Union[dict, Crystal]:\n from matador.utils.ase_utils import ase2dict\n\n return ase2dict(AseAtomsAdaptor.get_atoms(pmg), as_model=as_model)","title":""},{"docid":"f6b4fae9a291d80c9ce000de80bfe1a1","score":"0.58158517","text":"def to_dict(self):\n\n # Required meta-data\n out_dict = {'SpectrometerFrequency': self.SpectrometerFrequency,\n 'ResonantNucleus': self.ResonantNucleus}\n\n # Dimension information\n update_dict = {}\n for idx in range(5, 8):\n if self._dim_info[idx - 5]['tag'] is not None:\n update_dict[f'dim_{idx}'] = self._dim_info[idx - 5]['tag']\n if self._dim_info[idx - 5]['info'] is not None:\n update_dict[f'dim_{idx}_info'] = self._dim_info[idx - 5]['info']\n if self._dim_info[idx - 5]['hdr'] is not None:\n update_dict[f'dim_{idx}_header'] = self._dim_info[idx - 5]['hdr']\n out_dict.update(update_dict)\n\n # Add standard defined\n out_dict.update(self._standard_data)\n\n # Add user defined\n out_dict.update(self._user_data)\n\n return out_dict","title":""},{"docid":"c165562ef8e18e6ae02399d938883b03","score":"0.5814829","text":"def get_data_dict(\n filename: str\n) -> Dict[str, np.ndarray]:\n data_dict = defaultdict(np.ndarray)\n with open(filename, 'r') as f:\n for line in f:\n if \"[\" in line:\n key = line.split()[0]\n mat = []\n elif \"]\" in line:\n line = line.split(']')[0]\n mat.append([float(x) for x in line.split()])\n data_dict[key]=np.array(mat)\n else:\n mat.append([float(x) for x in line.split()])\n return data_dict","title":""},{"docid":"47ae5e2f187434bb04f0873ef680c66b","score":"0.5814463","text":"def to_dict(self):\n data_dict = {}\n data_dict['measurements'] = self.measurements\n data_dict['descriptors'] = self.descriptors\n data_dict['obs_descriptors'] = self.obs_descriptors\n data_dict['channel_descriptors'] = self.channel_descriptors\n data_dict['type'] = type(self).__name__\n return data_dict","title":""},{"docid":"9b32212054da027beacbaed373b23ac1","score":"0.5801208","text":"def to_dict(f):\n return dmp_to_dict(f.rep, 0, f.dom)","title":""},{"docid":"df1e08cdd2dd6d2aaaeecc5783a9c034","score":"0.5794283","text":"def get_util_table(self):\n result = dict()\n for key in self._matrix.keys():\n result[key] = self._matrix[key][1]\n return result","title":""},{"docid":"125886c9a9bcab3ec81d637191a91bdc","score":"0.5793791","text":"def _asdict(self):\n return {'measurements': self.measurements,\n 'attachments': [name for name in self.attachments],\n 'plugs': {\n k: v.__module__ + '.' + v.__class__.__name__\n for k, v in self.plugs.iteritems()}}","title":""},{"docid":"240b7670d3a5b79579072decd7abbd6e","score":"0.57933164","text":"def cs_to_dicts(cs, width, clean1=False):\n dicts = []\n for i, c in enumerate(cs):\n d = {}\n d['i'] = i\n if c.real > 0:\n d['re_sign'] = ''\n else:\n d['re_sign'] = '-'\n c = -c.real + (0+1j)*c.imag\n if c.imag > 0:\n d['im_sign'] = ''\n else:\n d['im_sign'] = '-'\n c = c.real - (0+1j)*c.imag\n d['re'] = str(f_to_sint(c.real, width/2, clean1=clean1))\n d['im'] = str(f_to_sint(c.imag, width/2, clean1=clean1))\n \n dicts.append(d)\n return dicts","title":""},{"docid":"826772fbf5e8857303e616e0aebcfe31","score":"0.5792228","text":"def dictionary_form(self) :\n \n toReturn = { }\n \n # if we only have stats for one data set\n if self.is_one_data_set :\n temp_prefix, temp_suffix = self.make_prefix_and_suffix(self.desc_text)\n toReturn = {\n temp_prefix + 'missing_value': self.missing_value,\n 'max' + temp_suffix: self.max,\n 'min' + temp_suffix: self.min,\n 'mean' + temp_suffix: self.mean,\n 'median' + temp_suffix: self.median,\n 'std_val' + temp_suffix: self.std_val,\n 'spatially_invalid_pts_ignored' + temp_suffix: self.spatially_invalid_pts_ignored,\n }\n \n if self.do_extras :\n toReturn['num_data_points'] = self.num_data_points\n toReturn['shape'] = self.shape\n \n # otherwise we must have stats for a comparison\n else :\n toReturn = {\n 'epsilon': self.epsilon,\n 'epsilon_percent': self.epsilon_percent,\n 'num_data_points': self.num_data_points,\n 'shape': self.shape,\n }\n a_dict = self.a_gen_stats.dictionary_form()\n toReturn.update(a_dict)\n b_dict = self.b_gen_stats.dictionary_form()\n toReturn.update(b_dict)\n \n return toReturn","title":""},{"docid":"113416b3444681b753a103e8a2c70f8c","score":"0.5789308","text":"def vector_to_dict(self, vector):\n pass","title":""},{"docid":"adcb37305085bfd0d563fbe18b7f2c77","score":"0.5783424","text":"def _matrix2dict(matrix, etype=False):\n n = len(matrix)\n adj = {k: {} for k in range(n)}\n for k in range(n):\n for j in range(n):\n if matrix[k, j] != 0:\n adj[k][j] = {} if not etype else matrix[k, j]\n\n return adj","title":""},{"docid":"7ac1a21f6a3ee40be15b3e506442cd5a","score":"0.57771266","text":"def axisheader2dic(header):\n\n dic = dict()\n\n dic[\"nucleus\"] = str(header[0]).strip('\\x00') \n dic[\"spectral_shift\"] = header[1]\n dic[\"npoints\"] = header[2]\n dic[\"size\"] = header[3]\n dic[\"bsize\"] = header[4]\n dic[\"spectrometer_freq\"] = header[5]\n dic[\"spectral_width\"] = header[6]\n dic[\"xmtr_freq\"] = header[7]\n dic[\"zero_order\"] = header[8]\n dic[\"first_order\"] = header[9]\n dic[\"first_pt_scale\"] = header[10]\n dic[\"extended\"] = header[11]\n\n return dic","title":""},{"docid":"0037295781b1cbfd9dede3020948a84f","score":"0.57759595","text":"def to_dict(self):\n output_dict = {\n \"kernel\" : self._kernel.to_dict(),\n \"tail\" : self._tail.to_dict(),\n \"eta\" : self._eta\n }\n if self._is_fitted():\n output_dict[\"X\"] = self._X.tolist()\n output_dict[\"y\"] = self._y.tolist()\n output_dict[\"lambda\"] = self._lambda.tolist()\n output_dict[\"LU\"] = self._LU.tolist()\n output_dict[\"piv\"] = self._piv.tolist()\n output_dict[\"loo\"] = self._loo_residuals.tolist()\n return output_dict","title":""},{"docid":"7122a529e7bb29c3d31fb21d7d5d2019","score":"0.57725054","text":"def todict(self):\n poscar = {}\n from numpy import array\n poscar[\"Lv\"] = array([map(float, l.split()[0:3]) for l in self.Lv])\n poscar[\"basis\"] = map(int, self.atom_counts.split())\n poscar[\"Bv\"] = array([map(float, l.split()[0:3]) for l in self.Bv])\n\n #The latpar is sometimes missing and we have a \"scale factor\" or\n #some other text their instead. If it is missing, just set it to 0.\n try:\n poscar[\"latpar\"] = float(self.latpar)\n except ValueError:\n poscar[\"latpar\"] = 0\n poscar[\"coordsys\"] = self.coordsys\n return poscar","title":""},{"docid":"586d7b079e7d4aafba6af9be46443dfc","score":"0.5762967","text":"def to_dictionary(self):\n a_dict = {\"id\": 0, \"width\": 0, \"height\": 0, \"x\": 0, \"y\": 0}\n for key in a_dict:\n if key == \"id\":\n a_dict[key] = self.id\n elif key == \"width\":\n a_dict[key] = self.width\n elif key == \"height\":\n a_dict[key] = self.height\n elif key == \"x\":\n a_dict[key] = self.x\n elif key == \"y\":\n a_dict[key] = self.y\n return a_dict","title":""},{"docid":"f0074c109de6a7de7349ef9c517b2a26","score":"0.5759627","text":"def _convertFinalizedDataRealizationToDict(self, rlz, unpackXarray=False):\n # TODO this has a lot of looping and might be slow for many variables. Bypass or rewrite where possible.\n new = {}\n for k,v in rlz.items():\n # if singular, eliminate dataarray container\n if len(v.dims) == 0:\n new[k] = v.item(0)\n # otherwise, trim NaN entries before returning\n else:\n for dim in v.dims:\n v = v.dropna(dim)\n if unpackXarray:\n new[dim] = v.coords[dim].values\n new[k] = v if not unpackXarray else v.values\n\n return new","title":""},{"docid":"f7db21c7f6ca5bdf9288c9eb6d1fca41","score":"0.57593644","text":"def numpy_serialize(obj: npt.NDArray[Any], **_kwargs: Any) -> dict[str, Any]:\n return {\"values\": obj.tolist(), \"shape\": obj.shape}","title":""},{"docid":"1c10fbb6a43f0ec9906dbcc827431a83","score":"0.5758683","text":"def asdict(self) -> Dict[str, Any]:\n return unflatten(self.data)","title":""},{"docid":"8e3651ed39812928a43319272a235035","score":"0.5752234","text":"def dictionary_form(self) :\n \n toReturn = {\n 'correlation': self.correlation,\n 'r-squared correlation': self.r_squared_correlation,\n 'diff_outside_epsilon_count': self.diff_outside_epsilon_count,\n 'diff_outside_epsilon_fraction': self.diff_outside_epsilon_fraction,\n 'perfect_match_count': self.perfect_match_count,\n 'perfect_match_fraction': self.perfect_match_fraction,\n 'mismatch_points_count': self.mismatch_points_count, \n 'mismatch_points_fraction': self.mismatch_points_fraction\n }\n toReturn.update(self.temp_analysis)\n \n return toReturn","title":""},{"docid":"faacc22e9e07fed4e85bdb01f37af0b5","score":"0.5749309","text":"def _getNumOfInstances(self):\n\n numb_dict = {}\n numb = mc.ls(\"*.hindleg\")\n for i in numb:\n\n item = i.split(\".\")[0]\n pos = mc.xform(item, q=1, ws=1, t=1)\n mat = mc.xform(item, q=1, ws=1, matrix=1)\n numb_dict[item.split(\"_\")[2]] = [item, pos, mat]\n\n return numb_dict","title":""},{"docid":"e92343a85ee2d4fa0781471bc833e4aa","score":"0.5747471","text":"def vector_to_dictionary(theta):\n parameters = {}\n parameters[\"W1\"] = theta[:40].reshape((20,2))\n parameters[\"b1\"] = theta[40:60].reshape((20,1))\n parameters[\"W2\"] = theta[60:120].reshape((3,20))\n parameters[\"b2\"] = theta[120:123].reshape((3,1))\n parameters[\"W3\"] = theta[123:126].reshape((1,3))\n parameters[\"b3\"] = theta[126:127].reshape((1,1))\n\n return parameters","title":""},{"docid":"6fa13d133cc637abfa4aaa56a90e4b16","score":"0.5744103","text":"def to_dict(self):\n return dict(breakpoints=self.breakpoints.tolist(),\n nord=self.nord,\n npoly=self.npoly,\n mask=self.mask.tolist(),\n coeff=self.coeff.tolist(),\n icoeff=self.icoeff.tolist(),\n xmin=self.xmin,\n xmax=self.xmax,\n funcname=self.funcname)","title":""},{"docid":"900cea78c6446c49dece567f15ef17de","score":"0.57366127","text":"def _extract_data_from_structure(self) -> dict:\n return {\n \"Chemical formula\": self._add_style(\n self._chemical_formula(self.structure.chemical_formula_reduced)\n ),\n \"Elements\": self._add_style(\", \".join(sorted(self.structure.elements))),\n \"Number of sites\": self._add_style(str(self.structure.nsites)),\n \"Unit cell\": self._unit_cell(self.structure.lattice_vectors),\n \"Unit cell volume\": (\n f\"{self._add_style('%.2f' % calc_cell_volume(self.structure.lattice_vectors))}\"\n \" Å<sup>3</sup>\"\n ),\n }","title":""},{"docid":"0e42e0029bacc9b1adc61bbafac0e72e","score":"0.57344186","text":"def mapGroupToDictionary(g):\n g_dict = {}\n for i in np.arange(g.size) + 1:\n g_dict[tuple(g[i])] = 1\n return g_dict","title":""},{"docid":"6cf3744132194f3d1555bef05823493d","score":"0.57329744","text":"def arrToDict(arr):\n return dict((x,1) for x in arr);","title":""},{"docid":"b29d5ccee4ce089a86cd42e613a7670f","score":"0.5730379","text":"def to_dict(self) -> Dict[str, Any]:\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"cd24214198d90e08997c285f8bc68664\",\n \"score\": \"0.6664811\",\n \"text\": \"def get_dict():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7550910142e0a82f7792a2ddcef64df3\",\n \"score\": \"0.6607125\",\n \"text\": \"def recarray2dict(ra):\\n d = { key:val for key, val in zip(ra.dtype.names, ra.tolist()) }\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f21acbcdc83379ccf47e1e31482768\",\n \"score\": \"0.65126914\",\n \"text\": \"def to_dict(self):\\n return {\\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__,\\n \\\"matrix\\\": self._matrix.tolist(),\\n \\\"a\\\": float(self.a),\\n \\\"b\\\": float(self.b),\\n \\\"c\\\": float(self.c),\\n \\\"alpha\\\": float(self.alpha),\\n \\\"beta\\\": float(self.beta),\\n \\\"gamma\\\": float(self.gamma),\\n \\\"volume\\\": float(self.volume)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc44883f9ed943825dc3865deb65e68a\",\n \"score\": \"0.6470545\",\n \"text\": \"def test_to_dict(self):\\n data = {(\\\"s1\\\", \\\"s2\\\"): 0.25, (\\\"s2\\\", \\\"s1\\\"): 0.25}\\n dmat = DistanceMatrix(data)\\n got = dmat.to_dict()\\n self.assertEqual(got, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efbdeb65d9cd38d16c8abe9862da5f3\",\n \"score\": \"0.6464569\",\n \"text\": \"def to_1D_dicts(self):\\n xyz, _ = self._get_coords_and_species(absolute=True, first=True)\\n dict0 = {\\\"smile\\\": self.molecule.smile}\\n dict0[\\\"rotor\\\"] = self.molecule.get_torsion_angles(xyz)\\n dict0[\\\"orientation\\\"], dict0[\\\"rmsd\\\"], dict0[\\\"reflect\\\"] = self.molecule.get_orientation(xyz)\\n angs = dict0[\\\"rotor\\\"]\\n #rdkit_mol = self.molecule.rdkit_mol(self.molecule.smile)\\n #conf0 = rdkit_mol.GetConformer(0)\\n #print(self.molecule.set_torsion_angles(conf0, angs))\\n #print(\\\"save matrix\\\"); print(self.orientation.r.as_matrix())\\n #print(\\\"save angle\\\"); print(self.orientation.r.as_euler('zxy', degrees=True))\\n #print(\\\"angle\\\"); print(dict0[\\\"orientation\\\"])\\n dict0[\\\"center\\\"] = self.position - np.floor(self.position) #self.molecule.get_center(xyz)\\n dict0[\\\"number\\\"] = self.wp.number\\n dict0[\\\"index\\\"] = self.wp.index\\n dict0[\\\"PBC\\\"] = self.wp.PBC\\n dict0[\\\"dim\\\"] = self.wp.dim\\n dict0[\\\"lattice\\\"] = self.lattice.matrix\\n dict0[\\\"lattice_type\\\"] = self.lattice.ltype\\n\\n return dict0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f241942fbefb16666e195a0eaa333a15\",\n \"score\": \"0.64108735\",\n \"text\": \"def to_dictionary(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f241942fbefb16666e195a0eaa333a15\",\n \"score\": \"0.64108735\",\n \"text\": \"def to_dictionary(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8360e3f3f816b6c7d74f68674597682b\",\n \"score\": \"0.63522875\",\n \"text\": \"def _todict(self, matobj):\\n dict = {}\\n for strg in matobj._fieldnames:\\n elem = matobj.__dict__[strg]\\n if isinstance(elem, scipy.io.matlab.mio5_params.mat_struct):\\n dict[strg] = self._todict(elem)\\n elif isinstance(elem, np.ndarray):\\n dict[strg] = self._tolist(elem)\\n else:\\n dict[strg] = elem\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75dcb916e550f359dc1ee2b807645d0d\",\n \"score\": \"0.6341674\",\n \"text\": \"def to_dict(self) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220adbd6bfd00e89e4f30a1cf3b53e19\",\n \"score\": \"0.63120365\",\n \"text\": \"def as_dict(self):\\n array = self._array\\n return {\\n 'Mrr': array[0],\\n 'Mtt': array[1],\\n 'Mpp': array[2],\\n 'Mrt': array[3],\\n 'Mrp': array[4],\\n 'Mtp': array[5],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effe7a26dc31190e60edce1a447e6ad3\",\n \"score\": \"0.628748\",\n \"text\": \"def to_dict(self):\\n return {\\n 'ul': tuple(self.ul),\\n 'crs': self.crs_wkt,\\n 'res': tuple(self.res),\\n 'size': tuple(self.size),\\n 'limits': tuple(self.limits),\\n 'name': self.name\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9cd50f69f4ffa2763723bc73addd92\",\n \"score\": \"0.62463206\",\n \"text\": \"def array2dict(arr):\\r\\n res = {}\\r\\n for k in arr.dtype.names:\\r\\n res[k] = arr[k]\\r\\n\\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72acbd485bff8472f28459ca157181c\",\n \"score\": \"0.61889505\",\n \"text\": \"def to_dict(self):\\n\\n if self.reciprocals:\\n self.reciprocal_str = [n.label for n in self.reciprocals]\\n\\n if self.clusters:\\n self.cluster_str = [n.label for n in self.clusters]\\n\\n dictionary = {\\\"label\\\": self.label,\\n \\\"intensity\\\": self.intensity,\\n \\\"c1\\\": self.c1, \\\"c2\\\": self.c2, \\\"h2\\\": self.h2,\\n \\\"reciprocals\\\": \\\" \\\".join(self.reciprocal_str),\\n \\\"clusters\\\": \\\" \\\".join(self.cluster_str),\\n \\\"short\\\": \\\"x\\\" if self.short_range else \\\"\\\"}\\n\\n if self.type == \\\"4D\\\":\\n dictionary[\\\"h1\\\"] = self.h1\\n\\n return dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c356a2f329f3ee0e53a7dd81e72b4cb6\",\n \"score\": \"0.6171276\",\n \"text\": \"def to_dictionary(self):\\n my_attrs = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n my_values = [self.id, self.size, self.x, self.y]\\n my_dict = {}\\n my_dict = dict(zip(my_attrs, my_values))\\n return my_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4e0b857935a9ea113fbd1bae1fe826\",\n \"score\": \"0.61708295\",\n \"text\": \"def data_type_dict():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"043172477347839192a7eb41957633dd\",\n \"score\": \"0.61694896\",\n \"text\": \"def as_dict(self):\\n\\t\\tretval = {}\\n\\t\\tretval['Nbits'] = self.n_bits\\n\\t\\tretval['Xmin'] = self.x_min\\n\\t\\tretval['Xmax'] = self.x_max\\n\\t\\tretval['Ymin'] = self.y_min\\n\\t\\tretval['Ymax'] = self.y_max\\n\\t\\treturn retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21868410835a86c6d61efb6fc2bab198\",\n \"score\": \"0.61533666\",\n \"text\": \"def _convertToDict(self):\\n if self.isEmpty:\\n self.raiseAnError(ValueError, f'DataObject named \\\"{self.name}\\\" is empty!')\\n self.raiseAWarning('DataObject._convertToDict can be a slow operation and should be avoided where possible!')\\n # container for all necessary information\\n dataDict = {}\\n # supporting data\\n dataDict['dims'] = self.getDimensions()\\n dataDict['metadata'] = self.getMeta(general=True)\\n dataDict['type'] = self.type\\n dataDict['inpVars'] = self.getVars('input')\\n dataDict['outVars'] = self.getVars('output')\\n dataDict['numberRealizations'] = self.size\\n dataDict['name'] = self.name\\n dataDict['metaKeys'] = self.getVars('meta')\\n # main data\\n if self.type == \\\"PointSet\\\":\\n # initialize with np arrays of objects\\n dataDict['data'] = dict((var, np.zeros(self.size, dtype=object)) for var in self.vars)\\n for var in self.vars:\\n dataDict['data'][var] = self.asDataset()[var].values\\n else:\\n dataDict['data'] = dict((var,np.zeros(self.size, dtype=object)) for var in self.vars+self.indexes)\\n # need to remove NaNs, so loop over slices\\n for s,rlz in enumerate(self.sliceByIndex(self.sampleTag)):\\n # loop over realizations to get distinct values without NaNs\\n for var in self.vars:\\n # how we get and store variables depends on the dimensionality of the variable\\n dims=self.getDimensions(var)[var]\\n # if scalar (no dims and not an index), just grab the values\\n if len(dims)==0 and var not in self.indexes:\\n dataDict['data'][var] = self.asDataset()[var].values\\n continue\\n # get data specific to this var for this realization (slice)\\n data = rlz[var]\\n # need to drop indexes for which no values are present\\n for index in dims:\\n data = data.dropna(index)\\n dataDict['data'][index][s] = data[index].values\\n dataDict['data'][var][s] = data.values\\n\\n return dataDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94deb64894cae4982512918d72fe275b\",\n \"score\": \"0.6138835\",\n \"text\": \"def as_dict(self, solver) -> dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7329ec60086a8870489a821beb908a0e\",\n \"score\": \"0.6102799\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapSLSL_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f062d64077d5005af0e9b14f2fa267\",\n \"score\": \"0.6098661\",\n \"text\": \"def to_dict(self):\\n data = dict()\\n data['group'] = self._group \\n data['character'] = self._character \\n #data['fullspace'] = self._fullspace\\n data['modular_symbols'] = self._modular_symbols\\n #data['newspace'] = self._newspace\\n data['newforms'] = self._newforms\\n data['new_modular_symbols'] = self._new_modular_symbols\\n data['galois_decomposition'] = self._galois_decomposition\\n data['galois_orbits_labels'] = self._galois_orbits_labels\\n data['oldspace_decomposition'] = self._oldspace_decomposition\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420c9efc9d10e74d9c7ea1d0b57443df\",\n \"score\": \"0.6096647\",\n \"text\": \"def to_dictionary(self):\\n dicc = ['id', 'width', 'height', 'x', 'y']\\n v = {}\\n for k in dicc:\\n v[k] = getattr(self, k)\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6013527bf8b1b64fefce0a12fd77dbbd\",\n \"score\": \"0.60948277\",\n \"text\": \"def _todict(matobj):\\n\\n dict = {}\\n for strg in matobj._fieldnames:\\n elem = matobj.__dict__[strg]\\n if isinstance(elem, scipy.io.matlab.mio5_params.mat_struct):\\n dict[strg] = _todict(elem)\\n else:\\n dict[strg] = elem\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a4982554899bb1614f320562c555909\",\n \"score\": \"0.6087999\",\n \"text\": \"def mat_to_dict(mat):\\n mat_dict = {}\\n state = 'getfields'\\n mat_lines = []\\n for line in mat.splitlines():\\n line = line.strip()\\n if line == '':\\n continue\\n if state == 'matrix':\\n assert not line.startswith('/')\\n mat_lines.append([float(v) for v in line.split()])\\n continue\\n assert state == 'getfields'\\n assert line.startswith('/')\\n if line == '/Matrix':\\n state = 'matrix'\\n continue\\n field_name, contents = _process_mat_line(line)\\n mat_dict[field_name] = contents\\n mat_dict['Matrix'] = np.array(mat_lines)\\n return mat_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591810e0a30ac0ce8aaf8b985a1eff56\",\n \"score\": \"0.60864925\",\n \"text\": \"def testSqrToDict(self):\\n d = self.sqr.to_dictionary()\\n self.assertEqual(\\n d, {'size': 5, 'id': 1, 'x': 7, 'y': 1})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a455b474ae02201b7e8215f4efeb628\",\n \"score\": \"0.608469\",\n \"text\": \"def ToDict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e534123a03bfc0b294fc9cabf79ce5\",\n \"score\": \"0.60813797\",\n \"text\": \"def _make_dict(self): \\n import collections\\n dd = collections.defaultdict(list)\\n \\n for d in self.weights:\\n dw = d.split(self.__delimiter)\\n dd[int(dw[0])].append((int(dw[1]),int(dw[2])))\\n \\n return dict(dd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301191cb428cdaf945183374d735e5d6\",\n \"score\": \"0.60686636\",\n \"text\": \"def _todict(matobj):\\n d = {}\\n for strg in matobj._fieldnames:\\n elem = matobj.__dict__[strg]\\n if isinstance(elem, spio.matlab.mio5_params.mat_struct):\\n d[strg] = _todict(elem)\\n elif isinstance(elem, numpy.ndarray):\\n d[strg] = _tolist(elem)\\n else:\\n d[strg] = elem\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"537322a73de144b644ccc2689131143d\",\n \"score\": \"0.60629106\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapSCSC_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f624578f7e84d240c812a273b2d1847\",\n \"score\": \"0.60505754\",\n \"text\": \"def analogue_dictionary(self,dom=1):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e6c4b58942b245489f5ffe6731ae1d7\",\n \"score\": \"0.6043747\",\n \"text\": \"def to_dict(self):\\n gp_dict = dict()\\n gp_dict[\\\"x\\\"] = self.x_train\\n gp_dict[\\\"y\\\"] = self.y_train\\n gp_dict[\\\"kern_types\\\"] = self.kern_types\\n gp_dict[\\\"hyp\\\"] = self.hyp\\n gp_dict[\\\"beta\\\"] = self.beta\\n gp_dict[\\\"inv_K\\\"] = self.inv_K\\n\\n return gp_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9284d8f388ca734aae58c6fa5ee8e9a9\",\n \"score\": \"0.6041814\",\n \"text\": \"def as_dict(self):\\n array = self._array\\n return {\\n 'Fr': array[0],\\n 'Ft': array[1],\\n 'Fp': array[2],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1385a29cc49d53290912956a634149c6\",\n \"score\": \"0.60277635\",\n \"text\": \"def _asdict(t):\\n return {'ssi_signo': t[0], 'ssi_errno': t[1], 'ssi_code': t[2], 'ssi_pid': t[3], 'ssi_uid': t[4], 'ssi_fd': t[5], 'ssi_tid': t[6], 'ssi_band': t[7], 'ssi_overrun': t[8], 'ssi_trapno': t[9], 'ssi_status': t[10], 'ssi_int': t[11], 'ssi_ptr': t[12], 'ssi_utime': t[13], 'ssi_stime': t[14], 'ssi_addr': t[15], 'padding': t[16]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8764af9790d2ff33b0a2dde3929ce924\",\n \"score\": \"0.60162866\",\n \"text\": \"def initalize_dict(lable_dataset:list)->dict:\\n label_dict = {}\\n for lable in lable_dataset:\\n label_dict[lable] = [0,0]\\n return label_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b729c5ac7ae59d96bdca03769b2d66\",\n \"score\": \"0.60042036\",\n \"text\": \"def convert_matlab_gens( genfile, genname='generators' ):\\n ca = spio.loadmat( genfile )#, squeeze_me=True )\\n\\n keys = ca.keys()\\n # there should only be one name for the cell array, the other keys\\n # should be metadata (eg., '__globals__', etc.)\\n try:\\n gens = ca[ genname ]\\n # this should work, as long as one does not use '__' in the name\\n # of the cell array.\\n except KeyError:\\n name = [ k for k in keys if not k.startswith('__') ][0]\\n gens = ca[name][0]\\n\\n return cell2dict( gens )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e4e19fba0fec0920e0c9f337c674b02\",\n \"score\": \"0.60035\",\n \"text\": \"def to_flat_dict(self) -> Dict[str, Any]:\\n data_dict = {}\\n for key in sparse_graph_properties:\\n val = getattr(self, key)\\n if sp.isspmatrix(val):\\n data_dict['{}.data'.format(key)] = val.data\\n data_dict['{}.indices'.format(key)] = val.indices\\n data_dict['{}.indptr'.format(key)] = val.indptr\\n data_dict['{}.shape'.format(key)] = val.shape\\n else:\\n data_dict[key] = val\\n return data_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670e7b3b880df2584a1937a26507f417\",\n \"score\": \"0.6002918\",\n \"text\": \"def Metadata2dict(md):\\n return dict(md)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d01343444b2ef8f6fcd1a726bb3d39\",\n \"score\": \"0.59990054\",\n \"text\": \"def io_dat2dict(file_name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30c3451ad2dd043d3babeab5aa2eb64\",\n \"score\": \"0.59981066\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapII_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b1a758e995eea2ae2f375bc0ae330c\",\n \"score\": \"0.5997023\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapSSSS_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf09c2d001cc72adf18d936aea3e11ff\",\n \"score\": \"0.5993429\",\n \"text\": \"def todict(self):\\n ylist = self.tolist()\\n return dict(zip(ylist[1], ylist[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef97fcd8e582a3175a89ce224fc9fb5\",\n \"score\": \"0.5988595\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapsetUL_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985bcd626e071cb4af61ee2afa640782\",\n \"score\": \"0.5986325\",\n \"text\": \"def toDict(self):\\n return {'':1,}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf041b0ae01ad45ea6f683f8414e7ee\",\n \"score\": \"0.598282\",\n \"text\": \"def to_dict(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37643b5832c366576645604d57598888\",\n \"score\": \"0.59806806\",\n \"text\": \"def value_dictionary(zma, angstrom=False, degree=False):\\n names = numpy.ravel(name_matrix(zma))\\n vals = numpy.ravel(value_matrix(zma, angstrom=angstrom, degree=degree))\\n val_dct = dict(zip(names, vals))\\n val_dct.pop(None)\\n return val_dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6f44fded154fbb8113dcca75164045\",\n \"score\": \"0.59765804\",\n \"text\": \"def toDict(self):\\n return dict(\\n sense=self.sense,\\n pi=self.pi,\\n constant=self.constant,\\n name=self.name,\\n coefficients=LpAffineExpression.toDict(self),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bee322c3528ef9433644e8b419d825\",\n \"score\": \"0.5970951\",\n \"text\": \"def to_dict(self, data: Any) -> Dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"becfc004873f2fc75ffbee9a2f2848b0\",\n \"score\": \"0.59578353\",\n \"text\": \"def to_dictionary(self):\\n attr = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n return {key: getattr(self, key) for key in attr}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66810ee4bfcca0ac9edbce4d637ccd89\",\n \"score\": \"0.5956648\",\n \"text\": \"def dict(self):\\n # type: () -> Dict[str, float]\\n return {\\n \\\"x0\\\": self.x0,\\n \\\"y0\\\": self.y0,\\n \\\"x1\\\": self.x1,\\n \\\"y1\\\": self.y1\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66810ee4bfcca0ac9edbce4d637ccd89\",\n \"score\": \"0.5956648\",\n \"text\": \"def dict(self):\\n # type: () -> Dict[str, float]\\n return {\\n \\\"x0\\\": self.x0,\\n \\\"y0\\\": self.y0,\\n \\\"x1\\\": self.x1,\\n \\\"y1\\\": self.y1\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cbacc7fe4ce365f8f8acb0e1215146f\",\n \"score\": \"0.5947385\",\n \"text\": \"def spectrum_to_dict(*args, **kwargs):\\n return gtlike_or_pointlike(gtlike_spectrum_to_dict, pointlike_spectrum_to_dict, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdcb1c60a2d5c5d53d031803ecdd0a8a\",\n \"score\": \"0.59460324\",\n \"text\": \"def __jdict__(self):\\n return dict(width=self.size[0], height=self.size[1], columns=self.columns,\\n start=self.start, end=self.end,\\n subplots=asdict(self.subplots),\\n aggregate=self.aggregate,\\n description=self.description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855586b8f1e98ee43847eacd056a06e5\",\n \"score\": \"0.5943951\",\n \"text\": \"def to_dictionary(self):\\n deec = self.__dict__\\n list = ['id', 'size', 'x', 'y']\\n res = {res: getattr(self, res) for res in list}\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba796d201bfc80315659fbacb518867\",\n \"score\": \"0.5943875\",\n \"text\": \"def to_dictionary(self):\\n my_attrs = [\\\"id\\\", \\\"height\\\", \\\"width\\\", \\\"x\\\", \\\"y\\\"]\\n my_values = [self.id, self.height, self.width, self.x, self.y]\\n my_dict = {}\\n my_dict = dict(zip(my_attrs, my_values))\\n return my_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f4fbddbe7628571e4c2874e7219b48\",\n \"score\": \"0.594206\",\n \"text\": \"def _todict(matobj):\\n\\n dict = {}\\n for strg in matobj._fieldnames:\\n elem = matobj.__dict__[strg]\\n if isinstance(elem, sp.io.matlab.mio5_params.mat_struct):\\n dict[strg] = _todict(elem)\\n else:\\n dict[strg] = elem\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ecd5167191629c299a5c9b3d895294d\",\n \"score\": \"0.59400815\",\n \"text\": \"def dictionary_form(self) :\\n \\n toReturn = { }\\n \\n # if we only have stats for one data set\\n if self.is_one_data_set :\\n temp_prefix, _ = self.make_prefix_and_suffix(self.desc_text)\\n toReturn = {\\n temp_prefix + 'nan_count': self.nan_count,\\n temp_prefix + 'nan_fraction': self.nan_fraction,\\n }\\n \\n # otherwise we must have stats for a comparison\\n else :\\n toReturn = {\\n 'common_nan_count': self.common_nan_count,\\n 'common_nan_fraction': self.common_nan_fraction\\n }\\n a_dict = self.a_nan_stats.dictionary_form()\\n toReturn.update(a_dict)\\n b_dict = self.b_nan_stats.dictionary_form()\\n toReturn.update(b_dict)\\n \\n return toReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e0e82098dbd14876d7010a5f7e067d4\",\n \"score\": \"0.5935981\",\n \"text\": \"def dimensions_as_dict(self):\\n dimension_dict = {}\\n for di in self.dimensions_as_list:\\n temp = di.split(\\\"=\\\")\\n dimension_dict[temp[0]] = temp[1]\\n return dimension_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02594d3b74b08512debc76acd6c2c5ac\",\n \"score\": \"0.59219396\",\n \"text\": \"def _asdict(self):\\n return convert_member_dict(self.__php_vars__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8e48efa03f6fb385eb25ac1e32919bb\",\n \"score\": \"0.5915815\",\n \"text\": \"def as_dict(self):\\n\\t\\tdata = {}\\n\\t\\tdata['structure'] = Poscar(self.structure).get_string()\\n\\t\\tdata['energies'] = self.energies\\n\\t\\tdata['densities'] = self.densities\\n\\t\\tdata['data'] = self.data\\n\\t\\tdata['vbm'] = self.vbm\\n\\t\\tdata['cbm'] = self.cbm\\n\\t\\tdata['efermi'] = self.efermi\\n\\t\\treturn data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91e8709406b2d5d0f432a5fb06140cf\",\n \"score\": \"0.59121424\",\n \"text\": \"def make_dict(self):\\n\\t\\td = dict()\\n\\n\\t\\td[7] = (0, 0) \\n\\t\\td[8] = (0, 1)\\n\\t\\td[9] = (0, 2)\\n\\t\\td[4] = (1, 0)\\n\\t\\td[5] = (1, 1)\\n\\t\\td[6] = (1, 2)\\n\\t\\td[1] = (2, 0)\\n\\t\\td[2] = (2, 1)\\n\\t\\td[3] = (2, 2)\\n\\n\\t\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232913781a7d27adfbd068dd6151710c\",\n \"score\": \"0.5910994\",\n \"text\": \"def getAxisDict():\\n a = {}\\n a[\\\"Dim\\\"] = 0\\n a[\\\"Sim\\\"] = 1\\n a[\\\"Fov\\\"] = 2\\n a[\\\"HOver\\\"] = 3\\n a[\\\"VOver\\\"] = 4\\n a[\\\"Acc\\\"] = 5\\n a[\\\"Ptu\\\"] = 6\\n a[\\\"Transp\\\"] = 7\\n a[\\\"Rand\\\"] = 8\\n a[\\\"Test\\\"] = 9\\n a[\\\"Algo\\\"] = 10\\n a[\\\"Metric\\\"] = 11\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b99d5e3a920188e827625da9a7d77d\",\n \"score\": \"0.5904304\",\n \"text\": \"def to_dictionary(self):\\n dicti = {'id': self.id, 'size': self.width,\\n 'x': self.x, 'y': self.y}\\n return dicti\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91bb811f8acff88b60d831cd2fc93064\",\n \"score\": \"0.5893366\",\n \"text\": \"def as_map(self):\\n return dict(zip('xyz', self.as_tuple()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48878024d4c519fcc033820d6e22ae85\",\n \"score\": \"0.5890842\",\n \"text\": \"def _sampling_to_dict(self):\\n return {'pos': _list_to_array(self.pos)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524a303ab478d73f9df7c47a2c4fe98f\",\n \"score\": \"0.588899\",\n \"text\": \"def getAxisDict():\\n a = {}\\n a[\\\"Dims\\\"] = 0\\n a[\\\"Sim\\\"] = 1\\n a[\\\"Fov\\\"] = 2\\n a[\\\"HOver\\\"] = 3\\n a[\\\"VOver\\\"] = 4\\n a[\\\"Acc\\\"] = 5\\n a[\\\"Ptu\\\"] = 6\\n a[\\\"Rand\\\"] = 7\\n a[\\\"Test\\\"] = 8\\n a[\\\"Alpha\\\"] = 9\\n a[\\\"Algo\\\"] = 10\\n a[\\\"Metric\\\"] = 11\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeaa79349666edbced055e179dd520dd\",\n \"score\": \"0.58782536\",\n \"text\": \"def to_dictionary(self):\\n my_dic = {\\n 'id': self.id,\\n 'size': self.width,\\n 'x': self.x,\\n 'y': self.y\\n }\\n return my_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac3741abd1d21e88e5dcaa0f9474643\",\n \"score\": \"0.58693254\",\n \"text\": \"def to_dictionary(self):\\n list_attr = ['id', 'x', 'size', 'y']\\n return {key: getattr(self, key) for key in list_attr}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"407fe9dd98bbd4658929ca9d09b6572f\",\n \"score\": \"0.586299\",\n \"text\": \"def create_dic(udic,datetimeobj=datetime.datetime.now(),user='user'):\\n\\n dic = dict()\\n \\n # determind shape of array\\n shape = [udic[k][\\\"size\\\"] for k in xrange(udic[\\\"ndim\\\"])]\\n\\n # populate the dictionary\\n dic[\\\"ident\\\"] = 'UCSF NMR'\\n dic[\\\"naxis\\\"] = udic[\\\"ndim\\\"]\\n dic[\\\"ncomponents\\\"] = 1\\n dic[\\\"encoding\\\"] = 0\\n dic[\\\"version\\\"] = 2\\n dic[\\\"owner\\\"] = user\\n dic[\\\"date\\\"] = datetimeobj.ctime()\\n dic[\\\"comment\\\"] = ''\\n dic[\\\"scratch\\\"] = ''\\n\\n # calc a good tile shape\\n tshape = calc_tshape(shape)\\n\\n # total number of tiles\\n ntiles = 1\\n for tlen,slen in zip(tshape,shape):\\n ntiles*=np.ceil(float(slen)/tlen)\\n \\n # points in tile\\n tpoints = np.array(tshape).prod()\\n\\n # data bytes\\n dbytes = tpoints*ntiles*4\\n\\n # total file size if data size plus leaders\\n dic[\\\"seek_pos\\\"] = int(dbytes+180+128*len(shape))\\n\\n\\n # populate the dictionary with axis dictionaries\\n for i,(tlen,dlen) in enumerate(zip(tshape,shape)):\\n \\n dic[\\\"w\\\"+str(i+1)] = create_axisdic(udic[i],tlen,dlen)\\n\\n return dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c45d4146c3c2d3ed23a90c48ac2123\",\n \"score\": \"0.58610034\",\n \"text\": \"def asdict(self) -> \\\"PyObject *\\\":\\n return _pyBasePython.mapUSUS_asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b907f1023d78017793eea207b040b7\",\n \"score\": \"0.5856626\",\n \"text\": \"def to_dictionary(self):\\n s_dict = {\\\"id\\\": self.id, \\\"x\\\": self.x, \\\"size\\\": self.size, \\\"y\\\": self.y}\\n return s_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6045257de6fe8971940fc85e39f515c\",\n \"score\": \"0.58564144\",\n \"text\": \"def get_data_dict(self):\\n ddict = dict()\\n ddict.update(self.parameters)\\n ddict['title'] = ddict['filename']\\n\\n # convert size to nm\\n ddict['boxlen'] = [i*1e9 for i in ddict['Pixel_size']]\\n\\n ddict['model'] = ddict['Detector_identifier']\\n ddict['expot'] = ddict['Exposure_time']\\n ddict['map'] = self.map\\n\\n return ddict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae9fdbf203292ecd96a83506a76cc49\",\n \"score\": \"0.58559066\",\n \"text\": \"def makedmat():\\n\\td=dict()\\n\\td['E']=list()\\n\\td['A']=list()\\n\\td['B']=list()\\n\\td['C']=list()\\n\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c444af3721dfc377fc62f8a792b166\",\n \"score\": \"0.5847421\",\n \"text\": \"def to_dict(self):\\n #print [s.to_dict() for s in self.sites]\\n return {\\n 'sites': [i.to_dict() for i in self.sites],\\n 'lattice': self.get_lattice().to_dict(),\\n 'space_group': self.space_group.to_dict(),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a339115436b6397f1317939970e4499b\",\n \"score\": \"0.5845245\",\n \"text\": \"def to_dictionary(self):\\n rec_dict = {\\n 'id': self.id,\\n 'size': self.width,\\n 'x': self.x,\\n 'y': self.y,\\n }\\n return rec_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fabac59fcc366d53d09de56c66b582\",\n \"score\": \"0.58416563\",\n \"text\": \"def to_dict(crystal):\\n xl_dict = CrystalAux._to_dict(crystal)\\n\\n # Get the mosaic parameters\\n half_mosaicity = crystal.get_half_mosaicity_deg()\\n xl_dict[\\\"ML_half_mosaicity_deg\\\"] = half_mosaicity\\n\\n domain_size = crystal.get_domain_size_ang()\\n xl_dict[\\\"ML_domain_size_ang\\\"] = domain_size\\n\\n return xl_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938898707800a7a53adfd287ee36aaf\",\n \"score\": \"0.58400524\",\n \"text\": \"def get_as_dict(self, *args, **kwargs):\\n try:\\n asfloat = kwargs['asfloat']\\n except:\\n asfloat = True\\n res = self.get(*args)\\n # an empty result will have a shape of (1,)\\n if len(res.shape) > 1:\\n descr = [x[0] for x in self.c.description]\\n if asfloat:\\n rd = {}\\n for x in range(res.shape[1]):\\n try:\\n r = res[:,x].astype('float')\\n except:\\n r = res[:,x]\\n rd[descr[x]] = r\\n else:\\n rd = dict([(descr[x],res[:,x]) for x in range(res.shape[1])])\\n else:\\n rd = {}\\n return rd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf4f649be91521b22986c9c7d562e17\",\n \"score\": \"0.5829836\",\n \"text\": \"def pmg2dict(pmg: Structure, as_model=False) -> Union[dict, Crystal]:\\n from matador.utils.ase_utils import ase2dict\\n\\n return ase2dict(AseAtomsAdaptor.get_atoms(pmg), as_model=as_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b4fae9a291d80c9ce000de80bfe1a1\",\n \"score\": \"0.58158517\",\n \"text\": \"def to_dict(self):\\n\\n # Required meta-data\\n out_dict = {'SpectrometerFrequency': self.SpectrometerFrequency,\\n 'ResonantNucleus': self.ResonantNucleus}\\n\\n # Dimension information\\n update_dict = {}\\n for idx in range(5, 8):\\n if self._dim_info[idx - 5]['tag'] is not None:\\n update_dict[f'dim_{idx}'] = self._dim_info[idx - 5]['tag']\\n if self._dim_info[idx - 5]['info'] is not None:\\n update_dict[f'dim_{idx}_info'] = self._dim_info[idx - 5]['info']\\n if self._dim_info[idx - 5]['hdr'] is not None:\\n update_dict[f'dim_{idx}_header'] = self._dim_info[idx - 5]['hdr']\\n out_dict.update(update_dict)\\n\\n # Add standard defined\\n out_dict.update(self._standard_data)\\n\\n # Add user defined\\n out_dict.update(self._user_data)\\n\\n return out_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c165562ef8e18e6ae02399d938883b03\",\n \"score\": \"0.5814829\",\n \"text\": \"def get_data_dict(\\n filename: str\\n) -> Dict[str, np.ndarray]:\\n data_dict = defaultdict(np.ndarray)\\n with open(filename, 'r') as f:\\n for line in f:\\n if \\\"[\\\" in line:\\n key = line.split()[0]\\n mat = []\\n elif \\\"]\\\" in line:\\n line = line.split(']')[0]\\n mat.append([float(x) for x in line.split()])\\n data_dict[key]=np.array(mat)\\n else:\\n mat.append([float(x) for x in line.split()])\\n return data_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47ae5e2f187434bb04f0873ef680c66b\",\n \"score\": \"0.5814463\",\n \"text\": \"def to_dict(self):\\n data_dict = {}\\n data_dict['measurements'] = self.measurements\\n data_dict['descriptors'] = self.descriptors\\n data_dict['obs_descriptors'] = self.obs_descriptors\\n data_dict['channel_descriptors'] = self.channel_descriptors\\n data_dict['type'] = type(self).__name__\\n return data_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b32212054da027beacbaed373b23ac1\",\n \"score\": \"0.5801208\",\n \"text\": \"def to_dict(f):\\n return dmp_to_dict(f.rep, 0, f.dom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1e08cdd2dd6d2aaaeecc5783a9c034\",\n \"score\": \"0.5794283\",\n \"text\": \"def get_util_table(self):\\n result = dict()\\n for key in self._matrix.keys():\\n result[key] = self._matrix[key][1]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"125886c9a9bcab3ec81d637191a91bdc\",\n \"score\": \"0.5793791\",\n \"text\": \"def _asdict(self):\\n return {'measurements': self.measurements,\\n 'attachments': [name for name in self.attachments],\\n 'plugs': {\\n k: v.__module__ + '.' + v.__class__.__name__\\n for k, v in self.plugs.iteritems()}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240b7670d3a5b79579072decd7abbd6e\",\n \"score\": \"0.57933164\",\n \"text\": \"def cs_to_dicts(cs, width, clean1=False):\\n dicts = []\\n for i, c in enumerate(cs):\\n d = {}\\n d['i'] = i\\n if c.real > 0:\\n d['re_sign'] = ''\\n else:\\n d['re_sign'] = '-'\\n c = -c.real + (0+1j)*c.imag\\n if c.imag > 0:\\n d['im_sign'] = ''\\n else:\\n d['im_sign'] = '-'\\n c = c.real - (0+1j)*c.imag\\n d['re'] = str(f_to_sint(c.real, width/2, clean1=clean1))\\n d['im'] = str(f_to_sint(c.imag, width/2, clean1=clean1))\\n \\n dicts.append(d)\\n return dicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826772fbf5e8857303e616e0aebcfe31\",\n \"score\": \"0.5792228\",\n \"text\": \"def dictionary_form(self) :\\n \\n toReturn = { }\\n \\n # if we only have stats for one data set\\n if self.is_one_data_set :\\n temp_prefix, temp_suffix = self.make_prefix_and_suffix(self.desc_text)\\n toReturn = {\\n temp_prefix + 'missing_value': self.missing_value,\\n 'max' + temp_suffix: self.max,\\n 'min' + temp_suffix: self.min,\\n 'mean' + temp_suffix: self.mean,\\n 'median' + temp_suffix: self.median,\\n 'std_val' + temp_suffix: self.std_val,\\n 'spatially_invalid_pts_ignored' + temp_suffix: self.spatially_invalid_pts_ignored,\\n }\\n \\n if self.do_extras :\\n toReturn['num_data_points'] = self.num_data_points\\n toReturn['shape'] = self.shape\\n \\n # otherwise we must have stats for a comparison\\n else :\\n toReturn = {\\n 'epsilon': self.epsilon,\\n 'epsilon_percent': self.epsilon_percent,\\n 'num_data_points': self.num_data_points,\\n 'shape': self.shape,\\n }\\n a_dict = self.a_gen_stats.dictionary_form()\\n toReturn.update(a_dict)\\n b_dict = self.b_gen_stats.dictionary_form()\\n toReturn.update(b_dict)\\n \\n return toReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"113416b3444681b753a103e8a2c70f8c\",\n \"score\": \"0.5789308\",\n \"text\": \"def vector_to_dict(self, vector):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcb37305085bfd0d563fbe18b7f2c77\",\n \"score\": \"0.5783424\",\n \"text\": \"def _matrix2dict(matrix, etype=False):\\n n = len(matrix)\\n adj = {k: {} for k in range(n)}\\n for k in range(n):\\n for j in range(n):\\n if matrix[k, j] != 0:\\n adj[k][j] = {} if not etype else matrix[k, j]\\n\\n return adj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac1a21f6a3ee40be15b3e506442cd5a\",\n \"score\": \"0.57771266\",\n \"text\": \"def axisheader2dic(header):\\n\\n dic = dict()\\n\\n dic[\\\"nucleus\\\"] = str(header[0]).strip('\\\\x00') \\n dic[\\\"spectral_shift\\\"] = header[1]\\n dic[\\\"npoints\\\"] = header[2]\\n dic[\\\"size\\\"] = header[3]\\n dic[\\\"bsize\\\"] = header[4]\\n dic[\\\"spectrometer_freq\\\"] = header[5]\\n dic[\\\"spectral_width\\\"] = header[6]\\n dic[\\\"xmtr_freq\\\"] = header[7]\\n dic[\\\"zero_order\\\"] = header[8]\\n dic[\\\"first_order\\\"] = header[9]\\n dic[\\\"first_pt_scale\\\"] = header[10]\\n dic[\\\"extended\\\"] = header[11]\\n\\n return dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0037295781b1cbfd9dede3020948a84f\",\n \"score\": \"0.57759595\",\n \"text\": \"def to_dict(self):\\n output_dict = {\\n \\\"kernel\\\" : self._kernel.to_dict(),\\n \\\"tail\\\" : self._tail.to_dict(),\\n \\\"eta\\\" : self._eta\\n }\\n if self._is_fitted():\\n output_dict[\\\"X\\\"] = self._X.tolist()\\n output_dict[\\\"y\\\"] = self._y.tolist()\\n output_dict[\\\"lambda\\\"] = self._lambda.tolist()\\n output_dict[\\\"LU\\\"] = self._LU.tolist()\\n output_dict[\\\"piv\\\"] = self._piv.tolist()\\n output_dict[\\\"loo\\\"] = self._loo_residuals.tolist()\\n return output_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7122a529e7bb29c3d31fb21d7d5d2019\",\n \"score\": \"0.57725054\",\n \"text\": \"def todict(self):\\n poscar = {}\\n from numpy import array\\n poscar[\\\"Lv\\\"] = array([map(float, l.split()[0:3]) for l in self.Lv])\\n poscar[\\\"basis\\\"] = map(int, self.atom_counts.split())\\n poscar[\\\"Bv\\\"] = array([map(float, l.split()[0:3]) for l in self.Bv])\\n\\n #The latpar is sometimes missing and we have a \\\"scale factor\\\" or\\n #some other text their instead. If it is missing, just set it to 0.\\n try:\\n poscar[\\\"latpar\\\"] = float(self.latpar)\\n except ValueError:\\n poscar[\\\"latpar\\\"] = 0\\n poscar[\\\"coordsys\\\"] = self.coordsys\\n return poscar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586d7b079e7d4aafba6af9be46443dfc\",\n \"score\": \"0.5762967\",\n \"text\": \"def to_dictionary(self):\\n a_dict = {\\\"id\\\": 0, \\\"width\\\": 0, \\\"height\\\": 0, \\\"x\\\": 0, \\\"y\\\": 0}\\n for key in a_dict:\\n if key == \\\"id\\\":\\n a_dict[key] = self.id\\n elif key == \\\"width\\\":\\n a_dict[key] = self.width\\n elif key == \\\"height\\\":\\n a_dict[key] = self.height\\n elif key == \\\"x\\\":\\n a_dict[key] = self.x\\n elif key == \\\"y\\\":\\n a_dict[key] = self.y\\n return a_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0074c109de6a7de7349ef9c517b2a26\",\n \"score\": \"0.5759627\",\n \"text\": \"def _convertFinalizedDataRealizationToDict(self, rlz, unpackXarray=False):\\n # TODO this has a lot of looping and might be slow for many variables. Bypass or rewrite where possible.\\n new = {}\\n for k,v in rlz.items():\\n # if singular, eliminate dataarray container\\n if len(v.dims) == 0:\\n new[k] = v.item(0)\\n # otherwise, trim NaN entries before returning\\n else:\\n for dim in v.dims:\\n v = v.dropna(dim)\\n if unpackXarray:\\n new[dim] = v.coords[dim].values\\n new[k] = v if not unpackXarray else v.values\\n\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7db21c7f6ca5bdf9288c9eb6d1fca41\",\n \"score\": \"0.57593644\",\n \"text\": \"def numpy_serialize(obj: npt.NDArray[Any], **_kwargs: Any) -> dict[str, Any]:\\n return {\\\"values\\\": obj.tolist(), \\\"shape\\\": obj.shape}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c10fbb6a43f0ec9906dbcc827431a83\",\n \"score\": \"0.5758683\",\n \"text\": \"def asdict(self) -> Dict[str, Any]:\\n return unflatten(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3651ed39812928a43319272a235035\",\n \"score\": \"0.5752234\",\n \"text\": \"def dictionary_form(self) :\\n \\n toReturn = {\\n 'correlation': self.correlation,\\n 'r-squared correlation': self.r_squared_correlation,\\n 'diff_outside_epsilon_count': self.diff_outside_epsilon_count,\\n 'diff_outside_epsilon_fraction': self.diff_outside_epsilon_fraction,\\n 'perfect_match_count': self.perfect_match_count,\\n 'perfect_match_fraction': self.perfect_match_fraction,\\n 'mismatch_points_count': self.mismatch_points_count, \\n 'mismatch_points_fraction': self.mismatch_points_fraction\\n }\\n toReturn.update(self.temp_analysis)\\n \\n return toReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faacc22e9e07fed4e85bdb01f37af0b5\",\n \"score\": \"0.5749309\",\n \"text\": \"def _getNumOfInstances(self):\\n\\n numb_dict = {}\\n numb = mc.ls(\\\"*.hindleg\\\")\\n for i in numb:\\n\\n item = i.split(\\\".\\\")[0]\\n pos = mc.xform(item, q=1, ws=1, t=1)\\n mat = mc.xform(item, q=1, ws=1, matrix=1)\\n numb_dict[item.split(\\\"_\\\")[2]] = [item, pos, mat]\\n\\n return numb_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92343a85ee2d4fa0781471bc833e4aa\",\n \"score\": \"0.5747471\",\n \"text\": \"def vector_to_dictionary(theta):\\n parameters = {}\\n parameters[\\\"W1\\\"] = theta[:40].reshape((20,2))\\n parameters[\\\"b1\\\"] = theta[40:60].reshape((20,1))\\n parameters[\\\"W2\\\"] = theta[60:120].reshape((3,20))\\n parameters[\\\"b2\\\"] = theta[120:123].reshape((3,1))\\n parameters[\\\"W3\\\"] = theta[123:126].reshape((1,3))\\n parameters[\\\"b3\\\"] = theta[126:127].reshape((1,1))\\n\\n return parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa13d133cc637abfa4aaa56a90e4b16\",\n \"score\": \"0.5744103\",\n \"text\": \"def to_dict(self):\\n return dict(breakpoints=self.breakpoints.tolist(),\\n nord=self.nord,\\n npoly=self.npoly,\\n mask=self.mask.tolist(),\\n coeff=self.coeff.tolist(),\\n icoeff=self.icoeff.tolist(),\\n xmin=self.xmin,\\n xmax=self.xmax,\\n funcname=self.funcname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900cea78c6446c49dece567f15ef17de\",\n \"score\": \"0.57366127\",\n \"text\": \"def _extract_data_from_structure(self) -> dict:\\n return {\\n \\\"Chemical formula\\\": self._add_style(\\n self._chemical_formula(self.structure.chemical_formula_reduced)\\n ),\\n \\\"Elements\\\": self._add_style(\\\", \\\".join(sorted(self.structure.elements))),\\n \\\"Number of sites\\\": self._add_style(str(self.structure.nsites)),\\n \\\"Unit cell\\\": self._unit_cell(self.structure.lattice_vectors),\\n \\\"Unit cell volume\\\": (\\n f\\\"{self._add_style('%.2f' % calc_cell_volume(self.structure.lattice_vectors))}\\\"\\n \\\" Å<sup>3</sup>\\\"\\n ),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e42e0029bacc9b1adc61bbafac0e72e\",\n \"score\": \"0.57344186\",\n \"text\": \"def mapGroupToDictionary(g):\\n g_dict = {}\\n for i in np.arange(g.size) + 1:\\n g_dict[tuple(g[i])] = 1\\n return g_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cf3744132194f3d1555bef05823493d\",\n \"score\": \"0.57329744\",\n \"text\": \"def arrToDict(arr):\\n return dict((x,1) for x in arr);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b29d5ccee4ce089a86cd42e613a7670f\",\n \"score\": \"0.5730379\",\n \"text\": \"def to_dict(self) -> Dict[str, Any]:\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993672,"cells":{"query_id":{"kind":"string","value":"6266c973d06d9a3db2f5dafb0c142c72"},"query":{"kind":"string","value":"Downloads documentation associated with IRS SOI Historical Table 2"},"positive_passages":{"kind":"list like","value":[{"docid":"190874280eeb34d573fc7d73a5b564a5","score":"0.62471473","text":"def get_docs(self):\n \n if self.year < self.mincompleteyr:\n raise ValueError(\"Documentation only available 2012 and onwards.\")\n\n filename = '{}incmdocguide'.format(self.yy)\n urlstub = 'https://www.irs.gov/pub/irs-soi/'\n url = urlstub + filename + \".doc\"\n urllib.request.urlretrieve(url, filename + \".doc\")\n return filename","title":""}],"string":"[\n {\n \"docid\": \"190874280eeb34d573fc7d73a5b564a5\",\n \"score\": \"0.62471473\",\n \"text\": \"def get_docs(self):\\n \\n if self.year < self.mincompleteyr:\\n raise ValueError(\\\"Documentation only available 2012 and onwards.\\\")\\n\\n filename = '{}incmdocguide'.format(self.yy)\\n urlstub = 'https://www.irs.gov/pub/irs-soi/'\\n url = urlstub + filename + \\\".doc\\\"\\n urllib.request.urlretrieve(url, filename + \\\".doc\\\")\\n return filename\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cdd5c1affe96db172149ff6160bcf39d","score":"0.61644614","text":"def get_doc(logya_inst, url):\n\n return logya_inst.docs.get(url)","title":""},{"docid":"4c0cbf68e7aca92819f0785808bf4984","score":"0.5851935","text":"def get_documentation(self):\n # call API without function to get hug to return standard 404 + documentation\n\n response = self.get(\"\")\n\n return response[\"documentation\"]","title":""},{"docid":"ca0a0e77ee4c7f2a2467250387265e00","score":"0.58089244","text":"def serviceDocumentation( pageTitle, pageDom, eCount=3 ):\n\ttemplate = \"single_source\"\n\tpublicXpath= \"//*/p/text()\"\n\ttemplatePublic = 'PUBLIC'\n\n# \n# \tNOTE: The code doesn't work, using the body content of the api pull has changed the structure of the DOM.\n# \t\tBecause we RC isn't going to go this direction with their document system structures we will now have\n# \t\tto pull from another system all together.\n# \n\n\n\t# validatePublic = pageDom.xpath( publicXpath )[0]\n\tvalidatePublic = pageDom.xpath( publicXpath )\n\tprint( validatePublic )\n\tif validatePublic == templatePublic:\n\t\trowCountXpath= \"count(//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr)\"\n\t\trowCount = int( pageDom.xpath( rowCountXpath ) ) \n\t\tpublicDictionary = {}\n\t\tfor index in range( 2, rowCount+1 ):\n\t\t\trowKeyXpath= \"//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr[{}]/td[1]/text()\".format( str( index ) )\n\t\t\trowValueXpath= \"//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr[{}]/td[2]/text()\".format( str( index ) )\n\t\t\tkey = pageDom.xpath( rowKeyXpath )\n\t\t\tvalue = pageDom.xpath( rowValueXpath )\n\n\t\t\tpublicDictionary[key[0]]=value[0]\n\n\t\tpublicFile = os.path.join( __data_mount_dir__, template, templatePublic, \"{}.json\".format( pageTitle.replace(\" \",\"_\") ) )\n\t\twriteToDisk( publicDictionary, publicFile )\n\n\t# //*[@id=\"main-content\"]/div/div[2]/div[1]/div/p\n\tuserXpath= \"//*[@id='main-content']/div/div[2]/div[1]/div/p/text()\"\n\ttemplateUser = \"USER\"\n\tvalidateUser = pageDom.xpath( userXpath )[0]\n\tif validateUser == templateUser:\n\t\trowCountXpath= \"count(//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr)\"\n\t\trowCount = int( pageDom.xpath( rowCountXpath ) ) \n\t\tpublicDictionary = {}\n\t\tfor index in range( 2, rowCount+1 ):\n\t\t\trowKeyXpath= \"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[1]/text()\".format( str( index ) )\n\t\t\trowValueXpath= \"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[2]/text()\".format( str( index ) )\n\t\t\trowSourceXpath= \"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[3]/text()\".format( str( index ) )\n\t\t\tkey = pageDom.xpath( rowKeyXpath )\n\t\t\tvalue = pageDom.xpath( rowValueXpath )\n\t\t\t# print(key, value)\n\t\t\tsource = pageDom.xpath( rowSourceXpath )\n\t\t\tprint(key, source.replace('\\n',None))\n\n\t\t# \tpublicDictionary[key[0]]=[value[0], source[0] if True else None]\n\n\t\tprint( publicDictionary )\n\n\t\tuserFile = os.path.join( __data_mount_dir__, template, templateUser, \"{}.json\".format( pageTitle.replace(\" \",\"_\") ) )\n\t\twriteToDisk( publicDictionary, userFile )","title":""},{"docid":"a0c9770db08a3524a8651032119b8014","score":"0.5693182","text":"def documentation():\n # display the page without any additional data\n return render_template(\"documentation.html\",\n title=\"Documentation - Bushing Failure Historian\")","title":""},{"docid":"f5545f8f982bf11f641936ba415c7254","score":"0.56810844","text":"def download_bench_data(self):","title":""},{"docid":"a9d1944633d913befd886dbfaca58ce4","score":"0.5672151","text":"def download_database(self):\n url =\"http://ndbserver.rutgers.edu\"\n url1 = url+\"/service/ndb/atlas/gallery/rna?polType=onlyRna&rnaFunc=all&protFunc=all&strGalType=rna&expMeth=all&seqType=all&galType=table&start=0&limit=50\"\n query = requests.get(url1)\n tree = html.fromstring(query.content)\n database_link = tree.xpath('//tr/td/h2/span/a[@id]/@href')\n urllib.urlretrieve(url+database_link[0], \"NDB_updated.xls\")\n csv_from_excel()\n return \"NDB Database was updated and converted to csv file\"","title":""},{"docid":"92b840b418376dd9a1efa882712046e7","score":"0.561059","text":"def download_dataset():\n # Parameters for the connection to the database\n Db2_j5_dsn = \"API key not publicly published. For the assignment, the keys are saved in the config.py within the \" \\\n \"folder submitted on moodle.\"\n # Establish the connection\n Db2_j5_connection = dbi.connect(Db2_j5_dsn)\n # Query to process\n query = 'SELECT * FROM \"CJH17542\".\"SN_20172994_IOT\"'\n # Get data from the database through the query created\n data_df_1 = pd.read_sql_query(query, con=Db2_j5_connection)\n # Close the connection\n Db2_j5_connection.close()\n return data_df_1","title":""},{"docid":"9c5928d0842c5005269e3e8f3e185256","score":"0.5594553","text":"def generate_docs(self) -> None:","title":""},{"docid":"975cb572177960b520d874d2b987ab54","score":"0.55297244","text":"def show_index():\n yield DOCUMENTATION_INTRO\n yield '<table id=\"api-endpoints\"><tbody>\\n'\n yield '<tr><th style=\"text-align: left\">Endpoint</th><th style=\"text-align: left\">Description</th>\\n'\n for route in application.routes:\n if route.rule == '/':\n yield '\\n<tr><td style=\"white-space: nowrap; font-weight: bold;\">/v2/</td><td>This documentation page for version 2 of the API.</td></tr>'\n elif route.rule.endswith('.json') and any(route.rule[:-4] + 'dat' == iter_route.rule for iter_route in application.routes):\n # JSONified version of an NBT endpoint\n continue\n elif route.rule.endswith('.dat'):\n if '<' in route.rule:\n yield '\\n<tr><td style=\"white-space: nowrap;\">/v2' + xml.sax.saxutils.escape(route.rule[:-4]) + '.json (or .dat)</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\n else:\n yield '\\n<tr><td style=\"white-space: nowrap;\"><a href=\"/v2' + route.rule[:-4] + '.json\">/v2' + route.rule[:-4] + '.json</a> (or <a href=\"/v2' + route.rule + '\">.dat</a>)</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\n else:\n if '<' in route.rule:\n yield '\\n<tr><td style=\"white-space: nowrap;\">/v2' + xml.sax.saxutils.escape(route.rule) + '</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\n else:\n yield '\\n<tr><td style=\"white-space: nowrap;\"><a href=\"/v2' + route.rule + '\">/v2' + route.rule + '</a></td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\n yield '</tbody></table>'","title":""},{"docid":"ffec8e79dd46612af0f4c2558b11bf75","score":"0.54715455","text":"def metadata_url(self):\n url = \"http://data.ssb.no/api/v0/no/table/\" + self.table_id\n return url","title":""},{"docid":"035f05f4ba0a8890f3c60ca95049d815","score":"0.54410917","text":"def generate_subsystems_doc() -> None:\n rst_dir = get_pkg_root() / \"doc\" / \"sal_interfaces\"\n rst_dir.mkdir(parents=True, exist_ok=True)\n\n # Write the rst table of contents.\n with open(rst_dir / \"index.rst\", \"w\") as f:\n write_heading(f, name=\"SAL Interfaces\", char=\"#\", overline=True)\n f.write(\n \"\"\"\n.. note:: This page is generated by python script ``generate_cet.py``.\n\nSAL Interfaces for all CSCs and other SAL components.\n\n.. toctree::\n :glob:\n :maxdepth: 1\n\n *\n\n\"\"\"\n )\n\n # Write one rst file for each SAL component.\n xml_dir = get_sal_interfaces_dir()\n subsystem_tree = ElementTree.parse(xml_dir / \"SALSubsystems.xml\")\n subsystem_tree_root = subsystem_tree.getroot()\n for subsystem_elt in subsystem_tree_root:\n subsystem = find_text_in_xml(subsystem_elt, \"Name\")\n # has_specific_topic_type starts empty for each CSC because it is\n # unknown if it has a topic file.\n has_specific_topic_type = []\n generics = find_text_in_xml(subsystem_elt, \"AddedGenerics\")\n has_generics = create_generics_dict(generics)\n\n with open(rst_dir / f\"{subsystem}.rst\", \"w\") as cf:\n cf.write(\":tocdepth: 3\\n\\n\")\n write_heading(cf, name=subsystem, char=\"#\", overline=True)\n cf.write(\n f\"\"\"\n.. note:: This page is generated by python script ``generate_cet.py``.\n\n:ref:`Back to table <index:csc-table:{subsystem.lower()}>`\n\n\"\"\"\n )\n for dds_type in [\"Commands\", \"Events\", \"Telemetry\"]:\n try:\n tree = ElementTree.parse(\n xml_dir / subsystem / f\"{subsystem}_{dds_type}.xml\"\n )\n has_specific_topic_type.append(dds_type[:-1])\n except FileNotFoundError:\n add_generics(\n cf,\n subsystem,\n set_name=f\"SAL{dds_type[:-1]}Set\",\n has_generics=has_generics,\n has_specific_topic_type=has_specific_topic_type,\n )\n continue\n if dds_type != \"Events\":\n write_heading(cf, name=dds_type)\n root = tree.getroot()\n set_name = root.tag\n root[:] = sorted(root, key=topic_sort_key)\n enumeration_seen = False\n first_event = True\n for topic in root:\n if topic.tag == \"Enumeration\":\n if dds_type == \"Events\" and not enumeration_seen:\n write_heading(cf, name=\"Enumerations\")\n enumeration_seen = True\n assert topic.text is not None\n states = topic.text.split(\",\")\n state_name = states[0].split(\"_\")[0].lstrip()\n cf.write(f\":{state_name}:\\n\")\n for state in states:\n # enum item format `\\n enumClassName_itemName\n # [=value]`\n # where itemName may include underscores,\n # and all whitespace is optional and of\n # arbitrary length.\n state = state.split(\"_\", 1)[\n 1\n ] # strip the beginning through the first underscore.\n cf.write(f\" * {state}\\n\") # noqa\n cf.write(\"\\n\")\n continue\n if dds_type == \"Events\" and first_event:\n write_heading(cf, name=\"Events\")\n first_event = False\n topic_name = find_text_in_xml(topic, \"EFDB_Topic\")\n if dds_type in [\"Commands\", \"Events\"]:\n short_name = topic_name.split(\"_\", 2)[-1]\n else:\n short_name = topic_name.split(\"_\", 1)[-1]\n cf.write(f\".. _{subsystem}:{dds_type}:{short_name}:\\n\\n\")\n write_heading(cf, name=short_name, char=\"~\")\n topic_description = topic.find(\"Description\")\n if topic_description is not None:\n cf.write(f\"**Description**: {topic_description.text}\\n\\n\")\n for field in topic:\n if field.tag == \"item\":\n efdb_name = find_text_in_xml(field, \"EFDB_Name\")\n cf.write(\n f\"\\n.. _{subsystem}:{dds_type}:{short_name}:{efdb_name}:\\n\\n\"\n )\n write_heading(cf, name=efdb_name, char=\"*\")\n for attribute in field:\n if attribute.tag in IGNORED_ATTRIBUTES:\n pass\n elif attribute.tag in [\"Count\", \"IDL_Size\"]:\n if attribute.text == \"1\":\n pass\n else:\n cf.write(\n f\":{attribute.tag}: {attribute.text}\\n\"\n )\n else:\n cf.write(f\":{attribute.tag}: {attribute.text}\\n\")\n description = find_text_in_xml(field, \"Description\")\n cf.write(f\"\\n**Description**: {description}\\n\\n\")\n elif field.tag in IGNORED_FIELDS:\n pass\n else:\n cf.write(f\":{field.tag}: {field.text}\\n\")\n cf.write(\"\\n\")\n add_generics(\n cf, subsystem, set_name, has_generics, has_specific_topic_type\n )","title":""},{"docid":"678c3998c6fb049f0c96ccdd124f6138","score":"0.5440025","text":"def test_rtd_extension_inventory():\n with open(BASE_DIR / 'sphinx-integration.html', 'r') as stream:\n page = stream.read()\n assert 'href=\"/en/latest/api/pydoctor.sphinx_ext.build_apidocs.html\"' in page, page","title":""},{"docid":"374e0709e627da1ba724befb42b94e03","score":"0.5403419","text":"def get_table(self):\n if self.table != \"2\":\n filename = \"histab\" + self.table + '.xls'\n else:\n if self.product == 'csv':\n filename = \"{}in54cmcsv.csv\".format(self.yy)\n \n elif self.product == 'xls':\n # need us, state, county, and one with us + state\n if self.level in '*':\n filename = \"{}in54cm.xlsx\".format(self.yy)\n \n elif self.level in ['state', 'us']:\n filename = \"{}in{}{}.xls\".format(self.yy, self.stnum, self.state)\n \n elif self.level == 'county':\n if self.state == None:\n filename = \"{}incyall.xls\".format(self.yy)\n else:\n filename = \"{}incy{}.xls\".format(self.yy, self.state)\n \n urlstub = 'https://www.irs.gov/pub/irs-soi/'\n \n url = urlstub + filename\n urllib.request.urlretrieve(url, filename)\n return filename","title":""},{"docid":"d45389042394f9ca00f0c17254757756","score":"0.5294146","text":"def main():\n df = pd.read_csv(URL, sep=\"\\t\")\n del df[df.columns[0]]\n df = df[df.description.notna()]\n df = df[df[\"prefix\"].map(lambda p: bioregistry.get_description(p) is None)]\n df = df[df[\"prefix\"].map(lambda p: bioregistry.get_obofoundry_prefix(p) is None)]\n click.echo(df.to_markdown())\n r = dict(bioregistry.read_registry())\n for prefix, description in df[[\"prefix\", \"description\"]].values:\n r[prefix].description = description\n bioregistry.write_registry(r)","title":""},{"docid":"d369dcc704668dc6c9cfc720aa928f9b","score":"0.5287122","text":"def get_data_source_from_lws(file_index, save=False, save_file_name=\"file.csv\"):\n url = \"https://learnwithshin.github.io/docs/files/\"\n content = get_content(url)\n soup = BeautifulSoup(content, \"html.parser\")\n article = soup.find(\"article\")\n anchors = article.find_all(\"a\")\n\n base_url = \"https://learnwithshin.github.io/docs/\"\n target_anchor = anchors[file_index]\n file_url = target_anchor[\"href\"]\n file_url = file_url.replace(\"../\", base_url)\n\n res = requests.get(file_url)\n if save:\n save_download_file(res.content, save_file_name)\n return res.content","title":""},{"docid":"861cf3819e843174fbf57f0feed47da6","score":"0.5251362","text":"def download(self):\n main = self.main\n delim = self.irs_delim\n current_yr = self.core_file_year #int\n\n main.logger.info('Beginning any necessary downloads from the IRS.')\n\n for form in main.forms:\n try:\n url = self.urls[form][current_yr]\n except KeyError:\n raise Exception('URL not found for core file year {}, form {}. Please check the \"urls\" folder.'.format(current_year, form))\n df = pd.read_csv(self.download_file(url), sep=delim, dtype='str')\n\n #Most IRS files have EIN in caps, but at least one (2012 EZ) has it in lowercase\n if 'ein' in df.columns:\n df.rename(columns={'ein':'EIN'}, inplace=True)\n\n df.set_index('EIN', inplace=True)\n\n #adds the source file name as a column\n df['SOURCE'] = url.split('/')[-1]\n self.data_dict[form] = df\n\n main.logger.info('Downloading complete.\\n')","title":""},{"docid":"3f0637d32360cb549a73abc684499f24","score":"0.5221541","text":"def get_docs(self):\n bot = self.bot\n\n # Icon sources.\n pdf_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/document-24'\n word_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/pdf-24'\n ppt_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/powerpoint-24'\n\n # Scrape\n soup = BeautifulSoup(bot.page_source, features=\"lxml\")\n divs = soup.findAll('div', attrs={'class' : 'activityinstance'})\n pdf_links = []\n for div in divs:\n href = div.find('a')['href']\n if div.find('img')['src'] in [pdf_icon, word_icon, ppt_icon]:\n pdf_links.append(href)\n\n # Keep pdf links\n cleaned_links = [x for x in pdf_links if x[:48] == 'https://moodle.concordia.ca/moodle/mod/resource/']\n \n all_files = []\n new_files = []\n for i, link in enumerate(cleaned_links, 1):\n all_files = os.listdir(self.outpath) # Before\n bot.find_element_by_xpath('//a[@href=\"' + link + '\"]').click()\n time.sleep(1.50)\n new_files = os.listdir(self.outpath) # After\n most_recent_file = list(set(new_files) - set(all_files))\n print('{}. {} saved.'.format(i, most_recent_file[0]))\n\n print(\"✓ Saved all available files.\")","title":""},{"docid":"93399eeef2d823480ec5b2c86598366f","score":"0.5219272","text":"def doc():","title":""},{"docid":"0ed5c59135682407fdc2b09ea5640035","score":"0.5203115","text":"def download_query_results(self):","title":""},{"docid":"14501e37b08e0b869d756fe0f98bf227","score":"0.5200712","text":"def open_documentation():\n webbrowser.open(FOXBMS_DOC_URL)","title":""},{"docid":"2ed4c1ea867577bb4cbb8818d1dea0a7","score":"0.5198265","text":"def test_developers(self):\n doc = self.s.go('http://%s/developers' % self.hostport)\n assert 'Downloading Data' in doc.text","title":""},{"docid":"04f6e2aafb6e27dcc2f9624e42018f0b","score":"0.5180838","text":"def ref_info(self):\n doc = self.get_doc()\n table = doc('table#officials')\n return utils.parse_officials_table(table)","title":""},{"docid":"f851b17dbce832d8b852321663bcc6ec","score":"0.5170102","text":"def get_docs(self):\n return \"SELECT * FROM doctor\"","title":""},{"docid":"d015bf19687208271e953d8abdfa8556","score":"0.51660705","text":"def test_n_search_download_ann(self) -> None:\n command = ['search', '--criteria=identifiers:10159402351973898',\n '--collection=KALCNES', '--server=kalideos',\n '--download=annexes']\n self.do_test_download_file(command, ['10159402351973898_ann.htm'])","title":""},{"docid":"0ed3ccead7f1a3d34d42b2533e57b8a4","score":"0.51632565","text":"def test_n_search_download_ql(self) -> None:\n command = ['search', '--criteria=identifiers:1355444872323826',\n '--collection=ROHAITI', '--server=ro',\n '--download=quicklook']\n self.do_test_download_file(command, ['1355444872323826_ql.jpg'])","title":""},{"docid":"9588d9a3a7b983e3480ed78b51a32c83","score":"0.51441884","text":"def rst(ctx):\r\n args_list = [\r\n ('locations', ''),\r\n ('download', ''),\r\n ('word2csv', ''),\r\n ('csv2df', '*tests*')\r\n #,('frontpage', '*markdown*')\r\n ]\r\n\r\n for args in args_list: \r\n command = apidoc(*args)\r\n ctx.run(command)","title":""},{"docid":"8f189ed24f57e8a4b048b8c623bb1799","score":"0.5134924","text":"def _get_nexsci_data(self, cache=\"update\"):\n NEXSCI_API = \"http://exoplanetarchive.ipac.caltech.edu/cgi-bin/nstedAPI/nph-nstedAPI\"\n url1 = NEXSCI_API + (\n \"?table=planets&select=pl_hostname,pl_letter,\"\n \"pl_disc,ra,dec,pl_trandep,pl_tranmid,pl_tranmiderr1,\"\n \"pl_tranmiderr2,pl_tranflag,pl_trandur,pl_pnum,pl_k2flag,\"\n \"pl_kepflag,pl_facility,pl_orbincl,pl_orbinclerr1,pl_orbinclerr2,\"\n \"pl_orblper,st_mass,st_masserr1,st_masserr2,st_rad,st_raderr1,\"\n \"st_raderr2,st_teff,st_tefferr1,st_tefferr2,st_optmag,st_j,st_h\"\n )\n url2 = NEXSCI_API + (\n \"?table=compositepars&select=fpl_hostname,fpl_letter,\"\n \"fpl_smax,fpl_smaxerr1,fpl_smaxerr2,fpl_radj,fpl_radjerr1,\"\n \"fpl_radjerr2,fpl_bmassj,fpl_bmassjerr1,fpl_bmassjerr2,\"\n \"fpl_bmassprov,fpl_eqt,fpl_orbper,fpl_orbpererr1,fpl_orbpererr2,\"\n \"fpl_eccen,fpl_eccenerr1,fpl_eccenerr2,fst_spt\"\n )\n try:\n path1 = download_file(\n url1, cache=cache, show_progress=False, pkgname=\"starry\"\n )\n path2 = download_file(\n url2, cache=cache, show_progress=False, pkgname=\"starry\"\n )\n except Exception as e:\n logger.warning(\n \"Couldn't obtain data from NEXSCI. Do you have an internet connection?\\n\"\n )\n df = pd.read_csv(\n f\"{_PACKAGEDIR}/extensions/nexsci/data/planetdata.csv\"\n )\n path1, path2 = [\n f\"{_PACKAGEDIR}/extensions/nexsci/data/planetdata.csv\"\n ] * 2\n return df, path1, path2\n\n planets = pd.read_csv(\n path1,\n comment=\"#\",\n skiprows=1,\n )\n composite = pd.read_csv(\n path2,\n comment=\"#\",\n skiprows=1,\n )\n composite.rename(\n {c: c[1:] for c in composite.columns if c.startswith(\"fpl\")},\n axis=\"columns\",\n inplace=True,\n )\n df = pd.merge(\n left=planets,\n right=composite,\n how=\"left\",\n left_on=[\"pl_hostname\", \"pl_letter\"],\n right_on=[\"pl_hostname\", \"pl_letter\"],\n )\n\n df[\"stripped_name\"] = np.asarray(\n [\n n.lower().replace(\"-\", \"\").replace(\" \", \"\")\n for n in df.pl_hostname\n ]\n )\n df = df[df.pl_tranflag == 1].reset_index(drop=True)\n df = self._fill_data(df)\n return df, path1, path2","title":""},{"docid":"d74dcd3a36a812355647fd3ad7c77168","score":"0.50901353","text":"def main():\n parser = argparse.ArgumentParser(\"Command line tool to download pdf via DOI from Scihub.\")\n # parser.add_argument('DOI', help=\"the DOI number of the paper\")\n parser.add_argument('-c', '--choose', help=\"choose scihub url by index\")\n parser.add_argument('-D', '--DOI', help=\"the DOI number of the paper\")\n parser.add_argument('-o', '--output', help=\"directory to download the pdf\")\n parser.add_argument('-u', '--update', action='store_true', help=\"update available Scihub links\")\n parser.add_argument('-b', '--brute-update', action='store_true', help=\"update available Scihub links by brute-force search\")\n parser.add_argument('-l', '--list', action='store_true', help=\"list current saved sichub urls\")\n args = parser.parse_args()\n\n if args.DOI:\n SCIHUB_URL_INDEX = int(open(get_resource_path('cur_scihub_index.txt'), 'r').read())\n if not args.output:\n sci = SciHub(args.DOI)\n else:\n sci = SciHub(args.DOI, args.output)\n sci.download(choose_scihub_url_index=SCIHUB_URL_INDEX)\n elif args.update:\n update_link()\n elif args.brute_update:\n update_link('b')\n elif args.list:\n link_file_path = get_resource_path('link.txt')\n cur_scihub_index = int(open(get_resource_path('cur_scihub_index.txt'), 'r').read())\n if not os.path.isfile(link_file_path):\n open(link_file_path, 'w')\n with open(link_file_path, 'r') as link_file:\n for i, link in enumerate(link_file.readlines()):\n if i == cur_scihub_index:\n print('* [{0}] {1}'.format(i, link[:-1]))\n else:\n print(' [{0}] {1}'.format(i, link[:-1]))\n elif args.choose:\n open(get_resource_path('cur_scihub_index.txt'), 'w').write(args.choose)\n cur_scihub_url = open(get_resource_path('link.txt'), 'r').readlines()[int(args.choose)].replace('\\n', '')\n print(\"Current scihub url: %s\" %(cur_scihub_url))\n else:\n print(\"Command line tool to download pdfs via DOI from Scihub.\")","title":""},{"docid":"b9e4f5c82567d38d259c61c04662a663","score":"0.50872064","text":"def do_GET(self):\n print(\"self.path\", self.path)\n if self.path == '/':\n path = './src/ui/dist/index.html'\n contenttype = 'text/html'\n print(\"path: \", path)\n file = open(path, 'rb')\n self.send_response(200)\n self.send_header('Content-type', contenttype)\n self.end_headers()\n self.wfile.write(file.read())\n file.close()\n elif self.path.startswith('/api?query='):\n query = self.path.split('&svd=')[0].replace('/api?query=', '')\n query = urllib.parse.unquote(query)\n print('query: ', query)\n use_svd = self.path.split('&')[1].replace('svd=', '')\n if \"&count=\" in self.path:\n count = int(self.path.split('&')[2].replace('count=', ''))\n else:\n count = 10\n if use_svd == 'true':\n documents, similarities = self.lsi_handler.get_ranking(query, number=count)\n else:\n documents, similarities = self.lsi_handler.get_ranking(query, use_SVD=False, number=count)\n\n ranked_list = []\n for i in range(0, len(similarities)):\n ranked_list.append({'doc': documents[i], 'rank': \"{:.9f}\".format(similarities[i])})\n\n contenttype = 'application/json'\n body = json.dumps(ranked_list)\n self.send_response(200)\n self.send_header('Content-type', contenttype)\n self.end_headers()\n self.wfile.write(bytearray(body, 'utf-8'))\n elif self.path.startswith('/api?doc='):\n doc = self.path.replace('/api?doc=', '')\n path = './data/20news-bydate/20news-bydate-train/' + doc\n contenttype = 'text/plain'\n print(\"path: \", path)\n file = open(path, 'rb')\n self.send_response(200)\n self.send_header('Content-type', contenttype)\n self.end_headers()\n self.wfile.write(file.read())\n file.close()\n else:\n path = './src/ui/dist' + self.path\n contenttype = 'application/javascript'\n print(\"path: \", path)\n file = open(path, 'rb')\n self.send_response(200)\n self.send_header('Content-type', contenttype)\n self.end_headers()\n self.wfile.write(file.read())\n file.close()\n return","title":""},{"docid":"af8c1171790ea005d07b0b1e7cc1e94d","score":"0.5084991","text":"def doc(self):","title":""},{"docid":"f58a94dc73ee2a3587a6d0cc47ad1954","score":"0.5073334","text":"def _docs(sub_url: Text) -> Text:\n return constants.DOCS_BASE_URL + sub_url","title":""},{"docid":"19fbe71bff75847d9047a1f9b65e7889","score":"0.5058602","text":"def open_dfhack_readme():\n from . import launcher\n index = paths.get('df', 'hack', 'docs', 'index.html')\n if os.path.isfile(index):\n launcher.open_file(index)\n else:\n launcher.open_url('https://dfhack.readthedocs.org')","title":""},{"docid":"2e70955ff6109100bbe6086bcfd8c0a1","score":"0.5050236","text":"def docs():\n res_file = resource.get_resource('manual_url')\n assert res_file is not None, \"User Manual URL file not found\"\n with click.open_file(res_file) as f:\n url = f.read().strip()\n click.launch(url)","title":""},{"docid":"93216072d2a7d346b5ee6873e05bdd17","score":"0.5050057","text":"def download_well_meta():\n well_meta_url = 'https://factpages.npd.no/ReportServer_npdpublic?/FactPages/TableView/wellbore_exploration_all&rs:Command=Render&rc:Toolbar=false&rc:Parameters=f&rs:Format=CSV&Top100=false&IpAddress=not_used&CultureCode=en'\n\n well_meta = pd.read_csv(well_meta_url)\n\n well_meta_path = OUTPUT_ROOT / 'well_meta_npd.csv'\n well_meta.to_csv(well_meta_path, index=False)","title":""},{"docid":"f8792e2f92070b01407f87b15c9c2273","score":"0.50432736","text":"def api_documentation():\n return \"WELCOME TO SEND_IT APP, THE SOLUTION TO ALL YOUR COUREER SERVICES\"","title":""},{"docid":"23a674e48cfc832d1b0c6bcdfdf98157","score":"0.50250465","text":"def docs_docstrings(session: nox.Session) -> None:\n session.install(\"interrogate\")\n session.run(\n f\"{session.bin}/python{session.python}\", \"-Im\", \"interrogate\", \"--version\"\n )\n session.run(\n f\"{session.bin}/python{session.python}\",\n \"-Im\",\n \"interrogate\",\n \"-v\",\n \"src/\",\n \"tests/\",\n \"noxfile.py\",\n )\n clean()","title":""},{"docid":"ec74aade76b70eaf29d8e90b8bada3af","score":"0.50124633","text":"def get_doc_summary(self):\n c = MySQLdb.cursors.DictCursor(self.conn)\n table = self.get_branch_table('imp_doc')\n query = \"SELECT * FROM \" + table + \" WHERE date=%s\"\n c.execute(query, (self.date,))\n return c.fetchone()","title":""},{"docid":"0d1e3bef9295b520c7c60688e34e1e68","score":"0.5010576","text":"def download_generators_info(cache):\n cache = hp._check_cache(cache)\n table = static_table(table_name=\"Generators and Scheduled Loads\", raw_data_location=cache)\n return table","title":""},{"docid":"74455460ef8eef3b8ccfebfeb03d9215","score":"0.50085306","text":"def download():\n secho('Downloading MIRIAM')\n get_miriam_df(force_download=True)\n\n secho('Download N2T')\n get_n2t(force=True)\n\n secho('Downloading NCBI')\n get_ncbi()\n\n secho('Downloading OBO Foundry')\n get_obofoundry_df(force_download=True)\n\n secho('Downloading OLS')\n get_ols_df(force_download=True)\n\n secho('Download Wikidata')\n get_wikidata_registry()\n\n secho('Download GO')\n get_go(force_download=True)\n\n secho('Download BioPortal')\n get_bioportal(force_download=True)","title":""},{"docid":"aa9d6add8c5e872e12cb82ccfa0c37fe","score":"0.5004742","text":"def fixture_response_get_docs(base_url: str, owner: str, repo: str) -> Response_Label:\n return {\n \"id\": 2222,\n \"node_id\": \"4444\",\n \"url\": f\"{base_url}/repos/{owner}/{repo}/labels/docs\",\n \"name\": \"docs\",\n \"description\": \"Tasks to write and update documentation\",\n \"color\": \"2abf88\",\n \"default\": False,\n }","title":""},{"docid":"ccd2611808f0543e9990be637e80f79e","score":"0.5002218","text":"def _documentation(self):\n pass","title":""},{"docid":"e0d63f1eb5db1a57d8b73ac7efcfa5cc","score":"0.49991462","text":"def test_get_document(self):\n response = self.index.get_document('500682')\n assert isinstance(response, object)\n assert 'title' in response\n assert response['title'] == 'The Highwaymen'","title":""},{"docid":"dc879504d81618f378a9de5be90b8800","score":"0.499403","text":"def click_download_experiment_data(experiment, output, tables):\n return download_experiment_data(experiment, output, tables)","title":""},{"docid":"e0ae40af14367edf4d2965532777ff94","score":"0.49801224","text":"def magic_docs(self, arg):\n with no_stdout():\n webbrowser.open(\"http://pml.readthedocs.org/en/latest/index.html\")","title":""},{"docid":"1d1750fa2ee8ba498e5a17bb9fb15f5a","score":"0.497243","text":"def download_all_data():\n download_from_hdx(\"wfp-food-prices-for-ethiopia\")\n download_from_hdx(\"faostat-prices-for-ethiopia\")\n return","title":""},{"docid":"620aa1f6a57c596aa472926f3bea1713","score":"0.49680912","text":"def document():\n html = flask.render_template('report_head.html',\n sample='Flask-OAuthlib')\n footer = flask.render_template('report_foot.html') \n content = ''\n\n endpoint = 'deviceManagement/deviceConfigurations'\n\n\n for item in get_api(endpoint)['value']:\n table = json2html.convert(json = item, table_attributes=\"id=\\\"info-table\\\"\")\n head = flask.render_template('report_table_head.html',\n endpoint= '/' + endpoint + '[' + item['displayName'] + ']')\n foot = flask.render_template('report_table_foot.html') \n content = content + head + table + foot\n html = html + content + footer\n return html","title":""},{"docid":"72eb1efd906acf8a31adef56c482fd3a","score":"0.4966787","text":"def index(self):\n\n self.logger.info(\"Start indexing documentation\")\n\n # 1. Fetch the available documentation from DevDocs website.\n r = requests.get(DEVDOCS_INDEX_ALL_URL)\n docs = r.json()\n\n docs = [x for x in docs if x['slug'] in self.docs_to_fetch]\n\n with open(os.path.join(self.index_file), 'w') as f:\n json.dump(docs, f)\n\n # For each doc fetch its associated entries.\n for doc in docs:\n self.fetch_doc_entries(doc['slug'])\n time.sleep(0.5)\n\n self.logger.info(\"Index Finished\")","title":""},{"docid":"91ac9b2dbd5e26b733dbea24b5c598f9","score":"0.49551314","text":"def test_n_search_download_th(self) -> None:\n command = ['search', '--criteria=identifiers:1355444872323826',\n '--collection=ROHAITI', '--server=ro',\n '--download=thumbnail']\n self.do_test_download_file(command, ['1355444872323826_th.jpg'])","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.49539682","text":"def download(self):\n pass","title":""},{"docid":"6862b93abc1ee414e40f40e9ebb68a02","score":"0.4949472","text":"def cb_open_documentation(_, **args):\n open_documentation()","title":""},{"docid":"3b80f28716158d02d5f4464bde98301e","score":"0.49491945","text":"def OnReadOnlineDocs(self, event):\n url = (\"https://gimel-studio.readthedocs.io/en/latest/\")\n webbrowser.open(url)","title":""},{"docid":"f2cffcc6ed3f9508832be22d397d919f","score":"0.49397767","text":"def test_readme_example_one_external():\n adapted_aroon = ta_adaptor(AroonIndicator, \"aroon_down\", window=1)\n signal_transformer = scikit_signal_factory(adapted_aroon)\n df = signal_transformer.fit_transform(simulated_market_data_4_years_gen())\n assert isinstance(df, pd.DataFrame)","title":""},{"docid":"53aee66f191ccb8d7148b0c75717fd6e","score":"0.49275732","text":"def api(index_name, start_date=datetime.date.today(), end_date=datetime.date.today()):\n def parse(response, index_name):\n parser = BeautifulSoup(response, 'html.parser')\n table = parser.find(\"section\", {\"class\": \"general-tables\"})\n results = []\n for row in table.findAll(\"tr\")[1:]:\n values = row.findAll(\"td\")\n if len(values) < 7:\n break\n\n date_raw, close_raw, var_raw, _, high_raw, low_raw, _ = [v.text for v in values]\n close_val = str2float(close_raw)\n var = str2float(var_raw)\n open_val = close_val - var\n date = datetime.datetime.strptime(date_raw, \"%d/%m/%y\").date()\n results.append({\n \"name\": index_name,\n \"date\": date,\n \"open\": open_val,\n \"high\": str2float(high_raw),\n \"low\": str2float(low_raw),\n \"close\": close_val\n })\n return results\n\n if index_name not in AVAILABLE_INDEX:\n raise Exception(\"Index %s not found\", index_name)\n if start_date > end_date:\n raise Exception(\"Invalid date range\")\n\n url = \"http://www.eleconomista.es/indice/IBEX-35/historico-fechas/{}/{}\".format(start_date.strftime(\"%Y-%m-%d\"), end_date.strftime(\"%Y-%m-%d\"))\n #TODO pagination\n return parse(requests.get(url).text, index_name)","title":""},{"docid":"e95d1925e11d5fdd6e5570a6e29e86fa","score":"0.49243712","text":"def describe_document(Name=None, DocumentVersion=None):\n pass","title":""},{"docid":"7d7a936a419a1e4b17f0f67fb65bd9cf","score":"0.49139595","text":"def _gettables(self):\n print('Downloading tables from ods sql.')\n start_processing=time.time()\n o = ODPS('LTAIVYhmNLQm0RPD', 'C7mhqOapX1iUSCYwis3lrZFN16nX5x', 'WS_BigData',\n endpoint='http://service.cn.maxcompute.aliyun.com/api')\n\n for table in self.tables:\n pre_time=time.time()\n print(table)\n table_data = o.get_table(table).to_df().to_pandas()\n print(\"connect \",table,\" successfully cost\",time.time()-pre_time)\n pre_time=time.time()\n table_data.to_csv('data/'+table+'.csv')\n print(\"save \",table,\" successfully cost\",time.time()-pre_time)\n print('finished --------Downloading tables from ods sql.',time.time()-start_processing)\n\n\n #--------------just save a single partition\n print('Fetch specific pt from downloaded tables.')\n fetch_pt_time=time.time()\n tables=[\n 'ods_app_ai_content_content_news_all_dt',\n 'ods_app_ai_aiid_account_all_dt',\n 'ods_app_ai_content_content_topic_all_dt',\n # 'dwd_user_app_browse_action_detail_cdc_dt',\n 'ods_app_ai_content_content_news_comment_all_dt',\n 'ods_app_ai_content_content_news_comment_liked_all_dt',\n 'ods_app_ai_content_content_news_liked_all_dt',\n 'ods_app_ai_content_content_topic_comment_all_dt',\n 'ods_app_ai_content_content_topic_comment_liked_all_dt',\n 'ods_app_ai_content_content_topic_discuss_all_dt',\n 'ods_app_ai_content_content_topic_discuss_comment_all_dt',\n 'ods_app_ai_content_content_topic_discuss_liked_all_dt',\n 'ods_app_ai_content_content_topic_liked_all_dt',\n 'ods_app_ai_content_content_topic_vote_all_dt',\n 'ods_app_ai_content_content_topic_vote_question_all_dt',\n 'ods_app_ai_content_content_topic_vote_option_all_dt',\n 'ods_app_ai_content_content_topic_vote_option_user_all_dt'\n ]\n for table in tables:\n pre_time=time.time()\n print(table+'.csv')\n table_data = pd.read_csv('data/'+table+'.csv',index_col=0,dtype=str)\n print(\"read \",table,\" cost\",time.time()-pre_time)\n pre_time=time.time()\n table_data[table_data['pt'] =='20190717'].to_csv('data/pt20190717'+table+'.csv')\n print(\"save pt20190717-\",table,\" successfully cost\",time.time()-pre_time)\n print('finished --------Fetch specific pt from downloaded tables.',time.time()-fetch_pt_time)","title":""},{"docid":"f76ec774b9588596c09023c89da50a95","score":"0.4913296","text":"def download_antibody_pdbs():\n # base url for SAbDab query\n url_base = 'http://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/'\n url_query_base = url_base + 'DBrowser.php?'\n\n # configure options for SAbDab query\n options = { 'submitted' : 'true',\n 'nonredundant' : 'true',\n 'ab_ident' : '99%25',# 25 need for html\n 'only_complete' : 'false', # might need capital F\n 'incomple_nr' : '*',\n 'agtype' : '*',\n 'ag_ident' : '99%25',\n 'aglengthnr' : '50',\n 'resolution' : '3.0',\n 'nonredundant_selected' : 'true' }\n\n # alternative options (all sub 3.0A structures)\n options2 = { 'submitted' : 'true',\n 'advanced' : 'true',\n 'abtype' : '*',\n 'method' : '*',\n 'organism' : '*',\n 'resolution' : '3.0',\n 'rfactor': '*',\n 'incomplex' : '*',\n 'ag_type' : '*',\n 'ag_length' : '50',\n 'lctype' : '*',\n 'hasconstant' : '*',\n 'hasaffinity' : '*' }\n\n # generate url from query\n #sabdab_url = url_query_base + '&'.join(['{0}={1}'.format(x,y) for x,y in options.items()])\n sabdab_url = url_query_base + '&'.join(['{0}={1}'.format(x,y) for x,y in options2.items()])\n\n # open page\n webpage = urllib2.urlopen(urllib2.Request(sabdab_url))\n\n # read html and extract summary file, and download chothia PDBs\n webpage_html = webpage.read() # PDBs in table, summary at end\n\n # use regex to extract PDBs\n chothia_pdb_re = re.compile('data/entries/\\w\\w\\w\\w/structure/chothia/\\w\\w\\w\\w.pdb')\n summary_re = re.compile('a href=.* download=\\\"summary.csv\\\"')\n pdb_urls = chothia_pdb_re.findall(webpage_html, re.MULTILINE)\n summary_url = summary_re.findall(webpage_html, re.MULTILINE) # should only find two\n summary_url = summary_url[0].split(\" download\")[0][8:-1] # drop unnecessary chars\n\n # make info and antibody_database directories if not present\n if not os.path.isdir(\"info\"): os.mkdir(\"info\")\n if not os.path.isdir(\"antibody_database\"): os.mkdir(\"antibody_database\")\n\n # download summary file, always overwrite\n with open(\"info/sabdab_summary.tsv\", \"w\") as f:\n u = urllib2.urlopen(urllib2.Request(url_base + summary_url))\n f.write(u.read())\n\n # download PDBs, check for *.pdb or *.pdb.bz before writing\n print(\"Found {} pdbs, beginning download.\".format(len(pdb_urls)))\n counter = 0\n for pdb in pdb_urls:\n if pdb[-8:-4] in [\"1qok\", \"1dzb\", \"6b0w\"]: # bad geometry -- skip\n continue\n if pdb[-8:-4] in [\"6db7\", \"6iut\"]: # AntibodyInfo construction issue\n continue\n counter += 1\n fpath = \"antibody_database/{}.pdb\".format(pdb[-8:-4])\n fpath_trunc = \"antibody_database/{}_trunc.pdb\".format(pdb[-8:-4]) # we use this naming later on\n #fpath_bz = \"antibody_database/{}.pdb.bz2\".format(pdb[-8:-4])\n #if not (os.path.isfile(fpath) or os.path.isfile(fpath_bz)):\n if not os.path.isfile(fpath_trunc):\n with open(fpath, \"w\") as f:\n print(\"Downloading {}... {}/{}\".format(pdb[-8:-4], counter, len(pdb_urls)))\n # won't timeout if no internet, so ...\n u = urllib2.urlopen(urllib2.Request(url_base + pdb))\n # write pdb ?\n f.write(u.read())\n # write compressed bz2\n #f.write(bz2.compress(u.read()))\n\n return","title":""},{"docid":"d3cb0cc1064737898d1d7866aa3ff0d8","score":"0.49113882","text":"def get_swr2(self):\n\n\t\tparams = 'swr2'\n\t\tbase = 'http://www.ndbc.noaa.gov/data/realtime2/'\n\t\tlink = base + str(self.buoy) + '.' + params\n\n\t\t#combine the first five date columns YY MM DD hh mm and make index\n\t\tdf = pd.read_csv(link,delim_whitespace=True,skiprows=1,\n\t\t\theader=None, parse_dates=[[0,1,2,3,4]], index_col=0)\n\n\t\t#convert the dates to datetimes\n\t\tdf.index = pd.to_datetime(df.index,format=\"%Y %m %d %H %M\")\n\n\t\tspecs = df.iloc[:,0::2]\n\t\tfreqs = df.iloc[0,1::2]\n\n\t\tspecs.columns=freqs\n\n\t\t#remove the parenthesis from the column index\n\t\tspecs.columns = [cname.replace('(','').replace(')','') \n\t\t\tfor cname in specs.columns]\n\n\t\treturn specs","title":""},{"docid":"119dbc8b853e2642560b127085eeb79d","score":"0.49097875","text":"def download_wl_file(self,fullpath):\n # #testing\n # print(self.api_parameters)\n # r = requests.get(\"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\",params=self.start_end_datetime)\n # print(r.url)\n # print(self.start_end_datetime)\n\n # Check the Folder's existence\n if os.path.exists(fullpath):\n self.metadata[\"Source File\"] = fullpath\n print('Folder exists:' + fullpath)\n else: # Raise a meaningful error\n raise RuntimeError('Folder of path ' + fullpath + ' does not exist')\n\n # # combine parameters and send a request to download data from website test\n # url_string = str(\"begin_date=\" + self.start_end_datetime[\"begin_date\"] + \"\" + self.start_end_datetime[\"begin_time\"] + \"&end_date=\" + \"&station=\" + self.station + \"&product=\" + self.product + \"&datum=\" + self.datum + \"&units=\" + self.units + \"&time_zone=\" + self.time_zone + \"&application=\" + self.application + \"&format=\" + self.format)\n #\n # url_parameters = str(\"begin_date=\" + self.begin_date + \"&end_date=\" + self.end_date + \"&station=\" + self.station + \"&product=\" + self.product + \"&datum=\" + self.datum + \"&units=\" + self.units + \"&time_zone=\" + self.time_zone + \"&application=\" + self.application + \"&format=\" + self.format)\n # url_api = \"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\"\n # url_download = url_api + url_parameters\n # result = requests.get(url_download)\n\n # combine parameters and send a request to download data from website\n url_parameters = str(\"begin_date=\" + self.begin_date + \"&end_date=\" + self.end_date + \"&station=\" + self.station + \"&product=\" + self.product + \"&datum=\" + self.datum + \"&units=\" + self.units + \"&time_zone=\" + self.time_zone + \"&application=\" + self.application + \"&format=\" + self.format)\n url_api = \"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\"\n url_download = url_api + url_parameters\n result = requests.get(url_download)\n\n # Parameters will be printed out\n print(\"Extracting data from:\" + url_download)\n print(\"Following parameters were used: \")\n print(\"begin_date:\", self.begin_date) # yyMMDD HH:MM\n print(\"end_date:\", self.end_date)\n print(\"station:\", self.station)\n print(\"product:\", self.product)\n print(\"datum:\", self.datum)\n print(\"units:\", self.units)\n print(\"timezone:\", self.time_zone)\n print(\"application:\", self.application)\n print(\"format:\", self.format)\n # print output\n #print(result.text)\n\n # export output as xml or csv file intow file directory\n # enter file output file name,format and destination folder\n #print(fullpath)\n filename = self.filename\n fileformat = str('.' + self.format) # takes the format of what you entered earlier\n #abs_path = fullpath \" # folder directory of where the data will be stored\n with open(fullpath + filename + fileformat, 'wb') as file:\n file.write(result.content)\n\n return print(\"File saved in \" + fullpath + filename + fileformat)","title":""},{"docid":"cda36bfd12c72fe830fe0dfab3715512","score":"0.4895257","text":"def testApiDocsContent(server):\n resp = server.request(path='/api/v1', method='GET', isJson=False, prefix='')\n assertStatusOk(resp)\n body = getResponseBody(resp)\n\n assert 'Girder REST API Documentation' in body\n assert 'This is not a sandbox' in body\n assert 'id=\"swagger-ui-container\"' in body","title":""},{"docid":"3bb06a12555e12ce5aeb633e28dc881f","score":"0.48949987","text":"def describe():\n dataset = utilities.opencsv()\n data = pd.read_csv(dataset)\n description = data.describe()\n description.to_csv('description.csv', encoding='utf-8')","title":""},{"docid":"5e1056170d1cd191bb7ff31aecc3881c","score":"0.48867595","text":"def open_documentation(self):\n url = QtCore.QUrl('https://github.com/jrsm1/EWAS_Application/blob/master/README.md')\n if not QtGui.QDesktopServices.openUrl(url):\n super().display_error('Could not open Help URL')\n\n pass","title":""},{"docid":"cf8386682a887af49dba745d14d7dd07","score":"0.4885031","text":"def test_rtd_pydoctor_call():\n # The pydoctor index is generated and overwrites the Sphinx files.\n with open(BASE_DIR / 'api' / 'index.html', 'r') as stream:\n page = stream.read()\n assert 'moduleIndex.html' in page, page","title":""},{"docid":"6d39108e964746d35fbeb78b67236184","score":"0.48793548","text":"def document_src(REQUEST=None, RESPONSE=None):","title":""},{"docid":"2f9b2f6646af7a9877ca3567a3189bec","score":"0.48705807","text":"def get_science_data_url(doi_meta = 'https://api.datacite.org/works/10.5066/P9IGEC9G'):\n \n #get doi metadata\n header = {'content-type':'application/json'}\n r=requests.get(doi_meta, header).json()\n url = r['data']['attributes']['url']\n\n #strip url to return sbid\n sbid = url.replace('https://www.sciencebase.gov/catalog/item/', '')\n\n #get sciencebase information\n item =sb.get_item(sbid)\n\n files = sb.get_item_file_info(item)\n for file in files:\n if re.search(\"^USGS_Dam_Removal_Science_Database_v.*csv$\", file['name']):\n file_url = file['url']\n break\n \n return file_url","title":""},{"docid":"12c5d95c5a3d09f051f6386ce41e14a2","score":"0.4862624","text":"def download_report(ctx):\n pass","title":""},{"docid":"5a5e41d39444ba7c8708b2fb4397d737","score":"0.4860026","text":"def get_iocs():\n response = http_request('GET', 'public/v3/iocs', params=handle_filters(), json_response=True)\n content = response.get('content')\n domains = []\n ip_infos = []\n url_infos = []\n hash_infos = []\n dbot_scores = []\n iocs_context = []\n iocs_readable = []\n\n for indicator in content:\n ioc_context, ioc_readable, dbot_score, domain, ip_info, url_info, hash_info = ioc_to_readable(indicator)\n iocs_context.append(ioc_context)\n iocs_readable.append(ioc_readable)\n dbot_scores.append(dbot_score)\n domains.append(domain)\n ip_infos.append(ip_info)\n url_infos.append(url_info)\n hash_infos.append(hash_info)\n\n headers = ['Type', 'Value', 'FirstSeen', 'LastSeen', 'Status', 'Severity', 'SourceID', 'SourceName',\n 'SourceConfidence', 'Geolocation']\n demisto.results(\n {\n 'Type': entryTypes['note'],\n 'EntryContext': {\n 'IntSights.Iocs(val.Value && val.Value === obj.Value)': iocs_context,\n 'DBotScore': dbot_scores,\n 'Domain': domains,\n 'IP': ip_infos,\n 'URL': url_infos,\n 'File': hash_infos\n },\n 'Contents': response,\n 'HumanReadable': tableToMarkdown('IOC Information', t=iocs_readable, headers=headers),\n 'ContentsFormat': formats['json']\n }\n )","title":""},{"docid":"bb572d010e4cd688578107a164003269","score":"0.48583165","text":"def download_doc(pmids_list):\n print(\"Downloading PMIDs for Not-DIDA\")\n all_data = pbmdh.download_publications(pmids_list)\n filename = CONFIG['NOTDIDA_DOCS'] + \".json\"\n exh.write_json(all_data, filename)\n display.display_info(\"Not-DIDA publications saved in {0}\".format(filename))","title":""},{"docid":"c9811d65dc90ab840c3544a0238e3c2f","score":"0.48534304","text":"def create_auto_documentation():\n create_screenshots()\n\n create_module_docs()\n create_py3_docs()","title":""},{"docid":"4dafc1ccb8a8d7b8d1f0cbf9bd53cc72","score":"0.48530278","text":"def getDocs(self):\n return self.DOCUMENTATION","title":""},{"docid":"0e4f706ad0df96bb36910e94c35da175","score":"0.4852924","text":"def docs():\n return render_template('docs.html')","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.48497438","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.48497438","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.48497438","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.48497438","text":"def download():\n return response.download(request,db)","title":""},{"docid":"d65896080e714cfd565052b771452c31","score":"0.4844849","text":"def test_download_model(self):\n response = self.client.get('/fit/john/1/model/')\n self.assertEqual(response.status_code, 200)\n self.assertTrue(response.content.startswith(\"# Reflectivity model\"))","title":""},{"docid":"9e81a726b61790eea864dbb93eccef04","score":"0.4837576","text":"def docs(self):\n\n # Root folder for the report files\n webbrowser.open(settings.DOCS)","title":""},{"docid":"44f3e9a2e8e747888463b2590655770f","score":"0.4835602","text":"def doc(self, topic=\"About\"): ###\r\n if __name__.startswith(\"BCPy2000.\"):\r\n try:\r\n from . import Documentation\r\n except ImportError:\r\n print(\"documentation not found\")\r\n else:\r\n Documentation.browse(topic)\r\n else:\r\n import webbrowser\r\n\r\n user = os.environ.get(\"USERNAME\", \"\")\r\n if len(user) == 0:\r\n user = os.environ.get(\"USER\", \"\")\r\n if len(user):\r\n user += \"/\"\r\n webbrowser.open(\r\n \"http://localhost/cgi-bin/\"\r\n + user\r\n + \"BCPy2000?\"\r\n + topic.replace(\" \", \"_\")\r\n )","title":""},{"docid":"a982935d3e4de683769a7919c0b43e11","score":"0.48344913","text":"def ireport():\n\n tablename = \"%s_%s\" % (module, resourcename)\n table = s3db[tablename]\n\n if \"open\" in get_vars:\n # Filter out Reports that are closed or Expired\n s3.crud_strings[tablename].title_list = T(\"Open Incidents\")\n response.s3.filter = (table.closed == False) & \\\n ((table.expiry == None) | \\\n (table.expiry > request.utcnow))\n\n # Non-Editors should only see a limited set of options\n if not auth.s3_has_role(\"EDITOR\"):\n irs_incident_type_opts = response.s3.irs_incident_type_opts\n ctable = s3db.irs_icategory\n allowed_opts = [irs_incident_type_opts.get(opt.code, opt.code) for opt in db().select(ctable.code)]\n allowed_opts.sort()\n table.category.requires = IS_EMPTY_OR(IS_IN_SET(allowed_opts))\n\n # Pre-processor\n def prep(r):\n table = r.table\n if r.method == \"ushahidi\":\n auth.settings.on_failed_authorization = r.url(method=\"\", vars=None)\n # Allow the 'XX' levels\n s3db.gis_location.level.requires = IS_EMPTY_OR(IS_IN_SET(\n gis.get_all_current_levels()))\n elif r.interactive or r.representation == \"aadata\":\n if r.method == \"update\":\n table.dispatch.writable = True\n table.verified.writable = True\n table.closed.writable = True\n if r.component:\n if r.component_name == \"image\":\n itable = s3db.doc_image\n itable.date.default = r.record.datetime.date()\n itable.location_id.default = r.record.location_id\n itable.location_id.readable = itable.location_id.writable = False\n itable.organisation_id.readable = itable.organisation_id.writable = False\n #itable.url.readable = itable.url.writable = False\n itable.person_id.readable = itable.person_id.writable = False\n elif r.component_name == \"document\":\n dtable = s3db.doc_document\n dtable.date.default = r.record.datetime.date()\n dtable.location_id.default = r.record.location_id\n dtable.location_id.readable = dtable.location_id.writable = False\n dtable.organisation_id.readable = dtable.organisation_id.writable = False\n #dtable.url.readable = dtable.url.writable = False\n dtable.person_id.readable = dtable.person_id.writable = False\n elif r.component_name == \"human_resource\":\n if not r.component_id:\n # Hide fields from create forms (list_fields override)\n htable = s3db.irs_ireport_human_resource\n htable.response.readable = htable.response.writable = False\n htable.reply.readable = htable.reply.writable = False\n s3.crud.submit_button = T(\"Assign\")\n s3.crud_strings[\"irs_ireport_human_resource\"] = Storage(\n label_create = T(\"Assign Human Resource\"),\n title_display = T(\"Human Resource Details\"),\n title_list = T(\"Human Resource Assignments\"),\n title_update = T(\"Edit Human Resource\"),\n label_list_button = T(\"List Assigned Human Resources\"),\n label_delete_button = T(\"Remove Human Resource from this incident\"),\n msg_record_created = T(\"Human Resource assigned\"),\n msg_record_modified = T(\"Human Resource Assignment updated\"),\n msg_record_deleted = T(\"Human Resource unassigned\"),\n msg_list_empty = T(\"No Human Resources currently assigned to this incident\"))\n elif r.component_name == \"task\":\n s3db.configure(\"project_task\", copyable=False)\n elif r.component_name == \"vehicle\":\n s3.crud.submit_button = T(\"Assign\")\n s3.crud_strings[\"irs_ireport_vehicle\"] = Storage(\n label_create = T(\"Assign Vehicle\"),\n title_display = T(\"Vehicle Details\"),\n title_list = T(\"Vehicle Assignments\"),\n title_update = T(\"Edit Vehicle Assignment\"),\n label_list_button = T(\"List Vehicle Assignments\"),\n label_delete_button = T(\"Remove Vehicle from this incident\"),\n msg_record_created = T(\"Vehicle assigned\"),\n msg_record_modified = T(\"Vehicle Assignment updated\"),\n msg_record_deleted = T(\"Vehicle unassigned\"),\n msg_list_empty = T(\"No Vehicles currently assigned to this incident\"))\n\n return True\n response.s3.prep = prep\n\n # Post-processor\n def postp(r, output):\n if r.representation == \"plain\" and \\\n r.method !=\"search\":\n # Map Popups\n # Look for a Photo\n # @ToDo: The default photo not the 1st\n ptable = s3db.doc_image\n query = (ptable.doc_id == r.record.doc_id) & \\\n (ptable.deleted == False)\n image = db(query).select(ptable.file,\n limitby=(0, 1)).first()\n if image and image.file:\n url = URL(c=\"default\", f=\"download\", args=image.file)\n output[\"item\"].append(IMG(_src=url,\n # @ToDo: capture the size on upload & have controller resize where-required on-download\n _width=400,\n _height=400))\n return output\n response.s3.postp = postp\n\n output = crud_controller(rheader=s3db.irs_rheader)\n\n # @ToDo: Add 'Dispatch' button to send OpenGeoSMS\n #try:\n # delete_btn = output[\"delete_btn\"]\n #except:\n # delete_btn = \"\"\n #buttons = DIV(delete_btn)\n #output.update(delete_btn=buttons)\n\n return output","title":""},{"docid":"135359b9eb8b807967d1d0fa0cab1391","score":"0.482283","text":"def download_intrinio_price(data_url, auth, show_progress=False):\n\n # Donwload price data\n if show_progress:\n log.info('Downloading price data @{url}'.format(url=data_url))\n\n data = download_without_progress(data_url, auth)\n table = pd.read_csv(data, skiprows=[0]) # Skip the first row\n table.insert(0, 'symbol', data_url.split('=')[-1]) # e.g. \"https://api.intrinio.com/prices.csv?identifier=AAPL\"\n table.columns = map(str.lower, table.columns)\n table.date = pd.to_datetime(table.date)\n return table","title":""},{"docid":"5c0e05327a402cbf9b5db62202639ea9","score":"0.48211864","text":"def search_doc(s, extra=''):\n cmd = 'sage -grepdoc \"%s\" | grep \"%s\"'%(s,extra)\n\n old_banner = os.environ.has_key('SAGE_BANNER')\n if old_banner:\n old_banner = os.environ['SAGE_BANNER']\n os.environ['SAGE_BANNER'] = \"no\"\n r = os.popen(cmd).read()\n if old_banner == False:\n del os.environ['SAGE_BANNER']\n else:\n os.environ['SAGE_BANNER'] = old_banner\n \n from sage.server.support import EMBEDDED_MODE\n if EMBEDDED_MODE: # I.e., running from the notebook\n print format_search_as_html('Documentation', r, s + extra)\n else:\n from sage.misc.all import pager\n pager()(r)","title":""},{"docid":"6a40d6895b940faa7874cdea8a2daff1","score":"0.48169506","text":"async def download(session, run_code):\n url = baseURL + 'genesis/sis/view'\n params = {\n 'module': 'reportWriter',\n 'category': 'reports',\n 'tab1': 'viewReport',\n 'runcode': run_code,\n 'action': 'rawopen'\n }\n async with session.get(url, params=params) as resp:\n return await resp.text()","title":""},{"docid":"b07b49a77ce5def3f82bf30dfa85398c","score":"0.48153055","text":"def download_dudetailsummary(cache, asof_date=None):\n if asof_date != None:\n latest = hp._validate_and_convert_date(asof_date, \"asof_date\")\n else:\n latest = datetime(datetime.now().year, datetime.now().month, 1) - timedelta(days = 90)\n \n cache = hp._check_cache(cache)\n overwrite_nemosis_defaults()\n\n if latest < datetime(2009,7,1):\n raise Exception(\"DATA UNAVAILABLE: DUDETAILSUMMARY table is not found in MMS prior to 07-2009. \" + \\\n \"Cannot correctly map emissions factors. Retry function without specifying `asof_date`\")\n\n # Data Retrieval\n df = mod_dynamic_data_fetch_loop(start_search=latest,\n start_time=latest - timedelta(hours=1),\n end_time=latest,\n table_name=\"DUDETAILSUMMARY\",\n raw_data_location=cache,\n select_columns=[\"START_DATE\", \"DUID\", \"DISPATCHTYPE\", \"REGIONID\", \"LASTCHANGED\"],\n date_filter=None,\n fformat=\"feather\",\n )\n df = pd.concat(df)\n df = df.drop(['file_year','file_month', 'LASTCHANGED'], axis=1)\n filtered = df.sort_values('START_DATE').drop_duplicates(['DUID'], keep='last')\n return filtered.sort_values(['DUID','START_DATE']).reset_index(drop=True)","title":""},{"docid":"bf6ac6025f06598c4ce9db39ab716ec8","score":"0.48119348","text":"def download_description(self):\n if os.path.exists(self.file_txt):\n return\n bf = BeautifulSoup(self.descr, 'lxml').contents[0].get_text()\n descr = f\"{self.name}\\n{bf}\\n\"\n with open(self.file_txt, mode=\"w\") as f:\n f.write(descr)","title":""},{"docid":"5100a187578cadfb845fed64a586fca0","score":"0.48112953","text":"def daily_api_update():\n\n yesterday = date.today() - timedelta(1)\n url_startdate = yesterday.strftime('%Y%m%d')\n url_enddate = date.today().strftime('%Y%m%d')\n\n get_historic_demand_api_data(url_startdate, url_enddate)\n\n # not using Net Imports now. may use in the future\n # get_historic_imports_api_data(url_startdate, url_enddate)","title":""},{"docid":"0515e9757b1874496f61576069455fd6","score":"0.48105806","text":"def print_doc(attrs):\n rstf.write(\"S-Link Attributes\\n\")\n rstf.write(\"-----------------\\n\")\n \n \n rstf.write(\".. table::\\n\")\n rstf.write(\" :widths: 10 30 10 10 20 50\\n\\n\")\n \n addr_size = len(\"Address \")\n name_size = len(\"Name \")\n ro_size = len(\"ReadOnly\")\n width_size = len(\"Width \")\n rst_size = len(\"Reset \")\n desc_size = len(\"Description \")\n \n \n for a in attrs:\n if(len(attrs[a].name) > name_size): name_size = len(attrs[a].name)\n if(len(str(attrs[a].width)) > width_size): width_size = len(str(attrs[a].width))\n if(len(str(attrs[a].reset)) > rst_size): rst_size = len(str(attrs[a].reset))\n if(len(attrs[a].desc) > desc_size): desc_size = len(attrs[a].desc)\n \n rstf.write(\" {0} {1} {2} {3} {4} {5}\\n\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))\n rstf.write(\" %-*s %-*s %-*s %-*s %-*s %-*s\\n\" % (addr_size, \"Address\", name_size, \"Name\", width_size, \"Width\", ro_size, \"ReadOnly\", rst_size, \"Reset\", desc_size, \"Description\"))\n rstf.write(\" {0} {1} {2} {3} {4} {5}\\n\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))\n \n for a in attrs:\n rstf.write(\" %-*s %-*s %-*s %-*s %-*s %-*s\\n\" % (addr_size, '0x'+str(format(a, 'x')), name_size, attrs[a].name, width_size, attrs[a].width, ro_size, str(attrs[a].ro), rst_size, attrs[a].reset, desc_size, attrs[a].desc))\n\n rstf.write(\" {0} {1} {2} {3} {4} {5}\\n\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))","title":""},{"docid":"a35ffa135c86ae42565a30ae792e020d","score":"0.4806526","text":"def fetch_bls_hist(api_key):\n \n # Header\n headers = {'Content-type': 'application/json'}\n \n # Payload\n # LAUMT123674000000003 : local unemployment for Orlando\n # LNU04000000: national unemployment\n # CUUR0000SA0 : national CPI\n \n series_list = ['LAUMT123674000000003','LNU04000000','CUUR0000SA0']\n \n payload = json.dumps({\n \"seriesid\": series_list,\n \"startyear\": \"2014\",\n \"endyear\": \"2019\",\n \"registrationkey\": api_key\n })\n \n # Request\n print('Sending request, may take a few minutes...')\n req = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=payload, headers=headers)\n \n # Dataframes\n local_unemp = make_bls_df(req=req, idx=0, metric='unemp_local')\n natl_unemp = make_bls_df(req=req, idx=1, metric='unemp_natl')\n natl_cpi = make_bls_df(req=req, idx=2, metric='cpi_natl')\n \n # Consolidated dataframe\n out_df = pd.merge(local_unemp, natl_unemp, how='outer', on=['month_of_year','year_of_calendar'])\n out_df = pd.merge(out_df, natl_cpi, how='outer', on=['month_of_year','year_of_calendar'])\n \n return out_df","title":""},{"docid":"0f66a71410c7ccbe929b463abe8e67ae","score":"0.4805448","text":"def get_api_data(endpoint_url, params, filepath, index_c='DATE'):\n with open(filepath, 'wb') as fi:\n response = requests.get(endpoint_url, stream=True, params=params)\n if not response.ok:\n print \"Something went wrong.\"\n for segment in response.iter_content(1024):\n fi.write(segment)\n odf = pd.read_csv(filepath, parse_dates=True, index_col=index_c)\n return odf","title":""},{"docid":"89e2306e382b2cf5d9b17618875b65d8","score":"0.48024186","text":"def ew_download(basefolder, logfolder, ddate): \n\n print(\"Downloading England and Wales data extract\")\n print(\"\\r\")\n\n \n # Create data folder\n\n dfolder = basefolder + \"/ew\"\n if not os.path.isdir(dfolder):\n os.mkdir(dfolder)\n else:\n print(\"{} already exists\".format(dfolder)) \n\n\n mfile = logfolder + \"/ew-download-metadata-\" + ddate + \".json\"\n\n\n # Request zip files\n \n tables = [\"charity\", \"charity_annual_return_history\", \"charity_annual_return_parta\", \"charity_annual_return_partb\", \n \"charity_area_of_operation\", \"charity_classification\", \"charity_event_history\", \"charity_governing_document\", \"charity_other_names\",\n \"charity_other_regulators\", \"charity_policy\", \"charity_published_report\", \"charity_trustee\"]\n webadd = \"https://ccewuksprdoneregsadata1.blob.core.windows.net/data/txt/publicextract.{}.zip\" # txt download (json also available)\n \n for table in tables:\n response = requests.get(webadd.format(table))\n print(response.status_code, response.headers)\n\n if response.status_code==200:\n\n outfile = dfolder + \"/\" + table + \"-\" + ddate + \".zip\"\n print(outfile)\n if os.path.isfile(outfile): # do not overwrite existing file\n print(\"File already exists, no need to overwrite\")\n else: # file does not currently exist, therefore create\n with open(outfile, \"wb\") as f:\n f.write(response.content)\n\n # Unzip files\n\n with zipfile.ZipFile(outfile, 'r') as zip_ref:\n \tzip_ref.extractall(dfolder)\n\n else:\n print(\"Unable to download data extract from link {}\".format(webadd))\n print(\"Status code {}\".format(response.status_code))\n\n\n # Write metadata to file\n\n mdata = dict(response.headers)\n mdata[\"file\"] = table\n mdata[\"file_link\"] = str(webadd)\n mdata[\"data_extract_last_modified\"] = response.headers[\"Last-Modified\"]\n\n with open(mfile, \"w\") as f:\n json.dump(mdata, f)\n \n\n print(\"\\r\") \n print(\"Successfully downloaded data extract\")\n print(\"Check log file for metadata about the download: {}\".format(mfile))","title":""},{"docid":"de43686f7dbf00b47c20c9728de90082","score":"0.48023462","text":"def onboarding_docs(self) -> List[dict]:\n docs = [\n BKitB.make_section_block(\n BKitB.plaintext_section(\n \"Welcome to OKR! We're glad to have you on board!\\nCheck out these links below \"\n \"to get familiar with OKR and the industry we support!\"\n )\n ),\n BKitB.make_section_block(\n BKitB.markdown_section(\n f\"\\t<{self.show_onboring_link()}|Onboring Doc>\\n\"\n )\n ),\n BKitB.make_section_block(\n BKitB.plaintext_section(\n \"For any questions, reach out to the CEO or our Head of Recruiting. \"\n \"Don't know who they are? Well, figure it out!\"\n )\n )\n ]\n return docs","title":""},{"docid":"e0ebd2be934d70a7a8d09b814f59c405","score":"0.47982448","text":"def showDocs():\n counter = 1\n print '\\n--------------------------------------'\n print main.__doc__\n print showUsage.__doc__\n print showDocs.__doc__\n print getTerms.__doc__\n print showResult.__doc__\n print Term.__doc__\n print parseTerm.__doc__\n print termValid.__doc__\n print cleanTerm.__doc__\n print generatePolynom.__doc__\n print recreateTerm.__doc__\n print debug.__doc__\n print setDebug.__doc__\n print setBigMode.__doc__\n print '--------------------------------------\\n'","title":""},{"docid":"0a5f58504c0842aaac0f287011529633","score":"0.47967297","text":"def import_soda(client_path, end_point, file_name, rxminer_token):\n client = Socrata(client_path, rxminer_token)\n results = client.get(end_point)\n results_df = pd.DataFrame.from_records(results)\n export_csv = results_df.to_csv(r'../test/rxdata/'+file_name, index=None, header=True)\n print('Finish downloading '+file_name)","title":""},{"docid":"2277977668b9cb9791984fea2e2ed51a","score":"0.47865814","text":"def getHistoricData(symbol, sDate=(1990,1,1),eDate=date.today().timetuple()[0:3],verbose=True):\r\n\r\n urlStr = 'http://ichart.finance.yahoo.com/table.csv?s={0}&a={1}&b={2}&c={3}&d={4}&e={5}&f={6}'.\\\r\n format(symbol.upper(),sDate[1]-1,sDate[2],sDate[0],eDate[1]-1,eDate[2],eDate[0])\r\n\r\n \r\n try:\r\n lines = urllib2.urlopen(urlStr).readlines()\r\n except Exception, e:\r\n s = \"Failed to download:\\n{0}\".format(e);\r\n print s\r\n\r\n dates = []\r\n data = [[] for i in range(6)]\r\n #high\r\n \r\n # header : Date,Open,High,Low,Close,Volume,Adj Close\r\n for line in lines[1:]:\r\n #print line\r\n fields = line.rstrip().split(',')\r\n dates.append(datetime.strptime( fields[0],'%Y-%m-%d'))\r\n for i,field in enumerate(fields[1:]):\r\n data[i].append(float(field))\r\n \r\n idx = Index(dates)\r\n data = dict(zip(['open','high','low','close','volume','adj_close'],data))\r\n \r\n # create a pandas dataframe structure \r\n df = DataFrame(data,index=idx).sort()\r\n \r\n if verbose:\r\n print 'Got %i days of data' % len(df)\r\n \r\n return df","title":""},{"docid":"e5b8edf042ea4fb7b9587157e0171742","score":"0.4783116","text":"def _show_docs(self):\n try:\n if sys.platform == 'darwin':\n os.system('open {}'.format(self.source_path))\n else:\n os.system('open-xdg {}'.format(self.source_path))\n except IOError:\n raise IOError(\"documentation file '{}' could not be opened\".format(self.source_path))","title":""},{"docid":"73a3949dde14c13263c9de48c1139666","score":"0.4779665","text":"def test_get_sys_internal_specs_openapi(self):\n pass","title":""},{"docid":"ad951e2ed84fb7378b85655e9d82c855","score":"0.4775592","text":"def _fetch_sis(self):\n\n\t\t# Generate all urls.\n\t\tgen_url = lambda s: \"http://67.220.92.23/bbs/forum-230-\"\\\n\t\t\t\t+ s + \".html\"\n\n\t\t# Prepare urls.\n\t\tall_urls = [gen_url(str(i)) for i in\\\n\t\t\t\trange(1, self.PAGES+1)]\n\n\t\timport time\n\t\timport urllib2\n\t\t# Set proxy because of GFW.\n\t\tproxy_handler = urllib2.ProxyHandler({\"http\":\"http://127.0.0.1:8087\"})\n\t\topener = urllib2.build_opener(proxy_handler)\n\t\t# Not using list conprehension because we need to\n\t\t# suspend a while in case the web site reject too\n\t\t# many requests in a short time. \n\t\t# Multi-threads can be used to enhance this part.\n\t\tfor url in all_urls:\n\t\t\tprint \"Fetching:\"+url\n\t\t\twhile True:\n\t\t\t\ttry:\n\t\t\t\t\tself.raw_data[self.SIS].append(opener.open(url,\\\n\t\t\t\t\t\ttimeout=5).read())\n\t\t\t\texcept socket.timeout:\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\ttime.sleep(1)\n\t\t\t\t\tbreak\n\n\t\t# Extract records.\n\t\tfrom sisHTMLProcessor import\\\n\t\t\t\tsisHTMLProcessor as SHP\n\t\tfor page in self.raw_data[self.SIS]:\n\t\t\tp = SHP()\n\t\t\tp.feed(page)\n\t\t\tself.all_records[self.SIS].extend(p.get_records())","title":""},{"docid":"f3ac6d1a2492d1c5fac6757d4c1b3563","score":"0.476675","text":"def fetch(self):\n\n # if you need to add a file_type add it to if clause below\n # and then create a new \"fetch\" method to download the data\n if self.file_type in self.file_list:\n if self.file_type == \"csv\":\n df = self.fetch_csv()\n\n if self.file_type == \"html-stream\":\n self.session = AsyncHTMLSession()\n results = self.session.run(self.fetch_html_stream)\n df = self.link_to_df(results)\n\n if self.file_type == \"findlink\":\n doc_url, txt = self.fetch_link()\n if doc_url:\n if self.file_ext == \"csv\":\n df = self.fetch_csv_stream(doc_url)\n if self.file_ext == \"xlsx\":\n df = pd.read_excel(doc_url)\n if self.file_ext == \"pdf\":\n front = re.findall(r\"(?<=\\/\\/).*?(?=\\/)\", doc_url)[0]\n front = front.replace(\"www.\",\"\")\n front = front.replace(\".\",\"-\")\n back = re.findall(r\"([^\\/]+$)\", doc_url)[0]\n curdir = os.getcwd()\n pdf_name = f\"{curdir}/{front}_{back}\"\n self.download_pdf(doc_url, pdf_name)\n df = parse_pdf(self.url, txt, pdf_name)\n\n else:\n print(\"Error: findlink did not return a document link\\n\")\n df = pd.DataFrame()\n\n if self.file_type == \"findlinks\":\n doc_urls, txts = self.fetch_links()\n df_list = []\n if doc_urls:\n print(\"\\tSearched for multiple links and found the following:\")\n for doc_url in doc_urls:\n if self.file_ext == \"csv\":\n df = self.fetch_csv_stream(doc_url)\n df_list.append(df)\n print(f\"\\t{doc_url}\")\n print(\"\")\n df = df_list\n df_list = []\n\n else:\n print(\"\\tError: findlinks did not return any document links\\n\")\n df = pd.DataFrame()\n\n if self.file_type == \"csv-git-scan\":\n df = self.fetch_csv_git_scan()\n\n if self.file_type == \"json\":\n data = self.fetch_json()\n\n # If no json retrieved, return empty DataFrame\n if not data:\n df = pd.DataFrame()\n # Otherwise convert json to DF\n else:\n # get validator name supplied by user in params\n validator = self.key_phrase.get(\"validator\")\n checkdata_key, results_key = self.key_phrase.get(\"keys\")\n\n # convert json data to DataFrame using\n # (1) validator or (2) direct conversion (no data validation)\n if validator:\n df = self.choose_validator(data) # validate than covnert\n else:\n df = self.json2df(data, results_key) # direct conversion\n\n else:\n # pp = pprint.PrettyPrinter(indent=1)\n if self.file_type:\n print(\n f\"\\nError: currently no capability to download file type: {self.file_type}\"\n )\n else:\n print(\"\\nError: no file type was provided.\")\n print(f\"Allowable file types are: \")\n pprint(self.file_list, width=5)\n print(\"Exiting....\")\n exit()\n\n print(\"\\tData fetched! Saved to DataFrame.\\n\")\n\n return df","title":""},{"docid":"5b00ac787b9ea02592a29527fdefcfaa","score":"0.47642988","text":"def _download(self, request_dict={}):\r\n html_l = super(Site, self)._download(request_dict)\r\n html_trees = []\r\n # this path reads the links for the last month in that year\r\n path = \"id('opinions')//h2[string-length()>2][last()]/following::a[string-length()=10]/@href[not(contains(., 'pdf'))]\"\r\n # to get all the dates in that page the following path can be used:\r\n # path = \"id('opinions')//a[string-length()=10]\"\r\n for url in html_l.xpath(path):\r\n html_tree = self._get_html_tree_by_url(url, request_dict)\r\n html_trees.append((html_tree, url))\r\n\r\n return html_trees","title":""},{"docid":"bf7985711a7c25d752fd9062f6af1ad4","score":"0.47642314","text":"def main():\n html = fetch('http://www.hanbit.co.kr/store/books/full_book_list.html')\n books = scrape(html)\n save('books.db', books)\n view('books.db')","title":""},{"docid":"73e71ccc9f7d4de974f87843619020a7","score":"0.4760326","text":"def get_ohlcv_data(ticker, interval, start, end):\n log = logging.getLogger(__name__)\n\n ric = oci.convert_ticker(ticker, 'Reuters')\n log.info('Download data for: {} - RIC: {}'.format(ticker, ric))\n try:\n # Directly prints to the console if there's an issue - TODO: how to catch this?\n df = ek.get_timeseries(ric, start_date=start, end_date=end, interval=interval, fields=['HIGH', 'LOW', 'OPEN', 'CLOSE', 'VOLUME'])\n df.index.names = [None]\n df.columns = ['High', 'Low', 'Open', 'Close', 'Volume']\n df = df[['Open', 'High', 'Low', 'Close', 'Volume']]\n # Remove weekend days\n df = df[df.index.dayofweek < 5]\n # Settlement Fix (old spreads don't have settlements so need to get the close)\n close = pd.isnull(df['Close'])\n if len(close[close]) > 1 and oci.get_future_type(ticker) == oci.FutureType.Spread:\n df = fix_settlement(ticker, df)\n # Fill NAs\n df['Open'].fillna(df['Close'], inplace=True)\n df['High'].fillna(df['Close'], inplace=True)\n df['Low'].fillna(df['Close'], inplace=True)\n df['Volume'].fillna(0, inplace=True)\n # Add OI: Only for outrights and if the outright has traded\n vdf = df[df['Volume'] > 0]\n if len(vdf) > 0 and oci.get_future_type(ticker) == oci.FutureType.Outright:\n # Have to do this as the OI would shift it we start early (and there's no OI for some days)\n first_trade = str(vdf.index[0])[0:10]\n df = add_oi(ticker, df, first_trade, end) if interval is 'daily' else df\n else:\n df['OI'] = pd.Series([0 for _ in range(len(df.index))], index=df.index)\n # Check if first line has NAs and discard them\n # TODO: Move this in a function?\n if oci.get_future_type(ticker) == oci.FutureType.Outright and len(df) > 20:\n drop = []\n for i in range(20):\n if df.iloc[i].isnull().sum():\n drop.append(i)\n if drop:\n log.warning('Dropping lines: {}'.format(drop))\n df = df.drop(df.index[drop])\n except (TypeError, IndexError, req.HTTPError, ek.eikonError.EikonError, json.decoder.JSONDecodeError) as e:\n log.error('Problem downloading data for {}, see: {}'.format(ticker, e))\n df = None\n\n return df","title":""},{"docid":"6e9b079fde0f3a2c8700345a965c33f8","score":"0.476012","text":"def access(self, ext=None):\n if self.anchor.id == 'N:organization:fake-organization-id':\n # FIXME need a way to generalize/fix this so that organization\n # can vary ...\n print('Not in correct organization for running access report.')\n sys.exit(9999)\n\n from sparcur.sheets import Overview, Master\n if self.options.server:\n def fmt(d): return hfn.atag(d.uri_human, d.id, new_tab=True)\n else:\n def fmt(d): return d.uri_human\n\n if self.options.server:\n uri_human = Master._uri_human()\n def fmtmc(s): return hfn.atag(uri_human, s, new_tab=True)\n else:\n def fmtmc(s): return s\n\n o = Overview()\n remote = self.anchor.remote\n remote_datasets = remote.children\n\n master_sheet_ids = o.dataset_ids()\n bf_api_ids = set(d.id for d in remote_datasets)\n\n missing_from_api = sorted(master_sheet_ids - bf_api_ids)\n missing_datasets = [self.BlackfynnRemote(i, local_only=True) for i in missing_from_api]\n missing_uris = [fmt(d) for d in missing_datasets]\n rows = [['', ''],\n [fmtmc('Master Count'), len(master_sheet_ids)],\n ['BF API Count', len(bf_api_ids)],\n ['No API Count', len(missing_from_api)],\n *[[i, u] for i, u in zip(missing_from_api, missing_uris)]\n ]\n return self._print_table(rows, title='Access Report', ext=ext)","title":""}],"string":"[\n {\n \"docid\": \"cdd5c1affe96db172149ff6160bcf39d\",\n \"score\": \"0.61644614\",\n \"text\": \"def get_doc(logya_inst, url):\\n\\n return logya_inst.docs.get(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0cbf68e7aca92819f0785808bf4984\",\n \"score\": \"0.5851935\",\n \"text\": \"def get_documentation(self):\\n # call API without function to get hug to return standard 404 + documentation\\n\\n response = self.get(\\\"\\\")\\n\\n return response[\\\"documentation\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0a0e77ee4c7f2a2467250387265e00\",\n \"score\": \"0.58089244\",\n \"text\": \"def serviceDocumentation( pageTitle, pageDom, eCount=3 ):\\n\\ttemplate = \\\"single_source\\\"\\n\\tpublicXpath= \\\"//*/p/text()\\\"\\n\\ttemplatePublic = 'PUBLIC'\\n\\n# \\n# \\tNOTE: The code doesn't work, using the body content of the api pull has changed the structure of the DOM.\\n# \\t\\tBecause we RC isn't going to go this direction with their document system structures we will now have\\n# \\t\\tto pull from another system all together.\\n# \\n\\n\\n\\t# validatePublic = pageDom.xpath( publicXpath )[0]\\n\\tvalidatePublic = pageDom.xpath( publicXpath )\\n\\tprint( validatePublic )\\n\\tif validatePublic == templatePublic:\\n\\t\\trowCountXpath= \\\"count(//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr)\\\"\\n\\t\\trowCount = int( pageDom.xpath( rowCountXpath ) ) \\n\\t\\tpublicDictionary = {}\\n\\t\\tfor index in range( 2, rowCount+1 ):\\n\\t\\t\\trowKeyXpath= \\\"//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr[{}]/td[1]/text()\\\".format( str( index ) )\\n\\t\\t\\trowValueXpath= \\\"//*[@id='main-content']/div/div[1]/div[2]/div/div[1]/table/tbody/tr[{}]/td[2]/text()\\\".format( str( index ) )\\n\\t\\t\\tkey = pageDom.xpath( rowKeyXpath )\\n\\t\\t\\tvalue = pageDom.xpath( rowValueXpath )\\n\\n\\t\\t\\tpublicDictionary[key[0]]=value[0]\\n\\n\\t\\tpublicFile = os.path.join( __data_mount_dir__, template, templatePublic, \\\"{}.json\\\".format( pageTitle.replace(\\\" \\\",\\\"_\\\") ) )\\n\\t\\twriteToDisk( publicDictionary, publicFile )\\n\\n\\t# //*[@id=\\\"main-content\\\"]/div/div[2]/div[1]/div/p\\n\\tuserXpath= \\\"//*[@id='main-content']/div/div[2]/div[1]/div/p/text()\\\"\\n\\ttemplateUser = \\\"USER\\\"\\n\\tvalidateUser = pageDom.xpath( userXpath )[0]\\n\\tif validateUser == templateUser:\\n\\t\\trowCountXpath= \\\"count(//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr)\\\"\\n\\t\\trowCount = int( pageDom.xpath( rowCountXpath ) ) \\n\\t\\tpublicDictionary = {}\\n\\t\\tfor index in range( 2, rowCount+1 ):\\n\\t\\t\\trowKeyXpath= \\\"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[1]/text()\\\".format( str( index ) )\\n\\t\\t\\trowValueXpath= \\\"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[2]/text()\\\".format( str( index ) )\\n\\t\\t\\trowSourceXpath= \\\"//*[@id='main-content']/div/div[2]/div[2]/div/div[1]/table/tbody/tr[{}]/td[3]/text()\\\".format( str( index ) )\\n\\t\\t\\tkey = pageDom.xpath( rowKeyXpath )\\n\\t\\t\\tvalue = pageDom.xpath( rowValueXpath )\\n\\t\\t\\t# print(key, value)\\n\\t\\t\\tsource = pageDom.xpath( rowSourceXpath )\\n\\t\\t\\tprint(key, source.replace('\\\\n',None))\\n\\n\\t\\t# \\tpublicDictionary[key[0]]=[value[0], source[0] if True else None]\\n\\n\\t\\tprint( publicDictionary )\\n\\n\\t\\tuserFile = os.path.join( __data_mount_dir__, template, templateUser, \\\"{}.json\\\".format( pageTitle.replace(\\\" \\\",\\\"_\\\") ) )\\n\\t\\twriteToDisk( publicDictionary, userFile )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c9770db08a3524a8651032119b8014\",\n \"score\": \"0.5693182\",\n \"text\": \"def documentation():\\n # display the page without any additional data\\n return render_template(\\\"documentation.html\\\",\\n title=\\\"Documentation - Bushing Failure Historian\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5545f8f982bf11f641936ba415c7254\",\n \"score\": \"0.56810844\",\n \"text\": \"def download_bench_data(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9d1944633d913befd886dbfaca58ce4\",\n \"score\": \"0.5672151\",\n \"text\": \"def download_database(self):\\n url =\\\"http://ndbserver.rutgers.edu\\\"\\n url1 = url+\\\"/service/ndb/atlas/gallery/rna?polType=onlyRna&rnaFunc=all&protFunc=all&strGalType=rna&expMeth=all&seqType=all&galType=table&start=0&limit=50\\\"\\n query = requests.get(url1)\\n tree = html.fromstring(query.content)\\n database_link = tree.xpath('//tr/td/h2/span/a[@id]/@href')\\n urllib.urlretrieve(url+database_link[0], \\\"NDB_updated.xls\\\")\\n csv_from_excel()\\n return \\\"NDB Database was updated and converted to csv file\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b840b418376dd9a1efa882712046e7\",\n \"score\": \"0.561059\",\n \"text\": \"def download_dataset():\\n # Parameters for the connection to the database\\n Db2_j5_dsn = \\\"API key not publicly published. For the assignment, the keys are saved in the config.py within the \\\" \\\\\\n \\\"folder submitted on moodle.\\\"\\n # Establish the connection\\n Db2_j5_connection = dbi.connect(Db2_j5_dsn)\\n # Query to process\\n query = 'SELECT * FROM \\\"CJH17542\\\".\\\"SN_20172994_IOT\\\"'\\n # Get data from the database through the query created\\n data_df_1 = pd.read_sql_query(query, con=Db2_j5_connection)\\n # Close the connection\\n Db2_j5_connection.close()\\n return data_df_1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5928d0842c5005269e3e8f3e185256\",\n \"score\": \"0.5594553\",\n \"text\": \"def generate_docs(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975cb572177960b520d874d2b987ab54\",\n \"score\": \"0.55297244\",\n \"text\": \"def show_index():\\n yield DOCUMENTATION_INTRO\\n yield '<table id=\\\"api-endpoints\\\"><tbody>\\\\n'\\n yield '<tr><th style=\\\"text-align: left\\\">Endpoint</th><th style=\\\"text-align: left\\\">Description</th>\\\\n'\\n for route in application.routes:\\n if route.rule == '/':\\n yield '\\\\n<tr><td style=\\\"white-space: nowrap; font-weight: bold;\\\">/v2/</td><td>This documentation page for version 2 of the API.</td></tr>'\\n elif route.rule.endswith('.json') and any(route.rule[:-4] + 'dat' == iter_route.rule for iter_route in application.routes):\\n # JSONified version of an NBT endpoint\\n continue\\n elif route.rule.endswith('.dat'):\\n if '<' in route.rule:\\n yield '\\\\n<tr><td style=\\\"white-space: nowrap;\\\">/v2' + xml.sax.saxutils.escape(route.rule[:-4]) + '.json (or .dat)</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\\n else:\\n yield '\\\\n<tr><td style=\\\"white-space: nowrap;\\\"><a href=\\\"/v2' + route.rule[:-4] + '.json\\\">/v2' + route.rule[:-4] + '.json</a> (or <a href=\\\"/v2' + route.rule + '\\\">.dat</a>)</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\\n else:\\n if '<' in route.rule:\\n yield '\\\\n<tr><td style=\\\"white-space: nowrap;\\\">/v2' + xml.sax.saxutils.escape(route.rule) + '</td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\\n else:\\n yield '\\\\n<tr><td style=\\\"white-space: nowrap;\\\"><a href=\\\"/v2' + route.rule + '\\\">/v2' + route.rule + '</a></td><td>' + route.callback.__doc__.format(host=api.util.CONFIG['host']) + '</td></tr>'\\n yield '</tbody></table>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffec8e79dd46612af0f4c2558b11bf75\",\n \"score\": \"0.54715455\",\n \"text\": \"def metadata_url(self):\\n url = \\\"http://data.ssb.no/api/v0/no/table/\\\" + self.table_id\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035f05f4ba0a8890f3c60ca95049d815\",\n \"score\": \"0.54410917\",\n \"text\": \"def generate_subsystems_doc() -> None:\\n rst_dir = get_pkg_root() / \\\"doc\\\" / \\\"sal_interfaces\\\"\\n rst_dir.mkdir(parents=True, exist_ok=True)\\n\\n # Write the rst table of contents.\\n with open(rst_dir / \\\"index.rst\\\", \\\"w\\\") as f:\\n write_heading(f, name=\\\"SAL Interfaces\\\", char=\\\"#\\\", overline=True)\\n f.write(\\n \\\"\\\"\\\"\\n.. note:: This page is generated by python script ``generate_cet.py``.\\n\\nSAL Interfaces for all CSCs and other SAL components.\\n\\n.. toctree::\\n :glob:\\n :maxdepth: 1\\n\\n *\\n\\n\\\"\\\"\\\"\\n )\\n\\n # Write one rst file for each SAL component.\\n xml_dir = get_sal_interfaces_dir()\\n subsystem_tree = ElementTree.parse(xml_dir / \\\"SALSubsystems.xml\\\")\\n subsystem_tree_root = subsystem_tree.getroot()\\n for subsystem_elt in subsystem_tree_root:\\n subsystem = find_text_in_xml(subsystem_elt, \\\"Name\\\")\\n # has_specific_topic_type starts empty for each CSC because it is\\n # unknown if it has a topic file.\\n has_specific_topic_type = []\\n generics = find_text_in_xml(subsystem_elt, \\\"AddedGenerics\\\")\\n has_generics = create_generics_dict(generics)\\n\\n with open(rst_dir / f\\\"{subsystem}.rst\\\", \\\"w\\\") as cf:\\n cf.write(\\\":tocdepth: 3\\\\n\\\\n\\\")\\n write_heading(cf, name=subsystem, char=\\\"#\\\", overline=True)\\n cf.write(\\n f\\\"\\\"\\\"\\n.. note:: This page is generated by python script ``generate_cet.py``.\\n\\n:ref:`Back to table <index:csc-table:{subsystem.lower()}>`\\n\\n\\\"\\\"\\\"\\n )\\n for dds_type in [\\\"Commands\\\", \\\"Events\\\", \\\"Telemetry\\\"]:\\n try:\\n tree = ElementTree.parse(\\n xml_dir / subsystem / f\\\"{subsystem}_{dds_type}.xml\\\"\\n )\\n has_specific_topic_type.append(dds_type[:-1])\\n except FileNotFoundError:\\n add_generics(\\n cf,\\n subsystem,\\n set_name=f\\\"SAL{dds_type[:-1]}Set\\\",\\n has_generics=has_generics,\\n has_specific_topic_type=has_specific_topic_type,\\n )\\n continue\\n if dds_type != \\\"Events\\\":\\n write_heading(cf, name=dds_type)\\n root = tree.getroot()\\n set_name = root.tag\\n root[:] = sorted(root, key=topic_sort_key)\\n enumeration_seen = False\\n first_event = True\\n for topic in root:\\n if topic.tag == \\\"Enumeration\\\":\\n if dds_type == \\\"Events\\\" and not enumeration_seen:\\n write_heading(cf, name=\\\"Enumerations\\\")\\n enumeration_seen = True\\n assert topic.text is not None\\n states = topic.text.split(\\\",\\\")\\n state_name = states[0].split(\\\"_\\\")[0].lstrip()\\n cf.write(f\\\":{state_name}:\\\\n\\\")\\n for state in states:\\n # enum item format `\\\\n enumClassName_itemName\\n # [=value]`\\n # where itemName may include underscores,\\n # and all whitespace is optional and of\\n # arbitrary length.\\n state = state.split(\\\"_\\\", 1)[\\n 1\\n ] # strip the beginning through the first underscore.\\n cf.write(f\\\" * {state}\\\\n\\\") # noqa\\n cf.write(\\\"\\\\n\\\")\\n continue\\n if dds_type == \\\"Events\\\" and first_event:\\n write_heading(cf, name=\\\"Events\\\")\\n first_event = False\\n topic_name = find_text_in_xml(topic, \\\"EFDB_Topic\\\")\\n if dds_type in [\\\"Commands\\\", \\\"Events\\\"]:\\n short_name = topic_name.split(\\\"_\\\", 2)[-1]\\n else:\\n short_name = topic_name.split(\\\"_\\\", 1)[-1]\\n cf.write(f\\\".. _{subsystem}:{dds_type}:{short_name}:\\\\n\\\\n\\\")\\n write_heading(cf, name=short_name, char=\\\"~\\\")\\n topic_description = topic.find(\\\"Description\\\")\\n if topic_description is not None:\\n cf.write(f\\\"**Description**: {topic_description.text}\\\\n\\\\n\\\")\\n for field in topic:\\n if field.tag == \\\"item\\\":\\n efdb_name = find_text_in_xml(field, \\\"EFDB_Name\\\")\\n cf.write(\\n f\\\"\\\\n.. _{subsystem}:{dds_type}:{short_name}:{efdb_name}:\\\\n\\\\n\\\"\\n )\\n write_heading(cf, name=efdb_name, char=\\\"*\\\")\\n for attribute in field:\\n if attribute.tag in IGNORED_ATTRIBUTES:\\n pass\\n elif attribute.tag in [\\\"Count\\\", \\\"IDL_Size\\\"]:\\n if attribute.text == \\\"1\\\":\\n pass\\n else:\\n cf.write(\\n f\\\":{attribute.tag}: {attribute.text}\\\\n\\\"\\n )\\n else:\\n cf.write(f\\\":{attribute.tag}: {attribute.text}\\\\n\\\")\\n description = find_text_in_xml(field, \\\"Description\\\")\\n cf.write(f\\\"\\\\n**Description**: {description}\\\\n\\\\n\\\")\\n elif field.tag in IGNORED_FIELDS:\\n pass\\n else:\\n cf.write(f\\\":{field.tag}: {field.text}\\\\n\\\")\\n cf.write(\\\"\\\\n\\\")\\n add_generics(\\n cf, subsystem, set_name, has_generics, has_specific_topic_type\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"678c3998c6fb049f0c96ccdd124f6138\",\n \"score\": \"0.5440025\",\n \"text\": \"def test_rtd_extension_inventory():\\n with open(BASE_DIR / 'sphinx-integration.html', 'r') as stream:\\n page = stream.read()\\n assert 'href=\\\"/en/latest/api/pydoctor.sphinx_ext.build_apidocs.html\\\"' in page, page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374e0709e627da1ba724befb42b94e03\",\n \"score\": \"0.5403419\",\n \"text\": \"def get_table(self):\\n if self.table != \\\"2\\\":\\n filename = \\\"histab\\\" + self.table + '.xls'\\n else:\\n if self.product == 'csv':\\n filename = \\\"{}in54cmcsv.csv\\\".format(self.yy)\\n \\n elif self.product == 'xls':\\n # need us, state, county, and one with us + state\\n if self.level in '*':\\n filename = \\\"{}in54cm.xlsx\\\".format(self.yy)\\n \\n elif self.level in ['state', 'us']:\\n filename = \\\"{}in{}{}.xls\\\".format(self.yy, self.stnum, self.state)\\n \\n elif self.level == 'county':\\n if self.state == None:\\n filename = \\\"{}incyall.xls\\\".format(self.yy)\\n else:\\n filename = \\\"{}incy{}.xls\\\".format(self.yy, self.state)\\n \\n urlstub = 'https://www.irs.gov/pub/irs-soi/'\\n \\n url = urlstub + filename\\n urllib.request.urlretrieve(url, filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45389042394f9ca00f0c17254757756\",\n \"score\": \"0.5294146\",\n \"text\": \"def main():\\n df = pd.read_csv(URL, sep=\\\"\\\\t\\\")\\n del df[df.columns[0]]\\n df = df[df.description.notna()]\\n df = df[df[\\\"prefix\\\"].map(lambda p: bioregistry.get_description(p) is None)]\\n df = df[df[\\\"prefix\\\"].map(lambda p: bioregistry.get_obofoundry_prefix(p) is None)]\\n click.echo(df.to_markdown())\\n r = dict(bioregistry.read_registry())\\n for prefix, description in df[[\\\"prefix\\\", \\\"description\\\"]].values:\\n r[prefix].description = description\\n bioregistry.write_registry(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d369dcc704668dc6c9cfc720aa928f9b\",\n \"score\": \"0.5287122\",\n \"text\": \"def get_data_source_from_lws(file_index, save=False, save_file_name=\\\"file.csv\\\"):\\n url = \\\"https://learnwithshin.github.io/docs/files/\\\"\\n content = get_content(url)\\n soup = BeautifulSoup(content, \\\"html.parser\\\")\\n article = soup.find(\\\"article\\\")\\n anchors = article.find_all(\\\"a\\\")\\n\\n base_url = \\\"https://learnwithshin.github.io/docs/\\\"\\n target_anchor = anchors[file_index]\\n file_url = target_anchor[\\\"href\\\"]\\n file_url = file_url.replace(\\\"../\\\", base_url)\\n\\n res = requests.get(file_url)\\n if save:\\n save_download_file(res.content, save_file_name)\\n return res.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861cf3819e843174fbf57f0feed47da6\",\n \"score\": \"0.5251362\",\n \"text\": \"def download(self):\\n main = self.main\\n delim = self.irs_delim\\n current_yr = self.core_file_year #int\\n\\n main.logger.info('Beginning any necessary downloads from the IRS.')\\n\\n for form in main.forms:\\n try:\\n url = self.urls[form][current_yr]\\n except KeyError:\\n raise Exception('URL not found for core file year {}, form {}. Please check the \\\"urls\\\" folder.'.format(current_year, form))\\n df = pd.read_csv(self.download_file(url), sep=delim, dtype='str')\\n\\n #Most IRS files have EIN in caps, but at least one (2012 EZ) has it in lowercase\\n if 'ein' in df.columns:\\n df.rename(columns={'ein':'EIN'}, inplace=True)\\n\\n df.set_index('EIN', inplace=True)\\n\\n #adds the source file name as a column\\n df['SOURCE'] = url.split('/')[-1]\\n self.data_dict[form] = df\\n\\n main.logger.info('Downloading complete.\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0637d32360cb549a73abc684499f24\",\n \"score\": \"0.5221541\",\n \"text\": \"def get_docs(self):\\n bot = self.bot\\n\\n # Icon sources.\\n pdf_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/document-24'\\n word_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/pdf-24'\\n ppt_icon = 'https://moodle.concordia.ca/moodle/theme/image.php/clean/core/1575553324/f/powerpoint-24'\\n\\n # Scrape\\n soup = BeautifulSoup(bot.page_source, features=\\\"lxml\\\")\\n divs = soup.findAll('div', attrs={'class' : 'activityinstance'})\\n pdf_links = []\\n for div in divs:\\n href = div.find('a')['href']\\n if div.find('img')['src'] in [pdf_icon, word_icon, ppt_icon]:\\n pdf_links.append(href)\\n\\n # Keep pdf links\\n cleaned_links = [x for x in pdf_links if x[:48] == 'https://moodle.concordia.ca/moodle/mod/resource/']\\n \\n all_files = []\\n new_files = []\\n for i, link in enumerate(cleaned_links, 1):\\n all_files = os.listdir(self.outpath) # Before\\n bot.find_element_by_xpath('//a[@href=\\\"' + link + '\\\"]').click()\\n time.sleep(1.50)\\n new_files = os.listdir(self.outpath) # After\\n most_recent_file = list(set(new_files) - set(all_files))\\n print('{}. {} saved.'.format(i, most_recent_file[0]))\\n\\n print(\\\"✓ Saved all available files.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93399eeef2d823480ec5b2c86598366f\",\n \"score\": \"0.5219272\",\n \"text\": \"def doc():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ed5c59135682407fdc2b09ea5640035\",\n \"score\": \"0.5203115\",\n \"text\": \"def download_query_results(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14501e37b08e0b869d756fe0f98bf227\",\n \"score\": \"0.5200712\",\n \"text\": \"def open_documentation():\\n webbrowser.open(FOXBMS_DOC_URL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed4c1ea867577bb4cbb8818d1dea0a7\",\n \"score\": \"0.5198265\",\n \"text\": \"def test_developers(self):\\n doc = self.s.go('http://%s/developers' % self.hostport)\\n assert 'Downloading Data' in doc.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f6e2aafb6e27dcc2f9624e42018f0b\",\n \"score\": \"0.5180838\",\n \"text\": \"def ref_info(self):\\n doc = self.get_doc()\\n table = doc('table#officials')\\n return utils.parse_officials_table(table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f851b17dbce832d8b852321663bcc6ec\",\n \"score\": \"0.5170102\",\n \"text\": \"def get_docs(self):\\n return \\\"SELECT * FROM doctor\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d015bf19687208271e953d8abdfa8556\",\n \"score\": \"0.51660705\",\n \"text\": \"def test_n_search_download_ann(self) -> None:\\n command = ['search', '--criteria=identifiers:10159402351973898',\\n '--collection=KALCNES', '--server=kalideos',\\n '--download=annexes']\\n self.do_test_download_file(command, ['10159402351973898_ann.htm'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ed3ccead7f1a3d34d42b2533e57b8a4\",\n \"score\": \"0.51632565\",\n \"text\": \"def test_n_search_download_ql(self) -> None:\\n command = ['search', '--criteria=identifiers:1355444872323826',\\n '--collection=ROHAITI', '--server=ro',\\n '--download=quicklook']\\n self.do_test_download_file(command, ['1355444872323826_ql.jpg'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9588d9a3a7b983e3480ed78b51a32c83\",\n \"score\": \"0.51441884\",\n \"text\": \"def rst(ctx):\\r\\n args_list = [\\r\\n ('locations', ''),\\r\\n ('download', ''),\\r\\n ('word2csv', ''),\\r\\n ('csv2df', '*tests*')\\r\\n #,('frontpage', '*markdown*')\\r\\n ]\\r\\n\\r\\n for args in args_list: \\r\\n command = apidoc(*args)\\r\\n ctx.run(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f189ed24f57e8a4b048b8c623bb1799\",\n \"score\": \"0.5134924\",\n \"text\": \"def _get_nexsci_data(self, cache=\\\"update\\\"):\\n NEXSCI_API = \\\"http://exoplanetarchive.ipac.caltech.edu/cgi-bin/nstedAPI/nph-nstedAPI\\\"\\n url1 = NEXSCI_API + (\\n \\\"?table=planets&select=pl_hostname,pl_letter,\\\"\\n \\\"pl_disc,ra,dec,pl_trandep,pl_tranmid,pl_tranmiderr1,\\\"\\n \\\"pl_tranmiderr2,pl_tranflag,pl_trandur,pl_pnum,pl_k2flag,\\\"\\n \\\"pl_kepflag,pl_facility,pl_orbincl,pl_orbinclerr1,pl_orbinclerr2,\\\"\\n \\\"pl_orblper,st_mass,st_masserr1,st_masserr2,st_rad,st_raderr1,\\\"\\n \\\"st_raderr2,st_teff,st_tefferr1,st_tefferr2,st_optmag,st_j,st_h\\\"\\n )\\n url2 = NEXSCI_API + (\\n \\\"?table=compositepars&select=fpl_hostname,fpl_letter,\\\"\\n \\\"fpl_smax,fpl_smaxerr1,fpl_smaxerr2,fpl_radj,fpl_radjerr1,\\\"\\n \\\"fpl_radjerr2,fpl_bmassj,fpl_bmassjerr1,fpl_bmassjerr2,\\\"\\n \\\"fpl_bmassprov,fpl_eqt,fpl_orbper,fpl_orbpererr1,fpl_orbpererr2,\\\"\\n \\\"fpl_eccen,fpl_eccenerr1,fpl_eccenerr2,fst_spt\\\"\\n )\\n try:\\n path1 = download_file(\\n url1, cache=cache, show_progress=False, pkgname=\\\"starry\\\"\\n )\\n path2 = download_file(\\n url2, cache=cache, show_progress=False, pkgname=\\\"starry\\\"\\n )\\n except Exception as e:\\n logger.warning(\\n \\\"Couldn't obtain data from NEXSCI. Do you have an internet connection?\\\\n\\\"\\n )\\n df = pd.read_csv(\\n f\\\"{_PACKAGEDIR}/extensions/nexsci/data/planetdata.csv\\\"\\n )\\n path1, path2 = [\\n f\\\"{_PACKAGEDIR}/extensions/nexsci/data/planetdata.csv\\\"\\n ] * 2\\n return df, path1, path2\\n\\n planets = pd.read_csv(\\n path1,\\n comment=\\\"#\\\",\\n skiprows=1,\\n )\\n composite = pd.read_csv(\\n path2,\\n comment=\\\"#\\\",\\n skiprows=1,\\n )\\n composite.rename(\\n {c: c[1:] for c in composite.columns if c.startswith(\\\"fpl\\\")},\\n axis=\\\"columns\\\",\\n inplace=True,\\n )\\n df = pd.merge(\\n left=planets,\\n right=composite,\\n how=\\\"left\\\",\\n left_on=[\\\"pl_hostname\\\", \\\"pl_letter\\\"],\\n right_on=[\\\"pl_hostname\\\", \\\"pl_letter\\\"],\\n )\\n\\n df[\\\"stripped_name\\\"] = np.asarray(\\n [\\n n.lower().replace(\\\"-\\\", \\\"\\\").replace(\\\" \\\", \\\"\\\")\\n for n in df.pl_hostname\\n ]\\n )\\n df = df[df.pl_tranflag == 1].reset_index(drop=True)\\n df = self._fill_data(df)\\n return df, path1, path2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74dcd3a36a812355647fd3ad7c77168\",\n \"score\": \"0.50901353\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(\\\"Command line tool to download pdf via DOI from Scihub.\\\")\\n # parser.add_argument('DOI', help=\\\"the DOI number of the paper\\\")\\n parser.add_argument('-c', '--choose', help=\\\"choose scihub url by index\\\")\\n parser.add_argument('-D', '--DOI', help=\\\"the DOI number of the paper\\\")\\n parser.add_argument('-o', '--output', help=\\\"directory to download the pdf\\\")\\n parser.add_argument('-u', '--update', action='store_true', help=\\\"update available Scihub links\\\")\\n parser.add_argument('-b', '--brute-update', action='store_true', help=\\\"update available Scihub links by brute-force search\\\")\\n parser.add_argument('-l', '--list', action='store_true', help=\\\"list current saved sichub urls\\\")\\n args = parser.parse_args()\\n\\n if args.DOI:\\n SCIHUB_URL_INDEX = int(open(get_resource_path('cur_scihub_index.txt'), 'r').read())\\n if not args.output:\\n sci = SciHub(args.DOI)\\n else:\\n sci = SciHub(args.DOI, args.output)\\n sci.download(choose_scihub_url_index=SCIHUB_URL_INDEX)\\n elif args.update:\\n update_link()\\n elif args.brute_update:\\n update_link('b')\\n elif args.list:\\n link_file_path = get_resource_path('link.txt')\\n cur_scihub_index = int(open(get_resource_path('cur_scihub_index.txt'), 'r').read())\\n if not os.path.isfile(link_file_path):\\n open(link_file_path, 'w')\\n with open(link_file_path, 'r') as link_file:\\n for i, link in enumerate(link_file.readlines()):\\n if i == cur_scihub_index:\\n print('* [{0}] {1}'.format(i, link[:-1]))\\n else:\\n print(' [{0}] {1}'.format(i, link[:-1]))\\n elif args.choose:\\n open(get_resource_path('cur_scihub_index.txt'), 'w').write(args.choose)\\n cur_scihub_url = open(get_resource_path('link.txt'), 'r').readlines()[int(args.choose)].replace('\\\\n', '')\\n print(\\\"Current scihub url: %s\\\" %(cur_scihub_url))\\n else:\\n print(\\\"Command line tool to download pdfs via DOI from Scihub.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e4f5c82567d38d259c61c04662a663\",\n \"score\": \"0.50872064\",\n \"text\": \"def do_GET(self):\\n print(\\\"self.path\\\", self.path)\\n if self.path == '/':\\n path = './src/ui/dist/index.html'\\n contenttype = 'text/html'\\n print(\\\"path: \\\", path)\\n file = open(path, 'rb')\\n self.send_response(200)\\n self.send_header('Content-type', contenttype)\\n self.end_headers()\\n self.wfile.write(file.read())\\n file.close()\\n elif self.path.startswith('/api?query='):\\n query = self.path.split('&svd=')[0].replace('/api?query=', '')\\n query = urllib.parse.unquote(query)\\n print('query: ', query)\\n use_svd = self.path.split('&')[1].replace('svd=', '')\\n if \\\"&count=\\\" in self.path:\\n count = int(self.path.split('&')[2].replace('count=', ''))\\n else:\\n count = 10\\n if use_svd == 'true':\\n documents, similarities = self.lsi_handler.get_ranking(query, number=count)\\n else:\\n documents, similarities = self.lsi_handler.get_ranking(query, use_SVD=False, number=count)\\n\\n ranked_list = []\\n for i in range(0, len(similarities)):\\n ranked_list.append({'doc': documents[i], 'rank': \\\"{:.9f}\\\".format(similarities[i])})\\n\\n contenttype = 'application/json'\\n body = json.dumps(ranked_list)\\n self.send_response(200)\\n self.send_header('Content-type', contenttype)\\n self.end_headers()\\n self.wfile.write(bytearray(body, 'utf-8'))\\n elif self.path.startswith('/api?doc='):\\n doc = self.path.replace('/api?doc=', '')\\n path = './data/20news-bydate/20news-bydate-train/' + doc\\n contenttype = 'text/plain'\\n print(\\\"path: \\\", path)\\n file = open(path, 'rb')\\n self.send_response(200)\\n self.send_header('Content-type', contenttype)\\n self.end_headers()\\n self.wfile.write(file.read())\\n file.close()\\n else:\\n path = './src/ui/dist' + self.path\\n contenttype = 'application/javascript'\\n print(\\\"path: \\\", path)\\n file = open(path, 'rb')\\n self.send_response(200)\\n self.send_header('Content-type', contenttype)\\n self.end_headers()\\n self.wfile.write(file.read())\\n file.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8c1171790ea005d07b0b1e7cc1e94d\",\n \"score\": \"0.5084991\",\n \"text\": \"def doc(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58a94dc73ee2a3587a6d0cc47ad1954\",\n \"score\": \"0.5073334\",\n \"text\": \"def _docs(sub_url: Text) -> Text:\\n return constants.DOCS_BASE_URL + sub_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbe71bff75847d9047a1f9b65e7889\",\n \"score\": \"0.5058602\",\n \"text\": \"def open_dfhack_readme():\\n from . import launcher\\n index = paths.get('df', 'hack', 'docs', 'index.html')\\n if os.path.isfile(index):\\n launcher.open_file(index)\\n else:\\n launcher.open_url('https://dfhack.readthedocs.org')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e70955ff6109100bbe6086bcfd8c0a1\",\n \"score\": \"0.5050236\",\n \"text\": \"def docs():\\n res_file = resource.get_resource('manual_url')\\n assert res_file is not None, \\\"User Manual URL file not found\\\"\\n with click.open_file(res_file) as f:\\n url = f.read().strip()\\n click.launch(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93216072d2a7d346b5ee6873e05bdd17\",\n \"score\": \"0.5050057\",\n \"text\": \"def download_well_meta():\\n well_meta_url = 'https://factpages.npd.no/ReportServer_npdpublic?/FactPages/TableView/wellbore_exploration_all&rs:Command=Render&rc:Toolbar=false&rc:Parameters=f&rs:Format=CSV&Top100=false&IpAddress=not_used&CultureCode=en'\\n\\n well_meta = pd.read_csv(well_meta_url)\\n\\n well_meta_path = OUTPUT_ROOT / 'well_meta_npd.csv'\\n well_meta.to_csv(well_meta_path, index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8792e2f92070b01407f87b15c9c2273\",\n \"score\": \"0.50432736\",\n \"text\": \"def api_documentation():\\n return \\\"WELCOME TO SEND_IT APP, THE SOLUTION TO ALL YOUR COUREER SERVICES\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a674e48cfc832d1b0c6bcdfdf98157\",\n \"score\": \"0.50250465\",\n \"text\": \"def docs_docstrings(session: nox.Session) -> None:\\n session.install(\\\"interrogate\\\")\\n session.run(\\n f\\\"{session.bin}/python{session.python}\\\", \\\"-Im\\\", \\\"interrogate\\\", \\\"--version\\\"\\n )\\n session.run(\\n f\\\"{session.bin}/python{session.python}\\\",\\n \\\"-Im\\\",\\n \\\"interrogate\\\",\\n \\\"-v\\\",\\n \\\"src/\\\",\\n \\\"tests/\\\",\\n \\\"noxfile.py\\\",\\n )\\n clean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec74aade76b70eaf29d8e90b8bada3af\",\n \"score\": \"0.50124633\",\n \"text\": \"def get_doc_summary(self):\\n c = MySQLdb.cursors.DictCursor(self.conn)\\n table = self.get_branch_table('imp_doc')\\n query = \\\"SELECT * FROM \\\" + table + \\\" WHERE date=%s\\\"\\n c.execute(query, (self.date,))\\n return c.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d1e3bef9295b520c7c60688e34e1e68\",\n \"score\": \"0.5010576\",\n \"text\": \"def download_generators_info(cache):\\n cache = hp._check_cache(cache)\\n table = static_table(table_name=\\\"Generators and Scheduled Loads\\\", raw_data_location=cache)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74455460ef8eef3b8ccfebfeb03d9215\",\n \"score\": \"0.50085306\",\n \"text\": \"def download():\\n secho('Downloading MIRIAM')\\n get_miriam_df(force_download=True)\\n\\n secho('Download N2T')\\n get_n2t(force=True)\\n\\n secho('Downloading NCBI')\\n get_ncbi()\\n\\n secho('Downloading OBO Foundry')\\n get_obofoundry_df(force_download=True)\\n\\n secho('Downloading OLS')\\n get_ols_df(force_download=True)\\n\\n secho('Download Wikidata')\\n get_wikidata_registry()\\n\\n secho('Download GO')\\n get_go(force_download=True)\\n\\n secho('Download BioPortal')\\n get_bioportal(force_download=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa9d6add8c5e872e12cb82ccfa0c37fe\",\n \"score\": \"0.5004742\",\n \"text\": \"def fixture_response_get_docs(base_url: str, owner: str, repo: str) -> Response_Label:\\n return {\\n \\\"id\\\": 2222,\\n \\\"node_id\\\": \\\"4444\\\",\\n \\\"url\\\": f\\\"{base_url}/repos/{owner}/{repo}/labels/docs\\\",\\n \\\"name\\\": \\\"docs\\\",\\n \\\"description\\\": \\\"Tasks to write and update documentation\\\",\\n \\\"color\\\": \\\"2abf88\\\",\\n \\\"default\\\": False,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd2611808f0543e9990be637e80f79e\",\n \"score\": \"0.5002218\",\n \"text\": \"def _documentation(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d63f1eb5db1a57d8b73ac7efcfa5cc\",\n \"score\": \"0.49991462\",\n \"text\": \"def test_get_document(self):\\n response = self.index.get_document('500682')\\n assert isinstance(response, object)\\n assert 'title' in response\\n assert response['title'] == 'The Highwaymen'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc879504d81618f378a9de5be90b8800\",\n \"score\": \"0.499403\",\n \"text\": \"def click_download_experiment_data(experiment, output, tables):\\n return download_experiment_data(experiment, output, tables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ae40af14367edf4d2965532777ff94\",\n \"score\": \"0.49801224\",\n \"text\": \"def magic_docs(self, arg):\\n with no_stdout():\\n webbrowser.open(\\\"http://pml.readthedocs.org/en/latest/index.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d1750fa2ee8ba498e5a17bb9fb15f5a\",\n \"score\": \"0.497243\",\n \"text\": \"def download_all_data():\\n download_from_hdx(\\\"wfp-food-prices-for-ethiopia\\\")\\n download_from_hdx(\\\"faostat-prices-for-ethiopia\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620aa1f6a57c596aa472926f3bea1713\",\n \"score\": \"0.49680912\",\n \"text\": \"def document():\\n html = flask.render_template('report_head.html',\\n sample='Flask-OAuthlib')\\n footer = flask.render_template('report_foot.html') \\n content = ''\\n\\n endpoint = 'deviceManagement/deviceConfigurations'\\n\\n\\n for item in get_api(endpoint)['value']:\\n table = json2html.convert(json = item, table_attributes=\\\"id=\\\\\\\"info-table\\\\\\\"\\\")\\n head = flask.render_template('report_table_head.html',\\n endpoint= '/' + endpoint + '[' + item['displayName'] + ']')\\n foot = flask.render_template('report_table_foot.html') \\n content = content + head + table + foot\\n html = html + content + footer\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72eb1efd906acf8a31adef56c482fd3a\",\n \"score\": \"0.4966787\",\n \"text\": \"def index(self):\\n\\n self.logger.info(\\\"Start indexing documentation\\\")\\n\\n # 1. Fetch the available documentation from DevDocs website.\\n r = requests.get(DEVDOCS_INDEX_ALL_URL)\\n docs = r.json()\\n\\n docs = [x for x in docs if x['slug'] in self.docs_to_fetch]\\n\\n with open(os.path.join(self.index_file), 'w') as f:\\n json.dump(docs, f)\\n\\n # For each doc fetch its associated entries.\\n for doc in docs:\\n self.fetch_doc_entries(doc['slug'])\\n time.sleep(0.5)\\n\\n self.logger.info(\\\"Index Finished\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ac9b2dbd5e26b733dbea24b5c598f9\",\n \"score\": \"0.49551314\",\n \"text\": \"def test_n_search_download_th(self) -> None:\\n command = ['search', '--criteria=identifiers:1355444872323826',\\n '--collection=ROHAITI', '--server=ro',\\n '--download=thumbnail']\\n self.do_test_download_file(command, ['1355444872323826_th.jpg'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.49539682\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6862b93abc1ee414e40f40e9ebb68a02\",\n \"score\": \"0.4949472\",\n \"text\": \"def cb_open_documentation(_, **args):\\n open_documentation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b80f28716158d02d5f4464bde98301e\",\n \"score\": \"0.49491945\",\n \"text\": \"def OnReadOnlineDocs(self, event):\\n url = (\\\"https://gimel-studio.readthedocs.io/en/latest/\\\")\\n webbrowser.open(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cffcc6ed3f9508832be22d397d919f\",\n \"score\": \"0.49397767\",\n \"text\": \"def test_readme_example_one_external():\\n adapted_aroon = ta_adaptor(AroonIndicator, \\\"aroon_down\\\", window=1)\\n signal_transformer = scikit_signal_factory(adapted_aroon)\\n df = signal_transformer.fit_transform(simulated_market_data_4_years_gen())\\n assert isinstance(df, pd.DataFrame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53aee66f191ccb8d7148b0c75717fd6e\",\n \"score\": \"0.49275732\",\n \"text\": \"def api(index_name, start_date=datetime.date.today(), end_date=datetime.date.today()):\\n def parse(response, index_name):\\n parser = BeautifulSoup(response, 'html.parser')\\n table = parser.find(\\\"section\\\", {\\\"class\\\": \\\"general-tables\\\"})\\n results = []\\n for row in table.findAll(\\\"tr\\\")[1:]:\\n values = row.findAll(\\\"td\\\")\\n if len(values) < 7:\\n break\\n\\n date_raw, close_raw, var_raw, _, high_raw, low_raw, _ = [v.text for v in values]\\n close_val = str2float(close_raw)\\n var = str2float(var_raw)\\n open_val = close_val - var\\n date = datetime.datetime.strptime(date_raw, \\\"%d/%m/%y\\\").date()\\n results.append({\\n \\\"name\\\": index_name,\\n \\\"date\\\": date,\\n \\\"open\\\": open_val,\\n \\\"high\\\": str2float(high_raw),\\n \\\"low\\\": str2float(low_raw),\\n \\\"close\\\": close_val\\n })\\n return results\\n\\n if index_name not in AVAILABLE_INDEX:\\n raise Exception(\\\"Index %s not found\\\", index_name)\\n if start_date > end_date:\\n raise Exception(\\\"Invalid date range\\\")\\n\\n url = \\\"http://www.eleconomista.es/indice/IBEX-35/historico-fechas/{}/{}\\\".format(start_date.strftime(\\\"%Y-%m-%d\\\"), end_date.strftime(\\\"%Y-%m-%d\\\"))\\n #TODO pagination\\n return parse(requests.get(url).text, index_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e95d1925e11d5fdd6e5570a6e29e86fa\",\n \"score\": \"0.49243712\",\n \"text\": \"def describe_document(Name=None, DocumentVersion=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7a936a419a1e4b17f0f67fb65bd9cf\",\n \"score\": \"0.49139595\",\n \"text\": \"def _gettables(self):\\n print('Downloading tables from ods sql.')\\n start_processing=time.time()\\n o = ODPS('LTAIVYhmNLQm0RPD', 'C7mhqOapX1iUSCYwis3lrZFN16nX5x', 'WS_BigData',\\n endpoint='http://service.cn.maxcompute.aliyun.com/api')\\n\\n for table in self.tables:\\n pre_time=time.time()\\n print(table)\\n table_data = o.get_table(table).to_df().to_pandas()\\n print(\\\"connect \\\",table,\\\" successfully cost\\\",time.time()-pre_time)\\n pre_time=time.time()\\n table_data.to_csv('data/'+table+'.csv')\\n print(\\\"save \\\",table,\\\" successfully cost\\\",time.time()-pre_time)\\n print('finished --------Downloading tables from ods sql.',time.time()-start_processing)\\n\\n\\n #--------------just save a single partition\\n print('Fetch specific pt from downloaded tables.')\\n fetch_pt_time=time.time()\\n tables=[\\n 'ods_app_ai_content_content_news_all_dt',\\n 'ods_app_ai_aiid_account_all_dt',\\n 'ods_app_ai_content_content_topic_all_dt',\\n # 'dwd_user_app_browse_action_detail_cdc_dt',\\n 'ods_app_ai_content_content_news_comment_all_dt',\\n 'ods_app_ai_content_content_news_comment_liked_all_dt',\\n 'ods_app_ai_content_content_news_liked_all_dt',\\n 'ods_app_ai_content_content_topic_comment_all_dt',\\n 'ods_app_ai_content_content_topic_comment_liked_all_dt',\\n 'ods_app_ai_content_content_topic_discuss_all_dt',\\n 'ods_app_ai_content_content_topic_discuss_comment_all_dt',\\n 'ods_app_ai_content_content_topic_discuss_liked_all_dt',\\n 'ods_app_ai_content_content_topic_liked_all_dt',\\n 'ods_app_ai_content_content_topic_vote_all_dt',\\n 'ods_app_ai_content_content_topic_vote_question_all_dt',\\n 'ods_app_ai_content_content_topic_vote_option_all_dt',\\n 'ods_app_ai_content_content_topic_vote_option_user_all_dt'\\n ]\\n for table in tables:\\n pre_time=time.time()\\n print(table+'.csv')\\n table_data = pd.read_csv('data/'+table+'.csv',index_col=0,dtype=str)\\n print(\\\"read \\\",table,\\\" cost\\\",time.time()-pre_time)\\n pre_time=time.time()\\n table_data[table_data['pt'] =='20190717'].to_csv('data/pt20190717'+table+'.csv')\\n print(\\\"save pt20190717-\\\",table,\\\" successfully cost\\\",time.time()-pre_time)\\n print('finished --------Fetch specific pt from downloaded tables.',time.time()-fetch_pt_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76ec774b9588596c09023c89da50a95\",\n \"score\": \"0.4913296\",\n \"text\": \"def download_antibody_pdbs():\\n # base url for SAbDab query\\n url_base = 'http://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/'\\n url_query_base = url_base + 'DBrowser.php?'\\n\\n # configure options for SAbDab query\\n options = { 'submitted' : 'true',\\n 'nonredundant' : 'true',\\n 'ab_ident' : '99%25',# 25 need for html\\n 'only_complete' : 'false', # might need capital F\\n 'incomple_nr' : '*',\\n 'agtype' : '*',\\n 'ag_ident' : '99%25',\\n 'aglengthnr' : '50',\\n 'resolution' : '3.0',\\n 'nonredundant_selected' : 'true' }\\n\\n # alternative options (all sub 3.0A structures)\\n options2 = { 'submitted' : 'true',\\n 'advanced' : 'true',\\n 'abtype' : '*',\\n 'method' : '*',\\n 'organism' : '*',\\n 'resolution' : '3.0',\\n 'rfactor': '*',\\n 'incomplex' : '*',\\n 'ag_type' : '*',\\n 'ag_length' : '50',\\n 'lctype' : '*',\\n 'hasconstant' : '*',\\n 'hasaffinity' : '*' }\\n\\n # generate url from query\\n #sabdab_url = url_query_base + '&'.join(['{0}={1}'.format(x,y) for x,y in options.items()])\\n sabdab_url = url_query_base + '&'.join(['{0}={1}'.format(x,y) for x,y in options2.items()])\\n\\n # open page\\n webpage = urllib2.urlopen(urllib2.Request(sabdab_url))\\n\\n # read html and extract summary file, and download chothia PDBs\\n webpage_html = webpage.read() # PDBs in table, summary at end\\n\\n # use regex to extract PDBs\\n chothia_pdb_re = re.compile('data/entries/\\\\w\\\\w\\\\w\\\\w/structure/chothia/\\\\w\\\\w\\\\w\\\\w.pdb')\\n summary_re = re.compile('a href=.* download=\\\\\\\"summary.csv\\\\\\\"')\\n pdb_urls = chothia_pdb_re.findall(webpage_html, re.MULTILINE)\\n summary_url = summary_re.findall(webpage_html, re.MULTILINE) # should only find two\\n summary_url = summary_url[0].split(\\\" download\\\")[0][8:-1] # drop unnecessary chars\\n\\n # make info and antibody_database directories if not present\\n if not os.path.isdir(\\\"info\\\"): os.mkdir(\\\"info\\\")\\n if not os.path.isdir(\\\"antibody_database\\\"): os.mkdir(\\\"antibody_database\\\")\\n\\n # download summary file, always overwrite\\n with open(\\\"info/sabdab_summary.tsv\\\", \\\"w\\\") as f:\\n u = urllib2.urlopen(urllib2.Request(url_base + summary_url))\\n f.write(u.read())\\n\\n # download PDBs, check for *.pdb or *.pdb.bz before writing\\n print(\\\"Found {} pdbs, beginning download.\\\".format(len(pdb_urls)))\\n counter = 0\\n for pdb in pdb_urls:\\n if pdb[-8:-4] in [\\\"1qok\\\", \\\"1dzb\\\", \\\"6b0w\\\"]: # bad geometry -- skip\\n continue\\n if pdb[-8:-4] in [\\\"6db7\\\", \\\"6iut\\\"]: # AntibodyInfo construction issue\\n continue\\n counter += 1\\n fpath = \\\"antibody_database/{}.pdb\\\".format(pdb[-8:-4])\\n fpath_trunc = \\\"antibody_database/{}_trunc.pdb\\\".format(pdb[-8:-4]) # we use this naming later on\\n #fpath_bz = \\\"antibody_database/{}.pdb.bz2\\\".format(pdb[-8:-4])\\n #if not (os.path.isfile(fpath) or os.path.isfile(fpath_bz)):\\n if not os.path.isfile(fpath_trunc):\\n with open(fpath, \\\"w\\\") as f:\\n print(\\\"Downloading {}... {}/{}\\\".format(pdb[-8:-4], counter, len(pdb_urls)))\\n # won't timeout if no internet, so ...\\n u = urllib2.urlopen(urllib2.Request(url_base + pdb))\\n # write pdb ?\\n f.write(u.read())\\n # write compressed bz2\\n #f.write(bz2.compress(u.read()))\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3cb0cc1064737898d1d7866aa3ff0d8\",\n \"score\": \"0.49113882\",\n \"text\": \"def get_swr2(self):\\n\\n\\t\\tparams = 'swr2'\\n\\t\\tbase = 'http://www.ndbc.noaa.gov/data/realtime2/'\\n\\t\\tlink = base + str(self.buoy) + '.' + params\\n\\n\\t\\t#combine the first five date columns YY MM DD hh mm and make index\\n\\t\\tdf = pd.read_csv(link,delim_whitespace=True,skiprows=1,\\n\\t\\t\\theader=None, parse_dates=[[0,1,2,3,4]], index_col=0)\\n\\n\\t\\t#convert the dates to datetimes\\n\\t\\tdf.index = pd.to_datetime(df.index,format=\\\"%Y %m %d %H %M\\\")\\n\\n\\t\\tspecs = df.iloc[:,0::2]\\n\\t\\tfreqs = df.iloc[0,1::2]\\n\\n\\t\\tspecs.columns=freqs\\n\\n\\t\\t#remove the parenthesis from the column index\\n\\t\\tspecs.columns = [cname.replace('(','').replace(')','') \\n\\t\\t\\tfor cname in specs.columns]\\n\\n\\t\\treturn specs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119dbc8b853e2642560b127085eeb79d\",\n \"score\": \"0.49097875\",\n \"text\": \"def download_wl_file(self,fullpath):\\n # #testing\\n # print(self.api_parameters)\\n # r = requests.get(\\\"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\\\",params=self.start_end_datetime)\\n # print(r.url)\\n # print(self.start_end_datetime)\\n\\n # Check the Folder's existence\\n if os.path.exists(fullpath):\\n self.metadata[\\\"Source File\\\"] = fullpath\\n print('Folder exists:' + fullpath)\\n else: # Raise a meaningful error\\n raise RuntimeError('Folder of path ' + fullpath + ' does not exist')\\n\\n # # combine parameters and send a request to download data from website test\\n # url_string = str(\\\"begin_date=\\\" + self.start_end_datetime[\\\"begin_date\\\"] + \\\"\\\" + self.start_end_datetime[\\\"begin_time\\\"] + \\\"&end_date=\\\" + \\\"&station=\\\" + self.station + \\\"&product=\\\" + self.product + \\\"&datum=\\\" + self.datum + \\\"&units=\\\" + self.units + \\\"&time_zone=\\\" + self.time_zone + \\\"&application=\\\" + self.application + \\\"&format=\\\" + self.format)\\n #\\n # url_parameters = str(\\\"begin_date=\\\" + self.begin_date + \\\"&end_date=\\\" + self.end_date + \\\"&station=\\\" + self.station + \\\"&product=\\\" + self.product + \\\"&datum=\\\" + self.datum + \\\"&units=\\\" + self.units + \\\"&time_zone=\\\" + self.time_zone + \\\"&application=\\\" + self.application + \\\"&format=\\\" + self.format)\\n # url_api = \\\"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\\\"\\n # url_download = url_api + url_parameters\\n # result = requests.get(url_download)\\n\\n # combine parameters and send a request to download data from website\\n url_parameters = str(\\\"begin_date=\\\" + self.begin_date + \\\"&end_date=\\\" + self.end_date + \\\"&station=\\\" + self.station + \\\"&product=\\\" + self.product + \\\"&datum=\\\" + self.datum + \\\"&units=\\\" + self.units + \\\"&time_zone=\\\" + self.time_zone + \\\"&application=\\\" + self.application + \\\"&format=\\\" + self.format)\\n url_api = \\\"https://api.tidesandcurrents.noaa.gov/api/prod/datagetter?\\\"\\n url_download = url_api + url_parameters\\n result = requests.get(url_download)\\n\\n # Parameters will be printed out\\n print(\\\"Extracting data from:\\\" + url_download)\\n print(\\\"Following parameters were used: \\\")\\n print(\\\"begin_date:\\\", self.begin_date) # yyMMDD HH:MM\\n print(\\\"end_date:\\\", self.end_date)\\n print(\\\"station:\\\", self.station)\\n print(\\\"product:\\\", self.product)\\n print(\\\"datum:\\\", self.datum)\\n print(\\\"units:\\\", self.units)\\n print(\\\"timezone:\\\", self.time_zone)\\n print(\\\"application:\\\", self.application)\\n print(\\\"format:\\\", self.format)\\n # print output\\n #print(result.text)\\n\\n # export output as xml or csv file intow file directory\\n # enter file output file name,format and destination folder\\n #print(fullpath)\\n filename = self.filename\\n fileformat = str('.' + self.format) # takes the format of what you entered earlier\\n #abs_path = fullpath \\\" # folder directory of where the data will be stored\\n with open(fullpath + filename + fileformat, 'wb') as file:\\n file.write(result.content)\\n\\n return print(\\\"File saved in \\\" + fullpath + filename + fileformat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda36bfd12c72fe830fe0dfab3715512\",\n \"score\": \"0.4895257\",\n \"text\": \"def testApiDocsContent(server):\\n resp = server.request(path='/api/v1', method='GET', isJson=False, prefix='')\\n assertStatusOk(resp)\\n body = getResponseBody(resp)\\n\\n assert 'Girder REST API Documentation' in body\\n assert 'This is not a sandbox' in body\\n assert 'id=\\\"swagger-ui-container\\\"' in body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb06a12555e12ce5aeb633e28dc881f\",\n \"score\": \"0.48949987\",\n \"text\": \"def describe():\\n dataset = utilities.opencsv()\\n data = pd.read_csv(dataset)\\n description = data.describe()\\n description.to_csv('description.csv', encoding='utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1056170d1cd191bb7ff31aecc3881c\",\n \"score\": \"0.48867595\",\n \"text\": \"def open_documentation(self):\\n url = QtCore.QUrl('https://github.com/jrsm1/EWAS_Application/blob/master/README.md')\\n if not QtGui.QDesktopServices.openUrl(url):\\n super().display_error('Could not open Help URL')\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8386682a887af49dba745d14d7dd07\",\n \"score\": \"0.4885031\",\n \"text\": \"def test_rtd_pydoctor_call():\\n # The pydoctor index is generated and overwrites the Sphinx files.\\n with open(BASE_DIR / 'api' / 'index.html', 'r') as stream:\\n page = stream.read()\\n assert 'moduleIndex.html' in page, page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d39108e964746d35fbeb78b67236184\",\n \"score\": \"0.48793548\",\n \"text\": \"def document_src(REQUEST=None, RESPONSE=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9b2f6646af7a9877ca3567a3189bec\",\n \"score\": \"0.48705807\",\n \"text\": \"def get_science_data_url(doi_meta = 'https://api.datacite.org/works/10.5066/P9IGEC9G'):\\n \\n #get doi metadata\\n header = {'content-type':'application/json'}\\n r=requests.get(doi_meta, header).json()\\n url = r['data']['attributes']['url']\\n\\n #strip url to return sbid\\n sbid = url.replace('https://www.sciencebase.gov/catalog/item/', '')\\n\\n #get sciencebase information\\n item =sb.get_item(sbid)\\n\\n files = sb.get_item_file_info(item)\\n for file in files:\\n if re.search(\\\"^USGS_Dam_Removal_Science_Database_v.*csv$\\\", file['name']):\\n file_url = file['url']\\n break\\n \\n return file_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c5d95c5a3d09f051f6386ce41e14a2\",\n \"score\": \"0.4862624\",\n \"text\": \"def download_report(ctx):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5e41d39444ba7c8708b2fb4397d737\",\n \"score\": \"0.4860026\",\n \"text\": \"def get_iocs():\\n response = http_request('GET', 'public/v3/iocs', params=handle_filters(), json_response=True)\\n content = response.get('content')\\n domains = []\\n ip_infos = []\\n url_infos = []\\n hash_infos = []\\n dbot_scores = []\\n iocs_context = []\\n iocs_readable = []\\n\\n for indicator in content:\\n ioc_context, ioc_readable, dbot_score, domain, ip_info, url_info, hash_info = ioc_to_readable(indicator)\\n iocs_context.append(ioc_context)\\n iocs_readable.append(ioc_readable)\\n dbot_scores.append(dbot_score)\\n domains.append(domain)\\n ip_infos.append(ip_info)\\n url_infos.append(url_info)\\n hash_infos.append(hash_info)\\n\\n headers = ['Type', 'Value', 'FirstSeen', 'LastSeen', 'Status', 'Severity', 'SourceID', 'SourceName',\\n 'SourceConfidence', 'Geolocation']\\n demisto.results(\\n {\\n 'Type': entryTypes['note'],\\n 'EntryContext': {\\n 'IntSights.Iocs(val.Value && val.Value === obj.Value)': iocs_context,\\n 'DBotScore': dbot_scores,\\n 'Domain': domains,\\n 'IP': ip_infos,\\n 'URL': url_infos,\\n 'File': hash_infos\\n },\\n 'Contents': response,\\n 'HumanReadable': tableToMarkdown('IOC Information', t=iocs_readable, headers=headers),\\n 'ContentsFormat': formats['json']\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb572d010e4cd688578107a164003269\",\n \"score\": \"0.48583165\",\n \"text\": \"def download_doc(pmids_list):\\n print(\\\"Downloading PMIDs for Not-DIDA\\\")\\n all_data = pbmdh.download_publications(pmids_list)\\n filename = CONFIG['NOTDIDA_DOCS'] + \\\".json\\\"\\n exh.write_json(all_data, filename)\\n display.display_info(\\\"Not-DIDA publications saved in {0}\\\".format(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9811d65dc90ab840c3544a0238e3c2f\",\n \"score\": \"0.48534304\",\n \"text\": \"def create_auto_documentation():\\n create_screenshots()\\n\\n create_module_docs()\\n create_py3_docs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dafc1ccb8a8d7b8d1f0cbf9bd53cc72\",\n \"score\": \"0.48530278\",\n \"text\": \"def getDocs(self):\\n return self.DOCUMENTATION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4f706ad0df96bb36910e94c35da175\",\n \"score\": \"0.4852924\",\n \"text\": \"def docs():\\n return render_template('docs.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.48497438\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.48497438\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.48497438\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.48497438\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65896080e714cfd565052b771452c31\",\n \"score\": \"0.4844849\",\n \"text\": \"def test_download_model(self):\\n response = self.client.get('/fit/john/1/model/')\\n self.assertEqual(response.status_code, 200)\\n self.assertTrue(response.content.startswith(\\\"# Reflectivity model\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e81a726b61790eea864dbb93eccef04\",\n \"score\": \"0.4837576\",\n \"text\": \"def docs(self):\\n\\n # Root folder for the report files\\n webbrowser.open(settings.DOCS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f3e9a2e8e747888463b2590655770f\",\n \"score\": \"0.4835602\",\n \"text\": \"def doc(self, topic=\\\"About\\\"): ###\\r\\n if __name__.startswith(\\\"BCPy2000.\\\"):\\r\\n try:\\r\\n from . import Documentation\\r\\n except ImportError:\\r\\n print(\\\"documentation not found\\\")\\r\\n else:\\r\\n Documentation.browse(topic)\\r\\n else:\\r\\n import webbrowser\\r\\n\\r\\n user = os.environ.get(\\\"USERNAME\\\", \\\"\\\")\\r\\n if len(user) == 0:\\r\\n user = os.environ.get(\\\"USER\\\", \\\"\\\")\\r\\n if len(user):\\r\\n user += \\\"/\\\"\\r\\n webbrowser.open(\\r\\n \\\"http://localhost/cgi-bin/\\\"\\r\\n + user\\r\\n + \\\"BCPy2000?\\\"\\r\\n + topic.replace(\\\" \\\", \\\"_\\\")\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a982935d3e4de683769a7919c0b43e11\",\n \"score\": \"0.48344913\",\n \"text\": \"def ireport():\\n\\n tablename = \\\"%s_%s\\\" % (module, resourcename)\\n table = s3db[tablename]\\n\\n if \\\"open\\\" in get_vars:\\n # Filter out Reports that are closed or Expired\\n s3.crud_strings[tablename].title_list = T(\\\"Open Incidents\\\")\\n response.s3.filter = (table.closed == False) & \\\\\\n ((table.expiry == None) | \\\\\\n (table.expiry > request.utcnow))\\n\\n # Non-Editors should only see a limited set of options\\n if not auth.s3_has_role(\\\"EDITOR\\\"):\\n irs_incident_type_opts = response.s3.irs_incident_type_opts\\n ctable = s3db.irs_icategory\\n allowed_opts = [irs_incident_type_opts.get(opt.code, opt.code) for opt in db().select(ctable.code)]\\n allowed_opts.sort()\\n table.category.requires = IS_EMPTY_OR(IS_IN_SET(allowed_opts))\\n\\n # Pre-processor\\n def prep(r):\\n table = r.table\\n if r.method == \\\"ushahidi\\\":\\n auth.settings.on_failed_authorization = r.url(method=\\\"\\\", vars=None)\\n # Allow the 'XX' levels\\n s3db.gis_location.level.requires = IS_EMPTY_OR(IS_IN_SET(\\n gis.get_all_current_levels()))\\n elif r.interactive or r.representation == \\\"aadata\\\":\\n if r.method == \\\"update\\\":\\n table.dispatch.writable = True\\n table.verified.writable = True\\n table.closed.writable = True\\n if r.component:\\n if r.component_name == \\\"image\\\":\\n itable = s3db.doc_image\\n itable.date.default = r.record.datetime.date()\\n itable.location_id.default = r.record.location_id\\n itable.location_id.readable = itable.location_id.writable = False\\n itable.organisation_id.readable = itable.organisation_id.writable = False\\n #itable.url.readable = itable.url.writable = False\\n itable.person_id.readable = itable.person_id.writable = False\\n elif r.component_name == \\\"document\\\":\\n dtable = s3db.doc_document\\n dtable.date.default = r.record.datetime.date()\\n dtable.location_id.default = r.record.location_id\\n dtable.location_id.readable = dtable.location_id.writable = False\\n dtable.organisation_id.readable = dtable.organisation_id.writable = False\\n #dtable.url.readable = dtable.url.writable = False\\n dtable.person_id.readable = dtable.person_id.writable = False\\n elif r.component_name == \\\"human_resource\\\":\\n if not r.component_id:\\n # Hide fields from create forms (list_fields override)\\n htable = s3db.irs_ireport_human_resource\\n htable.response.readable = htable.response.writable = False\\n htable.reply.readable = htable.reply.writable = False\\n s3.crud.submit_button = T(\\\"Assign\\\")\\n s3.crud_strings[\\\"irs_ireport_human_resource\\\"] = Storage(\\n label_create = T(\\\"Assign Human Resource\\\"),\\n title_display = T(\\\"Human Resource Details\\\"),\\n title_list = T(\\\"Human Resource Assignments\\\"),\\n title_update = T(\\\"Edit Human Resource\\\"),\\n label_list_button = T(\\\"List Assigned Human Resources\\\"),\\n label_delete_button = T(\\\"Remove Human Resource from this incident\\\"),\\n msg_record_created = T(\\\"Human Resource assigned\\\"),\\n msg_record_modified = T(\\\"Human Resource Assignment updated\\\"),\\n msg_record_deleted = T(\\\"Human Resource unassigned\\\"),\\n msg_list_empty = T(\\\"No Human Resources currently assigned to this incident\\\"))\\n elif r.component_name == \\\"task\\\":\\n s3db.configure(\\\"project_task\\\", copyable=False)\\n elif r.component_name == \\\"vehicle\\\":\\n s3.crud.submit_button = T(\\\"Assign\\\")\\n s3.crud_strings[\\\"irs_ireport_vehicle\\\"] = Storage(\\n label_create = T(\\\"Assign Vehicle\\\"),\\n title_display = T(\\\"Vehicle Details\\\"),\\n title_list = T(\\\"Vehicle Assignments\\\"),\\n title_update = T(\\\"Edit Vehicle Assignment\\\"),\\n label_list_button = T(\\\"List Vehicle Assignments\\\"),\\n label_delete_button = T(\\\"Remove Vehicle from this incident\\\"),\\n msg_record_created = T(\\\"Vehicle assigned\\\"),\\n msg_record_modified = T(\\\"Vehicle Assignment updated\\\"),\\n msg_record_deleted = T(\\\"Vehicle unassigned\\\"),\\n msg_list_empty = T(\\\"No Vehicles currently assigned to this incident\\\"))\\n\\n return True\\n response.s3.prep = prep\\n\\n # Post-processor\\n def postp(r, output):\\n if r.representation == \\\"plain\\\" and \\\\\\n r.method !=\\\"search\\\":\\n # Map Popups\\n # Look for a Photo\\n # @ToDo: The default photo not the 1st\\n ptable = s3db.doc_image\\n query = (ptable.doc_id == r.record.doc_id) & \\\\\\n (ptable.deleted == False)\\n image = db(query).select(ptable.file,\\n limitby=(0, 1)).first()\\n if image and image.file:\\n url = URL(c=\\\"default\\\", f=\\\"download\\\", args=image.file)\\n output[\\\"item\\\"].append(IMG(_src=url,\\n # @ToDo: capture the size on upload & have controller resize where-required on-download\\n _width=400,\\n _height=400))\\n return output\\n response.s3.postp = postp\\n\\n output = crud_controller(rheader=s3db.irs_rheader)\\n\\n # @ToDo: Add 'Dispatch' button to send OpenGeoSMS\\n #try:\\n # delete_btn = output[\\\"delete_btn\\\"]\\n #except:\\n # delete_btn = \\\"\\\"\\n #buttons = DIV(delete_btn)\\n #output.update(delete_btn=buttons)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135359b9eb8b807967d1d0fa0cab1391\",\n \"score\": \"0.482283\",\n \"text\": \"def download_intrinio_price(data_url, auth, show_progress=False):\\n\\n # Donwload price data\\n if show_progress:\\n log.info('Downloading price data @{url}'.format(url=data_url))\\n\\n data = download_without_progress(data_url, auth)\\n table = pd.read_csv(data, skiprows=[0]) # Skip the first row\\n table.insert(0, 'symbol', data_url.split('=')[-1]) # e.g. \\\"https://api.intrinio.com/prices.csv?identifier=AAPL\\\"\\n table.columns = map(str.lower, table.columns)\\n table.date = pd.to_datetime(table.date)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c0e05327a402cbf9b5db62202639ea9\",\n \"score\": \"0.48211864\",\n \"text\": \"def search_doc(s, extra=''):\\n cmd = 'sage -grepdoc \\\"%s\\\" | grep \\\"%s\\\"'%(s,extra)\\n\\n old_banner = os.environ.has_key('SAGE_BANNER')\\n if old_banner:\\n old_banner = os.environ['SAGE_BANNER']\\n os.environ['SAGE_BANNER'] = \\\"no\\\"\\n r = os.popen(cmd).read()\\n if old_banner == False:\\n del os.environ['SAGE_BANNER']\\n else:\\n os.environ['SAGE_BANNER'] = old_banner\\n \\n from sage.server.support import EMBEDDED_MODE\\n if EMBEDDED_MODE: # I.e., running from the notebook\\n print format_search_as_html('Documentation', r, s + extra)\\n else:\\n from sage.misc.all import pager\\n pager()(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a40d6895b940faa7874cdea8a2daff1\",\n \"score\": \"0.48169506\",\n \"text\": \"async def download(session, run_code):\\n url = baseURL + 'genesis/sis/view'\\n params = {\\n 'module': 'reportWriter',\\n 'category': 'reports',\\n 'tab1': 'viewReport',\\n 'runcode': run_code,\\n 'action': 'rawopen'\\n }\\n async with session.get(url, params=params) as resp:\\n return await resp.text()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b07b49a77ce5def3f82bf30dfa85398c\",\n \"score\": \"0.48153055\",\n \"text\": \"def download_dudetailsummary(cache, asof_date=None):\\n if asof_date != None:\\n latest = hp._validate_and_convert_date(asof_date, \\\"asof_date\\\")\\n else:\\n latest = datetime(datetime.now().year, datetime.now().month, 1) - timedelta(days = 90)\\n \\n cache = hp._check_cache(cache)\\n overwrite_nemosis_defaults()\\n\\n if latest < datetime(2009,7,1):\\n raise Exception(\\\"DATA UNAVAILABLE: DUDETAILSUMMARY table is not found in MMS prior to 07-2009. \\\" + \\\\\\n \\\"Cannot correctly map emissions factors. Retry function without specifying `asof_date`\\\")\\n\\n # Data Retrieval\\n df = mod_dynamic_data_fetch_loop(start_search=latest,\\n start_time=latest - timedelta(hours=1),\\n end_time=latest,\\n table_name=\\\"DUDETAILSUMMARY\\\",\\n raw_data_location=cache,\\n select_columns=[\\\"START_DATE\\\", \\\"DUID\\\", \\\"DISPATCHTYPE\\\", \\\"REGIONID\\\", \\\"LASTCHANGED\\\"],\\n date_filter=None,\\n fformat=\\\"feather\\\",\\n )\\n df = pd.concat(df)\\n df = df.drop(['file_year','file_month', 'LASTCHANGED'], axis=1)\\n filtered = df.sort_values('START_DATE').drop_duplicates(['DUID'], keep='last')\\n return filtered.sort_values(['DUID','START_DATE']).reset_index(drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6ac6025f06598c4ce9db39ab716ec8\",\n \"score\": \"0.48119348\",\n \"text\": \"def download_description(self):\\n if os.path.exists(self.file_txt):\\n return\\n bf = BeautifulSoup(self.descr, 'lxml').contents[0].get_text()\\n descr = f\\\"{self.name}\\\\n{bf}\\\\n\\\"\\n with open(self.file_txt, mode=\\\"w\\\") as f:\\n f.write(descr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5100a187578cadfb845fed64a586fca0\",\n \"score\": \"0.48112953\",\n \"text\": \"def daily_api_update():\\n\\n yesterday = date.today() - timedelta(1)\\n url_startdate = yesterday.strftime('%Y%m%d')\\n url_enddate = date.today().strftime('%Y%m%d')\\n\\n get_historic_demand_api_data(url_startdate, url_enddate)\\n\\n # not using Net Imports now. may use in the future\\n # get_historic_imports_api_data(url_startdate, url_enddate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0515e9757b1874496f61576069455fd6\",\n \"score\": \"0.48105806\",\n \"text\": \"def print_doc(attrs):\\n rstf.write(\\\"S-Link Attributes\\\\n\\\")\\n rstf.write(\\\"-----------------\\\\n\\\")\\n \\n \\n rstf.write(\\\".. table::\\\\n\\\")\\n rstf.write(\\\" :widths: 10 30 10 10 20 50\\\\n\\\\n\\\")\\n \\n addr_size = len(\\\"Address \\\")\\n name_size = len(\\\"Name \\\")\\n ro_size = len(\\\"ReadOnly\\\")\\n width_size = len(\\\"Width \\\")\\n rst_size = len(\\\"Reset \\\")\\n desc_size = len(\\\"Description \\\")\\n \\n \\n for a in attrs:\\n if(len(attrs[a].name) > name_size): name_size = len(attrs[a].name)\\n if(len(str(attrs[a].width)) > width_size): width_size = len(str(attrs[a].width))\\n if(len(str(attrs[a].reset)) > rst_size): rst_size = len(str(attrs[a].reset))\\n if(len(attrs[a].desc) > desc_size): desc_size = len(attrs[a].desc)\\n \\n rstf.write(\\\" {0} {1} {2} {3} {4} {5}\\\\n\\\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))\\n rstf.write(\\\" %-*s %-*s %-*s %-*s %-*s %-*s\\\\n\\\" % (addr_size, \\\"Address\\\", name_size, \\\"Name\\\", width_size, \\\"Width\\\", ro_size, \\\"ReadOnly\\\", rst_size, \\\"Reset\\\", desc_size, \\\"Description\\\"))\\n rstf.write(\\\" {0} {1} {2} {3} {4} {5}\\\\n\\\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))\\n \\n for a in attrs:\\n rstf.write(\\\" %-*s %-*s %-*s %-*s %-*s %-*s\\\\n\\\" % (addr_size, '0x'+str(format(a, 'x')), name_size, attrs[a].name, width_size, attrs[a].width, ro_size, str(attrs[a].ro), rst_size, attrs[a].reset, desc_size, attrs[a].desc))\\n\\n rstf.write(\\\" {0} {1} {2} {3} {4} {5}\\\\n\\\".format('='*addr_size, '='*name_size, '='*width_size, '='*ro_size, '='*rst_size, '='*desc_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35ffa135c86ae42565a30ae792e020d\",\n \"score\": \"0.4806526\",\n \"text\": \"def fetch_bls_hist(api_key):\\n \\n # Header\\n headers = {'Content-type': 'application/json'}\\n \\n # Payload\\n # LAUMT123674000000003 : local unemployment for Orlando\\n # LNU04000000: national unemployment\\n # CUUR0000SA0 : national CPI\\n \\n series_list = ['LAUMT123674000000003','LNU04000000','CUUR0000SA0']\\n \\n payload = json.dumps({\\n \\\"seriesid\\\": series_list,\\n \\\"startyear\\\": \\\"2014\\\",\\n \\\"endyear\\\": \\\"2019\\\",\\n \\\"registrationkey\\\": api_key\\n })\\n \\n # Request\\n print('Sending request, may take a few minutes...')\\n req = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=payload, headers=headers)\\n \\n # Dataframes\\n local_unemp = make_bls_df(req=req, idx=0, metric='unemp_local')\\n natl_unemp = make_bls_df(req=req, idx=1, metric='unemp_natl')\\n natl_cpi = make_bls_df(req=req, idx=2, metric='cpi_natl')\\n \\n # Consolidated dataframe\\n out_df = pd.merge(local_unemp, natl_unemp, how='outer', on=['month_of_year','year_of_calendar'])\\n out_df = pd.merge(out_df, natl_cpi, how='outer', on=['month_of_year','year_of_calendar'])\\n \\n return out_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f66a71410c7ccbe929b463abe8e67ae\",\n \"score\": \"0.4805448\",\n \"text\": \"def get_api_data(endpoint_url, params, filepath, index_c='DATE'):\\n with open(filepath, 'wb') as fi:\\n response = requests.get(endpoint_url, stream=True, params=params)\\n if not response.ok:\\n print \\\"Something went wrong.\\\"\\n for segment in response.iter_content(1024):\\n fi.write(segment)\\n odf = pd.read_csv(filepath, parse_dates=True, index_col=index_c)\\n return odf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e2306e382b2cf5d9b17618875b65d8\",\n \"score\": \"0.48024186\",\n \"text\": \"def ew_download(basefolder, logfolder, ddate): \\n\\n print(\\\"Downloading England and Wales data extract\\\")\\n print(\\\"\\\\r\\\")\\n\\n \\n # Create data folder\\n\\n dfolder = basefolder + \\\"/ew\\\"\\n if not os.path.isdir(dfolder):\\n os.mkdir(dfolder)\\n else:\\n print(\\\"{} already exists\\\".format(dfolder)) \\n\\n\\n mfile = logfolder + \\\"/ew-download-metadata-\\\" + ddate + \\\".json\\\"\\n\\n\\n # Request zip files\\n \\n tables = [\\\"charity\\\", \\\"charity_annual_return_history\\\", \\\"charity_annual_return_parta\\\", \\\"charity_annual_return_partb\\\", \\n \\\"charity_area_of_operation\\\", \\\"charity_classification\\\", \\\"charity_event_history\\\", \\\"charity_governing_document\\\", \\\"charity_other_names\\\",\\n \\\"charity_other_regulators\\\", \\\"charity_policy\\\", \\\"charity_published_report\\\", \\\"charity_trustee\\\"]\\n webadd = \\\"https://ccewuksprdoneregsadata1.blob.core.windows.net/data/txt/publicextract.{}.zip\\\" # txt download (json also available)\\n \\n for table in tables:\\n response = requests.get(webadd.format(table))\\n print(response.status_code, response.headers)\\n\\n if response.status_code==200:\\n\\n outfile = dfolder + \\\"/\\\" + table + \\\"-\\\" + ddate + \\\".zip\\\"\\n print(outfile)\\n if os.path.isfile(outfile): # do not overwrite existing file\\n print(\\\"File already exists, no need to overwrite\\\")\\n else: # file does not currently exist, therefore create\\n with open(outfile, \\\"wb\\\") as f:\\n f.write(response.content)\\n\\n # Unzip files\\n\\n with zipfile.ZipFile(outfile, 'r') as zip_ref:\\n \\tzip_ref.extractall(dfolder)\\n\\n else:\\n print(\\\"Unable to download data extract from link {}\\\".format(webadd))\\n print(\\\"Status code {}\\\".format(response.status_code))\\n\\n\\n # Write metadata to file\\n\\n mdata = dict(response.headers)\\n mdata[\\\"file\\\"] = table\\n mdata[\\\"file_link\\\"] = str(webadd)\\n mdata[\\\"data_extract_last_modified\\\"] = response.headers[\\\"Last-Modified\\\"]\\n\\n with open(mfile, \\\"w\\\") as f:\\n json.dump(mdata, f)\\n \\n\\n print(\\\"\\\\r\\\") \\n print(\\\"Successfully downloaded data extract\\\")\\n print(\\\"Check log file for metadata about the download: {}\\\".format(mfile))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de43686f7dbf00b47c20c9728de90082\",\n \"score\": \"0.48023462\",\n \"text\": \"def onboarding_docs(self) -> List[dict]:\\n docs = [\\n BKitB.make_section_block(\\n BKitB.plaintext_section(\\n \\\"Welcome to OKR! We're glad to have you on board!\\\\nCheck out these links below \\\"\\n \\\"to get familiar with OKR and the industry we support!\\\"\\n )\\n ),\\n BKitB.make_section_block(\\n BKitB.markdown_section(\\n f\\\"\\\\t<{self.show_onboring_link()}|Onboring Doc>\\\\n\\\"\\n )\\n ),\\n BKitB.make_section_block(\\n BKitB.plaintext_section(\\n \\\"For any questions, reach out to the CEO or our Head of Recruiting. \\\"\\n \\\"Don't know who they are? Well, figure it out!\\\"\\n )\\n )\\n ]\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ebd2be934d70a7a8d09b814f59c405\",\n \"score\": \"0.47982448\",\n \"text\": \"def showDocs():\\n counter = 1\\n print '\\\\n--------------------------------------'\\n print main.__doc__\\n print showUsage.__doc__\\n print showDocs.__doc__\\n print getTerms.__doc__\\n print showResult.__doc__\\n print Term.__doc__\\n print parseTerm.__doc__\\n print termValid.__doc__\\n print cleanTerm.__doc__\\n print generatePolynom.__doc__\\n print recreateTerm.__doc__\\n print debug.__doc__\\n print setDebug.__doc__\\n print setBigMode.__doc__\\n print '--------------------------------------\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5f58504c0842aaac0f287011529633\",\n \"score\": \"0.47967297\",\n \"text\": \"def import_soda(client_path, end_point, file_name, rxminer_token):\\n client = Socrata(client_path, rxminer_token)\\n results = client.get(end_point)\\n results_df = pd.DataFrame.from_records(results)\\n export_csv = results_df.to_csv(r'../test/rxdata/'+file_name, index=None, header=True)\\n print('Finish downloading '+file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2277977668b9cb9791984fea2e2ed51a\",\n \"score\": \"0.47865814\",\n \"text\": \"def getHistoricData(symbol, sDate=(1990,1,1),eDate=date.today().timetuple()[0:3],verbose=True):\\r\\n\\r\\n urlStr = 'http://ichart.finance.yahoo.com/table.csv?s={0}&a={1}&b={2}&c={3}&d={4}&e={5}&f={6}'.\\\\\\r\\n format(symbol.upper(),sDate[1]-1,sDate[2],sDate[0],eDate[1]-1,eDate[2],eDate[0])\\r\\n\\r\\n \\r\\n try:\\r\\n lines = urllib2.urlopen(urlStr).readlines()\\r\\n except Exception, e:\\r\\n s = \\\"Failed to download:\\\\n{0}\\\".format(e);\\r\\n print s\\r\\n\\r\\n dates = []\\r\\n data = [[] for i in range(6)]\\r\\n #high\\r\\n \\r\\n # header : Date,Open,High,Low,Close,Volume,Adj Close\\r\\n for line in lines[1:]:\\r\\n #print line\\r\\n fields = line.rstrip().split(',')\\r\\n dates.append(datetime.strptime( fields[0],'%Y-%m-%d'))\\r\\n for i,field in enumerate(fields[1:]):\\r\\n data[i].append(float(field))\\r\\n \\r\\n idx = Index(dates)\\r\\n data = dict(zip(['open','high','low','close','volume','adj_close'],data))\\r\\n \\r\\n # create a pandas dataframe structure \\r\\n df = DataFrame(data,index=idx).sort()\\r\\n \\r\\n if verbose:\\r\\n print 'Got %i days of data' % len(df)\\r\\n \\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b8edf042ea4fb7b9587157e0171742\",\n \"score\": \"0.4783116\",\n \"text\": \"def _show_docs(self):\\n try:\\n if sys.platform == 'darwin':\\n os.system('open {}'.format(self.source_path))\\n else:\\n os.system('open-xdg {}'.format(self.source_path))\\n except IOError:\\n raise IOError(\\\"documentation file '{}' could not be opened\\\".format(self.source_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a3949dde14c13263c9de48c1139666\",\n \"score\": \"0.4779665\",\n \"text\": \"def test_get_sys_internal_specs_openapi(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad951e2ed84fb7378b85655e9d82c855\",\n \"score\": \"0.4775592\",\n \"text\": \"def _fetch_sis(self):\\n\\n\\t\\t# Generate all urls.\\n\\t\\tgen_url = lambda s: \\\"http://67.220.92.23/bbs/forum-230-\\\"\\\\\\n\\t\\t\\t\\t+ s + \\\".html\\\"\\n\\n\\t\\t# Prepare urls.\\n\\t\\tall_urls = [gen_url(str(i)) for i in\\\\\\n\\t\\t\\t\\trange(1, self.PAGES+1)]\\n\\n\\t\\timport time\\n\\t\\timport urllib2\\n\\t\\t# Set proxy because of GFW.\\n\\t\\tproxy_handler = urllib2.ProxyHandler({\\\"http\\\":\\\"http://127.0.0.1:8087\\\"})\\n\\t\\topener = urllib2.build_opener(proxy_handler)\\n\\t\\t# Not using list conprehension because we need to\\n\\t\\t# suspend a while in case the web site reject too\\n\\t\\t# many requests in a short time. \\n\\t\\t# Multi-threads can be used to enhance this part.\\n\\t\\tfor url in all_urls:\\n\\t\\t\\tprint \\\"Fetching:\\\"+url\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tself.raw_data[self.SIS].append(opener.open(url,\\\\\\n\\t\\t\\t\\t\\t\\ttimeout=5).read())\\n\\t\\t\\t\\texcept socket.timeout:\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttime.sleep(1)\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t# Extract records.\\n\\t\\tfrom sisHTMLProcessor import\\\\\\n\\t\\t\\t\\tsisHTMLProcessor as SHP\\n\\t\\tfor page in self.raw_data[self.SIS]:\\n\\t\\t\\tp = SHP()\\n\\t\\t\\tp.feed(page)\\n\\t\\t\\tself.all_records[self.SIS].extend(p.get_records())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ac6d1a2492d1c5fac6757d4c1b3563\",\n \"score\": \"0.476675\",\n \"text\": \"def fetch(self):\\n\\n # if you need to add a file_type add it to if clause below\\n # and then create a new \\\"fetch\\\" method to download the data\\n if self.file_type in self.file_list:\\n if self.file_type == \\\"csv\\\":\\n df = self.fetch_csv()\\n\\n if self.file_type == \\\"html-stream\\\":\\n self.session = AsyncHTMLSession()\\n results = self.session.run(self.fetch_html_stream)\\n df = self.link_to_df(results)\\n\\n if self.file_type == \\\"findlink\\\":\\n doc_url, txt = self.fetch_link()\\n if doc_url:\\n if self.file_ext == \\\"csv\\\":\\n df = self.fetch_csv_stream(doc_url)\\n if self.file_ext == \\\"xlsx\\\":\\n df = pd.read_excel(doc_url)\\n if self.file_ext == \\\"pdf\\\":\\n front = re.findall(r\\\"(?<=\\\\/\\\\/).*?(?=\\\\/)\\\", doc_url)[0]\\n front = front.replace(\\\"www.\\\",\\\"\\\")\\n front = front.replace(\\\".\\\",\\\"-\\\")\\n back = re.findall(r\\\"([^\\\\/]+$)\\\", doc_url)[0]\\n curdir = os.getcwd()\\n pdf_name = f\\\"{curdir}/{front}_{back}\\\"\\n self.download_pdf(doc_url, pdf_name)\\n df = parse_pdf(self.url, txt, pdf_name)\\n\\n else:\\n print(\\\"Error: findlink did not return a document link\\\\n\\\")\\n df = pd.DataFrame()\\n\\n if self.file_type == \\\"findlinks\\\":\\n doc_urls, txts = self.fetch_links()\\n df_list = []\\n if doc_urls:\\n print(\\\"\\\\tSearched for multiple links and found the following:\\\")\\n for doc_url in doc_urls:\\n if self.file_ext == \\\"csv\\\":\\n df = self.fetch_csv_stream(doc_url)\\n df_list.append(df)\\n print(f\\\"\\\\t{doc_url}\\\")\\n print(\\\"\\\")\\n df = df_list\\n df_list = []\\n\\n else:\\n print(\\\"\\\\tError: findlinks did not return any document links\\\\n\\\")\\n df = pd.DataFrame()\\n\\n if self.file_type == \\\"csv-git-scan\\\":\\n df = self.fetch_csv_git_scan()\\n\\n if self.file_type == \\\"json\\\":\\n data = self.fetch_json()\\n\\n # If no json retrieved, return empty DataFrame\\n if not data:\\n df = pd.DataFrame()\\n # Otherwise convert json to DF\\n else:\\n # get validator name supplied by user in params\\n validator = self.key_phrase.get(\\\"validator\\\")\\n checkdata_key, results_key = self.key_phrase.get(\\\"keys\\\")\\n\\n # convert json data to DataFrame using\\n # (1) validator or (2) direct conversion (no data validation)\\n if validator:\\n df = self.choose_validator(data) # validate than covnert\\n else:\\n df = self.json2df(data, results_key) # direct conversion\\n\\n else:\\n # pp = pprint.PrettyPrinter(indent=1)\\n if self.file_type:\\n print(\\n f\\\"\\\\nError: currently no capability to download file type: {self.file_type}\\\"\\n )\\n else:\\n print(\\\"\\\\nError: no file type was provided.\\\")\\n print(f\\\"Allowable file types are: \\\")\\n pprint(self.file_list, width=5)\\n print(\\\"Exiting....\\\")\\n exit()\\n\\n print(\\\"\\\\tData fetched! Saved to DataFrame.\\\\n\\\")\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b00ac787b9ea02592a29527fdefcfaa\",\n \"score\": \"0.47642988\",\n \"text\": \"def _download(self, request_dict={}):\\r\\n html_l = super(Site, self)._download(request_dict)\\r\\n html_trees = []\\r\\n # this path reads the links for the last month in that year\\r\\n path = \\\"id('opinions')//h2[string-length()>2][last()]/following::a[string-length()=10]/@href[not(contains(., 'pdf'))]\\\"\\r\\n # to get all the dates in that page the following path can be used:\\r\\n # path = \\\"id('opinions')//a[string-length()=10]\\\"\\r\\n for url in html_l.xpath(path):\\r\\n html_tree = self._get_html_tree_by_url(url, request_dict)\\r\\n html_trees.append((html_tree, url))\\r\\n\\r\\n return html_trees\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf7985711a7c25d752fd9062f6af1ad4\",\n \"score\": \"0.47642314\",\n \"text\": \"def main():\\n html = fetch('http://www.hanbit.co.kr/store/books/full_book_list.html')\\n books = scrape(html)\\n save('books.db', books)\\n view('books.db')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e71ccc9f7d4de974f87843619020a7\",\n \"score\": \"0.4760326\",\n \"text\": \"def get_ohlcv_data(ticker, interval, start, end):\\n log = logging.getLogger(__name__)\\n\\n ric = oci.convert_ticker(ticker, 'Reuters')\\n log.info('Download data for: {} - RIC: {}'.format(ticker, ric))\\n try:\\n # Directly prints to the console if there's an issue - TODO: how to catch this?\\n df = ek.get_timeseries(ric, start_date=start, end_date=end, interval=interval, fields=['HIGH', 'LOW', 'OPEN', 'CLOSE', 'VOLUME'])\\n df.index.names = [None]\\n df.columns = ['High', 'Low', 'Open', 'Close', 'Volume']\\n df = df[['Open', 'High', 'Low', 'Close', 'Volume']]\\n # Remove weekend days\\n df = df[df.index.dayofweek < 5]\\n # Settlement Fix (old spreads don't have settlements so need to get the close)\\n close = pd.isnull(df['Close'])\\n if len(close[close]) > 1 and oci.get_future_type(ticker) == oci.FutureType.Spread:\\n df = fix_settlement(ticker, df)\\n # Fill NAs\\n df['Open'].fillna(df['Close'], inplace=True)\\n df['High'].fillna(df['Close'], inplace=True)\\n df['Low'].fillna(df['Close'], inplace=True)\\n df['Volume'].fillna(0, inplace=True)\\n # Add OI: Only for outrights and if the outright has traded\\n vdf = df[df['Volume'] > 0]\\n if len(vdf) > 0 and oci.get_future_type(ticker) == oci.FutureType.Outright:\\n # Have to do this as the OI would shift it we start early (and there's no OI for some days)\\n first_trade = str(vdf.index[0])[0:10]\\n df = add_oi(ticker, df, first_trade, end) if interval is 'daily' else df\\n else:\\n df['OI'] = pd.Series([0 for _ in range(len(df.index))], index=df.index)\\n # Check if first line has NAs and discard them\\n # TODO: Move this in a function?\\n if oci.get_future_type(ticker) == oci.FutureType.Outright and len(df) > 20:\\n drop = []\\n for i in range(20):\\n if df.iloc[i].isnull().sum():\\n drop.append(i)\\n if drop:\\n log.warning('Dropping lines: {}'.format(drop))\\n df = df.drop(df.index[drop])\\n except (TypeError, IndexError, req.HTTPError, ek.eikonError.EikonError, json.decoder.JSONDecodeError) as e:\\n log.error('Problem downloading data for {}, see: {}'.format(ticker, e))\\n df = None\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9b079fde0f3a2c8700345a965c33f8\",\n \"score\": \"0.476012\",\n \"text\": \"def access(self, ext=None):\\n if self.anchor.id == 'N:organization:fake-organization-id':\\n # FIXME need a way to generalize/fix this so that organization\\n # can vary ...\\n print('Not in correct organization for running access report.')\\n sys.exit(9999)\\n\\n from sparcur.sheets import Overview, Master\\n if self.options.server:\\n def fmt(d): return hfn.atag(d.uri_human, d.id, new_tab=True)\\n else:\\n def fmt(d): return d.uri_human\\n\\n if self.options.server:\\n uri_human = Master._uri_human()\\n def fmtmc(s): return hfn.atag(uri_human, s, new_tab=True)\\n else:\\n def fmtmc(s): return s\\n\\n o = Overview()\\n remote = self.anchor.remote\\n remote_datasets = remote.children\\n\\n master_sheet_ids = o.dataset_ids()\\n bf_api_ids = set(d.id for d in remote_datasets)\\n\\n missing_from_api = sorted(master_sheet_ids - bf_api_ids)\\n missing_datasets = [self.BlackfynnRemote(i, local_only=True) for i in missing_from_api]\\n missing_uris = [fmt(d) for d in missing_datasets]\\n rows = [['', ''],\\n [fmtmc('Master Count'), len(master_sheet_ids)],\\n ['BF API Count', len(bf_api_ids)],\\n ['No API Count', len(missing_from_api)],\\n *[[i, u] for i, u in zip(missing_from_api, missing_uris)]\\n ]\\n return self._print_table(rows, title='Access Report', ext=ext)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993673,"cells":{"query_id":{"kind":"string","value":"783f32bd4d6c58ca4ec0767ead5e1768"},"query":{"kind":"string","value":"A previously established connection was found. This will be the default connection to start."},"positive_passages":{"kind":"list like","value":[{"docid":"b8e6b341e69a7e5fbd22ce439bef94c5","score":"0.5911767","text":"def found_previous_connection(self,\n app: Application,\n server: Server,\n ) -> NetworkState:\n app.interface_transition('no_active_connection_found')\n return DisconnectedState()","title":""}],"string":"[\n {\n \"docid\": \"b8e6b341e69a7e5fbd22ce439bef94c5\",\n \"score\": \"0.5911767\",\n \"text\": \"def found_previous_connection(self,\\n app: Application,\\n server: Server,\\n ) -> NetworkState:\\n app.interface_transition('no_active_connection_found')\\n return DisconnectedState()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fd56e177497fcb7c271f65d8369b0672","score":"0.6363437","text":"def _reconnect(self):\n if self._connection.closed > 0:\n self._connection = self._connect()","title":""},{"docid":"acf5400480e3255106f644dd5c197160","score":"0.6337093","text":"def connect(self):\n if self._conn is None:\n self._conn = self._connect()","title":""},{"docid":"e7a7edb0775c269f68d5f631c6580f52","score":"0.629398","text":"def init_connection_state(self):\n pass","title":""},{"docid":"a27fe5b27d6668e335cb65fc3c540f51","score":"0.62428737","text":"def open(self):\n # unique_id = yield self.conn.configure()\n log = yield self.conn.verify_conn()\n unique_id = log.unique_id\n gen_log.info('CONNECTION OPENED WITH: %s' % unique_id)\n if unique_id:\n self.session_key = unique_id\n self.state = OPEN\n self.conn.on_open(unique_id)\n raise gen.Return(unique_id)\n raise gen.Return(conf.DISCONN_RESULT)","title":""},{"docid":"2e06784fa9486540c53f0d0b3ace65ff","score":"0.62002075","text":"def attempt_connect_to_server(self):\r\n\r\n\t\t#If connection was valid\r\n\t\tif(self._client_socket != None):\r\n\t\t\t#Check if connection still exists by sending quick request\r\n\t\t\ttry:\r\n\t\t\t\tresponse = self.send_request_to_server(666)\r\n\t\t\t\tif not response:\r\n\t\t\t\t\tself.connect_to_server()\r\n\t\t\t\telse:\r\n\t\t\t\t\tprint(\"Already established connection.\")\r\n\t\t\t\t\treturn \r\n\t\t\texcept:\r\n\t\t\t\tself.connect_to_server()\r\n\t\telse:\r\n\t\t\tself.connect_to_server()","title":""},{"docid":"cd796520caa4c038233989a2d3c2a00a","score":"0.61029947","text":"def retry_connection(self):\n self._connection_successful = self._test_connection()","title":""},{"docid":"ecb21ff3102914636cf72e5ea0e78863","score":"0.6065234","text":"def connection_possible(self):\n if self._connection_successful is None:\n self.retry_connection()\n return self._connection_successful","title":""},{"docid":"f3128e05880e395feac565d6418fa3e7","score":"0.60170174","text":"def _connect(self, port=None):\n super(Connection, self)._connect()\n if not self._connected: \n self._connected = True","title":""},{"docid":"1816969a8ce922d31a8eda3c9e0eb607","score":"0.5992339","text":"def _do_connect(self):\n raise NotImplementedError(\"Please Implement this method\")","title":""},{"docid":"a5f14b99516e5edff03d05f1000e3e8e","score":"0.5989074","text":"def connect(self):\r\n self._closed = False\r\n return self.connection","title":""},{"docid":"dd6bf87439a39d44a9c3910594e8db2b","score":"0.59796196","text":"def validate_connection(self):\n if self.database_connection:\n if threading.get_ident() != self.stored_thread_id:\n self.disconnect()\n self.connect()\n else:\n self.connect()","title":""},{"docid":"46e17e22a6670704cd6de7380cb0705e","score":"0.5954927","text":"def connection(self):\r\n if not self._closed:\r\n if not self.connected:\r\n self.declared_entities.clear()\r\n self._default_channel = None\r\n self._connection = self._establish_connection()\r\n self._closed = False\r\n return self._connection","title":""},{"docid":"c9cffdf518d56531b0c67ef74abcf01b","score":"0.5951426","text":"def connect(self):\n # sanity check\n if self.mode == 'connecting':\n return defer.fail(ex.AlreadyConnecting())\n \n elif self.mode == 'connected':\n return defer.fail(ex.AlreadyConnected())\n \n # we are connecting now\n self.transitionTo('connecting')\n return self.do_next()","title":""},{"docid":"1bc1de96753235e2641a2d92b8ddff83","score":"0.59378225","text":"def opened(self):\n connectionMsg = {\"msg\": \"connect\", \"version\":\"pre1\", \"support\":[\"pre1\"]}\n self._sendDict(connectionMsg)","title":""},{"docid":"70e63b4c40ce880cd6ca7b27f22aa221","score":"0.5933501","text":"def _prepare_connection(self, new=True):\n try:\n self.conn.close()\n except BaseException:\n pass\n if new:\n self.conn = self._new_connection()","title":""},{"docid":"8896d12f20a941c96f722da339179139","score":"0.5917346","text":"def get_started_connection(self):\n return self.started_connection","title":""},{"docid":"0fe163df3effabf74d1657754848b5c7","score":"0.59123474","text":"def _new_conn(self):\n return self._socket","title":""},{"docid":"bd2713bb1817420659f0cf26f3e534f8","score":"0.5903351","text":"def connectionLost(self):\n pass","title":""},{"docid":"1a25d199318d7f92a8322c4b01417a52","score":"0.59030616","text":"def no_previous_connection_found(self, app: Application) -> NetworkState:\n app.interface_transition('no_active_connection_found')\n return UnconnectedState()","title":""},{"docid":"9dd8fca42a417bdf3e98285ad79ed05b","score":"0.59001714","text":"def reconnect(self):\n LOG.info(\"STOMP attempting to connect\")\n self.fire(connect(host=self.host))","title":""},{"docid":"00303df79860b68df83338b2812bddf0","score":"0.5894534","text":"def connect():\n return Connection()","title":""},{"docid":"3fcb83b22b5d9df0da461737a0202dba","score":"0.58864605","text":"def reconnect(self):\n # This is the old connection IOLoop instance, stop its ioloop\n self._connection.ioloop.stop()\n if not self._closing:\n # Create a new connection\n self._connection = self.connect()\n\n # There is now a new connection, needs a new ioloop to run\n self._connection.ioloop.start()","title":""},{"docid":"2d12158d3e6fdf0fd7c12e1b4b76943d","score":"0.5879867","text":"def open(self):\n if self.fail_open is True:\n raise ConnectionError()","title":""},{"docid":"934d59ef0f3bead2e3c45b9c1d72a0cf","score":"0.5874695","text":"def connect(self):\n # Nothing to do here.\n self.conn.connect()","title":""},{"docid":"c4263ccaea34b3ef5ff5481a702e961c","score":"0.58718354","text":"def reconnect(self):\n # This is the old connection IOLoop instance, stop its ioloop\n self._connection.ioloop.stop()\n\n # Create a new connection\n self._connection = self.connect()\n\n # There is now a new connection, needs a new ioloop to run\n self._connection.ioloop.start()","title":""},{"docid":"b8c063bd9db14f19f1b67d6b6768d917","score":"0.5871469","text":"def connect(self):\n options = self.get_connection_options()\n\n if options:\n # Connect to an option\n choice = random.choice(options)\n choice.connected = True\n choice.connections.append(self)\n\n self.connections.append(choice)\n self.lineto(choice)\n choice.connect()\n\n else:\n # Backtrack\n self.backtrack()","title":""},{"docid":"5c6982a22ee5654b27074915459461d5","score":"0.58638966","text":"def connect():\n global _CONNECTED\n _CONNECTED = True","title":""},{"docid":"da0f71ac0c582fbbd46e72e54c4e30f6","score":"0.58624244","text":"def __enter__(self):\n self.start()\n self._connection_made.wait()\n if not self.alive:\n raise RuntimeError('connection_lost already called')\n return self.protocol","title":""},{"docid":"e0026fd10b14af46ac1086210741aaed","score":"0.5858484","text":"def reconnect(self):\n # This is the old connection IOLoop instance, stop its ioloop\n self._connection.ioloop.stop()\n\n if not self._closing:\n # Create a new connection\n self._connection = self.connect()\n\n # There is now a new connection, needs a new ioloop to run\n self._connection.ioloop.start()","title":""},{"docid":"9fee8bb8ec8f247fc9cb14d83be893a7","score":"0.5856494","text":"def connect(self, reconnect=True):\n\n pass","title":""},{"docid":"8469da9bff8a60c5e66a6bb695d9e49e","score":"0.5854051","text":"def reconnect(self):\n # This is the old connection IOLoop instance, stop its ioloop\n self.connection.ioloop.stop()\n\n if not self.closing:\n self.connection = self.connect()\n self.connection.ioloop.start()","title":""},{"docid":"eef40354be181a6ab936ac614bb6096d","score":"0.5837165","text":"def init_conn(self) -> None:\n\t\tself.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tself.socket.connect(self.address)","title":""},{"docid":"166a9fdf6ae4e17ee5aca04364b13929","score":"0.5829957","text":"def connect(self):\n return super().connect()","title":""},{"docid":"166a9fdf6ae4e17ee5aca04364b13929","score":"0.5829957","text":"def connect(self):\n return super().connect()","title":""},{"docid":"b5b8fb10ebe1ecc0c005bd08574ee647","score":"0.5821749","text":"def get_connect(self):\n\t\treturn self.CONNECTION","title":""},{"docid":"56a43cb7bfc7a61401ab93ca048c6606","score":"0.58057463","text":"def set_default_db_conn(db_conn):\n global _def_db_conn\n _def_db_conn = db_conn","title":""},{"docid":"b1b4a592afe44ede99c8c768a06ce19b","score":"0.57918876","text":"def open_conn(self):","title":""},{"docid":"6989fc00e7f6ef495dd28e7e3a642aa9","score":"0.5786339","text":"def establish_connection(self):\r\n conninfo = self.client\r\n for name, default_value in items(self.default_connection_params):\r\n if not getattr(conninfo, name, None):\r\n setattr(conninfo, name, default_value)\r\n if conninfo.hostname == 'localhost':\r\n conninfo.hostname = '127.0.0.1'\r\n conn = self.Connection(host=conninfo.host,\r\n userid=conninfo.userid,\r\n password=conninfo.password,\r\n login_method=conninfo.login_method,\r\n virtual_host=conninfo.virtual_host,\r\n insist=conninfo.insist,\r\n ssl=conninfo.ssl,\r\n connect_timeout=conninfo.connect_timeout)\r\n conn.client = self.client\r\n return conn","title":""},{"docid":"48f04e8c1888a1100fc8879e2a5b37aa","score":"0.57805777","text":"def before_request():\r\n try:\r\n g.conn = engine.connect()\r\n except:\r\n print(\"uh oh, problem connecting to database\")\r\n import traceback; traceback.print_exc()\r\n g.conn = None","title":""},{"docid":"21fcc329d483e6660ef20be04469e91d","score":"0.5767054","text":"def ensure_connection(self):\r\n if self.server_proxy is None:\r\n self._connect_to_server()\r\n if self.server_proxy is None:\r\n raise RuntimeError('Cannot connect to server')","title":""},{"docid":"ffa6110e250bbd496ec5a7d163ba5487","score":"0.5760746","text":"def connect(self) -> None:\n pass","title":""},{"docid":"2676d75a929ab4ebd71ffb0e5d9c1ce3","score":"0.5755266","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print(\"uh oh, problem connecting to database\")\n import traceback\n traceback.print_exc()\n g.conn = None","title":""},{"docid":"0c71340fcdd395681c95be8bd3d77f0c","score":"0.57498485","text":"def onTryconnect(self):\n try:\n self.s.connect(self.peerAddr)\n except socket.error as exc:\n code = exc.args[0]\n if code == errno.EINPROGRESS:\n return\n\n self.pollManager.removeTimeout(self.tid)\n self.tid = None\n\n if self._useSSL:\n # Do the handshaking while the socket is in blocking mode. In\n # theory you can do it in non-blocking mode, but I think there\n # is a nasty timing bug in Python's wrapper or some versions of\n # the SSL library itself.\n self.s.setblocking(1)\n self.s.do_handshake()\n self.s.setblocking(0)\n\n active, ret = self.onConnectCB(self.peerName, self)\n if not active:\n # TODO: Should we shutdown the connection.\n pass","title":""},{"docid":"6caffbf403c7ad1c75289b9ee8d653ca","score":"0.57475436","text":"def on_connect(self):\n self.log_event('connect')\n self.last_keep_alive = now()\n self.set_event(Events.EVENT_ONLINE)","title":""},{"docid":"4236266b4f77c2bae12d89c14e6fd985","score":"0.57460266","text":"def connect(self):\n pass","title":""},{"docid":"4236266b4f77c2bae12d89c14e6fd985","score":"0.57460266","text":"def connect(self):\n pass","title":""},{"docid":"4236266b4f77c2bae12d89c14e6fd985","score":"0.57460266","text":"def connect(self):\n pass","title":""},{"docid":"4236266b4f77c2bae12d89c14e6fd985","score":"0.57460266","text":"def connect(self):\n pass","title":""},{"docid":"49461f95e0e31a968343c86369f2f79c","score":"0.5742942","text":"def connect(self):\n self._conn.connect()","title":""},{"docid":"7b4227db559f2274ccbd9d12081e91e4","score":"0.5742328","text":"def connection(self, connection):\r\n if connection is None:\r\n if self.connection is not None:\r\n del connections[self.id]\r\n return\r\n if self.connection is not None:\r\n connection.notify('*** Booting old connection. ***')\r\n self.connection.notify(\r\n '*** Logging you in from somewhere else ***'\r\n )\r\n self.connection.object = None\r\n self.connection.transport.loseConnection()\r\n connections[self.id] = connection\r\n self.notify(f'Welcome, {self.name}.')","title":""},{"docid":"48817a53108a4d15c2cde6826b620f76","score":"0.57260793","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print (\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"48817a53108a4d15c2cde6826b620f76","score":"0.57260793","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print (\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"5e9474af55d4840ac95827c0733e093a","score":"0.5725989","text":"def connect(self):\n self._create_connection()","title":""},{"docid":"5685915742b4def22987c840f7e1f456","score":"0.5725675","text":"def get_conn(self):\r\n got_connection = False\r\n while not got_connection:\r\n try:\r\n conn = self.conn_pool.connection()\r\n got_connection = True\r\n except:\r\n time.sleep(0.1)\r\n return conn","title":""},{"docid":"39f3e466e86b96350f0ee43cbe2607f7","score":"0.5723511","text":"def before_request():\n try:\n g.conn = engine.connect()\n\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"556e9850d46b434584767c1ec3338ac9","score":"0.5722945","text":"def connect(self):\n return self._connect()","title":""},{"docid":"036417a1d87f9be48a573f68026ec2e4","score":"0.5722124","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print(\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"036417a1d87f9be48a573f68026ec2e4","score":"0.5722124","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print(\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"036417a1d87f9be48a573f68026ec2e4","score":"0.5722124","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print(\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"036417a1d87f9be48a573f68026ec2e4","score":"0.5722124","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print(\"uh oh, problem connecting to database\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"fe03d12ea70d5e71b99ef90b02067621","score":"0.571884","text":"def init_connection_file(self):\n if self.existing:\n try:\n cf = find_connection_file(self.existing)\n except Exception:\n self.log.critical(\"Could not find existing kernel connection file %s\", self.existing)\n self.exit(1)\n self.log.info(\"Connecting to existing kernel: %s\" % cf)\n self.connection_file = cf\n # should load_connection_file only be used for existing?\n # as it is now, this allows reusing ports if an existing\n # file is requested\n try:\n self.load_connection_file()\n except Exception:\n self.log.error(\"Failed to load connection file: %r\", self.connection_file, exc_info=True)\n self.exit(1)","title":""},{"docid":"c9f8731210ea9c61777612de432f6206","score":"0.57136357","text":"def db_connect(self, db_conn_name):\n\n\t\t# TODO: Is there a better way of doing this distinction?\n\t\t# TODO: This logic should be pushed down into vondi, and use the has_external_pooling() check.\n\t\tif db_conn_name.startswith('sqlr_') or db_conn_name.endswith('_sqlr'):\n\t\t\tconn = None\n\t\t\tif db_conn_name in self.database_connections and self.database_connections[db_conn_name] is not None:\n\t\t\t\t# If we already have a connection, then reuse it.\n\t\t\t\t# TODO: Should we check if it is open?\n\t\t\t\tconn = self.database_connections[db_conn_name]\n\t\t\telse:\n\t\t\t\tconn = vondbi.connect(db_conn_name)\n\t\t\t\tself.database_connections[db_conn_name] = conn\n\t\t\tself.log.debug(\"database connections: %s\" % self.database_connections[db_conn_name])\n\t\t\treturn conn\n\t\telse:\n\t\t\treturn vondbi.connect(db_conn_name)","title":""},{"docid":"520ecf62db44b59854c5f870607def60","score":"0.57117635","text":"def reconnect(self):\n return reconnect(self)","title":""},{"docid":"520ecf62db44b59854c5f870607def60","score":"0.57117635","text":"def reconnect(self):\n return reconnect(self)","title":""},{"docid":"520ecf62db44b59854c5f870607def60","score":"0.57117635","text":"def reconnect(self):\n return reconnect(self)","title":""},{"docid":"784eb885efe83602323134f95359a955","score":"0.5711433","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print (\"Error connecting to database.\")\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"aaa3174cfd3dda473de423bbcda41c44","score":"0.5709649","text":"def own_conn(self):\n return self.new_conn()","title":""},{"docid":"f37f5f99dbeddd0db614971d85847dd5","score":"0.57057834","text":"def reconnect(self):\r\n if self._channel is None:\r\n self._channel = grpc.insecure_channel(self._connection)\r\n self._stub = models_pb2_grpc.ModelsStub(self._channel)\r\n else:\r\n print(\"Connection for client already open\")","title":""},{"docid":"2903cd1912ec63c745edc2a6f00f2917","score":"0.56987613","text":"def conn(self):\n return self.new_conn()","title":""},{"docid":"f0e1b8a9483a2513632ea442c3280703","score":"0.5696181","text":"def connect(self, fail_silently=False):\n if not self.connection:\n self.connection = get_connection(fail_silently=fail_silently)\n return self.connection","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"79900e8c6ab19d3c1fb7bf01031d0ebc","score":"0.56957895","text":"def before_request():\n try:\n g.conn = engine.connect()\n except:\n print \"uh oh, problem connecting to database\"\n import traceback; traceback.print_exc()\n g.conn = None","title":""},{"docid":"6146ce3c37ab2e24e51e0d2b18dc527c","score":"0.5692646","text":"def connection(self, key=None):\n if key:\n self.active = key\n else:\n self.active = 'default'\n\n return self","title":""},{"docid":"1f1b78945e5d92ad006aab49e9c4c44a","score":"0.5691761","text":"def __init__(self):\n self.connections = {}","title":""},{"docid":"1f1b78945e5d92ad006aab49e9c4c44a","score":"0.5691761","text":"def __init__(self):\n self.connections = {}","title":""},{"docid":"1f1b78945e5d92ad006aab49e9c4c44a","score":"0.5691761","text":"def __init__(self):\n self.connections = {}","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.56900793","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.56900793","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"7ddae9852c0c404f68cc6af1cdd63f5a","score":"0.56823784","text":"def reconnect():\r\n return _connect_to_server()","title":""},{"docid":"ae918455b7577be43f265dbd2ec4fdc0","score":"0.56808025","text":"def __newConnection(self):\n sock = self.nextPendingConnection()\n peerAddress = sock.peerAddress().toString()\n if peerAddress not in Preferences.getDebugger(\"AllowedHosts\"):\n # the peer is not allowed to connect\n res = E5MessageBox.yesNo(\n None,\n self.tr(\"Connection from illegal host\"),\n self.tr(\n \"\"\"<p>A connection was attempted by the illegal host\"\"\"\n \"\"\" <b>{0}</b>. Accept this connection?</p>\"\"\")\n .format(peerAddress),\n icon=E5MessageBox.Warning)\n if not res:\n sock.abort()\n return\n else:\n allowedHosts = Preferences.getDebugger(\"AllowedHosts\")\n allowedHosts.append(peerAddress)\n Preferences.setDebugger(\"AllowedHosts\", allowedHosts)\n \n if self.passive:\n self.__createDebuggerInterface(\n Preferences.getDebugger(\"PassiveDbgType\"))\n \n accepted = self.debuggerInterface.newConnection(sock)\n if accepted:\n # Perform actions necessary, if client type has changed\n if self.lastClientType != self.clientType:\n self.lastClientType = self.clientType\n self.remoteBanner()\n elif self.__autoClearShell:\n self.__autoClearShell = False\n self.remoteBanner()\n elif self.passive:\n self.remoteBanner()\n \n self.debuggerInterface.flush()","title":""},{"docid":"8cbcb2172fd9da1cb165e6ba2b699c26","score":"0.5678966","text":"def default_channel(self):\r\n # make sure we're still connected, and if not refresh.\r\n self.connection\r\n if self._default_channel is None:\r\n self._default_channel = self.channel()\r\n return self._default_channel","title":""},{"docid":"ab5b01c4b585aba5fa120f6f940e4a59","score":"0.5678228","text":"def on_connection_open(self, unused_connection):\n self.my_local_logger.info('Connection opened')\n self.add_on_connection_close_callback()\n self.open_channel()","title":""},{"docid":"66df0af7987812cb6b268a81fc8a43d6","score":"0.5673222","text":"def on_connection_open(self, unused_connection):\n self.my_local_logger.debug('Connection opened')\n self.add_on_connection_close_callback()\n self.open_channel()","title":""},{"docid":"5d55ecd060278a66d5961a84c30c023c","score":"0.5672022","text":"def start_conn(self):\n config = self.config[\"watcher\"]\n Connection.__init__(self._logger, config[\"nick\"], None , config[\"server\"][0],\n config[\"server\"][1], config[\"realname\"], config[\"ident\"], False, True)\n self.connect()\n self._connection = IRC(self._bot, rc_watch=True)\n self._connection.connect()\n if self.is_running:\n self.loop()\n\n while self.is_running:\n self.stayin_alive()\n self._last_conn_check = time.time()\n time.sleep(320)","title":""},{"docid":"9ee42bcece1f5c0797bc4a4e8e32de8b","score":"0.5665967","text":"def open_connection(self):\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((HOST, PORT))\n except Exception, e:\n print 'Can not connect to server at', HOST, PORT\n return None\n else:\n return s","title":""},{"docid":"75b8947993665eb3a06fe50b6bf738d1","score":"0.56624246","text":"def connect(self, tag='PQ'):\n if self.connected:\n return self.connected\n\n try:\n self.engine = create_engine(self.url)\n except Exception as err_var:\n self.connected = False\n log(module=__name__, function='connect', msg='ERROR_{0}: BDOSE engine NOT created. ABORT !!'.format(tag))\n log(module=__name__, function='connect', msg='ERROR: EXCEPTION {}'.format(err_var))\n exit(1)\n\n try:\n self.conn = self.engine.connect()\n self.connected = True\n except Exception as err_var:\n self.connected = False\n log(module=__name__, function='connect', msg='ERROR_{0}: BDOSE NOT connected. ABORT !!'.format(tag))\n log(module=__name__, function='connect', msg='ERROR: EXCEPTION {}'.format(err_var))\n exit(1)\n\n return self.connected","title":""},{"docid":"c17121aaee05286f7b693cc311161b05","score":"0.566215","text":"def connect(self):\n self.my_local_logger.info('Connecting to %s', self._url)\n try:\n return pika.SelectConnection(pika.URLParameters(self._url),\n self.on_connection_open,\n stop_ioloop_on_close=False)\n except pika.exceptions.AMQPConnectionError as e:\n self.my_local_logger.error('NO CONNECTION to %s because %s', (self._url, e))\n exit()","title":""},{"docid":"33372ce98dc7df1d9d000bb845125262","score":"0.56606627","text":"def _DoConnection(self):\n started = time.time()\n while True:\n if self._Reconnect() and self._ReplaySync():\n break\n else:\n logging.warning(\"Had to connect to %s:%d but failed. Trying again...\",\n self.Address(), self.Port())\n # Sleep for some time before trying again.\n time.sleep(config_lib.CONFIG[\"HTTPDataStore.retry_time\"])\n if time.time() - started >= config_lib.CONFIG[\n \"HTTPDataStore.reconnect_timeout\"]:\n raise HTTPDataStoreError(\"Could not connect to %s:%d. Giving up.\" %\n (self.Address(), self.Port()))","title":""},{"docid":"46c52171653f22524f3c06529f1ff9d1","score":"0.5659421","text":"def connectionInitialized(self):\n self.join(self.jrooms, self.nick)","title":""},{"docid":"a6cd29826138e29d6a9d68786b6bca0f","score":"0.5659126","text":"def clientConnectionLost(self, connector, reason):\r\n connector.connect()","title":""},{"docid":"e38f9c807edcd5ac88a8582ae93fad4b","score":"0.56504047","text":"def _check_connection(self):\n if self.last_connection_callback != self.connection_state:\n self.last_connection_callback = self.connection_state\n if self.connection_state == 1:\n if self.on_online:\n self.on_online()\n else:\n if self.on_offline:\n self.on_offline()","title":""},{"docid":"14a8a0da7a2c6cd5fa651f9fbca5a89c","score":"0.5649131","text":"def connection_made(self):","title":""},{"docid":"136e7d6169f64c289f2d1e859a8d70c8","score":"0.5646008","text":"def connect(self):\n if self._connected or self.is_alive():\n raise ConnectionExistsError(\"Already connected\")\n # After starting the thread we wait for it to post us\n # an event signifying that connection is established. This\n # ensures that the caller only resumes when we are fully connected.\n self.start()\n with self._lock:\n self._connect_cond.wait_for(lambda: self._connected)","title":""},{"docid":"cbeb77163255de0061378dfdaee57c87","score":"0.5645996","text":"def connect(self):\n if self.is_connected:\n return True\n else:\n with self._lock:\n # Checking the state again in case it was set before we got the lock\n if self.is_connected:\n return True\n else:\n self._open()\n self.is_connected = True","title":""}],"string":"[\n {\n \"docid\": \"fd56e177497fcb7c271f65d8369b0672\",\n \"score\": \"0.6363437\",\n \"text\": \"def _reconnect(self):\\n if self._connection.closed > 0:\\n self._connection = self._connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf5400480e3255106f644dd5c197160\",\n \"score\": \"0.6337093\",\n \"text\": \"def connect(self):\\n if self._conn is None:\\n self._conn = self._connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a7edb0775c269f68d5f631c6580f52\",\n \"score\": \"0.629398\",\n \"text\": \"def init_connection_state(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27fe5b27d6668e335cb65fc3c540f51\",\n \"score\": \"0.62428737\",\n \"text\": \"def open(self):\\n # unique_id = yield self.conn.configure()\\n log = yield self.conn.verify_conn()\\n unique_id = log.unique_id\\n gen_log.info('CONNECTION OPENED WITH: %s' % unique_id)\\n if unique_id:\\n self.session_key = unique_id\\n self.state = OPEN\\n self.conn.on_open(unique_id)\\n raise gen.Return(unique_id)\\n raise gen.Return(conf.DISCONN_RESULT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e06784fa9486540c53f0d0b3ace65ff\",\n \"score\": \"0.62002075\",\n \"text\": \"def attempt_connect_to_server(self):\\r\\n\\r\\n\\t\\t#If connection was valid\\r\\n\\t\\tif(self._client_socket != None):\\r\\n\\t\\t\\t#Check if connection still exists by sending quick request\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tresponse = self.send_request_to_server(666)\\r\\n\\t\\t\\t\\tif not response:\\r\\n\\t\\t\\t\\t\\tself.connect_to_server()\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tprint(\\\"Already established connection.\\\")\\r\\n\\t\\t\\t\\t\\treturn \\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tself.connect_to_server()\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.connect_to_server()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd796520caa4c038233989a2d3c2a00a\",\n \"score\": \"0.61029947\",\n \"text\": \"def retry_connection(self):\\n self._connection_successful = self._test_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecb21ff3102914636cf72e5ea0e78863\",\n \"score\": \"0.6065234\",\n \"text\": \"def connection_possible(self):\\n if self._connection_successful is None:\\n self.retry_connection()\\n return self._connection_successful\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3128e05880e395feac565d6418fa3e7\",\n \"score\": \"0.60170174\",\n \"text\": \"def _connect(self, port=None):\\n super(Connection, self)._connect()\\n if not self._connected: \\n self._connected = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1816969a8ce922d31a8eda3c9e0eb607\",\n \"score\": \"0.5992339\",\n \"text\": \"def _do_connect(self):\\n raise NotImplementedError(\\\"Please Implement this method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f14b99516e5edff03d05f1000e3e8e\",\n \"score\": \"0.5989074\",\n \"text\": \"def connect(self):\\r\\n self._closed = False\\r\\n return self.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6bf87439a39d44a9c3910594e8db2b\",\n \"score\": \"0.59796196\",\n \"text\": \"def validate_connection(self):\\n if self.database_connection:\\n if threading.get_ident() != self.stored_thread_id:\\n self.disconnect()\\n self.connect()\\n else:\\n self.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e17e22a6670704cd6de7380cb0705e\",\n \"score\": \"0.5954927\",\n \"text\": \"def connection(self):\\r\\n if not self._closed:\\r\\n if not self.connected:\\r\\n self.declared_entities.clear()\\r\\n self._default_channel = None\\r\\n self._connection = self._establish_connection()\\r\\n self._closed = False\\r\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cffdf518d56531b0c67ef74abcf01b\",\n \"score\": \"0.5951426\",\n \"text\": \"def connect(self):\\n # sanity check\\n if self.mode == 'connecting':\\n return defer.fail(ex.AlreadyConnecting())\\n \\n elif self.mode == 'connected':\\n return defer.fail(ex.AlreadyConnected())\\n \\n # we are connecting now\\n self.transitionTo('connecting')\\n return self.do_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc1de96753235e2641a2d92b8ddff83\",\n \"score\": \"0.59378225\",\n \"text\": \"def opened(self):\\n connectionMsg = {\\\"msg\\\": \\\"connect\\\", \\\"version\\\":\\\"pre1\\\", \\\"support\\\":[\\\"pre1\\\"]}\\n self._sendDict(connectionMsg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e63b4c40ce880cd6ca7b27f22aa221\",\n \"score\": \"0.5933501\",\n \"text\": \"def _prepare_connection(self, new=True):\\n try:\\n self.conn.close()\\n except BaseException:\\n pass\\n if new:\\n self.conn = self._new_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8896d12f20a941c96f722da339179139\",\n \"score\": \"0.5917346\",\n \"text\": \"def get_started_connection(self):\\n return self.started_connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fe163df3effabf74d1657754848b5c7\",\n \"score\": \"0.59123474\",\n \"text\": \"def _new_conn(self):\\n return self._socket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd2713bb1817420659f0cf26f3e534f8\",\n \"score\": \"0.5903351\",\n \"text\": \"def connectionLost(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a25d199318d7f92a8322c4b01417a52\",\n \"score\": \"0.59030616\",\n \"text\": \"def no_previous_connection_found(self, app: Application) -> NetworkState:\\n app.interface_transition('no_active_connection_found')\\n return UnconnectedState()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dd8fca42a417bdf3e98285ad79ed05b\",\n \"score\": \"0.59001714\",\n \"text\": \"def reconnect(self):\\n LOG.info(\\\"STOMP attempting to connect\\\")\\n self.fire(connect(host=self.host))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00303df79860b68df83338b2812bddf0\",\n \"score\": \"0.5894534\",\n \"text\": \"def connect():\\n return Connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcb83b22b5d9df0da461737a0202dba\",\n \"score\": \"0.58864605\",\n \"text\": \"def reconnect(self):\\n # This is the old connection IOLoop instance, stop its ioloop\\n self._connection.ioloop.stop()\\n if not self._closing:\\n # Create a new connection\\n self._connection = self.connect()\\n\\n # There is now a new connection, needs a new ioloop to run\\n self._connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d12158d3e6fdf0fd7c12e1b4b76943d\",\n \"score\": \"0.5879867\",\n \"text\": \"def open(self):\\n if self.fail_open is True:\\n raise ConnectionError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934d59ef0f3bead2e3c45b9c1d72a0cf\",\n \"score\": \"0.5874695\",\n \"text\": \"def connect(self):\\n # Nothing to do here.\\n self.conn.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4263ccaea34b3ef5ff5481a702e961c\",\n \"score\": \"0.58718354\",\n \"text\": \"def reconnect(self):\\n # This is the old connection IOLoop instance, stop its ioloop\\n self._connection.ioloop.stop()\\n\\n # Create a new connection\\n self._connection = self.connect()\\n\\n # There is now a new connection, needs a new ioloop to run\\n self._connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c063bd9db14f19f1b67d6b6768d917\",\n \"score\": \"0.5871469\",\n \"text\": \"def connect(self):\\n options = self.get_connection_options()\\n\\n if options:\\n # Connect to an option\\n choice = random.choice(options)\\n choice.connected = True\\n choice.connections.append(self)\\n\\n self.connections.append(choice)\\n self.lineto(choice)\\n choice.connect()\\n\\n else:\\n # Backtrack\\n self.backtrack()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6982a22ee5654b27074915459461d5\",\n \"score\": \"0.58638966\",\n \"text\": \"def connect():\\n global _CONNECTED\\n _CONNECTED = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0f71ac0c582fbbd46e72e54c4e30f6\",\n \"score\": \"0.58624244\",\n \"text\": \"def __enter__(self):\\n self.start()\\n self._connection_made.wait()\\n if not self.alive:\\n raise RuntimeError('connection_lost already called')\\n return self.protocol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0026fd10b14af46ac1086210741aaed\",\n \"score\": \"0.5858484\",\n \"text\": \"def reconnect(self):\\n # This is the old connection IOLoop instance, stop its ioloop\\n self._connection.ioloop.stop()\\n\\n if not self._closing:\\n # Create a new connection\\n self._connection = self.connect()\\n\\n # There is now a new connection, needs a new ioloop to run\\n self._connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fee8bb8ec8f247fc9cb14d83be893a7\",\n \"score\": \"0.5856494\",\n \"text\": \"def connect(self, reconnect=True):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8469da9bff8a60c5e66a6bb695d9e49e\",\n \"score\": \"0.5854051\",\n \"text\": \"def reconnect(self):\\n # This is the old connection IOLoop instance, stop its ioloop\\n self.connection.ioloop.stop()\\n\\n if not self.closing:\\n self.connection = self.connect()\\n self.connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef40354be181a6ab936ac614bb6096d\",\n \"score\": \"0.5837165\",\n \"text\": \"def init_conn(self) -> None:\\n\\t\\tself.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n\\t\\tself.socket.connect(self.address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166a9fdf6ae4e17ee5aca04364b13929\",\n \"score\": \"0.5829957\",\n \"text\": \"def connect(self):\\n return super().connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166a9fdf6ae4e17ee5aca04364b13929\",\n \"score\": \"0.5829957\",\n \"text\": \"def connect(self):\\n return super().connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b8fb10ebe1ecc0c005bd08574ee647\",\n \"score\": \"0.5821749\",\n \"text\": \"def get_connect(self):\\n\\t\\treturn self.CONNECTION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56a43cb7bfc7a61401ab93ca048c6606\",\n \"score\": \"0.58057463\",\n \"text\": \"def set_default_db_conn(db_conn):\\n global _def_db_conn\\n _def_db_conn = db_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1b4a592afe44ede99c8c768a06ce19b\",\n \"score\": \"0.57918876\",\n \"text\": \"def open_conn(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6989fc00e7f6ef495dd28e7e3a642aa9\",\n \"score\": \"0.5786339\",\n \"text\": \"def establish_connection(self):\\r\\n conninfo = self.client\\r\\n for name, default_value in items(self.default_connection_params):\\r\\n if not getattr(conninfo, name, None):\\r\\n setattr(conninfo, name, default_value)\\r\\n if conninfo.hostname == 'localhost':\\r\\n conninfo.hostname = '127.0.0.1'\\r\\n conn = self.Connection(host=conninfo.host,\\r\\n userid=conninfo.userid,\\r\\n password=conninfo.password,\\r\\n login_method=conninfo.login_method,\\r\\n virtual_host=conninfo.virtual_host,\\r\\n insist=conninfo.insist,\\r\\n ssl=conninfo.ssl,\\r\\n connect_timeout=conninfo.connect_timeout)\\r\\n conn.client = self.client\\r\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f04e8c1888a1100fc8879e2a5b37aa\",\n \"score\": \"0.57805777\",\n \"text\": \"def before_request():\\r\\n try:\\r\\n g.conn = engine.connect()\\r\\n except:\\r\\n print(\\\"uh oh, problem connecting to database\\\")\\r\\n import traceback; traceback.print_exc()\\r\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21fcc329d483e6660ef20be04469e91d\",\n \"score\": \"0.5767054\",\n \"text\": \"def ensure_connection(self):\\r\\n if self.server_proxy is None:\\r\\n self._connect_to_server()\\r\\n if self.server_proxy is None:\\r\\n raise RuntimeError('Cannot connect to server')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa6110e250bbd496ec5a7d163ba5487\",\n \"score\": \"0.5760746\",\n \"text\": \"def connect(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2676d75a929ab4ebd71ffb0e5d9c1ce3\",\n \"score\": \"0.5755266\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print(\\\"uh oh, problem connecting to database\\\")\\n import traceback\\n traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c71340fcdd395681c95be8bd3d77f0c\",\n \"score\": \"0.57498485\",\n \"text\": \"def onTryconnect(self):\\n try:\\n self.s.connect(self.peerAddr)\\n except socket.error as exc:\\n code = exc.args[0]\\n if code == errno.EINPROGRESS:\\n return\\n\\n self.pollManager.removeTimeout(self.tid)\\n self.tid = None\\n\\n if self._useSSL:\\n # Do the handshaking while the socket is in blocking mode. In\\n # theory you can do it in non-blocking mode, but I think there\\n # is a nasty timing bug in Python's wrapper or some versions of\\n # the SSL library itself.\\n self.s.setblocking(1)\\n self.s.do_handshake()\\n self.s.setblocking(0)\\n\\n active, ret = self.onConnectCB(self.peerName, self)\\n if not active:\\n # TODO: Should we shutdown the connection.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6caffbf403c7ad1c75289b9ee8d653ca\",\n \"score\": \"0.57475436\",\n \"text\": \"def on_connect(self):\\n self.log_event('connect')\\n self.last_keep_alive = now()\\n self.set_event(Events.EVENT_ONLINE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4236266b4f77c2bae12d89c14e6fd985\",\n \"score\": \"0.57460266\",\n \"text\": \"def connect(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4236266b4f77c2bae12d89c14e6fd985\",\n \"score\": \"0.57460266\",\n \"text\": \"def connect(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4236266b4f77c2bae12d89c14e6fd985\",\n \"score\": \"0.57460266\",\n \"text\": \"def connect(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4236266b4f77c2bae12d89c14e6fd985\",\n \"score\": \"0.57460266\",\n \"text\": \"def connect(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49461f95e0e31a968343c86369f2f79c\",\n \"score\": \"0.5742942\",\n \"text\": \"def connect(self):\\n self._conn.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4227db559f2274ccbd9d12081e91e4\",\n \"score\": \"0.5742328\",\n \"text\": \"def connection(self, connection):\\r\\n if connection is None:\\r\\n if self.connection is not None:\\r\\n del connections[self.id]\\r\\n return\\r\\n if self.connection is not None:\\r\\n connection.notify('*** Booting old connection. ***')\\r\\n self.connection.notify(\\r\\n '*** Logging you in from somewhere else ***'\\r\\n )\\r\\n self.connection.object = None\\r\\n self.connection.transport.loseConnection()\\r\\n connections[self.id] = connection\\r\\n self.notify(f'Welcome, {self.name}.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48817a53108a4d15c2cde6826b620f76\",\n \"score\": \"0.57260793\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print (\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48817a53108a4d15c2cde6826b620f76\",\n \"score\": \"0.57260793\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print (\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e9474af55d4840ac95827c0733e093a\",\n \"score\": \"0.5725989\",\n \"text\": \"def connect(self):\\n self._create_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5685915742b4def22987c840f7e1f456\",\n \"score\": \"0.5725675\",\n \"text\": \"def get_conn(self):\\r\\n got_connection = False\\r\\n while not got_connection:\\r\\n try:\\r\\n conn = self.conn_pool.connection()\\r\\n got_connection = True\\r\\n except:\\r\\n time.sleep(0.1)\\r\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f3e466e86b96350f0ee43cbe2607f7\",\n \"score\": \"0.5723511\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556e9850d46b434584767c1ec3338ac9\",\n \"score\": \"0.5722945\",\n \"text\": \"def connect(self):\\n return self._connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036417a1d87f9be48a573f68026ec2e4\",\n \"score\": \"0.5722124\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print(\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036417a1d87f9be48a573f68026ec2e4\",\n \"score\": \"0.5722124\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print(\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036417a1d87f9be48a573f68026ec2e4\",\n \"score\": \"0.5722124\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print(\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036417a1d87f9be48a573f68026ec2e4\",\n \"score\": \"0.5722124\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print(\\\"uh oh, problem connecting to database\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe03d12ea70d5e71b99ef90b02067621\",\n \"score\": \"0.571884\",\n \"text\": \"def init_connection_file(self):\\n if self.existing:\\n try:\\n cf = find_connection_file(self.existing)\\n except Exception:\\n self.log.critical(\\\"Could not find existing kernel connection file %s\\\", self.existing)\\n self.exit(1)\\n self.log.info(\\\"Connecting to existing kernel: %s\\\" % cf)\\n self.connection_file = cf\\n # should load_connection_file only be used for existing?\\n # as it is now, this allows reusing ports if an existing\\n # file is requested\\n try:\\n self.load_connection_file()\\n except Exception:\\n self.log.error(\\\"Failed to load connection file: %r\\\", self.connection_file, exc_info=True)\\n self.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9f8731210ea9c61777612de432f6206\",\n \"score\": \"0.57136357\",\n \"text\": \"def db_connect(self, db_conn_name):\\n\\n\\t\\t# TODO: Is there a better way of doing this distinction?\\n\\t\\t# TODO: This logic should be pushed down into vondi, and use the has_external_pooling() check.\\n\\t\\tif db_conn_name.startswith('sqlr_') or db_conn_name.endswith('_sqlr'):\\n\\t\\t\\tconn = None\\n\\t\\t\\tif db_conn_name in self.database_connections and self.database_connections[db_conn_name] is not None:\\n\\t\\t\\t\\t# If we already have a connection, then reuse it.\\n\\t\\t\\t\\t# TODO: Should we check if it is open?\\n\\t\\t\\t\\tconn = self.database_connections[db_conn_name]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tconn = vondbi.connect(db_conn_name)\\n\\t\\t\\t\\tself.database_connections[db_conn_name] = conn\\n\\t\\t\\tself.log.debug(\\\"database connections: %s\\\" % self.database_connections[db_conn_name])\\n\\t\\t\\treturn conn\\n\\t\\telse:\\n\\t\\t\\treturn vondbi.connect(db_conn_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520ecf62db44b59854c5f870607def60\",\n \"score\": \"0.57117635\",\n \"text\": \"def reconnect(self):\\n return reconnect(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520ecf62db44b59854c5f870607def60\",\n \"score\": \"0.57117635\",\n \"text\": \"def reconnect(self):\\n return reconnect(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520ecf62db44b59854c5f870607def60\",\n \"score\": \"0.57117635\",\n \"text\": \"def reconnect(self):\\n return reconnect(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784eb885efe83602323134f95359a955\",\n \"score\": \"0.5711433\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print (\\\"Error connecting to database.\\\")\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa3174cfd3dda473de423bbcda41c44\",\n \"score\": \"0.5709649\",\n \"text\": \"def own_conn(self):\\n return self.new_conn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37f5f99dbeddd0db614971d85847dd5\",\n \"score\": \"0.57057834\",\n \"text\": \"def reconnect(self):\\r\\n if self._channel is None:\\r\\n self._channel = grpc.insecure_channel(self._connection)\\r\\n self._stub = models_pb2_grpc.ModelsStub(self._channel)\\r\\n else:\\r\\n print(\\\"Connection for client already open\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2903cd1912ec63c745edc2a6f00f2917\",\n \"score\": \"0.56987613\",\n \"text\": \"def conn(self):\\n return self.new_conn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e1b8a9483a2513632ea442c3280703\",\n \"score\": \"0.5696181\",\n \"text\": \"def connect(self, fail_silently=False):\\n if not self.connection:\\n self.connection = get_connection(fail_silently=fail_silently)\\n return self.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79900e8c6ab19d3c1fb7bf01031d0ebc\",\n \"score\": \"0.56957895\",\n \"text\": \"def before_request():\\n try:\\n g.conn = engine.connect()\\n except:\\n print \\\"uh oh, problem connecting to database\\\"\\n import traceback; traceback.print_exc()\\n g.conn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6146ce3c37ab2e24e51e0d2b18dc527c\",\n \"score\": \"0.5692646\",\n \"text\": \"def connection(self, key=None):\\n if key:\\n self.active = key\\n else:\\n self.active = 'default'\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1b78945e5d92ad006aab49e9c4c44a\",\n \"score\": \"0.5691761\",\n \"text\": \"def __init__(self):\\n self.connections = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1b78945e5d92ad006aab49e9c4c44a\",\n \"score\": \"0.5691761\",\n \"text\": \"def __init__(self):\\n self.connections = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1b78945e5d92ad006aab49e9c4c44a\",\n \"score\": \"0.5691761\",\n \"text\": \"def __init__(self):\\n self.connections = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.56900793\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.56900793\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ddae9852c0c404f68cc6af1cdd63f5a\",\n \"score\": \"0.56823784\",\n \"text\": \"def reconnect():\\r\\n return _connect_to_server()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae918455b7577be43f265dbd2ec4fdc0\",\n \"score\": \"0.56808025\",\n \"text\": \"def __newConnection(self):\\n sock = self.nextPendingConnection()\\n peerAddress = sock.peerAddress().toString()\\n if peerAddress not in Preferences.getDebugger(\\\"AllowedHosts\\\"):\\n # the peer is not allowed to connect\\n res = E5MessageBox.yesNo(\\n None,\\n self.tr(\\\"Connection from illegal host\\\"),\\n self.tr(\\n \\\"\\\"\\\"<p>A connection was attempted by the illegal host\\\"\\\"\\\"\\n \\\"\\\"\\\" <b>{0}</b>. Accept this connection?</p>\\\"\\\"\\\")\\n .format(peerAddress),\\n icon=E5MessageBox.Warning)\\n if not res:\\n sock.abort()\\n return\\n else:\\n allowedHosts = Preferences.getDebugger(\\\"AllowedHosts\\\")\\n allowedHosts.append(peerAddress)\\n Preferences.setDebugger(\\\"AllowedHosts\\\", allowedHosts)\\n \\n if self.passive:\\n self.__createDebuggerInterface(\\n Preferences.getDebugger(\\\"PassiveDbgType\\\"))\\n \\n accepted = self.debuggerInterface.newConnection(sock)\\n if accepted:\\n # Perform actions necessary, if client type has changed\\n if self.lastClientType != self.clientType:\\n self.lastClientType = self.clientType\\n self.remoteBanner()\\n elif self.__autoClearShell:\\n self.__autoClearShell = False\\n self.remoteBanner()\\n elif self.passive:\\n self.remoteBanner()\\n \\n self.debuggerInterface.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbcb2172fd9da1cb165e6ba2b699c26\",\n \"score\": \"0.5678966\",\n \"text\": \"def default_channel(self):\\r\\n # make sure we're still connected, and if not refresh.\\r\\n self.connection\\r\\n if self._default_channel is None:\\r\\n self._default_channel = self.channel()\\r\\n return self._default_channel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab5b01c4b585aba5fa120f6f940e4a59\",\n \"score\": \"0.5678228\",\n \"text\": \"def on_connection_open(self, unused_connection):\\n self.my_local_logger.info('Connection opened')\\n self.add_on_connection_close_callback()\\n self.open_channel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66df0af7987812cb6b268a81fc8a43d6\",\n \"score\": \"0.5673222\",\n \"text\": \"def on_connection_open(self, unused_connection):\\n self.my_local_logger.debug('Connection opened')\\n self.add_on_connection_close_callback()\\n self.open_channel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d55ecd060278a66d5961a84c30c023c\",\n \"score\": \"0.5672022\",\n \"text\": \"def start_conn(self):\\n config = self.config[\\\"watcher\\\"]\\n Connection.__init__(self._logger, config[\\\"nick\\\"], None , config[\\\"server\\\"][0],\\n config[\\\"server\\\"][1], config[\\\"realname\\\"], config[\\\"ident\\\"], False, True)\\n self.connect()\\n self._connection = IRC(self._bot, rc_watch=True)\\n self._connection.connect()\\n if self.is_running:\\n self.loop()\\n\\n while self.is_running:\\n self.stayin_alive()\\n self._last_conn_check = time.time()\\n time.sleep(320)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee42bcece1f5c0797bc4a4e8e32de8b\",\n \"score\": \"0.5665967\",\n \"text\": \"def open_connection(self):\\n try:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.connect((HOST, PORT))\\n except Exception, e:\\n print 'Can not connect to server at', HOST, PORT\\n return None\\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b8947993665eb3a06fe50b6bf738d1\",\n \"score\": \"0.56624246\",\n \"text\": \"def connect(self, tag='PQ'):\\n if self.connected:\\n return self.connected\\n\\n try:\\n self.engine = create_engine(self.url)\\n except Exception as err_var:\\n self.connected = False\\n log(module=__name__, function='connect', msg='ERROR_{0}: BDOSE engine NOT created. ABORT !!'.format(tag))\\n log(module=__name__, function='connect', msg='ERROR: EXCEPTION {}'.format(err_var))\\n exit(1)\\n\\n try:\\n self.conn = self.engine.connect()\\n self.connected = True\\n except Exception as err_var:\\n self.connected = False\\n log(module=__name__, function='connect', msg='ERROR_{0}: BDOSE NOT connected. ABORT !!'.format(tag))\\n log(module=__name__, function='connect', msg='ERROR: EXCEPTION {}'.format(err_var))\\n exit(1)\\n\\n return self.connected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17121aaee05286f7b693cc311161b05\",\n \"score\": \"0.566215\",\n \"text\": \"def connect(self):\\n self.my_local_logger.info('Connecting to %s', self._url)\\n try:\\n return pika.SelectConnection(pika.URLParameters(self._url),\\n self.on_connection_open,\\n stop_ioloop_on_close=False)\\n except pika.exceptions.AMQPConnectionError as e:\\n self.my_local_logger.error('NO CONNECTION to %s because %s', (self._url, e))\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33372ce98dc7df1d9d000bb845125262\",\n \"score\": \"0.56606627\",\n \"text\": \"def _DoConnection(self):\\n started = time.time()\\n while True:\\n if self._Reconnect() and self._ReplaySync():\\n break\\n else:\\n logging.warning(\\\"Had to connect to %s:%d but failed. Trying again...\\\",\\n self.Address(), self.Port())\\n # Sleep for some time before trying again.\\n time.sleep(config_lib.CONFIG[\\\"HTTPDataStore.retry_time\\\"])\\n if time.time() - started >= config_lib.CONFIG[\\n \\\"HTTPDataStore.reconnect_timeout\\\"]:\\n raise HTTPDataStoreError(\\\"Could not connect to %s:%d. Giving up.\\\" %\\n (self.Address(), self.Port()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c52171653f22524f3c06529f1ff9d1\",\n \"score\": \"0.5659421\",\n \"text\": \"def connectionInitialized(self):\\n self.join(self.jrooms, self.nick)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cd29826138e29d6a9d68786b6bca0f\",\n \"score\": \"0.5659126\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\r\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38f9c807edcd5ac88a8582ae93fad4b\",\n \"score\": \"0.56504047\",\n \"text\": \"def _check_connection(self):\\n if self.last_connection_callback != self.connection_state:\\n self.last_connection_callback = self.connection_state\\n if self.connection_state == 1:\\n if self.on_online:\\n self.on_online()\\n else:\\n if self.on_offline:\\n self.on_offline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a8a0da7a2c6cd5fa651f9fbca5a89c\",\n \"score\": \"0.5649131\",\n \"text\": \"def connection_made(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136e7d6169f64c289f2d1e859a8d70c8\",\n \"score\": \"0.5646008\",\n \"text\": \"def connect(self):\\n if self._connected or self.is_alive():\\n raise ConnectionExistsError(\\\"Already connected\\\")\\n # After starting the thread we wait for it to post us\\n # an event signifying that connection is established. This\\n # ensures that the caller only resumes when we are fully connected.\\n self.start()\\n with self._lock:\\n self._connect_cond.wait_for(lambda: self._connected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbeb77163255de0061378dfdaee57c87\",\n \"score\": \"0.5645996\",\n \"text\": \"def connect(self):\\n if self.is_connected:\\n return True\\n else:\\n with self._lock:\\n # Checking the state again in case it was set before we got the lock\\n if self.is_connected:\\n return True\\n else:\\n self._open()\\n self.is_connected = True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993674,"cells":{"query_id":{"kind":"string","value":"f460811000e7ba45eb2e7399a0b30c77"},"query":{"kind":"string","value":"Build context for view."},"positive_passages":{"kind":"list like","value":[{"docid":"e23049e8eb5b78724a9d00684fc7c34e","score":"0.0","text":"def get_context_data(self, **kwargs):\n context = super(PublicProfileView, self).get_context_data(**kwargs)\n request_user = User.objects.filter(username=self.kwargs['request_username'])\n imager_profile = ImagerProfile.objects.filter(user=request_user)\n context['photos_pub'] = (Photo.objects\n .filter(user=request_user)\n .filter(published='PB'))\n context['albums_pub'] = (Album.objects\n .filter(user=request_user)\n .filter(published='PB'))\n context['imager_user'] = imager_profile\n\n return context","title":""}],"string":"[\n {\n \"docid\": \"e23049e8eb5b78724a9d00684fc7c34e\",\n \"score\": \"0.0\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super(PublicProfileView, self).get_context_data(**kwargs)\\n request_user = User.objects.filter(username=self.kwargs['request_username'])\\n imager_profile = ImagerProfile.objects.filter(user=request_user)\\n context['photos_pub'] = (Photo.objects\\n .filter(user=request_user)\\n .filter(published='PB'))\\n context['albums_pub'] = (Album.objects\\n .filter(user=request_user)\\n .filter(published='PB'))\\n context['imager_user'] = imager_profile\\n\\n return context\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f5cfbe026e23b36604f93ffcd667d129","score":"0.6692236","text":"def generate_context(self):\n raise NotImplementedError","title":""},{"docid":"2c20dcefe742d6f2b76e08f388cf1e1a","score":"0.6588604","text":"def get_context(self, request, context, **kwargs):\n self.ctx = {\n 'base_template': self.base_template,\n }\n self.ctx.update(context)\n self.ctx.update(kwargs)\n return self.ctx","title":""},{"docid":"3a134ecc79c60150593e84c92b5d37d3","score":"0.64821","text":"def _make_context():\n return {'app': app}","title":""},{"docid":"75df5058cc0956e3781e01db3700eccb","score":"0.64800435","text":"def load_context(self):","title":""},{"docid":"7d5afcf50f736d83d939ba5016a09ff9","score":"0.6403232","text":"def build_context(self, environ):\n self.__run_startup_hooks()\n\n ctx.app = self\n ctx.url_adapter = adapter = self.url_map.bind_to_environ(environ)\n\n # ALWAYS set this after adapter\n ctx.request = self.request_class(environ)\n\n rule, url_values = adapter.match(return_rule=True)\n dispatcher = self # FIXME: self.get_dispatcher(rule)\n\n # Set up context variables\n ctx.url_values = url_values\n ctx.dispatcher = dispatcher\n ctx.resource = dispatcher.get_resource(rule)\n\n context = ExitStack()\n context.callback(self.__cleanup)\n # Add all the application's context managers to\n # the exit stack. If any of them return a value,\n # we'll add the value to the application context\n # with the function name.\n for hook in self.context_hooks:\n retval = context.enter_context(hook())\n if retval is not None:\n setattr(ctx, hook.__name__, retval)\n return context","title":""},{"docid":"22361e6b7fd296a7df81b6110d25b91b","score":"0.6321904","text":"def _make_context():\n return {'app': app, 'db': db}","title":""},{"docid":"1ab1f128b05f1d10462dfb7dd55c834a","score":"0.63080126","text":"def get_context_data(self, **kwargs):\n context = {\n 'view': self,\n 'instance': self.get_instance(),\n 'form': self.get_form(),\n 'model_admin': self.model_admin,\n }\n context.update(kwargs)\n return context","title":""},{"docid":"d4b71f5e84a0ea98d66c4f057d26e875","score":"0.627791","text":"def _make_context():\n return {'app': app, 'db': db, 'User': User}","title":""},{"docid":"d4b71f5e84a0ea98d66c4f057d26e875","score":"0.627791","text":"def _make_context():\n return {'app': app, 'db': db, 'User': User}","title":""},{"docid":"1d9d8de0b84de34045dba30e5ccdef23","score":"0.62776417","text":"def get_context():\n # TODO: Make this work","title":""},{"docid":"88a00abf63f449dc118999570db3a6b1","score":"0.6272035","text":"def get_context(self, request, context, *args, **kwargs): # noqa: E501\n # Build MVView and extent\n view, extent = self.build_map_extent_and_view(request, *args, **kwargs)\n\n # Compose the Map\n log.debug(\"Building MapView...\")\n map_view = self._build_map_view(request, view, extent, *args, **kwargs)\n\n # Add layers to the Map\n log.debug(\"Composing layers...\")\n layer_groups = self.compose_layers(\n request=request, map_view=map_view, *args, **kwargs\n )\n # Add layers to map view if not already added\n for layer_group in layer_groups:\n for layer in layer_group[\"layers\"]:\n if layer not in map_view.layers:\n map_view.layers.append(layer)\n\n log.debug(f\"Number of Layers: {len(map_view.layers)}\")\n\n # Check if we need to create a blank custom layer group\n create_custom_layer = True\n for layer_group in layer_groups:\n if layer_group[\"id\"] == \"custom_layers\":\n create_custom_layer = False\n break\n\n # Create the Custom Layers layer group\n if self.show_custom_layer and create_custom_layer:\n log.debug('Creating the \"Custom Layers\" layer group...')\n custom_layers = self.build_custom_layer_group()\n layer_groups.append(custom_layers)\n\n # Build legends\n log.debug(\"Building legends for each layer...\")\n legends = []\n for layer in map_view.layers:\n if not layer.data[\"show_legend\"]:\n continue\n\n legend = self.build_legend(layer)\n if legend is not None:\n legend_select_options = legend.get(\"select_options\") # None if not set\n if legend_select_options:\n # Create color ramp selector\n legend_select_input = SelectInput(\n name=f'tethys-color-ramp-picker-{legend[\"legend_id\"]}',\n options=legend.get(\"select_options\"),\n initial=legend.get(\"initial_option\"),\n classes=\"map-layout-color-ramp-picker form-select-sm\",\n original=True,\n )\n else:\n legend_select_input = None\n\n legends.append((legend, legend_select_input))\n\n # Override MapView with CesiumMapView if Cesium is the chosen map_type.\n if self.map_type == \"cesium_map_view\":\n log.debug(\"Converting MapView to CesiumMapView...\")\n map_view = self._build_ceisum_map_view(map_view)\n\n # Prepare context\n context.update(\n {\n \"geocode_enabled\": self.geocode_api_key is not None,\n \"layer_groups\": layer_groups,\n \"layer_tab_name\": self.layer_tab_name,\n \"legends\": legends,\n \"map_extent\": extent,\n \"map_type\": self.map_type,\n \"map_view\": map_view,\n \"nav_subtitle\": self.map_subtitle,\n \"nav_title\": self.map_title,\n \"plotly_version\": self.plotly_version,\n \"show_custom_layer\": self.show_custom_layer,\n \"show_properties_popup\": self.show_properties_popup,\n \"show_map_click_popup\": self.show_map_click_popup,\n \"show_legends\": self.show_legends,\n \"wide_nav\": self.wide_nav,\n \"workspace\": self.geoserver_workspace,\n }\n )\n\n if context.get(\"show_public_toggle\", False):\n layer_dropdown_toggle = ToggleSwitch(\n display_text=\"\",\n name=\"layer-dropdown-toggle\",\n on_label=\"Yes\",\n off_label=\"No\",\n on_style=\"success\",\n off_style=\"danger\",\n initial=True,\n size=\"small\",\n classes=\"layer-dropdown-toggle\",\n )\n context.update({\"layer_dropdown_toggle\": layer_dropdown_toggle})\n\n # Add plot slide sheet\n plot_slidesheet = SlideSheet(\n id=\"plot-slide-sheet\",\n title=\"Plot\",\n content_template=\"tethys_layouts/map_layout/map_plot.html\",\n )\n\n context.update({\"plot_slide_sheet\": plot_slidesheet})\n return context","title":""},{"docid":"7e0b4905d7525efafdd7ceb9896f9234","score":"0.62493235","text":"def _make_context():\n return {\n 'app': app,\n 'db': db,\n 'User': User\n }","title":""},{"docid":"403bfbf900045a88ed8bcd152f6a5990","score":"0.6246165","text":"def _get_context(self, *args, **kwargs):\n return Context(args=args, kwargs=kwargs)","title":""},{"docid":"60a083b62763255220492c22953047f8","score":"0.6240362","text":"def create_context(self, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"a72144265b038841cf66724ca94dfacb","score":"0.61794543","text":"def _create_request_context(self, *args, **kwargs):\n return {}","title":""},{"docid":"67e519b6525b27b1d6f41141edb6fc41","score":"0.61465245","text":"def get_build_context(ctx: Dict={}) -> Dict:\n return ctx","title":""},{"docid":"733477aa90283f492d0efc9756deb139","score":"0.61457914","text":"def get_context_data(self, **kwargs):\n\n context = super().get_context_data(**kwargs)\n context = self.decorate_context(context)\n return context","title":""},{"docid":"250ffcfd8a19d7fcc28c586848a6ccad","score":"0.6136131","text":"def get_context(self):\n _context = EditingMixin.get_context(self)\n # Add some specific HTML rendering context when editing HTML modules where we pass\n # the root /c4x/ url for assets. This allows client-side substitutions to occur.\n _context.update({\n 'base_asset_url': StaticContent.get_base_url_path_for_course_assets(self.location.course_key),\n 'enable_latex_compiler': self.use_latex_compiler,\n 'editor': self.editor\n })\n return _context","title":""},{"docid":"4354d3f8321dad7b028f120a2bc50017","score":"0.61036646","text":"def context(self, request):\n\n # Generate custom context data based on the particular report subclass\n context = self.get_context_data(request)\n\n context['base_url'] = common.models.InvenTreeSetting.get_setting('INVENTREE_BASE_URL')\n context['date'] = datetime.datetime.now().date()\n context['datetime'] = datetime.datetime.now()\n context['default_page_size'] = common.models.InvenTreeSetting.get_setting('REPORT_DEFAULT_PAGE_SIZE')\n context['report_description'] = self.description\n context['report_name'] = self.name\n context['report_revision'] = self.revision\n context['request'] = request\n context['user'] = request.user\n\n return context","title":""},{"docid":"dd7c5a38bf8fa3692d4748d4e968f766","score":"0.6099687","text":"def gen_extra_context(self):\n pass","title":""},{"docid":"d8786be95149391a1f96d29339bdcd73","score":"0.6064868","text":"def _create_request_context(self, *args, **kwargs):\n return {\n 'request': self.request,\n }","title":""},{"docid":"d8786be95149391a1f96d29339bdcd73","score":"0.6064868","text":"def _create_request_context(self, *args, **kwargs):\n return {\n 'request': self.request,\n }","title":""},{"docid":"d8786be95149391a1f96d29339bdcd73","score":"0.6064868","text":"def _create_request_context(self, *args, **kwargs):\n return {\n 'request': self.request,\n }","title":""},{"docid":"364f683dbb15cfebb3f7ad32c5107d77","score":"0.60129315","text":"def _get_rendering_context(self):\n return {}","title":""},{"docid":"07f53f60b23b1914998d7a18b516505d","score":"0.6008574","text":"def apply_to_context():","title":""},{"docid":"9123cf4613f498bc8265790ab626eebb","score":"0.5994399","text":"def get_context_data(self, **kwargs):\n kwargs[\"view\"] = self\n\n if getattr(self, \"object\", None) is not None:\n kwargs[\"object\"] = self.object\n context_object_name = self.get_context_object_name()\n if context_object_name:\n kwargs[context_object_name] = self.object\n\n if getattr(self, \"object_list\", None) is not None:\n kwargs[\"object_list\"] = self.object_list\n context_object_name = self.get_context_object_name(is_list=True)\n if context_object_name:\n kwargs[context_object_name] = self.object_list\n\n return kwargs","title":""},{"docid":"a241931231a0e136a931cb2301947d8c","score":"0.5934253","text":"def get_context_data(self, **kwargs):\n request = self.request\n extension = ReviewBotExtension.instance\n\n extension_config_attrs = {\n 'brokerURL': extension.settings.get('broker_url'),\n }\n\n # Add information on the Review Bot User entry.\n user = get_object_or_none(User, pk=extension.settings.get('user'))\n\n if user:\n extension_config_attrs['user'] = _serialize_user(request, user)\n\n return dict(super(ConfigureView, self).get_context_data(**kwargs), **{\n 'extension': extension,\n 'extension_config_attrs': extension_config_attrs,\n 'extension_config_options': {\n 'integrationConfigURL': local_site_reverse(\n 'integration-list',\n request=request),\n 'userConfigURL': local_site_reverse(\n 'reviewbot-configure-user',\n request=request),\n 'workerStatusURL': local_site_reverse(\n 'reviewbot-worker-status',\n request=request),\n },\n })","title":""},{"docid":"a173f1490a2126d6d1b792322fc0c9ad","score":"0.5927507","text":"def get_context(self, **kwargs):\n if callable(self.context):\n context = self.context()\n else:\n context = self.context\n context.update(kwargs)\n return context","title":""},{"docid":"78512e5377e9ce4ef25c1f733886afb2","score":"0.5925705","text":"def create_context(cls):\n pass","title":""},{"docid":"bf963934baf2b4cca55666f7111b2ec5","score":"0.58962077","text":"def get_context_data(self):\n context = super(HomeView, self).get_context_data()\n\n context['title'] = 'Dev.f'\n context['meta_description'] = 'Aprende a construir software en la ' +\\\n 'primera y más grande escuela de hackers ' +\\\n 'de México y LatAm'\n\n senseis_object_list = list(Sensei.objects.all())\n random.shuffle(senseis_object_list)\n context['senseis'] = senseis_object_list[:3]\n\n programs_object_list = Program.objects.all()\n for program in programs_object_list:\n program.requirements = program.requirements.split(',')\n context['programs'] = programs_object_list\n\n mentors_object_list = list(Mentor.objects.all())\n random.shuffle(mentors_object_list)\n context['mentors'] = mentors_object_list[:5]\n\n testimonials_object_list = list(Testimonial.objects.all())\n random.shuffle(testimonials_object_list)\n context['testimonials'] = testimonials_object_list[:5]\n\n context['press'] = Press.objects.all()\n\n return context","title":""},{"docid":"37a7e6c39e849c10fbd5b10c9795308a","score":"0.5883541","text":"def get_context_data(self, *args, **kwargs):\n return {}","title":""},{"docid":"da041ccb779e9ca554e15056bcc5773a","score":"0.58800846","text":"def get_context(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"bad110edfe808d7b6c2bab981612c33e","score":"0.58788097","text":"def get_context_data(self, **kwargs):\n context = super(LeafTemplateView, self).get_context_data(**kwargs)\n context['page'] = self.page\n return context","title":""},{"docid":"ef70faccd497faf0484efb2880f22949","score":"0.5873368","text":"def initial_context(self):\n context = {}\n\n if self.context:\n for key, value in self.context.items():\n util.set_nested_attr(context, key, value)\n\n if self.group:\n util.set_nested_attr(context, 'group._id', self.group)\n\n if self.project:\n # TODO: Check for <id:xyz> syntax\n util.set_nested_attr(context, 'project.label', self.project)\n\n return context","title":""},{"docid":"4147288dc8ec86349960ec6666bbd305","score":"0.58391714","text":"def new_context(self, context):\n pass","title":""},{"docid":"dd35120428af2675e8c3e41b485f63f1","score":"0.5817951","text":"def get_context(self):\n ctx = self.get_default_context()\n if isinstance(self.body, dict):\n ctx.update(self.body)\n else:\n ctx['body'] = self.body\n return ctx","title":""},{"docid":"86d10b0af433ee154b1366c316643706","score":"0.5816754","text":"def init_with_context(self, context):\n\t\tpass","title":""},{"docid":"86d10b0af433ee154b1366c316643706","score":"0.5816754","text":"def init_with_context(self, context):\n\t\tpass","title":""},{"docid":"4facba4e3c68cc7244281c4874f99304","score":"0.5814838","text":"def get_context_data(self, request):\n\n return {}","title":""},{"docid":"74d803e3c797ec87e34f3bf41e5a0e84","score":"0.5809814","text":"def update_context(self):\n pass","title":""},{"docid":"1de0f0d95cfbe7c0fdb2469d9387d5a5","score":"0.57954663","text":"def create_global_context(self, **kwargs):\n context = dict(**kwargs)\n return context","title":""},{"docid":"8a820ea99257b75602423d6e731a0009","score":"0.57869446","text":"def _build_collection_context_dictionary(self, collection, context, form_data):\n if form_data:\n queryset_filter = build_queryset_filters(\n form_data, {\"collection\": collection}\n )\n else:\n queryset_filter = Q(collection=collection)\n\n context = self._fill_chart_context(collection, context, queryset_filter)\n context = self._fill_statistics_table_context(context, queryset_filter)\n context = self._fill_totals_tables(context, queryset_filter)\n context = self._fill_latest_linkevents(collection, context, form_data)\n\n return context","title":""},{"docid":"da8f4e12dfd56a46c9523362959e2f3e","score":"0.57710624","text":"def get_context_data(self, request):\n\n my_build = self.object_to_print\n\n if not type(my_build) == build.models.Build:\n raise TypeError('Provided model is not a Build object')\n\n return {\n 'build': my_build,\n 'part': my_build.part,\n 'bom_items': my_build.part.get_bom_items(),\n 'reference': my_build.reference,\n 'quantity': my_build.quantity,\n 'title': str(my_build),\n }","title":""},{"docid":"2acd2949ced2f162cfed7b09a58af9c0","score":"0.57690144","text":"def _template_context():\n return {\n 'api': g.api,\n 'operating_currencies': g.api.options['operating_currency'],\n 'datetime': datetime,\n 'interval': request.args.get('interval', app.config['interval']),\n }","title":""},{"docid":"c0a3ae4d3d15e108301ee3197fcfdfcc","score":"0.5757258","text":"def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, **kwargs)\n\n # Get request query parmaters\n query_text = self.request.GET.get('q')\n selected_languages = self.request.GET.getlist('lang')\n selected_technological_areas = self.request.GET.getlist('tech_area')\n selected_progress_outcomes = self.request.GET.getlist('progress_outcome')\n selected_nzqa_standards = self.request.GET.getlist('nzqa_standard')\n selected_year_levels = self.request.GET.getlist('year_level')\n selected_curriculum_learning_areas = self.request.GET.getlist('curriculum_area')\n\n get_request = bool(self.request.GET)\n\n context['languages'] = Language.objects.annotate(\n selected=Case(\n When(id__in=selected_languages, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n context['technological_areas'] = TechnologicalArea.objects.annotate(\n selected=Case(\n When(id__in=selected_technological_areas, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n context['progress_outcomes'] = ProgressOutcome.objects.annotate(\n selected=Case(\n When(id__in=selected_progress_outcomes, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n context['nzqa_standards'] = NZQAStandard.objects.annotate(\n selected=Case(\n When(id__in=selected_nzqa_standards, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n context['year_levels'] = YearLevel.objects.annotate(\n selected=Case(\n When(id__in=selected_year_levels, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n context['curriculum_learning_areas'] = CurriculumLearningArea.objects.annotate(\n selected=Case(\n When(id__in=selected_curriculum_learning_areas, then=Value(True)),\n default=Value(False),\n output_field=BooleanField()\n )\n )\n if get_request:\n context['search'] = get_request\n\n # Start with all objects\n results = Resource.objects.filter(published=True)\n\n # Filter items by tags if given in query.\n if selected_languages:\n results = results.filter(languages__in=selected_languages)\n if selected_technological_areas:\n results = results.filter(technological_areas__in=selected_technological_areas)\n if selected_progress_outcomes:\n results = results.filter(progress_outcomes__in=selected_progress_outcomes)\n if selected_nzqa_standards:\n results = results.filter(nzqa_standards__in=selected_nzqa_standards)\n if selected_year_levels:\n results = results.filter(year_levels__in=selected_year_levels)\n if selected_curriculum_learning_areas:\n results = results.filter(curriculum_learning_areas__in=selected_curriculum_learning_areas)\n\n # Search by text query if provided\n if query_text:\n query = SearchQuery(query_text, search_type=\"websearch\")\n results = Resource.objects.filter(\n search_vector=query\n ).annotate(\n rank=SearchRank(F('search_vector'), query)\n ).filter(rank__gt=0).order_by('-rank')\n\n results = results.prefetch_related(\n 'author_entities',\n 'author_users',\n 'languages',\n 'technological_areas',\n 'progress_outcomes',\n 'nzqa_standards',\n 'year_levels',\n 'curriculum_learning_areas',\n ).distinct()\n\n context['query'] = query_text\n context['results'] = results\n context['results_count'] = len(results)\n return context","title":""},{"docid":"7c38100d240af0436397a7e09ebaf335","score":"0.57395786","text":"def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, **kwargs)\n context[\"form\"] = self.form_class(self.request.GET)\n return context","title":""},{"docid":"1b07e947f1fdff33c6d73d1038bf3fd4","score":"0.5738129","text":"def app_context(self):\r\n return AppContext(self)","title":""},{"docid":"53a974263d947f818ab719e7b231f61b","score":"0.5733511","text":"def build(self, request, args, kwargs):\n raise NotImplementedError()","title":""},{"docid":"53a974263d947f818ab719e7b231f61b","score":"0.5733511","text":"def build(self, request, args, kwargs):\n raise NotImplementedError()","title":""},{"docid":"b0f5e03d1acaaa443a21ae0bed6c0360","score":"0.57254964","text":"def get_context_data(self, **kwargs):\n\n context = super(IndexView, self).get_context_data(**kwargs)\n context['title'] = self.title\n\n return context","title":""},{"docid":"960857711b3cb14501df0920b287cf8f","score":"0.5712354","text":"def get_context_data(self):\n context = self.environment.get(constants.ENVI_KEY_CONTEXT, {})\n return {constants.ENVI_TEMPLATE_CONTEXT_ACCESSOR: context}","title":""},{"docid":"57709036b250eb10e2f732750b0bfba7","score":"0.57010514","text":"def trait_context ( self ):\r\n return { 'object': self.model, 'controller': self, 'handler': self }","title":""},{"docid":"55c9e895d5736311356c0c925ba27ec5","score":"0.56675446","text":"def get_context_data(self, **kwargs):\n\n context = super(DocumetationView, self).get_context_data(**kwargs)\n context['title'] = self.title\n\n return context","title":""},{"docid":"e403dc849aa117be0d59376cd49bf0db","score":"0.5665375","text":"def get_context(cls):\n return g","title":""},{"docid":"0b6c79a11f7d47cab2862dd7796c2c5b","score":"0.56628853","text":"def __init__(self):\n super(BaseContext, self).__init__()","title":""},{"docid":"8697e1626251947574d24a2911fd02d6","score":"0.56492245","text":"def generate_context(request):\n context = {\n 'categories': models.Category.objects.all(),\n }\n if request.user.is_authenticated:\n context['cart_items'] = CartItem.objects.filter(user=request.user)\n\n return context","title":""},{"docid":"b54ac7b7438ad1955ae705d621c92e52","score":"0.5648326","text":"def get_context_data(self, **kwargs):\n context = super(IndexView, self).get_context_data(**kwargs)\n context['title'] = 'Main'\n return context","title":""},{"docid":"4c8eaf9c29f25a609f47a4aa3e4fe831","score":"0.563907","text":"def common_context(self, request, form, topic):\n return render(request, self.template_name, {\"form\": form, \"topic\": topic})","title":""},{"docid":"4e202957ad196748e2013dd2265dfc4e","score":"0.5636333","text":"def user_context(request): # pragma: no cover\n # Disabled; this is bad practice\n raise NotImplementedError","title":""},{"docid":"a6d13cace0336a11134b1ec9f0c793f0","score":"0.56300473","text":"def app_context(self):\n with self.app.app_context() as ctx:\n yield ctx","title":""},{"docid":"ba91e4a7f5e325570464669d7e31614e","score":"0.5629707","text":"def get_view ( self ):","title":""},{"docid":"d6c6ebe11d90ad2fba82ef33c8b4eb2f","score":"0.5625829","text":"def trait_context ( self ):\r\n return { 'object': self, 'handler': self, 'model': self.model }","title":""},{"docid":"af3fdf0532cfb04d15f2602c7e06958f","score":"0.5624803","text":"def get_template_context(self, options):\n dburl = options.get(\"dburl\")\n db_settings = (\n dj_database_url.config(default=dburl)\n if dburl else settings.DATABASES[\"default\"])\n if \"sqlite\" in db_settings[\"ENGINE\"]:\n dbtype = \"sqlite\"\n elif \"postgresql\" in db_settings[\"ENGINE\"]:\n dbtype = \"postgres\"\n else:\n dbtype = \"mysql\"\n dbhost = db_settings.get(\"HOST\", \"127.0.0.1\")\n dbport = db_settings.get(\"PORT\", \"\")\n if len(dbport):\n dbhost += ':' + dbport\n\n commandline = \"{} {}\".format(\n os.path.basename(sys.argv[0]), \" \".join(sys.argv[1:]))\n context = {\n \"date\": timezone.now(),\n \"commandline\": commandline,\n \"dbtype\": dbtype,\n \"dbuser\": db_settings[\"USER\"],\n \"dbpass\": db_settings[\"PASSWORD\"],\n \"dbname\": db_settings[\"NAME\"],\n \"dbhost\": dbhost\n }\n return context","title":""},{"docid":"184ad99e1277aa3743fab16e53776dfc","score":"0.56201726","text":"def as_view(self, initial_context, view=None, attrs={}):\n if not view:\n view = self.handler.view\n \n # add handler view-attrs and views attr. The below will\n # copy info over, protecting the originals\n #? could be cached? Or init?\n #! use extend logic, not this\n attrs = attrs.copy()\n attrs.update(self.handler.get_view_attrs(view))\n attrs.update(view.attrs)\n return view.render(request=None, attrs=attrs, **initial_context)","title":""},{"docid":"1a8f060552d6fb3998f8c3d5f14fbb84","score":"0.5613834","text":"def context(self, request, instance, context):","title":""},{"docid":"ecaa39a1b5240f116dc6aaeecac6f38f","score":"0.5610289","text":"def get_context_data(self, **kwargs):\n context = super(SubmitReport,self).get_context_data(**kwargs)\n context = section1Context(self,context)\n context = section2Context(self,context)\n context = section3Context(self,context)\n context = section4Context(self,context)\n context['toDo'] = todoGetter(4,self.report)\n context['reqTodo'] = len(context['toDo']['r'])\n context['sugTodo'] = len(context['toDo']['s'])\n return context","title":""},{"docid":"9cfdbf8a580a3dce44bba2eae4fa24eb","score":"0.55917895","text":"def get_context_data(self, **kwargs):\n context = super(DetailView, self).get_context_data(**kwargs)\n obj = self._get_data()\n context['object'] = obj\n context['object_created_date'] = self._get_data_created_date(obj)\n context['object_bytes'] = self._get_data_bytes(obj)\n return context","title":""},{"docid":"9d1f48d509b425c517d6fd4f6ac51c32","score":"0.558336","text":"def get_serializer_context(self):\r\n return {\r\n 'request': self.request,\r\n 'view': self,\r\n }","title":""},{"docid":"370749d73f19e51964b376ec8bab6052","score":"0.5557711","text":"def _get_context ( self, context ):\r\n name = 'object'\r\n n = len( context )\r\n if (n == 2) and ('handler' in context):\r\n for name, value in context.items():\r\n if name != 'handler':\r\n break\r\n elif n == 1:\r\n name = context.keys()[0]\r\n\r\n value = context.get( name )\r\n if value is not None:\r\n context2 = value.trait_get()\r\n context2.update( context )\r\n else:\r\n context2 = context.copy()\r\n\r\n context2['ui'] = self\r\n\r\n return context2","title":""},{"docid":"51c5f728a89af383fc0d676763d7246e","score":"0.5543416","text":"def get_serializer_context(self):\r\n return {\r\n \"request\": self.request,\r\n \"format\": self.format_kwarg,\r\n \"view\": self,\r\n \"chat\": self.chat,\r\n }","title":""},{"docid":"d63e32658e44caf5fd897a4b43b83e3a","score":"0.5537822","text":"def pvx_context_processor(request: HttpRequest) -> dict:\n ctx = dict(\n custom_urls=ctadmin.custom_urls_reverse,\n custom_url_map=ctadmin.custom_url_map,\n ctadmin=ctadmin\n )\n # log.debug(\"pvx_context_processor :: URL = %s\", request.get_full_path())\n # log.debug(\"pvx_context_processor :: PATH = %s \", request.path)\n # log.debug(\"pvx_context_processor :: HEADERS = %s \", request.headers)\n # log.debug(\"pvx_context_processor :: Context = %s \", ctx)\n # log.debug(\" --------- pvx_context_processor --------- \")\n return ctx","title":""},{"docid":"ae6efc9f9ae2f3a3312fa7ce10d69a4a","score":"0.5534492","text":"def build(self):\n for detail_view in self.detail_views:\n view = self._get_view(detail_view)\n view().build_object(self)\n self._build_extra()\n self._build_related()","title":""},{"docid":"3bf50632f9dcbd889ef41bb14aef8c19","score":"0.55314094","text":"def get_context(self, request, *args, **kwargs):\n context = super().get_context(request, *args, **kwargs)\n\n context['posts'] = PostPage.objects.live().public().order_by('-first_published_at')[:9]\n context[\"categories\"] = BlogCategory.objects.all()\n\n return context","title":""},{"docid":"d179701a6df8e81084c78470bb84278e","score":"0.5528085","text":"def getBaseContext(request):\n if request.POST:\n source = request.POST.get(\"source\")\n else:\n source = getMessageFromKey(request, \"source\")\n returnDict = {\"toolbarDestinations\": {\"login\": constants.LOGIN,\n \"home\": constants.HOME,\n \"logout\": constants.LOGOUT,\n \"profile\": constants.PROFILE,\n \"browse\": constants.BROWSE,\n \"inbox\": constants.INBOX,\n \"post\": constants.CREATE_POST},\n \"source\": source,\n \"default\": constants.DEFAULT,\n \"cancel\": constants.CANCEL,\n \"images\": {\"noProfilePicture\": constants.NO_PROFILE_PICTURE_PATH,\n \"noPicture\": constants.NO_PICTURE_PATH,\n \"loadingGif\": constants.LOADING_GIF,\n \"plus\": constants.PLUS_SIGN_PATH,\n \"logo\": constants.LOGO_PATH,\n \"icons\": constants.ICON_PATHS_DICT,\n \"stock\": getStockImage(),\n \"loading\": {\"light\": constants.LOADING_GIF.replace(\"loading.gif\", \"loading_light.gif\"),\n \"mid\": constants.LOADING_GIF.replace(\"loading.gif\", \"loading_mid.gif\"),\n \"dark\": constants.LOADING_GIF.replace(\"loading.gif\", \"loading_dark.gif\")}}\n }\n returnDict[\"posterNameMap\"] = {}\n for account in models.UserAccount.objects.all():\n returnDict[\"posterNameMap\"][account.username] = capitalizeName(\"{0} {1}\".format(account.firstName, account.lastName))\n returnDict[\"posterNameMap\"] = json.dumps(returnDict[\"posterNameMap\"])\n return returnDict","title":""},{"docid":"9a73e07f2236b967c938e075ae8dc1df","score":"0.5527508","text":"def get_base_context(self, **kwargs):\n request = kwargs.get(\"request\")\n return dict(kwargs, site=Site.objects.get_current(request))","title":""},{"docid":"8470dbad7f650b2bccc39c966f8b71e0","score":"0.5524159","text":"def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, **kwargs)\n\n # include credebtors as a seperate queryset with annotations for total number and\n # amount of expenses and revenues\n context[\"credebtors\"] = Credebtor.objects.filter(\n chain=self.get_object()\n ).annotate(\n camp_expenses_amount=Sum(\n \"expenses__amount\", filter=Q(expenses__camp=self.camp), distinct=True\n ),\n camp_expenses_count=Count(\n \"expenses\", filter=Q(expenses__camp=self.camp), distinct=True\n ),\n camp_revenues_amount=Sum(\n \"revenues__amount\", filter=Q(revenues__camp=self.camp), distinct=True\n ),\n camp_revenues_count=Count(\n \"revenues\", filter=Q(revenues__camp=self.camp), distinct=True\n ),\n )\n\n # Include expenses and revenues for the Chain in context as seperate querysets,\n # since accessing them through the relatedmanager returns for all camps\n context[\"expenses\"] = Expense.objects.filter(\n camp=self.camp, creditor__chain=self.get_object()\n ).prefetch_related(\"responsible_team\", \"user\", \"creditor\")\n context[\"revenues\"] = Revenue.objects.filter(\n camp=self.camp, debtor__chain=self.get_object()\n ).prefetch_related(\"responsible_team\", \"user\", \"debtor\")\n return context","title":""},{"docid":"8133cbc139ee0a0fa90dcfa33135f4a4","score":"0.5521093","text":"def buildTree(self):\n super(Contexts, self).buildTree()\n if self._project.contexts:\n ctxtItems = []\n for ctxtObj in self._project.contexts:\n newItem = self.new_treeItem(ctxtObj)\n ctxtItems.append(newItem)\n self.tw_tree.addTopLevelItems(ctxtItems)\n self.rf_treeColumns()\n self.rf_itemStyle()","title":""},{"docid":"e291569a352687c4950fc70f60b8f3c9","score":"0.5513167","text":"def build(self, *args, **kwargs):","title":""},{"docid":"e291569a352687c4950fc70f60b8f3c9","score":"0.5513167","text":"def build(self, *args, **kwargs):","title":""},{"docid":"90a31d6d345025978d1d77acb67fbb4c","score":"0.5509685","text":"def build_views(self, experiment_config_path, output_dir):\n from celldom.execute import view\n from celldom.core import experiment\n exp_config = experiment_config.ExperimentConfig(experiment_config_path)\n view.build_all(experiment.Experiment(exp_config, output_dir), force=True)\n logger.info('View construction complete')","title":""},{"docid":"f9ecb15b84276c04fce2990e2cde3eac","score":"0.55093867","text":"def make_context(self, info_name, args, parent=None, **extra):\r\n if 'default_map' not in extra:\r\n default_map = None\r\n if parent is not None and parent.default_map is not None:\r\n default_map = parent.default_map.get(info_name)\r\n extra['default_map'] = default_map\r\n ctx = Context(self, info_name=info_name, parent=parent, **extra)\r\n self.parse_args(ctx, args)\r\n return ctx","title":""},{"docid":"6b30713e69d67ef41176a622fe0f3890","score":"0.54992914","text":"def _build(self):\n pass","title":""},{"docid":"6b30713e69d67ef41176a622fe0f3890","score":"0.54992914","text":"def _build(self):\n pass","title":""},{"docid":"9e11b9b03bbc06c93a7e2cdf78435d04","score":"0.5493782","text":"def template_context(request):\n context = {\n 'site_title': settings.APPLICATION_TITLE,\n 'site_acronym': settings.APPLICATION_ACRONYM,\n 'version_no': settings.APPLICATION_VERSION_NO,\n 'geoserver_wms_url': settings.GEOSERVER_WMS_URL,\n 'geoserver_wfs_url': settings.GEOSERVER_WFS_URL,\n 'prs_user_group': settings.PRS_USER_GROUP,\n 'managers': settings.MANAGERS,\n }\n if request.user.is_authenticated:\n context['prs_user'] = request.user.userprofile.is_prs_user()\n context['prs_power_user'] = request.user.userprofile.is_power_user()\n context['last_referral'] = request.user.userprofile.last_referral()\n context.update(settings.STATIC_CONTEXT_VARS)\n return context","title":""},{"docid":"962e410866e01c9152782206e3e3ce41","score":"0.5493135","text":"def __init__(self, context):\n self._context = context","title":""},{"docid":"efdbfc07e8aaeeb08f6290c64eef6cd6","score":"0.5489776","text":"def context(self):\n return self.__context","title":""},{"docid":"e1d0f000fcdba20f0d17f208dca8f072","score":"0.5485853","text":"def each_context(self, request):\n self.site_title = ugettext_lazy('Vendor')\n self.index_title = ugettext_lazy('Dashboard')\n self.site_header = ugettext_lazy('GLEMKO')\n \n script_name = request.META['SCRIPT_NAME']\n site_url = script_name if self.site_url == '/' and script_name else self.site_url\n return {\n 'site_title': self.site_title,\n 'site_header': self.site_header,\n 'site_url': site_url,\n 'has_permission': self.has_permission(request),\n 'available_apps': self.get_app_list(request),\n 'is_popup': False,\n }","title":""},{"docid":"5278a9b402c89a5b5f8e9f26005bdac3","score":"0.5482498","text":"def __call__(self, *args, **kwargs):\n self.collect_args(self.request)\n entry_point = None\n if isinstance(self.context, Exception):\n entry_point = EntryPoint(get_exception_entry_point_key(self.context))\n elif hasattr(self.context, 'entry_point'):\n logger.debug(\"%s\", self.context)\n entry_point = self.context.entry_point\n\n if entry_point is None:\n assert entry_point is not None, \"Entry point for view should not be None (context=%r)\" % self.context\n key = entry_point.key\n assert key, \"Entry point key for view should be truthy\"\n\n # todo it might be super helpful to sanity check this value, because this generates errors\n # later that t+race to here\n # self._response_dict['entry_point_key'] = entry_point.key\n # self._response_dict['entry_point_template'] = 'build/templates/entry_point/%s.jinja2' % key\n\n return self._response_dict","title":""},{"docid":"40a37ab4c02d7da4873c8d1dffa62fd4","score":"0.54652226","text":"def do(self, action_context, view, user, *args, **kwargs):\n return action_context","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.5454381","text":"def build(self):\n pass","title":""},{"docid":"36328765d258ca23e84c39ffdf5f4698","score":"0.54479337","text":"def build_view_from(self, cmdargs):\n experiment_view = ExperimentBuilder().build_view_from(cmdargs)\n self.connect_to_version_control_tree(experiment_view)\n\n return experiment_view","title":""},{"docid":"0d9b206dfa34e26f2c68544d7ed2f783","score":"0.5445825","text":"def get_context_data(self, **kwargs):\n return super().get_context_data(\n **{key: getattr(self, key) for key in self.attr_context},\n **kwargs,\n )","title":""},{"docid":"5521fca363d8f49e14f6ee7365ddcb8c","score":"0.5443454","text":"def make_shell_context():\n\n return dict(app=app, db=db, Photo=Photo)","title":""},{"docid":"521d82c1e76714ec30403e44d016175f","score":"0.5440685","text":"def get_context_data(self, **kwargs):\n logger.info(\"category info\")\n context = super().get_context_data(**kwargs)\n category = Category.objects.get(pk=self.kwargs[\"pk\"])\n context[\"category\"] = category\n\n view = self.request.GET.get(\"view\", \"list\")\n context['view'] = view\n return context","title":""},{"docid":"509187bcd1f0a779507e5df4672df363","score":"0.54373497","text":"def user_context(self) -> global___UserContext:","title":""},{"docid":"509187bcd1f0a779507e5df4672df363","score":"0.54373497","text":"def user_context(self) -> global___UserContext:","title":""},{"docid":"509187bcd1f0a779507e5df4672df363","score":"0.54373497","text":"def user_context(self) -> global___UserContext:","title":""},{"docid":"93613bbd16d271cde84f3794d1006ccf","score":"0.54191977","text":"def context(self):\n assert access_checker.isSet(self.data.conversation)\n assert access_checker.isSet(self.data.user)\n\n query = gciconversation_logic.queryConversationUserForConversationAndUser(\n self.data.conversation.key, ndb.Key.from_old_key(self.data.user.key()))\n conv_user_results = query.fetch(1)\n assert conv_user_results\n\n conv_user = conv_user_results[0]\n\n self.data.redirect.id()\n\n url = self.data.redirect.urlOf(\n url_names.GCI_CONVERSATION_NOTIFICATION_TOGGLE)\n\n enable_notifications = toggle_button_view.ToggleButtonTemplate(\n self.data, 'on_off', translation.ugettext('Enable Notifications'),\n 'notifications-enabled', url,\n checked=conv_user.enable_notifications,\n help_text=self.DEF_ENABLE_NOTIFICATIONS_HELP,\n labels={\n 'checked': 'Yes',\n 'unchecked': 'No'})\n self.toggle_buttons.append(enable_notifications)\n\n return {\n 'title': translation.ugettext('Conversation Actions'),\n 'toggle_buttons': self.toggle_buttons,\n }","title":""},{"docid":"c2c98e882d211f4288a5c9a029cb8ec6","score":"0.5406181","text":"def get_preview_context(self, request, mode_name):\n return {\"object\": self, \"request\": request}","title":""},{"docid":"200ba62308847e8decedffd1845bd529","score":"0.5404884","text":"def generate_context(self):\n coupons = Coupon.current_coupons.select_related(\n 'offer', 'offer__business', 'offer__business__advertiser'\n ).filter(location__isnull=False).order_by('offer__business')\n valid_days_dict = {}\n for coupon in coupons:\n valid_days_dict[coupon.id] = VALID_DAYS.create_valid_days_string(\n coupon)\n return {'coupons': coupons,\n 'shooger_category_dict': self.shooger_category_dict(),\n 'valid_days_dict': valid_days_dict\n }","title":""},{"docid":"56618ebffdea64ca439ce5537bca3928","score":"0.54044926","text":"def _render_view(self):\r\n\r\n return self.template.render({})","title":""}],"string":"[\n {\n \"docid\": \"f5cfbe026e23b36604f93ffcd667d129\",\n \"score\": \"0.6692236\",\n \"text\": \"def generate_context(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c20dcefe742d6f2b76e08f388cf1e1a\",\n \"score\": \"0.6588604\",\n \"text\": \"def get_context(self, request, context, **kwargs):\\n self.ctx = {\\n 'base_template': self.base_template,\\n }\\n self.ctx.update(context)\\n self.ctx.update(kwargs)\\n return self.ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a134ecc79c60150593e84c92b5d37d3\",\n \"score\": \"0.64821\",\n \"text\": \"def _make_context():\\n return {'app': app}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75df5058cc0956e3781e01db3700eccb\",\n \"score\": \"0.64800435\",\n \"text\": \"def load_context(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5afcf50f736d83d939ba5016a09ff9\",\n \"score\": \"0.6403232\",\n \"text\": \"def build_context(self, environ):\\n self.__run_startup_hooks()\\n\\n ctx.app = self\\n ctx.url_adapter = adapter = self.url_map.bind_to_environ(environ)\\n\\n # ALWAYS set this after adapter\\n ctx.request = self.request_class(environ)\\n\\n rule, url_values = adapter.match(return_rule=True)\\n dispatcher = self # FIXME: self.get_dispatcher(rule)\\n\\n # Set up context variables\\n ctx.url_values = url_values\\n ctx.dispatcher = dispatcher\\n ctx.resource = dispatcher.get_resource(rule)\\n\\n context = ExitStack()\\n context.callback(self.__cleanup)\\n # Add all the application's context managers to\\n # the exit stack. If any of them return a value,\\n # we'll add the value to the application context\\n # with the function name.\\n for hook in self.context_hooks:\\n retval = context.enter_context(hook())\\n if retval is not None:\\n setattr(ctx, hook.__name__, retval)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22361e6b7fd296a7df81b6110d25b91b\",\n \"score\": \"0.6321904\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab1f128b05f1d10462dfb7dd55c834a\",\n \"score\": \"0.63080126\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = {\\n 'view': self,\\n 'instance': self.get_instance(),\\n 'form': self.get_form(),\\n 'model_admin': self.model_admin,\\n }\\n context.update(kwargs)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b71f5e84a0ea98d66c4f057d26e875\",\n \"score\": \"0.627791\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db, 'User': User}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b71f5e84a0ea98d66c4f057d26e875\",\n \"score\": \"0.627791\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db, 'User': User}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9d8de0b84de34045dba30e5ccdef23\",\n \"score\": \"0.62776417\",\n \"text\": \"def get_context():\\n # TODO: Make this work\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a00abf63f449dc118999570db3a6b1\",\n \"score\": \"0.6272035\",\n \"text\": \"def get_context(self, request, context, *args, **kwargs): # noqa: E501\\n # Build MVView and extent\\n view, extent = self.build_map_extent_and_view(request, *args, **kwargs)\\n\\n # Compose the Map\\n log.debug(\\\"Building MapView...\\\")\\n map_view = self._build_map_view(request, view, extent, *args, **kwargs)\\n\\n # Add layers to the Map\\n log.debug(\\\"Composing layers...\\\")\\n layer_groups = self.compose_layers(\\n request=request, map_view=map_view, *args, **kwargs\\n )\\n # Add layers to map view if not already added\\n for layer_group in layer_groups:\\n for layer in layer_group[\\\"layers\\\"]:\\n if layer not in map_view.layers:\\n map_view.layers.append(layer)\\n\\n log.debug(f\\\"Number of Layers: {len(map_view.layers)}\\\")\\n\\n # Check if we need to create a blank custom layer group\\n create_custom_layer = True\\n for layer_group in layer_groups:\\n if layer_group[\\\"id\\\"] == \\\"custom_layers\\\":\\n create_custom_layer = False\\n break\\n\\n # Create the Custom Layers layer group\\n if self.show_custom_layer and create_custom_layer:\\n log.debug('Creating the \\\"Custom Layers\\\" layer group...')\\n custom_layers = self.build_custom_layer_group()\\n layer_groups.append(custom_layers)\\n\\n # Build legends\\n log.debug(\\\"Building legends for each layer...\\\")\\n legends = []\\n for layer in map_view.layers:\\n if not layer.data[\\\"show_legend\\\"]:\\n continue\\n\\n legend = self.build_legend(layer)\\n if legend is not None:\\n legend_select_options = legend.get(\\\"select_options\\\") # None if not set\\n if legend_select_options:\\n # Create color ramp selector\\n legend_select_input = SelectInput(\\n name=f'tethys-color-ramp-picker-{legend[\\\"legend_id\\\"]}',\\n options=legend.get(\\\"select_options\\\"),\\n initial=legend.get(\\\"initial_option\\\"),\\n classes=\\\"map-layout-color-ramp-picker form-select-sm\\\",\\n original=True,\\n )\\n else:\\n legend_select_input = None\\n\\n legends.append((legend, legend_select_input))\\n\\n # Override MapView with CesiumMapView if Cesium is the chosen map_type.\\n if self.map_type == \\\"cesium_map_view\\\":\\n log.debug(\\\"Converting MapView to CesiumMapView...\\\")\\n map_view = self._build_ceisum_map_view(map_view)\\n\\n # Prepare context\\n context.update(\\n {\\n \\\"geocode_enabled\\\": self.geocode_api_key is not None,\\n \\\"layer_groups\\\": layer_groups,\\n \\\"layer_tab_name\\\": self.layer_tab_name,\\n \\\"legends\\\": legends,\\n \\\"map_extent\\\": extent,\\n \\\"map_type\\\": self.map_type,\\n \\\"map_view\\\": map_view,\\n \\\"nav_subtitle\\\": self.map_subtitle,\\n \\\"nav_title\\\": self.map_title,\\n \\\"plotly_version\\\": self.plotly_version,\\n \\\"show_custom_layer\\\": self.show_custom_layer,\\n \\\"show_properties_popup\\\": self.show_properties_popup,\\n \\\"show_map_click_popup\\\": self.show_map_click_popup,\\n \\\"show_legends\\\": self.show_legends,\\n \\\"wide_nav\\\": self.wide_nav,\\n \\\"workspace\\\": self.geoserver_workspace,\\n }\\n )\\n\\n if context.get(\\\"show_public_toggle\\\", False):\\n layer_dropdown_toggle = ToggleSwitch(\\n display_text=\\\"\\\",\\n name=\\\"layer-dropdown-toggle\\\",\\n on_label=\\\"Yes\\\",\\n off_label=\\\"No\\\",\\n on_style=\\\"success\\\",\\n off_style=\\\"danger\\\",\\n initial=True,\\n size=\\\"small\\\",\\n classes=\\\"layer-dropdown-toggle\\\",\\n )\\n context.update({\\\"layer_dropdown_toggle\\\": layer_dropdown_toggle})\\n\\n # Add plot slide sheet\\n plot_slidesheet = SlideSheet(\\n id=\\\"plot-slide-sheet\\\",\\n title=\\\"Plot\\\",\\n content_template=\\\"tethys_layouts/map_layout/map_plot.html\\\",\\n )\\n\\n context.update({\\\"plot_slide_sheet\\\": plot_slidesheet})\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e0b4905d7525efafdd7ceb9896f9234\",\n \"score\": \"0.62493235\",\n \"text\": \"def _make_context():\\n return {\\n 'app': app,\\n 'db': db,\\n 'User': User\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403bfbf900045a88ed8bcd152f6a5990\",\n \"score\": \"0.6246165\",\n \"text\": \"def _get_context(self, *args, **kwargs):\\n return Context(args=args, kwargs=kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a083b62763255220492c22953047f8\",\n \"score\": \"0.6240362\",\n \"text\": \"def create_context(self, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72144265b038841cf66724ca94dfacb\",\n \"score\": \"0.61794543\",\n \"text\": \"def _create_request_context(self, *args, **kwargs):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e519b6525b27b1d6f41141edb6fc41\",\n \"score\": \"0.61465245\",\n \"text\": \"def get_build_context(ctx: Dict={}) -> Dict:\\n return ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"733477aa90283f492d0efc9756deb139\",\n \"score\": \"0.61457914\",\n \"text\": \"def get_context_data(self, **kwargs):\\n\\n context = super().get_context_data(**kwargs)\\n context = self.decorate_context(context)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250ffcfd8a19d7fcc28c586848a6ccad\",\n \"score\": \"0.6136131\",\n \"text\": \"def get_context(self):\\n _context = EditingMixin.get_context(self)\\n # Add some specific HTML rendering context when editing HTML modules where we pass\\n # the root /c4x/ url for assets. This allows client-side substitutions to occur.\\n _context.update({\\n 'base_asset_url': StaticContent.get_base_url_path_for_course_assets(self.location.course_key),\\n 'enable_latex_compiler': self.use_latex_compiler,\\n 'editor': self.editor\\n })\\n return _context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4354d3f8321dad7b028f120a2bc50017\",\n \"score\": \"0.61036646\",\n \"text\": \"def context(self, request):\\n\\n # Generate custom context data based on the particular report subclass\\n context = self.get_context_data(request)\\n\\n context['base_url'] = common.models.InvenTreeSetting.get_setting('INVENTREE_BASE_URL')\\n context['date'] = datetime.datetime.now().date()\\n context['datetime'] = datetime.datetime.now()\\n context['default_page_size'] = common.models.InvenTreeSetting.get_setting('REPORT_DEFAULT_PAGE_SIZE')\\n context['report_description'] = self.description\\n context['report_name'] = self.name\\n context['report_revision'] = self.revision\\n context['request'] = request\\n context['user'] = request.user\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7c5a38bf8fa3692d4748d4e968f766\",\n \"score\": \"0.6099687\",\n \"text\": \"def gen_extra_context(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8786be95149391a1f96d29339bdcd73\",\n \"score\": \"0.6064868\",\n \"text\": \"def _create_request_context(self, *args, **kwargs):\\n return {\\n 'request': self.request,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8786be95149391a1f96d29339bdcd73\",\n \"score\": \"0.6064868\",\n \"text\": \"def _create_request_context(self, *args, **kwargs):\\n return {\\n 'request': self.request,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8786be95149391a1f96d29339bdcd73\",\n \"score\": \"0.6064868\",\n \"text\": \"def _create_request_context(self, *args, **kwargs):\\n return {\\n 'request': self.request,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364f683dbb15cfebb3f7ad32c5107d77\",\n \"score\": \"0.60129315\",\n \"text\": \"def _get_rendering_context(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f53f60b23b1914998d7a18b516505d\",\n \"score\": \"0.6008574\",\n \"text\": \"def apply_to_context():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9123cf4613f498bc8265790ab626eebb\",\n \"score\": \"0.5994399\",\n \"text\": \"def get_context_data(self, **kwargs):\\n kwargs[\\\"view\\\"] = self\\n\\n if getattr(self, \\\"object\\\", None) is not None:\\n kwargs[\\\"object\\\"] = self.object\\n context_object_name = self.get_context_object_name()\\n if context_object_name:\\n kwargs[context_object_name] = self.object\\n\\n if getattr(self, \\\"object_list\\\", None) is not None:\\n kwargs[\\\"object_list\\\"] = self.object_list\\n context_object_name = self.get_context_object_name(is_list=True)\\n if context_object_name:\\n kwargs[context_object_name] = self.object_list\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a241931231a0e136a931cb2301947d8c\",\n \"score\": \"0.5934253\",\n \"text\": \"def get_context_data(self, **kwargs):\\n request = self.request\\n extension = ReviewBotExtension.instance\\n\\n extension_config_attrs = {\\n 'brokerURL': extension.settings.get('broker_url'),\\n }\\n\\n # Add information on the Review Bot User entry.\\n user = get_object_or_none(User, pk=extension.settings.get('user'))\\n\\n if user:\\n extension_config_attrs['user'] = _serialize_user(request, user)\\n\\n return dict(super(ConfigureView, self).get_context_data(**kwargs), **{\\n 'extension': extension,\\n 'extension_config_attrs': extension_config_attrs,\\n 'extension_config_options': {\\n 'integrationConfigURL': local_site_reverse(\\n 'integration-list',\\n request=request),\\n 'userConfigURL': local_site_reverse(\\n 'reviewbot-configure-user',\\n request=request),\\n 'workerStatusURL': local_site_reverse(\\n 'reviewbot-worker-status',\\n request=request),\\n },\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a173f1490a2126d6d1b792322fc0c9ad\",\n \"score\": \"0.5927507\",\n \"text\": \"def get_context(self, **kwargs):\\n if callable(self.context):\\n context = self.context()\\n else:\\n context = self.context\\n context.update(kwargs)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78512e5377e9ce4ef25c1f733886afb2\",\n \"score\": \"0.5925705\",\n \"text\": \"def create_context(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf963934baf2b4cca55666f7111b2ec5\",\n \"score\": \"0.58962077\",\n \"text\": \"def get_context_data(self):\\n context = super(HomeView, self).get_context_data()\\n\\n context['title'] = 'Dev.f'\\n context['meta_description'] = 'Aprende a construir software en la ' +\\\\\\n 'primera y más grande escuela de hackers ' +\\\\\\n 'de México y LatAm'\\n\\n senseis_object_list = list(Sensei.objects.all())\\n random.shuffle(senseis_object_list)\\n context['senseis'] = senseis_object_list[:3]\\n\\n programs_object_list = Program.objects.all()\\n for program in programs_object_list:\\n program.requirements = program.requirements.split(',')\\n context['programs'] = programs_object_list\\n\\n mentors_object_list = list(Mentor.objects.all())\\n random.shuffle(mentors_object_list)\\n context['mentors'] = mentors_object_list[:5]\\n\\n testimonials_object_list = list(Testimonial.objects.all())\\n random.shuffle(testimonials_object_list)\\n context['testimonials'] = testimonials_object_list[:5]\\n\\n context['press'] = Press.objects.all()\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a7e6c39e849c10fbd5b10c9795308a\",\n \"score\": \"0.5883541\",\n \"text\": \"def get_context_data(self, *args, **kwargs):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da041ccb779e9ca554e15056bcc5773a\",\n \"score\": \"0.58800846\",\n \"text\": \"def get_context(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad110edfe808d7b6c2bab981612c33e\",\n \"score\": \"0.58788097\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super(LeafTemplateView, self).get_context_data(**kwargs)\\n context['page'] = self.page\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef70faccd497faf0484efb2880f22949\",\n \"score\": \"0.5873368\",\n \"text\": \"def initial_context(self):\\n context = {}\\n\\n if self.context:\\n for key, value in self.context.items():\\n util.set_nested_attr(context, key, value)\\n\\n if self.group:\\n util.set_nested_attr(context, 'group._id', self.group)\\n\\n if self.project:\\n # TODO: Check for <id:xyz> syntax\\n util.set_nested_attr(context, 'project.label', self.project)\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4147288dc8ec86349960ec6666bbd305\",\n \"score\": \"0.58391714\",\n \"text\": \"def new_context(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd35120428af2675e8c3e41b485f63f1\",\n \"score\": \"0.5817951\",\n \"text\": \"def get_context(self):\\n ctx = self.get_default_context()\\n if isinstance(self.body, dict):\\n ctx.update(self.body)\\n else:\\n ctx['body'] = self.body\\n return ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d10b0af433ee154b1366c316643706\",\n \"score\": \"0.5816754\",\n \"text\": \"def init_with_context(self, context):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d10b0af433ee154b1366c316643706\",\n \"score\": \"0.5816754\",\n \"text\": \"def init_with_context(self, context):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4facba4e3c68cc7244281c4874f99304\",\n \"score\": \"0.5814838\",\n \"text\": \"def get_context_data(self, request):\\n\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d803e3c797ec87e34f3bf41e5a0e84\",\n \"score\": \"0.5809814\",\n \"text\": \"def update_context(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1de0f0d95cfbe7c0fdb2469d9387d5a5\",\n \"score\": \"0.57954663\",\n \"text\": \"def create_global_context(self, **kwargs):\\n context = dict(**kwargs)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a820ea99257b75602423d6e731a0009\",\n \"score\": \"0.57869446\",\n \"text\": \"def _build_collection_context_dictionary(self, collection, context, form_data):\\n if form_data:\\n queryset_filter = build_queryset_filters(\\n form_data, {\\\"collection\\\": collection}\\n )\\n else:\\n queryset_filter = Q(collection=collection)\\n\\n context = self._fill_chart_context(collection, context, queryset_filter)\\n context = self._fill_statistics_table_context(context, queryset_filter)\\n context = self._fill_totals_tables(context, queryset_filter)\\n context = self._fill_latest_linkevents(collection, context, form_data)\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8f4e12dfd56a46c9523362959e2f3e\",\n \"score\": \"0.57710624\",\n \"text\": \"def get_context_data(self, request):\\n\\n my_build = self.object_to_print\\n\\n if not type(my_build) == build.models.Build:\\n raise TypeError('Provided model is not a Build object')\\n\\n return {\\n 'build': my_build,\\n 'part': my_build.part,\\n 'bom_items': my_build.part.get_bom_items(),\\n 'reference': my_build.reference,\\n 'quantity': my_build.quantity,\\n 'title': str(my_build),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2acd2949ced2f162cfed7b09a58af9c0\",\n \"score\": \"0.57690144\",\n \"text\": \"def _template_context():\\n return {\\n 'api': g.api,\\n 'operating_currencies': g.api.options['operating_currency'],\\n 'datetime': datetime,\\n 'interval': request.args.get('interval', app.config['interval']),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a3ae4d3d15e108301ee3197fcfdfcc\",\n \"score\": \"0.5757258\",\n \"text\": \"def get_context_data(self, *args, **kwargs):\\n context = super().get_context_data(*args, **kwargs)\\n\\n # Get request query parmaters\\n query_text = self.request.GET.get('q')\\n selected_languages = self.request.GET.getlist('lang')\\n selected_technological_areas = self.request.GET.getlist('tech_area')\\n selected_progress_outcomes = self.request.GET.getlist('progress_outcome')\\n selected_nzqa_standards = self.request.GET.getlist('nzqa_standard')\\n selected_year_levels = self.request.GET.getlist('year_level')\\n selected_curriculum_learning_areas = self.request.GET.getlist('curriculum_area')\\n\\n get_request = bool(self.request.GET)\\n\\n context['languages'] = Language.objects.annotate(\\n selected=Case(\\n When(id__in=selected_languages, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n context['technological_areas'] = TechnologicalArea.objects.annotate(\\n selected=Case(\\n When(id__in=selected_technological_areas, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n context['progress_outcomes'] = ProgressOutcome.objects.annotate(\\n selected=Case(\\n When(id__in=selected_progress_outcomes, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n context['nzqa_standards'] = NZQAStandard.objects.annotate(\\n selected=Case(\\n When(id__in=selected_nzqa_standards, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n context['year_levels'] = YearLevel.objects.annotate(\\n selected=Case(\\n When(id__in=selected_year_levels, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n context['curriculum_learning_areas'] = CurriculumLearningArea.objects.annotate(\\n selected=Case(\\n When(id__in=selected_curriculum_learning_areas, then=Value(True)),\\n default=Value(False),\\n output_field=BooleanField()\\n )\\n )\\n if get_request:\\n context['search'] = get_request\\n\\n # Start with all objects\\n results = Resource.objects.filter(published=True)\\n\\n # Filter items by tags if given in query.\\n if selected_languages:\\n results = results.filter(languages__in=selected_languages)\\n if selected_technological_areas:\\n results = results.filter(technological_areas__in=selected_technological_areas)\\n if selected_progress_outcomes:\\n results = results.filter(progress_outcomes__in=selected_progress_outcomes)\\n if selected_nzqa_standards:\\n results = results.filter(nzqa_standards__in=selected_nzqa_standards)\\n if selected_year_levels:\\n results = results.filter(year_levels__in=selected_year_levels)\\n if selected_curriculum_learning_areas:\\n results = results.filter(curriculum_learning_areas__in=selected_curriculum_learning_areas)\\n\\n # Search by text query if provided\\n if query_text:\\n query = SearchQuery(query_text, search_type=\\\"websearch\\\")\\n results = Resource.objects.filter(\\n search_vector=query\\n ).annotate(\\n rank=SearchRank(F('search_vector'), query)\\n ).filter(rank__gt=0).order_by('-rank')\\n\\n results = results.prefetch_related(\\n 'author_entities',\\n 'author_users',\\n 'languages',\\n 'technological_areas',\\n 'progress_outcomes',\\n 'nzqa_standards',\\n 'year_levels',\\n 'curriculum_learning_areas',\\n ).distinct()\\n\\n context['query'] = query_text\\n context['results'] = results\\n context['results_count'] = len(results)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c38100d240af0436397a7e09ebaf335\",\n \"score\": \"0.57395786\",\n \"text\": \"def get_context_data(self, *args, **kwargs):\\n context = super().get_context_data(*args, **kwargs)\\n context[\\\"form\\\"] = self.form_class(self.request.GET)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b07e947f1fdff33c6d73d1038bf3fd4\",\n \"score\": \"0.5738129\",\n \"text\": \"def app_context(self):\\r\\n return AppContext(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a974263d947f818ab719e7b231f61b\",\n \"score\": \"0.5733511\",\n \"text\": \"def build(self, request, args, kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a974263d947f818ab719e7b231f61b\",\n \"score\": \"0.5733511\",\n \"text\": \"def build(self, request, args, kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f5e03d1acaaa443a21ae0bed6c0360\",\n \"score\": \"0.57254964\",\n \"text\": \"def get_context_data(self, **kwargs):\\n\\n context = super(IndexView, self).get_context_data(**kwargs)\\n context['title'] = self.title\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960857711b3cb14501df0920b287cf8f\",\n \"score\": \"0.5712354\",\n \"text\": \"def get_context_data(self):\\n context = self.environment.get(constants.ENVI_KEY_CONTEXT, {})\\n return {constants.ENVI_TEMPLATE_CONTEXT_ACCESSOR: context}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57709036b250eb10e2f732750b0bfba7\",\n \"score\": \"0.57010514\",\n \"text\": \"def trait_context ( self ):\\r\\n return { 'object': self.model, 'controller': self, 'handler': self }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c9e895d5736311356c0c925ba27ec5\",\n \"score\": \"0.56675446\",\n \"text\": \"def get_context_data(self, **kwargs):\\n\\n context = super(DocumetationView, self).get_context_data(**kwargs)\\n context['title'] = self.title\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e403dc849aa117be0d59376cd49bf0db\",\n \"score\": \"0.5665375\",\n \"text\": \"def get_context(cls):\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b6c79a11f7d47cab2862dd7796c2c5b\",\n \"score\": \"0.56628853\",\n \"text\": \"def __init__(self):\\n super(BaseContext, self).__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8697e1626251947574d24a2911fd02d6\",\n \"score\": \"0.56492245\",\n \"text\": \"def generate_context(request):\\n context = {\\n 'categories': models.Category.objects.all(),\\n }\\n if request.user.is_authenticated:\\n context['cart_items'] = CartItem.objects.filter(user=request.user)\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54ac7b7438ad1955ae705d621c92e52\",\n \"score\": \"0.5648326\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super(IndexView, self).get_context_data(**kwargs)\\n context['title'] = 'Main'\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c8eaf9c29f25a609f47a4aa3e4fe831\",\n \"score\": \"0.563907\",\n \"text\": \"def common_context(self, request, form, topic):\\n return render(request, self.template_name, {\\\"form\\\": form, \\\"topic\\\": topic})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e202957ad196748e2013dd2265dfc4e\",\n \"score\": \"0.5636333\",\n \"text\": \"def user_context(request): # pragma: no cover\\n # Disabled; this is bad practice\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d13cace0336a11134b1ec9f0c793f0\",\n \"score\": \"0.56300473\",\n \"text\": \"def app_context(self):\\n with self.app.app_context() as ctx:\\n yield ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba91e4a7f5e325570464669d7e31614e\",\n \"score\": \"0.5629707\",\n \"text\": \"def get_view ( self ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c6ebe11d90ad2fba82ef33c8b4eb2f\",\n \"score\": \"0.5625829\",\n \"text\": \"def trait_context ( self ):\\r\\n return { 'object': self, 'handler': self, 'model': self.model }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3fdf0532cfb04d15f2602c7e06958f\",\n \"score\": \"0.5624803\",\n \"text\": \"def get_template_context(self, options):\\n dburl = options.get(\\\"dburl\\\")\\n db_settings = (\\n dj_database_url.config(default=dburl)\\n if dburl else settings.DATABASES[\\\"default\\\"])\\n if \\\"sqlite\\\" in db_settings[\\\"ENGINE\\\"]:\\n dbtype = \\\"sqlite\\\"\\n elif \\\"postgresql\\\" in db_settings[\\\"ENGINE\\\"]:\\n dbtype = \\\"postgres\\\"\\n else:\\n dbtype = \\\"mysql\\\"\\n dbhost = db_settings.get(\\\"HOST\\\", \\\"127.0.0.1\\\")\\n dbport = db_settings.get(\\\"PORT\\\", \\\"\\\")\\n if len(dbport):\\n dbhost += ':' + dbport\\n\\n commandline = \\\"{} {}\\\".format(\\n os.path.basename(sys.argv[0]), \\\" \\\".join(sys.argv[1:]))\\n context = {\\n \\\"date\\\": timezone.now(),\\n \\\"commandline\\\": commandline,\\n \\\"dbtype\\\": dbtype,\\n \\\"dbuser\\\": db_settings[\\\"USER\\\"],\\n \\\"dbpass\\\": db_settings[\\\"PASSWORD\\\"],\\n \\\"dbname\\\": db_settings[\\\"NAME\\\"],\\n \\\"dbhost\\\": dbhost\\n }\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184ad99e1277aa3743fab16e53776dfc\",\n \"score\": \"0.56201726\",\n \"text\": \"def as_view(self, initial_context, view=None, attrs={}):\\n if not view:\\n view = self.handler.view\\n \\n # add handler view-attrs and views attr. The below will\\n # copy info over, protecting the originals\\n #? could be cached? Or init?\\n #! use extend logic, not this\\n attrs = attrs.copy()\\n attrs.update(self.handler.get_view_attrs(view))\\n attrs.update(view.attrs)\\n return view.render(request=None, attrs=attrs, **initial_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a8f060552d6fb3998f8c3d5f14fbb84\",\n \"score\": \"0.5613834\",\n \"text\": \"def context(self, request, instance, context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecaa39a1b5240f116dc6aaeecac6f38f\",\n \"score\": \"0.5610289\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super(SubmitReport,self).get_context_data(**kwargs)\\n context = section1Context(self,context)\\n context = section2Context(self,context)\\n context = section3Context(self,context)\\n context = section4Context(self,context)\\n context['toDo'] = todoGetter(4,self.report)\\n context['reqTodo'] = len(context['toDo']['r'])\\n context['sugTodo'] = len(context['toDo']['s'])\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cfdbf8a580a3dce44bba2eae4fa24eb\",\n \"score\": \"0.55917895\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super(DetailView, self).get_context_data(**kwargs)\\n obj = self._get_data()\\n context['object'] = obj\\n context['object_created_date'] = self._get_data_created_date(obj)\\n context['object_bytes'] = self._get_data_bytes(obj)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1f48d509b425c517d6fd4f6ac51c32\",\n \"score\": \"0.558336\",\n \"text\": \"def get_serializer_context(self):\\r\\n return {\\r\\n 'request': self.request,\\r\\n 'view': self,\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370749d73f19e51964b376ec8bab6052\",\n \"score\": \"0.5557711\",\n \"text\": \"def _get_context ( self, context ):\\r\\n name = 'object'\\r\\n n = len( context )\\r\\n if (n == 2) and ('handler' in context):\\r\\n for name, value in context.items():\\r\\n if name != 'handler':\\r\\n break\\r\\n elif n == 1:\\r\\n name = context.keys()[0]\\r\\n\\r\\n value = context.get( name )\\r\\n if value is not None:\\r\\n context2 = value.trait_get()\\r\\n context2.update( context )\\r\\n else:\\r\\n context2 = context.copy()\\r\\n\\r\\n context2['ui'] = self\\r\\n\\r\\n return context2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c5f728a89af383fc0d676763d7246e\",\n \"score\": \"0.5543416\",\n \"text\": \"def get_serializer_context(self):\\r\\n return {\\r\\n \\\"request\\\": self.request,\\r\\n \\\"format\\\": self.format_kwarg,\\r\\n \\\"view\\\": self,\\r\\n \\\"chat\\\": self.chat,\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d63e32658e44caf5fd897a4b43b83e3a\",\n \"score\": \"0.5537822\",\n \"text\": \"def pvx_context_processor(request: HttpRequest) -> dict:\\n ctx = dict(\\n custom_urls=ctadmin.custom_urls_reverse,\\n custom_url_map=ctadmin.custom_url_map,\\n ctadmin=ctadmin\\n )\\n # log.debug(\\\"pvx_context_processor :: URL = %s\\\", request.get_full_path())\\n # log.debug(\\\"pvx_context_processor :: PATH = %s \\\", request.path)\\n # log.debug(\\\"pvx_context_processor :: HEADERS = %s \\\", request.headers)\\n # log.debug(\\\"pvx_context_processor :: Context = %s \\\", ctx)\\n # log.debug(\\\" --------- pvx_context_processor --------- \\\")\\n return ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6efc9f9ae2f3a3312fa7ce10d69a4a\",\n \"score\": \"0.5534492\",\n \"text\": \"def build(self):\\n for detail_view in self.detail_views:\\n view = self._get_view(detail_view)\\n view().build_object(self)\\n self._build_extra()\\n self._build_related()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf50632f9dcbd889ef41bb14aef8c19\",\n \"score\": \"0.55314094\",\n \"text\": \"def get_context(self, request, *args, **kwargs):\\n context = super().get_context(request, *args, **kwargs)\\n\\n context['posts'] = PostPage.objects.live().public().order_by('-first_published_at')[:9]\\n context[\\\"categories\\\"] = BlogCategory.objects.all()\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d179701a6df8e81084c78470bb84278e\",\n \"score\": \"0.5528085\",\n \"text\": \"def getBaseContext(request):\\n if request.POST:\\n source = request.POST.get(\\\"source\\\")\\n else:\\n source = getMessageFromKey(request, \\\"source\\\")\\n returnDict = {\\\"toolbarDestinations\\\": {\\\"login\\\": constants.LOGIN,\\n \\\"home\\\": constants.HOME,\\n \\\"logout\\\": constants.LOGOUT,\\n \\\"profile\\\": constants.PROFILE,\\n \\\"browse\\\": constants.BROWSE,\\n \\\"inbox\\\": constants.INBOX,\\n \\\"post\\\": constants.CREATE_POST},\\n \\\"source\\\": source,\\n \\\"default\\\": constants.DEFAULT,\\n \\\"cancel\\\": constants.CANCEL,\\n \\\"images\\\": {\\\"noProfilePicture\\\": constants.NO_PROFILE_PICTURE_PATH,\\n \\\"noPicture\\\": constants.NO_PICTURE_PATH,\\n \\\"loadingGif\\\": constants.LOADING_GIF,\\n \\\"plus\\\": constants.PLUS_SIGN_PATH,\\n \\\"logo\\\": constants.LOGO_PATH,\\n \\\"icons\\\": constants.ICON_PATHS_DICT,\\n \\\"stock\\\": getStockImage(),\\n \\\"loading\\\": {\\\"light\\\": constants.LOADING_GIF.replace(\\\"loading.gif\\\", \\\"loading_light.gif\\\"),\\n \\\"mid\\\": constants.LOADING_GIF.replace(\\\"loading.gif\\\", \\\"loading_mid.gif\\\"),\\n \\\"dark\\\": constants.LOADING_GIF.replace(\\\"loading.gif\\\", \\\"loading_dark.gif\\\")}}\\n }\\n returnDict[\\\"posterNameMap\\\"] = {}\\n for account in models.UserAccount.objects.all():\\n returnDict[\\\"posterNameMap\\\"][account.username] = capitalizeName(\\\"{0} {1}\\\".format(account.firstName, account.lastName))\\n returnDict[\\\"posterNameMap\\\"] = json.dumps(returnDict[\\\"posterNameMap\\\"])\\n return returnDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a73e07f2236b967c938e075ae8dc1df\",\n \"score\": \"0.5527508\",\n \"text\": \"def get_base_context(self, **kwargs):\\n request = kwargs.get(\\\"request\\\")\\n return dict(kwargs, site=Site.objects.get_current(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8470dbad7f650b2bccc39c966f8b71e0\",\n \"score\": \"0.5524159\",\n \"text\": \"def get_context_data(self, *args, **kwargs):\\n context = super().get_context_data(*args, **kwargs)\\n\\n # include credebtors as a seperate queryset with annotations for total number and\\n # amount of expenses and revenues\\n context[\\\"credebtors\\\"] = Credebtor.objects.filter(\\n chain=self.get_object()\\n ).annotate(\\n camp_expenses_amount=Sum(\\n \\\"expenses__amount\\\", filter=Q(expenses__camp=self.camp), distinct=True\\n ),\\n camp_expenses_count=Count(\\n \\\"expenses\\\", filter=Q(expenses__camp=self.camp), distinct=True\\n ),\\n camp_revenues_amount=Sum(\\n \\\"revenues__amount\\\", filter=Q(revenues__camp=self.camp), distinct=True\\n ),\\n camp_revenues_count=Count(\\n \\\"revenues\\\", filter=Q(revenues__camp=self.camp), distinct=True\\n ),\\n )\\n\\n # Include expenses and revenues for the Chain in context as seperate querysets,\\n # since accessing them through the relatedmanager returns for all camps\\n context[\\\"expenses\\\"] = Expense.objects.filter(\\n camp=self.camp, creditor__chain=self.get_object()\\n ).prefetch_related(\\\"responsible_team\\\", \\\"user\\\", \\\"creditor\\\")\\n context[\\\"revenues\\\"] = Revenue.objects.filter(\\n camp=self.camp, debtor__chain=self.get_object()\\n ).prefetch_related(\\\"responsible_team\\\", \\\"user\\\", \\\"debtor\\\")\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8133cbc139ee0a0fa90dcfa33135f4a4\",\n \"score\": \"0.5521093\",\n \"text\": \"def buildTree(self):\\n super(Contexts, self).buildTree()\\n if self._project.contexts:\\n ctxtItems = []\\n for ctxtObj in self._project.contexts:\\n newItem = self.new_treeItem(ctxtObj)\\n ctxtItems.append(newItem)\\n self.tw_tree.addTopLevelItems(ctxtItems)\\n self.rf_treeColumns()\\n self.rf_itemStyle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e291569a352687c4950fc70f60b8f3c9\",\n \"score\": \"0.5513167\",\n \"text\": \"def build(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e291569a352687c4950fc70f60b8f3c9\",\n \"score\": \"0.5513167\",\n \"text\": \"def build(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a31d6d345025978d1d77acb67fbb4c\",\n \"score\": \"0.5509685\",\n \"text\": \"def build_views(self, experiment_config_path, output_dir):\\n from celldom.execute import view\\n from celldom.core import experiment\\n exp_config = experiment_config.ExperimentConfig(experiment_config_path)\\n view.build_all(experiment.Experiment(exp_config, output_dir), force=True)\\n logger.info('View construction complete')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ecb15b84276c04fce2990e2cde3eac\",\n \"score\": \"0.55093867\",\n \"text\": \"def make_context(self, info_name, args, parent=None, **extra):\\r\\n if 'default_map' not in extra:\\r\\n default_map = None\\r\\n if parent is not None and parent.default_map is not None:\\r\\n default_map = parent.default_map.get(info_name)\\r\\n extra['default_map'] = default_map\\r\\n ctx = Context(self, info_name=info_name, parent=parent, **extra)\\r\\n self.parse_args(ctx, args)\\r\\n return ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b30713e69d67ef41176a622fe0f3890\",\n \"score\": \"0.54992914\",\n \"text\": \"def _build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b30713e69d67ef41176a622fe0f3890\",\n \"score\": \"0.54992914\",\n \"text\": \"def _build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e11b9b03bbc06c93a7e2cdf78435d04\",\n \"score\": \"0.5493782\",\n \"text\": \"def template_context(request):\\n context = {\\n 'site_title': settings.APPLICATION_TITLE,\\n 'site_acronym': settings.APPLICATION_ACRONYM,\\n 'version_no': settings.APPLICATION_VERSION_NO,\\n 'geoserver_wms_url': settings.GEOSERVER_WMS_URL,\\n 'geoserver_wfs_url': settings.GEOSERVER_WFS_URL,\\n 'prs_user_group': settings.PRS_USER_GROUP,\\n 'managers': settings.MANAGERS,\\n }\\n if request.user.is_authenticated:\\n context['prs_user'] = request.user.userprofile.is_prs_user()\\n context['prs_power_user'] = request.user.userprofile.is_power_user()\\n context['last_referral'] = request.user.userprofile.last_referral()\\n context.update(settings.STATIC_CONTEXT_VARS)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962e410866e01c9152782206e3e3ce41\",\n \"score\": \"0.5493135\",\n \"text\": \"def __init__(self, context):\\n self._context = context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efdbfc07e8aaeeb08f6290c64eef6cd6\",\n \"score\": \"0.5489776\",\n \"text\": \"def context(self):\\n return self.__context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d0f000fcdba20f0d17f208dca8f072\",\n \"score\": \"0.5485853\",\n \"text\": \"def each_context(self, request):\\n self.site_title = ugettext_lazy('Vendor')\\n self.index_title = ugettext_lazy('Dashboard')\\n self.site_header = ugettext_lazy('GLEMKO')\\n \\n script_name = request.META['SCRIPT_NAME']\\n site_url = script_name if self.site_url == '/' and script_name else self.site_url\\n return {\\n 'site_title': self.site_title,\\n 'site_header': self.site_header,\\n 'site_url': site_url,\\n 'has_permission': self.has_permission(request),\\n 'available_apps': self.get_app_list(request),\\n 'is_popup': False,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5278a9b402c89a5b5f8e9f26005bdac3\",\n \"score\": \"0.5482498\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n self.collect_args(self.request)\\n entry_point = None\\n if isinstance(self.context, Exception):\\n entry_point = EntryPoint(get_exception_entry_point_key(self.context))\\n elif hasattr(self.context, 'entry_point'):\\n logger.debug(\\\"%s\\\", self.context)\\n entry_point = self.context.entry_point\\n\\n if entry_point is None:\\n assert entry_point is not None, \\\"Entry point for view should not be None (context=%r)\\\" % self.context\\n key = entry_point.key\\n assert key, \\\"Entry point key for view should be truthy\\\"\\n\\n # todo it might be super helpful to sanity check this value, because this generates errors\\n # later that t+race to here\\n # self._response_dict['entry_point_key'] = entry_point.key\\n # self._response_dict['entry_point_template'] = 'build/templates/entry_point/%s.jinja2' % key\\n\\n return self._response_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a37ab4c02d7da4873c8d1dffa62fd4\",\n \"score\": \"0.54652226\",\n \"text\": \"def do(self, action_context, view, user, *args, **kwargs):\\n return action_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.5454381\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36328765d258ca23e84c39ffdf5f4698\",\n \"score\": \"0.54479337\",\n \"text\": \"def build_view_from(self, cmdargs):\\n experiment_view = ExperimentBuilder().build_view_from(cmdargs)\\n self.connect_to_version_control_tree(experiment_view)\\n\\n return experiment_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9b206dfa34e26f2c68544d7ed2f783\",\n \"score\": \"0.5445825\",\n \"text\": \"def get_context_data(self, **kwargs):\\n return super().get_context_data(\\n **{key: getattr(self, key) for key in self.attr_context},\\n **kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5521fca363d8f49e14f6ee7365ddcb8c\",\n \"score\": \"0.5443454\",\n \"text\": \"def make_shell_context():\\n\\n return dict(app=app, db=db, Photo=Photo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521d82c1e76714ec30403e44d016175f\",\n \"score\": \"0.5440685\",\n \"text\": \"def get_context_data(self, **kwargs):\\n logger.info(\\\"category info\\\")\\n context = super().get_context_data(**kwargs)\\n category = Category.objects.get(pk=self.kwargs[\\\"pk\\\"])\\n context[\\\"category\\\"] = category\\n\\n view = self.request.GET.get(\\\"view\\\", \\\"list\\\")\\n context['view'] = view\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509187bcd1f0a779507e5df4672df363\",\n \"score\": \"0.54373497\",\n \"text\": \"def user_context(self) -> global___UserContext:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509187bcd1f0a779507e5df4672df363\",\n \"score\": \"0.54373497\",\n \"text\": \"def user_context(self) -> global___UserContext:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509187bcd1f0a779507e5df4672df363\",\n \"score\": \"0.54373497\",\n \"text\": \"def user_context(self) -> global___UserContext:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93613bbd16d271cde84f3794d1006ccf\",\n \"score\": \"0.54191977\",\n \"text\": \"def context(self):\\n assert access_checker.isSet(self.data.conversation)\\n assert access_checker.isSet(self.data.user)\\n\\n query = gciconversation_logic.queryConversationUserForConversationAndUser(\\n self.data.conversation.key, ndb.Key.from_old_key(self.data.user.key()))\\n conv_user_results = query.fetch(1)\\n assert conv_user_results\\n\\n conv_user = conv_user_results[0]\\n\\n self.data.redirect.id()\\n\\n url = self.data.redirect.urlOf(\\n url_names.GCI_CONVERSATION_NOTIFICATION_TOGGLE)\\n\\n enable_notifications = toggle_button_view.ToggleButtonTemplate(\\n self.data, 'on_off', translation.ugettext('Enable Notifications'),\\n 'notifications-enabled', url,\\n checked=conv_user.enable_notifications,\\n help_text=self.DEF_ENABLE_NOTIFICATIONS_HELP,\\n labels={\\n 'checked': 'Yes',\\n 'unchecked': 'No'})\\n self.toggle_buttons.append(enable_notifications)\\n\\n return {\\n 'title': translation.ugettext('Conversation Actions'),\\n 'toggle_buttons': self.toggle_buttons,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c98e882d211f4288a5c9a029cb8ec6\",\n \"score\": \"0.5406181\",\n \"text\": \"def get_preview_context(self, request, mode_name):\\n return {\\\"object\\\": self, \\\"request\\\": request}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"200ba62308847e8decedffd1845bd529\",\n \"score\": \"0.5404884\",\n \"text\": \"def generate_context(self):\\n coupons = Coupon.current_coupons.select_related(\\n 'offer', 'offer__business', 'offer__business__advertiser'\\n ).filter(location__isnull=False).order_by('offer__business')\\n valid_days_dict = {}\\n for coupon in coupons:\\n valid_days_dict[coupon.id] = VALID_DAYS.create_valid_days_string(\\n coupon)\\n return {'coupons': coupons,\\n 'shooger_category_dict': self.shooger_category_dict(),\\n 'valid_days_dict': valid_days_dict\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56618ebffdea64ca439ce5537bca3928\",\n \"score\": \"0.54044926\",\n \"text\": \"def _render_view(self):\\r\\n\\r\\n return self.template.render({})\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993675,"cells":{"query_id":{"kind":"string","value":"a301f3f1e7d6be942b7976f14714974d"},"query":{"kind":"string","value":"Update the scraper config and name by sending PUT request."},"positive_passages":{"kind":"list like","value":[{"docid":"52b21767f5464fe5c27ed2ef92b4fc98","score":"0.74320465","text":"def update(self, id_or_name, name, config):\n data = {'name': name, 'config': config}\n return self.client.put('/scrapers/{}'.format(id_or_name), data)","title":""}],"string":"[\n {\n \"docid\": \"52b21767f5464fe5c27ed2ef92b4fc98\",\n \"score\": \"0.74320465\",\n \"text\": \"def update(self, id_or_name, name, config):\\n data = {'name': name, 'config': config}\\n return self.client.put('/scrapers/{}'.format(id_or_name), data)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e8c57175ec238a519b8d1a42dd3983f9","score":"0.6280583","text":"def put(self, name):\n data = rest_utils.get_json_and_verify_params({\n 'value': {},\n 'force': {'type': bool, 'optional': True}\n })\n value = data['value']\n force = data.get('force', False)\n config.instance.update_db({name: value}, force)\n\n return self._get_config(name)","title":""},{"docid":"0576d1d0217357f9e1a177285ba6dd4a","score":"0.6191346","text":"def update_config(self, config):","title":""},{"docid":"4ebcf68663c471b633e6156d445d4c1c","score":"0.6129055","text":"def update_config(self, *args, **kwargs) -> None:","title":""},{"docid":"9f1dd3ce04eb4d5da69c60063a47e240","score":"0.5995362","text":"def update(self, config):\n request = self._build_update_request(config)\n self._broker.ignore_content(request)","title":""},{"docid":"77d07555b73a204a4094d0b22658952c","score":"0.5968278","text":"def update(self, config: dict[str, str]) -> None:\n self._config.update(config)","title":""},{"docid":"242236657abe9ec50b9dee51e19dff53","score":"0.59663147","text":"def update(config, hostname, fqdn, username, password):\n\n data = {}\n if fqdn:\n data['fqdn'] = fqdn\n if username:\n data['username'] = username\n if password:\n data['password'] = password\n\n response = make_api_request('PUT', config, '/machines/' + hostname,\n data=json.dumps(data))\n\n print 'Successfully updated', hostname","title":""},{"docid":"f3bd8c5891385cf8855051b1f1fcf01b","score":"0.59602666","text":"def update(self):\n body = json.dumps(self.__dict__)\n brand = self.client.request_json('PUT', '/brands/' + str(self.id), body)\n print brand['name']","title":""},{"docid":"0e20643e02d9e1e0fe96c08fc69bc35f","score":"0.5950279","text":"def update_web_conf(self):","title":""},{"docid":"141220edb02be4edc270d53beea5fff0","score":"0.5929865","text":"def do_PUT(self):\n self.do_GET()","title":""},{"docid":"c56b3695f36dec7296a1fb39424df19d","score":"0.5909611","text":"def update(self):\n data = utils.get_attrs(self, self.config_attrs)\n # Manually remove the name\n if \"name\" in data:\n del data[\"name\"]\n self.__modified = self._put_data(data)\n logging.info(\"SUCCESS: Updating %s\", self)\n return self.__modified","title":""},{"docid":"37b0fdec3ec246f0ec9135688f4fdfdc","score":"0.58972716","text":"def update(self, _id=None, fsso_config_data=None):\n data = DataObject()\n data.add_value(\"fsso_config_data\", fsso_config_data)\n endpoint = FSSO_CONFIG + \"/{}\".format(_id)\n response = self.client.put_json(endpoint, data.data)\n response.success = response.status_code == 204\n\n return response","title":""},{"docid":"b513d9ea21b2b4c3c83f2718dd482729","score":"0.5859707","text":"def update(self, acme_id, name):\n data = {\"name\": name}\n url = self._url(str(acme_id))\n result = self._client.put(url, data=data)\n\n return result.ok","title":""},{"docid":"9fb8e60daddff7333fbeef8c8ba18bf2","score":"0.58453053","text":"def update(seld, request, pk=None):\n return Response({'http_method' : 'PUT'})","title":""},{"docid":"9e7184cca8756df9bce47ca5f3a9dca2","score":"0.5843323","text":"def update(self, id: str, name: str) -> dict:\n payload = {'name': name}\n r = requests.put(self.url + '/{}'.format(id), headers=self.headers, params=payload)\n\n return r.json()","title":""},{"docid":"e86cbc5034180547dac82514d0c026c5","score":"0.580461","text":"async def put(self, dataset_id):\n data = json.loads(self.request.body)\n if 'dataset_id' not in data:\n data['dataset_id'] = dataset_id\n elif data['dataset_id'] != dataset_id:\n raise tornado.web.HTTPError(400, reason='dataset_id mismatch')\n ret = await self.db.config.replace_one({'dataset_id':dataset_id},\n data, upsert=True)\n self.write({})\n self.finish()","title":""},{"docid":"88cbabb8a1ca4695c8789a404b246421","score":"0.5787509","text":"def put(self, request, routename):\n pass","title":""},{"docid":"7e76c894768425700ecea1d68d931884","score":"0.57821435","text":"def update(self):\n r = self.pt.get(self.job_url)\n self.update_fields(r.json())","title":""},{"docid":"85588f7c5bdc872051fda4c752681cec","score":"0.57410663","text":"def update_configuration(self):\n pass","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"ef08da12dcf06a6737f9a6d9c2dfa3a8","score":"0.57223374","text":"def put(self, request, *args, **kwargs):\n return self.update(request, *args, **kwargs)","title":""},{"docid":"4192fbc167b93cda9097f27b41685c2e","score":"0.5683158","text":"def update(self, request, pk=None):\n return Response({'httl_method': 'PUT'})","title":""},{"docid":"cfc1dc1318e519896e9eb16430e63fd0","score":"0.5679416","text":"def put(self):\n request = transforms.loads(self.request.get('request'))\n\n if not self.assert_xsrf_token_or_fail(\n request, 'update-local_chapter', {}):\n return\n\n if not roles.Roles.is_super_admin():\n transforms.send_json_response(\n self, 401, 'Access denied.')\n return\n\n payload = request.get('payload')\n updated_dict = transforms.json_to_dict(\n transforms.loads(payload), self.get_schema_dict())\n\n errors = []\n self.apply_updates(updated_dict, errors)\n if not errors:\n transforms.send_json_response(self, 200, 'Saved.')\n else:\n transforms.send_json_response(self, 412, '\\n'.join(errors))","title":""},{"docid":"221abe4cce5af60b56dc5809fff19ca0","score":"0.5678848","text":"def update_config(self, config):\n self.__dict__.update(config)","title":""},{"docid":"acd16b53db989912015baa344c57ddbb","score":"0.56612843","text":"def put(self, settings_id):\n parser = reqparse.RequestParser()\n parser.add_argument(\"Authorization\",\n type=str, location=\"headers\", required=True)\n parser.add_argument(\"available\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"friends_only\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"men\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"women\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"age_lower_limit\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"age_upper_limit\",\n type=int, location=\"form\", required=False)\n parser.add_argument(\"radius\",\n type=float,location=\"form\",required=False)\n parser.add_argument(\"radius_unit\",\n type=str,location=\"form\",required=False)\n args = parser.parse_args()\n if type(args.friends_only)==int:\n args.friends_only = bool(args.friends_only)\n if type(args.men)==int:\n args.men = bool(args.men)\n if type(args.women)==int:\n args.women = bool(args.women)\n if type(args.available)==int:\n args.available = bool(args.available)\n \n try:\n #get search_settings\n settings = SearchSettings.query.get(settings_id)\n if not settings:\n return Response(status=404, message=\"Search settings not found.\")\\\n .__dict__,404\n\n #Ensure user is authorized to make change to settings\n user = settings.user\n if user.fitpals_secret != args.Authorization:\n return Response(status=401, message=\"Not Authorized.\").__dict__,401\n\n #update search_settings\n for arg in args.keys():\n if args[arg] != None:\n setattr(settings,arg,args[arg])\n db.session.commit()\n\n #send update to user's other devices\n send_message(user,request.path,request.method,\n value=settings.dict_repr())\n\n return Response(status=202,message=\"Search settings updated.\",\n value=settings.dict_repr()).__dict__,202\n except Exception as e:\n if exception_is_validation_error(e):\n return Response(status=400,\n message=\"Search settings data invalid.\").__dict__,400\n db.session.rollback()\n app.logger.error(e)\n return Response(status=500, message=\"Internal server error.\").__dict__,500","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5660111","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5660111","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5660111","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"20d30386881a9055000f0e7fb1fe2c0e","score":"0.5624645","text":"def update(self, request, pk=None):\n return Response({'method':'PUT'})","title":""},{"docid":"91d06bbb27877d9579ee892d251bb761","score":"0.56105995","text":"def edit(self, config, etag):\n\n data = self._json_encode(config)\n headers = self._default_headers()\n\n if etag is not None:\n headers[\"If-Match\"] = etag\n\n return self._request(self.name,\n ok_status=None,\n data=data,\n headers=headers,\n method=\"PUT\")","title":""},{"docid":"bee2c9065131bf36184e9ac052381f50","score":"0.5602912","text":"def put(self):\n data = request.json\n return update_category(data=data)","title":""},{"docid":"55c25499d9594b3d7efc6e90454a7a2f","score":"0.55731755","text":"def update(self, **kwargs):\n\n ...","title":""},{"docid":"11fbbd119d167bfa9451fa6ec27efa1b","score":"0.5544983","text":"def update_config(self, *args):\n updates = list(args)\n for key, value in updates:\n self.config[key] = value\n self.save_config()","title":""},{"docid":"ad7d43abaf8da5a950e99b44276122d1","score":"0.55409986","text":"def put(self, url: str, **kwargs: Any) -> '_BoxResponse':\n return self.request('PUT', url, **kwargs)","title":""},{"docid":"7dc093884c1171df2f463488401c6898","score":"0.55389273","text":"async def update(self, data=None):\n self._modified = False\n if data is not None:\n self.cache.update(data)\n await self.api.update_config(self.cache)","title":""},{"docid":"b6223a9931d792edf1e61a1919b8bf68","score":"0.5537873","text":"def update(self):\n self.datalink.request(self._addr, self._id)\n self._update()","title":""},{"docid":"a50256ae45db53ca71fe97b66cee4989","score":"0.55203116","text":"def updateConfig(config_data):\n print \"Updating Config File...\"\n with open('../config.json','w') as config_file:\n json.dump(config_data,config_file)\n print \"Updated Config File\"","title":""},{"docid":"a4b9b1a4ad5d3ba6ebfe4cfe0df0941e","score":"0.55100614","text":"def PUT(self):\n return dumps(self.update_request(cherrypy.request.body.read().strip()))","title":""},{"docid":"1d42dc5a3bd8c407e1bf5ebfa192f741","score":"0.55086267","text":"def update_config(cls, config):\n cls.config.update(config)","title":""},{"docid":"297a26ef854bc72af9be8a28fa4bb60b","score":"0.5504723","text":"def test_updating_of_the_company(self):\n\n res = self.client().put(\n '/api/v1/companies/{}'.format(self.company.id),\n data=json.dumps(self.params),\n content_type='application/json',\n headers={AUTH_HEADER: self.token}\n )\n assert self.company.name, self.params['name']","title":""},{"docid":"23a07e318695bb4758a2972a5c82e985","score":"0.5501319","text":"def update(self, page_name=None, page_content=None):\n get_remote_obj().update(page_name=page_name, page_content=page_content)","title":""},{"docid":"5a534fce9c7b8f8c0c0f0df9121d7629","score":"0.5494595","text":"def update(args):\n from tiddlywebplugins.instancer import Instance\n instance = Instance('.', config)\n instance.update_store()","title":""},{"docid":"259ed74d82d37a5e4afd938e6b3759ad","score":"0.54941314","text":"def put(self, *args, **kwargs):\r\n kwargs['method'] = 'PUT'\r\n return self.request(*args, **kwargs)","title":""},{"docid":"14a84e708f6de363c26d2c60edcb580a","score":"0.5455791","text":"def put(self):\n\n data = request.get_json()\n print(\"status update: \", data)\n\n user = g.user\n\n if not data:\n abort(400, \"Missing status update\")\n\n if \"status_update\" not in data.keys():\n abort(400, \"Missing status update\")\n\n food_request = FoodRequest.query.filter_by(\n delivered=False\n ).filter_by(driver_id=user.id).first()\n\n if not food_request:\n abort(400, \"Could not find food request for this driver\")\n\n food_request.status = data[\"status_update\"]\n\n db.session.commit()\n\n return make_response(f\"Food Request for restaurant {food_request.restaurant.name} updated\", 200)","title":""},{"docid":"c9c3821826cdf8c533c85051d9d29710","score":"0.54440683","text":"def put(self, url: str, **kwargs) -> requests.Response:\n return self.request('PUT', url=url, **kwargs)","title":""},{"docid":"fed2a5c9e21642afbd9d85628104f614","score":"0.5437134","text":"def update(self,request,pk=None):\n return Response({'http_method':'PUT'})","title":""},{"docid":"3daddf5b54796388216acc0f2dbeb42b","score":"0.5435571","text":"def update(self, **kwargs):\n headers = {'content-type': 'application/json'}\n\n url = self.get_update_url(**kwargs)\n if not url:\n raise ValueError('No update url found')\n\n response = self._request(self._meta['update_method'], url,\n data=self.serialize(),\n headers=headers)\n\n self.validate_update_response(response)\n self._full_url = url\n self.handle_update_response(response)","title":""},{"docid":"90fee805c38fb9f7ab0a4fcf7d8f5374","score":"0.5422259","text":"def _update(self, name, value):\n return object.__setattr__(self, name, value)","title":""},{"docid":"b38f05f92880a68b26eddacf480ae032","score":"0.5417347","text":"def put(self, request: HTTPRequest) -> None:\n pass","title":""},{"docid":"79f081b704f00b36cb15ee90ec47997d","score":"0.5413176","text":"def test_update(self):\n api = gist.API(name=config.USER, token=config.TOKEN)\n api.all()[0].update(\"this is a test\")","title":""},{"docid":"6372a52a58042015a7f64416ecf06602","score":"0.54060733","text":"def update():\n if not authenticated(session) or not tiene_permiso(session, \"configuracion_update\"):\n abort(401)\n sitio = Configuracion().sitio()\n return render_template(\"configuracion/update.html\", sitio=sitio)","title":""},{"docid":"fef50d3da74e8136389b24266dc3d045","score":"0.53918874","text":"def update(cls, package_id, openapi_spec_url):\n values = {\n \"openapi_spec_url\": openapi_spec_url\n }\n model.Session.query(cls) \\\n .filter(cls.package_id == package_id) \\\n .update(values)","title":""},{"docid":"8095d46610289cc6457adda9a792ebbe","score":"0.53907615","text":"def Club_Setting(self, request):\n param_to_change = request.data.get('Param_to_change', {})\n new_value = request.data.get('New_value', {})\n user = User.objects.get(pk=request.data.get('User_ID', {}))\n bookclub = BookClub.objects.get(pk=request.data.get('BookClub_ID', {}))\n\n if CheckPermissions(user).hasPerm('Set Club', bookclub):\n if param_to_change == 'Name':\n setattr(bookclub, \"Name\", new_value)\n\n elif param_to_change == 'Logo':\n setattr(bookclub, \"Profile_Pic\", new_value)\n\n elif param_to_change == 'Open':\n setattr(bookclub, \"Open\", new_value)\n\n else:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n\n bookclub.save()\n serializer = BookClubSearchSerializer(bookclub)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n return Response(status=status.HTTP_401_UNAUTHORIZED)","title":""},{"docid":"eecf888029eef79a6efb23ce8913ab03","score":"0.5387636","text":"def update_name(self, name: str) -> None:","title":""},{"docid":"7435624177995784463a588db1d49452","score":"0.5382173","text":"def put(self, id: int):\n data = api.payload\n store = StoreModel.fetch_by_id(id)\n if store:\n if u\"name\" in data[\"name\"]:\n store.name = name\n return {\"message\": OPERATION_SUCCESSFUL.format(\"update\")}, 201\n else:\n return {\"message\": STORE_NOT_FOUND}, 404","title":""},{"docid":"36327643d9831f32baf874a26b4377c0","score":"0.53772587","text":"def test_update(self):\n self.config.update({\"age\": 901, \"starring\": \"Star Wars\"})\n self.assertEqual(self.config.age, 901)\n self.assertEqual(self.config.starring, \"Star Wars\")","title":""},{"docid":"48c941ed26eef9f5246b67655a7fec9c","score":"0.5375388","text":"def put(self, url, **kwargs):\n return self.request(url, 'PUT', **kwargs)","title":""},{"docid":"0def0846d9e369355ec51e7b34386385","score":"0.53519154","text":"def update(self, api_url=None, username=None, password=None):\n self.manager.update(self, api_url, username, password)","title":""},{"docid":"30f7f4dad18cf2476615369abf5353d3","score":"0.53481877","text":"def set(self, config):\n for name in config:\n self[name].set(config[name])","title":""},{"docid":"b3e913ec313aba4f82ba50f11da0744e","score":"0.53302854","text":"def _put(url, data):\n return Consul._http('PUT', url, data)","title":""},{"docid":"5f918a38664a1dd1641d0b7a4ee8a3a7","score":"0.53285205","text":"def update(cls, apiclient, name, value=None, zoneid=None, clusterid=None, storageid=None, domainid=None, accountid=None):\n\n cmd = updateConfiguration.updateConfigurationCmd()\n cmd.name = name\n cmd.value = value\n\n if zoneid:\n cmd.zoneid = zoneid\n if clusterid:\n cmd.clusterid = clusterid\n if storageid:\n cmd.storageid = storageid\n if domainid:\n cmd.domainid = domainid\n if accountid:\n cmd.accountid = accountid\n apiclient.updateConfiguration(cmd)","title":""},{"docid":"211471852717e7c964ef4cbc802e3750","score":"0.5318861","text":"def updateOCIOConfig(self, ocioConfigPath) -> None:\n ...","title":""},{"docid":"9aa17f2394830ad6e3b63a14130246a9","score":"0.531513","text":"def update(self, config_dict ):\n for key, value in config_dict.items():\n setattr(self, key, value)","title":""},{"docid":"cca57d6d6faf6242f467207fa3900002","score":"0.53056204","text":"def update(self, path, data, **params):\n return self.query('put', path, data = data, **params).json()","title":""},{"docid":"9676ed3c4a28137950b244e9335fd0e6","score":"0.5299441","text":"def update_config(self):\n\t\tcf = open(self.config_file,\"a\")\n\t\tself.config_parser.write(cf)\n\t\tcf.close()","title":""},{"docid":"d040ed4e7685d9ea4a546f57ef1e2c5a","score":"0.52970964","text":"def do_PUT(self):\n self.server_version = ServerHeader\n self.sys_version = \"\"\n self.send_response(200)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()","title":""},{"docid":"019016e66189976b25a9e4ab449ab0c9","score":"0.5291243","text":"def put(self, request, pk=None):\n return Response({'method': 'PUT'})","title":""},{"docid":"019016e66189976b25a9e4ab449ab0c9","score":"0.5291243","text":"def put(self, request, pk=None):\n return Response({'method': 'PUT'})","title":""},{"docid":"019016e66189976b25a9e4ab449ab0c9","score":"0.5291243","text":"def put(self, request, pk=None):\n return Response({'method': 'PUT'})","title":""},{"docid":"f8689ad05bbfb3d3df73be3f52a6e68b","score":"0.528704","text":"def update(self, *args, **kwargs) -> None:\n pass","title":""},{"docid":"8362ebf09643559cf06dbb56713c406c","score":"0.52806985","text":"def put(self, request, pk=None):\n return(Response({'method':'PUT'}))","title":""},{"docid":"8da6522b7777c0ffb92c0b2ddf44fa47","score":"0.5275514","text":"def update(self, webhook_id, name, target_url):\n req = SparkRequest(self._token)\n req.set_data('name', name)\n req.set_data('targetUrl', target_url)\n req.put('webhooks/' + webhook_id)\n if req.response_code == 200:\n return True\n return False","title":""},{"docid":"99d39ad2feadb1bde02757896a4f837c","score":"0.52693385","text":"def put(self , request , pk=None):\n return Response({'method': 'PUT'})","title":""},{"docid":"111da3dea20c99fb6fc11a83b3f02b18","score":"0.5268641","text":"def put(self, bucketlist_id):\n args = parser.parse_args()\n name = args.name\n\n if not name:\n return {\"message\": \"Please provide a new name for your bucketlist\"}, 400\n bucketlist = Bucketlist.query.filter(Bucketlist.id == bucketlist_id).first()\n\n if not bucketlist:\n return {\"message\": \"The Buckelist \" + bucketlist_id + \" provided doesn't exist ....\"}, 404\n\n bucketlist.name = args.name\n db.session.add(bucketlist)\n db.session.commit()\n return {\"message\": \"Bucket list name updated or changed Successfully\"}, 204","title":""},{"docid":"12dcc552599bedaeb60fb836acc6cc0a","score":"0.52682066","text":"def update(self, connection=None):\n if connection is None:\n connection = self.connection\n\n uri = connection.uri(\"servers\", self.name,\n parameters=[\"group-id=\"+self.group_name()])\n struct = self.marshal()\n response = connection.put(uri, payload=struct, etag=self.etag)\n\n self.name = self._config['server-name']\n if 'etag' in response.headers:\n self.etag = response.headers['etag']\n return self","title":""},{"docid":"6fc9b4f74d2fccbaa1e57030f05f346d","score":"0.52529114","text":"def PUT(self):\n try:\n return dumps(self.update(cherrypy.request.body.read().strip()))\n except:\n self.logger.error('Failed to update an mccm from API')\n return dumps({'results': False, 'message': 'Failed to update an mccm from API'})","title":""},{"docid":"d57f351d3bd512b3acf776e6c72f6820","score":"0.5243985","text":"def put(self, *args, **kw):\r\n kw['method'] = 'PUT'\r\n return self.open(*args, **kw)","title":""},{"docid":"ecdc37464c6ff3ba9e2872facb71c2a6","score":"0.5241009","text":"def put(self, **params):\n return self.patch(**params)","title":""},{"docid":"d3d6b3bdafc857272a9c6a956135d255","score":"0.5237322","text":"def update(self):\n body = json.dumps(self.__dict__)\n coupon = self.client.request_json('PUT', '/coupons/%s' % self.id, body)","title":""},{"docid":"ddcfeaa77d1df620213ffa76ee79e009","score":"0.5234599","text":"def put(self, request, pk=None):\n return Response({'method':'PUT'})","title":""},{"docid":"38f57ca5367a72ea91d33473cb5699c3","score":"0.52313036","text":"def put(self, request, pk=None):\n\n return Response({'method': 'put'})","title":""}],"string":"[\n {\n \"docid\": \"e8c57175ec238a519b8d1a42dd3983f9\",\n \"score\": \"0.6280583\",\n \"text\": \"def put(self, name):\\n data = rest_utils.get_json_and_verify_params({\\n 'value': {},\\n 'force': {'type': bool, 'optional': True}\\n })\\n value = data['value']\\n force = data.get('force', False)\\n config.instance.update_db({name: value}, force)\\n\\n return self._get_config(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0576d1d0217357f9e1a177285ba6dd4a\",\n \"score\": \"0.6191346\",\n \"text\": \"def update_config(self, config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ebcf68663c471b633e6156d445d4c1c\",\n \"score\": \"0.6129055\",\n \"text\": \"def update_config(self, *args, **kwargs) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1dd3ce04eb4d5da69c60063a47e240\",\n \"score\": \"0.5995362\",\n \"text\": \"def update(self, config):\\n request = self._build_update_request(config)\\n self._broker.ignore_content(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d07555b73a204a4094d0b22658952c\",\n \"score\": \"0.5968278\",\n \"text\": \"def update(self, config: dict[str, str]) -> None:\\n self._config.update(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"242236657abe9ec50b9dee51e19dff53\",\n \"score\": \"0.59663147\",\n \"text\": \"def update(config, hostname, fqdn, username, password):\\n\\n data = {}\\n if fqdn:\\n data['fqdn'] = fqdn\\n if username:\\n data['username'] = username\\n if password:\\n data['password'] = password\\n\\n response = make_api_request('PUT', config, '/machines/' + hostname,\\n data=json.dumps(data))\\n\\n print 'Successfully updated', hostname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3bd8c5891385cf8855051b1f1fcf01b\",\n \"score\": \"0.59602666\",\n \"text\": \"def update(self):\\n body = json.dumps(self.__dict__)\\n brand = self.client.request_json('PUT', '/brands/' + str(self.id), body)\\n print brand['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e20643e02d9e1e0fe96c08fc69bc35f\",\n \"score\": \"0.5950279\",\n \"text\": \"def update_web_conf(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"141220edb02be4edc270d53beea5fff0\",\n \"score\": \"0.5929865\",\n \"text\": \"def do_PUT(self):\\n self.do_GET()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56b3695f36dec7296a1fb39424df19d\",\n \"score\": \"0.5909611\",\n \"text\": \"def update(self):\\n data = utils.get_attrs(self, self.config_attrs)\\n # Manually remove the name\\n if \\\"name\\\" in data:\\n del data[\\\"name\\\"]\\n self.__modified = self._put_data(data)\\n logging.info(\\\"SUCCESS: Updating %s\\\", self)\\n return self.__modified\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b0fdec3ec246f0ec9135688f4fdfdc\",\n \"score\": \"0.58972716\",\n \"text\": \"def update(self, _id=None, fsso_config_data=None):\\n data = DataObject()\\n data.add_value(\\\"fsso_config_data\\\", fsso_config_data)\\n endpoint = FSSO_CONFIG + \\\"/{}\\\".format(_id)\\n response = self.client.put_json(endpoint, data.data)\\n response.success = response.status_code == 204\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b513d9ea21b2b4c3c83f2718dd482729\",\n \"score\": \"0.5859707\",\n \"text\": \"def update(self, acme_id, name):\\n data = {\\\"name\\\": name}\\n url = self._url(str(acme_id))\\n result = self._client.put(url, data=data)\\n\\n return result.ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb8e60daddff7333fbeef8c8ba18bf2\",\n \"score\": \"0.58453053\",\n \"text\": \"def update(seld, request, pk=None):\\n return Response({'http_method' : 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7184cca8756df9bce47ca5f3a9dca2\",\n \"score\": \"0.5843323\",\n \"text\": \"def update(self, id: str, name: str) -> dict:\\n payload = {'name': name}\\n r = requests.put(self.url + '/{}'.format(id), headers=self.headers, params=payload)\\n\\n return r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86cbc5034180547dac82514d0c026c5\",\n \"score\": \"0.580461\",\n \"text\": \"async def put(self, dataset_id):\\n data = json.loads(self.request.body)\\n if 'dataset_id' not in data:\\n data['dataset_id'] = dataset_id\\n elif data['dataset_id'] != dataset_id:\\n raise tornado.web.HTTPError(400, reason='dataset_id mismatch')\\n ret = await self.db.config.replace_one({'dataset_id':dataset_id},\\n data, upsert=True)\\n self.write({})\\n self.finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cbabb8a1ca4695c8789a404b246421\",\n \"score\": \"0.5787509\",\n \"text\": \"def put(self, request, routename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e76c894768425700ecea1d68d931884\",\n \"score\": \"0.57821435\",\n \"text\": \"def update(self):\\n r = self.pt.get(self.job_url)\\n self.update_fields(r.json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85588f7c5bdc872051fda4c752681cec\",\n \"score\": \"0.57410663\",\n \"text\": \"def update_configuration(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08da12dcf06a6737f9a6d9c2dfa3a8\",\n \"score\": \"0.57223374\",\n \"text\": \"def put(self, request, *args, **kwargs):\\n return self.update(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4192fbc167b93cda9097f27b41685c2e\",\n \"score\": \"0.5683158\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'httl_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc1dc1318e519896e9eb16430e63fd0\",\n \"score\": \"0.5679416\",\n \"text\": \"def put(self):\\n request = transforms.loads(self.request.get('request'))\\n\\n if not self.assert_xsrf_token_or_fail(\\n request, 'update-local_chapter', {}):\\n return\\n\\n if not roles.Roles.is_super_admin():\\n transforms.send_json_response(\\n self, 401, 'Access denied.')\\n return\\n\\n payload = request.get('payload')\\n updated_dict = transforms.json_to_dict(\\n transforms.loads(payload), self.get_schema_dict())\\n\\n errors = []\\n self.apply_updates(updated_dict, errors)\\n if not errors:\\n transforms.send_json_response(self, 200, 'Saved.')\\n else:\\n transforms.send_json_response(self, 412, '\\\\n'.join(errors))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"221abe4cce5af60b56dc5809fff19ca0\",\n \"score\": \"0.5678848\",\n \"text\": \"def update_config(self, config):\\n self.__dict__.update(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd16b53db989912015baa344c57ddbb\",\n \"score\": \"0.56612843\",\n \"text\": \"def put(self, settings_id):\\n parser = reqparse.RequestParser()\\n parser.add_argument(\\\"Authorization\\\",\\n type=str, location=\\\"headers\\\", required=True)\\n parser.add_argument(\\\"available\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"friends_only\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"men\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"women\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"age_lower_limit\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"age_upper_limit\\\",\\n type=int, location=\\\"form\\\", required=False)\\n parser.add_argument(\\\"radius\\\",\\n type=float,location=\\\"form\\\",required=False)\\n parser.add_argument(\\\"radius_unit\\\",\\n type=str,location=\\\"form\\\",required=False)\\n args = parser.parse_args()\\n if type(args.friends_only)==int:\\n args.friends_only = bool(args.friends_only)\\n if type(args.men)==int:\\n args.men = bool(args.men)\\n if type(args.women)==int:\\n args.women = bool(args.women)\\n if type(args.available)==int:\\n args.available = bool(args.available)\\n \\n try:\\n #get search_settings\\n settings = SearchSettings.query.get(settings_id)\\n if not settings:\\n return Response(status=404, message=\\\"Search settings not found.\\\")\\\\\\n .__dict__,404\\n\\n #Ensure user is authorized to make change to settings\\n user = settings.user\\n if user.fitpals_secret != args.Authorization:\\n return Response(status=401, message=\\\"Not Authorized.\\\").__dict__,401\\n\\n #update search_settings\\n for arg in args.keys():\\n if args[arg] != None:\\n setattr(settings,arg,args[arg])\\n db.session.commit()\\n\\n #send update to user's other devices\\n send_message(user,request.path,request.method,\\n value=settings.dict_repr())\\n\\n return Response(status=202,message=\\\"Search settings updated.\\\",\\n value=settings.dict_repr()).__dict__,202\\n except Exception as e:\\n if exception_is_validation_error(e):\\n return Response(status=400,\\n message=\\\"Search settings data invalid.\\\").__dict__,400\\n db.session.rollback()\\n app.logger.error(e)\\n return Response(status=500, message=\\\"Internal server error.\\\").__dict__,500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5660111\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5660111\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5660111\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d30386881a9055000f0e7fb1fe2c0e\",\n \"score\": \"0.5624645\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d06bbb27877d9579ee892d251bb761\",\n \"score\": \"0.56105995\",\n \"text\": \"def edit(self, config, etag):\\n\\n data = self._json_encode(config)\\n headers = self._default_headers()\\n\\n if etag is not None:\\n headers[\\\"If-Match\\\"] = etag\\n\\n return self._request(self.name,\\n ok_status=None,\\n data=data,\\n headers=headers,\\n method=\\\"PUT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee2c9065131bf36184e9ac052381f50\",\n \"score\": \"0.5602912\",\n \"text\": \"def put(self):\\n data = request.json\\n return update_category(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c25499d9594b3d7efc6e90454a7a2f\",\n \"score\": \"0.55731755\",\n \"text\": \"def update(self, **kwargs):\\n\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11fbbd119d167bfa9451fa6ec27efa1b\",\n \"score\": \"0.5544983\",\n \"text\": \"def update_config(self, *args):\\n updates = list(args)\\n for key, value in updates:\\n self.config[key] = value\\n self.save_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7d43abaf8da5a950e99b44276122d1\",\n \"score\": \"0.55409986\",\n \"text\": \"def put(self, url: str, **kwargs: Any) -> '_BoxResponse':\\n return self.request('PUT', url, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc093884c1171df2f463488401c6898\",\n \"score\": \"0.55389273\",\n \"text\": \"async def update(self, data=None):\\n self._modified = False\\n if data is not None:\\n self.cache.update(data)\\n await self.api.update_config(self.cache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6223a9931d792edf1e61a1919b8bf68\",\n \"score\": \"0.5537873\",\n \"text\": \"def update(self):\\n self.datalink.request(self._addr, self._id)\\n self._update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a50256ae45db53ca71fe97b66cee4989\",\n \"score\": \"0.55203116\",\n \"text\": \"def updateConfig(config_data):\\n print \\\"Updating Config File...\\\"\\n with open('../config.json','w') as config_file:\\n json.dump(config_data,config_file)\\n print \\\"Updated Config File\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4b9b1a4ad5d3ba6ebfe4cfe0df0941e\",\n \"score\": \"0.55100614\",\n \"text\": \"def PUT(self):\\n return dumps(self.update_request(cherrypy.request.body.read().strip()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d42dc5a3bd8c407e1bf5ebfa192f741\",\n \"score\": \"0.55086267\",\n \"text\": \"def update_config(cls, config):\\n cls.config.update(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297a26ef854bc72af9be8a28fa4bb60b\",\n \"score\": \"0.5504723\",\n \"text\": \"def test_updating_of_the_company(self):\\n\\n res = self.client().put(\\n '/api/v1/companies/{}'.format(self.company.id),\\n data=json.dumps(self.params),\\n content_type='application/json',\\n headers={AUTH_HEADER: self.token}\\n )\\n assert self.company.name, self.params['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a07e318695bb4758a2972a5c82e985\",\n \"score\": \"0.5501319\",\n \"text\": \"def update(self, page_name=None, page_content=None):\\n get_remote_obj().update(page_name=page_name, page_content=page_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a534fce9c7b8f8c0c0f0df9121d7629\",\n \"score\": \"0.5494595\",\n \"text\": \"def update(args):\\n from tiddlywebplugins.instancer import Instance\\n instance = Instance('.', config)\\n instance.update_store()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"259ed74d82d37a5e4afd938e6b3759ad\",\n \"score\": \"0.54941314\",\n \"text\": \"def put(self, *args, **kwargs):\\r\\n kwargs['method'] = 'PUT'\\r\\n return self.request(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a84e708f6de363c26d2c60edcb580a\",\n \"score\": \"0.5455791\",\n \"text\": \"def put(self):\\n\\n data = request.get_json()\\n print(\\\"status update: \\\", data)\\n\\n user = g.user\\n\\n if not data:\\n abort(400, \\\"Missing status update\\\")\\n\\n if \\\"status_update\\\" not in data.keys():\\n abort(400, \\\"Missing status update\\\")\\n\\n food_request = FoodRequest.query.filter_by(\\n delivered=False\\n ).filter_by(driver_id=user.id).first()\\n\\n if not food_request:\\n abort(400, \\\"Could not find food request for this driver\\\")\\n\\n food_request.status = data[\\\"status_update\\\"]\\n\\n db.session.commit()\\n\\n return make_response(f\\\"Food Request for restaurant {food_request.restaurant.name} updated\\\", 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9c3821826cdf8c533c85051d9d29710\",\n \"score\": \"0.54440683\",\n \"text\": \"def put(self, url: str, **kwargs) -> requests.Response:\\n return self.request('PUT', url=url, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed2a5c9e21642afbd9d85628104f614\",\n \"score\": \"0.5437134\",\n \"text\": \"def update(self,request,pk=None):\\n return Response({'http_method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3daddf5b54796388216acc0f2dbeb42b\",\n \"score\": \"0.5435571\",\n \"text\": \"def update(self, **kwargs):\\n headers = {'content-type': 'application/json'}\\n\\n url = self.get_update_url(**kwargs)\\n if not url:\\n raise ValueError('No update url found')\\n\\n response = self._request(self._meta['update_method'], url,\\n data=self.serialize(),\\n headers=headers)\\n\\n self.validate_update_response(response)\\n self._full_url = url\\n self.handle_update_response(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90fee805c38fb9f7ab0a4fcf7d8f5374\",\n \"score\": \"0.5422259\",\n \"text\": \"def _update(self, name, value):\\n return object.__setattr__(self, name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38f05f92880a68b26eddacf480ae032\",\n \"score\": \"0.5417347\",\n \"text\": \"def put(self, request: HTTPRequest) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f081b704f00b36cb15ee90ec47997d\",\n \"score\": \"0.5413176\",\n \"text\": \"def test_update(self):\\n api = gist.API(name=config.USER, token=config.TOKEN)\\n api.all()[0].update(\\\"this is a test\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6372a52a58042015a7f64416ecf06602\",\n \"score\": \"0.54060733\",\n \"text\": \"def update():\\n if not authenticated(session) or not tiene_permiso(session, \\\"configuracion_update\\\"):\\n abort(401)\\n sitio = Configuracion().sitio()\\n return render_template(\\\"configuracion/update.html\\\", sitio=sitio)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef50d3da74e8136389b24266dc3d045\",\n \"score\": \"0.53918874\",\n \"text\": \"def update(cls, package_id, openapi_spec_url):\\n values = {\\n \\\"openapi_spec_url\\\": openapi_spec_url\\n }\\n model.Session.query(cls) \\\\\\n .filter(cls.package_id == package_id) \\\\\\n .update(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8095d46610289cc6457adda9a792ebbe\",\n \"score\": \"0.53907615\",\n \"text\": \"def Club_Setting(self, request):\\n param_to_change = request.data.get('Param_to_change', {})\\n new_value = request.data.get('New_value', {})\\n user = User.objects.get(pk=request.data.get('User_ID', {}))\\n bookclub = BookClub.objects.get(pk=request.data.get('BookClub_ID', {}))\\n\\n if CheckPermissions(user).hasPerm('Set Club', bookclub):\\n if param_to_change == 'Name':\\n setattr(bookclub, \\\"Name\\\", new_value)\\n\\n elif param_to_change == 'Logo':\\n setattr(bookclub, \\\"Profile_Pic\\\", new_value)\\n\\n elif param_to_change == 'Open':\\n setattr(bookclub, \\\"Open\\\", new_value)\\n\\n else:\\n return Response(status=status.HTTP_400_BAD_REQUEST)\\n\\n bookclub.save()\\n serializer = BookClubSearchSerializer(bookclub)\\n return Response(serializer.data, status=status.HTTP_200_OK)\\n\\n return Response(status=status.HTTP_401_UNAUTHORIZED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecf888029eef79a6efb23ce8913ab03\",\n \"score\": \"0.5387636\",\n \"text\": \"def update_name(self, name: str) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7435624177995784463a588db1d49452\",\n \"score\": \"0.5382173\",\n \"text\": \"def put(self, id: int):\\n data = api.payload\\n store = StoreModel.fetch_by_id(id)\\n if store:\\n if u\\\"name\\\" in data[\\\"name\\\"]:\\n store.name = name\\n return {\\\"message\\\": OPERATION_SUCCESSFUL.format(\\\"update\\\")}, 201\\n else:\\n return {\\\"message\\\": STORE_NOT_FOUND}, 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36327643d9831f32baf874a26b4377c0\",\n \"score\": \"0.53772587\",\n \"text\": \"def test_update(self):\\n self.config.update({\\\"age\\\": 901, \\\"starring\\\": \\\"Star Wars\\\"})\\n self.assertEqual(self.config.age, 901)\\n self.assertEqual(self.config.starring, \\\"Star Wars\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c941ed26eef9f5246b67655a7fec9c\",\n \"score\": \"0.5375388\",\n \"text\": \"def put(self, url, **kwargs):\\n return self.request(url, 'PUT', **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0def0846d9e369355ec51e7b34386385\",\n \"score\": \"0.53519154\",\n \"text\": \"def update(self, api_url=None, username=None, password=None):\\n self.manager.update(self, api_url, username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f7f4dad18cf2476615369abf5353d3\",\n \"score\": \"0.53481877\",\n \"text\": \"def set(self, config):\\n for name in config:\\n self[name].set(config[name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e913ec313aba4f82ba50f11da0744e\",\n \"score\": \"0.53302854\",\n \"text\": \"def _put(url, data):\\n return Consul._http('PUT', url, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f918a38664a1dd1641d0b7a4ee8a3a7\",\n \"score\": \"0.53285205\",\n \"text\": \"def update(cls, apiclient, name, value=None, zoneid=None, clusterid=None, storageid=None, domainid=None, accountid=None):\\n\\n cmd = updateConfiguration.updateConfigurationCmd()\\n cmd.name = name\\n cmd.value = value\\n\\n if zoneid:\\n cmd.zoneid = zoneid\\n if clusterid:\\n cmd.clusterid = clusterid\\n if storageid:\\n cmd.storageid = storageid\\n if domainid:\\n cmd.domainid = domainid\\n if accountid:\\n cmd.accountid = accountid\\n apiclient.updateConfiguration(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211471852717e7c964ef4cbc802e3750\",\n \"score\": \"0.5318861\",\n \"text\": \"def updateOCIOConfig(self, ocioConfigPath) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aa17f2394830ad6e3b63a14130246a9\",\n \"score\": \"0.531513\",\n \"text\": \"def update(self, config_dict ):\\n for key, value in config_dict.items():\\n setattr(self, key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca57d6d6faf6242f467207fa3900002\",\n \"score\": \"0.53056204\",\n \"text\": \"def update(self, path, data, **params):\\n return self.query('put', path, data = data, **params).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9676ed3c4a28137950b244e9335fd0e6\",\n \"score\": \"0.5299441\",\n \"text\": \"def update_config(self):\\n\\t\\tcf = open(self.config_file,\\\"a\\\")\\n\\t\\tself.config_parser.write(cf)\\n\\t\\tcf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d040ed4e7685d9ea4a546f57ef1e2c5a\",\n \"score\": \"0.52970964\",\n \"text\": \"def do_PUT(self):\\n self.server_version = ServerHeader\\n self.sys_version = \\\"\\\"\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", \\\"text/html\\\")\\n self.end_headers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019016e66189976b25a9e4ab449ab0c9\",\n \"score\": \"0.5291243\",\n \"text\": \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019016e66189976b25a9e4ab449ab0c9\",\n \"score\": \"0.5291243\",\n \"text\": \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019016e66189976b25a9e4ab449ab0c9\",\n \"score\": \"0.5291243\",\n \"text\": \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8689ad05bbfb3d3df73be3f52a6e68b\",\n \"score\": \"0.528704\",\n \"text\": \"def update(self, *args, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8362ebf09643559cf06dbb56713c406c\",\n \"score\": \"0.52806985\",\n \"text\": \"def put(self, request, pk=None):\\n return(Response({'method':'PUT'}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da6522b7777c0ffb92c0b2ddf44fa47\",\n \"score\": \"0.5275514\",\n \"text\": \"def update(self, webhook_id, name, target_url):\\n req = SparkRequest(self._token)\\n req.set_data('name', name)\\n req.set_data('targetUrl', target_url)\\n req.put('webhooks/' + webhook_id)\\n if req.response_code == 200:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d39ad2feadb1bde02757896a4f837c\",\n \"score\": \"0.52693385\",\n \"text\": \"def put(self , request , pk=None):\\n return Response({'method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111da3dea20c99fb6fc11a83b3f02b18\",\n \"score\": \"0.5268641\",\n \"text\": \"def put(self, bucketlist_id):\\n args = parser.parse_args()\\n name = args.name\\n\\n if not name:\\n return {\\\"message\\\": \\\"Please provide a new name for your bucketlist\\\"}, 400\\n bucketlist = Bucketlist.query.filter(Bucketlist.id == bucketlist_id).first()\\n\\n if not bucketlist:\\n return {\\\"message\\\": \\\"The Buckelist \\\" + bucketlist_id + \\\" provided doesn't exist ....\\\"}, 404\\n\\n bucketlist.name = args.name\\n db.session.add(bucketlist)\\n db.session.commit()\\n return {\\\"message\\\": \\\"Bucket list name updated or changed Successfully\\\"}, 204\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12dcc552599bedaeb60fb836acc6cc0a\",\n \"score\": \"0.52682066\",\n \"text\": \"def update(self, connection=None):\\n if connection is None:\\n connection = self.connection\\n\\n uri = connection.uri(\\\"servers\\\", self.name,\\n parameters=[\\\"group-id=\\\"+self.group_name()])\\n struct = self.marshal()\\n response = connection.put(uri, payload=struct, etag=self.etag)\\n\\n self.name = self._config['server-name']\\n if 'etag' in response.headers:\\n self.etag = response.headers['etag']\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc9b4f74d2fccbaa1e57030f05f346d\",\n \"score\": \"0.52529114\",\n \"text\": \"def PUT(self):\\n try:\\n return dumps(self.update(cherrypy.request.body.read().strip()))\\n except:\\n self.logger.error('Failed to update an mccm from API')\\n return dumps({'results': False, 'message': 'Failed to update an mccm from API'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57f351d3bd512b3acf776e6c72f6820\",\n \"score\": \"0.5243985\",\n \"text\": \"def put(self, *args, **kw):\\r\\n kw['method'] = 'PUT'\\r\\n return self.open(*args, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdc37464c6ff3ba9e2872facb71c2a6\",\n \"score\": \"0.5241009\",\n \"text\": \"def put(self, **params):\\n return self.patch(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d6b3bdafc857272a9c6a956135d255\",\n \"score\": \"0.5237322\",\n \"text\": \"def update(self):\\n body = json.dumps(self.__dict__)\\n coupon = self.client.request_json('PUT', '/coupons/%s' % self.id, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcfeaa77d1df620213ffa76ee79e009\",\n \"score\": \"0.5234599\",\n \"text\": \"def put(self, request, pk=None):\\n return Response({'method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f57ca5367a72ea91d33473cb5699c3\",\n \"score\": \"0.52313036\",\n \"text\": \"def put(self, request, pk=None):\\n\\n return Response({'method': 'put'})\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993676,"cells":{"query_id":{"kind":"string","value":"6a10b8f3ca995c7dceea823c5601d9aa"},"query":{"kind":"string","value":"Used inside transformed functions, recognized by , and the k is automatically supplied. Cannot be called in return position. Use thusly"},"positive_passages":{"kind":"list like","value":[{"docid":"64df00d179c567bff1d455e16d845c99","score":"0.0","text":"def raise_Ef(x, k):\n x._k = k\n return x","title":""}],"string":"[\n {\n \"docid\": \"64df00d179c567bff1d455e16d845c99\",\n \"score\": \"0.0\",\n \"text\": \"def raise_Ef(x, k):\\n x._k = k\\n return x\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2fd55c928bad1935d7a01e1647003a6d","score":"0.70614517","text":"def S(self, k):\n # TODO: 1 lines missing.\n raise NotImplementedError(\"Implement function body\")","title":""},{"docid":"7ab7375f0830bbb72d7dd22ad27d5089","score":"0.6948748","text":"def get(k):","title":""},{"docid":"55a1c9c3b8acbf2949b8ce478d149b1e","score":"0.6424929","text":"def __init__(self, k):\n self.k = k","title":""},{"docid":"0a1d481a2a58717c71279e359701aef9","score":"0.6182266","text":"def __init__(self, k: int):\n self.k = k","title":""},{"docid":"380e3a8538b48e6dc685c74186c7c5da","score":"0.5982535","text":"def __getitem__(self, k: int):\n if not 0 <= k < self.n:\n return IndexError('index k is out of bounds')\n return self.A[k]","title":""},{"docid":"d3029995752276ffee27428bee7f1b68","score":"0.5941593","text":"def kmer(text, i, k):\r\n return text[i:(i+k)]","title":""},{"docid":"f81973334ba2eb1c990d2cd73175f534","score":"0.59378266","text":"def __getitem__(self, k):\n if not 0 <= k < self._n:\n raise IndexError(\"Inavlid Index\")\n return self._A[k]","title":""},{"docid":"5e07300d9c8fd230d5ec20cf6a080724","score":"0.5927689","text":"def kmer(text, i, k):\r\n return text[i : (i + k)]","title":""},{"docid":"f55e334656ac4e4f1233f860172d0937","score":"0.59202003","text":"def __getitem__(self, k):\n if not 0 <= k <= self._len:\n raise IndexError(\"wrong index\")\n return self._A[k]","title":""},{"docid":"6d5f36b496ed5b0557955f18da1aa5a5","score":"0.58515364","text":"def get_k_training_values(self,k):\n\n return self._meta_k_training(self.values,k)","title":""},{"docid":"2d9a4f19ba2ba81f744f4761da1339a0","score":"0.5840473","text":"def __getitem__(self, k):\n if not 0 <= k < self._n:\n raise IndexError('invalid index')\n return self._A[k] # retrieve from array","title":""},{"docid":"bd922e7fbdf09e1d0a50325fcb026145","score":"0.58283967","text":"def _kValue(self, kpos): # pragma: no cover (all our classes override this)\n raise NotImplementedError(\"%s does not implement kValue\"%self.__class__.__name__)","title":""},{"docid":"4d1a57c4033ce06637ad68810305fd3d","score":"0.5802249","text":"def retire_ligne(M,k) :\n M.pop(k)","title":""},{"docid":"25ec237fd5865fb3075f765cc45402a7","score":"0.57935476","text":"def compute_batch(self, k):\n pass","title":""},{"docid":"d06d2d0f9f1c13c13bb81c4882287ae2","score":"0.57858425","text":"def test_kval(q, c):\n k = kval(q, c)\n print k","title":""},{"docid":"4139284b3ab3cc04a86f2cbd4551baa4","score":"0.57775605","text":"def get_n_k(self):\n return 1,2","title":""},{"docid":"2700066b1bcbef30cda80174374eab50","score":"0.57713586","text":"def pop(k, *args):","title":""},{"docid":"c56d1677e2ff4e080d0a17877927186f","score":"0.5743515","text":"def k(self):\r\n return self._m[0:3, 2]","title":""},{"docid":"749c1ce270e3845354ba0119e5838033","score":"0.5702902","text":"def next_batch(self, k):\n pass","title":""},{"docid":"62109d0ffdb3eb6e91acf3cece3907c9","score":"0.56902534","text":"def gen_kmers(seq, k=GLOBAL_K):\n return (seq[i:i + k] for i in range(len(seq) - k + 1))","title":""},{"docid":"3da8b5e61bf6d0a51cac630741a4c42c","score":"0.56844395","text":"def __init__(self, k=2):\r\n self.k = k","title":""},{"docid":"5c16f5a7c8b68a03a23aceb7f920fbf9","score":"0.56737053","text":"def __init__(self, k, model):\n self.k= k\n self.model= model\n self.args= [list() for i in range(k)]","title":""},{"docid":"f66788a8c37b00fcd940b7509d39edbd","score":"0.5640223","text":"def __call__(self, x_i, x_j):\n\t\tK\t= 0\n\n\t\tK\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\n\t\tK\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\n\t\tK\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\n\t\t\n\t\treturn K","title":""},{"docid":"f66788a8c37b00fcd940b7509d39edbd","score":"0.5640223","text":"def __call__(self, x_i, x_j):\n\t\tK\t= 0\n\n\t\tK\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\n\t\tK\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\n\t\tK\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\n\t\t\n\t\treturn K","title":""},{"docid":"f66788a8c37b00fcd940b7509d39edbd","score":"0.5640223","text":"def __call__(self, x_i, x_j):\n\t\tK\t= 0\n\n\t\tK\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\n\t\tK\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\n\t\tK\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\n\t\t\n\t\treturn K","title":""},{"docid":"65d0940bae4b922d2e1b888d9da43f1a","score":"0.56291914","text":"def __getitem__(self, k):\n if 0 <= k < self._n:\n return self._A[k] # retrieve from array\n elif k == -1 and self._n != 0:\n return self._A[-1]\n else:\n raise IndexError('invalid index')","title":""},{"docid":"1787481f9f06678376c4290011dfddf5","score":"0.56281745","text":"def k_slice(k,pspec_data):\n\n zs = n.array(pspec_data.keys())\n k_is = [n.abs(pspec_data[redshift][:,0]-k).argmin() for redshift in zs]\n ks = [pspec_data[redshift][k_i,0] for k_i in k_is]\n power = n.vstack([pspec_data[redshift][k_i,:] for k_i in k_is])\n return zs,power","title":""},{"docid":"977f1e444e63a4d6d7bf83ea27a13cbd","score":"0.562617","text":"def get_mapk(GT, predictions, k):\n\n return metrics.mapk(GT, predictions, k)","title":""},{"docid":"bd719b7be22e601ed3833dd95f399f83","score":"0.56210715","text":"def LT(self, k):\n\n raise NotImplementedError","title":""},{"docid":"70e8602020c9583395ee85d82f563de3","score":"0.561896","text":"def __K1(self, f, t, y, h):\n return f(t, y)","title":""},{"docid":"60202aa9603930fd74260ec78cb148de","score":"0.56135094","text":"def query(k, default=None):","title":""},{"docid":"065bb4b7f0432e35c7802a468b4e50a5","score":"0.5588886","text":"def select(self, k: int) -> Any:\n if k < 0 or k >= len(self):\n raise ValueError(\"argument to select() is invalid: \" + str(k))\n x = self.__select(self.root, k)\n return x.key","title":""},{"docid":"5ee92e395de69f8fc309cf0a7d380b85","score":"0.55650836","text":"def __getitem__(self, k):\n if (0 > k > self._n):\n raise IndexError('invalid index')\n return self._A[k] # get the element from the array","title":""},{"docid":"37c81b2f6c92c1a0216de79a4c01c36d","score":"0.5548808","text":"def __getitem__(self, k):\n #when k is positive run over k items (if possible and list is\n # smaller then k.\n if k >= 0:\n iterNode = self.head\n counter = 0\n while iterNode is not None:\n if counter == k:\n return iterNode.data\n counter += 1\n iterNode = iterNode.next\n\n #if k is negative, run from the end with 2 steps per loop.\n else:\n iterNode = self.tail\n counter = -1\n while iterNode is not None:\n if counter == k:\n return iterNode.data\n #in case the k is odd\n if counter == k + 1:\n #if we are on the edge, we have to raise Error\n if iterNode is self.head:\n break\n #if we are one step from the end' we have to return the\n # head\n if iterNode is self.head.next:\n return self.head.data\n #if none of the above, we found the k'th element\n return iterNode.skip_back.next.data\n counter -= 2\n iterNode = iterNode.skip_back\n #in case we can't find the k'th element within the list, it means\n # the list is smaller then k. so- ERROR\n raise IndexError","title":""},{"docid":"e653b0650f7201bb6b1ef12f4a52cabc","score":"0.5543345","text":"def k(self):\n return self._k","title":""},{"docid":"633c1ab1a62e79896fd67026f9d59286","score":"0.5532629","text":"def get_K(self, t):\n try:\n return self._K\n except AttributeError:\n return self.K(self.P1, self.P2, t)","title":""},{"docid":"fb9dee72e8546db9573963911e8764c7","score":"0.5523119","text":"def select(phi, k):\n assert (0 <= phi.outdim) and (k < phi.outdim)\n\n # optimization: prevent creating an affine transformation\n # if phi is one-dimensional\n if phi.outdim == 1 and k == 0:\n return phi\n\n # otherwise, select the appropriate component\n return amnet.Linear(\n np.eye(1, phi.outdim, k),\n phi\n )","title":""},{"docid":"ae8ad021c2f3338111437544aee67ce6","score":"0.5512552","text":"def kValue(self, *args, **kwargs):\n kpos = galsim.utilities.parse_pos_args(args,kwargs,'kx','ky')\n return self.SBProfile.kValue(kpos)","title":""},{"docid":"34136d9531de28345b25f9682eca9673","score":"0.5501693","text":"def get_key(k):\n return k[0]","title":""},{"docid":"649c927167d329dca75824556390d5b6","score":"0.5496699","text":"def _bucket_getitem(self, j, k):\n raise NotImplemented","title":""},{"docid":"cbfa2b66cf1fdccabc463fc56560b9b5","score":"0.5491472","text":"def get_k_test_values(self,k):\n\n return self._meta_k_test(self.values,k)","title":""},{"docid":"9ecd62ae111edee17b5cf31a04d84ee4","score":"0.548045","text":"def retrieve_data(self, k):\n raise NotImplementedError()","title":""},{"docid":"a949f252851f8292a50e5cf4bc3024b4","score":"0.5472738","text":"def __init__(self, k=0.001):\n self.k = k","title":""},{"docid":"c9af2cfd0f1a02213675b767911a6337","score":"0.5454085","text":"def at_most_k(x, k):\n global csp\n check_engine()\n return csp.at_most_k(x, k)","title":""},{"docid":"11368483580648c5f41abae2a4de7975","score":"0.5450626","text":"def kValue(self, *args, **kwargs):\n kpos = parse_pos_args(args,kwargs,'kx','ky')\n return self._kValue(kpos)","title":""},{"docid":"bdabe49a7bca0c805361e70c4cf0a5b8","score":"0.54485506","text":"def karg(name, value, kargs):\n kargs[name] = value if name not in kargs.keys() else kargs[name]\n return kargs","title":""},{"docid":"e44bab44b96b3ccbd53e3161c35b27ce","score":"0.5438856","text":"def __init__(self, K: int = 1):\n super().__init__()\n self.last_k_vals = None\n self.K = K","title":""},{"docid":"8b30b50ea2ff04133fc8d07682897054","score":"0.5437481","text":"def kapply(self, func, *args, **kwargs) -> 'TupList':\n return TupList(func(k, *args, **kwargs) for k, _ in enumerate(self))","title":""},{"docid":"ca871f719111a2e0fa269deb5895cddd","score":"0.54345703","text":"def __mul__(self, k):\n return StablePose(self.x * k, self.y * k, self.z * k,\n self.__normalize_ang(self.yaw * k))","title":""},{"docid":"5a7f5d34ea96fab2ebd9b57a397ae45f","score":"0.542305","text":"def _meta_k_training(self,array_to_slice,k):\n\n # Only use the real training set\n real_training = array_to_slice[self._k_length:]\n\n if k > self._kfold_size -2 or k < 0:\n err = \"k must be between 0 and {}.\\n\".format(0,self._kfold_size-2)\n raise ValueError(err)\n\n start = real_training[:(k*self._k_length)]\n finish = real_training[((k+1)*self._k_length):]\n\n return np.concatenate((start,finish))","title":""},{"docid":"c39b764a559f5621583a02845533202e","score":"0.5419522","text":"def __getitem__(self, k: Hashable) -> Any:\n k = keySearch(self, k)\n return super().__getitem__(k)","title":""},{"docid":"b0145c73d873acdb2994727752d67311","score":"0.5412345","text":"def LB(self, k):\n\n if self.p == -1:\n self.fillBuffer()\n\n if k == 0:\n return None\n\n if self.p - k < 0:\n return None\n\n i = self.p\n n = 1\n # find k good tokens looking backwards\n while n <= k:\n # skip off-channel tokens\n i = self.skipOffTokenChannelsReverse(i-1) # leave p on valid token\n n += 1\n\n if i < 0:\n return None\n\n return self.tokens[i]","title":""},{"docid":"e90b36f55045b075789a4466165e2781","score":"0.54071194","text":"def get_k_training_features(self,k):\n\n return self._meta_k_training(self.features,k)","title":""},{"docid":"b3ee19d99070f295d03307da0dad8993","score":"0.5405379","text":"def roundTrip_elements_k0(self, m, k):\n if not type(k)==np.ndarray and len(k) == 1 and k[0] == 0:\n raise ValueError(\"k has to be np.array([0]), given %s\" %(str(k)))\n lmin = max(1, m)\n ext_VZ = -1+2*self.ext_flag\n try:\n mieA_k0 = self.MieA_k0\n mieB_k0 = self.MieB_k0\n V_k0 = translation(self.lmax, self.lssmax, m, self.mat3, self.L, k,\n self.ext_flag, 0).trans_ab()\n V_k0 = [V_k0[0][:, np.newaxis, :], V_k0[0][:, np.newaxis,:].transpose(1,2,0),\n V_k0[1][:, np.newaxis, :]]\n if self.forceFlag:\n raise NotImplementedError(\"Force evaluation of zero frequency \"+\n \"is not implemented, yet.\")\n\n V.extend(Vdiff)\n return [mieA_k0[:, lmin-1::, :, :], mieB_k0[:, :, :, lmin-1::], V_k0]\n\n except AttributeError:\n if type(self.mat1)==list or type(self.mat2)==list:\n raise NotImplementedError(\"Evaluation of the zeroth Matsubara frequency\"+\n \" is not yet implemented for stratified spheres\")\n self.MieA_k0 = scattering(self.mat1, self.mat3\n ).mie_k0(lmin, self.lmax, self.r1\n )[:, :, np.newaxis, np.newaxis]\n if not self.ext_flag:\n self.MieB_k0 = scattering(self.mat3, self.mat2\n ).mie_internal_k0(lmin, self.lssmax, self.r2)[:, np.newaxis, np.newaxis,:]\n else:\n self.MieB_k0 = scattering(self.mat2, self.mat3\n ).mie_k0(lmin, self.lssmax, self.r2)[:,np.newaxis, np.newaxis,:]\n mieA_k0 = self.MieA_k0\n mieB_k0 = self.MieB_k0\n V_k0 = translation(self.lmax, self.lssmax, m, self.mat3, self.L, k,\n self.ext_flag, 0).trans_ab()\n V_k0 = [V_k0[0][:, np.newaxis, :], V_k0[0][:, np.newaxis,:].transpose(1,2,0),\n V_k0[1][:, np.newaxis, :]]\n if self.forceFlag:\n raise NotImplementedError(\"Force evaluation of zero frequency \"+\n \"is not implemented, yet.\")\n\n V.extend(Vdiff)\n return [mieA_k0, mieB_k0, V_k0]","title":""},{"docid":"fe007717c3902efe9560c89d68cb351f","score":"0.54037416","text":"def get_k_test_sequences(self,k):\n\n return self._meta_k_test(self.sequences,k)","title":""},{"docid":"d6e71b17b0cca48a88e0a6d2be7f0086","score":"0.5401706","text":"def __K3(self,f, t, y, h, K2):\n a = t + .5*h\n b = y + .5*h*K2\n return f(a, b)","title":""},{"docid":"daee6ed2d061038038ed5c6635e3ed4c","score":"0.53978217","text":"def __K4(self,f, t, y, h, K3):\n a = t + h\n b = y + h*K3\n return f(a, b)","title":""},{"docid":"0fbba957e0ca985a7e2cdcf6ff2c724e","score":"0.5395918","text":"def init_k_step_idx(self, k):\n if k <= 0:\n return\n self.k_step_idx = {}\n i = 0\n for state_name, state in self.other_states.items():\n if state.section <= k:\n self.k_step_idx[state_name] = i\n i += 1","title":""},{"docid":"c63bbf18304ebbbfdf6afdeb47a473a2","score":"0.53890723","text":"def graph(self, k):\n \n return self.MU[k]","title":""},{"docid":"f4e6514a00a92bb54f8b0b74fe3295d1","score":"0.5387237","text":"def rows_sorting_key(k):\n return map(numerical_sorting_key, k)","title":""},{"docid":"2c4932d43814e4ad62812a3da658e4e9","score":"0.53870976","text":"def nnakk(self, kut_ggool):","title":""},{"docid":"82a3e1d927e333462bc5b9100e96d022","score":"0.5383424","text":"def forward(self, q, k, v):\n d_k = k.size(-1) # get the size of the key\n assert q.size(-1) == d_k # otherwise the 2 metrics cannot be multiplied\n\n attn = torch.bmm(q, k.transpose(1, 2)) # (n, l, l), a look up matrix\n attn = attn / math.sqrt(d_k)\n # print(\"attn\", attn)\n attn = torch.exp(attn)\n attn = attn / attn.sum(-1, keepdim=True)\n attn = self.dropout(attn)\n output = torch.bmm(attn, v) # (n, l, d_feature)\n return output","title":""},{"docid":"1f9949c435758fd3c073f2744ede6cb0","score":"0.5373253","text":"def __call__(self, k, a, cosmo=None, *, derivative=False):\n # determine if logarithmic derivative is needed\n if not derivative:\n eval_func = lib.pk2d_eval_multi\n else:\n eval_func = lib.pk2d_der_eval_multi\n\n # handle scale factor extrapolation\n if cosmo is None:\n cosmo = self.__call__._cosmo\n self.psp.extrap_linear_growth = 404 # flag no extrapolation\n else:\n cosmo.compute_growth() # growth factors for extrapolation\n self.psp.extrap_linear_growth = 401 # flag extrapolation\n\n a_use = np.atleast_1d(a).astype(float)\n k_use = np.atleast_1d(k).astype(float)\n lk_use = np.log(k_use)\n\n status = 0\n out = np.zeros([len(a_use), len(k_use)])\n for ia, aa in enumerate(a_use):\n f, status = eval_func(self.psp, lk_use, aa,\n cosmo.cosmo, k_use.size, status)\n\n # Catch scale factor extrapolation bounds error.\n if status == lib.CCL_ERROR_SPLINE_EV:\n raise ValueError(\n \"Pk2D evaluation scale factor is outside of the \"\n \"interpolation range. To extrapolate, pass a Cosmology.\")\n check(status, cosmo)\n out[ia] = f\n\n if np.ndim(k) == 0:\n out = np.squeeze(out, axis=-1)\n if np.ndim(a) == 0:\n out = np.squeeze(out, axis=0)\n return out","title":""},{"docid":"3162dff66285763d9e286e0cc3c498fa","score":"0.53697264","text":"def kmer_list(s,k):\n kmer_list=[]\n p=len(s)\n for a in range(0,p-k+1) :\n kmer_list.append(s[a:a+k])\n return kmer_list","title":""},{"docid":"57817bf8de1b0e040b7715bac0d22431","score":"0.5368048","text":"def _partition_func(self, hist, k):\n if k >= 2:\n hash_k = 2\n else:\n hash_k = k\n hash_key = tuple(sorted(hist)) + (hash_k, )\n \n if hash_key in self._normalization_cache:\n res = self._normalization_cache[hash_key]\n else:\n index_list = [self.vectorizer.hist_and_tag_to_binary((hist, t))\n for t in self._get_tags(k)]\n\n res = np.sum([np.exp(dot_prod(self.weights, v)) for v in index_list])\n self._normalization_cache[hash_key] = res\n return res","title":""},{"docid":"41146da93420ed62cd02160439b46bb9","score":"0.5364209","text":"def __init__(self, k: float = 0.2) -> None:\n self.current_iteration_selected_nodes = {}\n self.current_trees = {}\n self.k = k","title":""},{"docid":"b9bf1af47e55d3ff7b85aade0a89803c","score":"0.5348454","text":"def accept(self, visitor):\n return visitor.visit_keyword_arguments_parameter(self)","title":""},{"docid":"13071f42f2163c1aac7b5b4629eb30d1","score":"0.5346715","text":"def _meta_k_test(self,array_to_slice,k):\n\n # Only use the real training set\n real_training = array_to_slice[self._k_length:]\n\n if k > self._kfold_size -2 or k < 0:\n err = \"k must be between 0 and {}.\\n\".format(0,self._kfold_size-2)\n raise ValueError(err)\n\n return real_training[(k*self._k_length):((k+1)*self._k_length)]","title":""},{"docid":"76b073a37026c9ea50a5f2f8b17b0904","score":"0.5345285","text":"def z(j, k):\n return (j + 0.7* k)**0.75","title":""},{"docid":"525f74a114cbfe589f974667456bc1f1","score":"0.5331689","text":"def generate_line(k,v):\n\n pass","title":""},{"docid":"aceaba21ac45d65d9b6f675ad2263f54","score":"0.53287977","text":"def indexend(k):\n return k","title":""},{"docid":"80a6a05a3c926a8eddba2c22ec2b9c73","score":"0.5325676","text":"def mapk(actual, predicted, k=10):\n\treturn np.mean([apk(a,p,k) for a,p in zip(actual, predicted)])","title":""},{"docid":"e75cff42b68dcdc2aa89e2e937c8aa79","score":"0.5324288","text":"def __getitem__(key):","title":""},{"docid":"e75cff42b68dcdc2aa89e2e937c8aa79","score":"0.5324288","text":"def __getitem__(key):","title":""},{"docid":"7cf28094339ac7c440e5d958f7db19e2","score":"0.53138536","text":"def __K2(self, f, t, y, h, K1):\n a = t + .5*h\n b = y + .5*h*K1\n return f(a, b)","title":""},{"docid":"3ed9faacd2b5ebf2730bb605035406a4","score":"0.5312227","text":"def handle(self, effect, k):\n raise NotImplementedError()","title":""},{"docid":"f4a2cf8eb2b253adb68082594c5ad608","score":"0.5306451","text":"def get_k_training_sequences(self,k):\n\n return self._meta_k_training(self.sequences,k)","title":""},{"docid":"5b75d45a66ef9ebdee860946da0fa54d","score":"0.5304756","text":"def _sample(self, X, K, k, n_samples=1):\n\n raise NotImplementedError(\"Not sure if necessary!\")","title":""},{"docid":"569450be194aa67ff844007fa043a79b","score":"0.5301153","text":"def _get_k_simplices(simplex, k: int = 1) -> Generator:\n d = len(simplex) - 1\n k_simplices = (\n [simplex[indx] for indx in indices] for indices in _k_simplex_index_list(d, k) # type: ignore\n )\n return k_simplices","title":""},{"docid":"33f15ad06707516db6c632fe4ae24c66","score":"0.52866244","text":"def expand_call(kargs):\n\n pass","title":""},{"docid":"c45373496963296b5179de3416a3bfbd","score":"0.5286466","text":"def smile_func(self, K):\n if K < self.f:\n payoff = 'Put'\n else:\n payoff = 'Call'\n \n price = self.option_price(K, payoff=payoff)\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)","title":""},{"docid":"c45373496963296b5179de3416a3bfbd","score":"0.5286466","text":"def smile_func(self, K):\n if K < self.f:\n payoff = 'Put'\n else:\n payoff = 'Call'\n \n price = self.option_price(K, payoff=payoff)\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)","title":""},{"docid":"c45373496963296b5179de3416a3bfbd","score":"0.5286466","text":"def smile_func(self, K):\n if K < self.f:\n payoff = 'Put'\n else:\n payoff = 'Call'\n \n price = self.option_price(K, payoff=payoff)\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)","title":""},{"docid":"c45373496963296b5179de3416a3bfbd","score":"0.5286466","text":"def smile_func(self, K):\n if K < self.f:\n payoff = 'Put'\n else:\n payoff = 'Call'\n \n price = self.option_price(K, payoff=payoff)\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)","title":""},{"docid":"c351a053c9f701c87fa725067c400098","score":"0.52846265","text":"def f(m, q, k):\r\n return array(tuple(fg(m, q, k))).sum()","title":""},{"docid":"0f1fea4e2e32774bef1d8d5f65d7d30a","score":"0.5282002","text":"def r3_anyk(n, j, k):\n return 0","title":""},{"docid":"1f83cb5159eaa5a0986db9b6081343f3","score":"0.525487","text":"def mapk(actual, predicted, k=10):\n # return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])\n res = []\n for a, p in zip(actual, predicted):\n if len(a) <= len(p):\n res.append(apk(a, p, k))\n else:\n for i in range(k, len(a), k):\n res.append(apk(a[i:i+k], p, k))\n return np.mean(res)","title":""},{"docid":"8896bef49b69f25210fd1d9900fcd006","score":"0.5254348","text":"def kleer_slekssun(self):","title":""},{"docid":"918188fa05dbe5930b428d68cc867587","score":"0.52479404","text":"def __call__(self, arg):\n\n result = self * arg\n order_self = max([len(key_self) for key_self in self.keys()])\n order_arg = max([len(key_arg) for key_arg in arg.keys()])\n for r in range(0, min(order_self, order_arg)):\n result = result.trace(order_self - r - 1, order_self - r)\n return result","title":""},{"docid":"e8a516473328a669e746c090f0d89b49","score":"0.5246523","text":"def rec_func(p, k, agps, arps):\n n = len(p)\n if k == n:\n return agps[n - 1]\n if k == 0:\n return arps[n - 1]\n\n return rec_func(p[:-1], k, agps, arps) * (1 - p[n-1]) + rec_func(p[:-1], k - 1, agps, arps) * p[n-1]","title":""},{"docid":"7c49d69344b2cf79dc651d3f0184a3b0","score":"0.52446175","text":"def macqueen(data, k):\n return data[:k]","title":""},{"docid":"c4705991228b41125dc2cadff1848e91","score":"0.52287084","text":"def __div__(self, k):\n return self.__mul__(1.0 / k)","title":""},{"docid":"9b1af9bdeba5e202185b16de26c773ab","score":"0.52270395","text":"def __getitem__(self, key: Any) -> 'Output[Any]':\n return self.apply(lambda v: v[key])","title":""},{"docid":"97793c8ea9084d8f20ff7f33a87d62c7","score":"0.5223092","text":"def kth_from_end(self, k):\n # edges : k < 0 or k is not int\n # k is greater than linked list\n # if k is not type(int) or k < 0:\n # return \"Exception, your k is not a positive integer\"\n outpoint = self.head\n current = self.head\n\n for i in range(k):\n outpoint = outpoint._next\n if outpoint._next is None:\n return \"Exception, k is longer than list\"\n\n while outpoint._next:\n current = current._next\n outpoint = outpoint._next\n\n return current.val","title":""},{"docid":"67698e4ceeb17d92e26bbf0aae413269","score":"0.52193683","text":"def get_k_test_features(self,k):\n\n return self._meta_k_test(self.features,k)","title":""},{"docid":"2eaa42334f1722e2ffbc707b26e9bf0d","score":"0.52172464","text":"def select(self, arms, k=1):","title":""},{"docid":"664422def54782595c2de856c129742b","score":"0.52058893","text":"def keys(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"2b6a7d96053c375207c0aba57c0ba196","score":"0.5197398","text":"def kperform():\n pass","title":""},{"docid":"9cea4a45b643f6f000c4d2183c8f6bd0","score":"0.51928025","text":"def hit_at_k(r, k):\n r = np.array(r)[:k]\n if np.sum(r) > 0:\n return 1.\n else:\n return 0.","title":""},{"docid":"5131a84d1c8cac6f5e5187ad83c6fe53","score":"0.5192546","text":"def keyByKey(records, k):\n L=[]\n for i in range(len(records)):\n L.append((records[i][k],records[i]))\n return L","title":""},{"docid":"cca527f3a2b48e6e6de17e16b77b08a3","score":"0.5189211","text":"def _get_tags(self, k):\n\n return [START] if k < 0 else self.vectorizer.list_available_tags()","title":""}],"string":"[\n {\n \"docid\": \"2fd55c928bad1935d7a01e1647003a6d\",\n \"score\": \"0.70614517\",\n \"text\": \"def S(self, k):\\n # TODO: 1 lines missing.\\n raise NotImplementedError(\\\"Implement function body\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab7375f0830bbb72d7dd22ad27d5089\",\n \"score\": \"0.6948748\",\n \"text\": \"def get(k):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a1c9c3b8acbf2949b8ce478d149b1e\",\n \"score\": \"0.6424929\",\n \"text\": \"def __init__(self, k):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1d481a2a58717c71279e359701aef9\",\n \"score\": \"0.6182266\",\n \"text\": \"def __init__(self, k: int):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380e3a8538b48e6dc685c74186c7c5da\",\n \"score\": \"0.5982535\",\n \"text\": \"def __getitem__(self, k: int):\\n if not 0 <= k < self.n:\\n return IndexError('index k is out of bounds')\\n return self.A[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3029995752276ffee27428bee7f1b68\",\n \"score\": \"0.5941593\",\n \"text\": \"def kmer(text, i, k):\\r\\n return text[i:(i+k)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81973334ba2eb1c990d2cd73175f534\",\n \"score\": \"0.59378266\",\n \"text\": \"def __getitem__(self, k):\\n if not 0 <= k < self._n:\\n raise IndexError(\\\"Inavlid Index\\\")\\n return self._A[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e07300d9c8fd230d5ec20cf6a080724\",\n \"score\": \"0.5927689\",\n \"text\": \"def kmer(text, i, k):\\r\\n return text[i : (i + k)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f55e334656ac4e4f1233f860172d0937\",\n \"score\": \"0.59202003\",\n \"text\": \"def __getitem__(self, k):\\n if not 0 <= k <= self._len:\\n raise IndexError(\\\"wrong index\\\")\\n return self._A[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d5f36b496ed5b0557955f18da1aa5a5\",\n \"score\": \"0.58515364\",\n \"text\": \"def get_k_training_values(self,k):\\n\\n return self._meta_k_training(self.values,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9a4f19ba2ba81f744f4761da1339a0\",\n \"score\": \"0.5840473\",\n \"text\": \"def __getitem__(self, k):\\n if not 0 <= k < self._n:\\n raise IndexError('invalid index')\\n return self._A[k] # retrieve from array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd922e7fbdf09e1d0a50325fcb026145\",\n \"score\": \"0.58283967\",\n \"text\": \"def _kValue(self, kpos): # pragma: no cover (all our classes override this)\\n raise NotImplementedError(\\\"%s does not implement kValue\\\"%self.__class__.__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d1a57c4033ce06637ad68810305fd3d\",\n \"score\": \"0.5802249\",\n \"text\": \"def retire_ligne(M,k) :\\n M.pop(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ec237fd5865fb3075f765cc45402a7\",\n \"score\": \"0.57935476\",\n \"text\": \"def compute_batch(self, k):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06d2d0f9f1c13c13bb81c4882287ae2\",\n \"score\": \"0.57858425\",\n \"text\": \"def test_kval(q, c):\\n k = kval(q, c)\\n print k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4139284b3ab3cc04a86f2cbd4551baa4\",\n \"score\": \"0.57775605\",\n \"text\": \"def get_n_k(self):\\n return 1,2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2700066b1bcbef30cda80174374eab50\",\n \"score\": \"0.57713586\",\n \"text\": \"def pop(k, *args):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56d1677e2ff4e080d0a17877927186f\",\n \"score\": \"0.5743515\",\n \"text\": \"def k(self):\\r\\n return self._m[0:3, 2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749c1ce270e3845354ba0119e5838033\",\n \"score\": \"0.5702902\",\n \"text\": \"def next_batch(self, k):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62109d0ffdb3eb6e91acf3cece3907c9\",\n \"score\": \"0.56902534\",\n \"text\": \"def gen_kmers(seq, k=GLOBAL_K):\\n return (seq[i:i + k] for i in range(len(seq) - k + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da8b5e61bf6d0a51cac630741a4c42c\",\n \"score\": \"0.56844395\",\n \"text\": \"def __init__(self, k=2):\\r\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c16f5a7c8b68a03a23aceb7f920fbf9\",\n \"score\": \"0.56737053\",\n \"text\": \"def __init__(self, k, model):\\n self.k= k\\n self.model= model\\n self.args= [list() for i in range(k)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66788a8c37b00fcd940b7509d39edbd\",\n \"score\": \"0.5640223\",\n \"text\": \"def __call__(self, x_i, x_j):\\n\\t\\tK\\t= 0\\n\\n\\t\\tK\\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\\n\\t\\tK\\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\\n\\t\\tK\\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\\n\\t\\t\\n\\t\\treturn K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66788a8c37b00fcd940b7509d39edbd\",\n \"score\": \"0.5640223\",\n \"text\": \"def __call__(self, x_i, x_j):\\n\\t\\tK\\t= 0\\n\\n\\t\\tK\\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\\n\\t\\tK\\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\\n\\t\\tK\\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\\n\\t\\t\\n\\t\\treturn K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66788a8c37b00fcd940b7509d39edbd\",\n \"score\": \"0.5640223\",\n \"text\": \"def __call__(self, x_i, x_j):\\n\\t\\tK\\t= 0\\n\\n\\t\\tK\\t= K + self.gammas['linear'] * lk(x_i=x_i, x_j=x_j)\\n\\t\\tK\\t= K + self.gammas['gaussian'] * gk(x_i=x_i, x_j=x_j, sigma=self.hyperparameters['gaussian'])\\n\\t\\tK\\t= K + self.gammas['polynomial'] * pk(x_i=x_i, x_j=x_j, q=self.hyperparameters['polynomial'])\\n\\t\\t\\n\\t\\treturn K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d0940bae4b922d2e1b888d9da43f1a\",\n \"score\": \"0.56291914\",\n \"text\": \"def __getitem__(self, k):\\n if 0 <= k < self._n:\\n return self._A[k] # retrieve from array\\n elif k == -1 and self._n != 0:\\n return self._A[-1]\\n else:\\n raise IndexError('invalid index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1787481f9f06678376c4290011dfddf5\",\n \"score\": \"0.56281745\",\n \"text\": \"def k_slice(k,pspec_data):\\n\\n zs = n.array(pspec_data.keys())\\n k_is = [n.abs(pspec_data[redshift][:,0]-k).argmin() for redshift in zs]\\n ks = [pspec_data[redshift][k_i,0] for k_i in k_is]\\n power = n.vstack([pspec_data[redshift][k_i,:] for k_i in k_is])\\n return zs,power\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977f1e444e63a4d6d7bf83ea27a13cbd\",\n \"score\": \"0.562617\",\n \"text\": \"def get_mapk(GT, predictions, k):\\n\\n return metrics.mapk(GT, predictions, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd719b7be22e601ed3833dd95f399f83\",\n \"score\": \"0.56210715\",\n \"text\": \"def LT(self, k):\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e8602020c9583395ee85d82f563de3\",\n \"score\": \"0.561896\",\n \"text\": \"def __K1(self, f, t, y, h):\\n return f(t, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60202aa9603930fd74260ec78cb148de\",\n \"score\": \"0.56135094\",\n \"text\": \"def query(k, default=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065bb4b7f0432e35c7802a468b4e50a5\",\n \"score\": \"0.5588886\",\n \"text\": \"def select(self, k: int) -> Any:\\n if k < 0 or k >= len(self):\\n raise ValueError(\\\"argument to select() is invalid: \\\" + str(k))\\n x = self.__select(self.root, k)\\n return x.key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee92e395de69f8fc309cf0a7d380b85\",\n \"score\": \"0.55650836\",\n \"text\": \"def __getitem__(self, k):\\n if (0 > k > self._n):\\n raise IndexError('invalid index')\\n return self._A[k] # get the element from the array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c81b2f6c92c1a0216de79a4c01c36d\",\n \"score\": \"0.5548808\",\n \"text\": \"def __getitem__(self, k):\\n #when k is positive run over k items (if possible and list is\\n # smaller then k.\\n if k >= 0:\\n iterNode = self.head\\n counter = 0\\n while iterNode is not None:\\n if counter == k:\\n return iterNode.data\\n counter += 1\\n iterNode = iterNode.next\\n\\n #if k is negative, run from the end with 2 steps per loop.\\n else:\\n iterNode = self.tail\\n counter = -1\\n while iterNode is not None:\\n if counter == k:\\n return iterNode.data\\n #in case the k is odd\\n if counter == k + 1:\\n #if we are on the edge, we have to raise Error\\n if iterNode is self.head:\\n break\\n #if we are one step from the end' we have to return the\\n # head\\n if iterNode is self.head.next:\\n return self.head.data\\n #if none of the above, we found the k'th element\\n return iterNode.skip_back.next.data\\n counter -= 2\\n iterNode = iterNode.skip_back\\n #in case we can't find the k'th element within the list, it means\\n # the list is smaller then k. so- ERROR\\n raise IndexError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e653b0650f7201bb6b1ef12f4a52cabc\",\n \"score\": \"0.5543345\",\n \"text\": \"def k(self):\\n return self._k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"633c1ab1a62e79896fd67026f9d59286\",\n \"score\": \"0.5532629\",\n \"text\": \"def get_K(self, t):\\n try:\\n return self._K\\n except AttributeError:\\n return self.K(self.P1, self.P2, t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9dee72e8546db9573963911e8764c7\",\n \"score\": \"0.5523119\",\n \"text\": \"def select(phi, k):\\n assert (0 <= phi.outdim) and (k < phi.outdim)\\n\\n # optimization: prevent creating an affine transformation\\n # if phi is one-dimensional\\n if phi.outdim == 1 and k == 0:\\n return phi\\n\\n # otherwise, select the appropriate component\\n return amnet.Linear(\\n np.eye(1, phi.outdim, k),\\n phi\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8ad021c2f3338111437544aee67ce6\",\n \"score\": \"0.5512552\",\n \"text\": \"def kValue(self, *args, **kwargs):\\n kpos = galsim.utilities.parse_pos_args(args,kwargs,'kx','ky')\\n return self.SBProfile.kValue(kpos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34136d9531de28345b25f9682eca9673\",\n \"score\": \"0.5501693\",\n \"text\": \"def get_key(k):\\n return k[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"649c927167d329dca75824556390d5b6\",\n \"score\": \"0.5496699\",\n \"text\": \"def _bucket_getitem(self, j, k):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbfa2b66cf1fdccabc463fc56560b9b5\",\n \"score\": \"0.5491472\",\n \"text\": \"def get_k_test_values(self,k):\\n\\n return self._meta_k_test(self.values,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ecd62ae111edee17b5cf31a04d84ee4\",\n \"score\": \"0.548045\",\n \"text\": \"def retrieve_data(self, k):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a949f252851f8292a50e5cf4bc3024b4\",\n \"score\": \"0.5472738\",\n \"text\": \"def __init__(self, k=0.001):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9af2cfd0f1a02213675b767911a6337\",\n \"score\": \"0.5454085\",\n \"text\": \"def at_most_k(x, k):\\n global csp\\n check_engine()\\n return csp.at_most_k(x, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11368483580648c5f41abae2a4de7975\",\n \"score\": \"0.5450626\",\n \"text\": \"def kValue(self, *args, **kwargs):\\n kpos = parse_pos_args(args,kwargs,'kx','ky')\\n return self._kValue(kpos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdabe49a7bca0c805361e70c4cf0a5b8\",\n \"score\": \"0.54485506\",\n \"text\": \"def karg(name, value, kargs):\\n kargs[name] = value if name not in kargs.keys() else kargs[name]\\n return kargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44bab44b96b3ccbd53e3161c35b27ce\",\n \"score\": \"0.5438856\",\n \"text\": \"def __init__(self, K: int = 1):\\n super().__init__()\\n self.last_k_vals = None\\n self.K = K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b30b50ea2ff04133fc8d07682897054\",\n \"score\": \"0.5437481\",\n \"text\": \"def kapply(self, func, *args, **kwargs) -> 'TupList':\\n return TupList(func(k, *args, **kwargs) for k, _ in enumerate(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca871f719111a2e0fa269deb5895cddd\",\n \"score\": \"0.54345703\",\n \"text\": \"def __mul__(self, k):\\n return StablePose(self.x * k, self.y * k, self.z * k,\\n self.__normalize_ang(self.yaw * k))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7f5d34ea96fab2ebd9b57a397ae45f\",\n \"score\": \"0.542305\",\n \"text\": \"def _meta_k_training(self,array_to_slice,k):\\n\\n # Only use the real training set\\n real_training = array_to_slice[self._k_length:]\\n\\n if k > self._kfold_size -2 or k < 0:\\n err = \\\"k must be between 0 and {}.\\\\n\\\".format(0,self._kfold_size-2)\\n raise ValueError(err)\\n\\n start = real_training[:(k*self._k_length)]\\n finish = real_training[((k+1)*self._k_length):]\\n\\n return np.concatenate((start,finish))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39b764a559f5621583a02845533202e\",\n \"score\": \"0.5419522\",\n \"text\": \"def __getitem__(self, k: Hashable) -> Any:\\n k = keySearch(self, k)\\n return super().__getitem__(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0145c73d873acdb2994727752d67311\",\n \"score\": \"0.5412345\",\n \"text\": \"def LB(self, k):\\n\\n if self.p == -1:\\n self.fillBuffer()\\n\\n if k == 0:\\n return None\\n\\n if self.p - k < 0:\\n return None\\n\\n i = self.p\\n n = 1\\n # find k good tokens looking backwards\\n while n <= k:\\n # skip off-channel tokens\\n i = self.skipOffTokenChannelsReverse(i-1) # leave p on valid token\\n n += 1\\n\\n if i < 0:\\n return None\\n\\n return self.tokens[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90b36f55045b075789a4466165e2781\",\n \"score\": \"0.54071194\",\n \"text\": \"def get_k_training_features(self,k):\\n\\n return self._meta_k_training(self.features,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ee19d99070f295d03307da0dad8993\",\n \"score\": \"0.5405379\",\n \"text\": \"def roundTrip_elements_k0(self, m, k):\\n if not type(k)==np.ndarray and len(k) == 1 and k[0] == 0:\\n raise ValueError(\\\"k has to be np.array([0]), given %s\\\" %(str(k)))\\n lmin = max(1, m)\\n ext_VZ = -1+2*self.ext_flag\\n try:\\n mieA_k0 = self.MieA_k0\\n mieB_k0 = self.MieB_k0\\n V_k0 = translation(self.lmax, self.lssmax, m, self.mat3, self.L, k,\\n self.ext_flag, 0).trans_ab()\\n V_k0 = [V_k0[0][:, np.newaxis, :], V_k0[0][:, np.newaxis,:].transpose(1,2,0),\\n V_k0[1][:, np.newaxis, :]]\\n if self.forceFlag:\\n raise NotImplementedError(\\\"Force evaluation of zero frequency \\\"+\\n \\\"is not implemented, yet.\\\")\\n\\n V.extend(Vdiff)\\n return [mieA_k0[:, lmin-1::, :, :], mieB_k0[:, :, :, lmin-1::], V_k0]\\n\\n except AttributeError:\\n if type(self.mat1)==list or type(self.mat2)==list:\\n raise NotImplementedError(\\\"Evaluation of the zeroth Matsubara frequency\\\"+\\n \\\" is not yet implemented for stratified spheres\\\")\\n self.MieA_k0 = scattering(self.mat1, self.mat3\\n ).mie_k0(lmin, self.lmax, self.r1\\n )[:, :, np.newaxis, np.newaxis]\\n if not self.ext_flag:\\n self.MieB_k0 = scattering(self.mat3, self.mat2\\n ).mie_internal_k0(lmin, self.lssmax, self.r2)[:, np.newaxis, np.newaxis,:]\\n else:\\n self.MieB_k0 = scattering(self.mat2, self.mat3\\n ).mie_k0(lmin, self.lssmax, self.r2)[:,np.newaxis, np.newaxis,:]\\n mieA_k0 = self.MieA_k0\\n mieB_k0 = self.MieB_k0\\n V_k0 = translation(self.lmax, self.lssmax, m, self.mat3, self.L, k,\\n self.ext_flag, 0).trans_ab()\\n V_k0 = [V_k0[0][:, np.newaxis, :], V_k0[0][:, np.newaxis,:].transpose(1,2,0),\\n V_k0[1][:, np.newaxis, :]]\\n if self.forceFlag:\\n raise NotImplementedError(\\\"Force evaluation of zero frequency \\\"+\\n \\\"is not implemented, yet.\\\")\\n\\n V.extend(Vdiff)\\n return [mieA_k0, mieB_k0, V_k0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe007717c3902efe9560c89d68cb351f\",\n \"score\": \"0.54037416\",\n \"text\": \"def get_k_test_sequences(self,k):\\n\\n return self._meta_k_test(self.sequences,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e71b17b0cca48a88e0a6d2be7f0086\",\n \"score\": \"0.5401706\",\n \"text\": \"def __K3(self,f, t, y, h, K2):\\n a = t + .5*h\\n b = y + .5*h*K2\\n return f(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daee6ed2d061038038ed5c6635e3ed4c\",\n \"score\": \"0.53978217\",\n \"text\": \"def __K4(self,f, t, y, h, K3):\\n a = t + h\\n b = y + h*K3\\n return f(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fbba957e0ca985a7e2cdcf6ff2c724e\",\n \"score\": \"0.5395918\",\n \"text\": \"def init_k_step_idx(self, k):\\n if k <= 0:\\n return\\n self.k_step_idx = {}\\n i = 0\\n for state_name, state in self.other_states.items():\\n if state.section <= k:\\n self.k_step_idx[state_name] = i\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63bbf18304ebbbfdf6afdeb47a473a2\",\n \"score\": \"0.53890723\",\n \"text\": \"def graph(self, k):\\n \\n return self.MU[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e6514a00a92bb54f8b0b74fe3295d1\",\n \"score\": \"0.5387237\",\n \"text\": \"def rows_sorting_key(k):\\n return map(numerical_sorting_key, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4932d43814e4ad62812a3da658e4e9\",\n \"score\": \"0.53870976\",\n \"text\": \"def nnakk(self, kut_ggool):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a3e1d927e333462bc5b9100e96d022\",\n \"score\": \"0.5383424\",\n \"text\": \"def forward(self, q, k, v):\\n d_k = k.size(-1) # get the size of the key\\n assert q.size(-1) == d_k # otherwise the 2 metrics cannot be multiplied\\n\\n attn = torch.bmm(q, k.transpose(1, 2)) # (n, l, l), a look up matrix\\n attn = attn / math.sqrt(d_k)\\n # print(\\\"attn\\\", attn)\\n attn = torch.exp(attn)\\n attn = attn / attn.sum(-1, keepdim=True)\\n attn = self.dropout(attn)\\n output = torch.bmm(attn, v) # (n, l, d_feature)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9949c435758fd3c073f2744ede6cb0\",\n \"score\": \"0.5373253\",\n \"text\": \"def __call__(self, k, a, cosmo=None, *, derivative=False):\\n # determine if logarithmic derivative is needed\\n if not derivative:\\n eval_func = lib.pk2d_eval_multi\\n else:\\n eval_func = lib.pk2d_der_eval_multi\\n\\n # handle scale factor extrapolation\\n if cosmo is None:\\n cosmo = self.__call__._cosmo\\n self.psp.extrap_linear_growth = 404 # flag no extrapolation\\n else:\\n cosmo.compute_growth() # growth factors for extrapolation\\n self.psp.extrap_linear_growth = 401 # flag extrapolation\\n\\n a_use = np.atleast_1d(a).astype(float)\\n k_use = np.atleast_1d(k).astype(float)\\n lk_use = np.log(k_use)\\n\\n status = 0\\n out = np.zeros([len(a_use), len(k_use)])\\n for ia, aa in enumerate(a_use):\\n f, status = eval_func(self.psp, lk_use, aa,\\n cosmo.cosmo, k_use.size, status)\\n\\n # Catch scale factor extrapolation bounds error.\\n if status == lib.CCL_ERROR_SPLINE_EV:\\n raise ValueError(\\n \\\"Pk2D evaluation scale factor is outside of the \\\"\\n \\\"interpolation range. To extrapolate, pass a Cosmology.\\\")\\n check(status, cosmo)\\n out[ia] = f\\n\\n if np.ndim(k) == 0:\\n out = np.squeeze(out, axis=-1)\\n if np.ndim(a) == 0:\\n out = np.squeeze(out, axis=0)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3162dff66285763d9e286e0cc3c498fa\",\n \"score\": \"0.53697264\",\n \"text\": \"def kmer_list(s,k):\\n kmer_list=[]\\n p=len(s)\\n for a in range(0,p-k+1) :\\n kmer_list.append(s[a:a+k])\\n return kmer_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57817bf8de1b0e040b7715bac0d22431\",\n \"score\": \"0.5368048\",\n \"text\": \"def _partition_func(self, hist, k):\\n if k >= 2:\\n hash_k = 2\\n else:\\n hash_k = k\\n hash_key = tuple(sorted(hist)) + (hash_k, )\\n \\n if hash_key in self._normalization_cache:\\n res = self._normalization_cache[hash_key]\\n else:\\n index_list = [self.vectorizer.hist_and_tag_to_binary((hist, t))\\n for t in self._get_tags(k)]\\n\\n res = np.sum([np.exp(dot_prod(self.weights, v)) for v in index_list])\\n self._normalization_cache[hash_key] = res\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41146da93420ed62cd02160439b46bb9\",\n \"score\": \"0.5364209\",\n \"text\": \"def __init__(self, k: float = 0.2) -> None:\\n self.current_iteration_selected_nodes = {}\\n self.current_trees = {}\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bf1af47e55d3ff7b85aade0a89803c\",\n \"score\": \"0.5348454\",\n \"text\": \"def accept(self, visitor):\\n return visitor.visit_keyword_arguments_parameter(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13071f42f2163c1aac7b5b4629eb30d1\",\n \"score\": \"0.5346715\",\n \"text\": \"def _meta_k_test(self,array_to_slice,k):\\n\\n # Only use the real training set\\n real_training = array_to_slice[self._k_length:]\\n\\n if k > self._kfold_size -2 or k < 0:\\n err = \\\"k must be between 0 and {}.\\\\n\\\".format(0,self._kfold_size-2)\\n raise ValueError(err)\\n\\n return real_training[(k*self._k_length):((k+1)*self._k_length)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b073a37026c9ea50a5f2f8b17b0904\",\n \"score\": \"0.5345285\",\n \"text\": \"def z(j, k):\\n return (j + 0.7* k)**0.75\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525f74a114cbfe589f974667456bc1f1\",\n \"score\": \"0.5331689\",\n \"text\": \"def generate_line(k,v):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aceaba21ac45d65d9b6f675ad2263f54\",\n \"score\": \"0.53287977\",\n \"text\": \"def indexend(k):\\n return k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80a6a05a3c926a8eddba2c22ec2b9c73\",\n \"score\": \"0.5325676\",\n \"text\": \"def mapk(actual, predicted, k=10):\\n\\treturn np.mean([apk(a,p,k) for a,p in zip(actual, predicted)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75cff42b68dcdc2aa89e2e937c8aa79\",\n \"score\": \"0.5324288\",\n \"text\": \"def __getitem__(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75cff42b68dcdc2aa89e2e937c8aa79\",\n \"score\": \"0.5324288\",\n \"text\": \"def __getitem__(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf28094339ac7c440e5d958f7db19e2\",\n \"score\": \"0.53138536\",\n \"text\": \"def __K2(self, f, t, y, h, K1):\\n a = t + .5*h\\n b = y + .5*h*K1\\n return f(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed9faacd2b5ebf2730bb605035406a4\",\n \"score\": \"0.5312227\",\n \"text\": \"def handle(self, effect, k):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a2cf8eb2b253adb68082594c5ad608\",\n \"score\": \"0.5306451\",\n \"text\": \"def get_k_training_sequences(self,k):\\n\\n return self._meta_k_training(self.sequences,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b75d45a66ef9ebdee860946da0fa54d\",\n \"score\": \"0.5304756\",\n \"text\": \"def _sample(self, X, K, k, n_samples=1):\\n\\n raise NotImplementedError(\\\"Not sure if necessary!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569450be194aa67ff844007fa043a79b\",\n \"score\": \"0.5301153\",\n \"text\": \"def _get_k_simplices(simplex, k: int = 1) -> Generator:\\n d = len(simplex) - 1\\n k_simplices = (\\n [simplex[indx] for indx in indices] for indices in _k_simplex_index_list(d, k) # type: ignore\\n )\\n return k_simplices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f15ad06707516db6c632fe4ae24c66\",\n \"score\": \"0.52866244\",\n \"text\": \"def expand_call(kargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45373496963296b5179de3416a3bfbd\",\n \"score\": \"0.5286466\",\n \"text\": \"def smile_func(self, K):\\n if K < self.f:\\n payoff = 'Put'\\n else:\\n payoff = 'Call'\\n \\n price = self.option_price(K, payoff=payoff)\\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45373496963296b5179de3416a3bfbd\",\n \"score\": \"0.5286466\",\n \"text\": \"def smile_func(self, K):\\n if K < self.f:\\n payoff = 'Put'\\n else:\\n payoff = 'Call'\\n \\n price = self.option_price(K, payoff=payoff)\\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45373496963296b5179de3416a3bfbd\",\n \"score\": \"0.5286466\",\n \"text\": \"def smile_func(self, K):\\n if K < self.f:\\n payoff = 'Put'\\n else:\\n payoff = 'Call'\\n \\n price = self.option_price(K, payoff=payoff)\\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45373496963296b5179de3416a3bfbd\",\n \"score\": \"0.5286466\",\n \"text\": \"def smile_func(self, K):\\n if K < self.f:\\n payoff = 'Put'\\n else:\\n payoff = 'Call'\\n \\n price = self.option_price(K, payoff=payoff)\\n return self.IV.vol_from_price(price, self.f, K, self.T_expiry, payoff=payoff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c351a053c9f701c87fa725067c400098\",\n \"score\": \"0.52846265\",\n \"text\": \"def f(m, q, k):\\r\\n return array(tuple(fg(m, q, k))).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1fea4e2e32774bef1d8d5f65d7d30a\",\n \"score\": \"0.5282002\",\n \"text\": \"def r3_anyk(n, j, k):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f83cb5159eaa5a0986db9b6081343f3\",\n \"score\": \"0.525487\",\n \"text\": \"def mapk(actual, predicted, k=10):\\n # return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])\\n res = []\\n for a, p in zip(actual, predicted):\\n if len(a) <= len(p):\\n res.append(apk(a, p, k))\\n else:\\n for i in range(k, len(a), k):\\n res.append(apk(a[i:i+k], p, k))\\n return np.mean(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8896bef49b69f25210fd1d9900fcd006\",\n \"score\": \"0.5254348\",\n \"text\": \"def kleer_slekssun(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918188fa05dbe5930b428d68cc867587\",\n \"score\": \"0.52479404\",\n \"text\": \"def __call__(self, arg):\\n\\n result = self * arg\\n order_self = max([len(key_self) for key_self in self.keys()])\\n order_arg = max([len(key_arg) for key_arg in arg.keys()])\\n for r in range(0, min(order_self, order_arg)):\\n result = result.trace(order_self - r - 1, order_self - r)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a516473328a669e746c090f0d89b49\",\n \"score\": \"0.5246523\",\n \"text\": \"def rec_func(p, k, agps, arps):\\n n = len(p)\\n if k == n:\\n return agps[n - 1]\\n if k == 0:\\n return arps[n - 1]\\n\\n return rec_func(p[:-1], k, agps, arps) * (1 - p[n-1]) + rec_func(p[:-1], k - 1, agps, arps) * p[n-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c49d69344b2cf79dc651d3f0184a3b0\",\n \"score\": \"0.52446175\",\n \"text\": \"def macqueen(data, k):\\n return data[:k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4705991228b41125dc2cadff1848e91\",\n \"score\": \"0.52287084\",\n \"text\": \"def __div__(self, k):\\n return self.__mul__(1.0 / k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1af9bdeba5e202185b16de26c773ab\",\n \"score\": \"0.52270395\",\n \"text\": \"def __getitem__(self, key: Any) -> 'Output[Any]':\\n return self.apply(lambda v: v[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97793c8ea9084d8f20ff7f33a87d62c7\",\n \"score\": \"0.5223092\",\n \"text\": \"def kth_from_end(self, k):\\n # edges : k < 0 or k is not int\\n # k is greater than linked list\\n # if k is not type(int) or k < 0:\\n # return \\\"Exception, your k is not a positive integer\\\"\\n outpoint = self.head\\n current = self.head\\n\\n for i in range(k):\\n outpoint = outpoint._next\\n if outpoint._next is None:\\n return \\\"Exception, k is longer than list\\\"\\n\\n while outpoint._next:\\n current = current._next\\n outpoint = outpoint._next\\n\\n return current.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67698e4ceeb17d92e26bbf0aae413269\",\n \"score\": \"0.52193683\",\n \"text\": \"def get_k_test_features(self,k):\\n\\n return self._meta_k_test(self.features,k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eaa42334f1722e2ffbc707b26e9bf0d\",\n \"score\": \"0.52172464\",\n \"text\": \"def select(self, arms, k=1):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664422def54782595c2de856c129742b\",\n \"score\": \"0.52058893\",\n \"text\": \"def keys(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6a7d96053c375207c0aba57c0ba196\",\n \"score\": \"0.5197398\",\n \"text\": \"def kperform():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cea4a45b643f6f000c4d2183c8f6bd0\",\n \"score\": \"0.51928025\",\n \"text\": \"def hit_at_k(r, k):\\n r = np.array(r)[:k]\\n if np.sum(r) > 0:\\n return 1.\\n else:\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5131a84d1c8cac6f5e5187ad83c6fe53\",\n \"score\": \"0.5192546\",\n \"text\": \"def keyByKey(records, k):\\n L=[]\\n for i in range(len(records)):\\n L.append((records[i][k],records[i]))\\n return L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca527f3a2b48e6e6de17e16b77b08a3\",\n \"score\": \"0.5189211\",\n \"text\": \"def _get_tags(self, k):\\n\\n return [START] if k < 0 else self.vectorizer.list_available_tags()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993677,"cells":{"query_id":{"kind":"string","value":"f588c36eb70338466bac35b9f64a9027"},"query":{"kind":"string","value":"Sets the value at a given indentation level."},"positive_passages":{"kind":"list like","value":[{"docid":"95ad4bcc077bf144a6dc4e3781251024","score":"0.0","text":"def __setitem__(self, index, value):\n if index < 0:\n raise ValueError\n while len(self.all) <= index:\n self.all.append(None)\n self.all[index+1:] = [value]","title":""}],"string":"[\n {\n \"docid\": \"95ad4bcc077bf144a6dc4e3781251024\",\n \"score\": \"0.0\",\n \"text\": \"def __setitem__(self, index, value):\\n if index < 0:\\n raise ValueError\\n while len(self.all) <= index:\\n self.all.append(None)\\n self.all[index+1:] = [value]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ad542c91c2251023fd1a5f6148c37cc5","score":"0.6587817","text":"def indent(self, value):\n self._indent += value","title":""},{"docid":"8c3976f43ef81edb18565c4fc254a835","score":"0.6459699","text":"def level(self, value):\n self._level = value","title":""},{"docid":"2f09e80d61fbf4705c9af22250c88b2e","score":"0.6370632","text":"def _set_level(self, value=None):\r\n if value is None and hasattr(self, '_level'):\r\n del self._level\r\n else:\r\n self._level = int(value)","title":""},{"docid":"3cb9e684a44889ef8b0d219d2cdb9f47","score":"0.6208918","text":"def set_level(self, lvl):\n self.level = lvl","title":""},{"docid":"06afac53aa36d77418e0560604616848","score":"0.6114194","text":"def set_level(self, level: int):\n self.level = level","title":""},{"docid":"2c44f912ce60b3fde1b5aa209eebe7f2","score":"0.59482896","text":"def indented(self, indented):\n\n self.container['indented'] = indented","title":""},{"docid":"d63360cc13586b26e2623eeff8c2ce47","score":"0.59054327","text":"def set_level(self, level):\n self._level = level\n return","title":""},{"docid":"6b2dd5404a65768765c821327acbcdf2","score":"0.57822526","text":"def indent(self,indent=1):\n\n self.indlev+=indent\n if self.indlev<0:\n self.indlev=0\n return self","title":""},{"docid":"320618635ccd141c3b3cdb28b99e844c","score":"0.5737219","text":"async def set_level(self, level: int) -> None:\n await self.set_state(level=level)","title":""},{"docid":"0a4fcf3dc280c9d263d45acdaf91e9e2","score":"0.56734335","text":"def set_levels(self, recursion_level=0):\n raise NotImplementedError()","title":""},{"docid":"3444d9e31df614bb25337e2a1dc860df","score":"0.5630703","text":"def level(self, level):\n\n self._level = level","title":""},{"docid":"a95af60fd89da8b87018a36d3b30fe33","score":"0.5464114","text":"def set_level(self, level):\n self.max_level = level","title":""},{"docid":"2138007c95ec2d05dcd23919db740e14","score":"0.54403096","text":"def set_value(transaction, value):\r\n transaction['value'] = value","title":""},{"docid":"6d98fc2efb8a7facaf96572b87b3b930","score":"0.5429109","text":"def h_setlevel(self):\n # TODO: implement setlevel heuristic\n # raise NotImplementedError","title":""},{"docid":"7f5f13d5d9592d1a8958f9391807a28b","score":"0.53935736","text":"def increase_indent(self):\n self._indent += 1","title":""},{"docid":"4371c83714f696facc172ac20d5f9582","score":"0.5390408","text":"def setLevel(self, level):\n self.level_thr = level\n self.level_thr_ind = self.levels.index(level)","title":""},{"docid":"c88dc0481812e986e6ac884d51db985f","score":"0.53661907","text":"def setValue(self, v):\n self.setPos(v)","title":""},{"docid":"669eb53f7a5f7785201f9165eae5c826","score":"0.5364355","text":"def indent(self):\n self.indent_amount += 4","title":""},{"docid":"9fd6f1962c233c578d8d308e85854944","score":"0.5351004","text":"def indent(self):\r\n self.indent_amount += self.INDENT_STEP","title":""},{"docid":"b2ed13b39b4c94dd20be5c7046da8a13","score":"0.532767","text":"def increaseindentlevel_future(self):\n if self.unindented > 0:\n self.unindented -= 1\n elif self.indentincrease_future:\n self.indentlevel = self.indentlevel + 1\n self.indentincrease_future = False","title":""},{"docid":"c67fc8e1f7a03cf702bce2ed24410079","score":"0.528773","text":"def indent(self, indent: int) -> None:\n self._indent = indent\n self._indent_str = \" \" * indent","title":""},{"docid":"c8a25f8a04bd547c3ad5bf5e21dbd0df","score":"0.52797306","text":"def set_value(self, value):\n self.setCurrentText(str(value))","title":""},{"docid":"46091f1a3cd1ae664f2a03bf1e8a8878","score":"0.5272158","text":"def SetValue(self, value): \n\n self.value = value","title":""},{"docid":"e154053943517750e7cfd251d852a8d4","score":"0.5256579","text":"def setFirstLevel(self, firstLevel): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"801c3c3bfb1f6cd5cec8fb30dabd2ed8","score":"0.52565074","text":"def depth(self, val):\n self.__depth = val","title":""},{"docid":"d21b03a8cf2821d7bf71258c81c36d45","score":"0.5245829","text":"def _indent(self, elem, level=0):\n i = \"\\n\" + level * \" \"\n child = None\n if len(elem):\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n for child in elem:\n self._indent(child, level+1)\n # This seeks for the las child processed in for, is not a code\n # indentation error\n if child is not None and (not child.tail) or (not child.tail.strip()):\n child.tail = i\n else:\n if level is not None and (not elem.tail or not elem.tail.strip()):\n elem.tail = i","title":""},{"docid":"df71180de2ab09d32920e3d163969e09","score":"0.52355725","text":"def set(self, value):\n self.value = self.fit_n_round(value)\n\n self.entry.delete(0, tk.END)\n self.entry.insert(0, self.value)","title":""},{"docid":"b5d9813a5d3e495bff0b77d7470fef43","score":"0.5228039","text":"def change_indent(self,count=None,spaces=\"\"):\n if count:\n self.indent = count\n self.indent_str = count * \" \"\n else:\n self.indent_str = spaces\n self.indent = len(spaces)","title":""},{"docid":"5061599256d99424cc74904a70d4bc6d","score":"0.5194992","text":"def changeLevel(self):\n if self.isCorrect:\n self.initialLevel = self.initialLevel + 1 if self.initialLevel < 12 else self.initialLevel\n else:\n self.initialLevel = self.firstNumber if self.initialLevel > 1 else self.initialLevel","title":""},{"docid":"b1d4566b5982fc9e58e6b11f3ffb63a5","score":"0.5181596","text":"def set(self, value):\n self.block.__class__.field.write_to(self.block, value)","title":""},{"docid":"3606a345f7108d5808cd7e2546cead2b","score":"0.5177938","text":"def _inc(self) -> None:\n self._indent += 4","title":""},{"docid":"e76a0f3dfb5755ac9e514623c9f5105f","score":"0.5168259","text":"def _bump_level(self, level, line):\n if 0 >= level:\n return line\n if not self._is_heading(line):\n return line\n\n # produce warning if the heading level is too deep\n #\n current_level = self._get_heading_level(line)\n if 6 < (current_level + level):\n sys.stderr.write(\n \"Warning: Heading level is increased beyond 6 for line:\\n\")\n sys.stderr.write(self._shorten_line(line) + \"\\n\")\n\n # adjust the heading level\n #\n prefix = '#' * level\n result = prefix + line\n return result","title":""},{"docid":"57f5c9d03d1ec3ec24fbfc161648e39b","score":"0.51396716","text":"def indent(element, context):\n\tcontext.set(\"indent\", 1 + (context.get(\"indent\") or 0))","title":""},{"docid":"7c11d4cb6450cba7edf0dcbb6490f330","score":"0.5122665","text":"def indent_factor(self):\n return 2","title":""},{"docid":"1b6fb729b3b1bd6915c76101d5d7ff3e","score":"0.5122029","text":"def set_level(self):\n self._visa.write(f\"TRIGGER:{self._designation}:SETLEVEL\")","title":""},{"docid":"48e02251714eebac6d69fa1a146e633c","score":"0.5120087","text":"def set_nested_key(mapping, key_path, val, level=None):\n assert key_path\n\n if level is None:\n # This should work for dict and most basic structures\n level = type(mapping)\n\n for key in key_path[:-1]:\n try:\n mapping = mapping[key]\n except KeyError:\n new_level = level()\n mapping[key] = new_level\n mapping = new_level\n\n mapping[key_path[-1]] = val","title":""},{"docid":"b5b17faf1d3d1e3553ce61a71b9ac4c7","score":"0.51079977","text":"def set_value(self, value):\n pass","title":""},{"docid":"e99e12cd92075567241d2f07743f60af","score":"0.51004833","text":"def SetLevel(vLevel='ERROR'):\n FCLogger.level = logging.NOTSET\n FCLogger.parent.level = logging.DEBUG # fc.log always contains DEBUG log\n\n if isinstance(vLevel, str):\n if vLevel == '5' or vLevel.upper() == 'CRITICAL':\n FCLogger.level = logging.CRITICAL\n\n elif vLevel == '4' or vLevel.upper() == 'ERROR':\n FCLogger.level = logging.ERROR\n\n elif vLevel == '3' or vLevel.upper() == 'WARNING':\n FCLogger.level = logging.WARNING\n\n elif vLevel == '2' or vLevel.upper() == 'INFO':\n FCLogger.level = logging.INFO\n\n elif vLevel == '1' or vLevel.upper() == 'DEBUG':\n FCLogger.level = logging.DEBUG","title":""},{"docid":"4e8a4ddb5079bad9c87c08397bf5452e","score":"0.50986934","text":"def set(self, value):\n self.state.log_stack[-1].append( (self, self.value) )\n return self.raw_set(value)","title":""},{"docid":"4035b539511043813f29422af20bb368","score":"0.5098117","text":"def set(self, value):\n self.block.field = value","title":""},{"docid":"5c4ce8d796884e807f996429d74722f2","score":"0.5083594","text":"def set_value(self, value):\r\n\r\n self.value = value","title":""},{"docid":"b6119eef856210a580488243d28e5fb8","score":"0.5081345","text":"def set_parameter(levels, value):\n sublevel = _config\n for level in levels[:-1]:\n if level not in sublevel:\n sublevel[level] = {}\n sublevel = sublevel[level]\n sublevel[levels[-1]] = value","title":""},{"docid":"60eeef0805fe1222ea0a0f6b7ed8952a","score":"0.5062511","text":"def set(self, value: Value) -> None:\n ...","title":""},{"docid":"1d9dc4e85f1856bc8fa40895cc83a5c7","score":"0.5061161","text":"def set_value(self, value):\n self.setValue(int(value))","title":""},{"docid":"4e85fc06bed00dc7f0e3dbc0e51be74d","score":"0.5049786","text":"def set_val(self, k, a):\n k += self.n - 1\n self.dat[k] = a\n while k > 0:\n k = (k - 1) // 2 # parent\n self.dat[k] = self.op(self.dat[k * 2 + 1], self.dat[k * 2 + 2])","title":""},{"docid":"4e85fc06bed00dc7f0e3dbc0e51be74d","score":"0.5049786","text":"def set_val(self, k, a):\n k += self.n - 1\n self.dat[k] = a\n while k > 0:\n k = (k - 1) // 2 # parent\n self.dat[k] = self.op(self.dat[k * 2 + 1], self.dat[k * 2 + 2])","title":""},{"docid":"faf7dc9f6c10623186db2f85a65bd991","score":"0.5045666","text":"def change_value (value):\r\n print(\"Inside, value is:\", value)\r\n value = 1\r\n print(\"Inside, value is changed to:\", value)","title":""},{"docid":"81f5478c1216013a764d43cb6d29816f","score":"0.50285876","text":"def indent_level(self) -> int:\n return self.__indent_level","title":""},{"docid":"2cdb37be2794f2315b8011754becd608","score":"0.5027473","text":"def set_lvl(self, lvl):\n if lvl < 0:\n raise ArgumentOutOfRangeError(\"lvl < 0\")\n\n response = self.__put('user', {'stats.lvl': lvl, 'stats.exp': 0})\n response.raise_for_status()\n return response.json()['data']['stats']['lvl']","title":""},{"docid":"140dbc0ac7cdb09887288d9aaf51953d","score":"0.50225365","text":"def SetValue(self, value):\n self.__ety.SetValue(value)","title":""},{"docid":"ad3365852ee215f37f5733145baebb3d","score":"0.50176716","text":"def var_at_level(self, level):","title":""},{"docid":"964ed08b3766317d5a5f2d34bd0974d3","score":"0.4995006","text":"def label_indent(self, indent):\n self._label_indent = indent","title":""},{"docid":"360a430e26e82242ff183a0d78609af2","score":"0.498991","text":"def set(self, path, value):","title":""},{"docid":"a24167e7d2735d7fe0ebfab09037ad6f","score":"0.49887198","text":"def evt_set_level(self, event: tk.Event) -> None:\n level = BOX_LEVELS[self.level_selector.current()]\n self.pipe.send(('level', level))","title":""},{"docid":"cd3f3864b10b9e5c5a38819057a47066","score":"0.49805024","text":"def set_value(self, value):\n self.matrix[self.current_item]['value'] = value","title":""},{"docid":"7e6ecb94fc50198ca3c60d385ff31243","score":"0.49762678","text":"def setDepth(self, depth):\n pass","title":""},{"docid":"3e91f3e2cd21e34e92a84b7d44537c9b","score":"0.49683082","text":"def set_value(name, value):","title":""},{"docid":"bb3b690ec621939fbd1336a2b8b42787","score":"0.49615255","text":"def indent(self, indent=\" \"):\r\n import ly.etreeutil\r\n ly.etreeutil.indent(self.root, indent)","title":""},{"docid":"ff1b2bb3a669cc11914e829c91737f62","score":"0.4951376","text":"def setLevel(self, level):\n\t\tif isinstance(level, int):\n\t\t\tself.logger.setLevel(level)\n\t\t\treturn\n\n\t\t# level en tant que string\n\t\tlevel = level.lower()\n\t\tif level == \"debug\":\n\t\t\tself.logger.setLevel(logging.DEBUG)\n\n\t\telif level == \"info\":\n\t\t\tself.logger.setLevel(logging.INFO)\n\n\t\telif level == \"warning\" or level == \"warning\":\n\t\t\tself.logger.setLevel(logging.WARN)\n\n\t\telif level == \"error\":\n\t\t\tself.logger.setLevel(logging.ERROR)\n\n\t\telse:\n\t\t\t# par défaut\n\t\t\tself.logger.setLevel(logging.INFO)","title":""},{"docid":"8c6d0647e35cc9f2023b9532eac6b8ff","score":"0.49465317","text":"def set_debug_level(self, level=1):\n ret = self.soapcomm(\"SetDebugLevel\", option=level)\n return ret","title":""},{"docid":"4e47a05db51fb70620939a5bba1c3029","score":"0.49286085","text":"def set_value(self, pymux, value):\n try:\n value = int(value)\n if value < 0:\n raise ValueError\n except ValueError:\n raise SetOptionError('Expecting an integer.')\n else:\n setattr(pymux, self.attribute_name, value)","title":""},{"docid":"5609d9b283f3b3854eb4134657259d11","score":"0.4919288","text":"def setValue(*args, **kwargs):\n \n pass","title":""},{"docid":"4017bc145df0e3a981748d27a17fa4ec","score":"0.4911414","text":"def set_root_value(root, new_value):\n\n root[0] = new_value","title":""},{"docid":"a9085cdbd27223f6202a7bfb32f6a11e","score":"0.49000582","text":"def setDepth(self, depth):\n self.__depth = depth","title":""},{"docid":"d93456ef4a9bd37cd6fec76f4e8f4553","score":"0.48883435","text":"def _dec(self) -> None:\n self._indent -= 4","title":""},{"docid":"24143c84721dc0aa85eb46ac2a1ed517","score":"0.48853365","text":"def set_value(self, value):\n self.value = value","title":""},{"docid":"24143c84721dc0aa85eb46ac2a1ed517","score":"0.48853365","text":"def set_value(self, value):\n self.value = value","title":""},{"docid":"82b582d5abd535a2d47c911613546500","score":"0.48733425","text":"def setValue(self,value):\n v = float(value)\n self.value = float(v)\n self.text = self.getText(float(v))","title":""},{"docid":"c7dc112a99d5cc17355e9eb79d43ba9b","score":"0.48677275","text":"def set_val(self, **kwargs) -> None:\n pass","title":""},{"docid":"3e77a4903bba7bed2190d13494e7eb26","score":"0.48665637","text":"def __setitem__(self, item, value):\n self.parser.set(self.section, item, value)","title":""},{"docid":"c9ccd7574f0b1c3f4c614d11d6a49903","score":"0.48625284","text":"def setLevel(self, level):\n self._JavaProxy__javaObject.setLevelSelection(level)\n return","title":""},{"docid":"adf0fefd78ebcd5f67173429a429f3ad","score":"0.48476654","text":"def update_value(self, value):\n self._value = value\n if self.parent_exists:\n self.parent.__update_value()","title":""},{"docid":"5d4d69fcb46757b2ac31ca5e565e9e2a","score":"0.4841033","text":"def set_level(level, root=True):\n name = None if root else _module_name()\n _get(name).setLevel(level)","title":""},{"docid":"934868ea061f82947fecf6e0e1632d47","score":"0.48326454","text":"def test_02_setlevel(self):\n msg = udocker.Msg(5)\n self._verify_descriptors(msg)\n self.assertEqual(msg.level, 5)\n msg.nullfp.close()\n msg = udocker.Msg(0)\n msg.setlevel(7)\n self._verify_descriptors(msg)\n self.assertEqual(msg.level, 7)\n msg.nullfp.close()","title":""},{"docid":"5e3e014e649014f8ed44a3c4c9d27bec","score":"0.4830314","text":"def set_value(self, etree, theory):\n setattr(theory, self.__name__, self.get_value(etree, theory))","title":""},{"docid":"67699e3e9b239f362d4451e3749d46d6","score":"0.4829288","text":"def setLevel(self, level):\n self.logger.setLevel(getattr(logging, level.upper()))","title":""},{"docid":"df5fa6c457ab4312a0683e9c59cbf790","score":"0.4820027","text":"def set_SafetyLevel(self, value):\n super(UploadInputSet, self)._set_input('SafetyLevel', value)","title":""},{"docid":"995c424b6422af1537ad358de091e542","score":"0.48176658","text":"def SetValue(self, value):\r\n item = self._tree.GetSelection()\r\n if not item or self._tree.GetItemText(item) != value:\r\n item = self.FindString(value)\r\n if self._readOnly and not item:\r\n return\r\n if item:\r\n if self._tree.GetSelection() != item:\r\n self._tree.SelectItem(item)\r\n else:\r\n self._tree.Unselect()","title":""},{"docid":"385cdc7548726dfda1e40291b262071b","score":"0.48174542","text":"def SetValue(self, value):\r\n self.Settle(value)\r\n self.value = value\r\n self.path.append(self.value)\r\n attrs = dir(self.value)\r\n self.strpath.append(self.select)\r\n self.table.Set(attrs)","title":""},{"docid":"d9b51aa544129971b8844cf154899190","score":"0.48164225","text":"def setNodeValue(self, newValue):\n if self.isEmpty(): \n print(\"Empty tree, can't change node value\")\n else:\n self.nodeValue = newValue","title":""},{"docid":"297594139b8e566454520802b8f830e3","score":"0.4808241","text":"def set_in_dict(data, path_map, value):\n try:\n get_from_dict(data, path_map[:-1])[path_map[-1]].update(value)\n except KeyError:\n get_from_dict(data, path_map[:-1])[path_map[-1]] = value\n except TypeError:\n get_from_dict(data, path_map[:-1])[path_map[-1]]['rowSpan'] = value(get_from_dict(data, path_map[:-1])[path_map[-1]]['rowSpan'])","title":""},{"docid":"d02c14c9048c62632ab6cd65d696118c","score":"0.47984546","text":"def set(self, key: str, value: Any) -> None:\n # default_flow_style=None means that it can use Block for\n # complex types (types that have nested types) but use flow\n # for simple types (like an array). Not all versions of PyYAML\n # have the same default style.\n encoded_value = yaml.dump(value, Dumper=_SimpleDumper, default_flow_style=None)\n content = yaml.dump(\n {key: encoded_value},\n default_style='|',\n default_flow_style=False,\n Dumper=_SimpleDumper)\n _run([\"state-set\", \"--file\", \"-\"], input=content, check=True)","title":""},{"docid":"a5afc011dcb88c1d11342fec6b346509","score":"0.47940058","text":"def set(self, path: str, value: str) -> None:\n ...","title":""},{"docid":"0ef6d82d71d6a014cfe2d31d3a874fd6","score":"0.47905493","text":"def set_difficulty(selected, value):\n print('Set difficulty to {} ({})'.format(selected[0], value))","title":""},{"docid":"991a6b960e78cb9c9395bf2e3096e6d5","score":"0.47784564","text":"def __setitem__(self, k, v):\n self.root = self._set_node(self.root, k, v)\n self.root.color = BLACK","title":""},{"docid":"9f55bfb5ff6c1bce2db550ed95c48242","score":"0.47746122","text":"def set_block_scalar_indent(token_class):\r\n def callback(lexer, match, context):\r\n text = match.group()\r\n context.block_scalar_indent = None\r\n if not text:\r\n return\r\n increment = match.group(1)\r\n if increment:\r\n current_indent = max(context.indent, 0)\r\n increment = int(increment)\r\n context.block_scalar_indent = current_indent + increment\r\n if text:\r\n yield match.start(), token_class, text\r\n context.pos = match.end()\r\n return callback","title":""},{"docid":"dff998a75f1ce4d9a1a87852c776f8d5","score":"0.47742367","text":"def set_depth(self, element, depth):\n self.client.hset(self.depth, element, depth)","title":""},{"docid":"b2d996119404615ae2eabef6dd902d4b","score":"0.47707453","text":"def indent(self):\n self._line.append(self._indent_count)","title":""},{"docid":"7c6c1cc263eaab0fff586e11c25d7cab","score":"0.4767662","text":"def format_indent(level=0, width=INDENTATION_WIDTH):\n return ' ' * level * width","title":""},{"docid":"a218d09a3a8404d2638e275ebf9fcd28","score":"0.47404677","text":"def set_value(self, arg1):\n self.value = arg1","title":""},{"docid":"10c6f080f447cf0c29f145940470ac59","score":"0.47401002","text":"def setInteger(self, value):","title":""},{"docid":"10c6f080f447cf0c29f145940470ac59","score":"0.47401002","text":"def setInteger(self, value):","title":""},{"docid":"dc170635ce26ee458201f88e8675809c","score":"0.47242895","text":"def changeValue(self, value):\n self.value = value","title":""},{"docid":"7811a097a62ef0433eb9bd04fd821e96","score":"0.47228664","text":"def set(self, val, scope=None):\n global _expr_dict\n scope = self._get_updated_scope(scope)\n \n if self.is_valid_assignee():\n # self.assignment_code is a compiled version of an assignment statement\n # of the form 'somevar = _local_setter', so we set _local_setter here\n # and the exec call will pull it out of the locals dict\n _local_setter = val \n if self._parse_needed:\n self._parse()\n exec(self._assignment_code, _expr_dict, locals())\n else: # self._allow_set is False\n raise ValueError(\"expression '%s' can't be set to a value\" % self.text)","title":""},{"docid":"ddd2ecdd68c00cf3b834161170ccc486","score":"0.47087893","text":"def set_item(self, item, value):\n try:\n self.parent.data_layer.set_setting(item, value)\n except:\n self.parent.reporting.logResult(False, \"Unable to set '{}' to '{}'.\".format(item, value))","title":""},{"docid":"9e48e37cb27cfdaa98ea03b6ffc53aa2","score":"0.47065476","text":"def set_value(self, arg1):\n\n if arg1 > self.upper or arg1 < self.lower:\n raise AssertionError(\"Given parameter \" + str(arg1) +\" for \" + str(self.name) + \" setting is outside range. [\" + str(self.lower) + \", \" + str(self.upper) + \"]\")\n\n self.value = arg1","title":""},{"docid":"9e48e37cb27cfdaa98ea03b6ffc53aa2","score":"0.47065476","text":"def set_value(self, arg1):\n\n if arg1 > self.upper or arg1 < self.lower:\n raise AssertionError(\"Given parameter \" + str(arg1) +\" for \" + str(self.name) + \" setting is outside range. [\" + str(self.lower) + \", \" + str(self.upper) + \"]\")\n\n self.value = arg1","title":""},{"docid":"e9db572ad35c18010ede5ac5735f2a64","score":"0.47046483","text":"def line_indent(self) -> None:\n self.clean('line-indent') # Defensive. Should never happen.\n self.add_token('line-indent', self.lws)","title":""},{"docid":"db2738c330201445389f068aeda90656","score":"0.47035587","text":"def update_tree_value(node, path, new_val):\n for k in path:\n node = node[\"children\"][k]\n node[\"value\"] = new_val","title":""},{"docid":"173071da877a5d68ab1302d1aa621631","score":"0.4699691","text":"def set_isolation_level(self, level):\n if self.m_connection is None:\n self._connect()\n database = self.model().database()\n database.set_isolation_level(self.m_connection, level)","title":""},{"docid":"e8820900c002e2696f19541943bffb6d","score":"0.46981135","text":"def set_value(self, pos: Xy, value: T) -> \"Grid[T]\":\n self.get_cell(pos).value = value\n return self","title":""}],"string":"[\n {\n \"docid\": \"ad542c91c2251023fd1a5f6148c37cc5\",\n \"score\": \"0.6587817\",\n \"text\": \"def indent(self, value):\\n self._indent += value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3976f43ef81edb18565c4fc254a835\",\n \"score\": \"0.6459699\",\n \"text\": \"def level(self, value):\\n self._level = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f09e80d61fbf4705c9af22250c88b2e\",\n \"score\": \"0.6370632\",\n \"text\": \"def _set_level(self, value=None):\\r\\n if value is None and hasattr(self, '_level'):\\r\\n del self._level\\r\\n else:\\r\\n self._level = int(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb9e684a44889ef8b0d219d2cdb9f47\",\n \"score\": \"0.6208918\",\n \"text\": \"def set_level(self, lvl):\\n self.level = lvl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06afac53aa36d77418e0560604616848\",\n \"score\": \"0.6114194\",\n \"text\": \"def set_level(self, level: int):\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c44f912ce60b3fde1b5aa209eebe7f2\",\n \"score\": \"0.59482896\",\n \"text\": \"def indented(self, indented):\\n\\n self.container['indented'] = indented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d63360cc13586b26e2623eeff8c2ce47\",\n \"score\": \"0.59054327\",\n \"text\": \"def set_level(self, level):\\n self._level = level\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2dd5404a65768765c821327acbcdf2\",\n \"score\": \"0.57822526\",\n \"text\": \"def indent(self,indent=1):\\n\\n self.indlev+=indent\\n if self.indlev<0:\\n self.indlev=0\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320618635ccd141c3b3cdb28b99e844c\",\n \"score\": \"0.5737219\",\n \"text\": \"async def set_level(self, level: int) -> None:\\n await self.set_state(level=level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4fcf3dc280c9d263d45acdaf91e9e2\",\n \"score\": \"0.56734335\",\n \"text\": \"def set_levels(self, recursion_level=0):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3444d9e31df614bb25337e2a1dc860df\",\n \"score\": \"0.5630703\",\n \"text\": \"def level(self, level):\\n\\n self._level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95af60fd89da8b87018a36d3b30fe33\",\n \"score\": \"0.5464114\",\n \"text\": \"def set_level(self, level):\\n self.max_level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2138007c95ec2d05dcd23919db740e14\",\n \"score\": \"0.54403096\",\n \"text\": \"def set_value(transaction, value):\\r\\n transaction['value'] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d98fc2efb8a7facaf96572b87b3b930\",\n \"score\": \"0.5429109\",\n \"text\": \"def h_setlevel(self):\\n # TODO: implement setlevel heuristic\\n # raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5f13d5d9592d1a8958f9391807a28b\",\n \"score\": \"0.53935736\",\n \"text\": \"def increase_indent(self):\\n self._indent += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4371c83714f696facc172ac20d5f9582\",\n \"score\": \"0.5390408\",\n \"text\": \"def setLevel(self, level):\\n self.level_thr = level\\n self.level_thr_ind = self.levels.index(level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c88dc0481812e986e6ac884d51db985f\",\n \"score\": \"0.53661907\",\n \"text\": \"def setValue(self, v):\\n self.setPos(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669eb53f7a5f7785201f9165eae5c826\",\n \"score\": \"0.5364355\",\n \"text\": \"def indent(self):\\n self.indent_amount += 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd6f1962c233c578d8d308e85854944\",\n \"score\": \"0.5351004\",\n \"text\": \"def indent(self):\\r\\n self.indent_amount += self.INDENT_STEP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ed13b39b4c94dd20be5c7046da8a13\",\n \"score\": \"0.532767\",\n \"text\": \"def increaseindentlevel_future(self):\\n if self.unindented > 0:\\n self.unindented -= 1\\n elif self.indentincrease_future:\\n self.indentlevel = self.indentlevel + 1\\n self.indentincrease_future = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67fc8e1f7a03cf702bce2ed24410079\",\n \"score\": \"0.528773\",\n \"text\": \"def indent(self, indent: int) -> None:\\n self._indent = indent\\n self._indent_str = \\\" \\\" * indent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8a25f8a04bd547c3ad5bf5e21dbd0df\",\n \"score\": \"0.52797306\",\n \"text\": \"def set_value(self, value):\\n self.setCurrentText(str(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46091f1a3cd1ae664f2a03bf1e8a8878\",\n \"score\": \"0.5272158\",\n \"text\": \"def SetValue(self, value): \\n\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e154053943517750e7cfd251d852a8d4\",\n \"score\": \"0.5256579\",\n \"text\": \"def setFirstLevel(self, firstLevel): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801c3c3bfb1f6cd5cec8fb30dabd2ed8\",\n \"score\": \"0.52565074\",\n \"text\": \"def depth(self, val):\\n self.__depth = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d21b03a8cf2821d7bf71258c81c36d45\",\n \"score\": \"0.5245829\",\n \"text\": \"def _indent(self, elem, level=0):\\n i = \\\"\\\\n\\\" + level * \\\" \\\"\\n child = None\\n if len(elem):\\n if not elem.text or not elem.text.strip():\\n elem.text = i + \\\" \\\"\\n if not elem.tail or not elem.tail.strip():\\n elem.tail = i\\n for child in elem:\\n self._indent(child, level+1)\\n # This seeks for the las child processed in for, is not a code\\n # indentation error\\n if child is not None and (not child.tail) or (not child.tail.strip()):\\n child.tail = i\\n else:\\n if level is not None and (not elem.tail or not elem.tail.strip()):\\n elem.tail = i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df71180de2ab09d32920e3d163969e09\",\n \"score\": \"0.52355725\",\n \"text\": \"def set(self, value):\\n self.value = self.fit_n_round(value)\\n\\n self.entry.delete(0, tk.END)\\n self.entry.insert(0, self.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d9813a5d3e495bff0b77d7470fef43\",\n \"score\": \"0.5228039\",\n \"text\": \"def change_indent(self,count=None,spaces=\\\"\\\"):\\n if count:\\n self.indent = count\\n self.indent_str = count * \\\" \\\"\\n else:\\n self.indent_str = spaces\\n self.indent = len(spaces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5061599256d99424cc74904a70d4bc6d\",\n \"score\": \"0.5194992\",\n \"text\": \"def changeLevel(self):\\n if self.isCorrect:\\n self.initialLevel = self.initialLevel + 1 if self.initialLevel < 12 else self.initialLevel\\n else:\\n self.initialLevel = self.firstNumber if self.initialLevel > 1 else self.initialLevel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d4566b5982fc9e58e6b11f3ffb63a5\",\n \"score\": \"0.5181596\",\n \"text\": \"def set(self, value):\\n self.block.__class__.field.write_to(self.block, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3606a345f7108d5808cd7e2546cead2b\",\n \"score\": \"0.5177938\",\n \"text\": \"def _inc(self) -> None:\\n self._indent += 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76a0f3dfb5755ac9e514623c9f5105f\",\n \"score\": \"0.5168259\",\n \"text\": \"def _bump_level(self, level, line):\\n if 0 >= level:\\n return line\\n if not self._is_heading(line):\\n return line\\n\\n # produce warning if the heading level is too deep\\n #\\n current_level = self._get_heading_level(line)\\n if 6 < (current_level + level):\\n sys.stderr.write(\\n \\\"Warning: Heading level is increased beyond 6 for line:\\\\n\\\")\\n sys.stderr.write(self._shorten_line(line) + \\\"\\\\n\\\")\\n\\n # adjust the heading level\\n #\\n prefix = '#' * level\\n result = prefix + line\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f5c9d03d1ec3ec24fbfc161648e39b\",\n \"score\": \"0.51396716\",\n \"text\": \"def indent(element, context):\\n\\tcontext.set(\\\"indent\\\", 1 + (context.get(\\\"indent\\\") or 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c11d4cb6450cba7edf0dcbb6490f330\",\n \"score\": \"0.5122665\",\n \"text\": \"def indent_factor(self):\\n return 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6fb729b3b1bd6915c76101d5d7ff3e\",\n \"score\": \"0.5122029\",\n \"text\": \"def set_level(self):\\n self._visa.write(f\\\"TRIGGER:{self._designation}:SETLEVEL\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e02251714eebac6d69fa1a146e633c\",\n \"score\": \"0.5120087\",\n \"text\": \"def set_nested_key(mapping, key_path, val, level=None):\\n assert key_path\\n\\n if level is None:\\n # This should work for dict and most basic structures\\n level = type(mapping)\\n\\n for key in key_path[:-1]:\\n try:\\n mapping = mapping[key]\\n except KeyError:\\n new_level = level()\\n mapping[key] = new_level\\n mapping = new_level\\n\\n mapping[key_path[-1]] = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b17faf1d3d1e3553ce61a71b9ac4c7\",\n \"score\": \"0.51079977\",\n \"text\": \"def set_value(self, value):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e99e12cd92075567241d2f07743f60af\",\n \"score\": \"0.51004833\",\n \"text\": \"def SetLevel(vLevel='ERROR'):\\n FCLogger.level = logging.NOTSET\\n FCLogger.parent.level = logging.DEBUG # fc.log always contains DEBUG log\\n\\n if isinstance(vLevel, str):\\n if vLevel == '5' or vLevel.upper() == 'CRITICAL':\\n FCLogger.level = logging.CRITICAL\\n\\n elif vLevel == '4' or vLevel.upper() == 'ERROR':\\n FCLogger.level = logging.ERROR\\n\\n elif vLevel == '3' or vLevel.upper() == 'WARNING':\\n FCLogger.level = logging.WARNING\\n\\n elif vLevel == '2' or vLevel.upper() == 'INFO':\\n FCLogger.level = logging.INFO\\n\\n elif vLevel == '1' or vLevel.upper() == 'DEBUG':\\n FCLogger.level = logging.DEBUG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8a4ddb5079bad9c87c08397bf5452e\",\n \"score\": \"0.50986934\",\n \"text\": \"def set(self, value):\\n self.state.log_stack[-1].append( (self, self.value) )\\n return self.raw_set(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4035b539511043813f29422af20bb368\",\n \"score\": \"0.5098117\",\n \"text\": \"def set(self, value):\\n self.block.field = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c4ce8d796884e807f996429d74722f2\",\n \"score\": \"0.5083594\",\n \"text\": \"def set_value(self, value):\\r\\n\\r\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6119eef856210a580488243d28e5fb8\",\n \"score\": \"0.5081345\",\n \"text\": \"def set_parameter(levels, value):\\n sublevel = _config\\n for level in levels[:-1]:\\n if level not in sublevel:\\n sublevel[level] = {}\\n sublevel = sublevel[level]\\n sublevel[levels[-1]] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60eeef0805fe1222ea0a0f6b7ed8952a\",\n \"score\": \"0.5062511\",\n \"text\": \"def set(self, value: Value) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9dc4e85f1856bc8fa40895cc83a5c7\",\n \"score\": \"0.5061161\",\n \"text\": \"def set_value(self, value):\\n self.setValue(int(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e85fc06bed00dc7f0e3dbc0e51be74d\",\n \"score\": \"0.5049786\",\n \"text\": \"def set_val(self, k, a):\\n k += self.n - 1\\n self.dat[k] = a\\n while k > 0:\\n k = (k - 1) // 2 # parent\\n self.dat[k] = self.op(self.dat[k * 2 + 1], self.dat[k * 2 + 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e85fc06bed00dc7f0e3dbc0e51be74d\",\n \"score\": \"0.5049786\",\n \"text\": \"def set_val(self, k, a):\\n k += self.n - 1\\n self.dat[k] = a\\n while k > 0:\\n k = (k - 1) // 2 # parent\\n self.dat[k] = self.op(self.dat[k * 2 + 1], self.dat[k * 2 + 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf7dc9f6c10623186db2f85a65bd991\",\n \"score\": \"0.5045666\",\n \"text\": \"def change_value (value):\\r\\n print(\\\"Inside, value is:\\\", value)\\r\\n value = 1\\r\\n print(\\\"Inside, value is changed to:\\\", value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f5478c1216013a764d43cb6d29816f\",\n \"score\": \"0.50285876\",\n \"text\": \"def indent_level(self) -> int:\\n return self.__indent_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cdb37be2794f2315b8011754becd608\",\n \"score\": \"0.5027473\",\n \"text\": \"def set_lvl(self, lvl):\\n if lvl < 0:\\n raise ArgumentOutOfRangeError(\\\"lvl < 0\\\")\\n\\n response = self.__put('user', {'stats.lvl': lvl, 'stats.exp': 0})\\n response.raise_for_status()\\n return response.json()['data']['stats']['lvl']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140dbc0ac7cdb09887288d9aaf51953d\",\n \"score\": \"0.50225365\",\n \"text\": \"def SetValue(self, value):\\n self.__ety.SetValue(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad3365852ee215f37f5733145baebb3d\",\n \"score\": \"0.50176716\",\n \"text\": \"def var_at_level(self, level):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"964ed08b3766317d5a5f2d34bd0974d3\",\n \"score\": \"0.4995006\",\n \"text\": \"def label_indent(self, indent):\\n self._label_indent = indent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360a430e26e82242ff183a0d78609af2\",\n \"score\": \"0.498991\",\n \"text\": \"def set(self, path, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24167e7d2735d7fe0ebfab09037ad6f\",\n \"score\": \"0.49887198\",\n \"text\": \"def evt_set_level(self, event: tk.Event) -> None:\\n level = BOX_LEVELS[self.level_selector.current()]\\n self.pipe.send(('level', level))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3f3864b10b9e5c5a38819057a47066\",\n \"score\": \"0.49805024\",\n \"text\": \"def set_value(self, value):\\n self.matrix[self.current_item]['value'] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e6ecb94fc50198ca3c60d385ff31243\",\n \"score\": \"0.49762678\",\n \"text\": \"def setDepth(self, depth):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e91f3e2cd21e34e92a84b7d44537c9b\",\n \"score\": \"0.49683082\",\n \"text\": \"def set_value(name, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3b690ec621939fbd1336a2b8b42787\",\n \"score\": \"0.49615255\",\n \"text\": \"def indent(self, indent=\\\" \\\"):\\r\\n import ly.etreeutil\\r\\n ly.etreeutil.indent(self.root, indent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1b2bb3a669cc11914e829c91737f62\",\n \"score\": \"0.4951376\",\n \"text\": \"def setLevel(self, level):\\n\\t\\tif isinstance(level, int):\\n\\t\\t\\tself.logger.setLevel(level)\\n\\t\\t\\treturn\\n\\n\\t\\t# level en tant que string\\n\\t\\tlevel = level.lower()\\n\\t\\tif level == \\\"debug\\\":\\n\\t\\t\\tself.logger.setLevel(logging.DEBUG)\\n\\n\\t\\telif level == \\\"info\\\":\\n\\t\\t\\tself.logger.setLevel(logging.INFO)\\n\\n\\t\\telif level == \\\"warning\\\" or level == \\\"warning\\\":\\n\\t\\t\\tself.logger.setLevel(logging.WARN)\\n\\n\\t\\telif level == \\\"error\\\":\\n\\t\\t\\tself.logger.setLevel(logging.ERROR)\\n\\n\\t\\telse:\\n\\t\\t\\t# par défaut\\n\\t\\t\\tself.logger.setLevel(logging.INFO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6d0647e35cc9f2023b9532eac6b8ff\",\n \"score\": \"0.49465317\",\n \"text\": \"def set_debug_level(self, level=1):\\n ret = self.soapcomm(\\\"SetDebugLevel\\\", option=level)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e47a05db51fb70620939a5bba1c3029\",\n \"score\": \"0.49286085\",\n \"text\": \"def set_value(self, pymux, value):\\n try:\\n value = int(value)\\n if value < 0:\\n raise ValueError\\n except ValueError:\\n raise SetOptionError('Expecting an integer.')\\n else:\\n setattr(pymux, self.attribute_name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5609d9b283f3b3854eb4134657259d11\",\n \"score\": \"0.4919288\",\n \"text\": \"def setValue(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4017bc145df0e3a981748d27a17fa4ec\",\n \"score\": \"0.4911414\",\n \"text\": \"def set_root_value(root, new_value):\\n\\n root[0] = new_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9085cdbd27223f6202a7bfb32f6a11e\",\n \"score\": \"0.49000582\",\n \"text\": \"def setDepth(self, depth):\\n self.__depth = depth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93456ef4a9bd37cd6fec76f4e8f4553\",\n \"score\": \"0.48883435\",\n \"text\": \"def _dec(self) -> None:\\n self._indent -= 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24143c84721dc0aa85eb46ac2a1ed517\",\n \"score\": \"0.48853365\",\n \"text\": \"def set_value(self, value):\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24143c84721dc0aa85eb46ac2a1ed517\",\n \"score\": \"0.48853365\",\n \"text\": \"def set_value(self, value):\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b582d5abd535a2d47c911613546500\",\n \"score\": \"0.48733425\",\n \"text\": \"def setValue(self,value):\\n v = float(value)\\n self.value = float(v)\\n self.text = self.getText(float(v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7dc112a99d5cc17355e9eb79d43ba9b\",\n \"score\": \"0.48677275\",\n \"text\": \"def set_val(self, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e77a4903bba7bed2190d13494e7eb26\",\n \"score\": \"0.48665637\",\n \"text\": \"def __setitem__(self, item, value):\\n self.parser.set(self.section, item, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ccd7574f0b1c3f4c614d11d6a49903\",\n \"score\": \"0.48625284\",\n \"text\": \"def setLevel(self, level):\\n self._JavaProxy__javaObject.setLevelSelection(level)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf0fefd78ebcd5f67173429a429f3ad\",\n \"score\": \"0.48476654\",\n \"text\": \"def update_value(self, value):\\n self._value = value\\n if self.parent_exists:\\n self.parent.__update_value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4d69fcb46757b2ac31ca5e565e9e2a\",\n \"score\": \"0.4841033\",\n \"text\": \"def set_level(level, root=True):\\n name = None if root else _module_name()\\n _get(name).setLevel(level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934868ea061f82947fecf6e0e1632d47\",\n \"score\": \"0.48326454\",\n \"text\": \"def test_02_setlevel(self):\\n msg = udocker.Msg(5)\\n self._verify_descriptors(msg)\\n self.assertEqual(msg.level, 5)\\n msg.nullfp.close()\\n msg = udocker.Msg(0)\\n msg.setlevel(7)\\n self._verify_descriptors(msg)\\n self.assertEqual(msg.level, 7)\\n msg.nullfp.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3e014e649014f8ed44a3c4c9d27bec\",\n \"score\": \"0.4830314\",\n \"text\": \"def set_value(self, etree, theory):\\n setattr(theory, self.__name__, self.get_value(etree, theory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67699e3e9b239f362d4451e3749d46d6\",\n \"score\": \"0.4829288\",\n \"text\": \"def setLevel(self, level):\\n self.logger.setLevel(getattr(logging, level.upper()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df5fa6c457ab4312a0683e9c59cbf790\",\n \"score\": \"0.4820027\",\n \"text\": \"def set_SafetyLevel(self, value):\\n super(UploadInputSet, self)._set_input('SafetyLevel', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995c424b6422af1537ad358de091e542\",\n \"score\": \"0.48176658\",\n \"text\": \"def SetValue(self, value):\\r\\n item = self._tree.GetSelection()\\r\\n if not item or self._tree.GetItemText(item) != value:\\r\\n item = self.FindString(value)\\r\\n if self._readOnly and not item:\\r\\n return\\r\\n if item:\\r\\n if self._tree.GetSelection() != item:\\r\\n self._tree.SelectItem(item)\\r\\n else:\\r\\n self._tree.Unselect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385cdc7548726dfda1e40291b262071b\",\n \"score\": \"0.48174542\",\n \"text\": \"def SetValue(self, value):\\r\\n self.Settle(value)\\r\\n self.value = value\\r\\n self.path.append(self.value)\\r\\n attrs = dir(self.value)\\r\\n self.strpath.append(self.select)\\r\\n self.table.Set(attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b51aa544129971b8844cf154899190\",\n \"score\": \"0.48164225\",\n \"text\": \"def setNodeValue(self, newValue):\\n if self.isEmpty(): \\n print(\\\"Empty tree, can't change node value\\\")\\n else:\\n self.nodeValue = newValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297594139b8e566454520802b8f830e3\",\n \"score\": \"0.4808241\",\n \"text\": \"def set_in_dict(data, path_map, value):\\n try:\\n get_from_dict(data, path_map[:-1])[path_map[-1]].update(value)\\n except KeyError:\\n get_from_dict(data, path_map[:-1])[path_map[-1]] = value\\n except TypeError:\\n get_from_dict(data, path_map[:-1])[path_map[-1]]['rowSpan'] = value(get_from_dict(data, path_map[:-1])[path_map[-1]]['rowSpan'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02c14c9048c62632ab6cd65d696118c\",\n \"score\": \"0.47984546\",\n \"text\": \"def set(self, key: str, value: Any) -> None:\\n # default_flow_style=None means that it can use Block for\\n # complex types (types that have nested types) but use flow\\n # for simple types (like an array). Not all versions of PyYAML\\n # have the same default style.\\n encoded_value = yaml.dump(value, Dumper=_SimpleDumper, default_flow_style=None)\\n content = yaml.dump(\\n {key: encoded_value},\\n default_style='|',\\n default_flow_style=False,\\n Dumper=_SimpleDumper)\\n _run([\\\"state-set\\\", \\\"--file\\\", \\\"-\\\"], input=content, check=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5afc011dcb88c1d11342fec6b346509\",\n \"score\": \"0.47940058\",\n \"text\": \"def set(self, path: str, value: str) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef6d82d71d6a014cfe2d31d3a874fd6\",\n \"score\": \"0.47905493\",\n \"text\": \"def set_difficulty(selected, value):\\n print('Set difficulty to {} ({})'.format(selected[0], value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991a6b960e78cb9c9395bf2e3096e6d5\",\n \"score\": \"0.47784564\",\n \"text\": \"def __setitem__(self, k, v):\\n self.root = self._set_node(self.root, k, v)\\n self.root.color = BLACK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f55bfb5ff6c1bce2db550ed95c48242\",\n \"score\": \"0.47746122\",\n \"text\": \"def set_block_scalar_indent(token_class):\\r\\n def callback(lexer, match, context):\\r\\n text = match.group()\\r\\n context.block_scalar_indent = None\\r\\n if not text:\\r\\n return\\r\\n increment = match.group(1)\\r\\n if increment:\\r\\n current_indent = max(context.indent, 0)\\r\\n increment = int(increment)\\r\\n context.block_scalar_indent = current_indent + increment\\r\\n if text:\\r\\n yield match.start(), token_class, text\\r\\n context.pos = match.end()\\r\\n return callback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff998a75f1ce4d9a1a87852c776f8d5\",\n \"score\": \"0.47742367\",\n \"text\": \"def set_depth(self, element, depth):\\n self.client.hset(self.depth, element, depth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d996119404615ae2eabef6dd902d4b\",\n \"score\": \"0.47707453\",\n \"text\": \"def indent(self):\\n self._line.append(self._indent_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c6c1cc263eaab0fff586e11c25d7cab\",\n \"score\": \"0.4767662\",\n \"text\": \"def format_indent(level=0, width=INDENTATION_WIDTH):\\n return ' ' * level * width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a218d09a3a8404d2638e275ebf9fcd28\",\n \"score\": \"0.47404677\",\n \"text\": \"def set_value(self, arg1):\\n self.value = arg1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c6f080f447cf0c29f145940470ac59\",\n \"score\": \"0.47401002\",\n \"text\": \"def setInteger(self, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c6f080f447cf0c29f145940470ac59\",\n \"score\": \"0.47401002\",\n \"text\": \"def setInteger(self, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc170635ce26ee458201f88e8675809c\",\n \"score\": \"0.47242895\",\n \"text\": \"def changeValue(self, value):\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7811a097a62ef0433eb9bd04fd821e96\",\n \"score\": \"0.47228664\",\n \"text\": \"def set(self, val, scope=None):\\n global _expr_dict\\n scope = self._get_updated_scope(scope)\\n \\n if self.is_valid_assignee():\\n # self.assignment_code is a compiled version of an assignment statement\\n # of the form 'somevar = _local_setter', so we set _local_setter here\\n # and the exec call will pull it out of the locals dict\\n _local_setter = val \\n if self._parse_needed:\\n self._parse()\\n exec(self._assignment_code, _expr_dict, locals())\\n else: # self._allow_set is False\\n raise ValueError(\\\"expression '%s' can't be set to a value\\\" % self.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd2ecdd68c00cf3b834161170ccc486\",\n \"score\": \"0.47087893\",\n \"text\": \"def set_item(self, item, value):\\n try:\\n self.parent.data_layer.set_setting(item, value)\\n except:\\n self.parent.reporting.logResult(False, \\\"Unable to set '{}' to '{}'.\\\".format(item, value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e48e37cb27cfdaa98ea03b6ffc53aa2\",\n \"score\": \"0.47065476\",\n \"text\": \"def set_value(self, arg1):\\n\\n if arg1 > self.upper or arg1 < self.lower:\\n raise AssertionError(\\\"Given parameter \\\" + str(arg1) +\\\" for \\\" + str(self.name) + \\\" setting is outside range. [\\\" + str(self.lower) + \\\", \\\" + str(self.upper) + \\\"]\\\")\\n\\n self.value = arg1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e48e37cb27cfdaa98ea03b6ffc53aa2\",\n \"score\": \"0.47065476\",\n \"text\": \"def set_value(self, arg1):\\n\\n if arg1 > self.upper or arg1 < self.lower:\\n raise AssertionError(\\\"Given parameter \\\" + str(arg1) +\\\" for \\\" + str(self.name) + \\\" setting is outside range. [\\\" + str(self.lower) + \\\", \\\" + str(self.upper) + \\\"]\\\")\\n\\n self.value = arg1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9db572ad35c18010ede5ac5735f2a64\",\n \"score\": \"0.47046483\",\n \"text\": \"def line_indent(self) -> None:\\n self.clean('line-indent') # Defensive. Should never happen.\\n self.add_token('line-indent', self.lws)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db2738c330201445389f068aeda90656\",\n \"score\": \"0.47035587\",\n \"text\": \"def update_tree_value(node, path, new_val):\\n for k in path:\\n node = node[\\\"children\\\"][k]\\n node[\\\"value\\\"] = new_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173071da877a5d68ab1302d1aa621631\",\n \"score\": \"0.4699691\",\n \"text\": \"def set_isolation_level(self, level):\\n if self.m_connection is None:\\n self._connect()\\n database = self.model().database()\\n database.set_isolation_level(self.m_connection, level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8820900c002e2696f19541943bffb6d\",\n \"score\": \"0.46981135\",\n \"text\": \"def set_value(self, pos: Xy, value: T) -> \\\"Grid[T]\\\":\\n self.get_cell(pos).value = value\\n return self\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993678,"cells":{"query_id":{"kind":"string","value":"e7452dc48b784be27fd3650a5f4fb04f"},"query":{"kind":"string","value":"Creates the Data Frame objects and returns back in array"},"positive_passages":{"kind":"list like","value":[{"docid":"c68653d6a73539c71116e0adefe4a58b","score":"0.0","text":"def __initiate_vars(self,file_path, delimiter):\n df_pandas = pd.read_csv(file_path,delimiter=delimiter)\n rzt_data = RZTData(cntx.experiment)\n df_rzt = rzt_data.read({\n 'path':file_path,\n 'delimiter':delimiter,\n 'encoding':'utf-8'\n })\n\n return [df_pandas,df_rzt]","title":""}],"string":"[\n {\n \"docid\": \"c68653d6a73539c71116e0adefe4a58b\",\n \"score\": \"0.0\",\n \"text\": \"def __initiate_vars(self,file_path, delimiter):\\n df_pandas = pd.read_csv(file_path,delimiter=delimiter)\\n rzt_data = RZTData(cntx.experiment)\\n df_rzt = rzt_data.read({\\n 'path':file_path,\\n 'delimiter':delimiter,\\n 'encoding':'utf-8'\\n })\\n\\n return [df_pandas,df_rzt]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f3cac4bb3a80488c58c07f43841fc51f","score":"0.7516557","text":"def create_dataframe(self):\n raise NotImplementedError","title":""},{"docid":"f3c404fb5f07c549abab0f693980de90","score":"0.71988475","text":"def _get_data(self):\n # TODO implement this method\n return pd.DataFrame()","title":""},{"docid":"31323537289e8db4fdf80e27a7609b2d","score":"0.6973086","text":"def data(self):\n\n if self._data is None:\n self._cur.execute(\"SELECT objectid::int, \"\n \"s10::int, s11::int, s12::int, \"\n \"k10/10::float, k11/10::float, k12/10::float, \"\n \"h10::float, h11::float, h12::float, \"\n \"a10::int, a11::int, a12::int, \"\n \"v10::float, v11::float, v12::float \"\n \"FROM lvm_data_all \"\n \"WHERE s10 IS NOT NULL;\")\n\n data = []\n\n for row in self._cur:\n data.append(list(row))\n\n self._data = pd.DataFrame(\n data,\n columns=[\n 'objectid',\n 's1', 's2', 's3',\n 'k1', 'k2', 'k3',\n 'h1', 'h2', 'h3',\n 'a1', 'a2', 'a3',\n 'v1', 'v2', 'v3',\n ]\n )\n\n return self._data","title":""},{"docid":"3b335852fe32ba62332a2536eced4571","score":"0.682229","text":"def _create_dataframe(self):\n columns = [\n \"Date\",\n \"Scenario\",\n \"Score\",\n \"Kills\",\n \"Acc.\",\n \"Avg Score\",\n \"High Score\",\n \"Diff Avg Score\",\n \"Diff High Score\",\n ]\n if self.table_entries:\n df = pd.DataFrame(np.array(self.table_entries), columns=columns)\n df = df.sort_values(by=\"Date\", ascending=False)\n # Corrects datatypes in Dataframe\n df[[\"Date\"]] = df[[\"Date\"]].apply(pd.to_datetime)\n df[[\"Score\"]] = df[[\"Score\"]].apply(pd.to_numeric)\n df[[\"Avg Score\"]] = df[[\"Avg Score\"]].apply(pd.to_numeric)\n df[[\"High Score\"]] = df[[\"High Score\"]].apply(pd.to_numeric)\n\n self.dataframe = df\n self._update_benchmark_bars()\n self._setup_homepage()","title":""},{"docid":"742edb75933731472c89cd9a11ae33a4","score":"0.6810718","text":"def toDataFrame(self):\n if self.data is None:\n self.eval(outputDF=True)\n if not isinstance(self.data, DataFrame):\n self.data = matrix.sqlContext.createDataFrame(self.toPandas())\n return self.data","title":""},{"docid":"a4d566454d4baed322245aaecb0803d8","score":"0.6728489","text":"def get_frame(self):\n return pd.DataFrame()","title":""},{"docid":"a77af1ec14cb9de15c33cdb785e7e68e","score":"0.6603054","text":"def prepare_data(array):\n stats = []\n for row in array:\n stats.append(pd.DataFrame(row.tolist()).values.flatten())\n return stats","title":""},{"docid":"f93b916c532699c494afd433fbc899a8","score":"0.6577323","text":"def make_pandas(self):\n columns = self.papers_list[0]._raw.keys()\n papers_df = pd.DataFrame(columns=columns)\n for paper in self.papers_list:\n papers_df = papers_df.append(paper._raw, ignore_index=True)\n # papers_df.set_index('id', inplace=True)\n return papers_df","title":""},{"docid":"322b3697b21b7740240a12c546ee801f","score":"0.65767074","text":"def create_data_frame_result(self):\r\n start = timer()\r\n self.data_frame_result = pd.DataFrame(self.dict_of_array_result)\r\n end = timer()\r\n print '::: Time to create result data frame:', end - start\r\n \r\n self.data_frame_result.to_pickle('results/'+self.output_tag + '/' + self.output_file)\r\n return self.data_frame_result","title":""},{"docid":"4ec788123c72658e2fe2b88fcdfdac01","score":"0.6538759","text":"def dataframe(self):\n return pandas.DataFrame(self.values, columns=self.header)","title":""},{"docid":"292fc53684ae35481afeb64f2d6799e1","score":"0.65293294","text":"def _make_df(self) -> pd.DataFrame:\n df = pd.DataFrame(self.raw_acs_data[1:], columns=self.raw_acs_data[0])\n df = df.loc[:,~df.columns.duplicated()] #remove duplicate columns\n return df","title":""},{"docid":"846227628fb296a0fbdbe6c91f2591a9","score":"0.6520269","text":"def get_data(self):\n self.cursor.execute(f\"SELECT * FROM {TABLE_NAME}\")\n data = self.cursor.fetchall()\n columns = [desc[0] for desc in self.cursor.description]\n return pd.DataFrame(data, columns=columns)","title":""},{"docid":"ea3b460213192f8862e69d30c89733aa","score":"0.64974713","text":"def create_df():\n return pd.DataFrame(columns=[COLUMN_AUTHOR, COLUMN_REVIEW_DATE, COLUMN_RATING, COLUMN_CONTENT])","title":""},{"docid":"e91e7a1e1327e641765f86028d00d2df","score":"0.64821476","text":"def batch_create_df(self, result: List[List[np.ndarray]]) -> List[List[dict]]:\n df = []\n for one_result in result:\n one_df = self.create_df(one_result)\n df.append(one_df)\n\n return df","title":""},{"docid":"fc2d6df4a6215e0bc7c1f522808457bb","score":"0.64701545","text":"def to_dataframe(self):\n self.df = pd.DataFrame(self.results)","title":""},{"docid":"6bd1be6b4b916f63f8af9f1c5ccc4604","score":"0.6434636","text":"def make_dataframe(self):\n dataframe = pd.DataFrame.from_records(\n self.reservations_json,\n columns=[\n 'ID',\n 'Corretor',\n 'Data do Turno',\n 'Hora de Cadastro',\n 'Posição',\n 'Turno',\n 'Imóvel'\n ]\n )\n return dataframe","title":""},{"docid":"a9520b2f1043b4001c313de88cf6652b","score":"0.64336663","text":"def to_df(self):\n # Find all source properties\n df = pd.DataFrame()\n for attr in self.__dict__.keys():\n parm = getattr(self, attr)\n if type(parm) is np.ndarray:\n df[attr] = parm\n\n return df","title":""},{"docid":"acf8a9f3c42ce9ec027d2ded377f0ef7","score":"0.6428806","text":"def create_tweet_dataframe(self):\n return pd.DataFrame(list(self._collection.find()))","title":""},{"docid":"2cefd27e782c5746bdb3eff81b850de0","score":"0.64203423","text":"def to_array(self):\n if self.__is_splitted:\n m_dataset = []\n for v in self.__dataset:\n train_df = v[0]\n test_df = v[1]\n # last two columns: event event_time ( eg PFI PFI.time)\n x_train_df = train_df.iloc[:, :-2]\n y_train_df = train_df.iloc[:, -2:]\n x_test_df = test_df.iloc[:, :-2]\n y_test_df = test_df.iloc[:, -2:]\n # convert to numpy array\n x_train = np.array(x_train_df)\n y_train = np.array(y_train_df)\n x_test = np.array(x_test_df)\n y_test = np.array(y_test_df)\n\n m_dataset.append((x_train, y_train, x_test, y_test))\n return m_dataset\n else:\n x_df = self.__df.iloc[:, :-2]\n y_df = self.__df.iloc[:, -2:]\n\n x_array = np.array(x_df, dtype=np.float)\n y_array = np.array(y_df, dtype=np.float)\n return x_array, y_array","title":""},{"docid":"20c410bce52f55f82bd151e3a4696729","score":"0.6399226","text":"def data():\n x_size = len(fasta)\n y_size = 10\n numpy.random.seed(0)\n M = numpy.random.randint(0, 1000, (x_size, y_size))\n df = pandas.DataFrame(M, index=[seq.id for seq in fasta], columns=[names.get_first_name() for j in range(y_size)])\n return df","title":""},{"docid":"a3dbc60752ec21cce4863a0dc8d1fcd6","score":"0.6368369","text":"def getDataFrame(self):\r\n \r\n # Load data if are not loaded\r\n if self.df is None:\r\n self._load_data()\r\n \r\n # Return data as uda.DataFrame\r\n return self.df","title":""},{"docid":"2ccad96875291a236a38ac214725fd69","score":"0.6352187","text":"def data_frame(self) -> pd.DataFrame:\n return self._data_frame","title":""},{"docid":"d5c88e00b26b332f64c41c035a78daba","score":"0.6336747","text":"def list(self) -> 'DataFrame':\n pass","title":""},{"docid":"42377933bf62f77a4ab7d5a6b4694e4f","score":"0.6321764","text":"def fixture_dataframe():\n return pd.DataFrame(data={\"x\": [0], \"y\": [0]})","title":""},{"docid":"18ec8e546cd065b00c0671ffc2a476ff","score":"0.63007814","text":"def as_df(self):\n self.load()\n return self._data","title":""},{"docid":"18ec8e546cd065b00c0671ffc2a476ff","score":"0.63007814","text":"def as_df(self):\n self.load()\n return self._data","title":""},{"docid":"984661ccbc7364c04c95c0034a156a0e","score":"0.62911385","text":"def dataframe(self):\n temp_index = self._index\n rows = []\n indices = []\n for season in self._season:\n self._index = self._season.index(season)\n rows.append(self._dataframe_fields())\n indices.append(season)\n self._index = temp_index\n return pd.DataFrame(rows, index=[indices])","title":""},{"docid":"8b78604ad92661d94cd467456431a42b","score":"0.62896055","text":"def create_dataframe(self, coll, query=None, find_all=True):\n frame = self.find_doc(coll, query, find_all)\n return pd.DataFrame(frame)","title":""},{"docid":"469f2cdfa9a008e1314eee885cee5237","score":"0.62818605","text":"def creaDataFrame(regiones):\r\n df=pd.DataFrame()\r\n for region in regiones: \r\n if region:\r\n regionData,pais,idioma,h,tiempo = regionPaisIdioma(region)\r\n d={\"Region\":regionData,\"City Name\":pais,\"Languaje\":h,\"Time\":tiempo}\r\n df=df.append(d , ignore_index=True)\r\n if logs:\r\n logging.debug('Tabla Pandas: \\n {}'.format(df))\r\n return df","title":""},{"docid":"12cb741b5ba5a2a96269f196b493396d","score":"0.62785035","text":"def dataframe(self, records: List[Dict]) -> Any:\n return pd.DataFrame(self.records(records))","title":""},{"docid":"923418a80e0e66494a492cfc9c853632","score":"0.62781024","text":"def to_dataframe(self, complete=False):\n\n import pandas as pd\n arr = self.to_numpy(complete=complete)\n df = pd.DataFrame.from_records(arr)\n df.index = np.arange(df.shape[0], dtype='int64')\n return df","title":""},{"docid":"6a4fc93fba84aae3968c18fba9294e84","score":"0.6270584","text":"def get_dataframe(self):\n return self.dataframe","title":""},{"docid":"b5ae2d94c42c7907e0e787cb8ff72d2a","score":"0.6253916","text":"def get_data(self): # TODO use ORM instead of queries\n # TODO format query against sql injection\n query = f\"\"\"\n Select * from \"transactions\" t \n join \"customers\" c on c.\"customer_id\"=t.\"customer_id\"\n join \"stations\" s on t.\"station_id\"=s.\"station_id\"\n where dataset='{self.dataset}'\n \"\"\"\n df = pd.read_sql(query, con=self.engine).drop_duplicates()\n df.index = pd.to_datetime(df.transaction_date)\n df = df.loc[:, ~df.columns.duplicated()]\n print(df.shape)\n return df","title":""},{"docid":"823117bb892deaee94f96d4b48f076dc","score":"0.62498295","text":"def getData(self) -> pd.DataFrame:\n return self.data.copy()","title":""},{"docid":"7edaec994bd0efa24970c6d64f22e2ca","score":"0.6247302","text":"def get_data_frame(self):\n return self.final_data_frame","title":""},{"docid":"fc376ecf057cf9064e38b2fcaace5bee","score":"0.62367254","text":"def data_frame(self) -> pd.DataFrame:\n return pd.DataFrame(self.parse())","title":""},{"docid":"cc57688be355e6ff146f6af052ec7559","score":"0.6227631","text":"def as_dataframe(self):\n result = self.properties.copy()\n if self.positions is not None:\n result['x'] = self.positions[:, 0]\n result['y'] = self.positions[:, 1]\n result['z'] = self.positions[:, 2]\n if self.orientations is not None:\n result['orientation'] = list(self.orientations)\n\n result.index = 1 + np.arange(len(result))\n\n result.columns = map(str, result.columns)\n\n return result","title":""},{"docid":"b7ace983eec6db2f4a882794c3c200c4","score":"0.62000483","text":"def get_raw_dataframe(self):\r\n return self.raw_data_df","title":""},{"docid":"7a41e276199ab5384249954271b45d11","score":"0.6186407","text":"def as_dataframe(self):\n\t\treturn pd.DataFrame(self.response, index = self.input)","title":""},{"docid":"6d542dc81072823413bd173873733966","score":"0.6154277","text":"def get_dataframe_apoyos(self):\r\n \r\n return self.v.df_apoyos.copy()","title":""},{"docid":"f3abdef97663907580db173100cc613e","score":"0.61516017","text":"def as_df(self) -> pd.DataFrame:\n return pd.DataFrame(self.as_dicts())","title":""},{"docid":"d31799ba995be038ec3c9468ebcba7ad","score":"0.61390465","text":"def prepare(self):\n self.add_duration()\n self.add_distance()\n self.add_acc()\n self.add_dayofweek()\n return self.df","title":""},{"docid":"3940ce365da7f4f21f52b695d3c43027","score":"0.612594","text":"def to_data_frame(self, fields: Optional[List[str]] = None) -> pd.DataFrame: # noqa 501","title":""},{"docid":"f55f52266096170d251f5629738b6793","score":"0.6122482","text":"def get_dataframe2(fcs):\n\tif not isinstance(fcs, (tuple, list)):\n\t\tfcs = fcs,\n\n\ttpls = []\n\tfor fc in fcs:\n\t\tfor f in fc.frames:\n\t\t\tfor d in f.detectionsUnion.detectionsDP:\n\t\t\t\td = Detection(d.idx, d.xpos, d.ypos, d.radius, d.zRotation, list(d.decodedId), f.frameIdx, f.timestamp, fc.camId, fc.id)\n\t\t\t\ttpls.append(d)\n\n\tdf = pd.DataFrame(tpls)\n\treturn df","title":""},{"docid":"ae54cf0c6fa802ffacab1d74120b5079","score":"0.61189175","text":"def get_dataframe(self):\n return self._df","title":""},{"docid":"43ee2dfc2d223390fb4bb87ac4780296","score":"0.61135054","text":"def dataframe(self, limit=None):\n\n from metapack.jupyter.pandas import MetatabDataFrame\n\n rg = self.row_generator\n\n # Maybe generator has it's own Dataframe method()\n try:\n return rg.dataframe()\n except AttributeError:\n pass\n\n # Just normal data, so use the iterator in this object.\n headers = next(islice(self, 0, 1))\n data = islice(self, 1, None)\n\n df = MetatabDataFrame(list(data), columns=headers, metatab_resource=self)\n\n self.errors = df.metatab_errors = rg.errors if hasattr(rg, 'errors') and rg.errors else {}\n\n return df","title":""},{"docid":"9091475c29754bf09011f17f28e846b7","score":"0.608062","text":"def close(self) -> pd.DataFrame:\n return pd.DataFrame(\n data=self.data,\n columns=self.columns,\n index=self.index,\n dtype=self.dtypes\n )","title":""},{"docid":"79d0657eaa2f3a2027652b10c950252f","score":"0.60781044","text":"def dataset():\n return pd.DataFrame(\n data=[\n ['Alice', 45],\n ['Bob', 23],\n ['Claudia', 25],\n ['Dave', 56]\n ],\n columns=['Name', 'Age']\n )","title":""},{"docid":"bef3382d278f0390932eab04783d3a44","score":"0.6062772","text":"def create_data_numpy(data_numpy, times, cols, num=0):\n df = pd.DataFrame(data_numpy)\n df.columns = cols\n df['Strain'] = [num] * df.shape[0]\n df = df.set_index('Strain')\n df['Time'] = times\n print('Shape of the dataframe is:', df.shape)\n return df","title":""},{"docid":"0697fb2b26ccc081615600957c8e4812","score":"0.60553914","text":"def data_as_dataframe(self):\n\n resource = request(self.apiUris[3].uri) # third element is icane json\n data = pd.DataFrame(list(flatten_data(resource)))\n headers = list(resource['headers'])\n headers.append(unicode('Valor'))\n data.columns = headers\n time_series = pd.DataFrame()\n for element in headers:\n if element.encode('utf-8') in ['Año', 'Trimestre', 'Mes']:\n time_series = data.set_index([unicode(element)])\n elif element == 'Municipios':\n index = 'Municipios'\n if time_series.empty:\n time_series = data.set_index(index)\n return time_series","title":""},{"docid":"52c3b1eec6e3197cf14d766540aeafae","score":"0.6055046","text":"def asarray(self):\n return [Frame(frame, self.info().copy()) for frame in self.data()]","title":""},{"docid":"04dc2a20c9334cc1e9dcd7de1e4aef34","score":"0.60513","text":"def GetArrayRepresentation(self):\n contents = self.GetContents()\n rows = len(contents)\n cols = 4\n \n \n #Dimension the list initally rather than appending\n #results = [[None]*rows]*cols\n results = [[], [], [], []]\n for row in range(rows):\n obj = contents[row][1]\n results[NAME_COL].append(obj.name)\n results[VALUE_COL].append(obj.value)\n results[INFOTYPE_COL].append(obj.infoType)\n results[OPTIONS_COL].append(obj.options)\n \n return results","title":""},{"docid":"c64321dd6f971be40e488d5322cc9297","score":"0.60492355","text":"def dframe(self):\n from pandas import DataFrame\n return DataFrame({(k.name if isinstance(k, Dimension)\n else k): [v] for k, v in self.data.items()})","title":""},{"docid":"c086fff266db922de8dd58f7727f0631","score":"0.60481715","text":"def _to_array(self, sets: str, window_size: int) -> np.ndarray:\n to_concat_x = []\n to_concat_y = []\n for i in range(4):\n dataset = 'FD_00{}'.format(i + 1)\n df = self.data[dataset]['df_' + sets]\n x, y = utils.to_array(df, window_size)\n to_concat_x.append(x)\n to_concat_y.append(y)\n\n x = np.concatenate(to_concat_x)\n y = np.concatenate(to_concat_y)\n return x, y","title":""},{"docid":"7239ba0bdc185c00ced88b6698d79331","score":"0.60388744","text":"def getDataFrame(self):\n return pd.read_csv(self.data_path).astype('float32')","title":""},{"docid":"e197fdcb730832fd2945c4a1a4b1475d","score":"0.60135585","text":"def to_frame(self) -> pd.DataFrame:\n return pd.DataFrame([mol.to_series() for mol in self.moleculeDict.values()])","title":""},{"docid":"4034406da049cb907d8e3030e3161430","score":"0.60098","text":"def dataframe_proyecto(self):\r\n \r\n pd = self.get_pandas()\r\n d_proyecto = self.get_proyecto()\r\n ser = pd.Series(d_proyecto)\r\n df = ser.to_frame()\r\n df = df.T\r\n \r\n return df","title":""},{"docid":"f6141c38fa9431eb49d9c7d76bfd4f83","score":"0.6006332","text":"def data_frame(self):\n\n # Import pyspark functions\n import pyspark.sql.functions as F\n\n # Checks first if there is a data frame, outputs None.\n if self._data_frame is None:\n logger_data_import.warning(\"Data frame has not been set\")\n return\n\n # Create casted features and labels\n feature_types = [GeneralDataImport.cast_to_right_type(feature)\n for feature in self._list_structfield_features\n ]\n id_types = [F.col(id.name).cast('string').alias(id.name)\n for id in self._list_structfield_id\n ]\n label_types = list(map(\n func=lambda c: F.col(c.name).cast('double').alias(c.name),\n iter1=self._list_structfield_label)\n )\n\n # Log the selected features and labels\n logger_data_import.info(\n \"Data frame exported with columns: {col}, missing: {mis}\"\n .format(col=self._list_structfield_label\n + self._list_structfield_id\n + self._list_structfield_features,\n mis=set(self._all_columns)\n - set(self._list_structfield_label\n + self._list_structfield_id\n + self._list_structfield_features\n )\n )\n )\n return self._data_frame.select(\n id_types+label_types+feature_types\n )","title":""},{"docid":"6df014f7b61a113cc7a2f6ea2df380d6","score":"0.60000485","text":"def get_dataframe_implantaciones(self):\r\n \r\n return self.v.df_implantaciones.copy()","title":""},{"docid":"d0cd330e2e632ba968321774484c07a3","score":"0.5990821","text":"def init_dataframe():\n index = _node_to_position.keys()\n columns = ['x', 'y', 'value']\n df = pd.DataFrame(index = index, columns=columns)\n for node_id, (x, y) in _node_to_position.items():\n df.loc[node_id]['x'] = x\n df.loc[node_id]['y'] = y\n df.loc[node_id]['value'] = 0\n return df","title":""},{"docid":"29d8f72839e42fab15df8e0b692a245f","score":"0.5982969","text":"def create_df(self):\n df = pd.DataFrame()\n df['orf_name'] = self.nodes\n df['deg'] = np.diag(self.L)\n eff_orig = np.sum(self.prs_mat, axis=1)\n sens_orig = np.sum(self.prs_mat, axis=0)\n eigvecs_df = pd.DataFrame(self.gnm.getEigvecs(\n )[:, self.coll_index_sorted[:8]], columns=[f'eig_{i}' for i in range(8)])\n\n df_ = df#pd.merge(df, eigvecs_df, left_index=True, right_index=True)\n df_['eff'] = eff_orig\n df_['sens'] = sens_orig\n eigenvector_centr = nx.eigenvector_centrality_numpy(self.graph_gc)\n closeness_centr = nx.closeness_centrality(self.graph_gc)\n# df_['btw'] = betweenness_nx(self.graph_gc, normalized=True)\n df_['trans'] = list((nx.clustering(self.graph_gc)).values())\n df_['eigenvec_centr'] = [eigenvector_centr[i]\n for i in eigenvector_centr]\n df_['closeness_centr'] = [closeness_centr[i] for i in closeness_centr]\n df_['smallest_eigenvec'] = self.gnm.getEigvecs()[:, 0]\n self.df = df_","title":""},{"docid":"fe13085b941dc5c3a913a6953382c413","score":"0.5981116","text":"def _convert_to_records(self):\n for dfname in self.data.keys():\n self.log.debug(\"Converting %s \" % dfname)\n # Set index at which next set of tuples begins\n self.data[dfname]['first_index'] = len(self.data[dfname]['tuples']) + \\\n self.data[dfname].get('first_index', 0)\n # Convert tuples to records\n newrecords = np.array(self.data[dfname]['tuples'],\n self.data[dfname]['dtype'])\n # Clear tuples. Enjoy the freed memory.\n self.data[dfname]['tuples'] = []\n # Append new records\n if self.data[dfname]['records'] is None:\n self.data[dfname]['records'] = newrecords\n else:\n self.data[dfname]['records'] = np.concatenate((self.data[dfname]['records'],\n newrecords))\n self.events_ready_for_conversion = 0","title":""},{"docid":"6b098cf38cf6d94613942fa4f08b58bf","score":"0.59746027","text":"def get_data():\n data = pd.DataFrame()\n for i in range(0, 8):\n split_path = os.path.join(DATA_PATH,\n \"updrs_splits/split_{}.csv\".format(i))\n # split = pd.read_csv(\"../../data/updrs_splits/split_{}.csv\".format(i))\n split = pd.read_csv(split_path)\n data = pd.concat([data, split])\n\n X = data.drop(columns=[\"patno\", \"score\", \"date_scan\"])\n y = data[\"score\"].astype(int)\n\n return X, y","title":""},{"docid":"43b59ba014a72149a2da07c05ab8a632","score":"0.5972888","text":"def xarray_to_dataframe(xr: Dataset) -> DataFrame:\n pass","title":""},{"docid":"46993677407c7124fb66f642db4e0611","score":"0.5966046","text":"def __init__(self):\n self.df = pd.DataFrame()","title":""},{"docid":"93c24a385f3237fd30e335da29603dd5","score":"0.5954832","text":"def createSimpleDataSet( numOfAttr, numOfObj ):\n database = []\n for i in range(numOfObj):\n data = dataObject(numOfAttr)\n\n w=[random.gauss(2.0, 2.0)]\n w.append(w[0]*3+random.gauss(0.0, 0.05))\n w.append(random.random()*6)\n\n data.setAttributes(w)\n database.append(data)\n return database","title":""},{"docid":"f38089d776adb6b58bf86d4a37763dbe","score":"0.5947937","text":"def get_data() -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:\r\n projects = select_projects_from_libraries_io()\r\n libraries = select_libraries()\r\n migrations = select_migrations()\r\n lib_names = set(libraries[\"name\"])\r\n rules = select_rules(lib_names)\r\n dep_changes = select_dependency_changes_all(lib_names)\r\n migrations.startCommitTime = pd.to_datetime(migrations.startCommitTime, utc=True)\r\n migrations.endCommitTime = pd.to_datetime(migrations.endCommitTime, utc=True)\r\n return projects, libraries, migrations, rules, dep_changes","title":""},{"docid":"5f4cff1c3ffb7a611da367f353808c33","score":"0.59470904","text":"def query_set_to_df(q_set : QuerySet):\n return pd.DataFrame(q_set.values())","title":""},{"docid":"34db4dcfa8b8dd58332dee6fc022c5d5","score":"0.5940663","text":"def to_dataframe(self, **kwargs):\n use_categories = popargs('use_categories', kwargs)\n # Step 1: Combine the entities, keys, and entity_keys table\n ent_key_df = self.entity_keys.to_dataframe()\n entities_mapped_df = self.entities.to_dataframe().iloc[ent_key_df['entities_idx']].reset_index(drop=True)\n keys_mapped_df = self.keys.to_dataframe().iloc[ent_key_df['keys_idx']].reset_index(drop=True)\n ent_key_df = pd.concat(objs=[ent_key_df, entities_mapped_df, keys_mapped_df],\n axis=1,\n verify_integrity=False)\n # Step 2: Combine the the files, object_keys and objects tables\n object_keys_df = self.object_keys.to_dataframe()\n objects_mapped_df = self.objects.to_dataframe().iloc[object_keys_df['objects_idx']].reset_index(drop=True)\n object_keys_df = pd.concat(objs=[object_keys_df, objects_mapped_df],\n axis=1,\n verify_integrity=False)\n files_df = self.files.to_dataframe().iloc[object_keys_df['files_idx']].reset_index(drop=True)\n file_object_object_key_df = pd.concat(objs=[object_keys_df, files_df],\n axis=1,\n verify_integrity=False)\n # Step 3: merge the combined entities_df and object_keys_df DataFrames\n result_df = pd.concat(\n # Create for each row in the objects_keys table a DataFrame with all corresponding data from all tables\n objs=[pd.merge(\n # Find all entities that correspond to the row i of the object_keys_table\n ent_key_df[ent_key_df['keys_idx'] == object_keys_df['keys_idx'].iloc[i]].reset_index(drop=True),\n # Get a DataFrame for row i of the objects_keys_table\n file_object_object_key_df.iloc[[i, ]],\n # Merge the entities and object_keys on the keys_idx column so that the values from the single\n # object_keys_table row are copied across all corresponding rows in the entities table\n on='keys_idx')\n for i in range(len(object_keys_df))],\n # Concatenate the rows of the objs\n axis=0,\n verify_integrity=False)\n # Step 4: Clean up the index and sort columns by table type and name\n result_df.reset_index(inplace=True, drop=True)\n # ADD files\n file_id_col = []\n for idx in result_df['files_idx']:\n file_id_val = self.files.to_dataframe().iloc[int(idx)]['file_object_id']\n file_id_col.append(file_id_val)\n\n result_df['file_object_id'] = file_id_col\n column_labels = [('files', 'file_object_id'),\n ('objects', 'objects_idx'), ('objects', 'object_id'), ('objects', 'files_idx'),\n ('objects', 'object_type'), ('objects', 'relative_path'), ('objects', 'field'),\n ('keys', 'keys_idx'), ('keys', 'key'),\n ('entities', 'entities_idx'), ('entities', 'entity_id'), ('entities', 'entity_uri')]\n # sort the columns based on our custom order\n result_df = result_df.reindex(labels=[c[1] for c in column_labels],\n axis=1)\n result_df = result_df.astype({'keys_idx': 'uint32',\n 'objects_idx': 'uint32',\n 'files_idx': 'uint32',\n 'entities_idx': 'uint32'})\n # Add the categories if requested\n if use_categories:\n result_df.columns = pd.MultiIndex.from_tuples(column_labels)\n # return the result\n return result_df","title":""},{"docid":"94f693839611812a61531db9e16495c8","score":"0.5940352","text":"def sample(self) -> pd.DataFrame:\n pass","title":""},{"docid":"1ce8505504878a6abcc740d2b6f71147","score":"0.59357053","text":"def dataset(self):\n data = tablib.Dataset()\n data.headers = self.keys()\n\n row = _reduce_datetimes(self.values())\n data.append(row)\n\n return data","title":""},{"docid":"bc1d7c1062e176c132051823fd750b0c","score":"0.59345746","text":"def to_numpy_data(df_X, df_y, seed=SEED):\n\n # Convert to numpy\n X = df_X.values.astype('int8')\n y = df_y.values.astype('int8')\n print('Shape of X: {}. Shape of y: {}.'.format(X.shape, y.shape))\n\n # Split into training and test sets\n train_ind, test_ind = train_test_split(\n np.arange(len(X)), test_size=0.2, random_state=seed)\n return Datasets(\n X=X, y=y,\n X_train=X[train_ind],\n y_train=y[train_ind],\n X_test=X[test_ind],\n y_test=y[test_ind],\n train_ind=train_ind,\n test_ind=test_ind\n )","title":""},{"docid":"e6254b904f8b016550ca83471014e6bb","score":"0.5926994","text":"def to_dataframe(self):\n time_arr = []\n lon_arr = []\n lat_arr = []\n for d in self.waypoints:\n time_arr.append(d['time'])\n lon_arr.append(d['longitude'])\n lat_arr.append(d['latitude'])\n return pd.DataFrame({'Longitude': lon_arr, 'Latitude': lat_arr}, index=time_arr)","title":""},{"docid":"f111ba27073d608234cc14684f4847f2","score":"0.5921907","text":"def generate_final_db(self):\n \n self.add_type_columns()\n self.transform_columns()\n self.df['elevetaion'] = self.df.elevetaion.apply(lambda x: self.binning_elev(x))\n self.df = self.df.rename({'elevetaion':'elevation'}, axis=1)\n self.calc_gio_not()\n self.calc_season()\n \n return self.df","title":""},{"docid":"42c69a118cf2c13cd6cb0b6882c16f13","score":"0.59205234","text":"def _get_data_frame(self):\n import pandas as pd\n data_dict = {}\n for key in list(self[0].__dict__.keys()):\n print(key)\n if key == 'sigma':\n #Convert sigma to string separated by ', '\n data_dict[key] = [', '.join(str(x) for x in config.__dict__[key]) for config in self]\n else:\n data_dict[key] = [config.__dict__[key] for config in self]\n return pd.DataFrame(data=data_dict)","title":""},{"docid":"1ed7a97250c21debe7d8bb4c9a41a754","score":"0.5915785","text":"def create_prov_entities(self, dataframe, instance=None):\n instance = self.instance if instance is None else instance\n columns = dataframe.columns\n indexes = dataframe.index\n \n # Create output array of entities:\n entities = np.empty(dataframe.shape, dtype=object)\n for i in range(self.current_m):\n record_id = str(uuid.uuid4())\n for j in range(self.current_n):\n ent_id = self.NAMESPACE_ENTITY + str(uuid.uuid4())\n value = str(dataframe.iat[i, j])\n # Add entity to current provenance document:\n entities[i][j] = self.create_entity(ent_id, record_id, value, columns[j], indexes[i], self.operation_number)\n\n return entities","title":""},{"docid":"92984a59014598d66adcfc358fd8de48","score":"0.5909709","text":"def as_dataframe(self, dframe):\n return self.__class__(dframe.reset_index(drop=True), self.meta.copy())","title":""},{"docid":"54575c6d3f4a8424edf790d07c3860b6","score":"0.5900126","text":"def to_dataframe(predictions):\n df_list = []\n for i, p in enumerate(predictions):\n boxes = p['boxes'].detach().cpu().tolist()\n labels = p['labels'].detach().cpu().tolist()\n scores = p['scores'].detach().cpu().tolist()\n df = pd.DataFrame(boxes, columns=['x1','y1','x2','y2'])\n df['class_id'] = labels\n df['score'] = scores\n df['frame_id'] = i\n df_list.append(df)\n df_detect = pd.concat(df_list, axis=0)\n df_detect = df_detect[['frame_id','class_id','score','x1','y1','x2','y2']]\n return df_detect","title":""},{"docid":"5a0a365dafff994b9bb0f034fbc8f279","score":"0.5899012","text":"def _get_data(self) -> None:\n\n if len(self._stations) > 0:\n\n # Get list of datasets\n datasets = self._get_datasets()\n\n # Data Processings\n return processing_handler(\n datasets, self._load_data, self.processes, self.threads\n )\n\n # Empty DataFrame\n return pd.DataFrame(columns=[*self._types])","title":""},{"docid":"0df8cd9f0c6d316a8da767ec62b4c91c","score":"0.58987474","text":"def prepare_data_frame(*, shape_file):\n column_names = [r[0] for r in shape_file.fields][1:]\n records = shape_file.records()\n shape_points = [s.points for s in shape_file.shapes()]\n data_frame = pd.DataFrame(columns=column_names, data=records)\n data_frame = data_frame.assign(coords=shape_points)\n return data_frame","title":""},{"docid":"23d6425897142f16824fe9d879c5a096","score":"0.5896701","text":"def read_data(self)->pd.DataFrame:\r\n if self._canRead:\r\n rawData = self._device.read(5000)\r\n rawCol = ['channel_1_raw','channel_2_raw','channel_3_raw','channel_4_raw']\r\n df= pd.DataFrame(columns = rawCol)\r\n df.channel_1_raw = rawData[:,5]\r\n df.channel_2_raw = rawData[:,6]\r\n df.channel_3_raw = rawData[:,7]\r\n df.channel_4_raw = rawData[:,8]\r\n #print(time.strftime(\"%H:%M:%S\", time.localtime()))\r\n df['time'] = time.strftime(\"%H:%M:%S\", time.localtime())\r\n #df.set_index('time',inplace=True)\r\n #print(df.head())\r\n return df\r\n else:\r\n print(f'Device must be started.')","title":""},{"docid":"f878911e6004c293ca90f6ad3b495b13","score":"0.5891539","text":"def get_dataframe(object):\n\n try:\n df = pd.DataFrame(object)\n except ValueError:\n raise\n else:\n return df","title":""},{"docid":"55fbd0f118ce77e0efcbc95062f9cf7c","score":"0.5891077","text":"def df(self):\n return self._df","title":""},{"docid":"6a234a87ddbb091e14d2cb0651df5d9e","score":"0.58888507","text":"def Create(self):\n df1 = pd.DataFrame(self.columns, index = [0])\n df1.drop(axis = 0, index = df1.index[0], inplace = True)\n df1.to_csv(self.path, index = False, encoding=self.encoding)\n\n return None","title":""},{"docid":"ca110fbc2c3d2ea120f27ab8cc3a4023","score":"0.5883441","text":"def dataframe(self):\n if self._away_points is None and self._home_points is None:\n return None\n fields_to_include = {\n 'away_first_downs': self.away_first_downs,\n 'away_fumbles': self.away_fumbles,\n 'away_fumbles_lost': self.away_fumbles_lost,\n 'away_interceptions': self.away_interceptions,\n 'away_pass_attempts': self.away_pass_attempts,\n 'away_pass_completions': self.away_pass_completions,\n 'away_pass_touchdowns': self.away_pass_touchdowns,\n 'away_pass_yards': self.away_pass_yards,\n 'away_penalties': self.away_penalties,\n 'away_points': self.away_points,\n 'away_rush_attempts': self.away_rush_attempts,\n 'away_rush_touchdowns': self.away_rush_touchdowns,\n 'away_rush_yards': self.away_rush_yards,\n 'away_total_yards': self.away_total_yards,\n 'away_turnovers': self.away_turnovers,\n 'away_yards_from_penalties': self.away_yards_from_penalties,\n 'date': self.date,\n 'home_first_downs': self.home_first_downs,\n 'home_fumbles': self.home_fumbles,\n 'home_fumbles_lost': self.home_fumbles_lost,\n 'home_interceptions': self.home_interceptions,\n 'home_pass_attempts': self.home_pass_attempts,\n 'home_pass_completions': self.home_pass_completions,\n 'home_pass_touchdowns': self.home_pass_touchdowns,\n 'home_pass_yards': self.home_pass_yards,\n 'home_penalties': self.home_penalties,\n 'home_points': self.home_points,\n 'home_rush_attempts': self.home_rush_attempts,\n 'home_rush_touchdowns': self.home_rush_touchdowns,\n 'home_rush_yards': self.home_rush_yards,\n 'home_total_yards': self.home_total_yards,\n 'home_turnovers': self.home_turnovers,\n 'home_yards_from_penalties': self.home_yards_from_penalties,\n 'losing_abbr': self.losing_abbr,\n 'losing_name': self.losing_name,\n 'stadium': self.stadium,\n 'time': self.time,\n 'winner': self.winner,\n 'winning_abbr': self.winning_abbr,\n 'winning_name': self.winning_name\n }\n return pd.DataFrame([fields_to_include], index=[self._uri])","title":""},{"docid":"66b656c650541ab8128040fcfb48a4da","score":"0.58776695","text":"def prepare(data):\n data = pd.DataFrame(data)\n X = data.drop('class', axis=1)\n y = data['class']\n return X, y","title":""},{"docid":"192e6b8ac8c4a9ab1cc4f226fd3c90aa","score":"0.58742565","text":"def _to_dataframe(self, features):\n df = pandas.DataFrame(features)\n\n properties = df.properties.apply(pandas.Series).copy()\n properties = properties.add_prefix('properties_')\n\n geometry = df.geometry.apply(pandas.Series).copy()\n coordinates = geometry.coordinates.apply(pandas.Series).copy()\n coordinates = coordinates.add_prefix('geometry_coordinates_')\n\n self.data = pandas.concat([properties, coordinates, df.id], axis = 1)","title":""},{"docid":"7629a6d8218e36d827dfa5dc399c3f49","score":"0.5872692","text":"def create_dataframe():\n df = pd.read_csv('DashflaskSAFC/data/safc1920.csv')\n return df","title":""},{"docid":"bd53beaa765f62b287a9a95ec044ab82","score":"0.58718675","text":"def to_dataframe(self, lazy=True, **kwargs):\n if lazy:\n kwargs.setdefault('exclude', ['_cache'])\n return query_to_dataframe(\n self,\n connection=self._connection_from_session(),\n **kwargs\n )\n else:\n return DataFrame([model.to_json() for model in self])","title":""},{"docid":"36927be974c6344f47e7465bd25ef757","score":"0.5868971","text":"def getData(self):\n df = self.getDataFrame()\n y = df.iloc[:,0].values\n X = df.iloc[:,1:].values \n print(\"dimension de X = \" + str(X.shape))\n print(\"dimension de y = \" + str(y.shape))\n return X,y","title":""},{"docid":"83ab0e359fcfd6103b98794c2709aaa5","score":"0.5864817","text":"def _to_df_internal(self) -> pd.DataFrame:\n pass","title":""},{"docid":"72e0f67ab1ed2ae5ac477a2a3b8b350b","score":"0.5860397","text":"def extract_arrays(db):\n cursor = db.cursor()\n return extract_sql_data(\"array\", cursor)","title":""},{"docid":"e991d32769a3f62bd9fe8227524e22c4","score":"0.585693","text":"def build_dataframe(data):\n df = pd.DataFrame(data.data,\n columns=data.feature_names)\n df['target'] = data.target\n return df","title":""},{"docid":"4b923d339acc756cc4e629b95734dd4d","score":"0.5855825","text":"def getDataFramePackets(self):\n return pd.DataFrame([item.getSingleDict() for item in self.packets])","title":""},{"docid":"ceb58e025ce62a48e14dab226a1c1b4b","score":"0.584923","text":"def run(self, dataframe: DataFrame) -> DataFrame:","title":""},{"docid":"7a8b49a28806928e1b849c5281972fed","score":"0.5846577","text":"def create_dataframe(path_to_datafile):\n df = # Your code here\n\n return df","title":""},{"docid":"fd8e8715f8f9f26318963b1a27ae8dbe","score":"0.584536","text":"def constructObjects(self, rows):\n results = []\n #self.log(\"Rows: %s\"%(rows,), level=logging.INFO)\n if rows:\n for row in rows:\n results.append(self.constructObject(row))\n return results","title":""},{"docid":"fac944a0a755638b6524cf6a5a2949d0","score":"0.58441114","text":"def test_as_data_frame():\r\n raw = Raw(fif_fname, preload=True)\r\n df = raw.as_data_frame()\r\n assert_true((df.columns == raw.ch_names).all())\r\n df = raw.as_data_frame(use_time_index=False)\r\n assert_true('time' in df.columns)\r\n assert_array_equal(df.values[:, 1], raw._data[0] * 1e13)\r\n assert_array_equal(df.values[:, 3], raw._data[2] * 1e15)","title":""},{"docid":"6afcb7a45dabc1d03164cafcc3a96b95","score":"0.58424526","text":"def create_data(data_tensor, times, cols, num=0):\n df = pd.DataFrame(data_tensor.numpy()[0])\n df.columns = cols\n df['Strain'] = [num] * df.shape[0]\n df = df.set_index('Strain')\n df['Time'] = times\n print('Shape of the dataframe is:', df.shape)\n return df","title":""},{"docid":"f1d8341ce6f27ad04abd84d230a42588","score":"0.58401895","text":"def __init__(self, dataframe):\n\t\tself.data = dataframe.copy(deep=True)","title":""},{"docid":"b3ccc0c64b4d72ffb44632bf05f925bc","score":"0.58399343","text":"def to_dataset(self, *args, **kwargs):\r\n dss = [f.to_dataset(*args, **kwargs) for f in self._frames]\r\n return dss","title":""}],"string":"[\n {\n \"docid\": \"f3cac4bb3a80488c58c07f43841fc51f\",\n \"score\": \"0.7516557\",\n \"text\": \"def create_dataframe(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c404fb5f07c549abab0f693980de90\",\n \"score\": \"0.71988475\",\n \"text\": \"def _get_data(self):\\n # TODO implement this method\\n return pd.DataFrame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31323537289e8db4fdf80e27a7609b2d\",\n \"score\": \"0.6973086\",\n \"text\": \"def data(self):\\n\\n if self._data is None:\\n self._cur.execute(\\\"SELECT objectid::int, \\\"\\n \\\"s10::int, s11::int, s12::int, \\\"\\n \\\"k10/10::float, k11/10::float, k12/10::float, \\\"\\n \\\"h10::float, h11::float, h12::float, \\\"\\n \\\"a10::int, a11::int, a12::int, \\\"\\n \\\"v10::float, v11::float, v12::float \\\"\\n \\\"FROM lvm_data_all \\\"\\n \\\"WHERE s10 IS NOT NULL;\\\")\\n\\n data = []\\n\\n for row in self._cur:\\n data.append(list(row))\\n\\n self._data = pd.DataFrame(\\n data,\\n columns=[\\n 'objectid',\\n 's1', 's2', 's3',\\n 'k1', 'k2', 'k3',\\n 'h1', 'h2', 'h3',\\n 'a1', 'a2', 'a3',\\n 'v1', 'v2', 'v3',\\n ]\\n )\\n\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b335852fe32ba62332a2536eced4571\",\n \"score\": \"0.682229\",\n \"text\": \"def _create_dataframe(self):\\n columns = [\\n \\\"Date\\\",\\n \\\"Scenario\\\",\\n \\\"Score\\\",\\n \\\"Kills\\\",\\n \\\"Acc.\\\",\\n \\\"Avg Score\\\",\\n \\\"High Score\\\",\\n \\\"Diff Avg Score\\\",\\n \\\"Diff High Score\\\",\\n ]\\n if self.table_entries:\\n df = pd.DataFrame(np.array(self.table_entries), columns=columns)\\n df = df.sort_values(by=\\\"Date\\\", ascending=False)\\n # Corrects datatypes in Dataframe\\n df[[\\\"Date\\\"]] = df[[\\\"Date\\\"]].apply(pd.to_datetime)\\n df[[\\\"Score\\\"]] = df[[\\\"Score\\\"]].apply(pd.to_numeric)\\n df[[\\\"Avg Score\\\"]] = df[[\\\"Avg Score\\\"]].apply(pd.to_numeric)\\n df[[\\\"High Score\\\"]] = df[[\\\"High Score\\\"]].apply(pd.to_numeric)\\n\\n self.dataframe = df\\n self._update_benchmark_bars()\\n self._setup_homepage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742edb75933731472c89cd9a11ae33a4\",\n \"score\": \"0.6810718\",\n \"text\": \"def toDataFrame(self):\\n if self.data is None:\\n self.eval(outputDF=True)\\n if not isinstance(self.data, DataFrame):\\n self.data = matrix.sqlContext.createDataFrame(self.toPandas())\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d566454d4baed322245aaecb0803d8\",\n \"score\": \"0.6728489\",\n \"text\": \"def get_frame(self):\\n return pd.DataFrame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77af1ec14cb9de15c33cdb785e7e68e\",\n \"score\": \"0.6603054\",\n \"text\": \"def prepare_data(array):\\n stats = []\\n for row in array:\\n stats.append(pd.DataFrame(row.tolist()).values.flatten())\\n return stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f93b916c532699c494afd433fbc899a8\",\n \"score\": \"0.6577323\",\n \"text\": \"def make_pandas(self):\\n columns = self.papers_list[0]._raw.keys()\\n papers_df = pd.DataFrame(columns=columns)\\n for paper in self.papers_list:\\n papers_df = papers_df.append(paper._raw, ignore_index=True)\\n # papers_df.set_index('id', inplace=True)\\n return papers_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322b3697b21b7740240a12c546ee801f\",\n \"score\": \"0.65767074\",\n \"text\": \"def create_data_frame_result(self):\\r\\n start = timer()\\r\\n self.data_frame_result = pd.DataFrame(self.dict_of_array_result)\\r\\n end = timer()\\r\\n print '::: Time to create result data frame:', end - start\\r\\n \\r\\n self.data_frame_result.to_pickle('results/'+self.output_tag + '/' + self.output_file)\\r\\n return self.data_frame_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec788123c72658e2fe2b88fcdfdac01\",\n \"score\": \"0.6538759\",\n \"text\": \"def dataframe(self):\\n return pandas.DataFrame(self.values, columns=self.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292fc53684ae35481afeb64f2d6799e1\",\n \"score\": \"0.65293294\",\n \"text\": \"def _make_df(self) -> pd.DataFrame:\\n df = pd.DataFrame(self.raw_acs_data[1:], columns=self.raw_acs_data[0])\\n df = df.loc[:,~df.columns.duplicated()] #remove duplicate columns\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846227628fb296a0fbdbe6c91f2591a9\",\n \"score\": \"0.6520269\",\n \"text\": \"def get_data(self):\\n self.cursor.execute(f\\\"SELECT * FROM {TABLE_NAME}\\\")\\n data = self.cursor.fetchall()\\n columns = [desc[0] for desc in self.cursor.description]\\n return pd.DataFrame(data, columns=columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3b460213192f8862e69d30c89733aa\",\n \"score\": \"0.64974713\",\n \"text\": \"def create_df():\\n return pd.DataFrame(columns=[COLUMN_AUTHOR, COLUMN_REVIEW_DATE, COLUMN_RATING, COLUMN_CONTENT])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91e7a1e1327e641765f86028d00d2df\",\n \"score\": \"0.64821476\",\n \"text\": \"def batch_create_df(self, result: List[List[np.ndarray]]) -> List[List[dict]]:\\n df = []\\n for one_result in result:\\n one_df = self.create_df(one_result)\\n df.append(one_df)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2d6df4a6215e0bc7c1f522808457bb\",\n \"score\": \"0.64701545\",\n \"text\": \"def to_dataframe(self):\\n self.df = pd.DataFrame(self.results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd1be6b4b916f63f8af9f1c5ccc4604\",\n \"score\": \"0.6434636\",\n \"text\": \"def make_dataframe(self):\\n dataframe = pd.DataFrame.from_records(\\n self.reservations_json,\\n columns=[\\n 'ID',\\n 'Corretor',\\n 'Data do Turno',\\n 'Hora de Cadastro',\\n 'Posição',\\n 'Turno',\\n 'Imóvel'\\n ]\\n )\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9520b2f1043b4001c313de88cf6652b\",\n \"score\": \"0.64336663\",\n \"text\": \"def to_df(self):\\n # Find all source properties\\n df = pd.DataFrame()\\n for attr in self.__dict__.keys():\\n parm = getattr(self, attr)\\n if type(parm) is np.ndarray:\\n df[attr] = parm\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf8a9f3c42ce9ec027d2ded377f0ef7\",\n \"score\": \"0.6428806\",\n \"text\": \"def create_tweet_dataframe(self):\\n return pd.DataFrame(list(self._collection.find()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cefd27e782c5746bdb3eff81b850de0\",\n \"score\": \"0.64203423\",\n \"text\": \"def to_array(self):\\n if self.__is_splitted:\\n m_dataset = []\\n for v in self.__dataset:\\n train_df = v[0]\\n test_df = v[1]\\n # last two columns: event event_time ( eg PFI PFI.time)\\n x_train_df = train_df.iloc[:, :-2]\\n y_train_df = train_df.iloc[:, -2:]\\n x_test_df = test_df.iloc[:, :-2]\\n y_test_df = test_df.iloc[:, -2:]\\n # convert to numpy array\\n x_train = np.array(x_train_df)\\n y_train = np.array(y_train_df)\\n x_test = np.array(x_test_df)\\n y_test = np.array(y_test_df)\\n\\n m_dataset.append((x_train, y_train, x_test, y_test))\\n return m_dataset\\n else:\\n x_df = self.__df.iloc[:, :-2]\\n y_df = self.__df.iloc[:, -2:]\\n\\n x_array = np.array(x_df, dtype=np.float)\\n y_array = np.array(y_df, dtype=np.float)\\n return x_array, y_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c410bce52f55f82bd151e3a4696729\",\n \"score\": \"0.6399226\",\n \"text\": \"def data():\\n x_size = len(fasta)\\n y_size = 10\\n numpy.random.seed(0)\\n M = numpy.random.randint(0, 1000, (x_size, y_size))\\n df = pandas.DataFrame(M, index=[seq.id for seq in fasta], columns=[names.get_first_name() for j in range(y_size)])\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3dbc60752ec21cce4863a0dc8d1fcd6\",\n \"score\": \"0.6368369\",\n \"text\": \"def getDataFrame(self):\\r\\n \\r\\n # Load data if are not loaded\\r\\n if self.df is None:\\r\\n self._load_data()\\r\\n \\r\\n # Return data as uda.DataFrame\\r\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ccad96875291a236a38ac214725fd69\",\n \"score\": \"0.6352187\",\n \"text\": \"def data_frame(self) -> pd.DataFrame:\\n return self._data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c88e00b26b332f64c41c035a78daba\",\n \"score\": \"0.6336747\",\n \"text\": \"def list(self) -> 'DataFrame':\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42377933bf62f77a4ab7d5a6b4694e4f\",\n \"score\": \"0.6321764\",\n \"text\": \"def fixture_dataframe():\\n return pd.DataFrame(data={\\\"x\\\": [0], \\\"y\\\": [0]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ec8e546cd065b00c0671ffc2a476ff\",\n \"score\": \"0.63007814\",\n \"text\": \"def as_df(self):\\n self.load()\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ec8e546cd065b00c0671ffc2a476ff\",\n \"score\": \"0.63007814\",\n \"text\": \"def as_df(self):\\n self.load()\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984661ccbc7364c04c95c0034a156a0e\",\n \"score\": \"0.62911385\",\n \"text\": \"def dataframe(self):\\n temp_index = self._index\\n rows = []\\n indices = []\\n for season in self._season:\\n self._index = self._season.index(season)\\n rows.append(self._dataframe_fields())\\n indices.append(season)\\n self._index = temp_index\\n return pd.DataFrame(rows, index=[indices])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b78604ad92661d94cd467456431a42b\",\n \"score\": \"0.62896055\",\n \"text\": \"def create_dataframe(self, coll, query=None, find_all=True):\\n frame = self.find_doc(coll, query, find_all)\\n return pd.DataFrame(frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469f2cdfa9a008e1314eee885cee5237\",\n \"score\": \"0.62818605\",\n \"text\": \"def creaDataFrame(regiones):\\r\\n df=pd.DataFrame()\\r\\n for region in regiones: \\r\\n if region:\\r\\n regionData,pais,idioma,h,tiempo = regionPaisIdioma(region)\\r\\n d={\\\"Region\\\":regionData,\\\"City Name\\\":pais,\\\"Languaje\\\":h,\\\"Time\\\":tiempo}\\r\\n df=df.append(d , ignore_index=True)\\r\\n if logs:\\r\\n logging.debug('Tabla Pandas: \\\\n {}'.format(df))\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12cb741b5ba5a2a96269f196b493396d\",\n \"score\": \"0.62785035\",\n \"text\": \"def dataframe(self, records: List[Dict]) -> Any:\\n return pd.DataFrame(self.records(records))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923418a80e0e66494a492cfc9c853632\",\n \"score\": \"0.62781024\",\n \"text\": \"def to_dataframe(self, complete=False):\\n\\n import pandas as pd\\n arr = self.to_numpy(complete=complete)\\n df = pd.DataFrame.from_records(arr)\\n df.index = np.arange(df.shape[0], dtype='int64')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4fc93fba84aae3968c18fba9294e84\",\n \"score\": \"0.6270584\",\n \"text\": \"def get_dataframe(self):\\n return self.dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ae2d94c42c7907e0e787cb8ff72d2a\",\n \"score\": \"0.6253916\",\n \"text\": \"def get_data(self): # TODO use ORM instead of queries\\n # TODO format query against sql injection\\n query = f\\\"\\\"\\\"\\n Select * from \\\"transactions\\\" t \\n join \\\"customers\\\" c on c.\\\"customer_id\\\"=t.\\\"customer_id\\\"\\n join \\\"stations\\\" s on t.\\\"station_id\\\"=s.\\\"station_id\\\"\\n where dataset='{self.dataset}'\\n \\\"\\\"\\\"\\n df = pd.read_sql(query, con=self.engine).drop_duplicates()\\n df.index = pd.to_datetime(df.transaction_date)\\n df = df.loc[:, ~df.columns.duplicated()]\\n print(df.shape)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"823117bb892deaee94f96d4b48f076dc\",\n \"score\": \"0.62498295\",\n \"text\": \"def getData(self) -> pd.DataFrame:\\n return self.data.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edaec994bd0efa24970c6d64f22e2ca\",\n \"score\": \"0.6247302\",\n \"text\": \"def get_data_frame(self):\\n return self.final_data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc376ecf057cf9064e38b2fcaace5bee\",\n \"score\": \"0.62367254\",\n \"text\": \"def data_frame(self) -> pd.DataFrame:\\n return pd.DataFrame(self.parse())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc57688be355e6ff146f6af052ec7559\",\n \"score\": \"0.6227631\",\n \"text\": \"def as_dataframe(self):\\n result = self.properties.copy()\\n if self.positions is not None:\\n result['x'] = self.positions[:, 0]\\n result['y'] = self.positions[:, 1]\\n result['z'] = self.positions[:, 2]\\n if self.orientations is not None:\\n result['orientation'] = list(self.orientations)\\n\\n result.index = 1 + np.arange(len(result))\\n\\n result.columns = map(str, result.columns)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ace983eec6db2f4a882794c3c200c4\",\n \"score\": \"0.62000483\",\n \"text\": \"def get_raw_dataframe(self):\\r\\n return self.raw_data_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a41e276199ab5384249954271b45d11\",\n \"score\": \"0.6186407\",\n \"text\": \"def as_dataframe(self):\\n\\t\\treturn pd.DataFrame(self.response, index = self.input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d542dc81072823413bd173873733966\",\n \"score\": \"0.6154277\",\n \"text\": \"def get_dataframe_apoyos(self):\\r\\n \\r\\n return self.v.df_apoyos.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3abdef97663907580db173100cc613e\",\n \"score\": \"0.61516017\",\n \"text\": \"def as_df(self) -> pd.DataFrame:\\n return pd.DataFrame(self.as_dicts())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31799ba995be038ec3c9468ebcba7ad\",\n \"score\": \"0.61390465\",\n \"text\": \"def prepare(self):\\n self.add_duration()\\n self.add_distance()\\n self.add_acc()\\n self.add_dayofweek()\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3940ce365da7f4f21f52b695d3c43027\",\n \"score\": \"0.612594\",\n \"text\": \"def to_data_frame(self, fields: Optional[List[str]] = None) -> pd.DataFrame: # noqa 501\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f55f52266096170d251f5629738b6793\",\n \"score\": \"0.6122482\",\n \"text\": \"def get_dataframe2(fcs):\\n\\tif not isinstance(fcs, (tuple, list)):\\n\\t\\tfcs = fcs,\\n\\n\\ttpls = []\\n\\tfor fc in fcs:\\n\\t\\tfor f in fc.frames:\\n\\t\\t\\tfor d in f.detectionsUnion.detectionsDP:\\n\\t\\t\\t\\td = Detection(d.idx, d.xpos, d.ypos, d.radius, d.zRotation, list(d.decodedId), f.frameIdx, f.timestamp, fc.camId, fc.id)\\n\\t\\t\\t\\ttpls.append(d)\\n\\n\\tdf = pd.DataFrame(tpls)\\n\\treturn df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae54cf0c6fa802ffacab1d74120b5079\",\n \"score\": \"0.61189175\",\n \"text\": \"def get_dataframe(self):\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ee2dfc2d223390fb4bb87ac4780296\",\n \"score\": \"0.61135054\",\n \"text\": \"def dataframe(self, limit=None):\\n\\n from metapack.jupyter.pandas import MetatabDataFrame\\n\\n rg = self.row_generator\\n\\n # Maybe generator has it's own Dataframe method()\\n try:\\n return rg.dataframe()\\n except AttributeError:\\n pass\\n\\n # Just normal data, so use the iterator in this object.\\n headers = next(islice(self, 0, 1))\\n data = islice(self, 1, None)\\n\\n df = MetatabDataFrame(list(data), columns=headers, metatab_resource=self)\\n\\n self.errors = df.metatab_errors = rg.errors if hasattr(rg, 'errors') and rg.errors else {}\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9091475c29754bf09011f17f28e846b7\",\n \"score\": \"0.608062\",\n \"text\": \"def close(self) -> pd.DataFrame:\\n return pd.DataFrame(\\n data=self.data,\\n columns=self.columns,\\n index=self.index,\\n dtype=self.dtypes\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79d0657eaa2f3a2027652b10c950252f\",\n \"score\": \"0.60781044\",\n \"text\": \"def dataset():\\n return pd.DataFrame(\\n data=[\\n ['Alice', 45],\\n ['Bob', 23],\\n ['Claudia', 25],\\n ['Dave', 56]\\n ],\\n columns=['Name', 'Age']\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef3382d278f0390932eab04783d3a44\",\n \"score\": \"0.6062772\",\n \"text\": \"def create_data_numpy(data_numpy, times, cols, num=0):\\n df = pd.DataFrame(data_numpy)\\n df.columns = cols\\n df['Strain'] = [num] * df.shape[0]\\n df = df.set_index('Strain')\\n df['Time'] = times\\n print('Shape of the dataframe is:', df.shape)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0697fb2b26ccc081615600957c8e4812\",\n \"score\": \"0.60553914\",\n \"text\": \"def data_as_dataframe(self):\\n\\n resource = request(self.apiUris[3].uri) # third element is icane json\\n data = pd.DataFrame(list(flatten_data(resource)))\\n headers = list(resource['headers'])\\n headers.append(unicode('Valor'))\\n data.columns = headers\\n time_series = pd.DataFrame()\\n for element in headers:\\n if element.encode('utf-8') in ['Año', 'Trimestre', 'Mes']:\\n time_series = data.set_index([unicode(element)])\\n elif element == 'Municipios':\\n index = 'Municipios'\\n if time_series.empty:\\n time_series = data.set_index(index)\\n return time_series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52c3b1eec6e3197cf14d766540aeafae\",\n \"score\": \"0.6055046\",\n \"text\": \"def asarray(self):\\n return [Frame(frame, self.info().copy()) for frame in self.data()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04dc2a20c9334cc1e9dcd7de1e4aef34\",\n \"score\": \"0.60513\",\n \"text\": \"def GetArrayRepresentation(self):\\n contents = self.GetContents()\\n rows = len(contents)\\n cols = 4\\n \\n \\n #Dimension the list initally rather than appending\\n #results = [[None]*rows]*cols\\n results = [[], [], [], []]\\n for row in range(rows):\\n obj = contents[row][1]\\n results[NAME_COL].append(obj.name)\\n results[VALUE_COL].append(obj.value)\\n results[INFOTYPE_COL].append(obj.infoType)\\n results[OPTIONS_COL].append(obj.options)\\n \\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64321dd6f971be40e488d5322cc9297\",\n \"score\": \"0.60492355\",\n \"text\": \"def dframe(self):\\n from pandas import DataFrame\\n return DataFrame({(k.name if isinstance(k, Dimension)\\n else k): [v] for k, v in self.data.items()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c086fff266db922de8dd58f7727f0631\",\n \"score\": \"0.60481715\",\n \"text\": \"def _to_array(self, sets: str, window_size: int) -> np.ndarray:\\n to_concat_x = []\\n to_concat_y = []\\n for i in range(4):\\n dataset = 'FD_00{}'.format(i + 1)\\n df = self.data[dataset]['df_' + sets]\\n x, y = utils.to_array(df, window_size)\\n to_concat_x.append(x)\\n to_concat_y.append(y)\\n\\n x = np.concatenate(to_concat_x)\\n y = np.concatenate(to_concat_y)\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7239ba0bdc185c00ced88b6698d79331\",\n \"score\": \"0.60388744\",\n \"text\": \"def getDataFrame(self):\\n return pd.read_csv(self.data_path).astype('float32')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e197fdcb730832fd2945c4a1a4b1475d\",\n \"score\": \"0.60135585\",\n \"text\": \"def to_frame(self) -> pd.DataFrame:\\n return pd.DataFrame([mol.to_series() for mol in self.moleculeDict.values()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4034406da049cb907d8e3030e3161430\",\n \"score\": \"0.60098\",\n \"text\": \"def dataframe_proyecto(self):\\r\\n \\r\\n pd = self.get_pandas()\\r\\n d_proyecto = self.get_proyecto()\\r\\n ser = pd.Series(d_proyecto)\\r\\n df = ser.to_frame()\\r\\n df = df.T\\r\\n \\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6141c38fa9431eb49d9c7d76bfd4f83\",\n \"score\": \"0.6006332\",\n \"text\": \"def data_frame(self):\\n\\n # Import pyspark functions\\n import pyspark.sql.functions as F\\n\\n # Checks first if there is a data frame, outputs None.\\n if self._data_frame is None:\\n logger_data_import.warning(\\\"Data frame has not been set\\\")\\n return\\n\\n # Create casted features and labels\\n feature_types = [GeneralDataImport.cast_to_right_type(feature)\\n for feature in self._list_structfield_features\\n ]\\n id_types = [F.col(id.name).cast('string').alias(id.name)\\n for id in self._list_structfield_id\\n ]\\n label_types = list(map(\\n func=lambda c: F.col(c.name).cast('double').alias(c.name),\\n iter1=self._list_structfield_label)\\n )\\n\\n # Log the selected features and labels\\n logger_data_import.info(\\n \\\"Data frame exported with columns: {col}, missing: {mis}\\\"\\n .format(col=self._list_structfield_label\\n + self._list_structfield_id\\n + self._list_structfield_features,\\n mis=set(self._all_columns)\\n - set(self._list_structfield_label\\n + self._list_structfield_id\\n + self._list_structfield_features\\n )\\n )\\n )\\n return self._data_frame.select(\\n id_types+label_types+feature_types\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6df014f7b61a113cc7a2f6ea2df380d6\",\n \"score\": \"0.60000485\",\n \"text\": \"def get_dataframe_implantaciones(self):\\r\\n \\r\\n return self.v.df_implantaciones.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cd330e2e632ba968321774484c07a3\",\n \"score\": \"0.5990821\",\n \"text\": \"def init_dataframe():\\n index = _node_to_position.keys()\\n columns = ['x', 'y', 'value']\\n df = pd.DataFrame(index = index, columns=columns)\\n for node_id, (x, y) in _node_to_position.items():\\n df.loc[node_id]['x'] = x\\n df.loc[node_id]['y'] = y\\n df.loc[node_id]['value'] = 0\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d8f72839e42fab15df8e0b692a245f\",\n \"score\": \"0.5982969\",\n \"text\": \"def create_df(self):\\n df = pd.DataFrame()\\n df['orf_name'] = self.nodes\\n df['deg'] = np.diag(self.L)\\n eff_orig = np.sum(self.prs_mat, axis=1)\\n sens_orig = np.sum(self.prs_mat, axis=0)\\n eigvecs_df = pd.DataFrame(self.gnm.getEigvecs(\\n )[:, self.coll_index_sorted[:8]], columns=[f'eig_{i}' for i in range(8)])\\n\\n df_ = df#pd.merge(df, eigvecs_df, left_index=True, right_index=True)\\n df_['eff'] = eff_orig\\n df_['sens'] = sens_orig\\n eigenvector_centr = nx.eigenvector_centrality_numpy(self.graph_gc)\\n closeness_centr = nx.closeness_centrality(self.graph_gc)\\n# df_['btw'] = betweenness_nx(self.graph_gc, normalized=True)\\n df_['trans'] = list((nx.clustering(self.graph_gc)).values())\\n df_['eigenvec_centr'] = [eigenvector_centr[i]\\n for i in eigenvector_centr]\\n df_['closeness_centr'] = [closeness_centr[i] for i in closeness_centr]\\n df_['smallest_eigenvec'] = self.gnm.getEigvecs()[:, 0]\\n self.df = df_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe13085b941dc5c3a913a6953382c413\",\n \"score\": \"0.5981116\",\n \"text\": \"def _convert_to_records(self):\\n for dfname in self.data.keys():\\n self.log.debug(\\\"Converting %s \\\" % dfname)\\n # Set index at which next set of tuples begins\\n self.data[dfname]['first_index'] = len(self.data[dfname]['tuples']) + \\\\\\n self.data[dfname].get('first_index', 0)\\n # Convert tuples to records\\n newrecords = np.array(self.data[dfname]['tuples'],\\n self.data[dfname]['dtype'])\\n # Clear tuples. Enjoy the freed memory.\\n self.data[dfname]['tuples'] = []\\n # Append new records\\n if self.data[dfname]['records'] is None:\\n self.data[dfname]['records'] = newrecords\\n else:\\n self.data[dfname]['records'] = np.concatenate((self.data[dfname]['records'],\\n newrecords))\\n self.events_ready_for_conversion = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b098cf38cf6d94613942fa4f08b58bf\",\n \"score\": \"0.59746027\",\n \"text\": \"def get_data():\\n data = pd.DataFrame()\\n for i in range(0, 8):\\n split_path = os.path.join(DATA_PATH,\\n \\\"updrs_splits/split_{}.csv\\\".format(i))\\n # split = pd.read_csv(\\\"../../data/updrs_splits/split_{}.csv\\\".format(i))\\n split = pd.read_csv(split_path)\\n data = pd.concat([data, split])\\n\\n X = data.drop(columns=[\\\"patno\\\", \\\"score\\\", \\\"date_scan\\\"])\\n y = data[\\\"score\\\"].astype(int)\\n\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b59ba014a72149a2da07c05ab8a632\",\n \"score\": \"0.5972888\",\n \"text\": \"def xarray_to_dataframe(xr: Dataset) -> DataFrame:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46993677407c7124fb66f642db4e0611\",\n \"score\": \"0.5966046\",\n \"text\": \"def __init__(self):\\n self.df = pd.DataFrame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c24a385f3237fd30e335da29603dd5\",\n \"score\": \"0.5954832\",\n \"text\": \"def createSimpleDataSet( numOfAttr, numOfObj ):\\n database = []\\n for i in range(numOfObj):\\n data = dataObject(numOfAttr)\\n\\n w=[random.gauss(2.0, 2.0)]\\n w.append(w[0]*3+random.gauss(0.0, 0.05))\\n w.append(random.random()*6)\\n\\n data.setAttributes(w)\\n database.append(data)\\n return database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38089d776adb6b58bf86d4a37763dbe\",\n \"score\": \"0.5947937\",\n \"text\": \"def get_data() -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:\\r\\n projects = select_projects_from_libraries_io()\\r\\n libraries = select_libraries()\\r\\n migrations = select_migrations()\\r\\n lib_names = set(libraries[\\\"name\\\"])\\r\\n rules = select_rules(lib_names)\\r\\n dep_changes = select_dependency_changes_all(lib_names)\\r\\n migrations.startCommitTime = pd.to_datetime(migrations.startCommitTime, utc=True)\\r\\n migrations.endCommitTime = pd.to_datetime(migrations.endCommitTime, utc=True)\\r\\n return projects, libraries, migrations, rules, dep_changes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4cff1c3ffb7a611da367f353808c33\",\n \"score\": \"0.59470904\",\n \"text\": \"def query_set_to_df(q_set : QuerySet):\\n return pd.DataFrame(q_set.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34db4dcfa8b8dd58332dee6fc022c5d5\",\n \"score\": \"0.5940663\",\n \"text\": \"def to_dataframe(self, **kwargs):\\n use_categories = popargs('use_categories', kwargs)\\n # Step 1: Combine the entities, keys, and entity_keys table\\n ent_key_df = self.entity_keys.to_dataframe()\\n entities_mapped_df = self.entities.to_dataframe().iloc[ent_key_df['entities_idx']].reset_index(drop=True)\\n keys_mapped_df = self.keys.to_dataframe().iloc[ent_key_df['keys_idx']].reset_index(drop=True)\\n ent_key_df = pd.concat(objs=[ent_key_df, entities_mapped_df, keys_mapped_df],\\n axis=1,\\n verify_integrity=False)\\n # Step 2: Combine the the files, object_keys and objects tables\\n object_keys_df = self.object_keys.to_dataframe()\\n objects_mapped_df = self.objects.to_dataframe().iloc[object_keys_df['objects_idx']].reset_index(drop=True)\\n object_keys_df = pd.concat(objs=[object_keys_df, objects_mapped_df],\\n axis=1,\\n verify_integrity=False)\\n files_df = self.files.to_dataframe().iloc[object_keys_df['files_idx']].reset_index(drop=True)\\n file_object_object_key_df = pd.concat(objs=[object_keys_df, files_df],\\n axis=1,\\n verify_integrity=False)\\n # Step 3: merge the combined entities_df and object_keys_df DataFrames\\n result_df = pd.concat(\\n # Create for each row in the objects_keys table a DataFrame with all corresponding data from all tables\\n objs=[pd.merge(\\n # Find all entities that correspond to the row i of the object_keys_table\\n ent_key_df[ent_key_df['keys_idx'] == object_keys_df['keys_idx'].iloc[i]].reset_index(drop=True),\\n # Get a DataFrame for row i of the objects_keys_table\\n file_object_object_key_df.iloc[[i, ]],\\n # Merge the entities and object_keys on the keys_idx column so that the values from the single\\n # object_keys_table row are copied across all corresponding rows in the entities table\\n on='keys_idx')\\n for i in range(len(object_keys_df))],\\n # Concatenate the rows of the objs\\n axis=0,\\n verify_integrity=False)\\n # Step 4: Clean up the index and sort columns by table type and name\\n result_df.reset_index(inplace=True, drop=True)\\n # ADD files\\n file_id_col = []\\n for idx in result_df['files_idx']:\\n file_id_val = self.files.to_dataframe().iloc[int(idx)]['file_object_id']\\n file_id_col.append(file_id_val)\\n\\n result_df['file_object_id'] = file_id_col\\n column_labels = [('files', 'file_object_id'),\\n ('objects', 'objects_idx'), ('objects', 'object_id'), ('objects', 'files_idx'),\\n ('objects', 'object_type'), ('objects', 'relative_path'), ('objects', 'field'),\\n ('keys', 'keys_idx'), ('keys', 'key'),\\n ('entities', 'entities_idx'), ('entities', 'entity_id'), ('entities', 'entity_uri')]\\n # sort the columns based on our custom order\\n result_df = result_df.reindex(labels=[c[1] for c in column_labels],\\n axis=1)\\n result_df = result_df.astype({'keys_idx': 'uint32',\\n 'objects_idx': 'uint32',\\n 'files_idx': 'uint32',\\n 'entities_idx': 'uint32'})\\n # Add the categories if requested\\n if use_categories:\\n result_df.columns = pd.MultiIndex.from_tuples(column_labels)\\n # return the result\\n return result_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f693839611812a61531db9e16495c8\",\n \"score\": \"0.5940352\",\n \"text\": \"def sample(self) -> pd.DataFrame:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce8505504878a6abcc740d2b6f71147\",\n \"score\": \"0.59357053\",\n \"text\": \"def dataset(self):\\n data = tablib.Dataset()\\n data.headers = self.keys()\\n\\n row = _reduce_datetimes(self.values())\\n data.append(row)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1d7c1062e176c132051823fd750b0c\",\n \"score\": \"0.59345746\",\n \"text\": \"def to_numpy_data(df_X, df_y, seed=SEED):\\n\\n # Convert to numpy\\n X = df_X.values.astype('int8')\\n y = df_y.values.astype('int8')\\n print('Shape of X: {}. Shape of y: {}.'.format(X.shape, y.shape))\\n\\n # Split into training and test sets\\n train_ind, test_ind = train_test_split(\\n np.arange(len(X)), test_size=0.2, random_state=seed)\\n return Datasets(\\n X=X, y=y,\\n X_train=X[train_ind],\\n y_train=y[train_ind],\\n X_test=X[test_ind],\\n y_test=y[test_ind],\\n train_ind=train_ind,\\n test_ind=test_ind\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6254b904f8b016550ca83471014e6bb\",\n \"score\": \"0.5926994\",\n \"text\": \"def to_dataframe(self):\\n time_arr = []\\n lon_arr = []\\n lat_arr = []\\n for d in self.waypoints:\\n time_arr.append(d['time'])\\n lon_arr.append(d['longitude'])\\n lat_arr.append(d['latitude'])\\n return pd.DataFrame({'Longitude': lon_arr, 'Latitude': lat_arr}, index=time_arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f111ba27073d608234cc14684f4847f2\",\n \"score\": \"0.5921907\",\n \"text\": \"def generate_final_db(self):\\n \\n self.add_type_columns()\\n self.transform_columns()\\n self.df['elevetaion'] = self.df.elevetaion.apply(lambda x: self.binning_elev(x))\\n self.df = self.df.rename({'elevetaion':'elevation'}, axis=1)\\n self.calc_gio_not()\\n self.calc_season()\\n \\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c69a118cf2c13cd6cb0b6882c16f13\",\n \"score\": \"0.59205234\",\n \"text\": \"def _get_data_frame(self):\\n import pandas as pd\\n data_dict = {}\\n for key in list(self[0].__dict__.keys()):\\n print(key)\\n if key == 'sigma':\\n #Convert sigma to string separated by ', '\\n data_dict[key] = [', '.join(str(x) for x in config.__dict__[key]) for config in self]\\n else:\\n data_dict[key] = [config.__dict__[key] for config in self]\\n return pd.DataFrame(data=data_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed7a97250c21debe7d8bb4c9a41a754\",\n \"score\": \"0.5915785\",\n \"text\": \"def create_prov_entities(self, dataframe, instance=None):\\n instance = self.instance if instance is None else instance\\n columns = dataframe.columns\\n indexes = dataframe.index\\n \\n # Create output array of entities:\\n entities = np.empty(dataframe.shape, dtype=object)\\n for i in range(self.current_m):\\n record_id = str(uuid.uuid4())\\n for j in range(self.current_n):\\n ent_id = self.NAMESPACE_ENTITY + str(uuid.uuid4())\\n value = str(dataframe.iat[i, j])\\n # Add entity to current provenance document:\\n entities[i][j] = self.create_entity(ent_id, record_id, value, columns[j], indexes[i], self.operation_number)\\n\\n return entities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92984a59014598d66adcfc358fd8de48\",\n \"score\": \"0.5909709\",\n \"text\": \"def as_dataframe(self, dframe):\\n return self.__class__(dframe.reset_index(drop=True), self.meta.copy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54575c6d3f4a8424edf790d07c3860b6\",\n \"score\": \"0.5900126\",\n \"text\": \"def to_dataframe(predictions):\\n df_list = []\\n for i, p in enumerate(predictions):\\n boxes = p['boxes'].detach().cpu().tolist()\\n labels = p['labels'].detach().cpu().tolist()\\n scores = p['scores'].detach().cpu().tolist()\\n df = pd.DataFrame(boxes, columns=['x1','y1','x2','y2'])\\n df['class_id'] = labels\\n df['score'] = scores\\n df['frame_id'] = i\\n df_list.append(df)\\n df_detect = pd.concat(df_list, axis=0)\\n df_detect = df_detect[['frame_id','class_id','score','x1','y1','x2','y2']]\\n return df_detect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a0a365dafff994b9bb0f034fbc8f279\",\n \"score\": \"0.5899012\",\n \"text\": \"def _get_data(self) -> None:\\n\\n if len(self._stations) > 0:\\n\\n # Get list of datasets\\n datasets = self._get_datasets()\\n\\n # Data Processings\\n return processing_handler(\\n datasets, self._load_data, self.processes, self.threads\\n )\\n\\n # Empty DataFrame\\n return pd.DataFrame(columns=[*self._types])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df8cd9f0c6d316a8da767ec62b4c91c\",\n \"score\": \"0.58987474\",\n \"text\": \"def prepare_data_frame(*, shape_file):\\n column_names = [r[0] for r in shape_file.fields][1:]\\n records = shape_file.records()\\n shape_points = [s.points for s in shape_file.shapes()]\\n data_frame = pd.DataFrame(columns=column_names, data=records)\\n data_frame = data_frame.assign(coords=shape_points)\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d6425897142f16824fe9d879c5a096\",\n \"score\": \"0.5896701\",\n \"text\": \"def read_data(self)->pd.DataFrame:\\r\\n if self._canRead:\\r\\n rawData = self._device.read(5000)\\r\\n rawCol = ['channel_1_raw','channel_2_raw','channel_3_raw','channel_4_raw']\\r\\n df= pd.DataFrame(columns = rawCol)\\r\\n df.channel_1_raw = rawData[:,5]\\r\\n df.channel_2_raw = rawData[:,6]\\r\\n df.channel_3_raw = rawData[:,7]\\r\\n df.channel_4_raw = rawData[:,8]\\r\\n #print(time.strftime(\\\"%H:%M:%S\\\", time.localtime()))\\r\\n df['time'] = time.strftime(\\\"%H:%M:%S\\\", time.localtime())\\r\\n #df.set_index('time',inplace=True)\\r\\n #print(df.head())\\r\\n return df\\r\\n else:\\r\\n print(f'Device must be started.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f878911e6004c293ca90f6ad3b495b13\",\n \"score\": \"0.5891539\",\n \"text\": \"def get_dataframe(object):\\n\\n try:\\n df = pd.DataFrame(object)\\n except ValueError:\\n raise\\n else:\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55fbd0f118ce77e0efcbc95062f9cf7c\",\n \"score\": \"0.5891077\",\n \"text\": \"def df(self):\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a234a87ddbb091e14d2cb0651df5d9e\",\n \"score\": \"0.58888507\",\n \"text\": \"def Create(self):\\n df1 = pd.DataFrame(self.columns, index = [0])\\n df1.drop(axis = 0, index = df1.index[0], inplace = True)\\n df1.to_csv(self.path, index = False, encoding=self.encoding)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca110fbc2c3d2ea120f27ab8cc3a4023\",\n \"score\": \"0.5883441\",\n \"text\": \"def dataframe(self):\\n if self._away_points is None and self._home_points is None:\\n return None\\n fields_to_include = {\\n 'away_first_downs': self.away_first_downs,\\n 'away_fumbles': self.away_fumbles,\\n 'away_fumbles_lost': self.away_fumbles_lost,\\n 'away_interceptions': self.away_interceptions,\\n 'away_pass_attempts': self.away_pass_attempts,\\n 'away_pass_completions': self.away_pass_completions,\\n 'away_pass_touchdowns': self.away_pass_touchdowns,\\n 'away_pass_yards': self.away_pass_yards,\\n 'away_penalties': self.away_penalties,\\n 'away_points': self.away_points,\\n 'away_rush_attempts': self.away_rush_attempts,\\n 'away_rush_touchdowns': self.away_rush_touchdowns,\\n 'away_rush_yards': self.away_rush_yards,\\n 'away_total_yards': self.away_total_yards,\\n 'away_turnovers': self.away_turnovers,\\n 'away_yards_from_penalties': self.away_yards_from_penalties,\\n 'date': self.date,\\n 'home_first_downs': self.home_first_downs,\\n 'home_fumbles': self.home_fumbles,\\n 'home_fumbles_lost': self.home_fumbles_lost,\\n 'home_interceptions': self.home_interceptions,\\n 'home_pass_attempts': self.home_pass_attempts,\\n 'home_pass_completions': self.home_pass_completions,\\n 'home_pass_touchdowns': self.home_pass_touchdowns,\\n 'home_pass_yards': self.home_pass_yards,\\n 'home_penalties': self.home_penalties,\\n 'home_points': self.home_points,\\n 'home_rush_attempts': self.home_rush_attempts,\\n 'home_rush_touchdowns': self.home_rush_touchdowns,\\n 'home_rush_yards': self.home_rush_yards,\\n 'home_total_yards': self.home_total_yards,\\n 'home_turnovers': self.home_turnovers,\\n 'home_yards_from_penalties': self.home_yards_from_penalties,\\n 'losing_abbr': self.losing_abbr,\\n 'losing_name': self.losing_name,\\n 'stadium': self.stadium,\\n 'time': self.time,\\n 'winner': self.winner,\\n 'winning_abbr': self.winning_abbr,\\n 'winning_name': self.winning_name\\n }\\n return pd.DataFrame([fields_to_include], index=[self._uri])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b656c650541ab8128040fcfb48a4da\",\n \"score\": \"0.58776695\",\n \"text\": \"def prepare(data):\\n data = pd.DataFrame(data)\\n X = data.drop('class', axis=1)\\n y = data['class']\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192e6b8ac8c4a9ab1cc4f226fd3c90aa\",\n \"score\": \"0.58742565\",\n \"text\": \"def _to_dataframe(self, features):\\n df = pandas.DataFrame(features)\\n\\n properties = df.properties.apply(pandas.Series).copy()\\n properties = properties.add_prefix('properties_')\\n\\n geometry = df.geometry.apply(pandas.Series).copy()\\n coordinates = geometry.coordinates.apply(pandas.Series).copy()\\n coordinates = coordinates.add_prefix('geometry_coordinates_')\\n\\n self.data = pandas.concat([properties, coordinates, df.id], axis = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7629a6d8218e36d827dfa5dc399c3f49\",\n \"score\": \"0.5872692\",\n \"text\": \"def create_dataframe():\\n df = pd.read_csv('DashflaskSAFC/data/safc1920.csv')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd53beaa765f62b287a9a95ec044ab82\",\n \"score\": \"0.58718675\",\n \"text\": \"def to_dataframe(self, lazy=True, **kwargs):\\n if lazy:\\n kwargs.setdefault('exclude', ['_cache'])\\n return query_to_dataframe(\\n self,\\n connection=self._connection_from_session(),\\n **kwargs\\n )\\n else:\\n return DataFrame([model.to_json() for model in self])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36927be974c6344f47e7465bd25ef757\",\n \"score\": \"0.5868971\",\n \"text\": \"def getData(self):\\n df = self.getDataFrame()\\n y = df.iloc[:,0].values\\n X = df.iloc[:,1:].values \\n print(\\\"dimension de X = \\\" + str(X.shape))\\n print(\\\"dimension de y = \\\" + str(y.shape))\\n return X,y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ab0e359fcfd6103b98794c2709aaa5\",\n \"score\": \"0.5864817\",\n \"text\": \"def _to_df_internal(self) -> pd.DataFrame:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e0f67ab1ed2ae5ac477a2a3b8b350b\",\n \"score\": \"0.5860397\",\n \"text\": \"def extract_arrays(db):\\n cursor = db.cursor()\\n return extract_sql_data(\\\"array\\\", cursor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e991d32769a3f62bd9fe8227524e22c4\",\n \"score\": \"0.585693\",\n \"text\": \"def build_dataframe(data):\\n df = pd.DataFrame(data.data,\\n columns=data.feature_names)\\n df['target'] = data.target\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b923d339acc756cc4e629b95734dd4d\",\n \"score\": \"0.5855825\",\n \"text\": \"def getDataFramePackets(self):\\n return pd.DataFrame([item.getSingleDict() for item in self.packets])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb58e025ce62a48e14dab226a1c1b4b\",\n \"score\": \"0.584923\",\n \"text\": \"def run(self, dataframe: DataFrame) -> DataFrame:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a8b49a28806928e1b849c5281972fed\",\n \"score\": \"0.5846577\",\n \"text\": \"def create_dataframe(path_to_datafile):\\n df = # Your code here\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd8e8715f8f9f26318963b1a27ae8dbe\",\n \"score\": \"0.584536\",\n \"text\": \"def constructObjects(self, rows):\\n results = []\\n #self.log(\\\"Rows: %s\\\"%(rows,), level=logging.INFO)\\n if rows:\\n for row in rows:\\n results.append(self.constructObject(row))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac944a0a755638b6524cf6a5a2949d0\",\n \"score\": \"0.58441114\",\n \"text\": \"def test_as_data_frame():\\r\\n raw = Raw(fif_fname, preload=True)\\r\\n df = raw.as_data_frame()\\r\\n assert_true((df.columns == raw.ch_names).all())\\r\\n df = raw.as_data_frame(use_time_index=False)\\r\\n assert_true('time' in df.columns)\\r\\n assert_array_equal(df.values[:, 1], raw._data[0] * 1e13)\\r\\n assert_array_equal(df.values[:, 3], raw._data[2] * 1e15)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afcb7a45dabc1d03164cafcc3a96b95\",\n \"score\": \"0.58424526\",\n \"text\": \"def create_data(data_tensor, times, cols, num=0):\\n df = pd.DataFrame(data_tensor.numpy()[0])\\n df.columns = cols\\n df['Strain'] = [num] * df.shape[0]\\n df = df.set_index('Strain')\\n df['Time'] = times\\n print('Shape of the dataframe is:', df.shape)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d8341ce6f27ad04abd84d230a42588\",\n \"score\": \"0.58401895\",\n \"text\": \"def __init__(self, dataframe):\\n\\t\\tself.data = dataframe.copy(deep=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ccc0c64b4d72ffb44632bf05f925bc\",\n \"score\": \"0.58399343\",\n \"text\": \"def to_dataset(self, *args, **kwargs):\\r\\n dss = [f.to_dataset(*args, **kwargs) for f in self._frames]\\r\\n return dss\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993679,"cells":{"query_id":{"kind":"string","value":"a3a1c554473d8173e7ec8040362da842"},"query":{"kind":"string","value":"Gets details about a specific instance"},"positive_passages":{"kind":"list like","value":[{"docid":"da8eb30b9db6664135e2b15ec7c0e2e5","score":"0.6746519","text":"def get_instance(self, instance_name):\n try:\n return self.driver.vms.get(name=instance_name)\n except Exception as e:\n return None","title":""}],"string":"[\n {\n \"docid\": \"da8eb30b9db6664135e2b15ec7c0e2e5\",\n \"score\": \"0.6746519\",\n \"text\": \"def get_instance(self, instance_name):\\n try:\\n return self.driver.vms.get(name=instance_name)\\n except Exception as e:\\n return None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"69447ec0eda44af05f3fe5ebfa6b9d16","score":"0.776595","text":"def show(self, instance):\n \n return self._list(\"/mgmt/instances/%s\" % base.getid(instance),\n 'instance')","title":""},{"docid":"f4d8eef76f2bdfe57093deb69f579519","score":"0.7303252","text":"def get_instance_info(self,instance_id):\n return self.client.lrange(instance_id,0,-1);","title":""},{"docid":"4a674ebc20c623e64f768e27b0953e0a","score":"0.7292461","text":"def get_instance(self, instance_id):\n return self.session.get(self.instance_url + instance_id)","title":""},{"docid":"16948589e0b9536a8a86a2f7904af353","score":"0.7243924","text":"def get_instance_details_from_metadata(self):\n metadata_url = \"http://169.254.169.254/opc/v1/instance\"\n # use ansible provided open_url to avoid external dependencies\n try:\n response = open_url(metadata_url, method=\"GET\")\n metadata_response = json.loads(response.read())\n self.log(\"Response from metadata: {0}\".format(metadata_response))\n except Exception as ex:\n raise Exception(\n \"Getting instance metadata details failed with error: {0}\".format(\n str(ex)\n )\n )\n instance_details = dict(\n compartmentId=metadata_response[\"compartmentId\"],\n region=self.get_region_long_name(metadata_response[\"region\"]),\n tenancy=self.params[\"tenancy\"]\n or getattr(self.create_instance_principal_signer(), \"tenancy_id\", None),\n )\n self.log(\"Instance parameters from metadata: {0}\".format(instance_details))\n if not (\n instance_details[\"region\"]\n or instance_details[\"compartmentId\"]\n or instance_details[\"tenancy\"]\n ):\n raise Exception(\"Error getting details from instance metadata.\")\n return instance_details","title":""},{"docid":"a00c575adf93e25c23b0d42014f0e1e2","score":"0.7213994","text":"def get_instance_access_details(instanceName=None, protocol=None):\n pass","title":""},{"docid":"9b70d16f7ff7d08b7e94b8e0ad639ccf","score":"0.71543896","text":"def get_info(self, instance):\n container = self.client.containers.get(instance.name)\n state = container.state()\n mem_kb = state.memory['usage'] >> 10\n max_mem_kb = state.memory['usage_peak'] >> 10\n return hardware.InstanceInfo(\n state=(\n power_state.RUNNING if state.status == 'Running'\n else power_state.SHUTDOWN),\n max_mem_kb=max_mem_kb,\n mem_kb=mem_kb,\n num_cpu=instance.flavor.vcpus,\n cpu_time_ns=0)","title":""},{"docid":"2063946fb2a61678017ce4440456156a","score":"0.7121789","text":"def describe_instance(instance_name):\n inst_info = _get_vmops().get_info(instance_name)\n return inst_info","title":""},{"docid":"46e1766548edd46fc285d93f004d1232","score":"0.7042225","text":"def get_info(self, instance):\n inst_name = instance['name']\n\n try:\n return self._get_instance_info(instance)\n except sdkexception.ZVMVirtualMachineNotExist:\n LOG.warning(_(\"z/VM instance %s does not exist\") % inst_name,\n instance=instance)\n raise nova_exception.InstanceNotFound(instance_id=inst_name)\n except Exception as err:\n # TODO(YDY): raise nova_exception.InstanceNotFound\n LOG.warning(_(\"Failed to get the info of z/VM instance %s\") %\n inst_name, instance=instance)\n raise err","title":""},{"docid":"63325d499e5473489abc266c40acaf24","score":"0.6984873","text":"def get_instance(instance_id):\n show_deleted = request.args.get(\"deleted\", \"false\").lower() == \"true\"\n return di.get(InstanceService).get_instance(instance_id=instance_id, deleted=show_deleted)","title":""},{"docid":"3bf4f006392eb3abe07b81500f90ac85","score":"0.6914418","text":"def get_instance():\n req = requests.get(\n \"http://169.254.169.254/latest/dynamic/instance-identity/document\")\n response_json = req.json()\n instancetype = response_json.get('instanceType')\n region = response_json.get('region')\n return {'instancetype': instancetype, 'region': region}","title":""},{"docid":"ba308e456acb2587ded4a4e8928625b8","score":"0.68700033","text":"def describe_instance(version, acs_access_key_id, private_key, response_format,\n timestamp, rndguid, instanceid):\n \n signature = generate_signature(timestamp, rndguid, private_key)\n url_data = generate_url_data(version, acs_access_key_id, response_format, \n timestamp, rndguid, signature)\n url_data['instanceid'] = instanceid\n return read_api_url(API_METHOD_URLS['describe-instance'], url_data)","title":""},{"docid":"eee8230c118b1346b391d0b2e6292224","score":"0.67945576","text":"def get_instance(instanceid,logger):\n instance = ec2_resource.Instance(instanceid)\n logger.append(\"Acquiring instance with id {}\".format(instanceid))\n\n return instance","title":""},{"docid":"56166089e5a85943955680f0d9a13768","score":"0.665295","text":"def get_instance_info(self,instance_id,start_time):\n self.__reconnect_db__();\n sql_cmd='''select cpu_usage,mem_free,mem_max,nic_in,nic_out,disk_read,disk_write,monitor_time uuid from vm_monitor where instance_id='%s' and monitor_time>='%s' '''%(instance_id,start_time);\n #print \" in get instance info by id sql cmd=%s\"%sql_cmd;\n self.cursor.execute(sql_cmd);\n return self.cursor.fetchall();","title":""},{"docid":"000a56c2701c1bc35d51a7635a59744d","score":"0.65126073","text":"def show(self, req, tenant_id, id):\n LOG.info(\"Showing a database instance %(id)s for tenant \"\n \"'%(tenant_id)s'\\n\"\n \"req : '%(req)s'\\n\\n\", {\n \"tenant_id\": tenant_id, \"req\": req, \"id\": id})\n context = req.environ[wsgi.CONTEXT_KEY]\n deleted_q = req.GET.get('deleted', '').lower()\n include_deleted = deleted_q == 'true'\n server = models.DetailedMgmtInstance.load(context, id,\n include_deleted)\n root_history = mysql_models.RootHistory.load(context=context,\n instance_id=id)\n return wsgi.Result(\n views.MgmtInstanceDetailView(\n server,\n req=req,\n root_history=root_history).data(),\n 200)","title":""},{"docid":"7917ed0ebf279d47caa51320a0614e22","score":"0.65098757","text":"def ptp_instance_get(self, ptp_instance_id):","title":""},{"docid":"5831f00d3e5bba6b625b6da492e7fd41","score":"0.64772743","text":"def get_info(self, instance, use_cache=True):\n return self._vmops.get_info(instance)","title":""},{"docid":"577889b8a6ec817e4828d97d15f97d53","score":"0.6476486","text":"def get_instance(instanceName=None):\n pass","title":""},{"docid":"5bfd2d2f0bb737a2757c8f2b1e444844","score":"0.6463787","text":"def get_this_instance():\n c = connect(boto.ec2.connection.EC2Connection)\n metadata = boto.utils.get_instance_metadata()\n this_instance = next(i for i in c.get_only_instances() if i.id == metadata['instance-id'])\n return this_instance","title":""},{"docid":"d77c1ae4c0d47a372a1c8d3338449251","score":"0.6446425","text":"def get_info(self, instance):\n _domain_info = self._conn.get_domain_info(instance['name'])\n state, max_mem, mem, num_cpu, cpu_time = _domain_info\n return {'state': state,\n 'max_mem': max_mem,\n 'mem': mem,\n 'num_cpu': num_cpu,\n 'cpu_time': cpu_time}","title":""},{"docid":"ed93ea4372e837179567b5bb77597455","score":"0.64433485","text":"def show_instance(name, call=None):\n if call != \"action\":\n raise SaltCloudSystemExit(\n \"The show_instance action must be called with -a or --action.\"\n )\n\n nodes = list_nodes_full()\n # Find under which cloud service the name is listed, if any\n if name not in nodes:\n return {}\n __utils__[\"cloud.cache_node\"](nodes[name], _get_active_provider_name(), __opts__)\n return nodes[name]","title":""},{"docid":"b8f75dde32f884f2ac917bfc2e25f202","score":"0.6414563","text":"def GetInstance(self, instance_name):\n\n instances = self.ListInstances()\n instance = instances.get(instance_name)\n if not instance:\n error_msg = 'Instance {0:s} was not found in project {1:s}'.format(\n instance_name, self.project_id)\n raise RuntimeError(error_msg)\n return instance","title":""},{"docid":"02502a234e1cea574a4fd7880ee57d3e","score":"0.64012486","text":"def _inspect_instance(self, instance_name):\n network = {}\n try:\n instace_info = self._vbox.show_vm_info(instance_name)\n except exception.InstanceNotFound:\n LOG.warning(_LW(\"Failed to get specification for `%(instance)s`\"),\n {\"instance\": instance_name})\n return\n\n description = instace_info.get(constants.VM_DESCRIPTION)\n if not self._process_description(description):\n LOG.warning(_LW(\"Invalid description for `%(instance)s`: \"\n \"%(description)s\"),\n {\"instance\": instance_name,\n \"description\": description})\n return\n\n for field, value in instace_info.items():\n if field[:-1] in constants.NETWORK_FIELDS:\n network.setdefault(field[-1], {})[field[:-1]] = value\n\n for nic_index in network:\n mac_address = network[nic_index].get(constants.MAC_ADDRESS)\n device_id = self._device_map.get(mac_address)\n nic_mode = network[nic_index].get(constants.NIC_MODE)\n\n if device_id and nic_mode != constants.NIC_MODE_NONE:\n self._nic[device_id] = network[nic_index]\n self._nic[device_id][\"index\"] = nic_index\n self._nic[device_id][\"instance\"] = instance_name\n self._nic[device_id][\"state\"] = instace_info.get(\n constants.VM_STATE)","title":""},{"docid":"a2c4dfc809ea78ab671ec33895c3ace0","score":"0.63797563","text":"def print_instance(index, instance):\n\n # Print the index with the correct format if the index is available\n index_str = \"\\t\" + (\"\" if index == -1 else str(index) + ':')\n\n # Print each of the requested instance attributes\n print index_str, instance.id, '-', instance.instance_type, '<' + str(instance.region) + \\\n '>: <Running since:', str(instance.launch_time) + '>'","title":""},{"docid":"e713174aa2bd384a6599c9e5067f469e","score":"0.6369597","text":"def instance_id():\n return boto.utils.get_instance_metadata()['instance-id']","title":""},{"docid":"9f32f7c23cb69ab60ee5b14153326be8","score":"0.63623035","text":"def GetInstance(self, instance_ref):\n request = (self.compute.instances,\n 'Get',\n self.messages.ComputeInstancesGetRequest(\n instance=instance_ref.Name(),\n project=instance_ref.project,\n zone=instance_ref.zone))\n\n errors = []\n objects = list(request_helper.MakeRequests(\n requests=[request],\n http=self.http,\n batch_url=self.batch_url,\n errors=errors))\n if errors:\n utils.RaiseToolException(\n errors,\n error_message='Could not fetch instance:')\n return objects[0]","title":""},{"docid":"78843947b5087554a485e562a6f1ca44","score":"0.6347738","text":"def test_get_server_instance_detail(self):\n pass","title":""},{"docid":"0a44e83cc1c22ff5cb5d6895217e420b","score":"0.6321201","text":"def instance_info(self) -> KeyValuePair:\n return self._instance_info","title":""},{"docid":"0a4eaeafdfdf9ca51ae74b3036d03caa","score":"0.63010913","text":"def get_instance(self, project_id, service_id, version_id, instances_id):\n try:\n results = self.repository.version_instances.get(\n project_id, target=instances_id, services_id=service_id,\n versions_id=version_id)\n LOGGER.debug('Getting information about a specific instance of'\n ' a service, project_id = %s, service_id = %s,'\n ' version_id = %s, instance_id = %s, results = %s',\n project_id, service_id, version_id, instances_id,\n results)\n return results\n except (errors.HttpError, HttpLib2Error) as e:\n if _is_status_not_found(e):\n return {}\n raise api_errors.ApiExecutionError(project_id, e)","title":""},{"docid":"5a01c6f77b429ad9d5f1b8ecbb6e306c","score":"0.62947637","text":"def get_instance_id():\n response = requests.get('http://169.254.169.254/latest/meta-data/instance-id')\n return response.text","title":""},{"docid":"86d8a9f751147d3777182f0caf80815c","score":"0.6285572","text":"def get_instance(ServiceId=None, InstanceId=None):\n pass","title":""},{"docid":"4cdba8ffddffde7a258cb797e567f53b","score":"0.62784135","text":"def myInstanceID():\n return loadInstanceMetadata()[\"instance-id\"]","title":""},{"docid":"daf65979268597baccfd055ebb8ed89e","score":"0.6273561","text":"def do_show(self, args):\n\n if len(args) == 0:\n print(\"** class name missing **\")\n return\n args_list = args.split()\n if (args_list[0] not in self.classes):\n print(\"** class doesn't exist **\")\n return\n if len(args_list) == 1:\n print(\"** instance id missing **\")\n return\n elif len(args_list) > 1:\n key = args_list[0] + \".\" + args_list[1]\n if key in models.storage.all():\n print(models.storage.all()[key])\n else:\n print(\"** no instance found **\")\n return","title":""},{"docid":"767a40eba23d20fc9e9001634bf9ee2e","score":"0.6266999","text":"def get_instance(self, instance_id):\n mask = [\n 'datacenter,billingItem[id,package[id,keyName],nextInvoiceChildren]'\n ]\n mask = \"mask[%s]\" % ','.join(mask)\n\n return self.hardware.getObject(id=instance_id, mask=mask)","title":""},{"docid":"3b19052ba91340206534d14006bf3931","score":"0.6255929","text":"def get_instance_nw_info(self, context, instance):\n return self._get_instance_nw_info(context, instance)","title":""},{"docid":"dffa342c4ef0165da24cf85f69f673f5","score":"0.6252949","text":"def get_instanced_item(self, instance_id):\n\n params = {\n \"components\": \"ItemInstances,ItemStats,ItemPerks\"\n }\n item_request = requests.get(\n self.client_object.root_endpoint + \"/Destiny2/\" + str(self.membership_type) + \"/Profile/\" + self.membership_id +\n \"/Item/\" + instance_id,\n params=params, headers=self.client_object.request_header)\n return item_request.json()[\"Response\"]","title":""},{"docid":"29a8c7dfbfb7693c043dde72c2bcddc5","score":"0.6250046","text":"def _get_instance(\n self, instance: InstanceId, token: Optional[str] = None\n ) -> Instance:\n client = self._get_client(token)\n if isinstance(instance, str):\n filters = [Instance.label == instance]\n elif isinstance(instance, int):\n filters = [Instance.id == instance]\n else:\n raise AssertionError(f'Invalid instance type: {type(instance)}')\n\n instances = client.linode.instances(*filters)\n assert instances, f'No such Linode instance: {instance}'\n return instances[0]","title":""},{"docid":"de37abbb9d1d449c7d861b36d811a42a","score":"0.6225575","text":"def get_instance(self, instance_name):\n found = [s for s in self.servers if s.name == instance_name]\n\n if len(found) != 1:\n error_msg = 'invalid instance identifier: %s' % instance_name\n log.error(error_msg)\n raise errors.InvalidServerError(error_msg)\n\n return found[0]","title":""},{"docid":"a55bfdb6efe280417d892d9f5bd3473f","score":"0.6219639","text":"def loadInstanceMetadata():\n return boto.utils.get_instance_metadata(timeout=1, num_retries=3)","title":""},{"docid":"70632cee92754536f2304f87c9809bbf","score":"0.6212463","text":"def _get_instance_nw_info(self, context, instance):\n args = {'instance_id': instance['id'],\n 'instance_uuid': instance['uuid'],\n 'rxtx_factor': instance['instance_type']['rxtx_factor'],\n 'host': instance['host'],\n 'project_id': instance['project_id']}\n nw_info = rpc.call(context, FLAGS.network_topic,\n {'method': 'get_instance_nw_info',\n 'args': args})\n\n return network_model.NetworkInfo.hydrate(nw_info)","title":""},{"docid":"5916990278c7e726f891c539f1494757","score":"0.6203749","text":"def do_show(self, arg):\n args = shlex.split(arg)\n if len(args) == 0:\n print(\"** class name missing **\")\n elif not args[0] in self.classes:\n print(args[0])\n print(\"** class doesn't exist **\")\n elif len(args) == 1:\n print(\"** instance id missing **\")\n else:\n all_obj = models.storage.all()\n for key, value in all_obj.items():\n id_current = key.split('.')\n if id_current[1] == args[1]:\n print(all_obj[key])\n return\n print(\"** no instance found **\")","title":""},{"docid":"0ca3cd60689259587bf306bb9529ebc5","score":"0.6198044","text":"def get_instance(self):\n data = self.unit.relation_get()\n port = data.get('port')\n address = data.get('hostname')\n\n if not port or not address:\n self.unit.log('Peer address not set, waiting for handshake')\n raise RetryLater()\n\n ec2 = self.get_ec2()\n instance = self.unit.get_instance(ec2)\n return instance.id, instance.placement","title":""},{"docid":"fef78a6ec68f9a6394314fa5476ed64c","score":"0.6197021","text":"def _get_instance(self, instance_ref, client):\n if not self._instance:\n request = (client.apitools_client.instances, 'Get',\n client.messages.ComputeInstancesGetRequest(\n **instance_ref.AsDict()))\n instance = client.MakeRequests(requests=[request])\n\n self._instance = instance[0]\n\n return self._instance","title":""},{"docid":"421c2873242b71d133d015e62a07d456","score":"0.6196085","text":"def get_performance(self, *args, **kargs):\n instance_id = int(kargs.get(\"instance_id\", [None])[0])\n return api.wlee.get_instance_performance(instance_id)","title":""},{"docid":"532768f397b9506455a949a2544ebd51","score":"0.61504257","text":"def instance(self, n):\n sopInstanceUID = self.__sopInstanceUIDs[n]\n return self.__instances[sopInstanceUID]","title":""},{"docid":"fa75c18659b05639369e8a4820df240a","score":"0.6127541","text":"def do_show(self, arg):\n list_args = arg.split()\n if not arg:\n print(\"** class name missing **\")\n return\n if list_args[0] not in HBNBCommand.classes:\n print(\"** class doesn't exist **\")\n return\n if len(list_args) < 2:\n print(\"** instance id missing **\")\n return\n else:\n dic = models.storage.all()\n key = list_args[0] + '.' + str(list_args[1])\n if key in dic:\n print(dic[key])\n else:\n print(\"** no instance found **\")\n return","title":""},{"docid":"beb35f20b18906c0e9996ecba7524d30","score":"0.6126248","text":"def query_instances():\n try:\n logging.info(\"Checking for instances.....\")\n for instance in ec2.instances.all():\n print (instance.tags, instance.id, instance.public_ip_address, instance.state)\n except botocore.exceptions.ClientError as e:\n logging.error(e.response['Error']['Code'])","title":""},{"docid":"c8e1b1d8ae64845f4909a2c23a748361","score":"0.61247444","text":"def get_instance(self) -> str:\n pass","title":""},{"docid":"107af48f508e24407b4399b14c65c9f9","score":"0.61246556","text":"def get_db(self):\n try:\n instance = self.client.instances.get(self.instance.id)\n self.logger.warning('Currently Active Instances')\n self.success = True\n self.logger.warning(instance.id + \" \" + instance.name)\n\n except trove_exceptions.NotFound as e:\n self.success, self.overall_success = False, False\n self.failure = e\n self.logger.error(\"<*>404 db not found get_db %s\", e)\n\n except Exception as e:\n self.success, self.overall_success = False, False\n self.failure = e\n self.logger.warning('Error getting Instance %s', e)","title":""},{"docid":"51b6b57950e3dc716e7225d803f647a6","score":"0.6102276","text":"def list_instance_of_user(self):\n try:\n json_obj = self._atmo_get_req(\"/api/v2/instances\")\n return json_obj[\"results\"]\n except HTTPError as e:\n raise HTTPError(str(e.args) + \"Fail to list all the instances\")\n except KeyError:\n raise IncompleteResponse(\"Missing field\")","title":""},{"docid":"9c8b6e7d3faae563b66f1b2fd962461a","score":"0.6096001","text":"def do_show(self, arg):\n if not arg:\n print('** class name missing **')\n return\n args = arg.split()\n for k, v in classes.items():\n if args[0] == k:\n break\n else:\n print(\"** class doesn't exist **\")\n return\n if len(args) < 2:\n print('** instance id missing **')\n return\n for key, val in models.storage.all().items():\n if args[0] + '.' + args[1] == key:\n print(val)\n break\n else:\n print('** no instance found **')","title":""},{"docid":"f4fd45cf3eb460b684f77627e67387b3","score":"0.609028","text":"def get_instance_name() -> str:\n\n res = requests.get(\n url='http://metadata/computeMetadata/v1/instance/name',\n headers={'Metadata-Flavor': 'Google'}\n )\n instance_name = str(res.content, 'utf-8')\n\n return instance_name","title":""},{"docid":"1603ec9d0db4104e9ba184f3088ffc6a","score":"0.6062181","text":"def _get(self, hostname, zone, role):\n\n volume_config = {'image': self._image, 'class': self.storage_class}\n inst = None\n try:\n _server = self._nova.servers.find(name=hostname)\n LOGGER.debug(\"Found instance %s\", hostname)\n inst = Instance(self._cinder,\n self._nova,\n _server.name,\n self.net,\n zone,\n role,\n volume_config,\n _server.flavor)\n try:\n inst.ports.append(_server.interface_list()[0])\n except IndexError:\n LOGGER.warning(\"No network found for %s\", hostname)\n\n inst.exists = True\n except NovaNotFound:\n pass\n\n return inst","title":""},{"docid":"33b437afeedb75713dd7ccacd230e38f","score":"0.6045457","text":"def do_show(self, args):\n\n my_dict = models.storage.all()\n split_args = get_args(args)\n\n if len(args) == 0:\n print(\"** class name missing **\")\n else:\n if split_args[0] in my_class:\n if len(split_args) > 1:\n my_search = split_args[0] + '.' + split_args[1]\n\n if my_search in my_dict:\n print(my_dict.get(my_search))\n\n else:\n print(\"** no instance found **\")\n else:\n print(\"** instance id missing **\")\n else:\n print(\"** class doesn't exist **\")","title":""},{"docid":"b19b6cc53f62ca860af5508cb9162cd5","score":"0.60379124","text":"def get_instance(self, name):\r\n instances = self._build_instance_dict()\r\n try:\r\n return instances[name]\r\n except KeyError:\r\n logger.error(\"No instance named %s found\", name)\r\n return None","title":""},{"docid":"c516d38d0943b3328b135dc8f8f2606c","score":"0.6035774","text":"def do_show(self, argument):\n tokens = shlex.split(argument)\n if len(tokens) == 0:\n print(\"** class name missing **\")\n elif len(tokens) == 1:\n print(\"** instance id missing **\")\n elif tokens[0] not in self.classes:\n print(\"** class doesn't exist **\")\n else:\n dic = models.storage.all()\n # Key has format <className>.id\n keyU = tokens[0] + '.' + str(tokens[1])\n if keyU in dic:\n print(dic[keyU])\n else:\n print(\"** no instance found **\")\n return","title":""},{"docid":"c0064930af65f67897a12ff454d45dcb","score":"0.6024477","text":"def get_instance_address(self):\n ec2 = self.get_ec2()\n instance = self.unit.get_instance(ec2)\n return instance.id, instance.ip_address","title":""},{"docid":"86c09d2c256f7b69164c5210d1d4a9c9","score":"0.59979236","text":"def do_show(self, line):\n args = line.split()\n objects = storage.all()\n\n if len(args) == 0:\n print('** class name missing **')\n elif args[0] not in self.__list_class:\n print(\"** class doesn't exist **\")\n elif len(args) == 1:\n print('** instance id missing **')\n else:\n key_find = args[0] + '.' + args[1]\n if key_find in objects.keys():\n print(objects[key_find])\n else:\n print('** no instance found **')","title":""},{"docid":"f1e4addef47bdac01099eb2772f8f64f","score":"0.5975401","text":"def get(self, instance, owner):\n pass","title":""},{"docid":"14c7e07adc0f18e5f5a50747c35d2cb4","score":"0.59743834","text":"def get_info(self, instance):\n return dict(\n state=power_state.RUNNING,\n max_mem=1024,\n mem=1024,\n num_cpu=1,\n cpu_time=0,\n )","title":""},{"docid":"4c4fe8ce42b09f9e0892849c9a1d7edd","score":"0.5963981","text":"def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"4c4fe8ce42b09f9e0892849c9a1d7edd","score":"0.5963981","text":"def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"4c4fe8ce42b09f9e0892849c9a1d7edd","score":"0.5963981","text":"def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"4c4fe8ce42b09f9e0892849c9a1d7edd","score":"0.5963981","text":"def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"4c4fe8ce42b09f9e0892849c9a1d7edd","score":"0.5963981","text":"def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"4715365547c577b215aef69bbecbb176","score":"0.59459674","text":"def instance_name(self) -> str:\n return pulumi.get(self, \"instance_name\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"79062c0bd1fffb0b154a56a18f74fe66","score":"0.5936568","text":"def instance_url(self) -> str:\n return pulumi.get(self, \"instance_url\")","title":""},{"docid":"03e3e5bd702313394e77e5dd0fd9969d","score":"0.593423","text":"def do_show(self, line):\n args = line.split(' ')\n if len(args) == 0:\n print('** class name missing **')\n return\n elif args[0] not in self.classes:\n print('** class doesn\\'t exist **')\n return\n elif len(args) == 1:\n print('** instance id missing **')\n return\n else:\n all_objs = storage.all()\n k_find = args[0] + '.' + args[1]\n if k_find in all_objs:\n print(all_objs[k_find])\n return\n else:\n print('** no instance found **')\n pass","title":""},{"docid":"9bd6413231331dd60309537d93731995","score":"0.5927","text":"def get_instance(self, spec):\n return self._instances[spec]","title":""},{"docid":"65385a4d2015e4068b3c04c1c6ab6aa3","score":"0.5912811","text":"def instance_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"instance_name\")","title":""},{"docid":"374e7d1b9139164d556127d1abf567b0","score":"0.5902694","text":"def get_instance_data(self):\n self._search_hosts()\n instances = self.hosts.values()\n ec2 = boto3.client('ec2')\n response = ec2.describe_instances(InstanceIds=instances)\n if response['Reservations']:\n instances = response['Reservations']\n return instances\n return []","title":""},{"docid":"eecc1258f6424500177ef55e84628668","score":"0.5888823","text":"def do_show(self, arg):\n arg = self.parse_arg(arg)\n\n if len(arg) == 0:\n print(\"** class name missing **\")\n return\n elif len(arg) == 1:\n print(\"** instance id missing **\")\n return\n\n class_in = arg[0]\n key_in = arg[1]\n\n is_class = False\n\n all_objs = storage.all()\n\n for index in all_objs.keys():\n if class_in in index.split(\".\")[0]:\n is_class = True\n if key_in in index.split(\".\")[1]:\n print(str(all_objs[index]))\n return\n\n if is_class is True:\n print(\"** no instance found **\")\n else:\n print(\"** class doesn't exist **\")","title":""},{"docid":"57a16d14640e6daea0c5308408f4e2f9","score":"0.5883925","text":"def __inst_display(self, instance, disp, pg, region):\n res_id_list = [instance.id]\n if disp.display == DisplayOptions.LONG:\n if disp.display_name:\n last_field = instance.tags.get(\"Name\", \"-\")\n else:\n last_field = amazon2localtime(instance.launch_time)\n pg.prt(\"%-12s %-10s %-12s %-10s %s\",\n instance.id, instance.state,\n instance.placement,\n instance.instance_type,\n last_field)\n elif disp.display == DisplayOptions.EXTENDED:\n pg.prt(\"%s\", instance.id)\n pg.prt(\"%15s : %-12s\", \"State\", instance.state)\n pg.prt(\"%15s : %s\", \"Launch-time\", \n amazon2localtime(instance.launch_time))\n pg.prt(\"%15s : %s\", \"Location\", instance.placement)\n pg.prt(\"%15s : %s %s %s %s\", \"Hardware\",\n instance.instance_type,\n instance.architecture,\n instance.virtualization_type,\n instance.hypervisor,\n )\n pg.prt(\"%15s : %s %s %s %s\", \"Software\",\n instance.platform,\n instance.image_id,\n instance.kernel,\n instance.ramdisk)\n res_id_list.append(instance.image_id)\n if instance.kernel:\n res_id_list.append(instance.kernel)\n if instance.ramdisk:\n res_id_list.append(instance.ramdisk)\n pg.prt(\"%15s : %-12s %s\", \"Root\",\n instance.root_device_name,\n instance.root_device_type)\n pg.prt(\"%15s : %s\", \"EBS-optimized\",\n \"True\" if instance.ebs_optimized else \"False\")\n if instance.vpc_id:\n pg.prt(\"%15s : %-14s %-16s\", \"VPC-info\",\n instance.vpc_id, instance.subnet_id)\n res_id_list.extend([instance.vpc_id, instance.subnet_id])\n if instance.private_ip_address:\n pg.prt(\"%15s : %s %s\", \"IP\",\n instance.ip_address,\n instance.private_ip_address)\n for netif in instance.interfaces:\n pg.prt(\"%15s : %-15s idx=%-2s %-16s SDC=%s\",\n \"Interface\",\n netif.id,\n netif.attachment.device_index,\n netif.private_ip_address,\n \"on\" if netif.source_dest_check else \"off\"\n )\n res_id_list.append(netif.id)\n if instance.groups:\n group_id_list = [group.id for group in instance.groups]\n pg.prt(\"%15s : %s\", \"Groups\", \" \".join(group_id_list))\n res_id_list.extend(group_id_list)\n bdev_list = instance.block_device_mapping.keys()\n bdev_list.sort()\n for bdev in bdev_list:\n bdev_info = instance.block_device_mapping[bdev]\n pg.prt(\"%15s : %-12s %-12s %s dot=%s\",\n \"Device\", bdev,\n bdev_info.volume_id,\n bdev_info.status,\n bdev_info.delete_on_termination)\n res_id_list.append(bdev_info.volume_id)\n if disp.display_tags:\n common.display_tags(instance.tags, pg)\n else:\n pg.prt(\"%s\", instance.id)\n if disp.display_tags:\n common.display_tags(instance.tags, pg)\n self.cache_insert(region, res_id_list)","title":""},{"docid":"7c074b1bde51dd59af5e2d8856789d01","score":"0.5880238","text":"def GetInstanceRef(args, client):\n validate.ValidateInstanceName(args.instance)\n return client.resource_parser.Parse(\n args.instance,\n params={'project': properties.VALUES.core.project.GetOrFail},\n collection='sql.instances')","title":""},{"docid":"6a480263fc11b4709cfabbc92eb50333","score":"0.58766186","text":"def instance_view(self) -> 'outputs.RestorePointInstanceViewResponse':\n return pulumi.get(self, \"instance_view\")","title":""},{"docid":"57967dadcb15e020a3a19707c679fb3e","score":"0.58708197","text":"def instance_view(self) -> 'outputs.CapacityReservationGroupInstanceViewResponse':\n return pulumi.get(self, \"instance_view\")","title":""},{"docid":"d2406195c7a04febcbeb5fa1ccf38ba5","score":"0.5854854","text":"def test_get_member_server_image_instance_detail(self):\n pass","title":""},{"docid":"a2ef2ce221be817ef60a23ab63b51e59","score":"0.58519864","text":"def get(instance, **kwargs):","title":""},{"docid":"0efa4191a129f820ebbabcb67e90cbfd","score":"0.58404243","text":"def do_show(self, args):\n sw = 0\n arg = args.split()\n if args == \"\":\n print(\"** class name missing **\")\n elif arg[0] not in HBNBCommand.class_list:\n print(\"** class doesn't exist **\")\n elif len(arg) < 2:\n print(\"** instance id missing **\")\n else:\n in_key = (arg[0] + \".\" + arg[1])\n for key, obj in storage.all().items():\n if key == in_key:\n print(obj)\n sw = 1\n if sw == 0:\n print(\"** no instance found **\")","title":""},{"docid":"4b0493bc24ad92b5db59d768ff8b7f79","score":"0.5836969","text":"def instance_name(instance):\n return getattr(instance, 'OS-EXT-SRV-ATTR:instance_name', None)","title":""},{"docid":"5ea1cfa3b6b8f5b1cf8e28a8b47dfd91","score":"0.5825013","text":"def get_instance(settings):\n\n session = get_session_info(settings)\n instance_url = session[\"instance_url\"]\n base_url = re.compile(\"//[\\s\\S]+?\\.\").search(instance_url).group()\n instance = base_url[2:-1]\n\n return instance","title":""},{"docid":"9754bc40870d3ba18c54972530a1f291","score":"0.58244944","text":"def scanInstanceGet(self, instanceId: str) -> list:\n\n if not isinstance(instanceId, str):\n raise TypeError(f\"instanceId is {type(instanceId)}; expected str()\") from None\n\n qry = \"SELECT name, seed_target, ROUND(created/1000) AS created, \\\n ROUND(started/1000) AS started, ROUND(ended/1000) AS ended, status \\\n FROM tbl_scan_instance WHERE guid = ?\"\n qvars = [instanceId]\n\n with self.dbhLock:\n try:\n self.dbh.execute(qry, qvars)\n return self.dbh.fetchone()\n except sqlite3.Error as e:\n raise IOError(\"SQL error encountered when retrieving scan instance\") from e","title":""},{"docid":"ff38aacdfa23737841f14029967f01dc","score":"0.58233666","text":"def get_instance_snapshot(instanceSnapshotName=None):\n pass","title":""},{"docid":"5c92be9c97c4621a5d06451797c3d211","score":"0.58217","text":"def get_details():\n return details","title":""},{"docid":"a0e5560355b324a01879c695aa9daa11","score":"0.5818443","text":"def get_instances():\n logging.debug('Serving instance data.')\n\n instance_types = bottle.request.query.instanceTypes or None\n instance_types = instance_types.split(',') if instance_types else []\n\n return instance_data.get_instances(filter_instance_types=instance_types)","title":""},{"docid":"56ee53f709a8702625532490e721aaa6","score":"0.58165205","text":"def read(self, resource_type, instance_id):\n self.validate_resource_type(resource_type)\n\n res = self.db[resource_type].find_one({\"id\": instance_id})\n if res is None:\n raise NotFoundError\n return res","title":""},{"docid":"ab9dfaa729d4430f4d5c8880fa2f2337","score":"0.58099234","text":"def GetOperation(self):\n\n gce_instance_client = self.project.GceApi().instances()\n request = gce_instance_client.get(\n instance=self.name, project=self.project.project_id, zone=self.zone)\n response = request.execute()\n return response","title":""},{"docid":"99e45a4e63aade00dcb04467acf980c6","score":"0.58059126","text":"def test_get_instance(self):\n\n mock_instance_id = 'instance-abyz'\n mock_instance = Mock()\n mock_instance.id = mock_instance_id\n mock_instance.name = 'test-instance'\n mock_instance.hypervisor_hostname = 'nova-abyz'\n self.conn.compute.get_server_metadata.return_value = mock_instance\n result = self.os_sfc.get_instance(mock_instance_id)\n self.assertEqual(result, mock_instance)","title":""},{"docid":"8080ced242373e31330a5f8f8f1bfd45","score":"0.5788852","text":"def instance_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"8080ced242373e31330a5f8f8f1bfd45","score":"0.5788852","text":"def instance_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"8080ced242373e31330a5f8f8f1bfd45","score":"0.5788852","text":"def instance_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"8080ced242373e31330a5f8f8f1bfd45","score":"0.5788852","text":"def instance_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"instance_id\")","title":""},{"docid":"d343c94bbca39f42f876b02d53365ef2","score":"0.57871383","text":"def get_instance_output(instance_id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[Optional[str]]] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetInstanceResult]:\n ...","title":""},{"docid":"bd6b3461723a0651bd07b043a3db209d","score":"0.57849914","text":"def gen_instance(iid):\n instance = awscli('ec2', 'describe-instances', '--instance-ids', iid)\n instance = instance['Reservations'][0]['Instances'][0]\n return gen_details(instance)","title":""}],"string":"[\n {\n \"docid\": \"69447ec0eda44af05f3fe5ebfa6b9d16\",\n \"score\": \"0.776595\",\n \"text\": \"def show(self, instance):\\n \\n return self._list(\\\"/mgmt/instances/%s\\\" % base.getid(instance),\\n 'instance')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d8eef76f2bdfe57093deb69f579519\",\n \"score\": \"0.7303252\",\n \"text\": \"def get_instance_info(self,instance_id):\\n return self.client.lrange(instance_id,0,-1);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a674ebc20c623e64f768e27b0953e0a\",\n \"score\": \"0.7292461\",\n \"text\": \"def get_instance(self, instance_id):\\n return self.session.get(self.instance_url + instance_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16948589e0b9536a8a86a2f7904af353\",\n \"score\": \"0.7243924\",\n \"text\": \"def get_instance_details_from_metadata(self):\\n metadata_url = \\\"http://169.254.169.254/opc/v1/instance\\\"\\n # use ansible provided open_url to avoid external dependencies\\n try:\\n response = open_url(metadata_url, method=\\\"GET\\\")\\n metadata_response = json.loads(response.read())\\n self.log(\\\"Response from metadata: {0}\\\".format(metadata_response))\\n except Exception as ex:\\n raise Exception(\\n \\\"Getting instance metadata details failed with error: {0}\\\".format(\\n str(ex)\\n )\\n )\\n instance_details = dict(\\n compartmentId=metadata_response[\\\"compartmentId\\\"],\\n region=self.get_region_long_name(metadata_response[\\\"region\\\"]),\\n tenancy=self.params[\\\"tenancy\\\"]\\n or getattr(self.create_instance_principal_signer(), \\\"tenancy_id\\\", None),\\n )\\n self.log(\\\"Instance parameters from metadata: {0}\\\".format(instance_details))\\n if not (\\n instance_details[\\\"region\\\"]\\n or instance_details[\\\"compartmentId\\\"]\\n or instance_details[\\\"tenancy\\\"]\\n ):\\n raise Exception(\\\"Error getting details from instance metadata.\\\")\\n return instance_details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00c575adf93e25c23b0d42014f0e1e2\",\n \"score\": \"0.7213994\",\n \"text\": \"def get_instance_access_details(instanceName=None, protocol=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b70d16f7ff7d08b7e94b8e0ad639ccf\",\n \"score\": \"0.71543896\",\n \"text\": \"def get_info(self, instance):\\n container = self.client.containers.get(instance.name)\\n state = container.state()\\n mem_kb = state.memory['usage'] >> 10\\n max_mem_kb = state.memory['usage_peak'] >> 10\\n return hardware.InstanceInfo(\\n state=(\\n power_state.RUNNING if state.status == 'Running'\\n else power_state.SHUTDOWN),\\n max_mem_kb=max_mem_kb,\\n mem_kb=mem_kb,\\n num_cpu=instance.flavor.vcpus,\\n cpu_time_ns=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2063946fb2a61678017ce4440456156a\",\n \"score\": \"0.7121789\",\n \"text\": \"def describe_instance(instance_name):\\n inst_info = _get_vmops().get_info(instance_name)\\n return inst_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e1766548edd46fc285d93f004d1232\",\n \"score\": \"0.7042225\",\n \"text\": \"def get_info(self, instance):\\n inst_name = instance['name']\\n\\n try:\\n return self._get_instance_info(instance)\\n except sdkexception.ZVMVirtualMachineNotExist:\\n LOG.warning(_(\\\"z/VM instance %s does not exist\\\") % inst_name,\\n instance=instance)\\n raise nova_exception.InstanceNotFound(instance_id=inst_name)\\n except Exception as err:\\n # TODO(YDY): raise nova_exception.InstanceNotFound\\n LOG.warning(_(\\\"Failed to get the info of z/VM instance %s\\\") %\\n inst_name, instance=instance)\\n raise err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63325d499e5473489abc266c40acaf24\",\n \"score\": \"0.6984873\",\n \"text\": \"def get_instance(instance_id):\\n show_deleted = request.args.get(\\\"deleted\\\", \\\"false\\\").lower() == \\\"true\\\"\\n return di.get(InstanceService).get_instance(instance_id=instance_id, deleted=show_deleted)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf4f006392eb3abe07b81500f90ac85\",\n \"score\": \"0.6914418\",\n \"text\": \"def get_instance():\\n req = requests.get(\\n \\\"http://169.254.169.254/latest/dynamic/instance-identity/document\\\")\\n response_json = req.json()\\n instancetype = response_json.get('instanceType')\\n region = response_json.get('region')\\n return {'instancetype': instancetype, 'region': region}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba308e456acb2587ded4a4e8928625b8\",\n \"score\": \"0.68700033\",\n \"text\": \"def describe_instance(version, acs_access_key_id, private_key, response_format,\\n timestamp, rndguid, instanceid):\\n \\n signature = generate_signature(timestamp, rndguid, private_key)\\n url_data = generate_url_data(version, acs_access_key_id, response_format, \\n timestamp, rndguid, signature)\\n url_data['instanceid'] = instanceid\\n return read_api_url(API_METHOD_URLS['describe-instance'], url_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee8230c118b1346b391d0b2e6292224\",\n \"score\": \"0.67945576\",\n \"text\": \"def get_instance(instanceid,logger):\\n instance = ec2_resource.Instance(instanceid)\\n logger.append(\\\"Acquiring instance with id {}\\\".format(instanceid))\\n\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56166089e5a85943955680f0d9a13768\",\n \"score\": \"0.665295\",\n \"text\": \"def get_instance_info(self,instance_id,start_time):\\n self.__reconnect_db__();\\n sql_cmd='''select cpu_usage,mem_free,mem_max,nic_in,nic_out,disk_read,disk_write,monitor_time uuid from vm_monitor where instance_id='%s' and monitor_time>='%s' '''%(instance_id,start_time);\\n #print \\\" in get instance info by id sql cmd=%s\\\"%sql_cmd;\\n self.cursor.execute(sql_cmd);\\n return self.cursor.fetchall();\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000a56c2701c1bc35d51a7635a59744d\",\n \"score\": \"0.65126073\",\n \"text\": \"def show(self, req, tenant_id, id):\\n LOG.info(\\\"Showing a database instance %(id)s for tenant \\\"\\n \\\"'%(tenant_id)s'\\\\n\\\"\\n \\\"req : '%(req)s'\\\\n\\\\n\\\", {\\n \\\"tenant_id\\\": tenant_id, \\\"req\\\": req, \\\"id\\\": id})\\n context = req.environ[wsgi.CONTEXT_KEY]\\n deleted_q = req.GET.get('deleted', '').lower()\\n include_deleted = deleted_q == 'true'\\n server = models.DetailedMgmtInstance.load(context, id,\\n include_deleted)\\n root_history = mysql_models.RootHistory.load(context=context,\\n instance_id=id)\\n return wsgi.Result(\\n views.MgmtInstanceDetailView(\\n server,\\n req=req,\\n root_history=root_history).data(),\\n 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7917ed0ebf279d47caa51320a0614e22\",\n \"score\": \"0.65098757\",\n \"text\": \"def ptp_instance_get(self, ptp_instance_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5831f00d3e5bba6b625b6da492e7fd41\",\n \"score\": \"0.64772743\",\n \"text\": \"def get_info(self, instance, use_cache=True):\\n return self._vmops.get_info(instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577889b8a6ec817e4828d97d15f97d53\",\n \"score\": \"0.6476486\",\n \"text\": \"def get_instance(instanceName=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bfd2d2f0bb737a2757c8f2b1e444844\",\n \"score\": \"0.6463787\",\n \"text\": \"def get_this_instance():\\n c = connect(boto.ec2.connection.EC2Connection)\\n metadata = boto.utils.get_instance_metadata()\\n this_instance = next(i for i in c.get_only_instances() if i.id == metadata['instance-id'])\\n return this_instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77c1ae4c0d47a372a1c8d3338449251\",\n \"score\": \"0.6446425\",\n \"text\": \"def get_info(self, instance):\\n _domain_info = self._conn.get_domain_info(instance['name'])\\n state, max_mem, mem, num_cpu, cpu_time = _domain_info\\n return {'state': state,\\n 'max_mem': max_mem,\\n 'mem': mem,\\n 'num_cpu': num_cpu,\\n 'cpu_time': cpu_time}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed93ea4372e837179567b5bb77597455\",\n \"score\": \"0.64433485\",\n \"text\": \"def show_instance(name, call=None):\\n if call != \\\"action\\\":\\n raise SaltCloudSystemExit(\\n \\\"The show_instance action must be called with -a or --action.\\\"\\n )\\n\\n nodes = list_nodes_full()\\n # Find under which cloud service the name is listed, if any\\n if name not in nodes:\\n return {}\\n __utils__[\\\"cloud.cache_node\\\"](nodes[name], _get_active_provider_name(), __opts__)\\n return nodes[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f75dde32f884f2ac917bfc2e25f202\",\n \"score\": \"0.6414563\",\n \"text\": \"def GetInstance(self, instance_name):\\n\\n instances = self.ListInstances()\\n instance = instances.get(instance_name)\\n if not instance:\\n error_msg = 'Instance {0:s} was not found in project {1:s}'.format(\\n instance_name, self.project_id)\\n raise RuntimeError(error_msg)\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02502a234e1cea574a4fd7880ee57d3e\",\n \"score\": \"0.64012486\",\n \"text\": \"def _inspect_instance(self, instance_name):\\n network = {}\\n try:\\n instace_info = self._vbox.show_vm_info(instance_name)\\n except exception.InstanceNotFound:\\n LOG.warning(_LW(\\\"Failed to get specification for `%(instance)s`\\\"),\\n {\\\"instance\\\": instance_name})\\n return\\n\\n description = instace_info.get(constants.VM_DESCRIPTION)\\n if not self._process_description(description):\\n LOG.warning(_LW(\\\"Invalid description for `%(instance)s`: \\\"\\n \\\"%(description)s\\\"),\\n {\\\"instance\\\": instance_name,\\n \\\"description\\\": description})\\n return\\n\\n for field, value in instace_info.items():\\n if field[:-1] in constants.NETWORK_FIELDS:\\n network.setdefault(field[-1], {})[field[:-1]] = value\\n\\n for nic_index in network:\\n mac_address = network[nic_index].get(constants.MAC_ADDRESS)\\n device_id = self._device_map.get(mac_address)\\n nic_mode = network[nic_index].get(constants.NIC_MODE)\\n\\n if device_id and nic_mode != constants.NIC_MODE_NONE:\\n self._nic[device_id] = network[nic_index]\\n self._nic[device_id][\\\"index\\\"] = nic_index\\n self._nic[device_id][\\\"instance\\\"] = instance_name\\n self._nic[device_id][\\\"state\\\"] = instace_info.get(\\n constants.VM_STATE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c4dfc809ea78ab671ec33895c3ace0\",\n \"score\": \"0.63797563\",\n \"text\": \"def print_instance(index, instance):\\n\\n # Print the index with the correct format if the index is available\\n index_str = \\\"\\\\t\\\" + (\\\"\\\" if index == -1 else str(index) + ':')\\n\\n # Print each of the requested instance attributes\\n print index_str, instance.id, '-', instance.instance_type, '<' + str(instance.region) + \\\\\\n '>: <Running since:', str(instance.launch_time) + '>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e713174aa2bd384a6599c9e5067f469e\",\n \"score\": \"0.6369597\",\n \"text\": \"def instance_id():\\n return boto.utils.get_instance_metadata()['instance-id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f32f7c23cb69ab60ee5b14153326be8\",\n \"score\": \"0.63623035\",\n \"text\": \"def GetInstance(self, instance_ref):\\n request = (self.compute.instances,\\n 'Get',\\n self.messages.ComputeInstancesGetRequest(\\n instance=instance_ref.Name(),\\n project=instance_ref.project,\\n zone=instance_ref.zone))\\n\\n errors = []\\n objects = list(request_helper.MakeRequests(\\n requests=[request],\\n http=self.http,\\n batch_url=self.batch_url,\\n errors=errors))\\n if errors:\\n utils.RaiseToolException(\\n errors,\\n error_message='Could not fetch instance:')\\n return objects[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78843947b5087554a485e562a6f1ca44\",\n \"score\": \"0.6347738\",\n \"text\": \"def test_get_server_instance_detail(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a44e83cc1c22ff5cb5d6895217e420b\",\n \"score\": \"0.6321201\",\n \"text\": \"def instance_info(self) -> KeyValuePair:\\n return self._instance_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4eaeafdfdf9ca51ae74b3036d03caa\",\n \"score\": \"0.63010913\",\n \"text\": \"def get_instance(self, project_id, service_id, version_id, instances_id):\\n try:\\n results = self.repository.version_instances.get(\\n project_id, target=instances_id, services_id=service_id,\\n versions_id=version_id)\\n LOGGER.debug('Getting information about a specific instance of'\\n ' a service, project_id = %s, service_id = %s,'\\n ' version_id = %s, instance_id = %s, results = %s',\\n project_id, service_id, version_id, instances_id,\\n results)\\n return results\\n except (errors.HttpError, HttpLib2Error) as e:\\n if _is_status_not_found(e):\\n return {}\\n raise api_errors.ApiExecutionError(project_id, e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a01c6f77b429ad9d5f1b8ecbb6e306c\",\n \"score\": \"0.62947637\",\n \"text\": \"def get_instance_id():\\n response = requests.get('http://169.254.169.254/latest/meta-data/instance-id')\\n return response.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d8a9f751147d3777182f0caf80815c\",\n \"score\": \"0.6285572\",\n \"text\": \"def get_instance(ServiceId=None, InstanceId=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdba8ffddffde7a258cb797e567f53b\",\n \"score\": \"0.62784135\",\n \"text\": \"def myInstanceID():\\n return loadInstanceMetadata()[\\\"instance-id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf65979268597baccfd055ebb8ed89e\",\n \"score\": \"0.6273561\",\n \"text\": \"def do_show(self, args):\\n\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n args_list = args.split()\\n if (args_list[0] not in self.classes):\\n print(\\\"** class doesn't exist **\\\")\\n return\\n if len(args_list) == 1:\\n print(\\\"** instance id missing **\\\")\\n return\\n elif len(args_list) > 1:\\n key = args_list[0] + \\\".\\\" + args_list[1]\\n if key in models.storage.all():\\n print(models.storage.all()[key])\\n else:\\n print(\\\"** no instance found **\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"767a40eba23d20fc9e9001634bf9ee2e\",\n \"score\": \"0.6266999\",\n \"text\": \"def get_instance(self, instance_id):\\n mask = [\\n 'datacenter,billingItem[id,package[id,keyName],nextInvoiceChildren]'\\n ]\\n mask = \\\"mask[%s]\\\" % ','.join(mask)\\n\\n return self.hardware.getObject(id=instance_id, mask=mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b19052ba91340206534d14006bf3931\",\n \"score\": \"0.6255929\",\n \"text\": \"def get_instance_nw_info(self, context, instance):\\n return self._get_instance_nw_info(context, instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffa342c4ef0165da24cf85f69f673f5\",\n \"score\": \"0.6252949\",\n \"text\": \"def get_instanced_item(self, instance_id):\\n\\n params = {\\n \\\"components\\\": \\\"ItemInstances,ItemStats,ItemPerks\\\"\\n }\\n item_request = requests.get(\\n self.client_object.root_endpoint + \\\"/Destiny2/\\\" + str(self.membership_type) + \\\"/Profile/\\\" + self.membership_id +\\n \\\"/Item/\\\" + instance_id,\\n params=params, headers=self.client_object.request_header)\\n return item_request.json()[\\\"Response\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a8c7dfbfb7693c043dde72c2bcddc5\",\n \"score\": \"0.6250046\",\n \"text\": \"def _get_instance(\\n self, instance: InstanceId, token: Optional[str] = None\\n ) -> Instance:\\n client = self._get_client(token)\\n if isinstance(instance, str):\\n filters = [Instance.label == instance]\\n elif isinstance(instance, int):\\n filters = [Instance.id == instance]\\n else:\\n raise AssertionError(f'Invalid instance type: {type(instance)}')\\n\\n instances = client.linode.instances(*filters)\\n assert instances, f'No such Linode instance: {instance}'\\n return instances[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de37abbb9d1d449c7d861b36d811a42a\",\n \"score\": \"0.6225575\",\n \"text\": \"def get_instance(self, instance_name):\\n found = [s for s in self.servers if s.name == instance_name]\\n\\n if len(found) != 1:\\n error_msg = 'invalid instance identifier: %s' % instance_name\\n log.error(error_msg)\\n raise errors.InvalidServerError(error_msg)\\n\\n return found[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55bfdb6efe280417d892d9f5bd3473f\",\n \"score\": \"0.6219639\",\n \"text\": \"def loadInstanceMetadata():\\n return boto.utils.get_instance_metadata(timeout=1, num_retries=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70632cee92754536f2304f87c9809bbf\",\n \"score\": \"0.6212463\",\n \"text\": \"def _get_instance_nw_info(self, context, instance):\\n args = {'instance_id': instance['id'],\\n 'instance_uuid': instance['uuid'],\\n 'rxtx_factor': instance['instance_type']['rxtx_factor'],\\n 'host': instance['host'],\\n 'project_id': instance['project_id']}\\n nw_info = rpc.call(context, FLAGS.network_topic,\\n {'method': 'get_instance_nw_info',\\n 'args': args})\\n\\n return network_model.NetworkInfo.hydrate(nw_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5916990278c7e726f891c539f1494757\",\n \"score\": \"0.6203749\",\n \"text\": \"def do_show(self, arg):\\n args = shlex.split(arg)\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n elif not args[0] in self.classes:\\n print(args[0])\\n print(\\\"** class doesn't exist **\\\")\\n elif len(args) == 1:\\n print(\\\"** instance id missing **\\\")\\n else:\\n all_obj = models.storage.all()\\n for key, value in all_obj.items():\\n id_current = key.split('.')\\n if id_current[1] == args[1]:\\n print(all_obj[key])\\n return\\n print(\\\"** no instance found **\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca3cd60689259587bf306bb9529ebc5\",\n \"score\": \"0.6198044\",\n \"text\": \"def get_instance(self):\\n data = self.unit.relation_get()\\n port = data.get('port')\\n address = data.get('hostname')\\n\\n if not port or not address:\\n self.unit.log('Peer address not set, waiting for handshake')\\n raise RetryLater()\\n\\n ec2 = self.get_ec2()\\n instance = self.unit.get_instance(ec2)\\n return instance.id, instance.placement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef78a6ec68f9a6394314fa5476ed64c\",\n \"score\": \"0.6197021\",\n \"text\": \"def _get_instance(self, instance_ref, client):\\n if not self._instance:\\n request = (client.apitools_client.instances, 'Get',\\n client.messages.ComputeInstancesGetRequest(\\n **instance_ref.AsDict()))\\n instance = client.MakeRequests(requests=[request])\\n\\n self._instance = instance[0]\\n\\n return self._instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421c2873242b71d133d015e62a07d456\",\n \"score\": \"0.6196085\",\n \"text\": \"def get_performance(self, *args, **kargs):\\n instance_id = int(kargs.get(\\\"instance_id\\\", [None])[0])\\n return api.wlee.get_instance_performance(instance_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"532768f397b9506455a949a2544ebd51\",\n \"score\": \"0.61504257\",\n \"text\": \"def instance(self, n):\\n sopInstanceUID = self.__sopInstanceUIDs[n]\\n return self.__instances[sopInstanceUID]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa75c18659b05639369e8a4820df240a\",\n \"score\": \"0.6127541\",\n \"text\": \"def do_show(self, arg):\\n list_args = arg.split()\\n if not arg:\\n print(\\\"** class name missing **\\\")\\n return\\n if list_args[0] not in HBNBCommand.classes:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n if len(list_args) < 2:\\n print(\\\"** instance id missing **\\\")\\n return\\n else:\\n dic = models.storage.all()\\n key = list_args[0] + '.' + str(list_args[1])\\n if key in dic:\\n print(dic[key])\\n else:\\n print(\\\"** no instance found **\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb35f20b18906c0e9996ecba7524d30\",\n \"score\": \"0.6126248\",\n \"text\": \"def query_instances():\\n try:\\n logging.info(\\\"Checking for instances.....\\\")\\n for instance in ec2.instances.all():\\n print (instance.tags, instance.id, instance.public_ip_address, instance.state)\\n except botocore.exceptions.ClientError as e:\\n logging.error(e.response['Error']['Code'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e1b1d8ae64845f4909a2c23a748361\",\n \"score\": \"0.61247444\",\n \"text\": \"def get_instance(self) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107af48f508e24407b4399b14c65c9f9\",\n \"score\": \"0.61246556\",\n \"text\": \"def get_db(self):\\n try:\\n instance = self.client.instances.get(self.instance.id)\\n self.logger.warning('Currently Active Instances')\\n self.success = True\\n self.logger.warning(instance.id + \\\" \\\" + instance.name)\\n\\n except trove_exceptions.NotFound as e:\\n self.success, self.overall_success = False, False\\n self.failure = e\\n self.logger.error(\\\"<*>404 db not found get_db %s\\\", e)\\n\\n except Exception as e:\\n self.success, self.overall_success = False, False\\n self.failure = e\\n self.logger.warning('Error getting Instance %s', e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b6b57950e3dc716e7225d803f647a6\",\n \"score\": \"0.6102276\",\n \"text\": \"def list_instance_of_user(self):\\n try:\\n json_obj = self._atmo_get_req(\\\"/api/v2/instances\\\")\\n return json_obj[\\\"results\\\"]\\n except HTTPError as e:\\n raise HTTPError(str(e.args) + \\\"Fail to list all the instances\\\")\\n except KeyError:\\n raise IncompleteResponse(\\\"Missing field\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8b6e7d3faae563b66f1b2fd962461a\",\n \"score\": \"0.6096001\",\n \"text\": \"def do_show(self, arg):\\n if not arg:\\n print('** class name missing **')\\n return\\n args = arg.split()\\n for k, v in classes.items():\\n if args[0] == k:\\n break\\n else:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n if len(args) < 2:\\n print('** instance id missing **')\\n return\\n for key, val in models.storage.all().items():\\n if args[0] + '.' + args[1] == key:\\n print(val)\\n break\\n else:\\n print('** no instance found **')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fd45cf3eb460b684f77627e67387b3\",\n \"score\": \"0.609028\",\n \"text\": \"def get_instance_name() -> str:\\n\\n res = requests.get(\\n url='http://metadata/computeMetadata/v1/instance/name',\\n headers={'Metadata-Flavor': 'Google'}\\n )\\n instance_name = str(res.content, 'utf-8')\\n\\n return instance_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1603ec9d0db4104e9ba184f3088ffc6a\",\n \"score\": \"0.6062181\",\n \"text\": \"def _get(self, hostname, zone, role):\\n\\n volume_config = {'image': self._image, 'class': self.storage_class}\\n inst = None\\n try:\\n _server = self._nova.servers.find(name=hostname)\\n LOGGER.debug(\\\"Found instance %s\\\", hostname)\\n inst = Instance(self._cinder,\\n self._nova,\\n _server.name,\\n self.net,\\n zone,\\n role,\\n volume_config,\\n _server.flavor)\\n try:\\n inst.ports.append(_server.interface_list()[0])\\n except IndexError:\\n LOGGER.warning(\\\"No network found for %s\\\", hostname)\\n\\n inst.exists = True\\n except NovaNotFound:\\n pass\\n\\n return inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b437afeedb75713dd7ccacd230e38f\",\n \"score\": \"0.6045457\",\n \"text\": \"def do_show(self, args):\\n\\n my_dict = models.storage.all()\\n split_args = get_args(args)\\n\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n else:\\n if split_args[0] in my_class:\\n if len(split_args) > 1:\\n my_search = split_args[0] + '.' + split_args[1]\\n\\n if my_search in my_dict:\\n print(my_dict.get(my_search))\\n\\n else:\\n print(\\\"** no instance found **\\\")\\n else:\\n print(\\\"** instance id missing **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19b6cc53f62ca860af5508cb9162cd5\",\n \"score\": \"0.60379124\",\n \"text\": \"def get_instance(self, name):\\r\\n instances = self._build_instance_dict()\\r\\n try:\\r\\n return instances[name]\\r\\n except KeyError:\\r\\n logger.error(\\\"No instance named %s found\\\", name)\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c516d38d0943b3328b135dc8f8f2606c\",\n \"score\": \"0.6035774\",\n \"text\": \"def do_show(self, argument):\\n tokens = shlex.split(argument)\\n if len(tokens) == 0:\\n print(\\\"** class name missing **\\\")\\n elif len(tokens) == 1:\\n print(\\\"** instance id missing **\\\")\\n elif tokens[0] not in self.classes:\\n print(\\\"** class doesn't exist **\\\")\\n else:\\n dic = models.storage.all()\\n # Key has format <className>.id\\n keyU = tokens[0] + '.' + str(tokens[1])\\n if keyU in dic:\\n print(dic[keyU])\\n else:\\n print(\\\"** no instance found **\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0064930af65f67897a12ff454d45dcb\",\n \"score\": \"0.6024477\",\n \"text\": \"def get_instance_address(self):\\n ec2 = self.get_ec2()\\n instance = self.unit.get_instance(ec2)\\n return instance.id, instance.ip_address\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c09d2c256f7b69164c5210d1d4a9c9\",\n \"score\": \"0.59979236\",\n \"text\": \"def do_show(self, line):\\n args = line.split()\\n objects = storage.all()\\n\\n if len(args) == 0:\\n print('** class name missing **')\\n elif args[0] not in self.__list_class:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(args) == 1:\\n print('** instance id missing **')\\n else:\\n key_find = args[0] + '.' + args[1]\\n if key_find in objects.keys():\\n print(objects[key_find])\\n else:\\n print('** no instance found **')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e4addef47bdac01099eb2772f8f64f\",\n \"score\": \"0.5975401\",\n \"text\": \"def get(self, instance, owner):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c7e07adc0f18e5f5a50747c35d2cb4\",\n \"score\": \"0.59743834\",\n \"text\": \"def get_info(self, instance):\\n return dict(\\n state=power_state.RUNNING,\\n max_mem=1024,\\n mem=1024,\\n num_cpu=1,\\n cpu_time=0,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4fe8ce42b09f9e0892849c9a1d7edd\",\n \"score\": \"0.5963981\",\n \"text\": \"def instance_id(self) -> str:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4fe8ce42b09f9e0892849c9a1d7edd\",\n \"score\": \"0.5963981\",\n \"text\": \"def instance_id(self) -> str:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4fe8ce42b09f9e0892849c9a1d7edd\",\n \"score\": \"0.5963981\",\n \"text\": \"def instance_id(self) -> str:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4fe8ce42b09f9e0892849c9a1d7edd\",\n \"score\": \"0.5963981\",\n \"text\": \"def instance_id(self) -> str:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4fe8ce42b09f9e0892849c9a1d7edd\",\n \"score\": \"0.5963981\",\n \"text\": \"def instance_id(self) -> str:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4715365547c577b215aef69bbecbb176\",\n \"score\": \"0.59459674\",\n \"text\": \"def instance_name(self) -> str:\\n return pulumi.get(self, \\\"instance_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79062c0bd1fffb0b154a56a18f74fe66\",\n \"score\": \"0.5936568\",\n \"text\": \"def instance_url(self) -> str:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e3e5bd702313394e77e5dd0fd9969d\",\n \"score\": \"0.593423\",\n \"text\": \"def do_show(self, line):\\n args = line.split(' ')\\n if len(args) == 0:\\n print('** class name missing **')\\n return\\n elif args[0] not in self.classes:\\n print('** class doesn\\\\'t exist **')\\n return\\n elif len(args) == 1:\\n print('** instance id missing **')\\n return\\n else:\\n all_objs = storage.all()\\n k_find = args[0] + '.' + args[1]\\n if k_find in all_objs:\\n print(all_objs[k_find])\\n return\\n else:\\n print('** no instance found **')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd6413231331dd60309537d93731995\",\n \"score\": \"0.5927\",\n \"text\": \"def get_instance(self, spec):\\n return self._instances[spec]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65385a4d2015e4068b3c04c1c6ab6aa3\",\n \"score\": \"0.5912811\",\n \"text\": \"def instance_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"instance_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374e7d1b9139164d556127d1abf567b0\",\n \"score\": \"0.5902694\",\n \"text\": \"def get_instance_data(self):\\n self._search_hosts()\\n instances = self.hosts.values()\\n ec2 = boto3.client('ec2')\\n response = ec2.describe_instances(InstanceIds=instances)\\n if response['Reservations']:\\n instances = response['Reservations']\\n return instances\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecc1258f6424500177ef55e84628668\",\n \"score\": \"0.5888823\",\n \"text\": \"def do_show(self, arg):\\n arg = self.parse_arg(arg)\\n\\n if len(arg) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n elif len(arg) == 1:\\n print(\\\"** instance id missing **\\\")\\n return\\n\\n class_in = arg[0]\\n key_in = arg[1]\\n\\n is_class = False\\n\\n all_objs = storage.all()\\n\\n for index in all_objs.keys():\\n if class_in in index.split(\\\".\\\")[0]:\\n is_class = True\\n if key_in in index.split(\\\".\\\")[1]:\\n print(str(all_objs[index]))\\n return\\n\\n if is_class is True:\\n print(\\\"** no instance found **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a16d14640e6daea0c5308408f4e2f9\",\n \"score\": \"0.5883925\",\n \"text\": \"def __inst_display(self, instance, disp, pg, region):\\n res_id_list = [instance.id]\\n if disp.display == DisplayOptions.LONG:\\n if disp.display_name:\\n last_field = instance.tags.get(\\\"Name\\\", \\\"-\\\")\\n else:\\n last_field = amazon2localtime(instance.launch_time)\\n pg.prt(\\\"%-12s %-10s %-12s %-10s %s\\\",\\n instance.id, instance.state,\\n instance.placement,\\n instance.instance_type,\\n last_field)\\n elif disp.display == DisplayOptions.EXTENDED:\\n pg.prt(\\\"%s\\\", instance.id)\\n pg.prt(\\\"%15s : %-12s\\\", \\\"State\\\", instance.state)\\n pg.prt(\\\"%15s : %s\\\", \\\"Launch-time\\\", \\n amazon2localtime(instance.launch_time))\\n pg.prt(\\\"%15s : %s\\\", \\\"Location\\\", instance.placement)\\n pg.prt(\\\"%15s : %s %s %s %s\\\", \\\"Hardware\\\",\\n instance.instance_type,\\n instance.architecture,\\n instance.virtualization_type,\\n instance.hypervisor,\\n )\\n pg.prt(\\\"%15s : %s %s %s %s\\\", \\\"Software\\\",\\n instance.platform,\\n instance.image_id,\\n instance.kernel,\\n instance.ramdisk)\\n res_id_list.append(instance.image_id)\\n if instance.kernel:\\n res_id_list.append(instance.kernel)\\n if instance.ramdisk:\\n res_id_list.append(instance.ramdisk)\\n pg.prt(\\\"%15s : %-12s %s\\\", \\\"Root\\\",\\n instance.root_device_name,\\n instance.root_device_type)\\n pg.prt(\\\"%15s : %s\\\", \\\"EBS-optimized\\\",\\n \\\"True\\\" if instance.ebs_optimized else \\\"False\\\")\\n if instance.vpc_id:\\n pg.prt(\\\"%15s : %-14s %-16s\\\", \\\"VPC-info\\\",\\n instance.vpc_id, instance.subnet_id)\\n res_id_list.extend([instance.vpc_id, instance.subnet_id])\\n if instance.private_ip_address:\\n pg.prt(\\\"%15s : %s %s\\\", \\\"IP\\\",\\n instance.ip_address,\\n instance.private_ip_address)\\n for netif in instance.interfaces:\\n pg.prt(\\\"%15s : %-15s idx=%-2s %-16s SDC=%s\\\",\\n \\\"Interface\\\",\\n netif.id,\\n netif.attachment.device_index,\\n netif.private_ip_address,\\n \\\"on\\\" if netif.source_dest_check else \\\"off\\\"\\n )\\n res_id_list.append(netif.id)\\n if instance.groups:\\n group_id_list = [group.id for group in instance.groups]\\n pg.prt(\\\"%15s : %s\\\", \\\"Groups\\\", \\\" \\\".join(group_id_list))\\n res_id_list.extend(group_id_list)\\n bdev_list = instance.block_device_mapping.keys()\\n bdev_list.sort()\\n for bdev in bdev_list:\\n bdev_info = instance.block_device_mapping[bdev]\\n pg.prt(\\\"%15s : %-12s %-12s %s dot=%s\\\",\\n \\\"Device\\\", bdev,\\n bdev_info.volume_id,\\n bdev_info.status,\\n bdev_info.delete_on_termination)\\n res_id_list.append(bdev_info.volume_id)\\n if disp.display_tags:\\n common.display_tags(instance.tags, pg)\\n else:\\n pg.prt(\\\"%s\\\", instance.id)\\n if disp.display_tags:\\n common.display_tags(instance.tags, pg)\\n self.cache_insert(region, res_id_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c074b1bde51dd59af5e2d8856789d01\",\n \"score\": \"0.5880238\",\n \"text\": \"def GetInstanceRef(args, client):\\n validate.ValidateInstanceName(args.instance)\\n return client.resource_parser.Parse(\\n args.instance,\\n params={'project': properties.VALUES.core.project.GetOrFail},\\n collection='sql.instances')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a480263fc11b4709cfabbc92eb50333\",\n \"score\": \"0.58766186\",\n \"text\": \"def instance_view(self) -> 'outputs.RestorePointInstanceViewResponse':\\n return pulumi.get(self, \\\"instance_view\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57967dadcb15e020a3a19707c679fb3e\",\n \"score\": \"0.58708197\",\n \"text\": \"def instance_view(self) -> 'outputs.CapacityReservationGroupInstanceViewResponse':\\n return pulumi.get(self, \\\"instance_view\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2406195c7a04febcbeb5fa1ccf38ba5\",\n \"score\": \"0.5854854\",\n \"text\": \"def test_get_member_server_image_instance_detail(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ef2ce221be817ef60a23ab63b51e59\",\n \"score\": \"0.58519864\",\n \"text\": \"def get(instance, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0efa4191a129f820ebbabcb67e90cbfd\",\n \"score\": \"0.58404243\",\n \"text\": \"def do_show(self, args):\\n sw = 0\\n arg = args.split()\\n if args == \\\"\\\":\\n print(\\\"** class name missing **\\\")\\n elif arg[0] not in HBNBCommand.class_list:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(arg) < 2:\\n print(\\\"** instance id missing **\\\")\\n else:\\n in_key = (arg[0] + \\\".\\\" + arg[1])\\n for key, obj in storage.all().items():\\n if key == in_key:\\n print(obj)\\n sw = 1\\n if sw == 0:\\n print(\\\"** no instance found **\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0493bc24ad92b5db59d768ff8b7f79\",\n \"score\": \"0.5836969\",\n \"text\": \"def instance_name(instance):\\n return getattr(instance, 'OS-EXT-SRV-ATTR:instance_name', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea1cfa3b6b8f5b1cf8e28a8b47dfd91\",\n \"score\": \"0.5825013\",\n \"text\": \"def get_instance(settings):\\n\\n session = get_session_info(settings)\\n instance_url = session[\\\"instance_url\\\"]\\n base_url = re.compile(\\\"//[\\\\s\\\\S]+?\\\\.\\\").search(instance_url).group()\\n instance = base_url[2:-1]\\n\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9754bc40870d3ba18c54972530a1f291\",\n \"score\": \"0.58244944\",\n \"text\": \"def scanInstanceGet(self, instanceId: str) -> list:\\n\\n if not isinstance(instanceId, str):\\n raise TypeError(f\\\"instanceId is {type(instanceId)}; expected str()\\\") from None\\n\\n qry = \\\"SELECT name, seed_target, ROUND(created/1000) AS created, \\\\\\n ROUND(started/1000) AS started, ROUND(ended/1000) AS ended, status \\\\\\n FROM tbl_scan_instance WHERE guid = ?\\\"\\n qvars = [instanceId]\\n\\n with self.dbhLock:\\n try:\\n self.dbh.execute(qry, qvars)\\n return self.dbh.fetchone()\\n except sqlite3.Error as e:\\n raise IOError(\\\"SQL error encountered when retrieving scan instance\\\") from e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff38aacdfa23737841f14029967f01dc\",\n \"score\": \"0.58233666\",\n \"text\": \"def get_instance_snapshot(instanceSnapshotName=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c92be9c97c4621a5d06451797c3d211\",\n \"score\": \"0.58217\",\n \"text\": \"def get_details():\\n return details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0e5560355b324a01879c695aa9daa11\",\n \"score\": \"0.5818443\",\n \"text\": \"def get_instances():\\n logging.debug('Serving instance data.')\\n\\n instance_types = bottle.request.query.instanceTypes or None\\n instance_types = instance_types.split(',') if instance_types else []\\n\\n return instance_data.get_instances(filter_instance_types=instance_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ee53f709a8702625532490e721aaa6\",\n \"score\": \"0.58165205\",\n \"text\": \"def read(self, resource_type, instance_id):\\n self.validate_resource_type(resource_type)\\n\\n res = self.db[resource_type].find_one({\\\"id\\\": instance_id})\\n if res is None:\\n raise NotFoundError\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9dfaa729d4430f4d5c8880fa2f2337\",\n \"score\": \"0.58099234\",\n \"text\": \"def GetOperation(self):\\n\\n gce_instance_client = self.project.GceApi().instances()\\n request = gce_instance_client.get(\\n instance=self.name, project=self.project.project_id, zone=self.zone)\\n response = request.execute()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e45a4e63aade00dcb04467acf980c6\",\n \"score\": \"0.58059126\",\n \"text\": \"def test_get_instance(self):\\n\\n mock_instance_id = 'instance-abyz'\\n mock_instance = Mock()\\n mock_instance.id = mock_instance_id\\n mock_instance.name = 'test-instance'\\n mock_instance.hypervisor_hostname = 'nova-abyz'\\n self.conn.compute.get_server_metadata.return_value = mock_instance\\n result = self.os_sfc.get_instance(mock_instance_id)\\n self.assertEqual(result, mock_instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8080ced242373e31330a5f8f8f1bfd45\",\n \"score\": \"0.5788852\",\n \"text\": \"def instance_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8080ced242373e31330a5f8f8f1bfd45\",\n \"score\": \"0.5788852\",\n \"text\": \"def instance_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8080ced242373e31330a5f8f8f1bfd45\",\n \"score\": \"0.5788852\",\n \"text\": \"def instance_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8080ced242373e31330a5f8f8f1bfd45\",\n \"score\": \"0.5788852\",\n \"text\": \"def instance_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"instance_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d343c94bbca39f42f876b02d53365ef2\",\n \"score\": \"0.57871383\",\n \"text\": \"def get_instance_output(instance_id: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[Optional[str]]] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetInstanceResult]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6b3461723a0651bd07b043a3db209d\",\n \"score\": \"0.57849914\",\n \"text\": \"def gen_instance(iid):\\n instance = awscli('ec2', 'describe-instances', '--instance-ids', iid)\\n instance = instance['Reservations'][0]['Instances'][0]\\n return gen_details(instance)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993680,"cells":{"query_id":{"kind":"string","value":"7070bd69164b7992eb7f720077323f67"},"query":{"kind":"string","value":"Defines the hessian matrix for the cost function"},"positive_passages":{"kind":"list like","value":[{"docid":"b536ae7f28eb22e92c4e3d9a75c5bf3c","score":"0.58042645","text":"def f_hess(x, *args):\n D, N, m_deg, nk = args[0], args[1], args[2], args[3]\n hess = [[0 for i in range(N)] for j in range(N)]\n n = sum(nk)\n for i in range(N):\n for j in range(N):\n hij_1 = 0\n for t in range(1, m_deg):\n t_coef = n ** (m_deg - t) * comb(m_deg, t - 1)\n for k in range(4):\n hij_1 += nk[k] ** (m - 1) * (D[k][m_deg - t + 1][i] * (t - 1) * np.dot(x, D[k][1]) ** (t - 2) * D[k][1][j] +\n (t - 1) * D[k][1][i] * np.dot(x, D[k][m - t + 1]) * (t-2) * np.dot(x, D[k][1]) ** (t-3) * D[k][1][j] +\n (t - 1) * D[k][1][i] * D[k][m - t + 1][j] * np.dot(x, D[k][1]) ** (t - 2))\n hij_1 *= t_coef\n hij_2 = 0\n for k in range(0, 4):\n hij_2 += nk[k] * D[k][1][i] * m_deg * (m_deg - 1) * np.dot(x, D[k][1]) ** (m_deg - 2) * D[k][1][j]\n hess[i][j] = hij_1 + hij_2\n return hess","title":""}],"string":"[\n {\n \"docid\": \"b536ae7f28eb22e92c4e3d9a75c5bf3c\",\n \"score\": \"0.58042645\",\n \"text\": \"def f_hess(x, *args):\\n D, N, m_deg, nk = args[0], args[1], args[2], args[3]\\n hess = [[0 for i in range(N)] for j in range(N)]\\n n = sum(nk)\\n for i in range(N):\\n for j in range(N):\\n hij_1 = 0\\n for t in range(1, m_deg):\\n t_coef = n ** (m_deg - t) * comb(m_deg, t - 1)\\n for k in range(4):\\n hij_1 += nk[k] ** (m - 1) * (D[k][m_deg - t + 1][i] * (t - 1) * np.dot(x, D[k][1]) ** (t - 2) * D[k][1][j] +\\n (t - 1) * D[k][1][i] * np.dot(x, D[k][m - t + 1]) * (t-2) * np.dot(x, D[k][1]) ** (t-3) * D[k][1][j] +\\n (t - 1) * D[k][1][i] * D[k][m - t + 1][j] * np.dot(x, D[k][1]) ** (t - 2))\\n hij_1 *= t_coef\\n hij_2 = 0\\n for k in range(0, 4):\\n hij_2 += nk[k] * D[k][1][i] * m_deg * (m_deg - 1) * np.dot(x, D[k][1]) ** (m_deg - 2) * D[k][1][j]\\n hess[i][j] = hij_1 + hij_2\\n return hess\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"70b133178455eb1f704a65c04f1163e3","score":"0.7606198","text":"def hessian(self, x):\r\n return numpy.array([[10, 8], [8, 10]], dtype = numpy.float)","title":""},{"docid":"45c382621e4d63d7ac2a62c425605811","score":"0.74376804","text":"def hessian(X,Etha):\n \n h_dim = X.shape[1]*Etha.shape[1]\n H = np.zeros((h_dim, h_dim))\n for i in range(Etha.shape[1]):\n for j in range(Etha.shape[1]):\n rf = Etha.shape[0] * i\n rl = Etha.shape[0] * (i+1)\n cf = Etha.shape[0] * j\n cl = Etha.shape[0] * (j+1) \n if i == j:\n W = np.diagflat([etha[:,i]*(1-etha[:,i]) for etha in Etha])\n print 'W:', type(W).__name__, len(W)\n print 'X:', type(X).__name__, X.shape\n H[rf:rl,cf:cl] = -np.dot(np.dot(X.transpose(),W),X)[:,:]\n else:\n W = np.diagflat([etha[:,i]*etha[:,j] for etha in Etha]) \n H[rf:rl,cf:cl] = np.dot(np.dot(X.transpose(),W),X)[:,:]\n return H","title":""},{"docid":"a5f5d5292d748328ab8194c497bd93ac","score":"0.7407189","text":"def calculate_hessian(y, tx, w, pred):\n\n n = int(y.shape[0])\n S = pred*(1-pred)*np.eye(n)\n print \"S\"\n b = tx.T.dot(S)\n print \"B\"\n hessian = b.dot(tx)\n print \"H\"\n return hessian","title":""},{"docid":"780a6fbd1348bf05628a9b5cac9193ab","score":"0.7277152","text":"def makeHessian(N):\n ia_null = ia(0.,0.)\n m1 = np.zeros((N,N),float)#np.matrix(np.zeros((N,N),float))\n HX=[]\n for i in range(N):\n HX.append([])\n for j in range(N):\n HX[i].append(ia_null*m1[i,j])\n HX = np.matrix(HX)#np.asarray(HX)#\n return HX","title":""},{"docid":"2d1c2b6cd0bae9bfed6838315b5a33dd","score":"0.72490305","text":"def hessian(self, q):\n x = self._g(q)\n J_g = self._g.jacobian(q)\n H_f = self._f.hessian(x)\n # print(\"H_f :\", H_f.shape)\n # print(\"J_g :\", J_g.shape)\n return J_g.T * H_f * J_g","title":""},{"docid":"6dae1a7844a5e2ec8c17529c4277da42","score":"0.7225836","text":"def hessian(self, params):\n raise NotImplementedError","title":""},{"docid":"efb33a516330f22a5e6c67f4e4c8cab1","score":"0.7152987","text":"def hessian(self, functions, eval_points):\n\t\traise NotImplementedError","title":""},{"docid":"07d48b2337af9a1f4a3104e4e5a01b51","score":"0.7127126","text":"def _hessian(yhat, dtrain):\n\n y = dtrain.get_label()\n return 1. / np.power(np.cosh(y - yhat), 2)","title":""},{"docid":"575101ecef88f7e8b9902c82e690151f","score":"0.71194786","text":"def get_Hessian(self,u,xi, theta):\n\t\tpass","title":""},{"docid":"75f352bf092fa71e84b84cc83ca23119","score":"0.71186304","text":"def _estimate_hessian(self, theta: np.ndarray) -> sparse.spmatrix:\n score = 1.0 / (1 + self._exp_affine(theta))\n D = sparse_diag_matrix(score * (1 - score))\n X = self.data.X\n full_hessian = X.T * D * X\n return full_hessian","title":""},{"docid":"56bffdebea4bb98d5a5beccc71fc55fc","score":"0.71115786","text":"def hessian(self, params):\n inter = 2 * self.f.gradient(self.x, params)[..., numpy.newaxis] * self.f.gradient(self.x, params)[..., numpy.newaxis, :]\n shape = inter.shape[-2], inter.shape[-1]\n inter.shape = (-1, inter.shape[-2] * inter.shape[-1])\n temp = numpy.sum(inter, axis = 0)\n temp.shape = shape\n return temp","title":""},{"docid":"37eeafddf5022fd5d5196e0639cef90f","score":"0.7090309","text":"def hessian(self, x, y, obj_factor):\n pass","title":""},{"docid":"4853d89b9623a7ef4a3b7ef4e7e5c2c0","score":"0.70627683","text":"def hessian(self, x):\n if self._symmetric and self._posdef:\n return self._a.T\n else:\n return 0.5 * (self._a + self._a.T)","title":""},{"docid":"24e2ee7197f77276c1157727a73b6a22","score":"0.70527565","text":"def calculate_hessian(y, tx, w):\n \"\"\"\"sig = np.apply_along_axis(sigmoid,axis=1,arr=np.dot(tx,w))\n S = np.diag(np.diag(np.dot(sig,np.ones((len(y),1))-sig)))\"\"\"\n S = np.zeros((len(y),len(y)))\n for i in range(len(y)):\n S[i][i] = np.dot(sigmoid(np.dot(tx[i],w)), 1 - sigmoid(np.dot(tx[i],w)))\n\n return np.dot(np.transpose(tx),np.dot(S,tx))","title":""},{"docid":"25673c5cb95df775b47bf2029a4ab201","score":"0.7051751","text":"def calculate_hessian(y, tx, w):\n pred = sigmoid(tx.dot(w))\n pred = np.diag(pred.T[0])\n r = np.multiply(pred, (1-pred))\n\n return tx.T.dot(r).dot(tx)","title":""},{"docid":"fef4ccf7ee7c8065e360f7bc55ff7ead","score":"0.7038425","text":"def hessian(self, X, y, alpha):\n n = len(y)\n W = np.diag(self.d2_loss(y * self.K_train @ alpha))\n return (1/n) * self.K_train @ W @ self.K_train + self.lambd * self.K_train","title":""},{"docid":"bbc68f480db6d0865d2f16f8c4471850","score":"0.703747","text":"def calculate_hessian(y, tx, w):\n N = len(y)\n S = np.eye(N)\n for n in range(N):\n pred = sigmoid(np.dot(tx[n,:].T,w))\n S[n,n] = pred*(1-pred)\n return tx.T.dot(S.dot(tx))","title":""},{"docid":"6c856ce0b7feddbc76ceb484e8857ccd","score":"0.70134056","text":"def scalarHessian(N):\n m1 = np.zeros((N,N),float)#np.matrix(np.zeros((N,N),float))\n HX=[]\n for i in range(N):\n HX.append([])\n for j in range(N):\n HX[i].append(m1[i,j])\n HX = np.matrix(HX)#np.asarray(HX)#\n return HX","title":""},{"docid":"7ea1f69f32a7ba31b0fc6d20e2271da2","score":"0.69780093","text":"def hessian(self, params):\n loglike = self.loglike\n return approx_hess(params, loglike)","title":""},{"docid":"3fcedaec13e8184f9c840eeb744e266b","score":"0.6952656","text":"def hessian(X, theta):\n\n # The diagonal sigmoid matrix\n # https://stats.stackexchange.com/a/316933\n # -1 * g(X, theta) * (1 - g(X, theta)) * X @ X.T\n G = h(X, theta)\n D = np.diag(G * (1 - G))\n\n return X.T @ D @ X","title":""},{"docid":"2ca965dbe8717fc353847a52dc29a872","score":"0.6929033","text":"def hessian(self, data, beta):\n raise NotImplementedError(\"abstract method\")","title":""},{"docid":"1a4aa6c02d0fc8b21e464f8d4826ac9b","score":"0.6865305","text":"def _update_full_hessian(self, new_theta: np.array) -> sparse.spmatrix:\n raise NotImplementedError","title":""},{"docid":"498aa212366e474c25ee053f1ffd6d3d","score":"0.6860637","text":"def _update_full_hessian(self, new_theta: np.ndarray) -> sparse.spmatrix:\n return (\n self.initial_model.hessian * self.param_delta # type: ignore\n + self._estimate_hessian(new_theta)\n + (1 - self.param_delta) * self.param_lambda\n )","title":""},{"docid":"ef7bc31d44c2e32abcac7364351e34e9","score":"0.6854312","text":"def hessian(theta,X,y,regTerm=0.0):\n (m,n) = X.shape\n tmp = (regTerm/m)*np.identity(n)\n tmp[0][0] = 0. #no regularization for first term\n mat1 = np.dot(X.T,np.diag(np.multiply(sigmoid(X,theta),1-sigmoid(X,theta))))\n mat2 = (1.0/m)*np.dot(mat1,X)\n return mat2 + tmp","title":""},{"docid":"4b134d12623eb6774bc8323b986209c1","score":"0.68359387","text":"def hessian(self, x, lagrange, obj_factor):\n return obj_factor * hnz","title":""},{"docid":"44eb2db0a309be9c9186d8e7a299a636","score":"0.67806596","text":"def Hessian(x):\n hessian = np.zeros((3, 3))\n return PotentialField._accumulate(PotentialField._hessian, x, hessian)","title":""},{"docid":"c89ba3147d8d776fb0f9d0cf1cf6b522","score":"0.67723465","text":"def hessian(self, q):\n x = self._g(q)\n J_g = self._g.jacobian(q)\n H_g = self._g.hessian(q)\n J_f = self._f.jacobian(x)\n H_f = self._f.hessian(x)\n a_x = J_g.T * H_f * J_g\n b_x = J_f * np.ones(self.input_dimension()) * H_g\n return a_x + b_x","title":""},{"docid":"4e513ed9e3a7649ab5c258f952118d36","score":"0.67086816","text":"def hessian(y, x):\n meta_batch_size, num_observations = y.shape[:2]\n grad_y = torch.ones_like(y[..., 0]).to(y.device)\n h = torch.zeros(meta_batch_size, num_observations, y.shape[-1], x.shape[-1], x.shape[-1]).to(y.device)\n for i in range(y.shape[-1]):\n # calculate dydx over batches for each feature value of y\n dydx = grad(y[..., i], x, grad_y, create_graph=True)[0]\n\n # calculate hessian on y for each x value\n for j in range(x.shape[-1]):\n tmp = grad(dydx[..., j], x, grad_y, create_graph=True)\n h[..., i, j, :] = tmp[0].unsqueeze(1)[..., :]\n\n return h","title":""},{"docid":"77a43b55295d9c0167b6c7d132924bfe","score":"0.66171896","text":"def _update_hessian(self, new_theta: np.array) -> sparse.spmatrix:\n if self.hessian_type == HessianType.NONE:\n hessian = None\n elif self.hessian_type == HessianType.IDENTITY:\n hessian = sparse_diag_matrix([1] * len(new_theta))\n elif self.hessian_type == HessianType.DIAGONAL:\n hessian = sparse_diag_matrix(self._update_full_hessian(new_theta).diagonal())\n elif self.hessian_type == HessianType.FULL:\n hessian = self._update_full_hessian(new_theta)\n else:\n raise ValueError(f\"{self.hessian_type} is not a supported HessianType.\")\n\n return hessian","title":""},{"docid":"930e7137eb9e9f5962b99569e5310eee","score":"0.6570693","text":"def hessian(x):\n x_grad = np.gradient(x) \n hessian = np.empty((x.ndim, x.ndim) + x.shape, dtype=x.dtype) \n for k, grad_k in enumerate(x_grad):\n # iterate over dimensions\n # apply gradient again to every component of the first derivative.\n tmp_grad = np.gradient(grad_k) \n for l, grad_kl in enumerate(tmp_grad):\n hessian[k, l, :, :] = grad_kl\n return hessian","title":""},{"docid":"930e7137eb9e9f5962b99569e5310eee","score":"0.6570693","text":"def hessian(x):\n x_grad = np.gradient(x) \n hessian = np.empty((x.ndim, x.ndim) + x.shape, dtype=x.dtype) \n for k, grad_k in enumerate(x_grad):\n # iterate over dimensions\n # apply gradient again to every component of the first derivative.\n tmp_grad = np.gradient(grad_k) \n for l, grad_kl in enumerate(tmp_grad):\n hessian[k, l, :, :] = grad_kl\n return hessian","title":""},{"docid":"ad166894a295106765ce177ef177a083","score":"0.65619","text":"def hessian(self, x):\n if self.viewer is not None:\n if self._draw_hessian:\n self.draw_gradient(x)\n else:\n self.draw(Trajectory(q_init=self.objective.q_init, x=x))\n return self.objective.objective.hessian(x)","title":""},{"docid":"08bb30c819019805fedec0bcd248eb52","score":"0.65548205","text":"def hessian(func, argnums=0):\n return jacfwd(jacrev(func, argnums), argnums)","title":""},{"docid":"b849897f75c93bef7d914945c5512abe","score":"0.65482","text":"def full_hessian(loss, params):\n\n flat_params, unravel = ravel_pytree(params)\n\n def loss_flat(flat_params):\n params = unravel(flat_params)\n return loss(params)\n\n def hessian(f):\n return jacfwd(jacrev(f))\n\n hessian_matrix = hessian(loss_flat)(flat_params)\n return hessian_matrix","title":""},{"docid":"12af9c7cc33dd0f56156583969057494","score":"0.64940876","text":"def BetheHessian(adjacency_matrix, r=None):\n d = np.sum(adjacency_matrix, axis=0)\n if r is None:\n r = np.sqrt(np.mean(d))\n\n print((r**2 - 1)*np.identity(len(d)) - r*adjacency_matrix + np.diag(d))\n return (r**2 - 1)*np.identity(len(d)) - r*adjacency_matrix + np.diag(d)","title":""},{"docid":"f88260385df0083c59b1637355f9b0eb","score":"0.64898974","text":"def hessian(self, x, lagrange, obj_factor):\n row, col = self.hessianstructure()\n \n return obj_hess(x)[row, col]","title":""},{"docid":"14aeb39a1b22153e8529e25bd7e3ab42","score":"0.647654","text":"def hessian(self, f: Callable, wrt: tuple or list, get_output: bool, get_gradient: bool) -> tuple:\n raise NotImplementedError(self)","title":""},{"docid":"5c1fdbea374a60d58a1356ead0a5c304","score":"0.64643323","text":"def hessian(self, x, lagrange, obj_factor):\n\n H = obj_factor*np.array((\n (2*x[3], 0, 0, 0),\n (x[3], 0, 0, 0),\n (x[3], 0, 0, 0),\n (2*x[0]+x[1]+x[2], x[0], x[0], 0)))\n\n H += lagrange[0]*np.array((\n (0, 0, 0, 0),\n (x[2]*x[3], 0, 0, 0),\n (x[1]*x[3], x[0]*x[3], 0, 0),\n (x[1]*x[2], x[0]*x[2], x[0]*x[1], 0)))\n\n H += lagrange[1]*2*np.eye(4)\n\n row, col = self.hessianstructure()\n\n return H[row, col]","title":""},{"docid":"86fc6c073b1a8950ee4a84ec17ef11c9","score":"0.644322","text":"def compute_hessian(x0, X1, X0, Z1, Z0, Y1, Y0):\n\n # def auxiliary parameters\n num_obs = X1.shape[0] + X0.shape[0]\n n_col_X1 = X1.shape[1]\n n_col_X0 = X0.shape[1]\n n_col_Z = Z1.shape[1]\n\n # parameters\n num_col_X1X0 = n_col_X1 + n_col_X0\n num_col_X1X0Z1 = num_col_X1X0 + n_col_Z\n\n beta1, beta0, gamma = (\n x0[:n_col_X1],\n x0[n_col_X1:num_col_X1X0],\n x0[num_col_X1X0:-4],\n )\n sd1, sd0, rho1v, rho0v = x0[-4], x0[-2], x0[-3], x0[-1]\n\n # aux_params\n nu1 = (Y1 - np.dot(beta1, X1.T)) / sd1\n lambda1 = (np.dot(gamma, Z1.T) - rho1v * nu1) / (np.sqrt(1 - rho1v ** 2))\n nu0 = (Y0 - np.dot(beta0, X0.T)) / sd0\n lambda0 = (np.dot(gamma, Z0.T) - rho0v * nu0) / (np.sqrt(1 - rho0v ** 2))\n\n eta1 = (\n -lambda1 * norm.pdf(lambda1) * norm.cdf(lambda1) - norm.pdf(lambda1) ** 2\n ) / (norm.cdf(lambda1) ** 2)\n eta0 = (\n lambda0 * norm.pdf(lambda0) * (1 - norm.cdf(lambda0)) - norm.pdf(lambda0) ** 2\n ) / (1 - norm.cdf(lambda0)) ** 2\n # combinations of obs\n X1X1 = np.einsum(\"ij, i ->ij\", X1, eta1).T @ X1\n X1Z1 = np.einsum(\"ij, i ->ij\", X1, eta1).T @ Z1\n\n X0X0 = np.einsum(\"ij, i ->ij\", X0, eta0).T @ X0\n X0Z0 = np.einsum(\"ij, i ->ij\", X0, eta0).T @ Z0\n\n Z1Z1 = np.einsum(\"ij, i ->ij\", Z1, eta1).T @ Z1\n Z0Z0 = np.einsum(\"ij, i ->ij\", Z0, eta0).T @ Z0\n\n # beginning with derivations of beta1\n derv_beta1 = calc_hess_beta1(\n X1X1, X1Z1, X1, sd1, rho1v, nu1, lambda1, eta1, n_col_X1, n_col_X0, num_obs\n )\n derv_beta0 = calc_hess_beta0(\n X0X0, X0Z0, X0, sd0, rho0v, nu0, lambda0, eta0, n_col_X1, n_col_X0, num_obs\n )\n derv_gamma = calc_hess_gamma(\n Z1Z1,\n Z0Z0,\n Z1,\n X1,\n Z0,\n X0,\n sd0,\n sd1,\n rho0v,\n rho1v,\n eta1,\n eta0,\n nu0,\n nu1,\n lambda0,\n lambda1,\n num_col_X1X0,\n num_obs,\n )\n derv_dist = calc_hess_dist(\n Z1,\n Z0,\n gamma,\n sd1,\n sd0,\n rho1v,\n rho0v,\n lambda1,\n lambda0,\n nu1,\n nu0,\n eta1,\n eta0,\n num_col_X1X0Z1,\n num_obs,\n )\n\n # convert results to a symmetric hessian matrix\n hessian_upper = np.triu(\n np.concatenate((derv_beta1, derv_beta0, derv_gamma, derv_dist), axis=0)\n )\n\n aux = hessian_upper.copy()\n for i in range(hessian_upper.shape[0]):\n hessian_upper[:, i][i + 1 :] = hessian_upper[i][i + 1 :]\n return hessian_upper, aux","title":""},{"docid":"c62c50d75bb2ee1d4981625ce3a20f9f","score":"0.64051557","text":"def hess(self, x):\n raise NotImplementedError('Hessian oracle is not implemented.')","title":""},{"docid":"8297841d0f5c08dfc045cc0a18ab53a5","score":"0.6360792","text":"def convenience_calc_hessian(self, params):\n shapes, intercepts, betas = self.convenience_split_params(params)\n\n args = [betas,\n self.design,\n self.alt_id_vector,\n self.rows_to_obs,\n self.rows_to_alts,\n self.utility_transform,\n self.calc_dh_d_shape,\n self.calc_dh_dv,\n self.calc_dh_d_alpha,\n self.block_matrix_idxs,\n intercepts,\n shapes,\n self.ridge]\n\n return cc.calc_hessian(*args)","title":""},{"docid":"1d8fc52a5d28e6cb94642f1c4fc75469","score":"0.63550186","text":"def hessian(self, theta):\n L = len(self.data)\n H = self._hessian_minibatch(theta, 0)\n\n for idx in range(1, L):\n print('{} of {}'.format(idx, L))\n H += self._hessian_minibatch(theta, idx)\n\n return H / float(L)","title":""},{"docid":"b9628479bc98ee7d2a38586cf5e6c760","score":"0.63359135","text":"def RebuildHessian(IC, H0, coord_seq, grad_seq, params):\n Na = len(coord_seq[0])/3\n history = 0\n for i in range(2, len(coord_seq)+1):\n disp = bohr2ang*(coord_seq[-i]-coord_seq[-1])\n rmsd = np.sqrt(np.sum(disp**2)/Na)\n if rmsd > params.trust: break\n history += 1\n if history < 1:\n return H0.copy()\n logger.info(\"Rebuilding Hessian using %i gradients\\n\" % history)\n y_seq = [IC.calculate(i) for i in coord_seq[-history-1:]]\n g_seq = [IC.calcGrad(i, j) for i, j in zip(coord_seq[-history-1:],grad_seq[-history-1:])]\n Yprev = y_seq[0]\n Gprev = g_seq[0]\n H = H0.copy()\n for i in range(1, len(y_seq)):\n Y = y_seq[i]\n G = g_seq[i]\n Yprev = y_seq[i-1]\n Gprev = g_seq[i-1]\n Dy = col(Y - Yprev)\n Dg = col(G - Gprev)\n # Mat1 = (Dg*Dg.T)/(Dg.T*Dy)[0,0]\n # Mat2 = ((H*Dy)*(H*Dy).T)/(Dy.T*H*Dy)[0,0]\n Mat1 = np.dot(Dg,Dg.T)/np.dot(Dg.T,Dy)[0,0]\n Mat2 = np.dot(np.dot(H,Dy),np.dot(H,Dy).T)/multi_dot([Dy.T,H,Dy])[0,0]\n Hstor = H.copy()\n H += Mat1-Mat2\n if np.min(np.linalg.eigh(H)[0]) < params.epsilon and params.reset:\n logger.info(\"Eigenvalues below %.4e (%.4e) - returning guess\\n\" % (params.epsilon, np.min(np.linalg.eigh(H)[0])))\n return H0.copy()\n return H","title":""},{"docid":"61fe75d20ee1dc291b5e8fc424934eca","score":"0.6315976","text":"def calc_quasi_hessian(self, delta_x, delta_gradient, prev_h):\n pass","title":""},{"docid":"70be000279ae7f13b855bd8cccfb43d0","score":"0.6301178","text":"def lossHessian(self, beta):\r\n return -self.hessian(beta)","title":""},{"docid":"7342fc246ec81e845cf0c9505448f76b","score":"0.6300913","text":"def get_hessian_matrix(self):\n\n\t\thessian_matrix = []\n\n\t\thessian_matrix_indices = self.get_line_indices_containing_string(Outcar.hessian_string)\n\n\t\tif len(hessian_matrix_indices) == 0:\n\t\t\traise Exception(\"No hessian matrix found in completed outcar file\")\n\n\n\t\thessian_matrix_starting_index = hessian_matrix_indices[-1] + 3\n\n\n\t\tnumber_of_degrees_of_freedom = 3*self.get_number_of_atoms()\n\n\t\tfor i in range(number_of_degrees_of_freedom):\n\t\t\trow_string = self[hessian_matrix_starting_index+i]\n\n\t\t\tcleaned_row_string = su.remove_extra_spaces(row_string.strip())\n\n\t\t\tcleaned_row_components_list = cleaned_row_string.strip().split(' ')\n\n\t\t\tnumerical_strings_list = cleaned_row_components_list[1:]\n\n\t\t\trow_values_list = [-1.0*float(component_string) for component_string in numerical_strings_list] #must take the negative because vasp gives values in terms of changes in forces, which are negative of dEnergy terms\n\n\t\t\tif not len(row_values_list) == number_of_degrees_of_freedom:\n\t\t\t\traise Exception(\"Number of degrees of freedom and number of columns in hessian row do not match. Row is\", row_values_list, \"number of dofs is\", number_of_degrees_of_freedom)\n\n\n\t\t\thessian_matrix.append(row_values_list)\n\n\t\tif len(hessian_matrix) != number_of_degrees_of_freedom:\n\t\t\traise Exception(\"Number of degrees of freedom and number of rows in hessian matrix do not match. Matrix row count is\", len(hessian_matrix), \"number of dofs is\", number_of_degrees_of_freedom)\n\n\t\treturn hessian_matrix","title":""},{"docid":"3bb54d355d301608e3bf768fa4dcf6e2","score":"0.62908304","text":"def estimate_inversed_hessian_matrix(func, param_0):\n n_param = len(param_0)\n np_hessian = numpy.zeros(shape=(n_param, n_param), dtype=float)\n np_first_der = numpy.zeros(shape=(n_param,), dtype=float)\n chi_sq = func(param_0)\n perc = 0.01\n \n for i_p_1, p_1 in enumerate(param_0):\n delta_p_1 = perc * numpy.abs(p_1)\n if delta_p_1 < 1e-5:\n delta_p_1 = 1e-5\n param_p = copy.deepcopy(param_0)\n param_m = copy.deepcopy(param_0)\n param_p[i_p_1] += delta_p_1\n param_m[i_p_1] -= delta_p_1\n # chi_sq_p = func(param_p)\n # chi_sq_m = func(param_m)\n # der_second = (chi_sq_p + chi_sq_m - 2.*chi_sq)/(delta_p_1**2)\n # np_hessian[i_p_1, i_p_1] = der_second\n\n # der_first = (chi_sq_p - chi_sq_m) / (2.*delta_p_1)\n # np_first_der[i_p_1] = der_first\n\n param_pp = copy.deepcopy(param_0)\n param_pm = copy.deepcopy(param_0)\n param_mp = copy.deepcopy(param_0)\n param_mm = copy.deepcopy(param_0)\n\n param_pp[i_p_1] += delta_p_1\n param_pm[i_p_1] += delta_p_1\n param_mp[i_p_1] -= delta_p_1\n param_mm[i_p_1] -= delta_p_1\n for i_p_2, p_2 in enumerate(param_0[:i_p_1+1]):\n delta_p_2 = perc * numpy.abs(p_2)\n if delta_p_2 < 1e-5:\n delta_p_2 = 1e-5\n param_pp[i_p_2] += delta_p_2\n param_pm[i_p_2] -= delta_p_2\n param_mp[i_p_2] += delta_p_2\n param_mm[i_p_2] -= delta_p_2\n \n chi_sq_pp = func(param_pp)\n chi_sq_pm = func(param_pm)\n chi_sq_mp = func(param_mp)\n chi_sq_mm = func(param_mm)\n\n der_second = (chi_sq_pp + chi_sq_mm - chi_sq_pm - chi_sq_mp) / (\n 4. * delta_p_1 * delta_p_2) \n np_hessian[i_p_1, i_p_2] = der_second\n np_hessian[i_p_2, i_p_1] = der_second\n\n param_pp[i_p_2] -= delta_p_2\n param_pm[i_p_2] += delta_p_2\n param_mp[i_p_2] -= delta_p_2\n param_mm[i_p_2] += delta_p_2\n\n try:\n np_hessian_inv = numpy.linalg.inv(np_hessian)\n except LinAlgError:\n np_hessian_inv = None\n func(param_0)\n return np_hessian_inv, np_first_der","title":""},{"docid":"5f4fe23ab7a9040209fbde678e6fcd05","score":"0.6274369","text":"def hessian(self, q):\n return finite_difference_hessian(self, q)","title":""},{"docid":"ee43b3c0dc6398b7bc6fc7f596041a0f","score":"0.62249756","text":"def calculate_hessian(self, finite_step):\n\n # Create the OpenMM coords list from the qm coordinates and convert to nm.\n input_coords = self.molecule.coordinates.flatten() * constants.ANGS_TO_NM\n\n # We get each hessian element from = [E(dx + dy) + E(-dx - dy) - E(dx - dy) - E(-dx + dy)] / 4 dx dy\n hessian = np.zeros((3 * len(self.molecule.atoms), 3 * len(self.molecule.atoms)))\n\n for i in range(3 * len(self.molecule.atoms)):\n for j in range(i, 3 * len(self.molecule.atoms)):\n # Mutate the atomic coords\n # Do fewer energy evaluations on the diagonal of the matrix\n if i == j:\n coords = deepcopy(input_coords)\n coords[i] += 2 * finite_step\n e1 = self.get_energy(self.format_coords(coords))\n coords = deepcopy(input_coords)\n coords[i] -= 2 * finite_step\n e2 = self.get_energy(self.format_coords(coords))\n hessian[i, j] = (e1 + e2) / (\n 4 * finite_step**2 * self.molecule.atoms[i // 3].atomic_mass\n )\n else:\n coords = deepcopy(input_coords)\n coords[i] += finite_step\n coords[j] += finite_step\n e1 = self.get_energy(self.format_coords(coords))\n coords = deepcopy(input_coords)\n coords[i] -= finite_step\n coords[j] -= finite_step\n e2 = self.get_energy(self.format_coords(coords))\n coords = deepcopy(input_coords)\n coords[i] += finite_step\n coords[j] -= finite_step\n e3 = self.get_energy(self.format_coords(coords))\n coords = deepcopy(input_coords)\n coords[i] -= finite_step\n coords[j] += finite_step\n e4 = self.get_energy(self.format_coords(coords))\n hessian[i, j] = (e1 + e2 - e3 - e4) / (\n 4 * finite_step**2 * self.molecule.atoms[i // 3].atomic_mass\n )\n\n # Hessian is currently just the upper right half of the matrix.\n # Transpose to get the lower left, then remove the extra diagonal terms to get the whole matrix.\n sym_hessian = hessian + hessian.T - np.diag(hessian.diagonal())\n return sym_hessian","title":""},{"docid":"8dfd6a02fe9e0a1bee9e727ca2fefeec","score":"0.621227","text":"def Hv(self):\n if self._Hv is None:\n self._Hv = hessian_vector_product(self.loss, self.weights, self.v)\n return self._Hv","title":""},{"docid":"aab53a0dcbd66a3dcefe99994cbb141e","score":"0.62032664","text":"def _hessian_minibatch(self, theta, data_index):\n\n # data\n r = self.data[data_index]['rate']\n\n # model\n u, z, zgrad, zhess, drdz, dr2dz2, rhat = self.rate(\n theta, self.data[data_index]['stim'], hess=True)\n\n # store the full Hessian\n N = np.prod(theta['W'].shape[1:]) # filter dimension\n M = theta['W'].shape[0] # number of subunits\n P = theta['f'].shape[1] # nonlinearity parameters\n T = r.size\n H = np.zeros((N * M + P * M, N * M + P * M))\n\n # gradient and Hessian factors\n grad_factor = 1 - r / rhat\n hess_factor = r / (rhat**2)\n\n # nonlinearity projection\n zproj = np.sum(theta['f'][:, np.newaxis, :] * zgrad, axis=2) # M by T\n zproj2 = np.sum(theta['f'][:, np.newaxis, :] * zhess, axis=2) # M by T\n\n # vectorized data\n x_vec = np.rollaxis(self.data[data_index]['stim'], -1).reshape(N, T) # N x T\n z_vec = np.rollaxis(z, -1).reshape(M * P, T) # M*P x T\n scale_factor = np.diag(grad_factor * dr2dz2 + hess_factor * drdz**2) # diagonal(T)\n\n # nonlinearity - nonlinearity portion\n H[-(M * P):, -(M * P):] = z_vec.dot(scale_factor.dot(z_vec.T))\n\n # loop over subunits\n for j1 in range(M):\n\n zgrad_j1 = zproj[j1, :]\n zhess_j1 = zproj2[j1, :]\n\n # scale factor grad\n sf_zgrad = scale_factor * np.diag(zgrad_j1)\n\n # subunit-nonlinearity block\n H[j1 * N:(j1 + 1) * N, -(M * P):] = x_vec.dot(sf_zgrad.dot(z_vec.T))\n H[-(M * P):, j1 * N:(j1 + 1) * N] = H[j1 * N:(j1 + 1) * N, -(M * P):].T\n\n # subunit-subunit block\n for j2 in range(M):\n\n zgrad_j2 = np.diag(zproj[j2, :])\n\n # update this portion of the Hessian\n H[j1 * N:(j1 + 1) * N, j2 * N:(j2 + 1) * N] = x_vec.dot((sf_zgrad * zgrad_j2).dot(x_vec.T))\n\n # diagonal term (for the same subunit)\n if j1 == j2:\n H[j1 * N:(j1 + 1) * N, j2 * N:(j2 + 1) * N] += x_vec.dot(np.diag(grad_factor * drdz * zhess_j1).dot(x_vec.T))\n\n return H / float(T)","title":""},{"docid":"431b506b192ff50d525551c70fd8be88","score":"0.6190988","text":"def Jacobian_H():\r\n Jacobian_H = np.array([[1.0, 0, 0],\r\n [0, 1.0, 0],\r\n [0, 0, 1.0]])\r\n return Jacobian_H","title":""},{"docid":"27fcb0df257ead84bdfa981f3a4bd687","score":"0.6121463","text":"def test_hessian_at_zero(self, torch_device, x, shift):\n dev = qml.device(\"default.qubit.torch\", wires=1, torch_device=torch_device)\n\n x = torch.tensor(x, requires_grad=True)\n\n @qml.qnode(dev, interface=\"torch\", diff_method=\"backprop\")\n def circuit(x):\n qml.RY(shift, wires=0)\n qml.RY(x, wires=0)\n return qml.expval(qml.PauliZ(0))\n\n grad = torch.autograd.functional.jacobian(circuit, x)\n hess = torch.autograd.functional.hessian(circuit, x)\n\n assert qml.math.isclose(grad, torch.tensor(0.0))\n assert qml.math.isclose(hess, torch.tensor(-1.0))","title":""},{"docid":"e4b204b62ed69ceec64cf0be2d2fce93","score":"0.61059403","text":"def compute_hessian_nll(y, x, w, lambda_=0):\n\n sgm = sigmoid(x @ w)\n s = sgm * (1 - sgm) + 2 * lambda_\n return (x.T * s) @ x","title":""},{"docid":"741f379846fe32bdae37e87c9575382e","score":"0.60802615","text":"def sum_hessian(self, hessian, p=None):\n raise NotImplementedError(\"sum_hessian method not implemented\")","title":""},{"docid":"b359e20efc06dfb0a9a10fdb338ec893","score":"0.60706824","text":"def numerical_hessian_G(grad,x0,h=1.0e-5):\n n = len(x0)\n hess = np.zeros((n,n))\n g0 = grad(x0)\n print \"numerical differentiation of forces requires %d gradient calculations\" % (2*n)\n for i in range(0, n):\n print \"%s of %d\" % (2*(i+1), 2*n)\n ei = np.zeros(n)\n ei[i] = 1.0\n x_phi = x0 + h*ei\n# hess[i,:] = (grad(x_phi) - g0)/h \n x_mhi = x0 - h*ei\n hess[i,:] = (grad(x_phi) - grad(x_mhi))/(2*h)\n\n # hessian should be symmetric\n hess = 0.5 * (hess + hess.transpose())\n \n return hess","title":""},{"docid":"25fe9debd8ced7f36c1cdc669ec97452","score":"0.60441446","text":"def hessian(sdf_im):\n batch_size, height, width = [sdf_im.shape[i].value for i in range(3)]\n sdf_im = tf.reshape(sdf_im, [batch_size, height, width, 1])\n # pyformat: disable\n xx_fda_kernel = tf.reshape(\n tf.constant([[0.0, 0.0, 0.0],\n [1.0, -2.0, 1.0],\n [0.0, 0.0, 0.0]], dtype=tf.float32), [3, 3, 1, 1])\n yy_fda_kernel = tf.reshape(\n tf.constant([[0.0, 1.0, 0.0],\n [0.0, -2.0, 0.0],\n [0.0, 1.0, 0.0]], dtype=tf.float32), [3, 3, 1, 1])\n xy_fda_kernel = tf.reshape(\n tf.constant([[0.25, 0.0, -0.25],\n [0.0, 0.0, 0.0],\n [-0.25, 0.0, 0.25]], dtype=tf.float32), [3, 3, 1, 1])\n # pyformat: enable\n fda_kernel = tf.concat(\n [xx_fda_kernel, xy_fda_kernel, xy_fda_kernel, yy_fda_kernel], axis=3)\n fda = tf.nn.conv2d(sdf_im, fda_kernel, [1, 1, 1, 1], padding='SAME')\n # Fda should have shape [batch_size, height, width, 4], because we duplicated\n # the xy partial channels.\n hess = tf.reshape(fda, [batch_size, height, width, 2, 2])\n # Because we used an fda method, we don't have to symmetrize, so just return:\n return hess","title":""},{"docid":"d37f53ab9217dab1d18f6732c6f170bb","score":"0.59972906","text":"def cost_matrix(self, Y, Y_hat):\n\n assert hasattr(Y_hat, 'owner')\n owner = Y_hat.owner\n assert owner is not None\n op = owner.op\n\n if not hasattr(op, 'scalar_op'):\n raise ValueError(\"Expected Y_hat to be generated by an Elemwise op, got \"+str(op)+\" of type \"+str(type(op)))\n assert isinstance(op.scalar_op, T.nnet.sigm.ScalarSigmoid)\n z ,= owner.inputs\n\n term_1 = Y * T.nnet.softplus(-z)\n term_2 = (1 - Y) * T.nnet.softplus(z)\n\n total = term_1 + term_2\n return total","title":""},{"docid":"726da6bf89da8f0dea61e6069f3bc776","score":"0.59852344","text":"def __init__(self, n_features=10000, hessian_thresh=100):\n self.n_features = n_features\n self.hessian_thresh = hessian_thresh","title":""},{"docid":"c69e0bb67fb642be1d4d7b9fe066a6c4","score":"0.59824437","text":"def _derivProblem_Hessian(self,fittingData,fittingDataDerivs,indepParamsList,\n priorLambda):\n J = self._derivProblem_Jacobian(fittingData,fittingDataDerivs,\n indepParamsList)\n HnoPrior = np.dot(J.T,J)\n\n # add parameter prior to diagonal\n numResiduals,numParameters = np.shape(J)\n priorDiag = scipy.diag(np.ones(numParameters)*priorLambda)\n H = HnoPrior + priorDiag\n\n return H","title":""},{"docid":"dc58065d76c1697f1f62611ef7e820cc","score":"0.5976115","text":"def compute_hessian_of_loss(xs, model, loss=\"cross_entropy\", n_out=10, noise_log_std=None):\n\n model.eval()\n with torch.no_grad():\n\n if loss == \"cross_entropy\":\n y_hat = torch.softmax(model(xs).detach(), dim=1) # B x O\n H_L = torch.diag_embed(\n y_hat) - contract('bi,bj->bij', y_hat, y_hat) # B x O x O\n\n elif loss == \"binary_cross_entropy\":\n y_hat = torch.sigmoid(model(xs).detach()) # B x 1\n H_L = (y_hat * (1 - y_hat)).unsqueeze(1) # B x 1 x 1\n\n elif loss == \"mse\" or loss == \"gaussian\":\n H_device = 'cuda:0' if isinstance(model, nn.DataParallel) else xs.device\n H_L = torch.eye(n_out, device=H_device).unsqueeze(0).expand(\n xs.shape[0], -1, -1) # B x O x O\n\n if loss == \"gaussian\":\n H_L = H_L * (noise_log_std * (-2)).exp()\n\n else:\n raise ValueError(\"Invalid loss function for GGN computation!\")\n\n return H_L","title":""},{"docid":"110f8dc01f9187b20fcff979b47714b4","score":"0.59666276","text":"def __init__(self, cg_iter, learning_rate=1.0, damping=1.0, fix_first_step=False, hv_method = 0, use_sgd=False, use_locking=False, name=\"HessianFree\"):\n super(HessianFreeOptimizer, self).__init__(use_locking, name)\n self._cg_iter = cg_iter\n self._learning_rate = learning_rate\n self._damping = damping\n self._fix_first_step = fix_first_step\n self._use_sgd = use_sgd\n self._Hv = Hv\n if hv_method == 0:\n self._Hv = Gv","title":""},{"docid":"368bb0dbfd37663b012f90ee75a45ac9","score":"0.59664834","text":"def convenience_calc_hessian(self, params):\n msg = \"Method should be defined by descendant classes\"\n raise NotImplementedError(msg)\n\n return None","title":""},{"docid":"7a3963c49f4c3bf0151e99282ef4c039","score":"0.59608096","text":"def test_ModelHawkesExpKernLogLik_hessian(self):\n for model in [self.model]:\n hessian = model.hessian(self.coeffs).todense()\n # Check that hessian is equal to its transpose\n np.testing.assert_array_almost_equal(hessian, hessian.T,\n decimal=10)\n\n # Check that for all dimension hessian row is consistent\n # with its corresponding gradient coordinate.\n for i in range(model.n_coeffs):\n\n def g_i(x):\n return model.grad(x)[i]\n\n def h_i(x):\n h = model.hessian(x).todense()\n return np.asarray(h)[i, :]\n\n self.assertLess(check_grad(g_i, h_i, self.coeffs), 1e-5)","title":""},{"docid":"e2d35d6812b9ed5b34c585a5cd4e4eb9","score":"0.59096104","text":"def calc_neg_hessian(self, params):\n return -1 * self.convenience_calc_hessian(params)","title":""},{"docid":"7a5a344700976caecee0ff81e106a45f","score":"0.59087557","text":"def nonlinear_hess_matrix(x, v):\n #D, N, m_deg, nk = args[0], args[1], args[2], args[3]\n # The four variables above are used instead by the global variables defined at the top of the code\n sum_1 = 0\n for i in range(2, m_deg):\n args_local = (D, N, i, nk)\n sum_1 += v[i - 2] * np.array(f_hess(x, *args_local))\n return sum_1","title":""},{"docid":"8f4f3917caaad79174b7aff7747cbe31","score":"0.5907711","text":"def mean_hessian(self, x, params):\n\n switch = self.f1.get_n_params(x)\n\n exp = self.f2.mean_f(x, params[switch:])\n nonzeroexp = True\n if np.allclose(exp, 0.):\n nonzeroexp = False\n nononeexp = True\n if np.allclose(exp, 1.):\n nononeexp = False\n\n hess = np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))\n\n if nonzeroexp and nononeexp:\n\n hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*\n self.f1.mean_hessian(x, params[:switch]) +\n exp*(exp - 1.)*self.f1.mean_f(x, params[:switch])**(exp - 2.)*\n self.f1.mean_deriv(x, params[:switch]))\n\n elif nonzeroexp:\n\n hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*\n self.f1.mean_hessian(x, params[:switch]))\n\n\n if not self.f2.get_n_params(x) == 0:\n\n if nonzeroexp:\n hess[:switch, switch:, :] = (self.f1.mean_f(x, params[:switch])**(exp - 1.)*\n (exp*np.log(self.f1.mean_f(x, params[:switch])) + 1.)*\n self.f1.mean_deriv(x, params[:switch])[:,np.newaxis,:]*\n self.f2.mean_deriv(x, params[switch:])[np.newaxis,:,:])\n hess[switch:, :switch, :] = np.transpose(hess[:switch, switch:, :], (1, 0, 2))\n\n hess[switch:, switch:] = (self.f1.mean_f(x, params[:switch])**exp*\n (np.log(self.f1.mean_f(x, params[:switch]))**2*\n self.f2.mean_deriv(x, params[switch:])**2 +\n np.log(self.f1.mean_f(x, params[:switch]))*\n self.f2.mean_hessian(x, params[switch:])))\n\n return hess","title":""},{"docid":"46f05ae42271dd85528fa5907d53d162","score":"0.59058034","text":"def uvc_get_hessian(x, data):\n\n def f(xi):\n val = 0.\n for d in data:\n val += error_single_test_uvc(xi, d)\n return val\n\n hess_fun = nda.Hessian(f)\n return hess_fun(x)","title":""},{"docid":"e538bd9a4ad2a6614059fa40f074a0ef","score":"0.5891991","text":"def hessian_scalar(expr, wrt):\n J = T.grad(expr, wrt, disconnected_inputs=\"ignore\")\n Q = T.stack([T.grad(g, wrt, disconnected_inputs=\"ignore\") for g in J])\n return Q","title":""},{"docid":"201b78742f4380524153bb68048b9abb","score":"0.5882084","text":"def mw_hessian(self, full_hessian):\n mass_xyz = np.zeros((self.molecule.natoms, 3))\n mass_array = np.zeros((self.molecule.natoms))\n for row in range(0, self.molecule.natoms):\n #adding 1/np.sqrt(amu) units to xyz coords\n x = element(self.molecule.atomtable[row][0])\n value = np.sqrt(x.atomic_weight)\n mass_array[row] = value\n mass_xyz[row] = np.array(self.molecule.atomtable[row][1:])*(1/value) #mass weighted coordinates\n for column in range(0, len(self.molecule.atomtable)): \n y = element(self.molecule.atomtable[column][0])\n z = x.atomic_weight*y.atomic_weight\n value = np.sqrt(z)**-1\n term = full_hessian[row][column]*value\n full_hessian[row][column] = term\n \n reshape_mass_hess = full_hessian.transpose(0, 2, 1, 3)\n x = reshape_mass_hess.reshape(reshape_mass_hess.shape[0]*reshape_mass_hess.shape[1],reshape_mass_hess.shape[2]*reshape_mass_hess.shape[3])\n e_values, modes = LA.eigh(x)\n\n #unit conversion of freq from H/B**2 amu -> 1/s**2\n #factor = (4.3597482*10**-18)/(1.6603145*10**-27)/(1.0*10**-20) #Angstrom to m\n factor = 1.8897259886**2*(4.3597482*10**-18)/(1.6603145*10**-27)/(1.0*10**-20) #Bohr to Angstrom\n freq = (np.sqrt(e_values*factor))/(2*np.pi*2.9979*10**10) #1/s^2 -> cm-1\n return freq, modes","title":""},{"docid":"771a22d9b7f13679d21e42d16d2b8643","score":"0.5860381","text":"def test_adjoint_hessian(self, interface):\n dev = qml.device(\"default.qubit\", wires=2)\n params = tf.Variable([0.543, -0.654])\n\n with tf.GradientTape() as t2:\n with tf.GradientTape() as t1:\n with qml.queuing.AnnotatedQueue() as q:\n qml.RX(params[0], wires=[0])\n qml.RY(params[1], wires=[1])\n qml.CNOT(wires=[0, 1])\n qml.expval(qml.PauliZ(0))\n\n tape = qml.tape.QuantumScript.from_queue(q)\n res = execute(\n [tape],\n dev,\n gradient_fn=\"device\",\n gradient_kwargs={\"method\": \"adjoint_jacobian\", \"use_device_state\": True},\n interface=interface,\n )[0]\n\n grad = t1.gradient(res, params)\n assert grad is not None\n assert grad.dtype == tf.float64\n assert grad.shape == params.shape\n\n hess = t2.jacobian(grad, params)\n assert hess is None","title":""},{"docid":"266d02393c3cf69e1e883a840c01b7bf","score":"0.58473873","text":"def logit_hessian(s, b, X, y):\n z = X.dot(b)\n z = phi(y * z)\n d = z * (1 - z)\n wa = d * X.dot(s)\n Hs = X.T.dot(wa)\n out = Hs\n return out","title":""},{"docid":"266d02393c3cf69e1e883a840c01b7bf","score":"0.58473873","text":"def logit_hessian(s, b, X, y):\n z = X.dot(b)\n z = phi(y * z)\n d = z * (1 - z)\n wa = d * X.dot(s)\n Hs = X.T.dot(wa)\n out = Hs\n return out","title":""},{"docid":"724e019749ea86f79608cbcaf5e019a5","score":"0.5832009","text":"def get_hessian(self, calc):\n\n try:\n line_idx = next(i for i, line in enumerate(calc.output.file_lines)\n if 'MASS-WEIGHTED NUCLEAR HESSIAN' in line)\n except StopIteration:\n raise CouldNotGetProperty('Hessian not found in the output file')\n\n hess_lines = [[] for _ in range(calc.molecule.n_atoms*3)]\n\n for hess_line in calc.output.file_lines[line_idx+6:]:\n\n if 'NORMAL MODE EIGENVECTORS' in hess_line:\n break # Finished the Hessian block\n\n if 'D' in hess_line:\n # e.g. 1 4.50945D-01 ...\n idx = hess_line.split()[0]\n try:\n _ = hess_lines[int(idx) - 1]\n except (ValueError, IndexError):\n raise CouldNotGetProperty(\"Unexpected hessian formating: \"\n f\"{hess_line}\")\n\n values = [float(x) for x in hess_line.replace('D', 'E').split()[1:]]\n hess_lines[int(idx)-1] += values\n\n atom_masses = self._atom_masses_from_hessian(calc)\n hess = symm_matrix_from_ltril(array=hess_lines)\n\n # Un-mass weight from Kamu^-1 to 1\n mass_arr = np.repeat(atom_masses, repeats=3, axis=np.newaxis) * 1E-3\n hess *= np.sqrt(np.outer(mass_arr, mass_arr))\n\n # and convert from atomic units (Ha/a0^2) to base units (Ha/Å^2)\n return hess / Constants.a0_to_ang**2","title":""},{"docid":"8fd2dd1b398197f57e995074ac2cc6df","score":"0.5828477","text":"def get_hessian(self, calc):\n\n hess_filename = calc.output.filename\n\n if calc.output.filename.endswith('.out'):\n hess_filename = calc.output.filename.replace('.out', '.hess')\n\n if not os.path.exists(hess_filename):\n raise CouldNotGetProperty('Could not find Hessian file')\n\n file_lines = open(hess_filename, 'r', encoding=\"utf-8\").readlines()\n\n hessian_blocks = []\n start_line = self._start_line_hessian(calc, file_lines)\n\n for j, h_line in enumerate(file_lines[start_line:]):\n\n if len(h_line.split()) == 0:\n # Assume we're at the end of the Hessian\n break\n\n # Skip blank lines in the file, marked by one or more fewer items\n # than the previous\n if len(h_line.split()) < len(file_lines[start_line+j-1].split()):\n continue\n\n # First item is the coordinate number, thus append all others\n hessian_blocks.append([float(v) for v in h_line.split()[1:]])\n\n n_atoms = calc.molecule.n_atoms\n hessian = [block for block in hessian_blocks[:3*n_atoms]]\n\n for i, block in enumerate(hessian_blocks[3*n_atoms:]):\n hessian[i % (3 * n_atoms)] += block\n\n # Hessians printed in Ha/a0^2, so convert to base Ha/Å^2\n return np.array(hessian, dtype='f8') / Constants.a0_to_ang**2","title":""},{"docid":"7791d5c7af8d935991fb8129c2c5e83d","score":"0.5827034","text":"def gen_cost(X, y):\n rows, cols = X.shape\n w_vars = [Var('w{}'.format(i)) for i in range(cols)]\n\n return Function(\n - 1 / rows * sum(\n y[i] * ln(y_pred(w_vars, X[i])) + (1 - y[i]) * ln(1 - y_pred(w_vars, X[i]))\n for i in range(rows))\n , *w_vars)","title":""},{"docid":"2ce25cf9de165e41cf94494b0e849883","score":"0.5819392","text":"def make_cost_matrix(profit_matrix, inversion_function):\n import munkres\n return munkres.make_cost_matrix(profit_matrix, inversion_function)","title":""},{"docid":"d1d31ecad71d41fec872d73ebc3a46a8","score":"0.58137983","text":"def _compute(self):\n\n if self.HX is None:\n if \"hessian\" in self.calculations_needed():\n if self.params.cart_hess_read:\n self.HX = hessian.from_file(self.params.hessian_file) # set ourselves if file\n _ = self.computer.compute(self.geom, driver=\"hessian\")\n self.gX = self.computer.external_gradient\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\n self._Hq = self.molsys.hessian_to_internals(self.HX)\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\n self.HX = None\n self.params.cart_hess_read = False\n self.params.hessian_file = None\n else:\n raise RuntimeError(\n \"Optking requested a hessian but was not provided one. \" \"This could be a driver issue\"\n )\n elif self.step_num == 0:\n logger.debug(\"Guessing hessian\")\n self._Hq = hessian.guess(self.molsys, guessType=self.params.intrafrag_hess)\n self.gX = self.computer.compute(self.geom, driver=\"gradient\", return_full=False)\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\n\n else:\n logger.debug(\"Updating hessian\")\n self.gX = self.computer.compute(self.geom, driver=\"gradient\", return_full=False)\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\n self._Hq = self.history.hessian_update(self._Hq, self.fq, self.molsys)\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\n else:\n result = self.computer.compute(self.geom, driver=\"hessian\")\n self.HX = self.computer.external_hessian\n self.gX = self.computer.external_gradient\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\n self._Hq = self.molsys.hessian_to_internals(self.HX)\n self.HX = None # set back to None\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)","title":""},{"docid":"2a663fe202c936733a2156c7b6ce5fb0","score":"0.5803068","text":"def trace_hessian(loss, logits, weights):\n weights = _AsList(weights)\n\n # Flatten logits with a well-specified dimension. This assumes any\n # non-trivial dimension will resolve to 1.\n # logits_flat = tf.reshape(logits, [-1])\n\n loss_logits_hessian = hessians(loss, logits)[0]\n tr_hessian_pieces = []\n\n for w in weights:\n J = jacobian(logits, w)\n # Contract along the weight indices\n # (first index is the logit index)\n weight_axes = list(range(logits.shape.ndims, J.shape.ndims))\n JJ = tf.tensordot(J, J, axes=[weight_axes, weight_axes])\n # Doesn't work for dynamic shape\n # assert loss_logits_hessian.shape == JJ.shape\n all_axes = list(range(JJ.shape.ndims))\n tr_hessian_pieces.append(\n tf.tensordot(loss_logits_hessian, JJ, axes=[all_axes, all_axes]))\n\n return tf.reduce_sum(tr_hessian_pieces)","title":""},{"docid":"679826fc72cc71ab0384d9f19d3fd04f","score":"0.57904613","text":"def finite_difference_hessian(f, q):\n assert q.size == f.input_dimension()\n assert f.output_dimension() == 1\n dt = 1e-4\n dt_half = dt / 2.\n H = np.zeros((\n f.input_dimension(), f.input_dimension()))\n for j in range(q.size):\n q_up = copy.deepcopy(q)\n q_up[j] += dt_half\n g_up = f.gradient(q_up)\n q_down = copy.deepcopy(q)\n q_down[j] -= dt_half\n g_down = f.gradient(q_down)\n H[:, j] = (g_up - g_down) / dt\n return np.matrix(H)","title":""},{"docid":"66ef85cb7c0f0503ab8f7289b188fb5d","score":"0.5790247","text":"def ml_hessian(self, phi, mode, degree):\n\n def f_sig(point):\n \"\"\"\n sigmoid wrapper\n :param point: np.array. row feature vector\n \"\"\"\n if mode is None:\n point = np.mat(point).T\n\n return self._sig(phi, point)\n\n if mode == 1:\n assert degree is not None\n hessian = np.sum([self.trans_pt(self.features[pt], mode, degree) *\n f_sig(self.trans_pt(self.features[pt], mode, degree)) *\n (1 - f_sig(self.trans_pt(self.features[pt], mode, degree))) *\n self.trans_pt(self.features[pt], mode, degree).T\n for pt in range(self.num_pts)], axis=0)\n\n elif mode is None:\n hessian = np.sum([np.mat(self.features[pt]).T *\n f_sig(self.features[pt]) *\n (1 - f_sig(self.features[pt])) *\n np.mat(self.features[pt])\n for pt in range(self.num_pts)], axis=0)\n else:\n assert False\n\n print 'hessian: {0}'.format(hessian)\n return hessian","title":""},{"docid":"966e6c5a06dbefc710080ee1ce23a779","score":"0.57859784","text":"def test_cost_hess(self): # noqa\n expected = np.array(\n [1.54801e3, 0.01, 0.01, 0.01, 1.56001e3, 0.01, 1.54801e3, 0.01,\n 1.56001e3, 0.01])\n hess_p = np.arange(300)\n actual = _cost_hess(\n self.input_cost, hess_p, *self.args)[:10]\n np.testing.assert_allclose(\n expected, actual,\n err_msg='Expected {}, but found {}.'.format(expected, actual))","title":""},{"docid":"5db5e806a7aaf77351b23c1d853d0111","score":"0.5784397","text":"def constructCouplingHJ(self):\n Ntot = np.prod(self.dimList)\n HJ = sp.csr_matrix((Ntot,Ntot)) \n \n if self.JDict is None:\n return HJ\n \n for (i,j), Jij in self.JDict.items():\n Sx_i,Sy_i,Sz_i = self.SList[i]\n Sx_j,Sy_j,Sz_j = self.SList[j]\n HJ += Jij*meV*(Sz_i*Sz_j + Sx_i*Sx_j + Sy_i*Sy_j)\n \n return HJ","title":""},{"docid":"efd33cc96104b9a34e6931ce5d568d1f","score":"0.5776834","text":"def hessian_vector(expr, wrt, size):\n return _tensor_map(lambda x: hessian_scalar(x, wrt), expr, size)","title":""},{"docid":"357b296a7e67977ea2ff40509d8310e0","score":"0.5767032","text":"def _construct_H(tx,ty,ix,iy,H):\n \n \n # H[i,j] = eigenFunction2D(tx[-i],ty[-i],ix,iy)\n i,j = cuda.grid(2)\n H[i,j] = exp(1j*2*pi*(ix[j]*tx[i]+iy[j]*ty[i]))","title":""},{"docid":"234682bcbbcf38db9fd5a3ab5f1243af","score":"0.57555276","text":"def hessianstructure(self):\n pass","title":""},{"docid":"3c73108f5e06a57d79382c3abd1d1cb4","score":"0.5749974","text":"def hessian_vector_product(loss, weights, v):\n grad = flatten_tensor_list(tf.gradients(loss, weights))\n grad_v = tf.reduce_sum(grad * tf.stop_gradient(v))\n H_v = flatten_tensor_list(tf.gradients(grad_v, weights))\n return H_v","title":""},{"docid":"c9a1a36eb6232afc3f5674a7294dc88d","score":"0.57403535","text":"def eval_hess_f(subs_counts, v, Y):\n Y = algopy.UTPM.init_hessian(Y)\n retval = eval_f_explicit(subs_counts, v, Y)\n return algopy.UTPM.extract_hessian(len(Y), retval)","title":""},{"docid":"af17785821cce9b4865ddbf065b20efe","score":"0.5739618","text":"def RHS_fun(j, u_j):\n\n s_j = np.zeros(u_j.size) # array containing distribution of heat source\n\n \n return s_j # return s_j = 0 for all i if no heat source required.","title":""},{"docid":"332cd6ae1515a2d1117ba34678ae5247","score":"0.5732078","text":"def fit(self, x, y):\n # *** START CODE HERE ***\n # Initialization Values\n theta = self.theta\n nValues = len(x)\n counter = 0\n\n #For Problem 6d\n # rho = 1/11\n # kappa = rho/(1-rho)\n \n if (theta == None):\n theta = np.zeros([3, 1])\n\n thetaPrev = np.ones([3, 1])\n \n \n while((np.linalg.norm(theta.T - thetaPrev.T,1) > self.eps) and counter < self.max_iter):\n \n # Initialize\n gradientSum = [] #Size of theta-vector\n hessianSum = [] #Size of Hessian matrix\n \n for n in range(0, nValues):\n \n #For Problem 6d\n # omega = 0\n # if (y[n]==1):\n # omega = 1/kappa\n # else:\n # omega = 1\n \n # Compute h_theta(x) as defined in Lecture 3 Notes\n hThetaX = 1/(1 + np.exp(-np.matmul(theta.T, x[n])))\n\n # Compute the gradient of the log likelihood function\n logGradient = (y[n] - hThetaX)*x[n]\n # logGradient = omega*logGradient #For Problem 6d\n \n gradientSum.append(logGradient)\n # Compute the Hessian of the log likelihood function wrt to theta1 and theta2 (first and second index of x vector)\n H = np.array([[x[n, 0], x[n, 1], x[n, 2]], [\n x[n, 1], x[n, 1]**2, x[n, 1]*x[n, 2]], [x[n, 2], x[n, 2]*x[n, 1], x[n, 2]**2]])\n \n \n logHessian = (1-hThetaX+hThetaX**2)*H\n # logHessian = omega*logHessian #For Problem 6d\n \n hessianSum.append(logHessian)\n \n #Compute the sum of the gradient and hessian\n hessian = -(1/nValues)*sum(hessianSum)\n gradient = -(1/nValues)*sum(gradientSum)\n \n #For Problem 6d\n # hessian = ( -(1+kappa)/(2*nValues) )*sum(hessianSum)\n # gradient = ( -(1+kappa)/(2*nValues) )*sum(gradientSum)\n \n #Update Theta\n thetaPrev = theta\n deltaTheta = self.step_size*(-np.linalg.solve(hessian,gradient))\n theta = theta.T - deltaTheta\n theta = theta.T\n counter = counter + 1\n # if (counter == 5):\n # break\n print((np.linalg.norm(theta.T - thetaPrev.T,1)) )\n self.theta = theta\n return theta","title":""},{"docid":"8a6b9a24f42a7876e98b746ba5704e63","score":"0.5727019","text":"def mean_hessian(self, x, params):\n\n x, params = self._check_inputs(x, params)\n\n n_params = self.get_n_params(x)\n\n hess = np.zeros((n_params, n_params, x.shape[0]))\n\n return hess","title":""},{"docid":"e775abf8a6476d13402dafa3e8fddbe4","score":"0.57104474","text":"def approximate_cost(x, u, Cf):\n assert x.shape[0] == u.shape[0]\n assert x.shape[1] == u.shape[1]\n T = x.shape[0]\n tau = F.concat((x, u), axis=2)\n costs = []\n hessians = []\n grads = []\n # for time\n for t in range(T):\n tau_t = tau[t]\n cost = Cf(tau_t) # value of cost function at tau\n assert list(cost.shape) == [x.shape[1]]\n # print(\"cost.shape\", cost.shape)\n grad = chainer.grad([F.sum(cost)], [tau_t], enable_double_backprop=True)[0] # need hessian\n hessian = []\n # for each dimension?\n for v_i in range(tau.shape[2]):\n # n_sc\n grad_line = F.sum(grad[:, v_i])\n hessian.append(chainer.grad([grad_line], [tau_t])[0])\n hessian = F.stack(hessian, axis=-1)\n costs.append(cost)\n # change to near 0?? Is this necessary ???\n grads.append(grad - bmv(hessian, tau_t))\n hessians.append(hessian)\n costs = F.stack(costs)\n grads = F.stack(grads)\n hessians = F.stack(hessians)\n return hessians, grads, costs","title":""},{"docid":"c5f36e1f676f33f62ab10844e1fc30dc","score":"0.56923467","text":"def __init__(self):\n super().__init__(LossHessianStrategy.EXACT, \"diag_ggn_exact\")","title":""},{"docid":"5e5bf83907b508c1617e91bce2558a65","score":"0.5688244","text":"def _update_h_from_cart_h(self, arr: Optional[\"Hessian\"]) -> None:\n\n if arr is None:\n self._x.h, self.h = None, None\n\n else:\n self._x.h = arr\n n = len(self)\n m = self.n_constraints\n\n self.h = np.zeros(shape=(n + m, n + m))\n\n # Fill in the upper left corner with d^2L/ds_i ds_j\n # where the second derivative of the constraint is zero\n self.h[:n, :n] = np.linalg.multi_dot(\n (self.B_T_inv.T, arr, self.B_T_inv)\n )\n\n # and the d^2L/ds_i dλ_i = -dC_i/ds_i = -1\n # d^2L/dλ_i dλ_j = 0\n\n for i in range(m):\n self.h[n + i, :] = self.h[:, n + i] = 0.0\n\n for i in range(m):\n self.h[n - m + i, n + i] = self.h[n + i, n - m + i] = -1.0\n\n return None","title":""},{"docid":"360a3a4401510465ccb3fe39c3024abd","score":"0.56736326","text":"def construct_reduced_hess(W, T_ti):\n tmp = np.dot(T_ti.transpose(), W)\n red_hess = np.dot(tmp, T_ti)\n\n return red_hess","title":""},{"docid":"961caa842851eeff10973e0da5467d6b","score":"0.5672992","text":"def test_lr_update_hessian(self):\n indexed_data, model = simple_mock_data()\n\n lr = TrainerLogisticLossWithL2(training_data=indexed_data, initial_model=model, penalty=10, hessian_type=HessianType.FULL)\n hessian = lr._update_full_hessian(model.theta)\n\n expected_hessian = np.array(\n [\n [10.076006603, 0.007826689207, 0.16766658587],\n [0.007826689207, 10.003913344, 0.023480067621],\n [0.16766658587, 0.023480067621, 10.382293306],\n ]\n )\n\n self.assertTrue(\n matrices_almost_equal(hessian, expected_hessian),\n f\"Hessian computation is correct. Actual {hessian} == Expected {expected_hessian}.\",\n )","title":""},{"docid":"5f6590e6ac05571d3c1164c02d96188c","score":"0.567169","text":"def gradient(self, X_tr, y_tr, w):\n n = np.shape(X_tr)[0]\n\n Xe = np.c_[X_tr, np.ones(n)] # Append X_tr with ones to include bias term\n\n eta = sigmoid(np.dot(Xe, w.T))\n\n grad = np.dot(Xe.T, np.reshape((y_tr - eta), (n, 1)))\n\n sigma = np.diag(eta * (1 - eta))\n\n hessian = np.dot(- Xe.T, np.dot(sigma, Xe))\n\n return grad, hessian","title":""},{"docid":"c92c258fddc4f34716d798f51b160693","score":"0.56699675","text":"def test_caching_param_shift_hessian(self, num_params, tol):\n dev = qml.device(\"default.qubit\", wires=2)\n params = tf.Variable(np.arange(1, num_params + 1) / 10, dtype=tf.float64)\n\n N = params.shape[0]\n\n def cost(x, cache):\n with qml.queuing.AnnotatedQueue() as q:\n qml.RX(x[0], wires=[0])\n qml.RY(x[1], wires=[1])\n\n for i in range(2, num_params):\n qml.RZ(x[i], wires=[i % 2])\n\n qml.CNOT(wires=[0, 1])\n qml.var(qml.PauliZ(0) @ qml.PauliX(1))\n\n tape = qml.tape.QuantumScript.from_queue(q)\n return execute(\n [tape], dev, gradient_fn=param_shift, cache=cache, interface=\"tf\", max_diff=2\n )[0]\n\n # No caching: number of executions is not ideal\n with tf.GradientTape() as t2:\n with tf.GradientTape() as t1:\n res = cost(params, cache=False)\n grad = t1.gradient(res, params)\n hess1 = t2.jacobian(grad, params)\n\n if num_params == 2:\n # compare to theoretical result\n x, y, *_ = params * 1.0\n expected = np.array(\n [\n [2 * np.cos(2 * x) * np.sin(y) ** 2, np.sin(2 * x) * np.sin(2 * y)],\n [np.sin(2 * x) * np.sin(2 * y), -2 * np.cos(x) ** 2 * np.cos(2 * y)],\n ]\n )\n assert np.allclose(expected, hess1, atol=tol, rtol=0)\n\n nonideal_runs = dev.num_executions\n\n # Use caching: number of executions is ideal\n dev._num_executions = 0\n with tf.GradientTape() as t2:\n with tf.GradientTape() as t1:\n res = cost(params, cache=True)\n grad = t1.gradient(res, params)\n hess2 = t2.jacobian(grad, params)\n\n assert np.allclose(hess1, hess2, atol=tol, rtol=0)\n\n expected_runs_ideal = 1 # forward pass\n expected_runs_ideal += 2 * N # Jacobian\n expected_runs_ideal += N + 1 # Hessian diagonal\n expected_runs_ideal += 4 * N * (N - 1) // 2 # Hessian off-diagonal\n assert dev.num_executions == expected_runs_ideal\n assert expected_runs_ideal < nonideal_runs","title":""},{"docid":"ea591869120d64c3eeba01aeda8467f2","score":"0.5663585","text":"def sparse_h(b, w ,X):\n act = g( (X.dot(w)) + b)\n return act.reshape((X.shape[0], 1))","title":""},{"docid":"969d70426313947f56fd4a1aefe8069e","score":"0.56634766","text":"def generate_Hessian_from_system():\n print 'Not implemented'\n return 'You want a Lagrangian, not this.'","title":""}],"string":"[\n {\n \"docid\": \"70b133178455eb1f704a65c04f1163e3\",\n \"score\": \"0.7606198\",\n \"text\": \"def hessian(self, x):\\r\\n return numpy.array([[10, 8], [8, 10]], dtype = numpy.float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45c382621e4d63d7ac2a62c425605811\",\n \"score\": \"0.74376804\",\n \"text\": \"def hessian(X,Etha):\\n \\n h_dim = X.shape[1]*Etha.shape[1]\\n H = np.zeros((h_dim, h_dim))\\n for i in range(Etha.shape[1]):\\n for j in range(Etha.shape[1]):\\n rf = Etha.shape[0] * i\\n rl = Etha.shape[0] * (i+1)\\n cf = Etha.shape[0] * j\\n cl = Etha.shape[0] * (j+1) \\n if i == j:\\n W = np.diagflat([etha[:,i]*(1-etha[:,i]) for etha in Etha])\\n print 'W:', type(W).__name__, len(W)\\n print 'X:', type(X).__name__, X.shape\\n H[rf:rl,cf:cl] = -np.dot(np.dot(X.transpose(),W),X)[:,:]\\n else:\\n W = np.diagflat([etha[:,i]*etha[:,j] for etha in Etha]) \\n H[rf:rl,cf:cl] = np.dot(np.dot(X.transpose(),W),X)[:,:]\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f5d5292d748328ab8194c497bd93ac\",\n \"score\": \"0.7407189\",\n \"text\": \"def calculate_hessian(y, tx, w, pred):\\n\\n n = int(y.shape[0])\\n S = pred*(1-pred)*np.eye(n)\\n print \\\"S\\\"\\n b = tx.T.dot(S)\\n print \\\"B\\\"\\n hessian = b.dot(tx)\\n print \\\"H\\\"\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"780a6fbd1348bf05628a9b5cac9193ab\",\n \"score\": \"0.7277152\",\n \"text\": \"def makeHessian(N):\\n ia_null = ia(0.,0.)\\n m1 = np.zeros((N,N),float)#np.matrix(np.zeros((N,N),float))\\n HX=[]\\n for i in range(N):\\n HX.append([])\\n for j in range(N):\\n HX[i].append(ia_null*m1[i,j])\\n HX = np.matrix(HX)#np.asarray(HX)#\\n return HX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1c2b6cd0bae9bfed6838315b5a33dd\",\n \"score\": \"0.72490305\",\n \"text\": \"def hessian(self, q):\\n x = self._g(q)\\n J_g = self._g.jacobian(q)\\n H_f = self._f.hessian(x)\\n # print(\\\"H_f :\\\", H_f.shape)\\n # print(\\\"J_g :\\\", J_g.shape)\\n return J_g.T * H_f * J_g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dae1a7844a5e2ec8c17529c4277da42\",\n \"score\": \"0.7225836\",\n \"text\": \"def hessian(self, params):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb33a516330f22a5e6c67f4e4c8cab1\",\n \"score\": \"0.7152987\",\n \"text\": \"def hessian(self, functions, eval_points):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d48b2337af9a1f4a3104e4e5a01b51\",\n \"score\": \"0.7127126\",\n \"text\": \"def _hessian(yhat, dtrain):\\n\\n y = dtrain.get_label()\\n return 1. / np.power(np.cosh(y - yhat), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575101ecef88f7e8b9902c82e690151f\",\n \"score\": \"0.71194786\",\n \"text\": \"def get_Hessian(self,u,xi, theta):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f352bf092fa71e84b84cc83ca23119\",\n \"score\": \"0.71186304\",\n \"text\": \"def _estimate_hessian(self, theta: np.ndarray) -> sparse.spmatrix:\\n score = 1.0 / (1 + self._exp_affine(theta))\\n D = sparse_diag_matrix(score * (1 - score))\\n X = self.data.X\\n full_hessian = X.T * D * X\\n return full_hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bffdebea4bb98d5a5beccc71fc55fc\",\n \"score\": \"0.71115786\",\n \"text\": \"def hessian(self, params):\\n inter = 2 * self.f.gradient(self.x, params)[..., numpy.newaxis] * self.f.gradient(self.x, params)[..., numpy.newaxis, :]\\n shape = inter.shape[-2], inter.shape[-1]\\n inter.shape = (-1, inter.shape[-2] * inter.shape[-1])\\n temp = numpy.sum(inter, axis = 0)\\n temp.shape = shape\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37eeafddf5022fd5d5196e0639cef90f\",\n \"score\": \"0.7090309\",\n \"text\": \"def hessian(self, x, y, obj_factor):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4853d89b9623a7ef4a3b7ef4e7e5c2c0\",\n \"score\": \"0.70627683\",\n \"text\": \"def hessian(self, x):\\n if self._symmetric and self._posdef:\\n return self._a.T\\n else:\\n return 0.5 * (self._a + self._a.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e2ee7197f77276c1157727a73b6a22\",\n \"score\": \"0.70527565\",\n \"text\": \"def calculate_hessian(y, tx, w):\\n \\\"\\\"\\\"\\\"sig = np.apply_along_axis(sigmoid,axis=1,arr=np.dot(tx,w))\\n S = np.diag(np.diag(np.dot(sig,np.ones((len(y),1))-sig)))\\\"\\\"\\\"\\n S = np.zeros((len(y),len(y)))\\n for i in range(len(y)):\\n S[i][i] = np.dot(sigmoid(np.dot(tx[i],w)), 1 - sigmoid(np.dot(tx[i],w)))\\n\\n return np.dot(np.transpose(tx),np.dot(S,tx))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25673c5cb95df775b47bf2029a4ab201\",\n \"score\": \"0.7051751\",\n \"text\": \"def calculate_hessian(y, tx, w):\\n pred = sigmoid(tx.dot(w))\\n pred = np.diag(pred.T[0])\\n r = np.multiply(pred, (1-pred))\\n\\n return tx.T.dot(r).dot(tx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef4ccf7ee7c8065e360f7bc55ff7ead\",\n \"score\": \"0.7038425\",\n \"text\": \"def hessian(self, X, y, alpha):\\n n = len(y)\\n W = np.diag(self.d2_loss(y * self.K_train @ alpha))\\n return (1/n) * self.K_train @ W @ self.K_train + self.lambd * self.K_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc68f480db6d0865d2f16f8c4471850\",\n \"score\": \"0.703747\",\n \"text\": \"def calculate_hessian(y, tx, w):\\n N = len(y)\\n S = np.eye(N)\\n for n in range(N):\\n pred = sigmoid(np.dot(tx[n,:].T,w))\\n S[n,n] = pred*(1-pred)\\n return tx.T.dot(S.dot(tx))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c856ce0b7feddbc76ceb484e8857ccd\",\n \"score\": \"0.70134056\",\n \"text\": \"def scalarHessian(N):\\n m1 = np.zeros((N,N),float)#np.matrix(np.zeros((N,N),float))\\n HX=[]\\n for i in range(N):\\n HX.append([])\\n for j in range(N):\\n HX[i].append(m1[i,j])\\n HX = np.matrix(HX)#np.asarray(HX)#\\n return HX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea1f69f32a7ba31b0fc6d20e2271da2\",\n \"score\": \"0.69780093\",\n \"text\": \"def hessian(self, params):\\n loglike = self.loglike\\n return approx_hess(params, loglike)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcedaec13e8184f9c840eeb744e266b\",\n \"score\": \"0.6952656\",\n \"text\": \"def hessian(X, theta):\\n\\n # The diagonal sigmoid matrix\\n # https://stats.stackexchange.com/a/316933\\n # -1 * g(X, theta) * (1 - g(X, theta)) * X @ X.T\\n G = h(X, theta)\\n D = np.diag(G * (1 - G))\\n\\n return X.T @ D @ X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ca965dbe8717fc353847a52dc29a872\",\n \"score\": \"0.6929033\",\n \"text\": \"def hessian(self, data, beta):\\n raise NotImplementedError(\\\"abstract method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4aa6c02d0fc8b21e464f8d4826ac9b\",\n \"score\": \"0.6865305\",\n \"text\": \"def _update_full_hessian(self, new_theta: np.array) -> sparse.spmatrix:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498aa212366e474c25ee053f1ffd6d3d\",\n \"score\": \"0.6860637\",\n \"text\": \"def _update_full_hessian(self, new_theta: np.ndarray) -> sparse.spmatrix:\\n return (\\n self.initial_model.hessian * self.param_delta # type: ignore\\n + self._estimate_hessian(new_theta)\\n + (1 - self.param_delta) * self.param_lambda\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7bc31d44c2e32abcac7364351e34e9\",\n \"score\": \"0.6854312\",\n \"text\": \"def hessian(theta,X,y,regTerm=0.0):\\n (m,n) = X.shape\\n tmp = (regTerm/m)*np.identity(n)\\n tmp[0][0] = 0. #no regularization for first term\\n mat1 = np.dot(X.T,np.diag(np.multiply(sigmoid(X,theta),1-sigmoid(X,theta))))\\n mat2 = (1.0/m)*np.dot(mat1,X)\\n return mat2 + tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b134d12623eb6774bc8323b986209c1\",\n \"score\": \"0.68359387\",\n \"text\": \"def hessian(self, x, lagrange, obj_factor):\\n return obj_factor * hnz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44eb2db0a309be9c9186d8e7a299a636\",\n \"score\": \"0.67806596\",\n \"text\": \"def Hessian(x):\\n hessian = np.zeros((3, 3))\\n return PotentialField._accumulate(PotentialField._hessian, x, hessian)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89ba3147d8d776fb0f9d0cf1cf6b522\",\n \"score\": \"0.67723465\",\n \"text\": \"def hessian(self, q):\\n x = self._g(q)\\n J_g = self._g.jacobian(q)\\n H_g = self._g.hessian(q)\\n J_f = self._f.jacobian(x)\\n H_f = self._f.hessian(x)\\n a_x = J_g.T * H_f * J_g\\n b_x = J_f * np.ones(self.input_dimension()) * H_g\\n return a_x + b_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e513ed9e3a7649ab5c258f952118d36\",\n \"score\": \"0.67086816\",\n \"text\": \"def hessian(y, x):\\n meta_batch_size, num_observations = y.shape[:2]\\n grad_y = torch.ones_like(y[..., 0]).to(y.device)\\n h = torch.zeros(meta_batch_size, num_observations, y.shape[-1], x.shape[-1], x.shape[-1]).to(y.device)\\n for i in range(y.shape[-1]):\\n # calculate dydx over batches for each feature value of y\\n dydx = grad(y[..., i], x, grad_y, create_graph=True)[0]\\n\\n # calculate hessian on y for each x value\\n for j in range(x.shape[-1]):\\n tmp = grad(dydx[..., j], x, grad_y, create_graph=True)\\n h[..., i, j, :] = tmp[0].unsqueeze(1)[..., :]\\n\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a43b55295d9c0167b6c7d132924bfe\",\n \"score\": \"0.66171896\",\n \"text\": \"def _update_hessian(self, new_theta: np.array) -> sparse.spmatrix:\\n if self.hessian_type == HessianType.NONE:\\n hessian = None\\n elif self.hessian_type == HessianType.IDENTITY:\\n hessian = sparse_diag_matrix([1] * len(new_theta))\\n elif self.hessian_type == HessianType.DIAGONAL:\\n hessian = sparse_diag_matrix(self._update_full_hessian(new_theta).diagonal())\\n elif self.hessian_type == HessianType.FULL:\\n hessian = self._update_full_hessian(new_theta)\\n else:\\n raise ValueError(f\\\"{self.hessian_type} is not a supported HessianType.\\\")\\n\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930e7137eb9e9f5962b99569e5310eee\",\n \"score\": \"0.6570693\",\n \"text\": \"def hessian(x):\\n x_grad = np.gradient(x) \\n hessian = np.empty((x.ndim, x.ndim) + x.shape, dtype=x.dtype) \\n for k, grad_k in enumerate(x_grad):\\n # iterate over dimensions\\n # apply gradient again to every component of the first derivative.\\n tmp_grad = np.gradient(grad_k) \\n for l, grad_kl in enumerate(tmp_grad):\\n hessian[k, l, :, :] = grad_kl\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930e7137eb9e9f5962b99569e5310eee\",\n \"score\": \"0.6570693\",\n \"text\": \"def hessian(x):\\n x_grad = np.gradient(x) \\n hessian = np.empty((x.ndim, x.ndim) + x.shape, dtype=x.dtype) \\n for k, grad_k in enumerate(x_grad):\\n # iterate over dimensions\\n # apply gradient again to every component of the first derivative.\\n tmp_grad = np.gradient(grad_k) \\n for l, grad_kl in enumerate(tmp_grad):\\n hessian[k, l, :, :] = grad_kl\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad166894a295106765ce177ef177a083\",\n \"score\": \"0.65619\",\n \"text\": \"def hessian(self, x):\\n if self.viewer is not None:\\n if self._draw_hessian:\\n self.draw_gradient(x)\\n else:\\n self.draw(Trajectory(q_init=self.objective.q_init, x=x))\\n return self.objective.objective.hessian(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bb30c819019805fedec0bcd248eb52\",\n \"score\": \"0.65548205\",\n \"text\": \"def hessian(func, argnums=0):\\n return jacfwd(jacrev(func, argnums), argnums)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b849897f75c93bef7d914945c5512abe\",\n \"score\": \"0.65482\",\n \"text\": \"def full_hessian(loss, params):\\n\\n flat_params, unravel = ravel_pytree(params)\\n\\n def loss_flat(flat_params):\\n params = unravel(flat_params)\\n return loss(params)\\n\\n def hessian(f):\\n return jacfwd(jacrev(f))\\n\\n hessian_matrix = hessian(loss_flat)(flat_params)\\n return hessian_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12af9c7cc33dd0f56156583969057494\",\n \"score\": \"0.64940876\",\n \"text\": \"def BetheHessian(adjacency_matrix, r=None):\\n d = np.sum(adjacency_matrix, axis=0)\\n if r is None:\\n r = np.sqrt(np.mean(d))\\n\\n print((r**2 - 1)*np.identity(len(d)) - r*adjacency_matrix + np.diag(d))\\n return (r**2 - 1)*np.identity(len(d)) - r*adjacency_matrix + np.diag(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f88260385df0083c59b1637355f9b0eb\",\n \"score\": \"0.64898974\",\n \"text\": \"def hessian(self, x, lagrange, obj_factor):\\n row, col = self.hessianstructure()\\n \\n return obj_hess(x)[row, col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14aeb39a1b22153e8529e25bd7e3ab42\",\n \"score\": \"0.647654\",\n \"text\": \"def hessian(self, f: Callable, wrt: tuple or list, get_output: bool, get_gradient: bool) -> tuple:\\n raise NotImplementedError(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c1fdbea374a60d58a1356ead0a5c304\",\n \"score\": \"0.64643323\",\n \"text\": \"def hessian(self, x, lagrange, obj_factor):\\n\\n H = obj_factor*np.array((\\n (2*x[3], 0, 0, 0),\\n (x[3], 0, 0, 0),\\n (x[3], 0, 0, 0),\\n (2*x[0]+x[1]+x[2], x[0], x[0], 0)))\\n\\n H += lagrange[0]*np.array((\\n (0, 0, 0, 0),\\n (x[2]*x[3], 0, 0, 0),\\n (x[1]*x[3], x[0]*x[3], 0, 0),\\n (x[1]*x[2], x[0]*x[2], x[0]*x[1], 0)))\\n\\n H += lagrange[1]*2*np.eye(4)\\n\\n row, col = self.hessianstructure()\\n\\n return H[row, col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86fc6c073b1a8950ee4a84ec17ef11c9\",\n \"score\": \"0.644322\",\n \"text\": \"def compute_hessian(x0, X1, X0, Z1, Z0, Y1, Y0):\\n\\n # def auxiliary parameters\\n num_obs = X1.shape[0] + X0.shape[0]\\n n_col_X1 = X1.shape[1]\\n n_col_X0 = X0.shape[1]\\n n_col_Z = Z1.shape[1]\\n\\n # parameters\\n num_col_X1X0 = n_col_X1 + n_col_X0\\n num_col_X1X0Z1 = num_col_X1X0 + n_col_Z\\n\\n beta1, beta0, gamma = (\\n x0[:n_col_X1],\\n x0[n_col_X1:num_col_X1X0],\\n x0[num_col_X1X0:-4],\\n )\\n sd1, sd0, rho1v, rho0v = x0[-4], x0[-2], x0[-3], x0[-1]\\n\\n # aux_params\\n nu1 = (Y1 - np.dot(beta1, X1.T)) / sd1\\n lambda1 = (np.dot(gamma, Z1.T) - rho1v * nu1) / (np.sqrt(1 - rho1v ** 2))\\n nu0 = (Y0 - np.dot(beta0, X0.T)) / sd0\\n lambda0 = (np.dot(gamma, Z0.T) - rho0v * nu0) / (np.sqrt(1 - rho0v ** 2))\\n\\n eta1 = (\\n -lambda1 * norm.pdf(lambda1) * norm.cdf(lambda1) - norm.pdf(lambda1) ** 2\\n ) / (norm.cdf(lambda1) ** 2)\\n eta0 = (\\n lambda0 * norm.pdf(lambda0) * (1 - norm.cdf(lambda0)) - norm.pdf(lambda0) ** 2\\n ) / (1 - norm.cdf(lambda0)) ** 2\\n # combinations of obs\\n X1X1 = np.einsum(\\\"ij, i ->ij\\\", X1, eta1).T @ X1\\n X1Z1 = np.einsum(\\\"ij, i ->ij\\\", X1, eta1).T @ Z1\\n\\n X0X0 = np.einsum(\\\"ij, i ->ij\\\", X0, eta0).T @ X0\\n X0Z0 = np.einsum(\\\"ij, i ->ij\\\", X0, eta0).T @ Z0\\n\\n Z1Z1 = np.einsum(\\\"ij, i ->ij\\\", Z1, eta1).T @ Z1\\n Z0Z0 = np.einsum(\\\"ij, i ->ij\\\", Z0, eta0).T @ Z0\\n\\n # beginning with derivations of beta1\\n derv_beta1 = calc_hess_beta1(\\n X1X1, X1Z1, X1, sd1, rho1v, nu1, lambda1, eta1, n_col_X1, n_col_X0, num_obs\\n )\\n derv_beta0 = calc_hess_beta0(\\n X0X0, X0Z0, X0, sd0, rho0v, nu0, lambda0, eta0, n_col_X1, n_col_X0, num_obs\\n )\\n derv_gamma = calc_hess_gamma(\\n Z1Z1,\\n Z0Z0,\\n Z1,\\n X1,\\n Z0,\\n X0,\\n sd0,\\n sd1,\\n rho0v,\\n rho1v,\\n eta1,\\n eta0,\\n nu0,\\n nu1,\\n lambda0,\\n lambda1,\\n num_col_X1X0,\\n num_obs,\\n )\\n derv_dist = calc_hess_dist(\\n Z1,\\n Z0,\\n gamma,\\n sd1,\\n sd0,\\n rho1v,\\n rho0v,\\n lambda1,\\n lambda0,\\n nu1,\\n nu0,\\n eta1,\\n eta0,\\n num_col_X1X0Z1,\\n num_obs,\\n )\\n\\n # convert results to a symmetric hessian matrix\\n hessian_upper = np.triu(\\n np.concatenate((derv_beta1, derv_beta0, derv_gamma, derv_dist), axis=0)\\n )\\n\\n aux = hessian_upper.copy()\\n for i in range(hessian_upper.shape[0]):\\n hessian_upper[:, i][i + 1 :] = hessian_upper[i][i + 1 :]\\n return hessian_upper, aux\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c62c50d75bb2ee1d4981625ce3a20f9f\",\n \"score\": \"0.64051557\",\n \"text\": \"def hess(self, x):\\n raise NotImplementedError('Hessian oracle is not implemented.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8297841d0f5c08dfc045cc0a18ab53a5\",\n \"score\": \"0.6360792\",\n \"text\": \"def convenience_calc_hessian(self, params):\\n shapes, intercepts, betas = self.convenience_split_params(params)\\n\\n args = [betas,\\n self.design,\\n self.alt_id_vector,\\n self.rows_to_obs,\\n self.rows_to_alts,\\n self.utility_transform,\\n self.calc_dh_d_shape,\\n self.calc_dh_dv,\\n self.calc_dh_d_alpha,\\n self.block_matrix_idxs,\\n intercepts,\\n shapes,\\n self.ridge]\\n\\n return cc.calc_hessian(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d8fc52a5d28e6cb94642f1c4fc75469\",\n \"score\": \"0.63550186\",\n \"text\": \"def hessian(self, theta):\\n L = len(self.data)\\n H = self._hessian_minibatch(theta, 0)\\n\\n for idx in range(1, L):\\n print('{} of {}'.format(idx, L))\\n H += self._hessian_minibatch(theta, idx)\\n\\n return H / float(L)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9628479bc98ee7d2a38586cf5e6c760\",\n \"score\": \"0.63359135\",\n \"text\": \"def RebuildHessian(IC, H0, coord_seq, grad_seq, params):\\n Na = len(coord_seq[0])/3\\n history = 0\\n for i in range(2, len(coord_seq)+1):\\n disp = bohr2ang*(coord_seq[-i]-coord_seq[-1])\\n rmsd = np.sqrt(np.sum(disp**2)/Na)\\n if rmsd > params.trust: break\\n history += 1\\n if history < 1:\\n return H0.copy()\\n logger.info(\\\"Rebuilding Hessian using %i gradients\\\\n\\\" % history)\\n y_seq = [IC.calculate(i) for i in coord_seq[-history-1:]]\\n g_seq = [IC.calcGrad(i, j) for i, j in zip(coord_seq[-history-1:],grad_seq[-history-1:])]\\n Yprev = y_seq[0]\\n Gprev = g_seq[0]\\n H = H0.copy()\\n for i in range(1, len(y_seq)):\\n Y = y_seq[i]\\n G = g_seq[i]\\n Yprev = y_seq[i-1]\\n Gprev = g_seq[i-1]\\n Dy = col(Y - Yprev)\\n Dg = col(G - Gprev)\\n # Mat1 = (Dg*Dg.T)/(Dg.T*Dy)[0,0]\\n # Mat2 = ((H*Dy)*(H*Dy).T)/(Dy.T*H*Dy)[0,0]\\n Mat1 = np.dot(Dg,Dg.T)/np.dot(Dg.T,Dy)[0,0]\\n Mat2 = np.dot(np.dot(H,Dy),np.dot(H,Dy).T)/multi_dot([Dy.T,H,Dy])[0,0]\\n Hstor = H.copy()\\n H += Mat1-Mat2\\n if np.min(np.linalg.eigh(H)[0]) < params.epsilon and params.reset:\\n logger.info(\\\"Eigenvalues below %.4e (%.4e) - returning guess\\\\n\\\" % (params.epsilon, np.min(np.linalg.eigh(H)[0])))\\n return H0.copy()\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61fe75d20ee1dc291b5e8fc424934eca\",\n \"score\": \"0.6315976\",\n \"text\": \"def calc_quasi_hessian(self, delta_x, delta_gradient, prev_h):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70be000279ae7f13b855bd8cccfb43d0\",\n \"score\": \"0.6301178\",\n \"text\": \"def lossHessian(self, beta):\\r\\n return -self.hessian(beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7342fc246ec81e845cf0c9505448f76b\",\n \"score\": \"0.6300913\",\n \"text\": \"def get_hessian_matrix(self):\\n\\n\\t\\thessian_matrix = []\\n\\n\\t\\thessian_matrix_indices = self.get_line_indices_containing_string(Outcar.hessian_string)\\n\\n\\t\\tif len(hessian_matrix_indices) == 0:\\n\\t\\t\\traise Exception(\\\"No hessian matrix found in completed outcar file\\\")\\n\\n\\n\\t\\thessian_matrix_starting_index = hessian_matrix_indices[-1] + 3\\n\\n\\n\\t\\tnumber_of_degrees_of_freedom = 3*self.get_number_of_atoms()\\n\\n\\t\\tfor i in range(number_of_degrees_of_freedom):\\n\\t\\t\\trow_string = self[hessian_matrix_starting_index+i]\\n\\n\\t\\t\\tcleaned_row_string = su.remove_extra_spaces(row_string.strip())\\n\\n\\t\\t\\tcleaned_row_components_list = cleaned_row_string.strip().split(' ')\\n\\n\\t\\t\\tnumerical_strings_list = cleaned_row_components_list[1:]\\n\\n\\t\\t\\trow_values_list = [-1.0*float(component_string) for component_string in numerical_strings_list] #must take the negative because vasp gives values in terms of changes in forces, which are negative of dEnergy terms\\n\\n\\t\\t\\tif not len(row_values_list) == number_of_degrees_of_freedom:\\n\\t\\t\\t\\traise Exception(\\\"Number of degrees of freedom and number of columns in hessian row do not match. Row is\\\", row_values_list, \\\"number of dofs is\\\", number_of_degrees_of_freedom)\\n\\n\\n\\t\\t\\thessian_matrix.append(row_values_list)\\n\\n\\t\\tif len(hessian_matrix) != number_of_degrees_of_freedom:\\n\\t\\t\\traise Exception(\\\"Number of degrees of freedom and number of rows in hessian matrix do not match. Matrix row count is\\\", len(hessian_matrix), \\\"number of dofs is\\\", number_of_degrees_of_freedom)\\n\\n\\t\\treturn hessian_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb54d355d301608e3bf768fa4dcf6e2\",\n \"score\": \"0.62908304\",\n \"text\": \"def estimate_inversed_hessian_matrix(func, param_0):\\n n_param = len(param_0)\\n np_hessian = numpy.zeros(shape=(n_param, n_param), dtype=float)\\n np_first_der = numpy.zeros(shape=(n_param,), dtype=float)\\n chi_sq = func(param_0)\\n perc = 0.01\\n \\n for i_p_1, p_1 in enumerate(param_0):\\n delta_p_1 = perc * numpy.abs(p_1)\\n if delta_p_1 < 1e-5:\\n delta_p_1 = 1e-5\\n param_p = copy.deepcopy(param_0)\\n param_m = copy.deepcopy(param_0)\\n param_p[i_p_1] += delta_p_1\\n param_m[i_p_1] -= delta_p_1\\n # chi_sq_p = func(param_p)\\n # chi_sq_m = func(param_m)\\n # der_second = (chi_sq_p + chi_sq_m - 2.*chi_sq)/(delta_p_1**2)\\n # np_hessian[i_p_1, i_p_1] = der_second\\n\\n # der_first = (chi_sq_p - chi_sq_m) / (2.*delta_p_1)\\n # np_first_der[i_p_1] = der_first\\n\\n param_pp = copy.deepcopy(param_0)\\n param_pm = copy.deepcopy(param_0)\\n param_mp = copy.deepcopy(param_0)\\n param_mm = copy.deepcopy(param_0)\\n\\n param_pp[i_p_1] += delta_p_1\\n param_pm[i_p_1] += delta_p_1\\n param_mp[i_p_1] -= delta_p_1\\n param_mm[i_p_1] -= delta_p_1\\n for i_p_2, p_2 in enumerate(param_0[:i_p_1+1]):\\n delta_p_2 = perc * numpy.abs(p_2)\\n if delta_p_2 < 1e-5:\\n delta_p_2 = 1e-5\\n param_pp[i_p_2] += delta_p_2\\n param_pm[i_p_2] -= delta_p_2\\n param_mp[i_p_2] += delta_p_2\\n param_mm[i_p_2] -= delta_p_2\\n \\n chi_sq_pp = func(param_pp)\\n chi_sq_pm = func(param_pm)\\n chi_sq_mp = func(param_mp)\\n chi_sq_mm = func(param_mm)\\n\\n der_second = (chi_sq_pp + chi_sq_mm - chi_sq_pm - chi_sq_mp) / (\\n 4. * delta_p_1 * delta_p_2) \\n np_hessian[i_p_1, i_p_2] = der_second\\n np_hessian[i_p_2, i_p_1] = der_second\\n\\n param_pp[i_p_2] -= delta_p_2\\n param_pm[i_p_2] += delta_p_2\\n param_mp[i_p_2] -= delta_p_2\\n param_mm[i_p_2] += delta_p_2\\n\\n try:\\n np_hessian_inv = numpy.linalg.inv(np_hessian)\\n except LinAlgError:\\n np_hessian_inv = None\\n func(param_0)\\n return np_hessian_inv, np_first_der\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4fe23ab7a9040209fbde678e6fcd05\",\n \"score\": \"0.6274369\",\n \"text\": \"def hessian(self, q):\\n return finite_difference_hessian(self, q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee43b3c0dc6398b7bc6fc7f596041a0f\",\n \"score\": \"0.62249756\",\n \"text\": \"def calculate_hessian(self, finite_step):\\n\\n # Create the OpenMM coords list from the qm coordinates and convert to nm.\\n input_coords = self.molecule.coordinates.flatten() * constants.ANGS_TO_NM\\n\\n # We get each hessian element from = [E(dx + dy) + E(-dx - dy) - E(dx - dy) - E(-dx + dy)] / 4 dx dy\\n hessian = np.zeros((3 * len(self.molecule.atoms), 3 * len(self.molecule.atoms)))\\n\\n for i in range(3 * len(self.molecule.atoms)):\\n for j in range(i, 3 * len(self.molecule.atoms)):\\n # Mutate the atomic coords\\n # Do fewer energy evaluations on the diagonal of the matrix\\n if i == j:\\n coords = deepcopy(input_coords)\\n coords[i] += 2 * finite_step\\n e1 = self.get_energy(self.format_coords(coords))\\n coords = deepcopy(input_coords)\\n coords[i] -= 2 * finite_step\\n e2 = self.get_energy(self.format_coords(coords))\\n hessian[i, j] = (e1 + e2) / (\\n 4 * finite_step**2 * self.molecule.atoms[i // 3].atomic_mass\\n )\\n else:\\n coords = deepcopy(input_coords)\\n coords[i] += finite_step\\n coords[j] += finite_step\\n e1 = self.get_energy(self.format_coords(coords))\\n coords = deepcopy(input_coords)\\n coords[i] -= finite_step\\n coords[j] -= finite_step\\n e2 = self.get_energy(self.format_coords(coords))\\n coords = deepcopy(input_coords)\\n coords[i] += finite_step\\n coords[j] -= finite_step\\n e3 = self.get_energy(self.format_coords(coords))\\n coords = deepcopy(input_coords)\\n coords[i] -= finite_step\\n coords[j] += finite_step\\n e4 = self.get_energy(self.format_coords(coords))\\n hessian[i, j] = (e1 + e2 - e3 - e4) / (\\n 4 * finite_step**2 * self.molecule.atoms[i // 3].atomic_mass\\n )\\n\\n # Hessian is currently just the upper right half of the matrix.\\n # Transpose to get the lower left, then remove the extra diagonal terms to get the whole matrix.\\n sym_hessian = hessian + hessian.T - np.diag(hessian.diagonal())\\n return sym_hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfd6a02fe9e0a1bee9e727ca2fefeec\",\n \"score\": \"0.621227\",\n \"text\": \"def Hv(self):\\n if self._Hv is None:\\n self._Hv = hessian_vector_product(self.loss, self.weights, self.v)\\n return self._Hv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab53a0dcbd66a3dcefe99994cbb141e\",\n \"score\": \"0.62032664\",\n \"text\": \"def _hessian_minibatch(self, theta, data_index):\\n\\n # data\\n r = self.data[data_index]['rate']\\n\\n # model\\n u, z, zgrad, zhess, drdz, dr2dz2, rhat = self.rate(\\n theta, self.data[data_index]['stim'], hess=True)\\n\\n # store the full Hessian\\n N = np.prod(theta['W'].shape[1:]) # filter dimension\\n M = theta['W'].shape[0] # number of subunits\\n P = theta['f'].shape[1] # nonlinearity parameters\\n T = r.size\\n H = np.zeros((N * M + P * M, N * M + P * M))\\n\\n # gradient and Hessian factors\\n grad_factor = 1 - r / rhat\\n hess_factor = r / (rhat**2)\\n\\n # nonlinearity projection\\n zproj = np.sum(theta['f'][:, np.newaxis, :] * zgrad, axis=2) # M by T\\n zproj2 = np.sum(theta['f'][:, np.newaxis, :] * zhess, axis=2) # M by T\\n\\n # vectorized data\\n x_vec = np.rollaxis(self.data[data_index]['stim'], -1).reshape(N, T) # N x T\\n z_vec = np.rollaxis(z, -1).reshape(M * P, T) # M*P x T\\n scale_factor = np.diag(grad_factor * dr2dz2 + hess_factor * drdz**2) # diagonal(T)\\n\\n # nonlinearity - nonlinearity portion\\n H[-(M * P):, -(M * P):] = z_vec.dot(scale_factor.dot(z_vec.T))\\n\\n # loop over subunits\\n for j1 in range(M):\\n\\n zgrad_j1 = zproj[j1, :]\\n zhess_j1 = zproj2[j1, :]\\n\\n # scale factor grad\\n sf_zgrad = scale_factor * np.diag(zgrad_j1)\\n\\n # subunit-nonlinearity block\\n H[j1 * N:(j1 + 1) * N, -(M * P):] = x_vec.dot(sf_zgrad.dot(z_vec.T))\\n H[-(M * P):, j1 * N:(j1 + 1) * N] = H[j1 * N:(j1 + 1) * N, -(M * P):].T\\n\\n # subunit-subunit block\\n for j2 in range(M):\\n\\n zgrad_j2 = np.diag(zproj[j2, :])\\n\\n # update this portion of the Hessian\\n H[j1 * N:(j1 + 1) * N, j2 * N:(j2 + 1) * N] = x_vec.dot((sf_zgrad * zgrad_j2).dot(x_vec.T))\\n\\n # diagonal term (for the same subunit)\\n if j1 == j2:\\n H[j1 * N:(j1 + 1) * N, j2 * N:(j2 + 1) * N] += x_vec.dot(np.diag(grad_factor * drdz * zhess_j1).dot(x_vec.T))\\n\\n return H / float(T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"431b506b192ff50d525551c70fd8be88\",\n \"score\": \"0.6190988\",\n \"text\": \"def Jacobian_H():\\r\\n Jacobian_H = np.array([[1.0, 0, 0],\\r\\n [0, 1.0, 0],\\r\\n [0, 0, 1.0]])\\r\\n return Jacobian_H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27fcb0df257ead84bdfa981f3a4bd687\",\n \"score\": \"0.6121463\",\n \"text\": \"def test_hessian_at_zero(self, torch_device, x, shift):\\n dev = qml.device(\\\"default.qubit.torch\\\", wires=1, torch_device=torch_device)\\n\\n x = torch.tensor(x, requires_grad=True)\\n\\n @qml.qnode(dev, interface=\\\"torch\\\", diff_method=\\\"backprop\\\")\\n def circuit(x):\\n qml.RY(shift, wires=0)\\n qml.RY(x, wires=0)\\n return qml.expval(qml.PauliZ(0))\\n\\n grad = torch.autograd.functional.jacobian(circuit, x)\\n hess = torch.autograd.functional.hessian(circuit, x)\\n\\n assert qml.math.isclose(grad, torch.tensor(0.0))\\n assert qml.math.isclose(hess, torch.tensor(-1.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b204b62ed69ceec64cf0be2d2fce93\",\n \"score\": \"0.61059403\",\n \"text\": \"def compute_hessian_nll(y, x, w, lambda_=0):\\n\\n sgm = sigmoid(x @ w)\\n s = sgm * (1 - sgm) + 2 * lambda_\\n return (x.T * s) @ x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741f379846fe32bdae37e87c9575382e\",\n \"score\": \"0.60802615\",\n \"text\": \"def sum_hessian(self, hessian, p=None):\\n raise NotImplementedError(\\\"sum_hessian method not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b359e20efc06dfb0a9a10fdb338ec893\",\n \"score\": \"0.60706824\",\n \"text\": \"def numerical_hessian_G(grad,x0,h=1.0e-5):\\n n = len(x0)\\n hess = np.zeros((n,n))\\n g0 = grad(x0)\\n print \\\"numerical differentiation of forces requires %d gradient calculations\\\" % (2*n)\\n for i in range(0, n):\\n print \\\"%s of %d\\\" % (2*(i+1), 2*n)\\n ei = np.zeros(n)\\n ei[i] = 1.0\\n x_phi = x0 + h*ei\\n# hess[i,:] = (grad(x_phi) - g0)/h \\n x_mhi = x0 - h*ei\\n hess[i,:] = (grad(x_phi) - grad(x_mhi))/(2*h)\\n\\n # hessian should be symmetric\\n hess = 0.5 * (hess + hess.transpose())\\n \\n return hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25fe9debd8ced7f36c1cdc669ec97452\",\n \"score\": \"0.60441446\",\n \"text\": \"def hessian(sdf_im):\\n batch_size, height, width = [sdf_im.shape[i].value for i in range(3)]\\n sdf_im = tf.reshape(sdf_im, [batch_size, height, width, 1])\\n # pyformat: disable\\n xx_fda_kernel = tf.reshape(\\n tf.constant([[0.0, 0.0, 0.0],\\n [1.0, -2.0, 1.0],\\n [0.0, 0.0, 0.0]], dtype=tf.float32), [3, 3, 1, 1])\\n yy_fda_kernel = tf.reshape(\\n tf.constant([[0.0, 1.0, 0.0],\\n [0.0, -2.0, 0.0],\\n [0.0, 1.0, 0.0]], dtype=tf.float32), [3, 3, 1, 1])\\n xy_fda_kernel = tf.reshape(\\n tf.constant([[0.25, 0.0, -0.25],\\n [0.0, 0.0, 0.0],\\n [-0.25, 0.0, 0.25]], dtype=tf.float32), [3, 3, 1, 1])\\n # pyformat: enable\\n fda_kernel = tf.concat(\\n [xx_fda_kernel, xy_fda_kernel, xy_fda_kernel, yy_fda_kernel], axis=3)\\n fda = tf.nn.conv2d(sdf_im, fda_kernel, [1, 1, 1, 1], padding='SAME')\\n # Fda should have shape [batch_size, height, width, 4], because we duplicated\\n # the xy partial channels.\\n hess = tf.reshape(fda, [batch_size, height, width, 2, 2])\\n # Because we used an fda method, we don't have to symmetrize, so just return:\\n return hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37f53ab9217dab1d18f6732c6f170bb\",\n \"score\": \"0.59972906\",\n \"text\": \"def cost_matrix(self, Y, Y_hat):\\n\\n assert hasattr(Y_hat, 'owner')\\n owner = Y_hat.owner\\n assert owner is not None\\n op = owner.op\\n\\n if not hasattr(op, 'scalar_op'):\\n raise ValueError(\\\"Expected Y_hat to be generated by an Elemwise op, got \\\"+str(op)+\\\" of type \\\"+str(type(op)))\\n assert isinstance(op.scalar_op, T.nnet.sigm.ScalarSigmoid)\\n z ,= owner.inputs\\n\\n term_1 = Y * T.nnet.softplus(-z)\\n term_2 = (1 - Y) * T.nnet.softplus(z)\\n\\n total = term_1 + term_2\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726da6bf89da8f0dea61e6069f3bc776\",\n \"score\": \"0.59852344\",\n \"text\": \"def __init__(self, n_features=10000, hessian_thresh=100):\\n self.n_features = n_features\\n self.hessian_thresh = hessian_thresh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69e0bb67fb642be1d4d7b9fe066a6c4\",\n \"score\": \"0.59824437\",\n \"text\": \"def _derivProblem_Hessian(self,fittingData,fittingDataDerivs,indepParamsList,\\n priorLambda):\\n J = self._derivProblem_Jacobian(fittingData,fittingDataDerivs,\\n indepParamsList)\\n HnoPrior = np.dot(J.T,J)\\n\\n # add parameter prior to diagonal\\n numResiduals,numParameters = np.shape(J)\\n priorDiag = scipy.diag(np.ones(numParameters)*priorLambda)\\n H = HnoPrior + priorDiag\\n\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc58065d76c1697f1f62611ef7e820cc\",\n \"score\": \"0.5976115\",\n \"text\": \"def compute_hessian_of_loss(xs, model, loss=\\\"cross_entropy\\\", n_out=10, noise_log_std=None):\\n\\n model.eval()\\n with torch.no_grad():\\n\\n if loss == \\\"cross_entropy\\\":\\n y_hat = torch.softmax(model(xs).detach(), dim=1) # B x O\\n H_L = torch.diag_embed(\\n y_hat) - contract('bi,bj->bij', y_hat, y_hat) # B x O x O\\n\\n elif loss == \\\"binary_cross_entropy\\\":\\n y_hat = torch.sigmoid(model(xs).detach()) # B x 1\\n H_L = (y_hat * (1 - y_hat)).unsqueeze(1) # B x 1 x 1\\n\\n elif loss == \\\"mse\\\" or loss == \\\"gaussian\\\":\\n H_device = 'cuda:0' if isinstance(model, nn.DataParallel) else xs.device\\n H_L = torch.eye(n_out, device=H_device).unsqueeze(0).expand(\\n xs.shape[0], -1, -1) # B x O x O\\n\\n if loss == \\\"gaussian\\\":\\n H_L = H_L * (noise_log_std * (-2)).exp()\\n\\n else:\\n raise ValueError(\\\"Invalid loss function for GGN computation!\\\")\\n\\n return H_L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110f8dc01f9187b20fcff979b47714b4\",\n \"score\": \"0.59666276\",\n \"text\": \"def __init__(self, cg_iter, learning_rate=1.0, damping=1.0, fix_first_step=False, hv_method = 0, use_sgd=False, use_locking=False, name=\\\"HessianFree\\\"):\\n super(HessianFreeOptimizer, self).__init__(use_locking, name)\\n self._cg_iter = cg_iter\\n self._learning_rate = learning_rate\\n self._damping = damping\\n self._fix_first_step = fix_first_step\\n self._use_sgd = use_sgd\\n self._Hv = Hv\\n if hv_method == 0:\\n self._Hv = Gv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368bb0dbfd37663b012f90ee75a45ac9\",\n \"score\": \"0.59664834\",\n \"text\": \"def convenience_calc_hessian(self, params):\\n msg = \\\"Method should be defined by descendant classes\\\"\\n raise NotImplementedError(msg)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3963c49f4c3bf0151e99282ef4c039\",\n \"score\": \"0.59608096\",\n \"text\": \"def test_ModelHawkesExpKernLogLik_hessian(self):\\n for model in [self.model]:\\n hessian = model.hessian(self.coeffs).todense()\\n # Check that hessian is equal to its transpose\\n np.testing.assert_array_almost_equal(hessian, hessian.T,\\n decimal=10)\\n\\n # Check that for all dimension hessian row is consistent\\n # with its corresponding gradient coordinate.\\n for i in range(model.n_coeffs):\\n\\n def g_i(x):\\n return model.grad(x)[i]\\n\\n def h_i(x):\\n h = model.hessian(x).todense()\\n return np.asarray(h)[i, :]\\n\\n self.assertLess(check_grad(g_i, h_i, self.coeffs), 1e-5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d35d6812b9ed5b34c585a5cd4e4eb9\",\n \"score\": \"0.59096104\",\n \"text\": \"def calc_neg_hessian(self, params):\\n return -1 * self.convenience_calc_hessian(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5a344700976caecee0ff81e106a45f\",\n \"score\": \"0.59087557\",\n \"text\": \"def nonlinear_hess_matrix(x, v):\\n #D, N, m_deg, nk = args[0], args[1], args[2], args[3]\\n # The four variables above are used instead by the global variables defined at the top of the code\\n sum_1 = 0\\n for i in range(2, m_deg):\\n args_local = (D, N, i, nk)\\n sum_1 += v[i - 2] * np.array(f_hess(x, *args_local))\\n return sum_1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4f3917caaad79174b7aff7747cbe31\",\n \"score\": \"0.5907711\",\n \"text\": \"def mean_hessian(self, x, params):\\n\\n switch = self.f1.get_n_params(x)\\n\\n exp = self.f2.mean_f(x, params[switch:])\\n nonzeroexp = True\\n if np.allclose(exp, 0.):\\n nonzeroexp = False\\n nononeexp = True\\n if np.allclose(exp, 1.):\\n nononeexp = False\\n\\n hess = np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))\\n\\n if nonzeroexp and nononeexp:\\n\\n hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*\\n self.f1.mean_hessian(x, params[:switch]) +\\n exp*(exp - 1.)*self.f1.mean_f(x, params[:switch])**(exp - 2.)*\\n self.f1.mean_deriv(x, params[:switch]))\\n\\n elif nonzeroexp:\\n\\n hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*\\n self.f1.mean_hessian(x, params[:switch]))\\n\\n\\n if not self.f2.get_n_params(x) == 0:\\n\\n if nonzeroexp:\\n hess[:switch, switch:, :] = (self.f1.mean_f(x, params[:switch])**(exp - 1.)*\\n (exp*np.log(self.f1.mean_f(x, params[:switch])) + 1.)*\\n self.f1.mean_deriv(x, params[:switch])[:,np.newaxis,:]*\\n self.f2.mean_deriv(x, params[switch:])[np.newaxis,:,:])\\n hess[switch:, :switch, :] = np.transpose(hess[:switch, switch:, :], (1, 0, 2))\\n\\n hess[switch:, switch:] = (self.f1.mean_f(x, params[:switch])**exp*\\n (np.log(self.f1.mean_f(x, params[:switch]))**2*\\n self.f2.mean_deriv(x, params[switch:])**2 +\\n np.log(self.f1.mean_f(x, params[:switch]))*\\n self.f2.mean_hessian(x, params[switch:])))\\n\\n return hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f05ae42271dd85528fa5907d53d162\",\n \"score\": \"0.59058034\",\n \"text\": \"def uvc_get_hessian(x, data):\\n\\n def f(xi):\\n val = 0.\\n for d in data:\\n val += error_single_test_uvc(xi, d)\\n return val\\n\\n hess_fun = nda.Hessian(f)\\n return hess_fun(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e538bd9a4ad2a6614059fa40f074a0ef\",\n \"score\": \"0.5891991\",\n \"text\": \"def hessian_scalar(expr, wrt):\\n J = T.grad(expr, wrt, disconnected_inputs=\\\"ignore\\\")\\n Q = T.stack([T.grad(g, wrt, disconnected_inputs=\\\"ignore\\\") for g in J])\\n return Q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201b78742f4380524153bb68048b9abb\",\n \"score\": \"0.5882084\",\n \"text\": \"def mw_hessian(self, full_hessian):\\n mass_xyz = np.zeros((self.molecule.natoms, 3))\\n mass_array = np.zeros((self.molecule.natoms))\\n for row in range(0, self.molecule.natoms):\\n #adding 1/np.sqrt(amu) units to xyz coords\\n x = element(self.molecule.atomtable[row][0])\\n value = np.sqrt(x.atomic_weight)\\n mass_array[row] = value\\n mass_xyz[row] = np.array(self.molecule.atomtable[row][1:])*(1/value) #mass weighted coordinates\\n for column in range(0, len(self.molecule.atomtable)): \\n y = element(self.molecule.atomtable[column][0])\\n z = x.atomic_weight*y.atomic_weight\\n value = np.sqrt(z)**-1\\n term = full_hessian[row][column]*value\\n full_hessian[row][column] = term\\n \\n reshape_mass_hess = full_hessian.transpose(0, 2, 1, 3)\\n x = reshape_mass_hess.reshape(reshape_mass_hess.shape[0]*reshape_mass_hess.shape[1],reshape_mass_hess.shape[2]*reshape_mass_hess.shape[3])\\n e_values, modes = LA.eigh(x)\\n\\n #unit conversion of freq from H/B**2 amu -> 1/s**2\\n #factor = (4.3597482*10**-18)/(1.6603145*10**-27)/(1.0*10**-20) #Angstrom to m\\n factor = 1.8897259886**2*(4.3597482*10**-18)/(1.6603145*10**-27)/(1.0*10**-20) #Bohr to Angstrom\\n freq = (np.sqrt(e_values*factor))/(2*np.pi*2.9979*10**10) #1/s^2 -> cm-1\\n return freq, modes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771a22d9b7f13679d21e42d16d2b8643\",\n \"score\": \"0.5860381\",\n \"text\": \"def test_adjoint_hessian(self, interface):\\n dev = qml.device(\\\"default.qubit\\\", wires=2)\\n params = tf.Variable([0.543, -0.654])\\n\\n with tf.GradientTape() as t2:\\n with tf.GradientTape() as t1:\\n with qml.queuing.AnnotatedQueue() as q:\\n qml.RX(params[0], wires=[0])\\n qml.RY(params[1], wires=[1])\\n qml.CNOT(wires=[0, 1])\\n qml.expval(qml.PauliZ(0))\\n\\n tape = qml.tape.QuantumScript.from_queue(q)\\n res = execute(\\n [tape],\\n dev,\\n gradient_fn=\\\"device\\\",\\n gradient_kwargs={\\\"method\\\": \\\"adjoint_jacobian\\\", \\\"use_device_state\\\": True},\\n interface=interface,\\n )[0]\\n\\n grad = t1.gradient(res, params)\\n assert grad is not None\\n assert grad.dtype == tf.float64\\n assert grad.shape == params.shape\\n\\n hess = t2.jacobian(grad, params)\\n assert hess is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266d02393c3cf69e1e883a840c01b7bf\",\n \"score\": \"0.58473873\",\n \"text\": \"def logit_hessian(s, b, X, y):\\n z = X.dot(b)\\n z = phi(y * z)\\n d = z * (1 - z)\\n wa = d * X.dot(s)\\n Hs = X.T.dot(wa)\\n out = Hs\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266d02393c3cf69e1e883a840c01b7bf\",\n \"score\": \"0.58473873\",\n \"text\": \"def logit_hessian(s, b, X, y):\\n z = X.dot(b)\\n z = phi(y * z)\\n d = z * (1 - z)\\n wa = d * X.dot(s)\\n Hs = X.T.dot(wa)\\n out = Hs\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"724e019749ea86f79608cbcaf5e019a5\",\n \"score\": \"0.5832009\",\n \"text\": \"def get_hessian(self, calc):\\n\\n try:\\n line_idx = next(i for i, line in enumerate(calc.output.file_lines)\\n if 'MASS-WEIGHTED NUCLEAR HESSIAN' in line)\\n except StopIteration:\\n raise CouldNotGetProperty('Hessian not found in the output file')\\n\\n hess_lines = [[] for _ in range(calc.molecule.n_atoms*3)]\\n\\n for hess_line in calc.output.file_lines[line_idx+6:]:\\n\\n if 'NORMAL MODE EIGENVECTORS' in hess_line:\\n break # Finished the Hessian block\\n\\n if 'D' in hess_line:\\n # e.g. 1 4.50945D-01 ...\\n idx = hess_line.split()[0]\\n try:\\n _ = hess_lines[int(idx) - 1]\\n except (ValueError, IndexError):\\n raise CouldNotGetProperty(\\\"Unexpected hessian formating: \\\"\\n f\\\"{hess_line}\\\")\\n\\n values = [float(x) for x in hess_line.replace('D', 'E').split()[1:]]\\n hess_lines[int(idx)-1] += values\\n\\n atom_masses = self._atom_masses_from_hessian(calc)\\n hess = symm_matrix_from_ltril(array=hess_lines)\\n\\n # Un-mass weight from Kamu^-1 to 1\\n mass_arr = np.repeat(atom_masses, repeats=3, axis=np.newaxis) * 1E-3\\n hess *= np.sqrt(np.outer(mass_arr, mass_arr))\\n\\n # and convert from atomic units (Ha/a0^2) to base units (Ha/Å^2)\\n return hess / Constants.a0_to_ang**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd2dd1b398197f57e995074ac2cc6df\",\n \"score\": \"0.5828477\",\n \"text\": \"def get_hessian(self, calc):\\n\\n hess_filename = calc.output.filename\\n\\n if calc.output.filename.endswith('.out'):\\n hess_filename = calc.output.filename.replace('.out', '.hess')\\n\\n if not os.path.exists(hess_filename):\\n raise CouldNotGetProperty('Could not find Hessian file')\\n\\n file_lines = open(hess_filename, 'r', encoding=\\\"utf-8\\\").readlines()\\n\\n hessian_blocks = []\\n start_line = self._start_line_hessian(calc, file_lines)\\n\\n for j, h_line in enumerate(file_lines[start_line:]):\\n\\n if len(h_line.split()) == 0:\\n # Assume we're at the end of the Hessian\\n break\\n\\n # Skip blank lines in the file, marked by one or more fewer items\\n # than the previous\\n if len(h_line.split()) < len(file_lines[start_line+j-1].split()):\\n continue\\n\\n # First item is the coordinate number, thus append all others\\n hessian_blocks.append([float(v) for v in h_line.split()[1:]])\\n\\n n_atoms = calc.molecule.n_atoms\\n hessian = [block for block in hessian_blocks[:3*n_atoms]]\\n\\n for i, block in enumerate(hessian_blocks[3*n_atoms:]):\\n hessian[i % (3 * n_atoms)] += block\\n\\n # Hessians printed in Ha/a0^2, so convert to base Ha/Å^2\\n return np.array(hessian, dtype='f8') / Constants.a0_to_ang**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7791d5c7af8d935991fb8129c2c5e83d\",\n \"score\": \"0.5827034\",\n \"text\": \"def gen_cost(X, y):\\n rows, cols = X.shape\\n w_vars = [Var('w{}'.format(i)) for i in range(cols)]\\n\\n return Function(\\n - 1 / rows * sum(\\n y[i] * ln(y_pred(w_vars, X[i])) + (1 - y[i]) * ln(1 - y_pred(w_vars, X[i]))\\n for i in range(rows))\\n , *w_vars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce25cf9de165e41cf94494b0e849883\",\n \"score\": \"0.5819392\",\n \"text\": \"def make_cost_matrix(profit_matrix, inversion_function):\\n import munkres\\n return munkres.make_cost_matrix(profit_matrix, inversion_function)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d31ecad71d41fec872d73ebc3a46a8\",\n \"score\": \"0.58137983\",\n \"text\": \"def _compute(self):\\n\\n if self.HX is None:\\n if \\\"hessian\\\" in self.calculations_needed():\\n if self.params.cart_hess_read:\\n self.HX = hessian.from_file(self.params.hessian_file) # set ourselves if file\\n _ = self.computer.compute(self.geom, driver=\\\"hessian\\\")\\n self.gX = self.computer.external_gradient\\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\\n self._Hq = self.molsys.hessian_to_internals(self.HX)\\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\\n self.HX = None\\n self.params.cart_hess_read = False\\n self.params.hessian_file = None\\n else:\\n raise RuntimeError(\\n \\\"Optking requested a hessian but was not provided one. \\\" \\\"This could be a driver issue\\\"\\n )\\n elif self.step_num == 0:\\n logger.debug(\\\"Guessing hessian\\\")\\n self._Hq = hessian.guess(self.molsys, guessType=self.params.intrafrag_hess)\\n self.gX = self.computer.compute(self.geom, driver=\\\"gradient\\\", return_full=False)\\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\\n\\n else:\\n logger.debug(\\\"Updating hessian\\\")\\n self.gX = self.computer.compute(self.geom, driver=\\\"gradient\\\", return_full=False)\\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\\n self._Hq = self.history.hessian_update(self._Hq, self.fq, self.molsys)\\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\\n else:\\n result = self.computer.compute(self.geom, driver=\\\"hessian\\\")\\n self.HX = self.computer.external_hessian\\n self.gX = self.computer.external_gradient\\n self.fq = self.molsys.gradient_to_internals(self.gX, -1.0)\\n self._Hq = self.molsys.hessian_to_internals(self.HX)\\n self.HX = None # set back to None\\n self.fq, self._Hq = self.molsys.apply_external_forces(self.fq, self._Hq)\\n self.fq, self._Hq = self.molsys.project_redundancies_and_constraints(self.fq, self._Hq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a663fe202c936733a2156c7b6ce5fb0\",\n \"score\": \"0.5803068\",\n \"text\": \"def trace_hessian(loss, logits, weights):\\n weights = _AsList(weights)\\n\\n # Flatten logits with a well-specified dimension. This assumes any\\n # non-trivial dimension will resolve to 1.\\n # logits_flat = tf.reshape(logits, [-1])\\n\\n loss_logits_hessian = hessians(loss, logits)[0]\\n tr_hessian_pieces = []\\n\\n for w in weights:\\n J = jacobian(logits, w)\\n # Contract along the weight indices\\n # (first index is the logit index)\\n weight_axes = list(range(logits.shape.ndims, J.shape.ndims))\\n JJ = tf.tensordot(J, J, axes=[weight_axes, weight_axes])\\n # Doesn't work for dynamic shape\\n # assert loss_logits_hessian.shape == JJ.shape\\n all_axes = list(range(JJ.shape.ndims))\\n tr_hessian_pieces.append(\\n tf.tensordot(loss_logits_hessian, JJ, axes=[all_axes, all_axes]))\\n\\n return tf.reduce_sum(tr_hessian_pieces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679826fc72cc71ab0384d9f19d3fd04f\",\n \"score\": \"0.57904613\",\n \"text\": \"def finite_difference_hessian(f, q):\\n assert q.size == f.input_dimension()\\n assert f.output_dimension() == 1\\n dt = 1e-4\\n dt_half = dt / 2.\\n H = np.zeros((\\n f.input_dimension(), f.input_dimension()))\\n for j in range(q.size):\\n q_up = copy.deepcopy(q)\\n q_up[j] += dt_half\\n g_up = f.gradient(q_up)\\n q_down = copy.deepcopy(q)\\n q_down[j] -= dt_half\\n g_down = f.gradient(q_down)\\n H[:, j] = (g_up - g_down) / dt\\n return np.matrix(H)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ef85cb7c0f0503ab8f7289b188fb5d\",\n \"score\": \"0.5790247\",\n \"text\": \"def ml_hessian(self, phi, mode, degree):\\n\\n def f_sig(point):\\n \\\"\\\"\\\"\\n sigmoid wrapper\\n :param point: np.array. row feature vector\\n \\\"\\\"\\\"\\n if mode is None:\\n point = np.mat(point).T\\n\\n return self._sig(phi, point)\\n\\n if mode == 1:\\n assert degree is not None\\n hessian = np.sum([self.trans_pt(self.features[pt], mode, degree) *\\n f_sig(self.trans_pt(self.features[pt], mode, degree)) *\\n (1 - f_sig(self.trans_pt(self.features[pt], mode, degree))) *\\n self.trans_pt(self.features[pt], mode, degree).T\\n for pt in range(self.num_pts)], axis=0)\\n\\n elif mode is None:\\n hessian = np.sum([np.mat(self.features[pt]).T *\\n f_sig(self.features[pt]) *\\n (1 - f_sig(self.features[pt])) *\\n np.mat(self.features[pt])\\n for pt in range(self.num_pts)], axis=0)\\n else:\\n assert False\\n\\n print 'hessian: {0}'.format(hessian)\\n return hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966e6c5a06dbefc710080ee1ce23a779\",\n \"score\": \"0.57859784\",\n \"text\": \"def test_cost_hess(self): # noqa\\n expected = np.array(\\n [1.54801e3, 0.01, 0.01, 0.01, 1.56001e3, 0.01, 1.54801e3, 0.01,\\n 1.56001e3, 0.01])\\n hess_p = np.arange(300)\\n actual = _cost_hess(\\n self.input_cost, hess_p, *self.args)[:10]\\n np.testing.assert_allclose(\\n expected, actual,\\n err_msg='Expected {}, but found {}.'.format(expected, actual))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db5e806a7aaf77351b23c1d853d0111\",\n \"score\": \"0.5784397\",\n \"text\": \"def constructCouplingHJ(self):\\n Ntot = np.prod(self.dimList)\\n HJ = sp.csr_matrix((Ntot,Ntot)) \\n \\n if self.JDict is None:\\n return HJ\\n \\n for (i,j), Jij in self.JDict.items():\\n Sx_i,Sy_i,Sz_i = self.SList[i]\\n Sx_j,Sy_j,Sz_j = self.SList[j]\\n HJ += Jij*meV*(Sz_i*Sz_j + Sx_i*Sx_j + Sy_i*Sy_j)\\n \\n return HJ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efd33cc96104b9a34e6931ce5d568d1f\",\n \"score\": \"0.5776834\",\n \"text\": \"def hessian_vector(expr, wrt, size):\\n return _tensor_map(lambda x: hessian_scalar(x, wrt), expr, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357b296a7e67977ea2ff40509d8310e0\",\n \"score\": \"0.5767032\",\n \"text\": \"def _construct_H(tx,ty,ix,iy,H):\\n \\n \\n # H[i,j] = eigenFunction2D(tx[-i],ty[-i],ix,iy)\\n i,j = cuda.grid(2)\\n H[i,j] = exp(1j*2*pi*(ix[j]*tx[i]+iy[j]*ty[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"234682bcbbcf38db9fd5a3ab5f1243af\",\n \"score\": \"0.57555276\",\n \"text\": \"def hessianstructure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c73108f5e06a57d79382c3abd1d1cb4\",\n \"score\": \"0.5749974\",\n \"text\": \"def hessian_vector_product(loss, weights, v):\\n grad = flatten_tensor_list(tf.gradients(loss, weights))\\n grad_v = tf.reduce_sum(grad * tf.stop_gradient(v))\\n H_v = flatten_tensor_list(tf.gradients(grad_v, weights))\\n return H_v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a1a36eb6232afc3f5674a7294dc88d\",\n \"score\": \"0.57403535\",\n \"text\": \"def eval_hess_f(subs_counts, v, Y):\\n Y = algopy.UTPM.init_hessian(Y)\\n retval = eval_f_explicit(subs_counts, v, Y)\\n return algopy.UTPM.extract_hessian(len(Y), retval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af17785821cce9b4865ddbf065b20efe\",\n \"score\": \"0.5739618\",\n \"text\": \"def RHS_fun(j, u_j):\\n\\n s_j = np.zeros(u_j.size) # array containing distribution of heat source\\n\\n \\n return s_j # return s_j = 0 for all i if no heat source required.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332cd6ae1515a2d1117ba34678ae5247\",\n \"score\": \"0.5732078\",\n \"text\": \"def fit(self, x, y):\\n # *** START CODE HERE ***\\n # Initialization Values\\n theta = self.theta\\n nValues = len(x)\\n counter = 0\\n\\n #For Problem 6d\\n # rho = 1/11\\n # kappa = rho/(1-rho)\\n \\n if (theta == None):\\n theta = np.zeros([3, 1])\\n\\n thetaPrev = np.ones([3, 1])\\n \\n \\n while((np.linalg.norm(theta.T - thetaPrev.T,1) > self.eps) and counter < self.max_iter):\\n \\n # Initialize\\n gradientSum = [] #Size of theta-vector\\n hessianSum = [] #Size of Hessian matrix\\n \\n for n in range(0, nValues):\\n \\n #For Problem 6d\\n # omega = 0\\n # if (y[n]==1):\\n # omega = 1/kappa\\n # else:\\n # omega = 1\\n \\n # Compute h_theta(x) as defined in Lecture 3 Notes\\n hThetaX = 1/(1 + np.exp(-np.matmul(theta.T, x[n])))\\n\\n # Compute the gradient of the log likelihood function\\n logGradient = (y[n] - hThetaX)*x[n]\\n # logGradient = omega*logGradient #For Problem 6d\\n \\n gradientSum.append(logGradient)\\n # Compute the Hessian of the log likelihood function wrt to theta1 and theta2 (first and second index of x vector)\\n H = np.array([[x[n, 0], x[n, 1], x[n, 2]], [\\n x[n, 1], x[n, 1]**2, x[n, 1]*x[n, 2]], [x[n, 2], x[n, 2]*x[n, 1], x[n, 2]**2]])\\n \\n \\n logHessian = (1-hThetaX+hThetaX**2)*H\\n # logHessian = omega*logHessian #For Problem 6d\\n \\n hessianSum.append(logHessian)\\n \\n #Compute the sum of the gradient and hessian\\n hessian = -(1/nValues)*sum(hessianSum)\\n gradient = -(1/nValues)*sum(gradientSum)\\n \\n #For Problem 6d\\n # hessian = ( -(1+kappa)/(2*nValues) )*sum(hessianSum)\\n # gradient = ( -(1+kappa)/(2*nValues) )*sum(gradientSum)\\n \\n #Update Theta\\n thetaPrev = theta\\n deltaTheta = self.step_size*(-np.linalg.solve(hessian,gradient))\\n theta = theta.T - deltaTheta\\n theta = theta.T\\n counter = counter + 1\\n # if (counter == 5):\\n # break\\n print((np.linalg.norm(theta.T - thetaPrev.T,1)) )\\n self.theta = theta\\n return theta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6b9a24f42a7876e98b746ba5704e63\",\n \"score\": \"0.5727019\",\n \"text\": \"def mean_hessian(self, x, params):\\n\\n x, params = self._check_inputs(x, params)\\n\\n n_params = self.get_n_params(x)\\n\\n hess = np.zeros((n_params, n_params, x.shape[0]))\\n\\n return hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e775abf8a6476d13402dafa3e8fddbe4\",\n \"score\": \"0.57104474\",\n \"text\": \"def approximate_cost(x, u, Cf):\\n assert x.shape[0] == u.shape[0]\\n assert x.shape[1] == u.shape[1]\\n T = x.shape[0]\\n tau = F.concat((x, u), axis=2)\\n costs = []\\n hessians = []\\n grads = []\\n # for time\\n for t in range(T):\\n tau_t = tau[t]\\n cost = Cf(tau_t) # value of cost function at tau\\n assert list(cost.shape) == [x.shape[1]]\\n # print(\\\"cost.shape\\\", cost.shape)\\n grad = chainer.grad([F.sum(cost)], [tau_t], enable_double_backprop=True)[0] # need hessian\\n hessian = []\\n # for each dimension?\\n for v_i in range(tau.shape[2]):\\n # n_sc\\n grad_line = F.sum(grad[:, v_i])\\n hessian.append(chainer.grad([grad_line], [tau_t])[0])\\n hessian = F.stack(hessian, axis=-1)\\n costs.append(cost)\\n # change to near 0?? Is this necessary ???\\n grads.append(grad - bmv(hessian, tau_t))\\n hessians.append(hessian)\\n costs = F.stack(costs)\\n grads = F.stack(grads)\\n hessians = F.stack(hessians)\\n return hessians, grads, costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f36e1f676f33f62ab10844e1fc30dc\",\n \"score\": \"0.56923467\",\n \"text\": \"def __init__(self):\\n super().__init__(LossHessianStrategy.EXACT, \\\"diag_ggn_exact\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5bf83907b508c1617e91bce2558a65\",\n \"score\": \"0.5688244\",\n \"text\": \"def _update_h_from_cart_h(self, arr: Optional[\\\"Hessian\\\"]) -> None:\\n\\n if arr is None:\\n self._x.h, self.h = None, None\\n\\n else:\\n self._x.h = arr\\n n = len(self)\\n m = self.n_constraints\\n\\n self.h = np.zeros(shape=(n + m, n + m))\\n\\n # Fill in the upper left corner with d^2L/ds_i ds_j\\n # where the second derivative of the constraint is zero\\n self.h[:n, :n] = np.linalg.multi_dot(\\n (self.B_T_inv.T, arr, self.B_T_inv)\\n )\\n\\n # and the d^2L/ds_i dλ_i = -dC_i/ds_i = -1\\n # d^2L/dλ_i dλ_j = 0\\n\\n for i in range(m):\\n self.h[n + i, :] = self.h[:, n + i] = 0.0\\n\\n for i in range(m):\\n self.h[n - m + i, n + i] = self.h[n + i, n - m + i] = -1.0\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360a3a4401510465ccb3fe39c3024abd\",\n \"score\": \"0.56736326\",\n \"text\": \"def construct_reduced_hess(W, T_ti):\\n tmp = np.dot(T_ti.transpose(), W)\\n red_hess = np.dot(tmp, T_ti)\\n\\n return red_hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961caa842851eeff10973e0da5467d6b\",\n \"score\": \"0.5672992\",\n \"text\": \"def test_lr_update_hessian(self):\\n indexed_data, model = simple_mock_data()\\n\\n lr = TrainerLogisticLossWithL2(training_data=indexed_data, initial_model=model, penalty=10, hessian_type=HessianType.FULL)\\n hessian = lr._update_full_hessian(model.theta)\\n\\n expected_hessian = np.array(\\n [\\n [10.076006603, 0.007826689207, 0.16766658587],\\n [0.007826689207, 10.003913344, 0.023480067621],\\n [0.16766658587, 0.023480067621, 10.382293306],\\n ]\\n )\\n\\n self.assertTrue(\\n matrices_almost_equal(hessian, expected_hessian),\\n f\\\"Hessian computation is correct. Actual {hessian} == Expected {expected_hessian}.\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6590e6ac05571d3c1164c02d96188c\",\n \"score\": \"0.567169\",\n \"text\": \"def gradient(self, X_tr, y_tr, w):\\n n = np.shape(X_tr)[0]\\n\\n Xe = np.c_[X_tr, np.ones(n)] # Append X_tr with ones to include bias term\\n\\n eta = sigmoid(np.dot(Xe, w.T))\\n\\n grad = np.dot(Xe.T, np.reshape((y_tr - eta), (n, 1)))\\n\\n sigma = np.diag(eta * (1 - eta))\\n\\n hessian = np.dot(- Xe.T, np.dot(sigma, Xe))\\n\\n return grad, hessian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92c258fddc4f34716d798f51b160693\",\n \"score\": \"0.56699675\",\n \"text\": \"def test_caching_param_shift_hessian(self, num_params, tol):\\n dev = qml.device(\\\"default.qubit\\\", wires=2)\\n params = tf.Variable(np.arange(1, num_params + 1) / 10, dtype=tf.float64)\\n\\n N = params.shape[0]\\n\\n def cost(x, cache):\\n with qml.queuing.AnnotatedQueue() as q:\\n qml.RX(x[0], wires=[0])\\n qml.RY(x[1], wires=[1])\\n\\n for i in range(2, num_params):\\n qml.RZ(x[i], wires=[i % 2])\\n\\n qml.CNOT(wires=[0, 1])\\n qml.var(qml.PauliZ(0) @ qml.PauliX(1))\\n\\n tape = qml.tape.QuantumScript.from_queue(q)\\n return execute(\\n [tape], dev, gradient_fn=param_shift, cache=cache, interface=\\\"tf\\\", max_diff=2\\n )[0]\\n\\n # No caching: number of executions is not ideal\\n with tf.GradientTape() as t2:\\n with tf.GradientTape() as t1:\\n res = cost(params, cache=False)\\n grad = t1.gradient(res, params)\\n hess1 = t2.jacobian(grad, params)\\n\\n if num_params == 2:\\n # compare to theoretical result\\n x, y, *_ = params * 1.0\\n expected = np.array(\\n [\\n [2 * np.cos(2 * x) * np.sin(y) ** 2, np.sin(2 * x) * np.sin(2 * y)],\\n [np.sin(2 * x) * np.sin(2 * y), -2 * np.cos(x) ** 2 * np.cos(2 * y)],\\n ]\\n )\\n assert np.allclose(expected, hess1, atol=tol, rtol=0)\\n\\n nonideal_runs = dev.num_executions\\n\\n # Use caching: number of executions is ideal\\n dev._num_executions = 0\\n with tf.GradientTape() as t2:\\n with tf.GradientTape() as t1:\\n res = cost(params, cache=True)\\n grad = t1.gradient(res, params)\\n hess2 = t2.jacobian(grad, params)\\n\\n assert np.allclose(hess1, hess2, atol=tol, rtol=0)\\n\\n expected_runs_ideal = 1 # forward pass\\n expected_runs_ideal += 2 * N # Jacobian\\n expected_runs_ideal += N + 1 # Hessian diagonal\\n expected_runs_ideal += 4 * N * (N - 1) // 2 # Hessian off-diagonal\\n assert dev.num_executions == expected_runs_ideal\\n assert expected_runs_ideal < nonideal_runs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea591869120d64c3eeba01aeda8467f2\",\n \"score\": \"0.5663585\",\n \"text\": \"def sparse_h(b, w ,X):\\n act = g( (X.dot(w)) + b)\\n return act.reshape((X.shape[0], 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969d70426313947f56fd4a1aefe8069e\",\n \"score\": \"0.56634766\",\n \"text\": \"def generate_Hessian_from_system():\\n print 'Not implemented'\\n return 'You want a Lagrangian, not this.'\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993681,"cells":{"query_id":{"kind":"string","value":"200254087000ab1eccf0b7aeca357dcc"},"query":{"kind":"string","value":"Generate network layers based on configs"},"positive_passages":{"kind":"list like","value":[{"docid":"6c90944aa679d129f8a5e0ecfcb59f05","score":"0.0","text":"def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):\n downsample = None\n expanded_in_channels = in_channels * self.block.expansion\n expanded_out_channels = out_channels * self.block.expansion\n if expanded_in_channels != expanded_out_channels:\n downsample = nn.Sequential(\n nn.Conv2d(expanded_in_channels, expanded_out_channels, kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(expanded_out_channels),\n )\n\n layers = []\n layers.append(self.block(in_channels, out_channels, stride, downsample))\n for i in range(1, num_blocks):\n layers.append(self.block(expanded_out_channels, out_channels))\n\n return nn.Sequential(*layers)","title":""}],"string":"[\n {\n \"docid\": \"6c90944aa679d129f8a5e0ecfcb59f05\",\n \"score\": \"0.0\",\n \"text\": \"def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):\\n downsample = None\\n expanded_in_channels = in_channels * self.block.expansion\\n expanded_out_channels = out_channels * self.block.expansion\\n if expanded_in_channels != expanded_out_channels:\\n downsample = nn.Sequential(\\n nn.Conv2d(expanded_in_channels, expanded_out_channels, kernel_size=1, stride=stride, bias=False),\\n nn.BatchNorm2d(expanded_out_channels),\\n )\\n\\n layers = []\\n layers.append(self.block(in_channels, out_channels, stride, downsample))\\n for i in range(1, num_blocks):\\n layers.append(self.block(expanded_out_channels, out_channels))\\n\\n return nn.Sequential(*layers)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c03843b0a4bb02acb8811d8b04215eaa","score":"0.738548","text":"def _make_layers(self, configs: list):\r\n layers = []\r\n for config in configs:\r\n layer = self._make_layer(config['class'], config['args'])\r\n layers.append(layer)\r\n\r\n self.net = Sequential(*layers).cuda()","title":""},{"docid":"29792cf738fab7f5fbe76237d925fae6","score":"0.7220404","text":"def build_network(self): \n self.setup_cnn_layers() \n self.setup_pose_emb_layer() \n self.setup_smpl_layers()\n self.setup_cam_mesh_relation_module()\n self.setup_renderer_layer()","title":""},{"docid":"1b18c0794c768ff925058357f241b8f2","score":"0.6897156","text":"def _build(self):\n for (lt, ls, lo), d, li in zip(self.layer_config, self.dropouts, range(len(self.layer_config))):\n if li == 0:\n in_dim = self.input_dim\n si = True\n else:\n in_dim = self.layer_config[li-1][1]\n si = False\n if li == len(self.layer_config):\n out_dim = self.output_dim\n else:\n out_dim = ls\n \n if lt.lower() in (\"gc\", \"graph\", \"graphconvolution\"):\n self.layers.append(GraphConvolution(input_dim=in_dim, output_dim=out_dim,\n placeholders=self.placeholders, adjacency=lo, \n act=tf.nn.relu, dropout=d, sparse_inputs=si, \n logging=self.logging))\n elif lt.lower() in ('d', 'dense'):\n self.layers.append(Dense(input_dim=in_dim, output_dim=out_dim,\n placeholders=self.placeholders, act=tf.nn.relu,\n dropout=d, sparse_inputs=si, logging=self.logging))","title":""},{"docid":"a311d027a808cf77b29138b6f595679b","score":"0.67131996","text":"def _build_networks(\n self,\n net_config: NetworkConfig = None,\n conv_config: ConvolutionConfig = None,\n ):\n if net_config is None:\n net_config = self.net_config\n\n if conv_config is None and self.config.use_conv_net:\n conv_config = self.conv_config\n\n xshape = (self.xdim, 2)\n if self.config.use_conv_net:\n xshape = (*self.lattice_shape[1:], 2)\n\n kinit = None\n if self.config.zero_init:\n kinit = 'zeros'\n\n # ====\n # xNet configuration\n xnet_cfg = {\n 'factor': 2.0,\n 'net_config': net_config,\n 'conv_config': conv_config,\n 'kernel_initializer': kinit,\n 'lattice_shape': self.lattice_shape,\n 'input_shapes': {\n 'x': xshape, 'v': (self.xdim,), 't': (2,)\n }\n }\n\n # ====\n # xNet configuration\n vnet_cfg = {\n 'factor': 1.0,\n 'net_config': net_config,\n 'conv_config': None,\n 'kernel_initializer': kinit,\n 'lattice_shape': self.lattice_shape,\n 'input_shapes': {\n 'x': (self.xdim,), 'v': (self.xdim,), 't': (2,)\n }\n }\n\n if self.config.separate_networks:\n # ====\n # Build separate networks\n vnet = [\n get_gauge_network(**vnet_cfg, name=f'VNet{i}')\n for i in range(self.config.num_steps)\n ]\n\n xnet = [\n get_gauge_network(**xnet_cfg, name=f'XNet{i}')\n for i in range(self.config.num_steps)\n ]\n\n else:\n # ====\n # Build single network\n vnet = get_gauge_network(**vnet_cfg, name='VNet')\n xnet = get_gauge_network(**xnet_cfg, name='XNet')\n\n return xnet, vnet","title":""},{"docid":"3cded75c6a54310788fa4e24959a33a3","score":"0.6697693","text":"def _build_network(self):\n\n network = caffe_pb2.NetParameter()\n network.name = 'inception-%s' % self.task_name\n\n layers = []\n\n name_generator = self._connection_name_generator()\n\n train_layer = self._make_train_layer(network)\n layers.append(train_layer)\n test_layer = self._make_test_layer(network)\n layers.append(test_layer)\n\n conv1 = self._make_conv_layer(\n network, kernel_size=7, stride=2, num_output=64, pad=3)\n layers.append(conv1)\n relu1 = self._make_relu_layer(network)\n layers.append(relu1)\n\n maxpool1 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\n layers.append(maxpool1)\n lrn1 = self._make_lrn_layer(network)\n layers.append(lrn1)\n\n conv2 = self._make_conv_layer(\n network, kernel_size=1, num_output=64)\n layers.append(conv2)\n relu2 = self._make_relu_layer(network)\n layers.append(relu2)\n\n conv3 = self._make_conv_layer(network, kernel_size=3, num_output=64, pad=1)\n layers.append(conv3)\n relu3 = self._make_relu_layer(network)\n layers.append(relu3)\n\n lrn2 = self._make_lrn_layer(network)\n layers.append(lrn2)\n maxpool2 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\n layers.append(maxpool2)\n\n inception1 = self._make_inception(\n network, 64, 96, 128, 16, 32, 32, name_generator, lr=self.spearmint_args['inc1-2_lr'])\n layers += inception1\n\n inception2 = self._make_inception(\n network, 128, 128, 192, 32, 96, 64, name_generator, lr=self.spearmint_args['inc1-2_lr'])\n layers += inception2\n\n maxpool3 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\n layers.append(maxpool3)\n\n inception3 = self._make_inception(\n network, 192, 96, 208, 16, 48, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\n layers += inception3\n\n inception4 = self._make_inception(\n network, 160, 112, 224, 24, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\n layers += inception4\n\n inception5 = self._make_inception(\n network, 128, 128, 256, 24, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\n layers += inception5\n\n inception6 = self._make_inception(\n network, 112, 144, 288, 32, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\n layers += inception6\n\n inception7 = self._make_inception(\n network, 256, 160, 320, 32, 128, 128, name_generator, lr=self.spearmint_args['inc3-7_lr'])\n layers += inception7\n\n maxpool4 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\n layers.append(maxpool4)\n\n inception8 = self._make_inception(\n network, 256, 160, 320, 32, 128, 128, name_generator, lr=self.spearmint_args['inc8-9_lr'])\n layers += inception8\n\n inception9 = self._make_inception(\n network, 384, 192, 384, 48, 128, 128, name_generator, lr=self.spearmint_args['inc-8-9_lr'])\n layers += inception9\n\n avgpool = self._make_avgpool_layer(network, kernel_size=7)\n layers.append(avgpool)\n\n dropout = self._make_dropout_layer(network, dropout_ratio=0.4)\n layers.append(dropout)\n\n aux1 = self._make_aux(network, 1, name_generator)\n layers += aux1\n\n aux2 = self._make_aux(network, 2, name_generator)\n layers += aux2\n\n final_layers = self._make_final_classifiers(network, name_generator)\n layers += final_layers\n\n connections = [\n (conv1.name, (conv1.top, relu1.bottom, relu1.top, maxpool1.bottom)),\n (maxpool1.name, (maxpool1.top, lrn1.bottom)),\n (lrn1.name, (lrn1.top, conv2.bottom)),\n (conv2.name, (conv2.top, relu2.bottom, relu2.top, conv3.bottom)),\n (conv3.name, (conv3.top, relu3.bottom, relu3.top, lrn2.bottom)),\n (lrn2.name, (lrn2.top, maxpool2.bottom)),\n (maxpool2.name, (maxpool2.top, inception1[0].bottom)),\n (inception1[-1].name, (inception1[-1].top, inception2[0].bottom)),\n (inception2[-1].name, (inception2[-1].top, maxpool3.bottom)),\n (maxpool3.name, (maxpool3.top, inception3[0].bottom)),\n (inception3[-1].name, (inception3[-1].top, inception4[0].bottom, aux1[0].bottom)),\n (inception4[-1].name, (inception4[-1].top, inception5[0].bottom)),\n (inception5[-1].name, (inception5[-1].top, inception6[0].bottom)),\n (inception6[-1].name, (inception6[-1].top, inception7[0].bottom, aux2[0].bottom)),\n (inception7[-1].name, (inception7[-1].top, maxpool4.bottom)),\n (maxpool4.name, (maxpool4.top, inception8[0].bottom)),\n (inception8[-1].name, (inception8[-1].top, inception9[0].bottom)),\n (inception9[-1].name, (inception9[-1].top, avgpool.bottom)),\n (avgpool.name, (avgpool.top, dropout.bottom)),\n (dropout.name, (dropout.top, final_layers[0].bottom)),\n ]\n\n for connection in connections:\n self._tie(connection, name_generator)\n\n conv1.bottom.append('data')\n\n for pos, layer in enumerate(layers):\n print layer\n layer.name += '_%d' % pos\n\n return network","title":""},{"docid":"235fa73f887c8f20d1e0a381adab5d40","score":"0.66376126","text":"def setup_layers(self, params):\n\n # Input and encoding layers\n in_shape = [None]\n if isinstance(params.in_size, (list, tuple)):\n in_shape.extend(params.in_size)\n else:\n in_shape.extend([params.in_size, 1])\n\n encode_layers = [\n tf.placeholder(\n \"float\",\n name=\"input\",\n shape=in_shape\n )\n ]\n\n for idx, num_nodes in enumerate(params.encode_nodes):\n encode_layers.append(\n self.make_conv_layer(\n encode_layers[-1],\n num_nodes,\n 'encode_{}'.format(idx),\n params.encode_params\n )\n )\n\n # Bottleneck and decoding layers\n decode_layers = [\n self.make_conv_layer(\n encode_layers[-1],\n params.bottleneck_dim,\n 'bottleneck',\n params.bottleneck_params\n )\n ]\n\n for idx, num_nodes in enumerate(params.decode_nodes):\n decode_layers.append(\n self.make_deconv_layer(\n decode_layers[-1],\n num_nodes,\n 'decode_{}'.format(idx),\n params.decode_params\n )\n )\n\n # Standardizing the input/output layer names\n in_layer = encode_layers[0]\n\n target_layer = tf.placeholder(\n \"float\",\n name=\"target\",\n shape=in_layer.get_shape().as_list()\n )\n\n out_layer = tops.fit_to_shape(\n self.make_deconv_layer(\n decode_layers[-1],\n 1,\n 'output_layer',\n params.output_params\n ),\n target_layer.get_shape().as_list()\n )\n\n loss = tops.loss_mse(\n target_layer,\n out_layer\n )\n\n return in_layer, out_layer, target_layer, loss","title":""},{"docid":"5c15dd36695bb54001dbdecefa20caa6","score":"0.65856797","text":"def generate_net(self):\n span = 1\n #Input Layer\n inputs = np.zeros(self.layer_sizes[0])\n biases = np.random.uniform(-span,span,self.layer_sizes[0])\n self.net.add_node_layer(inputs, biases)\n #Hidden Layer and Output Layer\n for i in range(1,self.length):\n data = np.zeros(self.layer_sizes[i])\n biases = np.random.uniform(-span,span,self.layer_sizes[i])\n connections = np.random.uniform(-span,span,(self.layer_sizes[i],self.layer_sizes[i-1]))\n self.net.add_node_layer(data, biases)\n self.net.add_connection_layer(connections)\n return self.net","title":""},{"docid":"77481307e8ee6b679b8bbc8f7c15802c","score":"0.6505242","text":"def init_network_from_config(self):\n nn_modules = []\n orig_net = self.config.get_value('network')\n net = copy.deepcopy(orig_net)\n for layer_name, layer_desc in net.items():\n assert isinstance(layer_desc, dict)\n assert 'class' in layer_desc, 'class is not defined for layer: {}'.format(layer_name)\n layer_class = layer_desc['class']\n layer_module = nn_module.get_module(layer_class)\n del layer_desc['class']\n nn_modules.append((layer_module, layer_desc))\n self.net_model = model.NNModel(nn_modules)","title":""},{"docid":"be2dd1312c1633b1334eaa190707e729","score":"0.6453071","text":"def buildNetwork(mode='train'):\n \n if mode == 'train':\n batch_size = 360# this is n rows expected by placeholder - files produce multiple rows\n \n elif mode == 'test':\n batch_size = 50\n \n \n \n nBatch = batch_size\n patch_size = 5\n # depth = 1\n num_hidden = 560\n nTest = 1216#testFiles.shape[0]\n nTrainBatch = round(nBatch*(1-validProp))\n nValidBatch = round(nBatch*validProp)\n nValidBatch = 160\n # nValid = cValid.shape[0]*0.5\n nValid = 200\n graph = tf.Graph()\n \n \n with graph.as_default():\n\n # Input data.\n trainSubset = tf.placeholder(tf.float32, shape=(nTrainBatch, \n zSize, ySize, xSize, num_channels), \n name='PHTrain')\n \n trainSubsetLabels = tf.placeholder(tf.float32, shape=(nTrainBatch,2), \n name='PHTrainLabs')\n \n validSubset = tf.placeholder(tf.float32, shape=(nValidBatch, \n zSize, ySize, xSize, num_channels), \n name='PHValid')\n \n validSubsetLabels = tf.placeholder(tf.float32, shape=(nValidBatch,2), \n name='PHValidLabs')\n \n testDataset = tf.placeholder(tf.float32, shape = (nTest, \n zSize, ySize, xSize, num_channels), \n name='PHTest')\n \n LR = tf.Variable(1)\n outChans = 80\n \n # Layer sizes not defined in functions\n layer4_weights = tf.Variable(tf.truncated_normal(\n [num_hidden, num_labels], stddev=0.1))\n \n layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n \n # Model.\n def model(smData, outChans=80, patchSize=5,\n fcNum = num_hidden, mName=''):\n \n act1 = convLayer(smData, chanIn=1, chanOut=outChans,\n strides=[1, 2, 2, 2, 1], \n patchSize=patchSize, \n layerName=mName+'conv1', act='tanh')\n \n act2 = convLayer(act1, chanIn=outChans, chanOut=outChans, \n strides=[1, 2, 2, 2, 1],\n patchSize=patchSize, \n layerName=mName+'conv2', act='drop')\n \n # Reshape for FC\n shape = act2.get_shape().as_list()\n linSize = shape[1] * shape[2] * shape[3] * shape[4]\n reshape = tf.reshape(act2, [shape[0], linSize])\n \n act3 = fcLayer(reshape, chanIn=linSize, chanOut=fcNum, \n layerName=mName+'fc1', act='drop')\n \n output = tf.matmul(act3, layer4_weights) + layer4_biases\n \n return output\n \n # Training computation.\n with tf.name_scope(\"Models\"): \n logits = model(trainSubset, outChans=outChans, patchSize=patch_size, \n fcNum=num_hidden, mName='train') \n logitsV = model(validSubset, outChans=outChans, patchSize=patch_size, \n fcNum=num_hidden, mName='valid')\n \n with tf.name_scope(\"xent\"): \n \n loss = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits(labels=trainSubsetLabels, \n logits=logits))\n \n lossV = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits(labels=validSubsetLabels, \n logits=logitsV))\n \n # Optimizer.\n with tf.name_scope(\"train\"):\n optimizer = tf.train.GradientDescentOptimizer(0.00001).minimize(loss)\n \n # Predictions for the training, validation, and test data.\n with tf.name_scope(\"Preds\"):\n train_prediction = tf.nn.softmax(logits)\n valid_prediction = tf.nn.softmax(logitsV)\n \n test_prediction = tf.nn.softmax(model(testDataset, outChans=outChans, \n patchSize=patch_size, \n fcNum=num_hidden))\n \n with tf.name_scope(\"Accuracy\"):\n \n cp = tf.equal(tf.argmax(logits,1), tf.argmax(trainSubsetLabels,1))\n trAcc2 = tf.reduce_mean(tf.cast(cp, tf.float32))\n \n cp2 = tf.equal(tf.argmax(logitsV,1), tf.argmax(validSubsetLabels,1))\n vAcc2 = tf.reduce_mean(tf.cast(cp2, tf.float32))\n \n # Add ops to save and restore all the variables.\n saver = tf.train.Saver()\n # Add summary writer\n \n tf.summary.scalar(\"va\", vAcc2)\n # Create a summary to monitor accuracy tensor\n tf.summary.scalar(\"ta\", trAcc2)\n variable_summaries(trAcc2, 'TrainingAcc')\n variable_summaries(vAcc2, 'ValidAcc')\n variable_summaries(lossV, 'Trainingloss')\n variable_summaries(loss, 'Validloss')\n merged_summary = tf.summary.merge_all()","title":""},{"docid":"df47338cc7a85c92b0e2ba989985737d","score":"0.64382416","text":"def _setUpNetwork(self):\n\n #so initally we want 50 subclasses so 50 neurons amongst 784 features\n\n # s is 50 R is 784\n\n W_1_0 = np.matrix(np.random.uniform(low = -1, high = 1,size = (50,784))) # 50 X 784\n layer1 = {'weights': W_1_0.T,\n 'transFunc':'Compet',\n 'bias':0}\n W_2_0 = layer2Weight()\n layer2 = {'weights': W_2_0,\n 'transFunc':'Purlin',\n 'bias':0}\n\n #popualte the layers\n self.layers.append(layer1)\n self.layers.append(layer2)\n\n\n print(W_1_0.shape)\n print(W_2_0.shape)\n #continue to append\n #self.plotWeights()","title":""},{"docid":"488f5e06129a538997961dca3b61d727","score":"0.6435688","text":"def _build_layers(self):\n layers = []\n n_layers = len(self._topology)\n for i, (name, params) in enumerate(self._topology):\n # Construct Keras layer to be built from string 'name' which is\n # part of topology list.\n layer_cls = getattr(keras.layers, name)\n\n # In order to get the accepteble arguments for each layer we need\n # to use inspect because of keras's legacy support decorators.\n try:\n args = getargspec(layer_cls.__init__._original_function)[0]\n except AttributeError:\n args = getargspec(layer_cls)[0]\n\n # Add activation and other input arguments to params dict when\n # necessary. For example, MaxPooling1D does not\n # accept the argument 'kernel_initializer'.\n if 'kernel_initializer' in args:\n params['kernel_initializer'] = self._rnd_init\n\n # The 'return_squences' argument is necessary for stacking RNNs.\n if 'return_sequences' in args and i < n_layers - 1:\n params['return_sequences'] = True\n\n # Create Keras layer, but make sure that the input shape is\n # passed to the first later.\n if i == 0:\n layer = layer_cls(**params, input_shape=self._input_shape)\n else:\n layer = layer_cls(**params)\n layers.append(layer)\n\n # Add at least a dense layer for output with output shape of the\n # time series dimensionality.\n params = {}\n params['units'] = self._input_shape[1]\n if not self._topology:\n params['input_shape'] = self._input_shape\n layers.append(keras.layers.Dense(**params))\n\n return layers","title":""},{"docid":"601f9ed2dba692e7915c5361305b55b5","score":"0.64308953","text":"def setup_networks(self):\n\t\tself.encoder = Encoder(self.image_dim, self.label_dim, self.z_dim)\n\t\tself.decoder = Decoder(self.image_dim, self.label_dim, self.z_dim)\n\t\t# using GPUs for faster training of the networks\n\t\tif self.use_cuda:\n\t\t self.cuda()","title":""},{"docid":"c0d6ff6920c26c44f8d0fdc14914272f","score":"0.63918954","text":"def _create_network(self):\n\n #1st convolution layer w/ 64 5x5 kernels and 2x2 maxpool\n layer1_conv_W = weight_variable([5,5,1,64], name=\"layer1_W\")\n layer1_conv_b = bias_variable([64], name=\"layer1_b\")\n layer1_conv = tf.nn.relu(conv2d(self.x, layer1_conv_W, name='layer1_conv') + layer1_conv_b, name='layer1_relu')\n tf.histogram_summary('layer1_relu', layer1_conv)\n layer1_pool = max_pool_2x2(layer1_conv, name='layer1_pool')\n # layer1_drop = tf.nn.dropout(layer1_pool, self.keep_prob, name='layer1_drop')\n\n #2nd conv layer w/ 128 5x5 kernels and 2x2 maxpool\n layer2_conv_W = weight_variable([5,5,64,128], name='layer2_W')\n layer2_conv_b = bias_variable([128], name='layer2_b')\n layer2_conv = tf.nn.relu(conv2d(layer1_pool, layer2_conv_W) + layer2_conv_b, name='layer2_relu')\n tf.histogram_summary('layer2_relu', layer2_conv)\n layer2_pool = max_pool_2x2(layer2_conv, name='layer2_pool')\n\n #3rd conv layer w/ 256 kernels and 2x2 maxpool\n image_size_8 = self.image_size / 8\n layer3_conv_W = weight_variable([5,5,128,256], name='layer3_W')\n layer3_conv_b = bias_variable([256], name='layer3_b')\n layer3_conv = tf.nn.relu(conv2d(layer2_pool, layer3_conv_W) + layer3_conv_b, name='layer3_relu')\n tf.histogram_summary('layer3_relu', layer3_conv)\n layer3_pool = max_pool_2x2(layer3_conv, name='layer3_pool')\n # layer3_drop = tf.nn.dropout(layer3_pool, self.keep_prob, name='layer3_drop')\n\n #Flatten output into 1D tensor for input into fully connected layer\n layer3_flat = tf.reshape(layer3_pool, [-1, image_size_8 * image_size_8 * 256], name='layer2_pool_flat')\n\n #Fully connected layer; output is 300 neurons\n layer4_full_W = weight_variable(shape=[image_size_8 * image_size_8 * 256, 300], name='layer4_W')\n layer4_full_b = bias_variable([300], name='layer4_b')\n layer4_full = tf.nn.relu(tf.matmul(layer3_flat, layer4_full_W, name='layer4_matmull') + layer4_full_b, name='layer4_full')\n layer4_drop = tf.nn.dropout(layer4_full, self.keep_prob, name='layer4_drop')\n\n #Output layer of 2 neurons \n layer5_soft_W = weight_variable(shape=[300, OUTPUT_SIZE], name='layer5_W')\n layer5_soft_b = bias_variable([OUTPUT_SIZE], name='layer5_b')\n layer5_soft = tf.nn.relu(tf.matmul(layer4_drop, layer5_soft_W) + layer5_soft_b)\n\n #Cost and training calculations\n cross_entropy = -tf.reduce_sum(self.y_*tf.log(layer5_soft + 1e50))\n tf.scalar_summary = ('cost', cross_entropy)\n train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cross_entropy)\n init_ops = tf.initialize_all_variables()\n\n return train_step, layer5_soft, init_ops","title":""},{"docid":"de342aecfb9bccfe506148aa43550ac4","score":"0.6364454","text":"def _build_net(self):\n output = tf.layers.conv2d(\n inputs=self.input_ph,\n filters=self.config.filters[0],\n strides=self.config.strides[0],\n kernel_size=self.config.kernel[0],\n activation=tf.nn.relu,\n name='c0')\n\n for i, layer in enumerate(self.config.filters[1:]):\n output = tf.layers.conv2d(\n inputs=output,\n filters=layer,\n strides=self.config.strides[i+1],\n kernel_size=self.config.kernel_size[i+1],\n activation=tf.nn.relu,\n name='c{}'.format(i+1))\n\n # Fully connected layers\n output = tf.layers.flatten(output)\n for i, layer in enumerate(self.config.fc_layers):\n output = tf.layers.dense(\n output,\n layer,\n activation=tf.nn.relu,\n name='fc{}'.format(i))\n\n self.output = tf.layers.dense(\n output,\n self.dataset.n_labels,\n activation=tf.nn.softmax,\n name='output')","title":""},{"docid":"5c212e665ea681e4161bad558ff93564","score":"0.63355863","text":"def _build_networks(self):\n\n # set up the optimizers\n d_opt, g_opt = self._build_optimizers()\n\n # now build all of the networks\n for i in range(self.num_levels):\n Gz, d_loss, g_loss = self._build_network(self.X, self.Z, self.alpha)\n\n d_vars, g_vars = self.get_training_variables(i)\n d_solver = d_opt.minimize(d_loss, var_list=d_vars)\n g_solver = g_opt.minimize(g_loss, var_list=g_vars,\n global_step=self.global_step)\n\n # self._build_summaries(Gz, d_loss, g_loss)\n\n self.networks.append((Gz, d_loss, g_loss, d_solver, g_solver))\n\n self.expand()","title":""},{"docid":"6ed391ea6babc8c6b7209079286b2ee8","score":"0.6328495","text":"def build_network(self):\n\n # Create output neurons\n for ii in range(self.output_size):\n label = self.output_labels[ii]\n\n self.g_dict[self.node_id] = {\n 'type': 'output',\n 'label': label,\n 'connections': None,\n 'function': ActivationFunction(function=self.o_function)\n }\n\n self.node_id += 1\n\n # Create input neurons and connect them to the output neurons\n for ii in range(self.input_size):\n label = self.input_labels[ii]\n cons = {}\n\n for nn in self.g_dict.keys():\n if self.g_dict[nn]['type'] == 'output':\n cons[nn] = {\n 'weight': random.gauss(0, 1),\n 'enabled': True\n }\n\n self.g_dict[self.node_id] = {\n 'type': 'input',\n 'label': label,\n 'connections': cons,\n 'function': ActivationFunction(function='identity')\n }\n\n self.node_id += 1\n\n return","title":""},{"docid":"96e9b1f67706cf0b6460d7ca3c4f6e88","score":"0.6288556","text":"def generate_netlist(self):\n pass","title":""},{"docid":"6aac05f0370cbb446ad214cd12274ba0","score":"0.62730634","text":"def _write_create_layers(nw, of):\n to_write = \"\"\"std::vector<Layer *> create_layers(void)\n{\n std::cout << \"Creating the layers...\" << std::endl;\n\n std::vector<Layer *> layers;\"\"\" + os.linesep + os.linesep\n\n for i, layer in enumerate(nw.layers):\n layer_id = \"layer_\" + str(i)\n to_write += \" Layer *\" + layer_id + \" = new Layer();\" + os.linesep\n\n to_write += \" for (unsigned int i = 0; i < \" + str(len(layer))\n to_write += \"; i++)\" + os.linesep\n to_write += \" {\" + os.linesep\n\n # neuron_id is like: neuron_0_1\n neuron_id = \"\\\"neuron_\" + str(i) + \"_\\\"\" + \" + std::to_string(i)\"\n is_input = i == 0\n is_output = i == len(nw.layers) - 1\n to_write += \" Neuron n(\" + neuron_id + \", \" + str(is_input).lower()\n to_write += \", \" + str(is_output).lower() + \");\" + os.linesep\n\n to_write += \" \" + layer_id + \"->add_neuron(n);\" + os.linesep\n to_write += \" }\" + os.linesep\n to_write += \" layers.push_back(\" + layer_id + \");\" + os.linesep\n to_write += os.linesep\n to_write += \" return layers;\" + os.linesep\n to_write += \"}\" + os.linesep\n\n of.write(to_write)","title":""},{"docid":"f01618beda122392316035b947672416","score":"0.6265136","text":"def build_network():\n if FLAGS.model_architecture == 'mobilenet_v2':\n network_func = functools.partial(\n mobilenetv2_model.mobilenet_v2,\n expansion_factor=FLAGS.expansion_factor)\n elif FLAGS.model_architecture == 'mobilenet_v1':\n network_func = functools.partial(mobilenetv1_model.mobilenet_v1)\n elif FLAGS.model_architecture == 'resnet':\n prune_first_layer = FLAGS.first_layer_sparsity != 0.\n network_func = functools.partial(\n resnet_model.resnet_v1_,\n resnet_depth=FLAGS.resnet_depth,\n init_method=FLAGS.init_method,\n end_sparsity=FLAGS.end_sparsity,\n prune_first_layer=prune_first_layer)\n elif FLAGS.model_architecture.startswith('vgg'):\n network_func = functools.partial(\n vgg.vgg,\n vgg_type=FLAGS.model_architecture,\n init_method=FLAGS.init_method,\n end_sparsity=FLAGS.end_sparsity)\n else:\n raise ValueError('Unknown archiecture ' + FLAGS.archiecture)\n prune_last_layer = FLAGS.last_layer_sparsity != 0.\n network = network_func(\n num_classes=FLAGS.num_label_classes,\n # TODO remove the pruning_method option.\n pruning_method='threshold',\n width=width,\n prune_last_layer=prune_last_layer,\n data_format=FLAGS.data_format,\n weight_decay=FLAGS.weight_decay)\n\n is_training = (mode == tf_estimator.ModeKeys.TRAIN)\n if FLAGS.use_batch_statistics:\n is_training = True\n return network(inputs=features, is_training=is_training)","title":""},{"docid":"af4d1d775ef70676043620b9daeeddfd","score":"0.62396497","text":"def train_comparison_networks_CD(conv_layers,dense_layers,train_generator,validation_generator,nbr_epochs=80,network_name=\"CD_normal\",path=\"\",input_shape=(150,150,3)):\n for i in range(len(conv_layers)):\n nbr_layers_added = i+1\n model = get_normal_network_CD(conv_layers[0:nbr_layers_added],dense_layers=[],input_shape=input_shape)\n model.compile(loss='binary_crossentropy',\n optimizer=optimizers.RMSprop(lr=1e-4),\n metrics=['acc'])\n model.summary()\n history = model.fit_generator(\n train_generator,\n steps_per_epoch=100,\n epochs=nbr_epochs,\n validation_data=validation_generator,\n validation_steps=50)\n model_name = network_name+\"_conv_\"+str(nbr_epochs)+\"e_\"+str(nbr_layers_added)+\"L\"\n file_name = model_name+\"results.txt\"#change so that this\n print_results_to_file(model,history,file_name,model_name,path=path)\n model_name = path+model_name+\".h5\"\n model.save(model_name)\n ### Then add the dense layers\n for i in range(len(dense_layers)):\n nbr_layers_added = i+1\n model = get_normal_network_CD(conv_layers,dense_layers[0:nbr_layers_added],input_shape=input_shape)\n model.compile(loss='binary_crossentropy',\n optimizer=optimizers.RMSprop(lr=1e-4),\n metrics=['acc'])\n model.summary()\n history = model.fit_generator(\n train_generator,\n steps_per_epoch=100,\n epochs=nbr_epochs,\n validation_data=validation_generator,\n validation_steps=50)\n model_name = network_name+\"_dense_\"+str(nbr_epochs)+\"e_\"+str(nbr_layers_added)+\"L\"\n file_name = model_name+\"results.txt\"#change so that this\n print_results_to_file(model,history,file_name,model_name,path=path)\n model_name = path+model_name+\".h5\"\n model.save(model_name)\n\n return \"Success\"","title":""},{"docid":"86f3ed5c0bc25c70218d6fc482398f0b","score":"0.623451","text":"def network(self):\n nbr_patches=cfg.nbr_patches\n weights = []\n tensor = None\n padding='SAME'\n\n #### with one image as input\n for i in range(cfg.nbr_layers):\n ## Parameters for first layer\n if i==0:\n filter,bias_size=[3,3,cfg.channel,nbr_patches],[nbr_patches]\n entry=self.LR\n ## Parameters for last layer\n elif i==cfg.nbr_layers-1:\n filter,bias_size= [3,3,nbr_patches,3],[3]\n entry=tensor\n ## Parameters for hidden layers\n else:\n filter,bias_size= [3,3,nbr_patches,nbr_patches],[nbr_patches]\n entry=tensor\n\n ## Construction of the CNN based on parameters above\n conv_w = tf.get_variable(\"conv_w_%s\" % (i), filter, initializer=tf.random_normal_initializer(stddev=np.sqrt(2.0/9/64)))\n conv_b = tf.get_variable(\"conv_b_%s\" % (i), bias_size, initializer=tf.constant_initializer(0))\n weights.append(conv_w)\n weights.append(conv_b)\n if i==cfg.nbr_layers-1:\n ## We don't put the ReLu layer at the end of the network\n tensor = tf.nn.bias_add(tf.nn.conv2d(entry, conv_w, strides=[1,1,1,1], padding=padding), conv_b)\n else:\n tensor = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(entry, conv_w, strides=[1,1,1,1], padding=padding), conv_b)) #, strides=[1,1,1,1]\n\n ## Reconstruction of the image (adding low-res and residual)\n tensor_out=tf.add(tensor,self.LR_losses)\n\n ## output generated image, weights of the network and the residuals image\n return tensor_out,weights,tensor","title":""},{"docid":"f956fb1cc3be4e990a976b02454d26c4","score":"0.6225239","text":"def gen_net_conf(self,args):\n\t\t# Ref: http://docker-py.readthedocs.io/en/stable/api.html#docker.api.container.ContainerApiMixin.create_networking_config\n\t\tnetwork_dict=self.handle.create_networking_config({args['network_name']: self.gen_ep_conf(args)})\n\t\treturn network_dict","title":""},{"docid":"d087d1fabe22fcbfc6d9802ff11001f5","score":"0.6212829","text":"def create_layer_map(self):\n\t\tself._layer_map[0]=\t{'name':'data',\t\t\t\t\t'inputs':[],\t\t\t\t\t'fl':7,'type':'data'} \n\t\tself._layer_map[1]=\t{'name':'conv1',\t\t\t\t'inputs':[0],\t\t\t\t\t'fl':6, 'fl_w':16,'type':'conv'}\n\t\tself._layer_map[2]=\t{'name':'conv2',\t\t\t\t'inputs':[1],\t\t\t\t\t'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[3]=\t{'name':'conv3',\t\t\t\t'inputs':[2],\t\t\t\t\t'fl':6, 'fl_w':16,'type':'conv'}\n\t\t# Inception 3a ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[4]=\t{'name':'inc3a/pool1',\t\t\t'inputs':[3],\t\t\t\t\t 'fl':6,'type':'plmx'}\n\t\tself._layer_map[5]=\t{'name':'inc3a/conv1',\t\t\t'inputs':[4],\t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[6]=\t{'name':'inc3a/conv3_1',\t\t'inputs':[3],\t\t\t\t\t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[7]=\t{'name':'inc3a/conv3_2',\t\t'inputs':[6],\t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[8]=\t{'name':'inc3a/conv5_1',\t\t'inputs':[3],\t\t\t\t\t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[9]=\t{'name':'inc3a/conv5_2',\t\t'inputs':[8],\t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[10]={'name':'inc3a/conv5_3',\t\t'inputs':[9],\t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 3b ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[11]={'name':'inc3b/conv1',\t\t\t'inputs':[5,7,10], \t\t\t\t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[12]={'name':'inc3b/conv3_1',\t\t'inputs':[5,7,10], \t\t\t\t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[13]={'name':'inc3b/conv3_2',\t\t'inputs':[12], \t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[14]={'name':'inc3b/conv5_1',\t\t'inputs':[5,7,10], \t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[15]={'name':'inc3b/conv5_2',\t\t'inputs':[14], \t\t\t\t\t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[16]={'name':'inc3b/conv5_3',\t\t'inputs':[15],\t\t\t\t\t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 3c ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[17]={'name':'inc3c/conv1',\t\t\t'inputs':[11,13,16],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[18]={'name':'inc3c/conv3_1',\t\t'inputs':[11,13,16],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[19]={'name':'inc3c/conv3_2',\t\t'inputs':[18], \t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[20]={'name':'inc3c/conv5_1',\t\t'inputs':[11,13,16],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[21]={'name':'inc3c/conv5_2',\t\t'inputs':[20], \t\t\t\t \t 'fl':6, 'fl_w':13,'type':'conv'}\n\t\tself._layer_map[22]={'name':'inc3c/conv5_3',\t\t'inputs':[21],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 3d ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[23]={'name':'inc3d/conv1',\t\t\t'inputs':[17,19,22],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[24]={'name':'inc3d/conv3_1',\t\t'inputs':[17,19,22],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[25]={'name':'inc3d/conv3_2',\t\t'inputs':[24],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[26]={'name':'inc3d/conv5_1',\t\t'inputs':[17,19,22],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[27]={'name':'inc3d/conv5_2',\t\t'inputs':[26],\t\t\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[28]={'name':'inc3d/conv5_3',\t\t'inputs':[27],\t\t\t\t \t 'fl':6, 'fl_w':16,'type':'conv'}\n\t\t# Inception 3e ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[29]={'name':'inc3e/conv1',\t\t\t'inputs':[23,25,28],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[30]={'name':'inc3e/conv3_1',\t\t'inputs':[23,25,28],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[31]={'name':'inc3e/conv3_2',\t\t'inputs':[30],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[32]={'name':'inc3e/conv5_1',\t\t'inputs':[23,25,28],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[33]={'name':'inc3e/conv5_2',\t\t'inputs':[32],\t\t\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[34]={'name':'inc3e/conv5_3',\t\t'inputs':[33],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 4a ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[35]={'name':'inc4a/pool1',\t\t\t'inputs':[29,31,34],\t\t \t 'fl':6,'type':'plmx'}\n\t\tself._layer_map[36]={'name':'inc4a/conv1',\t\t\t'inputs':[35], \t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[37]={'name':'inc4a/conv3_1',\t\t'inputs':[29,31,34],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[38]={'name':'inc4a/conv3_2',\t\t'inputs':[37],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[39]={'name':'inc4a/conv5_1',\t\t'inputs':[29,31,34],\t\t \t 'fl':6, 'fl_w':16,'type':'conv'}\n\t\tself._layer_map[40]={'name':'inc4a/conv5_2',\t\t'inputs':[39],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[41]={'name':'inc4a/conv5_3',\t\t'inputs':[40],\t\t\t\t \t 'fl':6, 'fl_w':16,'type':'conv'}\n\t\t# Inception 4b ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[42]={'name':'inc4b/conv1',\t\t\t'inputs':[36,38,41],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[43]={'name':'inc4b/conv3_1',\t\t'inputs':[36,38,41],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[44]={'name':'inc4b/conv3_2',\t\t'inputs':[43],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[45]={'name':'inc4b/conv5_1',\t\t'inputs':[36,38,41],\t\t \t 'fl':6, 'fl_w':16,'type':'conv'}\n\t\tself._layer_map[46]={'name':'inc4b/conv5_2',\t\t'inputs':[45],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[47]={'name':'inc4b/conv5_3',\t\t'inputs':[46],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 4c ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[48]={'name':'inc4c/conv1',\t\t\t'inputs':[42,44,47],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[49]={'name':'inc4c/conv3_1',\t\t'inputs':[42,44,47],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[50]={'name':'inc4c/conv3_2',\t\t'inputs':[49],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[51]={'name':'inc4c/conv5_1',\t\t'inputs':[42,44,47],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[52]={'name':'inc4c/conv5_2',\t\t'inputs':[51],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[53]={'name':'inc4c/conv5_3',\t\t'inputs':[52],\t\t\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\t# Inception 4d ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[54]={'name':'inc4d/conv1',\t\t\t'inputs':[48,50,53],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[55]={'name':'inc4d/conv3_1',\t\t'inputs':[48,50,53],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[56]={'name':'inc4d/conv3_2',\t\t'inputs':[55], \t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[57]={'name':'inc4d/conv5_1',\t\t'inputs':[48,50,53],\t\t \t 'fl':6, 'fl_w':16,'type':'conv'}\n\t\tself._layer_map[58]={'name':'inc4d/conv5_2',\t\t'inputs':[57],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[59]={'name':'inc4d/conv5_3',\t\t'inputs':[58],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Inception 4e ~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[60]={'name':'inc4e/conv1',\t\t\t'inputs':[54,56,59],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[61]={'name':'inc4e/conv3_1',\t\t'inputs':[54,56,59],\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[62]={'name':'inc4e/conv3_2',\t\t'inputs':[61],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\tself._layer_map[63]={'name':'inc4e/conv5_1',\t\t'inputs':[54,56,59],\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[64]={'name':'inc4e/conv5_2',\t\t'inputs':[63],\t\t\t\t \t 'fl':6, 'fl_w':14,'type':'conv'}\n\t\tself._layer_map[65]={'name':'inc4e/conv5_3',\t\t'inputs':[64],\t\t\t\t \t 'fl':6, 'fl_w':15,'type':'conv'}\n\t\t# Downsample ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\t\tself._layer_map[66]={'name':'downsample', \t\t\t'inputs':[3], \t\t\t\t\t 'fl':6,'type':'plmx'}\n\t\tself._layer_map[67]={'name':'convf', \t\t\t\t'inputs':[66,29,31,34,60,62,65], 'fl':8, 'fl_w':16,'type':'conv'}\t\t\t\t\t\n\t\tself._layer_map[68]={'name':'convf_up2',\t\t\t'inputs':[67],\t\t\t\t\t 'fl':8, 'fl_w':16,'type':'decv'}\t# TODO: fl and fl_w values\n\t\tself._layer_map[69]={'name':'rpn_conv1', \t\t\t'inputs':[67], \t\t\t\t\t 'fl':10, 'fl_w':17,'type':'conv'}\n\n\t\tself._layer_map[70]={'name':'rpn_cls_score_fabu',\t'inputs':[69], \t\t\t\t\t 'fl':10, 'fl_w':17,'type':'conv'}\n\t\tself._layer_map[71]={'name':'rpn_bbox_pred_fabu', \t'inputs':[69], \t\t\t\t\t 'fl':13, 'fl_w':18,'type':'conv'}\n\t\tself._layer_map[72]={'name':'proposal', \t\t\t'inputs':[70,71], \t\t\t\t 'fl':4,'type':'prop'}\t\t\t\t\t\t\t\n\t\tself._layer_map[73]={'name':'roi_pool_conv5', \t\t'inputs':[68,72], \t\t\t\t 'fl':8,'type':'roip'}\t\t\t\t\t\t\t\n\t\tself._layer_map[74]={'name':'fc6_L', \t\t\t\t'inputs':[73],\t\t\t\t\t 'fl':6, 'fl_w':17,'type':'fcon'}\n\t\tself._layer_map[75]={'name':'fc6_U', \t\t\t\t'inputs':[74],\t\t\t\t\t 'fl':7, 'fl_w':17,'type':'fcon'}\n\t\tself._layer_map[76]={'name':'fc7_L', \t\t\t\t'inputs':[75],\t\t\t\t\t 'fl':7, 'fl_w':17,'type':'fcon'}\n\t\tself._layer_map[77]={'name':'fc7_U', \t\t\t\t'inputs':[76],\t\t\t\t\t 'fl':9, 'fl_w':17,'type':'fcon'}\n\t\tself._layer_map[78]={'name':'cls_score_fabu', \t\t'inputs':[77],\t\t\t\t\t 'fl':9, 'fl_w':17,'type':'fcon'}\n\t\tself._layer_map[79]={'name':'bbox_pred_fabu', \t\t'inputs':[77],\t\t\t\t\t 'fl':9, 'fl_w':17,'type':'fcon'}","title":""},{"docid":"83c34c195ab1048c43e4d02e501b03d4","score":"0.6130811","text":"def _build_net(self):\n\n np.random.seed(self.rand_seed)\n\n model = Sequential()\n\n for layer in self.layers:\n model = self._add_layer(model, layer)\n\n return model","title":""},{"docid":"2630326f3dc226a08fca03ebb840968b","score":"0.6126979","text":"def test_create_base(self):\n drive.convert_nets(networks, 'weight')","title":""},{"docid":"599de5985ddb9f6be495c27aae7dc31e","score":"0.61083186","text":"def generator_network(z, filters, start_shape=(4,4)):\n\n with tf.variable_scope('latent'):\n\n # pop the first filter, this ensures we make the correct number of\n # convolutional layers later on...\n\n # calculate the number of features\n initial_shape = start_shape+(filters[0],)\n num_units = np.prod(initial_shape)\n\n dense = tf.layers.dense(inputs=pixel_norm(z),\n units=num_units,\n activation=k_leaky_relu_alpha,\n name='dense1',\n reuse=tf.AUTO_REUSE)\n\n # pixel norm and reshape to an image\n reshaped = pixel_norm(tf.reshape(dense, (-1,)+initial_shape))\n\n # # first convolutional layer\n conv0 = weighted_conv2d(inputs=reshaped,\n filters=filters[0],\n kernel_size=[3, 3],\n padding=\"same\",\n activation=k_leaky_relu_alpha,\n name='conv',\n reuse=tf.AUTO_REUSE,\n norm=True)\n\n # now set the value of x (the output of the previous layer)\n conv_layers = [conv0]\n\n # now build the layers\n for l, f in enumerate(filters[1:]):\n with tf.variable_scope('layer_{0:d}'.format(l)):\n\n upscale = double_size(conv_layers[-1])\n\n # convolutional layers\n conv1 = weighted_conv2d(inputs=upscale,\n filters=f,\n kernel_size=[3, 3],\n padding=\"same\",\n activation=k_leaky_relu_alpha,\n name='conv1',\n reuse=tf.AUTO_REUSE,\n norm=True)\n\n conv2 = weighted_conv2d(inputs=conv1,\n filters=f,\n kernel_size=[3, 3],\n padding=\"same\",\n activation=k_leaky_relu_alpha,\n name='conv2',\n reuse=tf.AUTO_REUSE,\n norm=True)\n\n # convolutional layer\n conv_layers.append(conv2)\n\n outputs = []\n with tf.variable_scope(\"to_image\", auxiliary_name_scope=True):\n for l, conv in enumerate(conv_layers):\n output = to_image(conv, filters=2, n=l)\n outputs.append(output)\n\n return outputs, output","title":""},{"docid":"b773034ec95a72beed9756d3032a9827","score":"0.61083186","text":"def add_onenet_config(cfg):\n cfg.MODEL.OneNet = CN()\n cfg.MODEL.OneNet.NUM_CLASSES = 80\n cfg.MODEL.OneNet.NMS = False # for ablation\n cfg.MODEL.OneNet.HEAD = \"CenterNet\" # or FCOS or RetinaNet\n cfg.MODEL.OneNet.PRE_DEFINE = False\n \n # CenterNet Head.\n cfg.MODEL.OneNet.IN_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.OneNet.ACTIVATION = 'relu'\n cfg.MODEL.OneNet.DECONV_CHANNEL= [2048, 256, 128, 64]\n cfg.MODEL.OneNet.DECONV_KERNEL = [4, 4, 4]\n cfg.MODEL.OneNet.DCN = True\n cfg.MODEL.OneNet.MODULATE_DEFORM = True\n \n # FCOS or RetinaNet Head.\n # cfg.MODEL.OneNet.IN_FEATURES = [\"p3\", \"p4\", \"p5\", \"p6\", \"p7\"]\n cfg.MODEL.OneNet.FEATURES_STRIDE = [8, 16, 32, 64, 128]\n cfg.MODEL.OneNet.OBJECT_SIZES_OF_INTEREST=[\n [-1, 64],\n [64, 128],\n [128, 256],\n [256, 512],\n [512, float(\"inf\")],]\n cfg.MODEL.OneNet.NUM_CONV = 4\n cfg.MODEL.OneNet.CONV_NORM = \"GN\"\n cfg.MODEL.OneNet.CONV_CHANNELS = 256 \n \n # Cost.\n cfg.MODEL.OneNet.CLASS_COST = 2.0\n cfg.MODEL.OneNet.GIOU_COST = 2.0\n cfg.MODEL.OneNet.L1_COST = 5.0\n \n # Loss.\n cfg.MODEL.OneNet.CLASS_WEIGHT = 2.0\n cfg.MODEL.OneNet.GIOU_WEIGHT = 2.0\n cfg.MODEL.OneNet.L1_WEIGHT = 5.0\n \n # Focal Loss.\n cfg.MODEL.OneNet.ALPHA = 0.25\n cfg.MODEL.OneNet.GAMMA = 2.0\n cfg.MODEL.OneNet.PRIOR_PROB = 0.01\n \n # Optimizer.\n cfg.SOLVER.OPTIMIZER = \"ADAMW\"\n cfg.SOLVER.BACKBONE_MULTIPLIER = 1.0","title":""},{"docid":"c5ec2238dded7c2e5a805fab27c6933c","score":"0.6086006","text":"def build_layers(self, input_shape, layer_key='first'):\n x = torch.zeros(input_shape) # create dummy inputs to be used to infer shapes of layers\n out = self.process(x)\n print(\"Building basic block of Convolutional Network using input shape {} for layer key {}\".format(out.shape, layer_key))\n context_list = []\n\n for i in range(self.num_layers): # for number of layers times\n if i > 0:\n # to give every layer access to all prev layers (DenseNet connectivity)\n out = torch.cat(context_list, dim=1)\n\n dilation = int(DILATION_PARAM**i)\n self.layer_dict['conv_{}_{}'.format(i, layer_key)] = nn.Conv1d(in_channels=out.shape[1],\n # add a conv layer in the module dict\n kernel_size=3,\n out_channels=self.num_filters,\n padding=dilation,\n bias=False,\n dilation=dilation)\n\n out = self.layer_dict['conv_{}_{}'.format(i, layer_key)](out) # use layer on inputs to get an output\n self.layer_dict['batch_norm_{}_{}'.format(i, layer_key)] = nn.BatchNorm1d(num_features=out.shape[1])\n out = self.layer_dict['batch_norm_{}_{}'.format(i, layer_key)](out)\n out = F.leaky_relu(out)\n out = self.drop(out)\n context_list.append(out)\n\n out = torch.cat(context_list, dim=1)\n out = F.max_pool1d(out, out.shape[-1])\n out = out.view(out.shape[0], -1)\n self.layer_dict['fc_layer_{}'.format(layer_key)] = nn.Linear(in_features=out.shape[1], # add a linear layer\n out_features=self.num_output_classes,\n bias=self.use_bias)\n\n out = self.layer_dict['fc_layer_{}'.format(layer_key)](out) # apply linear layer on flattened inputs\n print(\"Block is built, output volume is {} for layer key {}\".format(out.shape, layer_key))","title":""},{"docid":"f928f8ae4a7c898f0ad9f30338be493f","score":"0.60796314","text":"def load_layers():\r\n import arcpy\r\n net = nx.DiGraph()\r\n current_mxd = arcpy.mapping.MapDocument('CURRENT')\r\n layer_list = arcpy.mapping.ListLayers(current_mxd)\r\n \r\n for layer in layer_list:\r\n describe_layer = arcpy.Describe(layer)\r\n \r\n if describe_layer.shapeType == 'Point':\r\n desc = arcpy.Describe(layer)\r\n shape = desc.ShapeFieldName\r\n rows = arcpy.SearchCursor(layer)\r\n for row in rows:\r\n feat = row.getValue(shape)\r\n node = feat.getPart()\r\n nodeXY = node.X, node.Y\r\n net.add_node(nodeXY) \r\n del row, rows\r\n\r\n if describe_layer.shapeType == 'Polyline':\r\n desc = arcpy.Describe(layer)\r\n shape = desc.ShapeFieldName\r\n rows = arcpy.SearchCursor(layer)\r\n for row in rows:\r\n feat = row.getValue(shape)\r\n edge_pt1 = feat.firstPoint\r\n edge_pt2 = feat.lastPoint\r\n edge_pt1XY = edge_pt1.X, edge_pt1.Y\r\n edge_pt2XY = edge_pt2.X, edge_pt2.Y\r\n net.add_edge(edge_pt1XY, edge_pt2XY)\r\n del row, rows\r\n return net","title":""},{"docid":"4bb701d7603a4b0eabe2a90eb9d88559","score":"0.607124","text":"def load_layers():\n weights, biases = load_pretrained('model_without_order_info_224.mat')\n\n model_layers = []\n\n # L1\n model_layers.append(layers.Conv2D(\n 64,\n (15, 15),\n strides=3,\n padding='valid',\n kernel_initializer=initializers.Constant(weights['conv1']),\n bias_initializer=initializers.Constant(biases['conv1']),\n activation='relu',\n input_shape=(225, 225, 1),\n name='L1conv'\n ))\n model_layers.append(layers.MaxPooling2D(\n pool_size=(3, 3),\n strides=2,\n padding='valid',\n name='Pool1'\n ))\n\n # L2\n model_layers.append(layers.Conv2D(\n 128,\n (5, 5),\n strides=1,\n padding='valid',\n kernel_initializer=initializers.Constant(weights['conv2']),\n bias_initializer=initializers.Constant(biases['conv2']),\n activation='relu',\n name='L2conv'\n ))\n model_layers.append(layers.MaxPooling2D(\n pool_size=(3, 3),\n strides=2,\n padding='valid',\n name='Pool2'\n ))\n\n # L3\n model_layers.append(layers.Conv2D(\n 256,\n (3, 3),\n strides=1,\n padding='same',\n kernel_initializer=initializers.Constant(weights['conv3']),\n bias_initializer=initializers.Constant(biases['conv3']),\n activation='relu',\n name='L3conv'\n ))\n\n # L4\n model_layers.append(layers.Conv2D(\n 256,\n (3, 3),\n strides=1,\n padding='same',\n kernel_initializer=initializers.Constant(weights['conv4']),\n bias_initializer=initializers.Constant(biases['conv4']),\n activation='relu',\n name='L4conv'\n ))\n\n # L5\n model_layers.append(layers.Conv2D(\n 256,\n (3, 3),\n strides=1,\n padding='same',\n kernel_initializer=initializers.Constant(weights['conv5']),\n bias_initializer=initializers.Constant(biases['conv5']),\n activation='relu',\n name='L5conv'\n ))\n model_layers.append(layers.MaxPooling2D(\n pool_size=(3, 3),\n strides=2,\n padding='valid',\n name='Pool5'\n ))\n\n # L6\n model_layers.append(layers.Conv2D(\n 512,\n (7, 7),\n strides=1,\n padding='valid',\n kernel_initializer=initializers.Constant(weights['conv6']),\n bias_initializer=initializers.Constant(biases['conv6']),\n activation='relu',\n name='L6conv'\n ))\n model_layers.append(layers.Dropout(\n rate=0.5,\n name='Dropout8'\n ))\n\n # L7\n model_layers.append(layers.Conv2D(\n 512,\n (1, 1),\n strides=1,\n padding='valid',\n kernel_initializer=initializers.Constant(weights['conv7']),\n bias_initializer=initializers.Constant(biases['conv7']),\n activation='relu',\n name='L7conv'\n ))\n model_layers.append(layers.Dropout(\n rate=0.5,\n name='Dropout7'\n ))\n\n # L8\n model_layers.append(layers.Conv2D(\n 250,\n (1, 1),\n strides=1,\n padding='valid',\n kernel_initializer=initializers.Constant(weights['conv8']),\n bias_initializer=initializers.Constant(biases['conv8']),\n activation='relu',\n name='L8conv'\n ))\n\n return model_layers","title":""},{"docid":"02fa631837c16af7bda81194f11e4bf9","score":"0.6062069","text":"def make_net(bottom, arch_str, is_generator, concat_list=[], norm_layer='BN', reuse=False, is_training=None,\r\n do_print=True):\r\n\r\n assert is_training is not None, \"is_training must be set\"\r\n\r\n # Create arch list from arch string\r\n arch_list = arch_string2list(arch_str, is_generator=is_generator, norm_layer=norm_layer)\r\n\r\n # Make sure that the generator is being created if any input is given (other than bottom)\r\n assert len(concat_list) == 0 or is_generator, \"Discriminator should not have concat_list\"\r\n\r\n # Print network architecture\r\n if do_print:\r\n print('Creating {} network:'.format('generator' if is_generator else 'discriminator'))\r\n print_arch_list(arch_list)\r\n\r\n # Create dictionary of layer names\r\n name2layer = {'bottom': bottom}\r\n\r\n # Create layers one by one\r\n for layer in arch_list:\r\n if reuse:\r\n tf.get_variable_scope().reuse_variables()\r\n\r\n # Set batchnorm mode\r\n if layer['op'] == 'batchnorm':\r\n layer['params'] = {'training': is_training}\r\n\r\n # If layer has parameters, apply them. For deconvolution perform concatenation.\r\n if 'params' in layer.keys():\r\n if layer['op'] == 'deconv' and len(concat_list) > 0:\r\n tensor_to_concat = concat_list.pop(0) # Pop the first tensor on list\r\n # If it's None, don't concat anything. Otherwise, concat it to layer's input.\r\n if tensor_to_concat is None:\r\n bottom_ = name2layer[layer['bottom']]\r\n else:\r\n deconv_bottom = name2layer[layer['bottom']]\r\n # If there is no bottom, just take tensor_to_concat (e.g. if dim_z_global = dim_z_local = 0)\r\n if deconv_bottom is not None:\r\n bottom_ = tf.concat([deconv_bottom, tensor_to_concat], axis=3)\r\n else:\r\n bottom_ = tensor_to_concat\r\n else:\r\n bottom_ = name2layer[layer['bottom']]\r\n\r\n layer_tf = op2layer(layer['op'])(bottom_, **layer['params'], name=layer['name'])\r\n if do_print:\r\n print(\"Creating {} layer {} with parameters {}\".format(layer['op'], layer['name'], layer['params']))\r\n elif layer['op'] == 'instancenorm': # Instace norm layer does not take a name parameter for some reason...\r\n layer_tf = op2layer(layer['op'])(name2layer[layer['bottom']], reuse=reuse, scope=layer['name'])\r\n if do_print:\r\n print(\"Creating {} layer {}\".format(layer['op'], layer['name']))\r\n else:\r\n layer_tf = op2layer(layer['op'])(name2layer[layer['bottom']], name=layer['name'])\r\n if do_print:\r\n print(\"Creating {} layer {}\".format(layer['op'], layer['name']))\r\n\r\n name2layer[layer['name']] = layer_tf\r\n\r\n assert len(concat_list) == 0, \"Not all concat tensors were used (length at the end is {})\".format(len(concat_list))\r\n\r\n # For generator, find kernel size (which we assert is constant for all deconv layers)\r\n # and crop k - 2 pixels from each of the two dimensions\r\n if is_generator:\r\n for layer in arch_list:\r\n if layer['op'] == 'deconv':\r\n kernel_size = layer['params']['kernel_size']\r\n break\r\n return layer_tf[:, :-(kernel_size-2), :-(kernel_size-2), :]\r\n\r\n return layer_tf","title":""},{"docid":"3458617c39658bbae6388d9d1e022b92","score":"0.6059142","text":"def build_architecture(opts, sample):\n opts.architecture = {}\n if opts.network_type == 'heading_network':\n opts.architecture['nlayers'] = opts.nlayers\n opts.architecture['filters'] = [ 2**(6+i) for i in range(opts.nlayers) ]\n opts.architecture['strides'] = [ 2 ] * opts.nlayers\n opts.architecture['kernel_sizes'] = [ 3 ] * opts.nlayers\n opts.architecture['paddings'] = [ 'VALID' ] * opts.nlayers\n opts.architecture['activations'] = [ 'relu' ] * opts.nlayers\n elif opts.network_type == 'heading_resnet':\n opts.architecture['nlayers'] = opts.nlayers\n opts.architecture['filters'] = [ [2**(6+i)]*2 for i in range(opts.nlayers) ]\n opts.architecture['strides'] = [ 2 ] * opts.nlayers\n opts.architecture['kernel_sizes'] = [ 3 ] * opts.nlayers\n opts.architecture['activations'] = [ 'relu' ] * opts.nlayers\n # TODO: Add back polymix\n # if opts['activation-type'] == \"polymix\":\n # opts['activations'][0] = \"relusq\"\n elif opts.network_type == 'hourglass':\n opts.architecture['nb_stack'] = 1\n opts.architecture['nlayers'] = opts.nlayers\n opts.architecture['width'] = 256\n opts.architecture['bn_decay'] = 0.9\n opts.architecture['epsilon'] = 1e-5\n\n opts.architecture['noutputs'] = opts.noutputs\n\n regularizer = tf.contrib.layers.l1_l2_regularizer(scale_l1=opts.weight_l1_decay,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tscale_l2=opts.weight_decay)\n if opts.network_type == 'heading_network':\n return build_heading_network(opts, sample[\"flow\"], regularizer=regularizer)\n elif opts.network_type == 'heading_resnet':\n return build_heading_resnet(opts, sample[\"flow\"], regularizer=regularizer)\n elif opts.network_type == 'heading_resnet_2':\n return build_heading_resnet_2(opts, sample[\"flow\"], regularizer=regularizer)\n elif opts.network_type == 'hourglass':\n hg = hourglass.single_hourglass(opts,\n verbose=False,\n name='network',\n regularizer=regularizer)\n return hg(sample[\"image\"])\n else:\n return None # TODO: Proper error handling","title":""},{"docid":"fc7bacb3fbbcd159a029e50adac60332","score":"0.6037738","text":"def __generate_network(\n self\n ) -> None:\n for i in range(0, self.__nb_network):\n net = self.__random_network()\n local = True if i == 0 else False\n if net not in self.__hosts.keys():\n self.__hosts[net.replace('/24','')] = list()\n self.__networks.append((net, local))","title":""},{"docid":"07c38b5688b2147bf69801a20eef6382","score":"0.60345113","text":"def build_network(self):\n self.network = input_data(shape=[None, 48, 48, 1])\n print(\"Input data \", self.network.shape[1:])\n\n self.network = conv_2d(self.network, 64, 5, activation='relu')\n print(\"Conv1 \", self.network.shape[1:])\n\n self.network = max_pool_2d(self.network, 3, strides=2)\n print(\"Maxpool1 \", self.network.shape[1:])\n\n self.network = conv_2d(self.network, 64, 5, activation='relu')\n print(\"Conv2 \", self.network.shape[1:])\n\n self.network = max_pool_2d(self.network, 3, strides=2)\n print(\"Maxpool2 \", self.network.shape[1:])\n\n self.network = conv_2d(self.network, 128, 4, activation='relu')\n print(\"Conv3 \", self.network.shape[1:])\n\n self.network = fully_connected(self.network, 3072, activation='relu')\n print(\"Fully connected\", self.network.shape[1:])\n\n self.network = fully_connected(self.network, len(\n self.target_classes), activation='softmax')\n print(\"Output \", self.network.shape[1:])\n # Generates a TrainOp which contains the information about optimization process - optimizer, loss function, etc\n\n self.network = regression(\n self.network, optimizer='momentum', metric='accuracy', loss='categorical_crossentropy')\n\n # Creates a model instance.\n self.model = tflearn.DNN(\n self.network, checkpoint_path='./utility/emotionDetection/model_1_atul', max_checkpoints=1, tensorboard_verbose=2)\n\n # Loads the model weights from the checkpoint\n self.load_model()","title":""},{"docid":"a1a0f3e4b98cfb04930da262fe66d03b","score":"0.60299116","text":"def connect_layers(self):\n logging.info('Converted layer info')\n logging.info(\"{:22s} {:22s} {:12s} {:5s} {:18s} {:8s} {:s}\".format(\n 'Input Node', 'Output Node', 'Node Type', 'Live Range',\n 'Output Dimension', 'Addr', 'Size'))\n\n class LayerLiveRange(object):\n def __init__(self, layer, index):\n self.layer = layer\n self.birth_index = index\n self.death_index = index\n self.output_concat_lr = None\n self.allocated = False\n live_ranges = []\n weight_size = 0\n for index, layer in enumerate(self.layer):\n # calc weight and increment the counter\n if layer.type is LayerType.Convolution:\n for run in layer.run:\n weight_size += get_weight_size(run.conv, self.quantization)\n\n # add to output_layer\n if layer.is_output:\n self.num_output_layers += 1\n self.output_layer.append(layer)\n\n # create LayerLiveRange\n lr = LayerLiveRange(layer, index)\n for node_in, lr_in in itertools.product(layer.node_in.input_nodes,\n live_ranges):\n if node_in is lr_in.layer.node_out:\n lr.layer.layer_in.append(lr_in.layer)\n if lr_in.death_index < index:\n lr_in.death_index = index\n if lr.layer.is_output:\n lr.death_index = len(self.layer) - 1\n live_ranges.append(lr)\n\n # handle concat layer and update live_ranges\n def _lr_is_partof_concat(lr, concat_lr):\n if concat_lr.layer.type is not LayerType.Concatenate:\n return False\n while lr:\n if lr is concat_lr:\n return True\n lr = lr.output_concat_lr\n return False\n\n for index, lr in enumerate(reversed(live_ranges)):\n index = len(live_ranges) - index\n if lr.layer.type is LayerType.Concatenate:\n for prev_lr in live_ranges[:index]:\n if prev_lr.layer.node_out in lr.layer.node_in.input_nodes:\n prev_lr.output_concat_lr = lr\n\n for index, lr in enumerate(reversed(live_ranges)):\n index = len(live_ranges) - index\n if lr.layer.type is LayerType.Concatenate:\n for prev_lr in live_ranges[:index]:\n if _lr_is_partof_concat(prev_lr, lr):\n max_di = max(prev_lr.death_index, lr.death_index)\n prev_lr.death_index = max_di\n lr.death_index = max_di\n min_di = min(prev_lr.birth_index, lr.birth_index)\n prev_lr.birth_index = min_di\n lr.birth_index = min_di\n\n def get_dst_concat_lr(lr):\n \"\"\"\n search final concat node Life Range\n If lr.output_concat_lr is None, lr is returned.\n \"\"\"\n _lr = lr\n while _lr.output_concat_lr:\n _lr = _lr.output_concat_lr\n return _lr\n\n allocated_size = 0 # size to be allocated\n for lr in sorted(reversed(live_ranges),\n key=(lambda x : x.death_index),\n reverse=True):\n if lr.output_concat_lr:\n offset = 0\n for node in lr.output_concat_lr.layer.node_in.input_nodes:\n if node == lr.layer.node_out:\n break\n offset += node.output_size\n lr.layer.output_addr_offset = lr.output_concat_lr.layer.output_addr_offset + offset\n lr.allocated = True\n\n necessary_size = make_align_size(lr.layer.node_out.output_size)\n if not lr.allocated:\n current_live_ranges = [_lr for _lr in live_ranges\n if not\n (_lr.birth_index > lr.death_index or\n _lr.death_index < lr.birth_index)]\n current_live_ranges = sorted(\n current_live_ranges,\n key=(lambda x: x.layer.output_addr_offset))\n\n # find if can re-use empty spaces in current live ranges\n empty_found = False\n current_offset = 0\n for clr in current_live_ranges:\n if not clr.allocated:\n continue\n\n empty_found = (clr.layer.output_addr_offset\n - current_offset >= necessary_size)\n if empty_found:\n lr.layer.output_addr_offset = current_offset\n break\n else:\n # to next, update current_offset\n increment_size = make_align_size(\n clr.layer.node_out.output_size)\n if clr.output_concat_lr:\n increment_size = make_align_size(\n clr.output_concat_lr.layer.node_out.output_size)\n current_offset = make_align_size(\n clr.layer.output_addr_offset + increment_size)\n\n # if not, put it in the end of current buffer\n if not empty_found:\n lr.layer.output_addr_offset = current_offset\n if current_offset + necessary_size > allocated_size:\n allocated_size = current_offset + necessary_size\n lr.allocated = True\n\n for lr in live_ranges:\n logging.info(\"{:22s} {:22s} {:12s} {:02d} {:02d} {:18s} {:08X} {:08X}{:s}\".format(\n lr.layer.node_in.name, lr.layer.node_out.name,\n str(lr.layer.type),\n lr.birth_index, lr.death_index,\n str(lr.layer.node_out.output_dim),\n lr.layer.output_addr_offset,\n lr.layer.node_out.output_size,\n ('*' if lr.layer.is_output else '')))\n logging.info('allocated size:{:d}'.format(allocated_size))\n self.weight_size = weight_size\n self.buffer_size = allocated_size","title":""},{"docid":"3d5d82de158387791533ca7fb8876e47","score":"0.6023222","text":"def build(self,inputs):\n assert inputs.get_shape().as_list()[1:]==[224,224,3], 'the size of inputs is incorrect!'\n \n # start to build the densenet model\n net = inputs\n print \"start-shape：\"+str(inputs.shape)\n \n net = self.tf_op.conv_layer(net, kernel_num=2*cfg.growth_rate, kernel_size=7, stride=2, layer_name=\"conv1\",padding='SAME')\n net = self.tf_op.batch_normalization(net,scope_name=\"bn1\")\n net = tf.nn.relu(net) \n net = self.tf_op.max_pool(net,layer_name=\"pool1\", kernel_size=3, stride=2, padding='SAME')\n print \"the 1th stage-shape：\"+str(net.shape)\n \n net = self.dense_block(net,6,\"denseblock1\") \n net = self.compression_transitionLayer(net,\"transition1\")\n print \"the 2th stage-shape：\"+str(net.shape)\n \n net = self.dense_block(net,12,\"denseblock2\") \n net = self.compression_transitionLayer(net,\"transition2\")\n print \"the 3th stage-shape：\"+str(net.shape)\n \n net = self.dense_block(net,24,\"denseblock3\") \n net = self.compression_transitionLayer(net,\"transition3\")\n print \"the 4th stage-shape：\"+str(net.shape)\n \n net = self.dense_block(net,16,\"denseblock4\") \n net = self.tf_op.batch_normalization(net,scope_name=\"bn4\")\n net = tf.nn.relu(net) \n net = self.tf_op.global_avg_pool(net, \"pool4\", stride=1)\n print \"the 5th stage-shape：\"+str(net.shape)\n \n shape = net.get_shape()\n in_size = shape[1]*shape[2]*shape[3]\n net = self.tf_op.fc_layer(net, int(in_size), self.class_num, \"fc5\") \n net = self.tf_op.drop_out(net,dropout_rate=self.dropout_rate)\n result = tf.nn.softmax(net,name=\"prob\")\n print \"the 6th stage-shape：\"+str(result.shape)\n return result","title":""},{"docid":"736963c08b4a158106b6d70d5e595380","score":"0.6016884","text":"def _build_net(x_placeholder, shape, weight_generator, bias_generator, activator, activate_output):\n a_set = [x_placeholder]\n for i in range(len(shape) - 1):\n up_size = shape[i]\n down_size = shape[i + 1]\n\n if verbose:\n print('mid-layer [{0}, {1}]'.format(up_size, down_size))\n\n w = tf.Variable(weight_generator([up_size, down_size]))\n b = tf.Variable(bias_generator([down_size]))\n if i == len(shape) - 2: # output\n if activate_output:\n a_set.append(activator(tf.matmul(a_set[-1], w) + b, name='y'))\n else:\n a_set.append(tf.add(tf.matmul(a_set[-1], w), b, name='y'))\n else: # upstream\n a_set.append(tf.matmul(a_set[-1], w) + b)\n return a_set[-1]","title":""},{"docid":"af8614955959bc981d30d2e8dacee80b","score":"0.60113466","text":"def create_neural_network(blocks):\n\n previous_filters = 3 # starts out as 3, since we have a RGB image with 3 colour channels\n\n for block in blocks:\n module=nn.Sequential()\n if block[type]==\"net\":\n continue\n # Adding convolutional layer\n elif block[type]==\"convolutional\":\n activation=block[\"activation\"]\n kernel_size=block[\"size\"]\n pad=block[\"pad\"]\n stride=block[\"stride\"]\n filters=block[\"filter\"]\n\n # Generally, bias does not exist in layers that have batch normalization.\n # This is so because batch normalization inherently takes care of the necessary bias factor.\n # Thus, we check below if the block defined has batch normalization and make a decision on bias accordingly.\n\n if \"batch_normalize\" in block.keys():\n bias=False\n batch_normalization=block[\"batch_normalize\"]\n else:\n bias=True\n batch_normalization=0\n\n # Adding layers now\n conv_layer=nn.Conv2d(previous_filters, filters, kernel_size, stride, pad, bias=bias)\n module.add_module(conv_layer)\n\n # Add Batch Normalization\n if batch_normalization:\n bn_layer=nn.BatchNorm2d(filters)\n module.add_module(bn_layer)\n\n # Add Activation layer\n # Activation layer can be either a Leaky-Relu layer or a linear activation layer.\n if activation==\"leaky\":\n activation_layer=nn.LeakyReLU(0.1, inplace=True)","title":""},{"docid":"a65134cb50461f59b00cd5d20f39ab9f","score":"0.6006831","text":"def make_stages(cfg_dict):\n layers = []\n for i in range(len(cfg_dict) - 1):\n one_ = cfg_dict[i]\n for k, v in one_.items():\n if 'pool' in k:\n layers += [nn.MaxPool2d(kernel_size=v[0], stride=v[1],\n padding=v[2])]\n else:\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\n kernel_size=v[2], stride=v[3],\n padding=v[4])\n layers += [conv2d, nn.ReLU(inplace=True)]\n one_ = list(cfg_dict[-1].keys())\n k = one_[0]\n v = cfg_dict[-1][k]\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\n kernel_size=v[2], stride=v[3], padding=v[4])\n layers += [conv2d]\n return nn.Sequential(*layers)","title":""},{"docid":"a65134cb50461f59b00cd5d20f39ab9f","score":"0.6006831","text":"def make_stages(cfg_dict):\n layers = []\n for i in range(len(cfg_dict) - 1):\n one_ = cfg_dict[i]\n for k, v in one_.items():\n if 'pool' in k:\n layers += [nn.MaxPool2d(kernel_size=v[0], stride=v[1],\n padding=v[2])]\n else:\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\n kernel_size=v[2], stride=v[3],\n padding=v[4])\n layers += [conv2d, nn.ReLU(inplace=True)]\n one_ = list(cfg_dict[-1].keys())\n k = one_[0]\n v = cfg_dict[-1][k]\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\n kernel_size=v[2], stride=v[3], padding=v[4])\n layers += [conv2d]\n return nn.Sequential(*layers)","title":""},{"docid":"fdc01e7dda44cf781d93b855033d2337","score":"0.60021186","text":"def create_network_deployment(context):\n network_config = {}\n network_config_array = []\n for nics in range(context.properties['numNics']):\n if nics == 0:\n net_name = 'mgmt'\n subnet_name = 'mgmt'\n subnet_description = 'Subnetwork used for management traffic'\n elif context.properties['numNics'] != 1 and nics == 1:\n net_name = 'external'\n subnet_name = 'external'\n subnet_description = 'Subnetwork used for external traffic'\n else:\n net_name = 'internal' + str(nics)\n subnet_name = 'internal' + str(nics)\n subnet_description = 'Subnetwork used for internal traffic'\n subnet_config = [{\n 'description': subnet_description,\n 'name': subnet_name,\n 'region': context.properties['region'],\n 'ipCidrRange': '10.0.' + str(nics) + '.0/24'\n }]\n if nics + 1 == context.properties['numNics']:\n app_subnet_config = {\n 'description': 'Subnetwork used for POC application.',\n 'name': 'app',\n 'region': context.properties['region'],\n 'ipCidrRange': '10.0.' + str(nics + 1) + '.0/24'\n }\n subnet_config.append(app_subnet_config)\n network_config = {\n 'name': 'network' + str(nics),\n 'type': '../modules/network/network.py',\n 'properties': {\n 'name': net_name,\n 'provisionPublicIp': context.properties['provisionPublicIp'],\n 'region': context.properties['region'],\n 'uniqueString': context.properties['uniqueString'],\n 'subnets': subnet_config\n }\n }\n network_config_array.append(network_config)\n return network_config_array","title":""},{"docid":"875c8aac86890ccc4aa054da06b681dd","score":"0.599502","text":"def init_nets():\n global pnet, rnet, onet\n pnet_model = td.Model('pnet.pkl')\n rnet_model = td.Model('rnet.pkl')\n onet_model = td.Model('onet.pkl')\n print pnet_model.roots\n print rnet_model.roots\n print onet_model.roots\n\n # define all nets i/o\n pnet_input, pnet_output1, pnet_output2 = pnet_model.get('pnet/input:0', 'pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0')\n print pnet_input, pnet_output1, pnet_output2\n rnet_input, rnet_output1, rnet_output2 = rnet_model.get('rnet/input:0', 'rnet/conv5-2/conv5-2:0', 'rnet/prob1:0')\n print rnet_input, rnet_output1, rnet_output2\n onet_input, onet_output1, onet_output2, onet_output3 = onet_model.get('onet/input:0', 'onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0')\n print onet_input, onet_output1, onet_output2, onet_output3\n\n # define all nets processing function\n pnet = lambda img: (pnet_output1.eval({pnet_input: img}), pnet_output2.eval({pnet_input: img}))\n rnet = lambda img: (rnet_output1.eval({rnet_input: img}), rnet_output2.eval({rnet_input: img}))\n onet = lambda img: (onet_output1.eval({onet_input: img}), onet_output2.eval({onet_input: img}), onet_output3.eval({onet_input: img}))","title":""},{"docid":"e875f7fb3bec50459b0a5273bfb47fc9","score":"0.59947395","text":"def nnet_builder(self):\n \n # encoder \n with tf.variable_scope('encoder'):\n # input to hidden layer 1\n self.layer_enc_hidden1, self.params['layer_enc_hidden1_weights'], self.params['layer_enc_hidden1_biases'] = layer_fc(self.x,\n self.n_hidden,0.5,'layer_enc_hidden1',self.initializer,self.nonlinearity)\n\n self.layer_enc_hidden2, self.params['layer_enc_hidden2_weights'], self.params['layer_enc_hidden2_biases'] = layer_fc(self.layer_enc_hidden1,\n self.n_hidden,0.5,'layer_enc_hidden2',self.initializer,self.nonlinearity)\n\n\n # latent sampler\n with tf.variable_scope('latent'): \n self.layer_z_mu, self.params['layer_z_mu_weights'], self.params['layer_z_mu_biases'] = layer_fc(self.layer_enc_hidden1,self.n_latent,0.5,'z_mu',self.initializer)\n\n self.layer_z_logvar, self.params['layer_z_logvar_weights'], self.params['layer_z_logvar_biases'] = layer_fc(self.layer_enc_hidden1,self.n_latent,0.5,'z_logvar',self.initializer)\n\n self.epsilon = tf.random_normal(tf.stack([tf.shape(self.layer_enc_hidden1)[0], self.n_latent]),mean=0.0,stddev=1.0, dtype = tf.float32)\n self.layer_z = tf.add(self.layer_z_mu,tf.multiply(tf.sqrt(tf.exp(self.layer_z_logvar)),self.epsilon)) \n\n \n # decoder \n with tf.variable_scope('decoder'): \n self.layer_dec_hidden1, self.params['layer_dec_hidden1_weights'], self.params['layer_dec_hidden1_biases'] = layer_fc(self.layer_z,\n self.n_hidden,0.5,'layer_dec_hidden1',self.initializer,self.nonlinearity)\n \n self.y_hat, self.params['y_hat_weights'], self.params['y_hat_biases'] = layer_fc(self.layer_dec_hidden1,self.dim_outputs[1],0.5,'y_hat',nonlinearity=tf.nn.sigmoid)\n \n return","title":""},{"docid":"3a5e6d4576736831467812c7dd1be397","score":"0.5990629","text":"def makeNet_fullyFeatured(self, n, training_boards=[]):\n # We seperate the features of the input \n input_A = Input(shape=(4,))\n input_PAR = Input(shape=(2*n+2,))\n input_B = Input(shape=(n**2,))\n input_C = Input(shape=(n**2,))\n input_D = Input(shape=(n**2,))\n\n tempo_layer = Dense(4, activation=self.layer1actfn)(input_A)\n parity_layer = Dense(2*n+2, activation=self.layer1actfn)(input_PAR)\n taken_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_B)\n dead_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_C)\n available_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_D)\n\n # We merge the features, and then include layers for high-level feature extraction and output.\n merged_layer1 = Concatenate()([taken_squares_layer, dead_squares_layer, available_squares_layer])\n abstract_layer1 = Dense(2*(n**2), activation=self.layer2actfn)(merged_layer1)\n \n merged_layer2 = Concatenate()([abstract_layer1, parity_layer, tempo_layer])\n abstract_layer2 = Dense(n**2, activation=self.layer2actfn)(merged_layer2)\n \n output_layer = Dense(1, activation=self.layer_out_actfn)(abstract_layer2)\n\n self.model = tf.keras.Model(inputs=[input_A, input_PAR, input_B, input_C, input_D], outputs=output_layer)\n\n # Sanity checks\n #self.model.summary()\n #tf.keras.utils.plot_model(self.model, show_shapes=True)\n\n if training_boards != []:\n input_A = []\n input_PAR = []\n input_B = []\n input_C = []\n input_D = []\n y_train = []\n for board, value in training_boards:\n x_train = self.parseBoardAsInput(board)\n input_A.append(x_train[0])\n input_PAR.append(x_train[1])\n input_B.append(x_train[2])\n input_C.append(x_train[3])\n input_D.append(x_train[4])\n y_train.append(value)\n\n self.train([\n numpy.asarray(input_A), \n numpy.asarray(input_PAR),\n numpy.asarray(input_B), \n numpy.asarray(input_C), \n numpy.asarray(input_D)\n ], \n numpy.asarray([numpy.asarray([y]) for y in y_train]))","title":""},{"docid":"464643318e214218996d524a8e99fc37","score":"0.5988742","text":"def create_network():\n network = {}\n dim0 = 784 # 28x28 (pixels in an image)\n dim1 = 100 # chosen arbitrarily\n dim2 = 50 # chosen arbitrarily\n dim3 = 10 # the number of kinds of labels (0, 1, .. , 9)\n\n # layer-1: (dim0, 1)-matrix -> (dim1, 1)-matrix\n network['W1'] = np.random.rand(dim0, dim1)\n network['b1'] = np.random.rand(dim1)\n\n # layer-2: (dim1, 1)-matrix -> (dim2, 1)-matrix\n network['W2'] = np.random.rand(dim1, dim2)\n network['b2'] = np.random.rand(dim2)\n\n # layer-3: (dim2, 1)-matrix -> (dim3, 1)-matrix\n network['W3'] = np.random.rand(dim2, dim3)\n network['b3'] = np.random.rand(dim3)\n return network","title":""},{"docid":"3a0c731528835b837e6734c99bee0afc","score":"0.59876484","text":"def build(self):\n for i in range(1, len(self.layers)):\n self.layers[i](self.layers[i - 1].n_neuron)","title":""},{"docid":"732605c4d2212be0ac417ee595748970","score":"0.59767497","text":"def _make_final_classifiers(self, network, name_generator):\n\n layers = []\n\n split = self._make_split_layer(network)\n layers.append(split)\n\n connections = []\n\n softmaxes = []\n for label, num_output in self.other_args['labels']:\n inner_product = self._make_inner_product_layer(\n network, num_output=num_output)\n layers.append(inner_product)\n\n softmax_loss = self._make_softmax_loss_layer(network, loss_id=label)\n layers.append(softmax_loss)\n\n accuracy = self._make_accuracy_layer(network, accuracy_id=label)\n layers.append(accuracy)\n\n connections.append((split.name, (split.top, inner_product.bottom)))\n connections.append((inner_product.name, (inner_product.top, softmax_loss.bottom, accuracy.bottom)))\n\n softmaxes.append((label, softmax_loss))\n softmaxes.append((label, accuracy))\n\n for connection in connections:\n self._tie(connection, name_generator)\n\n for label, loss in softmaxes:\n loss.bottom.append(label)\n\n return layers","title":""},{"docid":"9dd09df62c77ae1b336079615cfe3754","score":"0.59734416","text":"def create_layers(self, defaultExtension=''):\n # Make sure that the layer listing exists\n if self.metadata is None:\n self.list_layers()\n # no raster data availabile?\n if self.metadata is not None:\n for fileName, layerMeta in self.metadata.items():\n self.create_layer(fileName, layerMeta, defaultExtension=defaultExtension)","title":""},{"docid":"3616cc1c154ff53e7c2bd5a16a5694d5","score":"0.5964093","text":"def __init__( self, layers_def):\n\t\tprint \"=> NET:\"\n\t\tself.layers = []\n\t\tfor layer_def in layers_def:\n\t\t\t# Convolutional layer\n\t\t\tif layer_def['layer'] == 'conv':\n\t\t\t\tlayer = ConvLayer(input_size, layer_def['n'], (layer_def['kern_size'], layer_def['kern_size']), layer_def['pad'], layer_def['stride'])\n\t\t\t\tinput_size = layer.output_shape\n\t\t\t# Fully connected layer\n\t\t\telif layer_def['layer'] == 'fc':\n\t\t\t\tlayer = FCLayer(input_size, layer_def['n'], layer_def['act_type'])\n\t\t\t\tinput_size = (layer.n_neurons,)\n\t\t\t# Maxpool layer\n\t\t\telif layer_def['layer'] == 'maxpool':\n\t\t\t\tlayer = MaxPoolLayer(input_size, (layer_def['kern_size'], layer_def['kern_size']), layer_def['stride'])\n\t\t\t\tinput_size = layer.output_shape\n\t\t\t# Input size definition\n\t\t\telif layer_def['layer'] == 'input':\n\t\t\t\tinput_size = (layer_def['c'], layer_def['h'], layer_def['w'])\n\t\t\t\tself.input_size = input_size\n\t\t\t\tprint \"* Input -> Size: \", input_size\n\t\t\t\tcontinue\n\t\t\t# Error layer\n\t\t\telif layer_def['layer'] == 'error':\n\t\t\t\tlayer = ErrorLayer(layer_def['type'])\n\t\t\telse:\n\t\t\t\traise ValueError(\"*** UNKOWN LAYER TYPE (\"+layer_def['layer']+\")\")\n\t\t\tself.layers.append(layer)\n\n\t\tself.n_classes = int(input_size[0])\n\t\tself.layers_def = layers_def\n\t\tprint \"* NET: done initializing net!\"","title":""},{"docid":"2336c5091d72636e0ff84eca534e62a8","score":"0.5960152","text":"def create_model_from_cfg(cfg):\n\n # create the model\n model = tf.keras.models.Sequential()\n block_index = 0\n \n def add_conv_or_local(is_conv, section):\n \n filters = section['filters']\n kernel_size = section['size']\n strides = section['stride']\n\n # add padding\n padding = section['pad']\n if padding == 1:\n pad = (kernel_size - 1) // 2\n else:\n pad = 0\n model.add(ZeroPadding2D(pad, name=f'pad_{block_index}'))\n\n # check batch norm\n try:\n batch_normalize = (section['batch_normalize'] == 1)\n except:\n batch_normalize = False\n use_bias = not batch_normalize\n\n # add the 2d convolution or locally connected layer\n if is_conv:\n layer_class = Conv2D\n layer_name = f'conv_{block_index}'\n else:\n layer_class = LocallyConnected2D\n layer_name = f'local_{block_index}'\n model.add(layer_class(filters, kernel_size, strides, use_bias=use_bias, name=layer_name))\n\n # add batch norm layer\n if batch_normalize:\n model.add(BatchNormalization(name=f'batchnorm_{block_index}'))\n\n # add the activation\n activation = section['activation']\n if activation == 'leaky':\n model.add(LeakyReLU(alpha=0.1, name=f'leaky_{block_index}'))\n\n for name, section in cfg:\n if name == 'net':\n input_shape = (section['height'], section['width'], section['channels'])\n model.add(InputLayer(input_shape=input_shape, name='input_0'))\n elif name == 'convolutional':\n add_conv_or_local(True, section)\n elif name == 'maxpool':\n pool_size = section['size']\n strides = section['stride']\n model.add(MaxPooling2D(pool_size, strides, name=f'maxpool_{block_index}'))\n elif name == 'local':\n add_conv_or_local(False, section)\n elif name == 'dropout':\n rate = section['probability']\n model.add(Dropout(rate, name=f'dropout_{block_index}'))\n elif name == 'connected':\n units = section['output']\n activation = section['activation']\n \n model.add(Flatten(name=f'flatten_{block_index}'))\n model.add(Dense(units, name=f'connected_{block_index}'))\n \n if activation == 'leaky':\n model.add(LeakyReLU(alpha=0.1, name=f'leaky_{block_index}'))\n elif name == 'detection':\n s = section['side']\n b = section['num']\n c = section['classes']\n\n model.add(YOLODetection(s, b, c, name=f'detection_{block_index}'))\n \n block_index += 1\n \n return model","title":""},{"docid":"c07261d07ed5de244fe048c0088a0cd1","score":"0.59579855","text":"def make_net(in_channels, conf, include_last_relu=True):\r\n def make_layer(layer_cfg):\r\n nonlocal in_channels\r\n \r\n # Possible patterns:\r\n # ( 256, 3, {}) -> conv\r\n # ( 256,-2, {}) -> deconv\r\n # (None,-2, {}) -> bilinear interpolate\r\n # ('cat',[],{}) -> concat the subnetworks in the list\r\n #\r\n # You know it would have probably been simpler just to adopt a 'c' 'd' 'u' naming scheme.\r\n # Whatever, it's too late now.\r\n if isinstance(layer_cfg[0], str):\r\n layer_name = layer_cfg[0]\r\n\r\n if layer_name == 'cat':\r\n nets = [make_net(in_channels, x) for x in layer_cfg[1]]\r\n layer = Concat([net[0] for net in nets], layer_cfg[2])\r\n num_channels = sum([net[1] for net in nets])\r\n else:\r\n num_channels = layer_cfg[0]\r\n kernel_size = layer_cfg[1]\r\n\r\n if kernel_size > 0:\r\n layer = nn.Conv2d(in_channels, num_channels, kernel_size, **layer_cfg[2])\r\n else:\r\n if num_channels is None:\r\n layer = InterpolateModule(scale_factor=-kernel_size, mode='bilinear', align_corners=False, **layer_cfg[2])\r\n else:\r\n layer = nn.ConvTranspose2d(in_channels, num_channels, -kernel_size, **layer_cfg[2])\r\n \r\n in_channels = num_channels if num_channels is not None else in_channels\r\n\r\n # Don't return a ReLU layer if we're doing an upsample. This probably doesn't affect anything\r\n # output-wise, but there's no need to go through a ReLU here.\r\n # Commented out for backwards compatibility with previous models\r\n # if num_channels is None:\r\n # return [layer]\r\n # else:\r\n return [layer, nn.ReLU(inplace=True)]\r\n\r\n # Use sum to concat together all the component layer lists\r\n net = sum([make_layer(x) for x in conf], [])\r\n if not include_last_relu:\r\n net = net[:-1]\r\n\r\n return nn.Sequential(*(net)), in_channels","title":""},{"docid":"963218937b4ac07330b34e4597ce447b","score":"0.59327227","text":"def _build_network(self, X, Z, alpha):\n\n num_layers = self.current_level\n filters = self.filters[:(num_layers+1)]\n d_filters_crop = filters[::-1] # reversed\n g_filters_crop = filters\n\n print num_layers, d_filters_crop, g_filters_crop\n\n print \"Building new graph with {0:d} layers...\".format(num_layers)\n with tf.variable_scope('GAN', reuse=tf.AUTO_REUSE):\n\n # generator discriminator pair\n g_layers, Gz_raw = self.generator(Z, g_filters_crop)\n\n # resize the real input\n X_resized = tf.image.resize_images(X,\n self.current_size,\n align_corners=True)\n\n # make a mixed output if we have more than one layer...\n if num_layers > 0:\n\n prev_Gz = double_size(g_layers[-2])\n Gz = tf.add(alpha*Gz_raw, (1.-alpha)*prev_Gz)\n\n # do the same for the real data\n prev_X = double_size(half_size(X_resized))\n X_resized = tf.add(alpha*X_resized, (1.-alpha)*prev_X)\n\n else:\n Gz = Gz_raw\n\n # discriminator for arbitrary inputs\n d_layers, Dz = self.discriminator(Gz, d_filters_crop)\n _, Dx = self.discriminator(X_resized, d_filters_crop)\n\n print X_resized.shape, Gz.shape\n\n # sanity check, make sure the same number of layers in each network\n assert(len(g_layers) == len(d_layers))\n\n # operations to mix the input and generated\n r = tf.random_uniform(shape=[self.batch_size, 1, 1, 1],\n minval=0.0,\n maxval=1.0,\n dtype=tf.float32)\n mix = tf.add(r*X_resized, (1-r)*Gz)\n _, Dmix = self.discriminator(mix, d_filters_crop)\n\n\n # now make the Wasserstein Loss Function\n with tf.variable_scope('WGAN-GP_loss'):\n loss_lambda = 10.\n\n grad = tf.gradients(Dmix, [mix])[0]\n grad_normed = tf.sqrt(tf.reduce_sum(tf.square(grad),[1,2,3]))\n # grad_penalty = tf.square((grad_normed-1.0))\n lipschitz_penalty = tf.square(tf.maximum((grad_normed-1.0), 0))\n scaled_penalty = loss_lambda*lipschitz_penalty\n eps_penalty = 0.001 * tf.square(Dx)\n\n g_loss = tf.reduce_mean(-Dz)\n d_loss = tf.reduce_mean(-Dx + Dz + scaled_penalty + eps_penalty)\n\n # hand back the un-mixed Gz\n return Gz_raw, d_loss, g_loss","title":""},{"docid":"65a37ea7d431362e80342f0469e913cb","score":"0.59243923","text":"def create_output_layers(self):\n output_layers = nn.ModuleList([])\n input_dim = self.input_dim_into_final_layer\n if not isinstance(self.layers_info[-1][0], list) : self.layers_info[-1] = [self.layers_info[-1]]\n for output_layer in self.layers_info[-1]:\n self.create_and_append_layer(input_dim, output_layer, output_layers)\n return output_layers","title":""},{"docid":"3d17603f0f93f50b14273661ec943e9e","score":"0.59229696","text":"def spawn_network(patch_size: int):\n name = \"tprime_psize{}\".format(patch_size)\n inputs = keras.layers.Input(shape=(patch_size, patch_size, 3))\n if (patch_size == 17):\n conv1 = _conv2d(inputs, n_filter=128, ksize=5)\n conv2 = _conv2d(conv1, n_filter=256, ksize=5)\n conv3 = _conv2d(conv2, n_filter=256, ksize=5)\n conv4 = _conv2d(conv3, n_filter=128, ksize=5)\n decode = _conv2d(conv4, n_filter=512, ksize=1)\n elif (patch_size == 33):\n conv1 = _conv2d(inputs, n_filter=128, ksize=3)\n maxp1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv1)\n conv2 = _conv2d(maxp1, n_filter=256, ksize=5)\n maxp2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv2)\n conv3 = _conv2d(maxp2, n_filter=256, ksize=2)\n conv4 = _conv2d(conv3, n_filter=128, ksize=4)\n decode = _conv2d(conv4, n_filter=512, ksize=1)\n elif (patch_size==65):\n conv1 = _conv2d(inputs, n_filter=128, ksize=5)\n maxp1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv1)\n conv2 = _conv2d(maxp1, n_filter=128, ksize=5)\n maxp2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv2)\n conv3 = _conv2d(maxp2, n_filter=128, ksize=5)\n maxp3 = MaxPool2D(pool_size=(2, 2), strides=2)(conv3)\n conv4 = _conv2d(maxp3, n_filter=256, ksize=4)\n conv5 = _conv2d(conv4, n_filter=128, ksize=1)\n decode = _conv2d(conv5, n_filter=512, ksize=1)\n else:\n raise ValueError(\"Unspported size: {}\".format(patch_size))\n return keras.Model(inputs=inputs, outputs=decode, name=name)","title":""},{"docid":"4bc9e116a4ced17639773b46c2eec06f","score":"0.5920516","text":"def ghostnet_base(inputs,\r\n\t\t\t\t final_endpoint=None,\r\n\t\t\t\t min_depth=8,\r\n\t\t\t\t depth_multiplier=1.0,\r\n\t\t\t\t depth=1.0,\r\n\t\t\t\t conv_defs=None,\r\n\t\t\t\t output_stride=None,\r\n\t\t\t\t dw_code=None,\r\n\t\t\t\t ratio_code=None,\r\n\t\t\t\t se=1,\r\n\t\t\t\t scope=None,\r\n\t\t\t\t is_training=False):\r\n\r\n\tdef depth(d):\r\n\t\td = max(int(d * depth_multiplier), min_depth)\r\n\t\td = round(d / 4) * 4\r\n\t\treturn d\r\n\r\n\tend_points = {}\r\n\r\n\t# Used to find thinned depths for each layer.\r\n\tif depth_multiplier <= 0:\r\n\t\traise ValueError('depth_multiplier is not greater than zero.')\r\n\r\n\tif conv_defs is None:\r\n\t\tconv_defs = _CONV_DEFS_0\r\n\r\n\tif dw_code is None or len(dw_code) < len(conv_defs):\r\n\t\tdw_code = [3] * len(conv_defs)\r\n\tprint('dw_code', dw_code)\r\n\r\n\tif ratio_code is None or len(ratio_code) < len(conv_defs):\r\n\t\tratio_code = [2] * len(conv_defs)\r\n\tprint('ratio_code', ratio_code)\r\n\r\n\tse_code = [x.se for x in conv_defs]\r\n\tprint('se_code', se_code)\r\n\r\n\tif final_endpoint is None:\r\n\t\tfinal_endpoint = 'Conv2d_%d' % (len(conv_defs) - 1)\r\n\r\n\tif output_stride is not None and output_stride not in [8, 16, 32]:\r\n\t\traise ValueError('Only allowed output_stride values are 8, 16, 32.')\r\n\r\n\twith tf.variable_scope(scope, 'MobilenetV2', [inputs]):\r\n\r\n\t\t# The current_stride variable keeps track of the output stride of the\r\n\t\t# activations, i.e., the running product of convolution strides up to the\r\n\t\t# current network layer. This allows us to invoke atrous convolution\r\n\t\t# whenever applying the next convolution would result in the activations\r\n\t\t# having output stride larger than the target output_stride.\r\n\t\tcurrent_stride = 1\r\n\r\n\t\t# The atrous convolution rate parameter.\r\n\t\trate = 1\r\n\t\tnet = inputs\r\n\t\tin_depth = 3\r\n\t\tgi = 0\r\n\t\tfor i, conv_def in enumerate(conv_defs):\r\n\t\t\tprint('---')\r\n\t\t\tend_point_base = 'Conv2d_%d' % i\r\n\t\t\tif output_stride is not None and current_stride == output_stride:\r\n\t\t\t\t# If we have reached the target output_stride, then we need to employ\r\n\t\t\t\t# atrous convolution with stride=1 and multiply the atrous rate by the\r\n\t\t\t\t# current unit's stride for use in subsequent layers.\r\n\t\t\t\tlayer_stride = 1\r\n\t\t\t\tlayer_rate = rate\r\n\t\t\t\trate *= conv_def.stride\r\n\t\t\telse:\r\n\t\t\t\tlayer_stride = conv_def.stride\r\n\t\t\t\tlayer_rate = 1\r\n\t\t\t\tcurrent_stride *= conv_def.stride\r\n\r\n\t\t\t# change last bottleneck\r\n\t\t\tif i + 2 == len(conv_defs):\t\t\t# remove last two head layers\r\n\t\t\t\treturn [\r\n\t\t\t\t\tend_points['Conv2d_1_residual'],\r\n\t\t\t\t\tend_points['Conv2d_3_residual'],\r\n\t\t\t\t\tend_points['Conv2d_5_residual'],\r\n\t\t\t\t\tend_points['Conv2d_11_residual'],\r\n\t\t\t\t\tend_points['Conv2d_16_residual']\r\n\t\t\t\t]\r\n\r\n\t\t\telif isinstance(conv_def, Conv):\r\n\t\t\t\tend_point = end_point_base\r\n\t\t\t\tnet = ConvBlock(net, depth(conv_def.depth), conv_def.kernel, stride=conv_def.stride,\r\n\t\t\t\t\t\t\t\tname='ConvBlock_{}'.format(i), is_training=is_training)\r\n\t\t\t\tend_points[end_point] = net\r\n\r\n\t\t\t# Bottleneck block.\r\n\t\t\telif isinstance(conv_def, Bottleneck):\r\n\t\t\t\t# Stride > 1 or different depth: no residual part.\r\n\t\t\t\tif layer_stride == 1 and in_depth == conv_def.depth:\r\n\t\t\t\t\tres = net\r\n\t\t\t\telse:\r\n\t\t\t\t\tres = DepthConv(net, conv_def.kernel, stride=layer_stride, name='Bottleneck_block_{}_shortcut_dw'.format(i))\r\n\t\t\t\t\tres = tf.layers.batch_normalization(res, training=is_training, name='Bottleneck_block_{}_shortcut_dw_BN'.format(i), axis=1)\r\n\t\t\t\t\tres = ConvBlock(res, depth(conv_def.depth), (1, 1), (1, 1),\r\n\t\t\t\t\t\t\t\t\tname='Bottleneck_block_{}_shortcut_1x1'.format(i), is_training=is_training)\r\n\r\n\t\t\t\t# Increase depth with 1x1 conv.\r\n\t\t\t\tend_point = end_point_base + '_up_pointwise'\r\n\t\t\t\tnet = MyConv('Bottleneck_block_{}_up_pointwise'.format(i), net, depth(in_depth * conv_def.factor), [1, 1],\r\n\t\t\t\t\t\t\t dw_code[gi],\r\n\t\t\t\t\t\t\t ratio_code[gi], strides=1, use_bias=False, is_training=is_training, activation=True)\r\n\r\n\t\t\t\tend_points[end_point] = net\r\n\r\n\t\t\t\t# Depthwise conv2d.\r\n\t\t\t\tif layer_stride > 1:\r\n\t\t\t\t\tend_point = end_point_base + '_depthwise'\r\n\t\t\t\t\tnet = DepthConv(net, conv_def.kernel, stride=layer_stride, name='Bottleneck_block_{}_depthwise'.format(i))\r\n\t\t\t\t\tnet = tf.layers.batch_normalization(net, training=is_training, name='Bottleneck_block_{}_depthwise_BN'.format(i), axis=1)\r\n\t\t\t\t\tend_points[end_point] = net\r\n\t\t\t\t# SE\r\n\t\t\t\tif se_code[i] > 0 and se > 0:\r\n\t\t\t\t\tend_point = end_point_base + '_se'\r\n\t\t\t\t\tnet = SELayer(net, depth(in_depth * conv_def.factor), 4)\r\n\t\t\t\t\tend_points[end_point] = net\r\n\r\n\t\t\t\t# Downscale 1x1 conv.\r\n\t\t\t\tnet = MyConv('Bottleneck_block_{}_down_pointwise'.format(i), net, depth(conv_def.depth), [1, 1], dw_code[gi],\r\n\t\t\t\t\t\t\t ratio_code[gi], strides=1, use_bias=False, is_training=is_training, activation=False)\r\n\t\t\t\tnet = tf.layers.batch_normalization(net, training=is_training, name='Bottleneck_block_{}_down_pointwise_BN'.format(i), axis=1)\r\n\r\n\t\t\t\tgi += 1\r\n\r\n\t\t\t\t# Residual connection?\r\n\t\t\t\tend_point = end_point_base + '_residual'\r\n\t\t\t\tnet = tf.add(res, net, name='Bottleneck_block_{}_Add'.format(i)) if res is not None else net\r\n\t\t\t\tend_points[end_point] = net\r\n\r\n\t\t\tin_depth = conv_def.depth\r\n\t\t\t# Final end point?\r\n\r\n\t\t\tif final_endpoint in end_points:\r\n\r\n\t\t\t\treturn [\r\n\t\t\t\t\tend_points['Conv2d_1_residual'],\r\n\t\t\t\t\tend_points['Conv2d_3_residual'],\r\n\t\t\t\t\tend_points['Conv2d_5_residual'],\r\n\t\t\t\t\tend_points['Conv2d_11_residual'],\r\n\t\t\t\t\tend_points['Conv2d_16_residual']\r\n\t\t\t\t]","title":""},{"docid":"d08d97a73b33ce6e858e877fa8ba5024","score":"0.5914832","text":"def setup(self, layer):\n\n layers = OrderedDict()\n self.layer_map = self.CFG_MAP[self.cnn_type]\n\n if self.cnn_type.startswith('vgg'):\n assert len(self._base_cnn.features) == len(self.layer_map)\n\n # There's no named modules inside VGG, all integers\n for module, params in zip(self.layer_map, self._base_cnn.features):\n layers[module] = params\n # 'penultimate' takes all conv layers by default\n if layer != 'penultimate' and module == layer:\n break\n\n if layer == 'penultimate':\n # Add flatten layer\n layers['flatten'] = Flatten()\n\n # Exclude final classification layer\n for i in range(len(self._base_cnn.classifier) - 1):\n mod = self._base_cnn.classifier[i]\n name = \"{}{}\".format(mod.__class__.__name__, i)\n layers[name] = mod\n\n elif self.cnn_type.startswith('resnet'):\n assert layer in self.layer_map.values(), \\\n \"The given layer {} is not known.\".format(layer)\n for module, params in self._base_cnn.named_children():\n # Add the layer with our naming scheme\n layers[self.layer_map[module]] = params\n # If we've hit the extraction point, break the loop\n if self.layer_map[module] == layer:\n break\n\n self.cnn = torch.nn.Sequential(layers)\n\n # Disable requires_grad by default\n self.set_requires_grad(False)","title":""},{"docid":"c88cd32e853b1a69a3bbdde80d98a1bb","score":"0.59111285","text":"def FCDenseNet_103Layers(input_shape, \n class_num, \n growth_rate,\n layer_depths,\n model_name,\n probes=[[],[]], \n **kwargs):\n \n # growth rate\n k = growth_rate\n\n # dimension\n dim = len(input_shape) - 1\n\n # first layer\n # dim=2 : [48, [3,3]]\n # dim=3 : [48, [3,3,3]]\n pw_dict = {'first': ['conv', [48, [3]*dim], 'MA']}\n\n \n ''' DB+TD '''\n # number of Dense block layers in the downward path\n Ls = layer_depths[:5]\n for i in range(len(Ls)):\n # dense block\n DB = {'DB%d_%d'%(i,j): ['conv', [k, [3]*dim], 'BAM']\n for j in range(Ls[i])}\n pw_dict.update(DB)\n # transition down\n nfmap = 48+np.sum(Ls[:i+1])*k\n TD = {'T_%d'%i: ['conv', [nfmap, [1]*dim], 'BMA'],\n 'pool_%d'%i: ['pool', [2]*dim]}\n pw_dict.update(TD)\n\n ''' Bottleneck Dense Block''' \n L = 15\n BT = {'BottleDB_%d'%j: ['conv', [k, [3]*dim], 'BAM']\n for j in range(L)}\n pw_dict.update(BT)\n\n ''' TU+DB '''\n Ls = np.flip(Ls+[layer_depths[5]], 0)\n for i in range(1,len(Ls)):\n # transition up\n nfmap = Ls[i-1]*k\n TU = {'TU_%d'%(i-1): ['conv_transpose', \n [nfmap, [3]*dim, [2]*dim], 'M']}\n pw_dict.update(TU)\n # dense block\n DB = {'DB%d_%d'%(5+i-1,j): ['conv', [k, [3]*dim], 'BAM']\n for j in range(Ls[i])}\n pw_dict.update(DB)\n\n # last layer\n pw_dict.update({'last': ['conv', [class_num,[1]*dim], 'M']})\n\n\n ''' Establishing the Skip Connections '''\n layer_names = np.array(list(pw_dict.keys()))\n stype = 'con' # concatenation\n skips = []\n\n # intra-DB skip connections of downward path\n for i in range(5):\n # starting index\n start_ind = np.where('DB%d_0'%i==layer_names)[0][0]\n # number of layers\n L = np.sum(['DB%d'%i in name for \n name in layer_names])\n for j in range(L):\n skips += [[start_ind-1+j,\n list(np.arange(start_ind+1+j,\n start_ind+L+1)),\n stype]]\n # bottleneck DB\n start_ind = np.where('BottleDB_0'==layer_names)[0][0]\n L = np.sum(['BottleDB' in name for name in layer_names])\n # for bottleneck, the input of the first layer (or output\n # of pool_4) is connected to all intermediate layers\n # but the last output (input of TU_0)\n skips += [[start_ind-1, \n list(np.arange(start_ind+1,\n start_ind+L)), stype]]\n # but the rest will be connected to the last one too\n for j in range(1,L):\n skips += [[start_ind-1+j, \n list(np.arange(start_ind+1+j,\n start_ind+L+1)),\n stype]]\n\n # inter-DB skip connections\n skipped_nodes = np.array([skips[i][0] for i in\n range(len(skips))])\n # NOTE1: we know that output nodes of DBs in downward\n # path are not already skipped somewhere else, hence\n # we don't need to check that with an if\n #\n # NOTE2: more importantly, for source nodes, we only \n # save their outuputs before combining them with other\n # sources. For example, in the first connection below\n # the output of DB4_11 (end node of DB4) has to be\n # connected to the input of DB5. However, the output\n # node of DB4 (which is the same as input node of\n # T_4) is also combined with many other previous\n # nodes. Hence, what we actually have to do here is\n # to combine output node of DB4_11 together with \n # outputs of all other previous nodes that have\n # already combined with input of T_4. In other words,\n # input of DB5_0 should be the destination of (output\n # of) DB4_11 and all connections of T_4\n # \n # output of DB4_11 + connections to T_4 \n # --> \n # output of TU_0 (input of DB5_0)\n DB4_end_node = np.where(layer_names=='DB4_{}'.format(layer_depths[4]-1))[0][0]\n DB5_start_node = np.where(layer_names=='DB5_0')[0][0]\n T4_node = np.where(layer_names=='T_4')[0][0]\n # go through all nodes, and if T_4 was in their\n # destination, put DB5_0 as a destination too\n for i in range(len(skips)):\n if T4_node in skips[i][1]:\n skips[i][1] += [DB5_start_node]\n skips += [[DB4_end_node,[DB5_start_node], stype]]\n\n # output of DB3 + connections to T_3\n # --> \n # output of TU_1 (input of DB6_0)\n DB3_end_node = np.where(layer_names=='DB3_{}'.format(layer_depths[3]-1))[0][0]\n DB6_start_node = np.where(layer_names=='DB6_0')[0][0]\n T3_node = np.where(layer_names=='T_3')[0][0]\n for i in range(len(skips)):\n if T3_node in skips[i][1]:\n skips[i][1] += [DB6_start_node]\n skips += [[DB3_end_node, [DB6_start_node], stype]]\n\n # output of DB2 + connections to T_2 \n # --> \n # output of TU_2 (input of DB7_0)\n DB2_end_node = np.where(layer_names=='DB2_{}'.format(layer_depths[2]-1))[0][0]\n DB7_start_node = np.where(layer_names=='DB7_0')[0][0]\n T2_node = np.where(layer_names=='T_2')[0][0]\n for i in range(len(skips)):\n if T2_node in skips[i][1]:\n skips[i][1] += [DB7_start_node]\n skips += [[DB2_end_node, [DB7_start_node], stype]]\n\n # output of DB1 + connections to T_1 \n # -->\n # output of TU_3 (input of DB8_0)\n DB1_end_node = np.where(layer_names=='DB1_{}'.format(layer_depths[1]-1))[0][0]\n DB8_start_node = np.where(layer_names=='DB8_0')[0][0]\n T1_node = np.where(layer_names=='T_1')[0][0]\n for i in range(len(skips)):\n if T1_node in skips[i][1]:\n skips[i][1] += [DB8_start_node]\n skips += [[DB1_end_node, [DB8_start_node], stype]]\n\n # output of DB0 + connections to T_0\n # --> \n # output of TU_4 (input of DB9_0)\n DB0_end_node = np.where(layer_names=='DB0_{}'.format(layer_depths[0]-1))[0][0]\n DB9_start_node = np.where(layer_names=='DB9_0')[0][0]\n T0_node = np.where(layer_names=='T_0')[0][0]\n for i in range(len(skips)):\n if T0_node in skips[i][1]:\n skips[i][1] += [DB9_start_node]\n skips += [[DB0_end_node, [DB9_start_node], stype]]\n\n # After estalishing the skip connections between the \n # downward and upward paths, for the intra-DB skips of\n # the upward path. It is different than the intra-DB skips\n # of downward path, because here for connecting the \n # layers inside a DB, the first layer itself has \n # combination of outputs of many other layers from\n # the downward path that should be taken into account.\n # For instance, skips of DB5, we should also consider layers\n # of DB4, which hare connected to the input of DB5_0\n for i in range(5,9):\n # starting index\n start_ind = np.where('DB%d_0'%i==layer_names)[0][0]\n # first do the connections to the last layer\n # because this one does not receive the first layer\n # number of layers\n L = np.sum(['DB%d'%i in name for \n name in layer_names])\n skips += [[start_ind-1, \n list(np.arange(start_ind+1,\n start_ind+L)), stype]]\n for j in range(1,L):\n skips += [[start_ind-1+j,\n list(np.arange(start_ind+1+j,\n start_ind+L+1)),\n stype]]\n # also add the intermediate layers as destination\n # of the skipped connections (i.e. those layers\n # that already had the start_ind as their destination)\n for j in range(len(skips)):\n if start_ind in skips[j][1]:\n skips[j][1] += list(np.arange(start_ind+1,\n start_ind+L))\n\n # for DB9 (last DB) do the same thing, except its input\n # should also be connected to the last layer\n start_ind = np.where('DB9_0'==layer_names)[0][0]\n L = np.sum(['DB%d'%i in name for name in layer_names])\n for j in range(L):\n skips += [[start_ind-1+j,\n list(np.arange(start_ind+1+j,\n start_ind+L+1)),stype]]\n for j in range(len(skips)):\n if start_ind in skips[j][1]:\n skips[j][1] += list(np.arange(start_ind+1,\n start_ind+L+1))\n\n # sorting the skips in terms of the sources\n sort_inds = np.argsort([skips[i][0] for i in range(len(skips))])\n sorted_skips = []\n for ind in sort_inds:\n sorted_skips += [skips[ind]]\n\n\n ''' Specifying Drop-out Layers '''\n # including all the layers, except max-pooling and\n # upscaling layers\n dp_layers = np.arange(len(pw_dict)).tolist()\n no_dp = ['pool_0', 'pool_1', 'pool_2', 'pool_3',\n 'pool_4', 'TU_0', 'TU_1', 'TU_2', 'TU_3',\n 'TU_4']\n for name in no_dp:\n loc = np.where(layer_names==name)[0][0]\n dp_layers.remove(loc)\n dp_rate = 0.1\n\n \n ''' Creating the Model '''\n x = tf.placeholder(tf.float32, [None,]+input_shape)\n model = NN_extended.CNN(x, pw_dict, model_name, \n sorted_skips,None,\n [dp_layers, dp_rate],\n probes,\n **kwargs)\n\n return model","title":""},{"docid":"57b75396319b93aa5a18b1ec2f280840","score":"0.5904189","text":"def _build_net(self):\n # network define\n x = constant_op.constant(1.0, shape=[1, 28, 28, 3], dtype=dtypes.float32)\n is_training = ops.convert_to_tensor(True)\n\n # conv1\n x = self._conv2d(x, 'conv1', self.conv1_kernel, self.conv1_bias)\n x = self._batchnorm(x, 'bn1', self.bn1_gamma, self.bn1_beta, \\\n self.bn1_moving_mean, self.bn1_moving_variance, \\\n is_training)\n x = nn_ops.relu(x)\n\n # conv2a\n residual_x = self._conv2d(x,\n 'conv2a_1',\n self.conv2a_1_kernel,\n self.conv2a_1_bias)\n residual_x = self._batchnorm(residual_x,\n 'bn2a_1',\n self.bn2a_1_gamma,\n self.bn2a_1_beta,\n self.bn2a_1_moving_mean,\n self.bn2a_1_moving_variance,\n is_training)\n residual_x = nn_ops.relu(residual_x)\n\n card_in = gap_finetune.split(residual_x,\n num_or_size_splits=4,\n axis=-1,\n gap=self.gap,\n gap_vars=self.gap_vars)\n card_out = []\n for i in range(4):\n out = self._conv2d(card_in[i],\n 'conv2a_2_%d'%i,\n self.conv2a_2_kernel[i],\n self.conv2a_2_bias[i])\n card_out.append(out)\n residual_x = array_ops.concat(card_out, axis=-1)\n residual_x = self._batchnorm(residual_x,\n 'bn2a_2',\n self.bn2a_2_gamma,\n self.bn2a_2_beta,\n self.bn2a_2_moving_mean,\n self.bn2a_2_moving_variance,\n is_training)\n residual_x = nn_ops.relu(residual_x)\n\n residual_x = self._conv2d(residual_x,\n 'conv2a_3',\n self.conv2a_3_kernel,\n self.conv2a_3_bias)\n residual_x = self._batchnorm(residual_x,\n 'bn2a_3',\n self.bn2a_3_gamma,\n self.bn2a_3_beta,\n self.bn2a_3_moving_mean,\n self.bn2a_3_moving_variance,\n is_training)\n\n # conv2b\n shortcut_x = self._conv2d(x, 'conv2b', self.conv2b_kernel, self.conv2b_bias)\n shortcut_x = self._batchnorm(shortcut_x,\n 'bn2b',\n self.bn2b_gamma,\n self.bn2b_beta,\n self.bn2b_moving_mean,\n self.bn2b_moving_variance,\n is_training)\n\n x = nn_ops.relu(residual_x + shortcut_x)\n\n # conv3\n x = self._conv2d(x, 'conv3', self.conv3_kernel, self.conv3_bias)\n x = self._batchnorm(x, 'bn3', self.bn3_gamma, self.bn3_beta, \\\n self.bn3_moving_mean, self.bn3_moving_variance,\\\n is_training)\n conv_output = nn_ops.relu(x)\n\n # loss\n self.loss = nn_ops.l2_loss(conv_output)","title":""},{"docid":"1fabd55903fc34d6d876270b74b58a2e","score":"0.58965385","text":"def get_net(batch_size, num_actions=18, image_shape=(4, 84, 84), dtype=\"float32\", layout=\"NCHW\"):\n data_shape = (batch_size,) + image_shape\n data = relay.var(\"data\", shape=data_shape, dtype=dtype)\n\n bias_axis = layout.index(\"C\")\n\n conv1_bias = relay.var(\"conv1_bias\")\n conv1 = layers.conv2d(\n data,\n kernel_size=(8, 8),\n strides=(4, 4),\n padding=(0, 0),\n channels=32,\n name=\"conv1\",\n data_layout=layout,\n kernel_layout=layers.conv_kernel_layout(layout),\n )\n conv1 = relay.nn.bias_add(conv1, conv1_bias, bias_axis)\n relu1 = relay.nn.relu(conv1)\n\n conv2_bias = relay.var(\"conv2_bias\")\n conv2 = layers.conv2d(\n relu1,\n kernel_size=(4, 4),\n strides=(2, 2),\n padding=(0, 0),\n channels=64,\n name=\"conv2\",\n data_layout=layout,\n kernel_layout=layers.conv_kernel_layout(layout),\n )\n conv2 = relay.nn.bias_add(conv2, conv2_bias, bias_axis)\n relu2 = relay.nn.relu(conv2)\n\n conv3_bias = relay.var(\"conv3_bias\")\n conv3 = layers.conv2d(\n relu2,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=(0, 0),\n channels=64,\n name=\"conv3\",\n data_layout=layout,\n kernel_layout=layers.conv_kernel_layout(layout),\n )\n conv3 = relay.nn.bias_add(conv3, conv3_bias, bias_axis)\n relu3 = relay.nn.relu(conv3)\n\n bf1 = relay.nn.batch_flatten(relu3)\n dense1 = layers.dense_add_bias(bf1, units=512, name=\"dense1\")\n relu4 = relay.nn.relu(dense1)\n dense2 = layers.dense_add_bias(relu4, units=num_actions, name=\"dense2\")\n\n args = relay.analysis.free_vars(dense2)\n return relay.Function(args, dense2)","title":""},{"docid":"ccd113f9b6e77dd2c6ddde8a5f37119d","score":"0.58961946","text":"def _buildNetwork(self):\n\n #Core Network (reusable)\n conv1 = self._convLayer(self.obsPH, filterDims = 8, outputFilters = 16, strides = 4,name=\"conv1\", activation = tf.nn.relu)\n conv2 = self._convLayer(conv1 , filterDims = 4, outputFilters = 32, strides = 2,name=\"conv2\", activation = tf.nn.relu)\n fc1 = self._fcLayer (conv2 , numOutputs = 256,name=\"fc1\", activation = tf.nn.relu )\n\n #Outputs\n self.valueEst = tf.squeeze(self._fcLayer(fc1, numOutputs = 1,name=\"valueEst\", activation = lambda x: x), axis=[1])\n self.policyProb = self._fcLayer(fc1, numOutputs = self.actionSpace,name=\"policyProb\", activation = tf.nn.softmax)\n \n #Log results\n self.log.addHist(\"valueEst\", self.valueEst)\n self.log.addHist(\"policyProb\", self.policyProb)","title":""},{"docid":"7668b1092f2c12bddc3d1c9bf25763bd","score":"0.5895739","text":"def layers(self):\n if self._layers is not None:\n return self._layers\n\n neurons = self._config['neurons']\n\n self._layers = [\n partial(self.convolutional_layer, [5, 5, 1, 32], [32]),\n partial(self.convolutional_layer, [5, 5, 32, 64], [64]),\n partial(self.connected_layer,\n [7 * 7 * 64, neurons], [neurons], [-1, 7 * 7 * 64]),\n partial(self.dropout_layer, self.keep_prob),\n partial(self.readout_layer, [neurons, self.DIGITS], [self.DIGITS])\n ]\n\n return self._layers","title":""},{"docid":"3452bd6fa78532fe6451c643a87efd15","score":"0.5890459","text":"def buildNN(architecture,training_params,input_dim):\n current_units = input_dim\n \n model = Sequential()\n is_first_layer = True\n \n for layer in architecture:\n layer_name = layer.keys()[0]\n layer_params = layer[layer_name]\n if layer_name == 'Activation':\n model.add(Activation(layer_params['type']))\n if layer_name == 'PReLU':\n model.add(PReLU())\n if layer_name == 'Dropout':\n if is_first_layer:\n model.add(Dropout(layer_params['p'], input_shape=(None, current_units)))\n else:\n model.add(Dropout(layer_params['p']))\n if layer_name == 'Dense':\n model.add(Dense(layer_params['num_units']))\n current_units = layer_params['num_units']\n if layer_name == 'Conv':\n # a filter covers all predictions for a single time sample\n # strides are made between time samples\n model.add(Conv2D(layer_params['nb_filters'], 1))\n current_units = layer_params['nb_filters']*training_params['num_strides']\n if layer_name == 'GRU':\n model.add(GRU(layer_params['num_units'], \n input_shape=((None, input_dim) if is_first_layer else None),\n dropout=(layer_params['dropout'] if 'dropout' in layer_params else 0.0),\n return_sequences=layer_params['next_GRU']))\n current_units = layer_params['num_units']\n if layer_name == 'LSTM':\n model.add(LSTM(layer_params['num_units'], return_sequences=layer_params['next_GRU']))\n current_units = layer_params['num_units']\n if layer_name == 'BatchNormalization':\n model.add(BatchNormalization())\n if layer_name == 'Flatten':\n model.add(Flatten())\n if layer_name == 'Output':\n model.add(Dense(N_EVENTS))\n model.add(Activation('sigmoid'))\n is_first_layer = False\n\n if not training_params.has_key('optim') or training_params['optim'] == 'sgd':\n optim = SGD(lr=training_params['lr'], decay=float(training_params['decay']), momentum=training_params['momentum'], nesterov=True) \n elif training_params['optim'] == 'adam':\n optim = Adam(lr=training_params['lr'])\n \n model.compile(loss='binary_crossentropy', optimizer=optim)\n \n return model","title":""},{"docid":"24c9126f9b64fa6d7ca79a84bd0643df","score":"0.5889604","text":"def load_networks(self, epoch):\n for model_name in self.model_names:\n if isinstance(model_name, str):\n load_filename = '{:s}_net_{:s}.pth'.format(epoch, model_name)\n load_path = os.path.join(self.load_net_dir, load_filename)\n # Use the str to get the attribute aka the network (self.netG / self.netD)\n net = getattr(self, 'net' + model_name)\n # If we use multi GPUs and apply the data parallel\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n print('Loading the model from %s' % load_path)\n state_dict = torch.load(load_path, map_location=self.device)\n if hasattr(state_dict, '_metadata'):\n del state_dict._metadata\n\n net.load_state_dict(state_dict)","title":""},{"docid":"bde20fbb7ed82e712438ff8a6867f4ff","score":"0.5888223","text":"def build(self, input_shape):\n params = self.get_config()\n for lid in range(params[\"num_layers\"]):\n self._stacking_layers.append([\n build_transformer_component({\n \"base_layer.class\": LightConvolutionLayer.__name__,\n \"base_layer.params\": dict(\n kernel_size=params[\"conv_kernel_size_list\"][lid],\n num_heads=params[\"num_conv_heads\"],\n conv_type=params[\"conv_type\"],\n conv_dim=params[\"conv_hidden_size\"],\n use_glu=params[\"glu_after_proj\"],\n weight_dropout_rate=params[\"conv_weight_dropout_rate\"],\n name=\"light_conv\"\n )},\n dropout_rate=params[\"layer_postprocess_dropout_rate\"],\n epsilon=params[\"layer_postprocess_epsilon\"]),\n build_transformer_component({\n \"base_layer.class\": MultiHeadAttention.__name__,\n \"base_layer.params\": dict(\n num_heads=params[\"num_attention_heads\"],\n num_units=input_shape[-1],\n attention_dropout_rate=params[\"attention_dropout_rate\"],\n attention_type=params[\"attention_type\"],\n name=\"encdec_attention\")},\n dropout_rate=params[\"layer_postprocess_dropout_rate\"],\n epsilon=params[\"layer_postprocess_epsilon\"]),\n build_transformer_component({\n \"base_layer.class\": TransformerFFN.__name__,\n \"base_layer.params\": dict(\n filter_size=params[\"filter_size\"],\n output_size=input_shape[-1],\n dropout_rate=params[\"ffn_dropout_rate\"],\n activation=params[\"ffn_activation\"],\n name=\"ffn\")},\n dropout_rate=params[\"layer_postprocess_dropout_rate\"],\n epsilon=params[\"layer_postprocess_epsilon\"])])\n self._output_norm_layer = tf.keras.layers.LayerNormalization(\n epsilon=params[\"layer_postprocess_epsilon\"],\n dtype=\"float32\", name=\"output_ln\")\n super(LightConvolutionDecoder, self).build(input_shape)","title":""},{"docid":"d0da8e992d5e67856dbd98a496a61121","score":"0.58795035","text":"def network(configs):\n tf.reset_default_graph()\n # input: historical talent flow\n flow_input = tf.placeholder(tf.float32,\n [configs[\"batch_size\"],\n configs[\"en_steps\"],\n configs[\"flow_features\"]],\n name=\"flow_input\")\n # input: stock series\n stock_input = tf.placeholder(tf.float32,\n [configs[\"batch_size\"],\n configs[\"en_steps\"],\n configs[\"stock_features\"]],\n name=\"stock_input\")\n # input: profiles\n profile_input = tf.placeholder(tf.float32,\n [configs[\"batch_size\"],\n configs[\"profile_features\"]],\n name=\"profile_input\")\n # result\n decoder_targets = tf.placeholder(tf.float32,\n [configs[\"batch_size\"],\n configs[\"de_steps\"]],\n name=\"decoder_targets\")\n ###########\n\n # profile encoder\n profile_vec = ann_layer(profile_input, configs[\"profile_cells\"],\n scope=\"profile_vec\")\n\n # flow encoder\n flow_outputs, flow_final_state = encoder(flow_input, scope=\"flow_encoder\",\n cell_units=configs[\"en_flow_units\"])\n # stock encoder\n stock_outputs, stock_final_state = encoder(stock_input, scope=\"stock_encoder\",\n cell_units=configs[\"en_stock_units\"])\n\n en_outputs = tf.concat([flow_outputs, stock_outputs], 2)\n en_final_state = tf.concat([flow_final_state, stock_final_state], 1)\n\n # decoder\n train_res, train_alphas, test_res, test_alphas = decoder(en_final_state,\n en_outputs, profile_vec,\n decoder_targets,\n configs[\"de_cell_units\"],\n configs[\"de_out_units\"],\n configs[\"de_steps\"],\n configs[\"attention_units\"])\n\n # loss function\n vars = tf.trainable_variables()\n lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars]) * configs[\"l2e\"]\n loss = lossL2 + tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(train_res, decoder_targets)), axis=1)))\n opt = tf.train.AdamOptimizer(configs[\"learning_rate\"]).minimize(loss)\n return dict(loss=loss, opt=opt, predictions=test_res,\n train_res=train_res,\n stock_input=stock_input,\n profile_input=profile_input,\n alphas=test_alphas,\n flow_input=flow_input,\n decoder_targets=decoder_targets)","title":""},{"docid":"bd0508b891a48d88c249932d51fc43a1","score":"0.5876715","text":"def build_layers(inputs):\n resolution = inputs.shape[1]\n if resolution > 2:\n kernel_shapes = ((1, 1), (3, 3), (3, 3), (1, 1))\n else:\n kernel_shapes = ((1, 1), (1, 1), (1, 1), (1, 1))\n\n conv_layers = []\n aux_conv_layers = []\n for layer_idx, kernel_shape in enumerate(kernel_shapes):\n is_last = layer_idx == _NUM_CONV_LAYER_PER_BLOCK - 1\n num_channels = self.output_channels if is_last else self.internal_channels\n weights_scale = self.last_weights_scale if is_last else 1.\n conv_layers.append(\n get_vdvae_convolution(\n num_channels,\n kernel_shape,\n weights_scale,\n name=f'c{layer_idx}',\n precision=self.precision))\n aux_conv_layers.append(\n get_vdvae_convolution(\n num_channels, (1, 1),\n 0.,\n name=f'aux_c{layer_idx}',\n precision=self.precision))\n\n return conv_layers, aux_conv_layers","title":""},{"docid":"920bc82978b87b89c0f2838f919415b8","score":"0.5874086","text":"def build_standard_net(layer_sizes, normalize, L2_reg=0.0, L1_reg=0.0, activation_function=relu,\n nll_func=mean_squared_error, num_outputs=1):\n # How to change the output size here?\n layer_sizes = layer_sizes + [num_outputs]\n # Add number of outputs here\n\n parser = WeightsParser()\n for i, shape in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n parser.add_weights(('weights', i), shape)\n parser.add_weights(('biases', i), (1, shape[1]))\n\n def predictions(W_vect, X):\n cur_units = X\n for layer in range(len(layer_sizes) - 1):\n cur_W = parser.get(W_vect, ('weights', layer))\n cur_B = parser.get(W_vect, ('biases', layer))\n cur_units = np.dot(cur_units, cur_W) + cur_B\n if layer < len(layer_sizes) - 2:\n if normalize:\n cur_units = batch_normalize(cur_units)\n cur_units = activation_function(cur_units)\n # Note, cur_units will not be normalized for the categorical_nll case.\n # will let categorical_nll handle it.\n return cur_units\n \n\n def loss(w, X, targets):\n assert len(w) > 0\n log_prior = -L2_reg * np.dot(w, w) / len(w) - L1_reg * np.mean(np.abs(w))\n preds = predictions(w, X)\n if nll_func == categorical_nll:\n return nll_func(preds, targets, num_outputs) - log_prior\n return nll_func(preds, targets) - log_prior\n\n return loss, predictions, parser","title":""},{"docid":"bdab737468a42818f73a91fdb9c8255c","score":"0.58727676","text":"def create_network_topology(n_layers, init_n_neurons, n_neurons_decay_factor, n_decaying_layers):\n\n random_network_topology = [init_n_neurons]*n_layers\n for decaying_layer in range(n_decaying_layers, 0, -1):\n random_network_topology[n_layers-decaying_layer] = \\\n int(random_network_topology[n_layers-decaying_layer-1] / n_neurons_decay_factor)\n\n return random_network_topology","title":""},{"docid":"fc3bb415e909723549d2dde2b144fb4f","score":"0.5865945","text":"def build_yolonet(module_params):\n # get the hyperparameters of the neural net.\n net_param = module_params.pop(0)\n channels = [int(net_param['channels'])]\n modules = nn.ModuleList()\n detect_param = module_params.pop(-1)\n\n # hyperparameters:\n momentum = float(net_param['momentum'])\n\n # detection layer\n\n for i, layer in enumerate(module_params):\n module = nn.Sequential()\n layer_type = layer['type']\n # convolutional layer\n if layer_type == \"convolutional\":\n kernel = int(layer['size'])\n out_channel = int(layer['filters'])\n stride = int(layer['stride'])\n # pad = int(layer['pad'])\n pad = (kernel - 1) // 2\n bn = int(layer['batch_normalize']\n ) if \"batch_normalize\" in layer else 0\n # print(type(kernel), type(out_channel), type(stride), type(pad), type(bn))\n\n conv_layer = nn.Conv2d(in_channels=channels[-1], out_channels=out_channel,\n kernel_size=kernel, stride=stride, padding=pad, bias=not bn)\n module.add_module(\"conv_layer_{}\".format(i), conv_layer)\n if bn:\n batch_norm = nn.BatchNorm2d(\n num_features=out_channel, momentum=momentum)\n module.add_module(\"batch_norm_{}\".format(i), batch_norm)\n if \"activation\" in layer and layer[\"activation\"] == \"leaky\":\n leaky = nn.LeakyReLU(negative_slope=0.1, inplace=False)\n module.add_module(\"leaky_relu_{}\".format(i), leaky)\n # update channels\n channels.append(out_channel)\n\n # max-pooling layer\n elif layer_type == \"maxpool\":\n kernel = int(layer['size'])\n stride = int(layer['stride'])\n maxpool = nn.MaxPool2d(kernel_size=kernel, stride=stride)\n module.add_module(\"max_pool_{}\".format(i), maxpool)\n\n # local\n elif layer_type == \"local\":\n kernel = int(layer['size'])\n stride = int(layer['stride'])\n pad = int(layer['pad'])\n out_channel = int(layer['filters']) * \\\n (kernel + pad) * (kernel + pad)\n gird_size = int(2 * kernel + stride)\n\n # local_layer = Local(in_channels=channels[-1], out_channels=out_channel,\n # kernel_size=kernel, stride=stride, padding=pad)\n module.add_module(\"Flatten_{}\".format(i), Flatten())\n local_layer = nn.Linear(in_features=(\n gird_size * gird_size * channels[-1]), out_features=out_channel)\n module.add_module(\"local_layer_{}\".format(i), local_layer)\n if \"activation\" in layer and layer[\"activation\"] == \"leaky\":\n leaky = nn.LeakyReLU(negative_slope=0.1, inplace=False)\n module.add_module(\"leaky_relu_{}\".format(i), leaky)\n # update channels\n channels.append(out_channel)\n\n # dropout\n elif layer_type == \"dropout\":\n prob = float(layer['probability'])\n dropout = nn.Dropout(p=prob, inplace=False)\n module.add_module(\"dropout_{}\".format(i), dropout)\n\n # connection layer\n elif layer_type == \"connected\":\n out_channel = int(layer['output'])\n if \"activation\" in layer and layer[\"activation\"] == \"linear\":\n linear = nn.Linear(\n in_features=channels[-1], out_features=out_channel)\n module.add_module(\"linear_{}\".format(i), linear)\n sigmoid_layer = nn.Sigmoid()\n module.add_module(\"sigmoid_{}\".format(i), sigmoid_layer)\n\n # skip the detection layer (detection_cuda will do detection)\n elif layer_type == \"detection\": # like yolo layer in yolo-v3\n continue\n\n # add module\n modules.append(module)\n # print(type(modules))\n return net_param, detect_param, modules","title":""},{"docid":"e2237214673b4f3082246952a89dd4d6","score":"0.58587134","text":"def create_layer(self, in_shape: list):\n if len(in_shape) == 4:\n batch, channels, h, w = in_shape\n elif len(in_shape) == 2:\n batch, channels = in_shape\n else:\n raise Exception('Invalid input with shape: ' + str(in_shape))\n\n reshape_layer: Optional[Reshape] = None\n img_flat_size = int(reduce(lambda x, y: x * y, in_shape) / batch)\n\n # Calculating out feature size, creating deep layer and reshaping if necessary\n if self.module_node.layer_type.value == nn.Conv2d:\n if len(in_shape) == 2:\n h = w = int(math.sqrt(img_flat_size / channels))\n reshape_layer = Reshape(batch, channels, h, w)\n\n # TODO make kernel size and stride a tuple\n window_size = self.module_node.layer_type.get_sub_value('conv_window_size')\n stride = self.module_node.layer_type.get_sub_value('conv_stride')\n padding = math.ceil((window_size - h) / 2)\n padding = padding if padding >= 0 else 0\n # dilation = 1\n\n # h_out = math.floor((h + 2 * padding - dilation * (window_size - 1) - 1) / stride + 1)\n # w_out = math.floor((w + 2 * padding - dilation * (window_size - 1) - 1) / stride + 1) # same cause square\n #\n # # If using max pooling out feature size changes\n # neat_reduction = self.module_node.layer_type.get_sub_value('reduction', return_mutagen=True)\n # if neat_reduction is not None and neat_reduction.value is not None: # Using max pooling\n # pool_size = neat_reduction.get_sub_value('pool_size')\n # h_out = math.ceil((h_out - pool_size) / pool_size + 1)\n # w_out = math.ceil((w_out - pool_size) / pool_size + 1)\n\n deep_layer = nn.Conv2d(channels, self.out_features, window_size, stride, padding)\n else: # self.module_node.layer_type.value == nn.Linear:\n if len(in_shape) != 2 or channels != img_flat_size:\n reshape_layer = Reshape(batch, img_flat_size)\n\n deep_layer = nn.Linear(img_flat_size, self.out_features)\n\n self.sequential = nn.Sequential(*[module for module in\n [reshape_layer, deep_layer, *self._create_regularisers()]\n if module is not None])\n self.out_shape = list(self.forward(zeros(in_shape)).size())\n\n return self.out_shape","title":""},{"docid":"1e68455b6c8d5a18992d4cc3de075f2a","score":"0.5857208","text":"def _network_setup(\n max_iteration: int,\n ak_raw: ArrayKey,\n ak_mask: ArrayKey,\n labels: List[CNNectome.utils.label.Label],\n) -> Tuple[\n Dict[str, str],\n int,\n Dict[str, gunpowder.array.ArrayKey],\n Dict[str, gunpowder.array.ArrayKey],\n]:\n # load net_io_names.json\n with open(\"net_io_names.json\", \"r\") as f:\n net_io_names = json.load(f)\n\n # find checkpoint from previous training, start a new one if not found\n if tf.train.latest_checkpoint(\".\"):\n start_iteration = int(tf.train.latest_checkpoint(\".\").split(\"_\")[-1])\n if start_iteration >= max_iteration:\n logging.info(\n \"Network has already been trained for {0:} iterations\".format(\n start_iteration\n )\n )\n else:\n logging.info(\"Resuming training from {0:}\".format(start_iteration))\n else:\n start_iteration = 0\n logging.info(\"Starting fresh training\")\n\n # define network inputs\n inputs = dict()\n inputs[net_io_names[\"raw\"]] = ak_raw\n inputs[net_io_names[\"mask\"]] = ak_mask\n for label in labels:\n inputs[net_io_names[\"mask_\" + label.labelname]] = label.mask_key\n inputs[net_io_names[\"gt_\" + label.labelname]] = label.gt_dist_key\n if label.scale_loss or label.scale_key is not None:\n inputs[net_io_names[\"w_\" + label.labelname]] = label.scale_key\n\n # define network outputs\n outputs = dict()\n for label in labels:\n outputs[net_io_names[label.labelname]] = label.pred_dist_key\n return net_io_names, start_iteration, inputs, outputs","title":""},{"docid":"3d70dfee50d60fa68232f6ba7919ff95","score":"0.585505","text":"def build(width, height, depth, reg=0.0005):\r\n # initialize the input shape to be \"channels last\" and the\r\n # channels dimension itself\r\n input_shape = height, width, depth\r\n cha_dim = -1\r\n # if we are using \"channels first\", update the input shape\r\n # and channels dimension\r\n if K.image_data_format() == \"channels_first\":\r\n input_shape = (depth, height, width)\r\n cha_dim = 1\r\n\r\n inputs = Input(shape=input_shape)\r\n x = GoogLeNet.conv_module(inputs, 32, (3, 3), (1, 1), cha_dim, reg=reg, name=\"block1\")\r\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool1\")(x)\r\n x = GoogLeNet.conv_module(x, 32, (1, 1), (1, 1), cha_dim, reg=reg, name=\"block2\")\r\n x = GoogLeNet.conv_module(x, 32, (3, 3), (1, 1), cha_dim, reg=reg, name=\"block3\")\r\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool2\")(x)\r\n\r\n # apply two Inception modules followed by a POOL\r\n x = GoogLeNet.inception_module(x, 64, 32, 64, 16, 32, 64, 64, cha_dim, \"3\", reg=reg)\r\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool3\")(x)\r\n\r\n x = GoogLeNet.inception_module(x, 128, 32, 128, 24, 48, 64, 64, cha_dim, \"4\", reg=reg)\r\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool4\")(x)\r\n\r\n x = GoogLeNet.inception_module(x, 128, 32, 128, 24, 48, 64, 64, cha_dim, \"5\", reg=reg)\r\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool5\")(x)\r\n\r\n x = GoogLeNet.inception_module(x, 256, 112, 256, 32, 64, 128, 128, cha_dim, \"6\", reg=reg)\r\n # apply a POOL layer (average) followed by dropout\r\n x = GlobalAveragePooling2D(K.image_data_format())(x)\r\n x = Dropout(0.5, name=\"do\")(x)\r\n\r\n # outputs\r\n model = Model(inputs=inputs, outputs=x)\r\n return model","title":""},{"docid":"0ac00d191387b7f41368aea8fda054f8","score":"0.58516353","text":"def make_configs(self):\n raise NotImplementedError","title":""},{"docid":"0ac00d191387b7f41368aea8fda054f8","score":"0.58516353","text":"def make_configs(self):\n raise NotImplementedError","title":""},{"docid":"92b8a7f184b69eeb43964b95f618af60","score":"0.58484864","text":"def _configure_layers(self):\n nvis = self.Train[0].meta.shape[1]\n layers = []\n layer_sizes = get_layer_size_iterator(self.SizeHiddenLayers.value)\n for _ in range(self.NumHiddenLayers.value):\n nhid = layer_sizes.next()\n layer = rbm.RBM(nvis=nvis, nhid=nhid,\n irange=4, init_bias_hid=4,\n init_bias_vis=0,\n monitor_reconstruction=True)\n nvis = nhid\n layers.append(layer)\n\n n_out = self.Train[1].meta.shape[1]\n output = mlp.Softmax(n_out, 'output', irange=.1)\n layers.append(output)\n self._layers = layers","title":""},{"docid":"5a50163fb3cd09bca48462922d0e4edf","score":"0.5844027","text":"def create_dnn(first_output_dim, second_output_dim, input_dim, first_layer_count, second_layer_count, interm_fraction, neuron_count):\n def dnn_model_builder(layer_count, inp_dim, out_dim, loss, act):\n model = Sequential()\n for inx in range(int(layer_count)):\n if inx == 0:\n model.add(Dense(neuron_count, activation='relu', input_dim=inp_dim, name='dense-{}'.format(inx+1)))\n elif inx == layer_count - 1:\n model.add(Dense(out_dim, activation=act, name='output-{}'.format(act)))\n model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])\n return model\n else:\n model.add(Dense(neuron_count, activation='relu', name='dense-{}'.format(inx+1)))\n\n # Create first model with specified number of layers\n first_model = dnn_model_builder(first_layer_count, input_dim, first_output_dim, loss='categorical_crossentropy', act='softmax')\n\n # Create intermediate layers with interm_fraction of first_model layers\n intermediate = Sequential()\n for inx in range(int(Decimal(first_layer_count*interm_fraction).quantize(0, ROUND_HALF_UP))):\n if inx == 0:\n intermediate.add(Dense(neuron_count, activation='relu', input_dim=input_dim, name='dense-{}'.format(inx+1)))\n else:\n intermediate.add(Dense(neuron_count, activation='relu', name='dense-{}'.format(inx+1)))\n intermediate.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n\n\n second_model = dnn_model_builder(second_layer_count, input_dim, second_output_dim, loss='binary_crossentropy', act='sigmoid')\n latent_model = dnn_model_builder(second_layer_count, neuron_count, second_output_dim, loss='binary_crossentropy', act='sigmoid')\n\n return first_model, intermediate, second_model, latent_model","title":""},{"docid":"e0ec85ecdaa6dd5d21ea5a998d924133","score":"0.5823734","text":"def networks_build_mlp(\n hid_sizes: Iterable[int],\n name: Optional[str] = None,\n activation: Optional[Callable] = tf.nn.relu,\n initializer: Optional[Callable] = None,\n) -> LayersDict:\n layers = collections.OrderedDict()\n\n # Hidden layers\n for i, size in enumerate(hid_sizes):\n key = f\"{name}_dense{i}\"\n layer = tf.layers.Dense(\n size, activation=activation, kernel_initializer=initializer, name=key\n ) # type: tf.layers.Layer\n layers[key] = layer\n\n # Final layer\n layer = tf.layers.Dense(\n 1, kernel_initializer=initializer, name=f\"{name}_dense_final\"\n ) # type: tf.layers.Layer\n layers[f\"{name}_dense_final\"] = layer\n\n return layers","title":""},{"docid":"1a40cdbbbf3f2b1d20ae445cecb37e31","score":"0.5820634","text":"def _make_aux(self, network, aux_id, name_generator):\n\n layers = []\n\n avgpool = self._make_avgpool_layer(network, kernel_size=5, stride=3)\n self._set_mode(avgpool, 'TRAIN')\n layers.append(avgpool)\n\n conv = self._make_conv_layer(\n network, kernel_size=1, stride=1, num_output=128, bias_value=0)\n self._set_mode(conv, 'TRAIN')\n layers.append(conv)\n\n relu1 = self._make_relu_layer(network)\n self._set_mode(relu1, 'TRAIN')\n layers.append(relu1)\n\n inner_product1 = self._make_inner_product_layer(network, num_output=1024)\n self._set_mode(inner_product1, 'TRAIN')\n layers.append(inner_product1)\n\n relu2 = self._make_relu_layer(network)\n self._set_mode(relu2, 'TRAIN')\n layers.append(relu2)\n\n dropout = self._make_dropout_layer(network, dropout_ratio=0.7)\n self._set_mode(dropout, 'TRAIN')\n layers.append(dropout)\n\n final_layers = self._make_aux_classifiers(\n network, aux_id, name_generator, loss_weight=0.3)\n layers += final_layers\n\n connections = [\n (avgpool.name, (avgpool.top, conv.bottom)),\n (conv.name, (conv.top, relu1.bottom, relu1.top, inner_product1.bottom)),\n (inner_product1.name, (inner_product1.top, relu2.bottom, relu2.top, dropout.bottom)),\n (dropout.name, (dropout.top, final_layers[0].bottom)),\n ]\n\n for connection in connections:\n self._tie(connection, name_generator)\n\n return layers","title":""},{"docid":"fc5d89b8e3fd982bd0fd3daeb5b12354","score":"0.58150876","text":"def initialize_networks(self, opt):\n\n generator = co_mod_gan.Generator()\n\n return generator","title":""},{"docid":"ce8aab13475e9ed18715b9fa84704d68","score":"0.5810496","text":"def createRMCfg(model, remoteIns, deviceOuts, index):\n deviceOuts = [i.name for i in deviceOuts]\n# remoteIns = [i.name for i in remoteIns]\n modelCfg = model.get_config()\n\n remoteIns = [createInpCfg(i) for i in remoteIns]\n\n modelLayers = modelCfg['layers'][index+1:]\n\n for i in remoteIns:\n modelLayers.insert(0, i)\n modelCfg['layers'] = modelLayers\n\n return modelCfg","title":""},{"docid":"6bfa3e91d5389e105babc54afa3b6fc7","score":"0.5808745","text":"def build_model(opt):\n # TODO stage 1.2 train mapping network\n print(\"build 2 vae and a transfer network\")\n model = mapping_model.Pix2PixHDModel_Mapping()\n model.initialize(opt)\n\n ##### define networks\n print(\"build vae1 and vae2 ...\")\n vae1 = networks.GlobalGenerator_DCDCv2(\n opt.input_nc,\n opt.output_nc,\n opt.ngf,\n opt.k_size,\n opt.n_downsample_global,\n networks.get_norm_layer(norm_type=opt.norm),\n opt=opt,\n )\n vae2 = networks.GlobalGenerator_DCDCv2(\n opt.input_nc,\n opt.output_nc,\n opt.ngf,\n opt.k_size,\n opt.n_downsample_global,\n networks.get_norm_layer(norm_type=opt.norm),\n opt=opt,\n )\n vae1.apply(networks.weights_init)\n vae2.apply(networks.weights_init)\n print(\"build vae1 and vae2 finish!\")\n print(\"build D ...\")\n xr_recon_d = networks.Z_xr_Discriminator(input_nc=3, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\n z_xr_d = networks.Z_xr_Discriminator(input_nc=opt.feat_dim, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\n y_recon_d =networks.Z_xr_Discriminator(input_nc=3, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\n print(\"build D finish\")\n \"\"\" Optim \"\"\"\n optimizer_vae1 = torch.optim.Adam(vae1.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\n optimizer_d1 = torch.optim.Adam(itertools.chain(xr_recon_d.parameters(), z_xr_d.parameters()),lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\n optimizer_vae2 = torch.optim.Adam(vae2.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\n optimizer_d2 = torch.optim.Adam(y_recon_d.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\n return vae1, xr_recon_d, z_xr_d, vae2, y_recon_d, optimizer_vae1, optimizer_d1, optimizer_vae2, optimizer_d2","title":""},{"docid":"f2c148e704b5f8f0bf3cdb36eebcebb4","score":"0.5782471","text":"def generate_config(self, indent_level: int = 0) -> str:\n idl: int = indent_level\n sp: int = indent_level * 4\n conf: str = self._generate_config(indent_level, skip = \"layers\")\n\n conf += \" \"*sp + \"'layers': [\\n\"\n for layer in self.layers:\n conf += \" \"*(idl+1)*4 + \"(\" + type(layer).__name__ + \", \"\n conf += \"'\" + layer.name + \"',\\n\"\n conf += \" \"*(idl+2)*4 + \"{\\n\"\n conf += layer.generate_config(idl+3)\n #for key in layer[2]:\n # conf += \" \"*16 + \"'\" + key + \"': \" + get_str(layer[2][key])\n # conf += \",\\n\"\n conf += \" \"*(idl+2)*4 + \"}\\n\"\n conf += \" \"*(idl+1)*4 + \"),\\n\"\n\n conf += \" \"*sp + \"]\\n\"\n return conf","title":""},{"docid":"cf2c345014e70e990639ec6993a85f27","score":"0.57783914","text":"def create_lenet(self, data, train=True):\n\n # Creating the architecture\n # First convolutional\n with tf.name_scope('conv_1') as scope:\n conv1 = create_conv2d(data, self.W_conv1)\n # relu1 = create_relu(conv1, self.b_conv1)\n # relu1 = create_sigmoid(conv1, self.b_conv1)\n\n with tf.name_scope('tanh_1') as scope:\n tanh_1 = create_tanh(conv1, self.b_conv1)\n\n\n # Pooling\n # pool1 = create_max_pool(relu1)\n # pool1 = create_max_pool(relu1)\n with tf.name_scope('pool_1') as scope:\n pool1 = create_max_pool(tanh_1)\n\n\n # Second convolutional\n with tf.name_scope('conv_2') as scope:\n conv2 = create_conv2d(pool1, self.W_conv2)\n # relu2 = create_relu(conv2, self.b_conv2)\n # relu2 = create_sigmoid(conv2, self.b_conv2)\n\n\n with tf.name_scope('tanh_2') as scope:\n # pool2 = create_max_pool(relu2)\n #tanh_2 = create_relu(conv2, self.b_conv2)\n # pool2 = create_max_pool(conv2)\n tanh_2 = create_tanh(conv2, self.b_conv2)\n\n # Pooling\n with tf.name_scope('pool_2') as scope:\n pool2 = create_max_pool(tanh_2)\n\n\n #if train:\n #pool2 = tf.nn.dropout(pool2, 0.5, seed=self.seed)\n\n # Reshaping all the convolved images to 2D to feed the FC layers\n # FC1\n with tf.name_scope('fc_1') as scope:\n pool_shape = pool2.get_shape().as_list()\n reshape = tf.reshape(pool2, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\n #fc1 = tf.nn.relu(tf.matmul(reshape, self.W_fc1) + self.b_fc1)\n fc1 = tf.nn.tanh(tf.matmul(reshape, self.W_fc1) + self.b_fc1)\n\n #if train:\n #fc1 = tf.nn.dropout(fc1, 0.5, seed=self.seed)\n\n # FC2\n with tf.name_scope('fc_2') as scope:\n fc2 = tf.matmul(fc1, self.W_fc2) + self.b_fc2\n #fc2 = tf.nn.softmax(tf.matmul(fc1, self.W_fc2) + self.b_fc2)\n\n return fc2","title":""},{"docid":"155d961763c65f921e915574c0ac40ea","score":"0.5775039","text":"def build_rpn(cfg, in_channels):\n # add by hui ###############################\n if cfg.MODEL.LOC_ON:\n return build_location_net(cfg, in_channels)\n if cfg.MODEL.FCOS_ON:\n if cfg.MODEL.FCOS.CASCADE_ON:\n return build_cascade_fcos(cfg, in_channels)\n else:\n return build_fcos(cfg, in_channels)\n if cfg.MODEL.GAU_ON:\n return build_gaussian_net(cfg, in_channels)\n ############################################################\n if cfg.MODEL.RETINANET_ON:\n # add by hui ###############################\n if cfg.FREEANCHOR.FREEANCHOR_ON:\n return retinanet_fa.build_retinanet(cfg, in_channels)\n ############################################################\n return build_retinanet(cfg, in_channels)\n\n return RPNModule(cfg, in_channels)","title":""},{"docid":"fd08270219ac74700a0ae31cd7264ff8","score":"0.57739896","text":"def generate_net(hidden_layers=2, n_neurons=128):\n # Initialize Sequential model\n model = tf.keras.models.Sequential()\n\n # Add input layer\n model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))\n\n # Add hidden layers\n for i in range(hidden_layers):\n model.add(tf.keras.layers.Dense(n_neurons, activation='relu'))\n # Add output layer\n model.add(tf.keras.layers.Dense(10))\n\n model.summary()\n\n return model","title":""},{"docid":"9c0dfa91c85e3280a80b3c821a0b8b2d","score":"0.5769726","text":"def _build(self, inputs, is_training, dropout_keep_prob=1.0):\n\n target_layers = []\n\n if self._final_endpoint not in self.VALID_ENDPOINTS:\n raise ValueError('Unknown final endpoint %s' % self._final_endpoint)\n\n net = inputs\n end_points = {}\n end_point = 'Conv3d_1a_7x7'\n net = Unit3D(output_channels=64, kernel_shape=[7, 7, 7],\n stride=[2, 2, 2], name=end_point)(net, is_training=is_training)\n end_points[end_point] = net\n\n target_layers.append(net)\n net = rank_layer(net)\n\n if self._final_endpoint == end_point: return net, end_points, target_layers\n end_point = 'MaxPool3d_2a_3x3'\n net = tf.nn.max_pool3d(net, ksize=[1, 1, 3, 3, 1], strides=[1, 1, 2, 2, 1],\n padding=snt.SAME, name=end_point)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n end_point = 'Conv3d_2b_1x1'\n net = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name=end_point)(net, is_training=is_training)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n end_point = 'Conv3d_2c_3x3'\n net = Unit3D(output_channels=192, kernel_shape=[3, 3, 3],\n name=end_point)(net, is_training=is_training)\n end_points[end_point] = net\n\n target_layers.append(net)\n net = rank_layer(net)\n\n if self._final_endpoint == end_point: return net, end_points, target_layers\n end_point = 'MaxPool3d_3a_3x3'\n net = tf.nn.max_pool3d(net, ksize=[1, 1, 3, 3, 1], strides=[1, 1, 2, 2, 1],\n padding=snt.SAME, name=end_point)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_3b'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=96, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=16, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=32, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_3c'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=192, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=96, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n\n target_layers.append(net)\n net = rank_layer(net)\n\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'MaxPool3d_4a_3x3'\n net = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1],\n padding=snt.SAME, name=end_point)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_4b'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=192, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=96, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=208, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=16, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=48, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_4c'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=112, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=224, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=24, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_4d'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=256, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=24, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_4e'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=112, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=144, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=288, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_4f'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=256, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=320, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n\n target_layers.append(net)\n net = rank_layer(net)\n\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'MaxPool3d_5a_2x2'\n net = tf.nn.max_pool3d(net, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1],\n padding=snt.SAME, name=end_point)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_5b'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=256, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=320, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\n name='Conv3d_0a_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Mixed_5c'\n with tf.variable_scope(end_point):\n with tf.variable_scope('Branch_0'):\n branch_0 = Unit3D(output_channels=384, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n with tf.variable_scope('Branch_1'):\n branch_1 = Unit3D(output_channels=192, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_1 = Unit3D(output_channels=384, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_1,\n is_training=is_training)\n with tf.variable_scope('Branch_2'):\n branch_2 = Unit3D(output_channels=48, kernel_shape=[1, 1, 1],\n name='Conv3d_0a_1x1')(net, is_training=is_training)\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\n name='Conv3d_0b_3x3')(branch_2,\n is_training=is_training)\n with tf.variable_scope('Branch_3'):\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\n name='MaxPool3d_0a_3x3')\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\n name='Conv3d_0b_1x1')(branch_3,\n is_training=is_training)\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\n end_points[end_point] = net\n\n target_layers.append(net)\n net = rank_layer(net)\n\n if self._final_endpoint == end_point: return net, end_points, target_layers\n\n end_point = 'Logits'\n with tf.variable_scope(end_point):\n net = tf.nn.avg_pool3d(net, ksize=[1, 2, 7, 7, 1],\n strides=[1, 1, 1, 1, 1], padding=snt.VALID)\n net = tf.nn.dropout(net, dropout_keep_prob)\n logits = Unit3D(output_channels=self._num_classes,\n kernel_shape=[1, 1, 1],\n activation_fn=None,\n use_batch_norm=False,\n use_bias=True,\n name='Conv3d_0c_1x1')(net, is_training=is_training)\n if self._spatial_squeeze:\n logits = tf.squeeze(logits, [2, 3], name='SpatialSqueeze')\n averaged_logits = tf.reduce_mean(logits, axis=1)\n end_points[end_point] = averaged_logits\n if self._final_endpoint == end_point: return averaged_logits, end_points, target_layers\n\n end_point = 'Predictions'\n predictions = tf.nn.softmax(averaged_logits)\n end_points[end_point] = predictions\n return predictions, end_points, target_layers","title":""},{"docid":"f066d639318df1e83b4d3a48228d173e","score":"0.5768032","text":"def convert_to_layers(self):","title":""},{"docid":"16685cf3dcd6c8a80d8b271ec2cf1a10","score":"0.5765233","text":"def build_module(self):\n print(\"Building basic block of ConvolutionalNetwork using input shape\", self.input_shape)\n x = torch.zeros((self.input_shape)) # create dummy inputs to be used to infer shapes of layers\n out = x\n#------------------------conv layer-----------------------------------------------------------------\n out = self.conv_layer(out.shape[1], 0, 64, out)\n out = self.conv_layer(out.shape[1], 1, 64, out)\n self.layer_dict['dim_reduction_max_pool_{}'.format(1)] = nn.MaxPool2d(3, stride=2)\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(1)](out)\n self.dim_reduction_layer[1] = 1\n print(out.shape)\n\n out = self.conv_layer(out.shape[1], 2, 128, out)\n out = self.conv_layer(out.shape[1], 3, 128, out)\n out = self.conv_layer(out.shape[1], 4, 128, out)\n self.layer_dict['dim_reduction_max_pool_{}'.format(4)] = nn.MaxPool2d(3, stride=2)\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(4)](out)\n self.dim_reduction_layer[4] = 1\n print(out.shape)\n\n\n out = self.conv_layer(out.shape[1], 5, 256, out)\n self.layer_dict['dim_reduction_max_pool_{}'.format(5)] = nn.MaxPool2d(3, stride=2)\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(5)](out)\n self.dim_reduction_layer[5] = 1\n print(out.shape)\n\n if out.shape[-1] != 2:\n out = F.adaptive_avg_pool2d(out,\n 2) # apply adaptive pooling to make sure output of conv layers is always (2, 2) spacially (helps with comparisons).\n print('shape before final linear layer', out.shape)\n out = out.view(out.shape[0], -1)\n\n#-----------------------fully connected layer-------------------------------------------------------\n self.layer_dict['fcc_{}'.format(6)] = nn.Linear(in_features=out.shape[1], # add a linear layer\n out_features=128,\n bias=self.use_bias)\n out = self.layer_dict['fcc_{}'.format(6)](out) # apply ith fcc layer to the previous layers outputs\n out = F.relu(out) # apply a ReLU on the outputs\n\n self.layer_dict['fcc_{}'.format(7)] = nn.Linear(in_features=out.shape[1], # add a linear layer\n out_features=64,\n bias=self.use_bias)\n out = self.layer_dict['fcc_{}'.format(7)](out) # apply ith fcc layer to the previous layers outputs\n out = F.relu(out) # apply a ReLU on the outputs\n\n self.softmax_layer = nn.Linear(in_features=out.shape[1], # add a linear layer\n out_features=self.num_output_classes,\n bias=self.use_bias)\n out = self.softmax_layer(out) # apply ith fcc layer to the previous layers outputs\n out = F.softmax(out, dim=0) # apply a softmax on the outputs\n print(\"Block is built, output volume is\", out.shape)\n return out","title":""},{"docid":"d7deb0b30bbb63ce4fa92d72f91aae25","score":"0.5764015","text":"def generate_output_layer_info(self):\n output_layers = []\n\n for i in range(len(self.layer_info['layer_names'])):\n start = 1.5\n anchor_size = self.layer_info['anchor_sizes'][i]\n layer_name = self.layer_info['layer_names'][i]\n window_scale = self.layer_info['window_scales'][i]\n window_length = self.layer_info['window_lengths'][i]\n if anchor_size % 3 == 0:\n start = -anchor_size / 3.0\n elif anchor_size % 2 == 0:\n start = -anchor_size / 2.0 + 0.5\n k = 1\n for row in range(anchor_size):\n for col in range(anchor_size):\n out_layer = FaceDetectionLayerOutput(\n prob_name=layer_name + '/prob',\n reg_name=layer_name + '/bb',\n anchor_index=k - 1,\n anchor_size=anchor_size * anchor_size,\n win_scale=window_scale,\n win_length=window_length,\n win_trans_x=float((start + col) / anchor_size),\n win_trans_y=float((start + row) / anchor_size)\n )\n output_layers.append(out_layer)\n k += 1\n return output_layers","title":""},{"docid":"2f64ebaba1565ff266c9169007223eec","score":"0.5761241","text":"def set_train_layers(self, layer_names=[]):\n\n for k, p in self.day_encoder.named_parameters():\n bool_list = []\n for layer in layer_names:\n bool_list.append(k.startswith(layer))\n if not any(bool_list):\n p.requires_grad = False\n for k, p in self.night_encoder.named_parameters():\n bool_list = []\n for layer in layer_names:\n bool_list.append(k.startswith(layer))\n if not any(bool_list):\n p.requires_grad = False","title":""},{"docid":"17180b415ace1d755d252be79b853349","score":"0.5747913","text":"def build_model(is_training, inputs, params):\n images = inputs['images']\n \n assert images.get_shape().as_list() == [None, params.image_size, params.image_size, 3]\n\n out = images\n \n conv1 = convLayer(out, 3, 3, 1, 1, 32, \"conv1\")\n pool1 = maxPoolLayer(conv1, 2, 2, 2, 2, \"pool1\")\n #dropout1 = dropout(pool1, 0.5)\n \n conv2 = convLayer(pool1, 3, 3, 1, 1, 32, \"conv2\")\n pool2 = maxPoolLayer(conv2, 2, 2, 2, 2, \"pool2\")\n dropout1 = dropout(pool2, 0.5)\n\n conv3 = convLayer(dropout1, 3, 3, 1, 1, 64, \"conv3\")\n pool3 = maxPoolLayer(conv3, 2, 2, 2, 2, \"pool3\")\n #dropout3 = dropout(pool3, 0.5)\n \n conv4 = convLayer(pool3, 3, 3, 1, 1, 64, \"conv4\")\n pool4 = maxPoolLayer(conv4, 2, 2, 2, 2, \"pool4\")\n #dropout4 = dropout(pool4, 0.5)\n \n conv5 = convLayer(pool4, 3, 3, 1, 1, 128, \"conv5\")\n pool5 = maxPoolLayer(conv5, 2, 2, 2, 2, \"pool5\")\n #dropout5 = dropout(pool5, 0.5)\n \n fcIn = tf.reshape(pool5, [-1, 2 * 2 * 128])\n dense1 = tf.layers.dense(fcIn, 128, name = 'dense1', kernel_regularizer=regularizers.l2(0.005))\n dense1r = tf.nn.relu(dense1)\n \n dropout2 = dropout(dense1r, 0.5)\n dense2 = tf.layers.dense(dropout2, params.num_labels, name = 'dense2', kernel_regularizer=regularizers.l2(0.005))\n \n #fc7 = fcLayer(dropout1, 4096, 4096, True, \"fc7\")\n #dropout2 = dropout(fc7, 0.5)\n\n #fc8 = fcLayer(dropout2, 4096, params.num_labels, True, \"fc8\")\n #print(fc8.shape)\n return dense2","title":""},{"docid":"90a078e0a28428953cd3c5b024b505f9","score":"0.57444566","text":"def _get_network_config(self):\n return {\n 'default_category': 'front_panel',\n 'mapping': {\n 'oobm': {\n 'netns': None,\n 'managed_by': 'docker',\n 'prefix': ''\n },\n 'front_panel': {\n 'netns': 'front_panel',\n 'managed_by': 'platform',\n 'prefix': ''\n }\n }\n }","title":""},{"docid":"99b7c2d62d8996948168cc09c0bc6086","score":"0.5740701","text":"def build_network(net_name, rep_dim=64, bias_terms=False):\n\n implemented_networks = ('mnist_LeNet', 'cifar10_LeNet', 'imagenet_WideResNet', 'toy_Net',\n 'mnist_LeNet_classifier', 'cifar10_LeNet_classifier', 'imagenet_WideResNet_classifier',\n 'toy_Net_classifier')\n assert net_name in implemented_networks\n\n net = None\n\n if net_name == 'mnist_LeNet':\n net = MNIST_LeNet(rep_dim=rep_dim, bias_terms=bias_terms)\n\n if net_name == 'cifar10_LeNet':\n net = CIFAR10_LeNet(rep_dim=rep_dim, bias_terms=bias_terms)\n\n if net_name == 'imagenet_WideResNet':\n net = ImageNet_WideResNet(rep_dim=rep_dim)\n\n if net_name == 'toy_Net':\n net = Toy_Net(rep_dim=rep_dim)\n\n if net_name in ['mnist_LeNet_classifier', 'cifar10_LeNet_classifier', 'imagenet_WideResNet_classifier',\n 'toy_Net_classifier']:\n net = ClassifierNet(net_name, rep_dim=rep_dim, bias_terms=bias_terms)\n\n return net","title":""},{"docid":"7eae1c470391de479bc7d02b5cee839b","score":"0.5740444","text":"def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.retina_cls = nn.Conv2d(\n self.feat_channels,\n self.num_anchors * self.cls_out_channels,\n 3,\n padding=1)\n self.retina_reg = nn.Conv2d(\n self.feat_channels, self.num_anchors * 4, 3, padding=1)","title":""},{"docid":"0c26aebb8fecc1bb7a85e42294f6c933","score":"0.57372904","text":"def build_layers(self, model_pre, seq_len, conv_feats, fc_feats, conv_k_size,\n conv_stride, LOGDIR, gamma=0, batch_size=0, learning_rate=0):\n\n for i in range(0, len(conv_feats)):\n layer = tf.keras.layers.Conv2D(filters=conv_feats[i],\n kernel_size=conv_k_size[i],\n strides=conv_stride[i],\n data_format=\"channels_last\",\n activation=tf.nn.relu,\n use_bias=True,\n kernel_initializer='glorot_normal',\n bias_initializer='zeros')\n self.layer_dict[model_pre+\"_cnn_layer\"+str(i)] = layer\n\n self.layer_dict[model_pre+\"_flatten\"] = tf.keras.layers.Flatten()\n\n for i in range(0, len(fc_feats)):\n layer = tf.keras.layers.Dense(fc_feats[i],\n activation=tf.nn.relu,\n use_bias=True,\n kernel_initializer='glorot_normal',\n bias_initializer='zeros')\n self.layer_dict[model_pre+\"_dense\"+str(i)] = layer\n\n self.layer_dict[model_pre+\"_fdense\"] = tf.keras.layers.Dense(3, activation=None, use_bias=True,\n kernel_initializer='glorot_normal',\n bias_initializer='zeros')","title":""},{"docid":"36111726233655887c728adc6cdf6953","score":"0.57331663","text":"def build_inference_network(self, model):","title":""},{"docid":"626cf80ea79cdc7ebc8293d4347e2344","score":"0.5733042","text":"def create(self):\n # 1st Layer: Conv (w ReLu) -> Lrn -> Pool\n conv1 = conv(self.X, 11, 11, 96, 4, 4, padding='VALID', name=self.scope_initial + 'conv1')\n norm1 = lrn(conv1, 2, 1e-04, 0.75, name=self.scope_initial + 'norm1')\n pool1 = max_pool(norm1, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool1')\n \n # 2nd Layer: Conv (w ReLu) -> Lrn -> Pool with 2 groups\n conv2 = conv(pool1, 5, 5, 256, 1, 1, groups=2, name=self.scope_initial + 'conv2')\n norm2 = lrn(conv2, 2, 1e-04, 0.75, name=self.scope_initial + 'norm2')\n pool2 = max_pool(norm2, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool2')\n \n # 3rd Layer: Conv (w ReLu)\n conv3 = conv(pool2, 3, 3, 384, 1, 1, name=self.scope_initial + 'conv3')\n\n # 4th Layer: Conv (w ReLu) splitted into two groups\n conv4 = conv(conv3, 3, 3, 384, 1, 1, groups=2, name=self.scope_initial + 'conv4')\n\n # 5th Layer: Conv (w ReLu) -> Pool splitted into two groups\n conv5 = conv(conv4, 3, 3, 256, 1, 1, groups=2, name=self.scope_initial + 'conv5')\n pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool5')\n\n # 6th Layer: Flatten -> FC (w ReLu) -> Dropout\n flattened = tf.reshape(pool5, [-1, 6*6*256])\n fc6 = fc(flattened, 6*6*256, 4096, name=self.scope_initial + 'fc6')\n dropout6 = dropout(fc6, self.KEEP_PROB)\n\n # 7th Layer: FC (w ReLu) -> Dropout\n fc7 = fc(dropout6, 4096, 4096, name=self.scope_initial + 'fc7')\n dropout7 = dropout(fc7, self.KEEP_PROB)\n\n # 8th Layer: FC and return unscaled activations\n self.fc8 = fc_8(dropout7, 4096, self.NUM_CLASSES, relu=False, name=self.scope_initial + 'fc8')","title":""},{"docid":"5f6adf73ed9376313b198d1d6eddb65e","score":"0.5732038","text":"def layers_list(self, spectral_norm, shared_activation=False, shared_channels=False, depth=7, n_channels=64, image_channels=1, kernel_size=3, padding=1):\n if spectral_norm == \"Chen\":\n if self.bias is False:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n shared_activations = self.init_activation(('conv', 1))\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n shared_activations = self.init_activation(('conv', n_channels))\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n for _ in range(depth - 2):\n if self.bias is False:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\n if self.batchnorm is True:\n self.layers.append(nn.BatchNorm2d(n_channels))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n if self.bias is False:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding)))\n\n elif spectral_norm == \"Normal\":\n\n if self.bias is False:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n shared_activations = self.init_activation(('conv', 1))\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n shared_activations = self.init_activation(('conv', n_channels))\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n for _ in range(depth - 2):\n if self.bias is False:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\n if self.batchnorm is True:\n self.layers.append(nn.BatchNorm2d(n_channels))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n if self.bias is False:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False)))\n elif self.bias is True:\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding)))\n\n elif spectral_norm == \"None\":\n\n if self.bias is False:\n self.layers.append(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n shared_activations = self.init_activation(('conv', 1))\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n shared_activations = self.init_activation(('conv', n_channels))\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n for _ in range(depth - 2):\n if self.bias is False:\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding))\n if self.batchnorm is True:\n self.layers.append(nn.BatchNorm2d(n_channels))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n if self.bias is False:\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding))\n\n elif spectral_norm == \"Parseval\":\n\n if self.bias is False:\n self.layers.append(PersevalConv(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(PersevalConv(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=True))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n shared_activations = self.init_activation(('conv', 1))\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n shared_activations = self.init_activation(('conv', n_channels))\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n for _ in range(depth - 2):\n if self.bias is False:\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size,padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size,padding=padding, bias=True))\n if self.batchnorm is True:\n self.layers.append(nn.BatchNorm2d(n_channels))\n if self.activation == \"relu\":\n self.layers.append(nn.ReLU(inplace=True))\n elif self.activation == \"leaky_relu\":\n self.layers.append(nn.LeakyReLU(inplace=True))\n elif self.activation == \"prelu\":\n self.layers.append(nn.PReLU(num_parameters=n_channels))\n else:\n if (shared_activation is True) and (shared_channels is True):\n self.layers.append(shared_activations)\n elif (shared_activation is True) and (shared_channels is False):\n self.layers.append(shared_activations)\n elif (shared_activation is False) and (shared_channels is True):\n self.layers.append(self.init_activation(('conv', 1)))\n elif (shared_activation is False) and (shared_channels is False):\n self.layers.append(self.init_activation(('conv', n_channels)))\n if self.bias is False:\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False))\n elif self.bias is True:\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=True))\n\n else:\n raise ValueError(\"The normalization specified is not provided choose : None, Normal, Chen or Perseval\")","title":""},{"docid":"2626959a70c557c902d08b1148e5174b","score":"0.57236356","text":"def _build_models(output_c_dim, df_dim, gf_dim, latent_dim, f_size, is_batchnorm, is_bicycle):\n\n discr = Discriminator(df_dim, f_size, 3, is_batchnorm, is_bicycle)\n discr2 = Discriminator(df_dim, f_size, 4, is_batchnorm, is_bicycle)\n\n \"\"\"\n #Generator used for adding latent values only at the geerator input\n gen = Generator(output_c_dim, gf_dim, f_size, latent_dim, is_batchnorm, is_bicycle)\n \n \"\"\"\n # Generator used for adding latent values at all the input layers\n gen = Generator_add_all(output_c_dim, gf_dim, f_size, latent_dim, is_batchnorm, is_bicycle)\n\n enc = ResNet18(df_dim, latent_dim, f_size, is_batchnorm)\n\n # A separate discriminator for latent values\n #discr_z = Discriminator_zz()\n\n return discr, discr2, gen, enc","title":""},{"docid":"324592ae4bacdb03d130c6a40466ba2e","score":"0.5720984","text":"def create_output_layers(self):\n output_layers = nn.ModuleList([])\n if len(self.layers_info) >= 2: input_dim = self.layers_info[-2]\n else: input_dim = self.input_dim\n if not isinstance(self.layers_info[-1], list): output_layer = [self.layers_info[-1]]\n else: output_layer = self.layers_info[-1]\n for output_dim in output_layer:\n output_layers.extend([nn.Linear(input_dim, output_dim)])\n return output_layers","title":""},{"docid":"12b7b36b15eeae9bd95816aaf685d8d7","score":"0.57189214","text":"def build_resnet152_model(self,inputs):\n eps = 1.1e-5\n #img_rows = self.x_shape[0]\n #img_cols = self.x_shape[1]\n #color_type = self.x_shape[2]\n\n # Handle Dimension Ordering for different backends\n global bn_axis\n bn_axis = 3\n img_input = inputs\n\n x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)\n x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)\n x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x)\n x = Scale(axis=bn_axis, name='scale_conv1')(x)\n x = Activation('relu', name='conv1_relu')(x)\n x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)\n\n x = self.conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n\n x = self.conv_block(x, 3, [128, 128, 512], stage=3, block='a')\n for i in range(1,8):\n x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i))\n\n x = self.conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\n for i in range(1,36):\n x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i))\n\n x = self.conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\n x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n\n x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)\n x_fc = Flatten()(x_fc)\n x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)\n\n model = Model(img_input, x_fc)\n return [model, x]","title":""}],"string":"[\n {\n \"docid\": \"c03843b0a4bb02acb8811d8b04215eaa\",\n \"score\": \"0.738548\",\n \"text\": \"def _make_layers(self, configs: list):\\r\\n layers = []\\r\\n for config in configs:\\r\\n layer = self._make_layer(config['class'], config['args'])\\r\\n layers.append(layer)\\r\\n\\r\\n self.net = Sequential(*layers).cuda()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29792cf738fab7f5fbe76237d925fae6\",\n \"score\": \"0.7220404\",\n \"text\": \"def build_network(self): \\n self.setup_cnn_layers() \\n self.setup_pose_emb_layer() \\n self.setup_smpl_layers()\\n self.setup_cam_mesh_relation_module()\\n self.setup_renderer_layer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b18c0794c768ff925058357f241b8f2\",\n \"score\": \"0.6897156\",\n \"text\": \"def _build(self):\\n for (lt, ls, lo), d, li in zip(self.layer_config, self.dropouts, range(len(self.layer_config))):\\n if li == 0:\\n in_dim = self.input_dim\\n si = True\\n else:\\n in_dim = self.layer_config[li-1][1]\\n si = False\\n if li == len(self.layer_config):\\n out_dim = self.output_dim\\n else:\\n out_dim = ls\\n \\n if lt.lower() in (\\\"gc\\\", \\\"graph\\\", \\\"graphconvolution\\\"):\\n self.layers.append(GraphConvolution(input_dim=in_dim, output_dim=out_dim,\\n placeholders=self.placeholders, adjacency=lo, \\n act=tf.nn.relu, dropout=d, sparse_inputs=si, \\n logging=self.logging))\\n elif lt.lower() in ('d', 'dense'):\\n self.layers.append(Dense(input_dim=in_dim, output_dim=out_dim,\\n placeholders=self.placeholders, act=tf.nn.relu,\\n dropout=d, sparse_inputs=si, logging=self.logging))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a311d027a808cf77b29138b6f595679b\",\n \"score\": \"0.67131996\",\n \"text\": \"def _build_networks(\\n self,\\n net_config: NetworkConfig = None,\\n conv_config: ConvolutionConfig = None,\\n ):\\n if net_config is None:\\n net_config = self.net_config\\n\\n if conv_config is None and self.config.use_conv_net:\\n conv_config = self.conv_config\\n\\n xshape = (self.xdim, 2)\\n if self.config.use_conv_net:\\n xshape = (*self.lattice_shape[1:], 2)\\n\\n kinit = None\\n if self.config.zero_init:\\n kinit = 'zeros'\\n\\n # ====\\n # xNet configuration\\n xnet_cfg = {\\n 'factor': 2.0,\\n 'net_config': net_config,\\n 'conv_config': conv_config,\\n 'kernel_initializer': kinit,\\n 'lattice_shape': self.lattice_shape,\\n 'input_shapes': {\\n 'x': xshape, 'v': (self.xdim,), 't': (2,)\\n }\\n }\\n\\n # ====\\n # xNet configuration\\n vnet_cfg = {\\n 'factor': 1.0,\\n 'net_config': net_config,\\n 'conv_config': None,\\n 'kernel_initializer': kinit,\\n 'lattice_shape': self.lattice_shape,\\n 'input_shapes': {\\n 'x': (self.xdim,), 'v': (self.xdim,), 't': (2,)\\n }\\n }\\n\\n if self.config.separate_networks:\\n # ====\\n # Build separate networks\\n vnet = [\\n get_gauge_network(**vnet_cfg, name=f'VNet{i}')\\n for i in range(self.config.num_steps)\\n ]\\n\\n xnet = [\\n get_gauge_network(**xnet_cfg, name=f'XNet{i}')\\n for i in range(self.config.num_steps)\\n ]\\n\\n else:\\n # ====\\n # Build single network\\n vnet = get_gauge_network(**vnet_cfg, name='VNet')\\n xnet = get_gauge_network(**xnet_cfg, name='XNet')\\n\\n return xnet, vnet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cded75c6a54310788fa4e24959a33a3\",\n \"score\": \"0.6697693\",\n \"text\": \"def _build_network(self):\\n\\n network = caffe_pb2.NetParameter()\\n network.name = 'inception-%s' % self.task_name\\n\\n layers = []\\n\\n name_generator = self._connection_name_generator()\\n\\n train_layer = self._make_train_layer(network)\\n layers.append(train_layer)\\n test_layer = self._make_test_layer(network)\\n layers.append(test_layer)\\n\\n conv1 = self._make_conv_layer(\\n network, kernel_size=7, stride=2, num_output=64, pad=3)\\n layers.append(conv1)\\n relu1 = self._make_relu_layer(network)\\n layers.append(relu1)\\n\\n maxpool1 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\\n layers.append(maxpool1)\\n lrn1 = self._make_lrn_layer(network)\\n layers.append(lrn1)\\n\\n conv2 = self._make_conv_layer(\\n network, kernel_size=1, num_output=64)\\n layers.append(conv2)\\n relu2 = self._make_relu_layer(network)\\n layers.append(relu2)\\n\\n conv3 = self._make_conv_layer(network, kernel_size=3, num_output=64, pad=1)\\n layers.append(conv3)\\n relu3 = self._make_relu_layer(network)\\n layers.append(relu3)\\n\\n lrn2 = self._make_lrn_layer(network)\\n layers.append(lrn2)\\n maxpool2 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\\n layers.append(maxpool2)\\n\\n inception1 = self._make_inception(\\n network, 64, 96, 128, 16, 32, 32, name_generator, lr=self.spearmint_args['inc1-2_lr'])\\n layers += inception1\\n\\n inception2 = self._make_inception(\\n network, 128, 128, 192, 32, 96, 64, name_generator, lr=self.spearmint_args['inc1-2_lr'])\\n layers += inception2\\n\\n maxpool3 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\\n layers.append(maxpool3)\\n\\n inception3 = self._make_inception(\\n network, 192, 96, 208, 16, 48, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\\n layers += inception3\\n\\n inception4 = self._make_inception(\\n network, 160, 112, 224, 24, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\\n layers += inception4\\n\\n inception5 = self._make_inception(\\n network, 128, 128, 256, 24, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\\n layers += inception5\\n\\n inception6 = self._make_inception(\\n network, 112, 144, 288, 32, 64, 64, name_generator, lr=self.spearmint_args['inc3-7_lr'])\\n layers += inception6\\n\\n inception7 = self._make_inception(\\n network, 256, 160, 320, 32, 128, 128, name_generator, lr=self.spearmint_args['inc3-7_lr'])\\n layers += inception7\\n\\n maxpool4 = self._make_maxpool_layer(network, kernel_size=3, stride=2)\\n layers.append(maxpool4)\\n\\n inception8 = self._make_inception(\\n network, 256, 160, 320, 32, 128, 128, name_generator, lr=self.spearmint_args['inc8-9_lr'])\\n layers += inception8\\n\\n inception9 = self._make_inception(\\n network, 384, 192, 384, 48, 128, 128, name_generator, lr=self.spearmint_args['inc-8-9_lr'])\\n layers += inception9\\n\\n avgpool = self._make_avgpool_layer(network, kernel_size=7)\\n layers.append(avgpool)\\n\\n dropout = self._make_dropout_layer(network, dropout_ratio=0.4)\\n layers.append(dropout)\\n\\n aux1 = self._make_aux(network, 1, name_generator)\\n layers += aux1\\n\\n aux2 = self._make_aux(network, 2, name_generator)\\n layers += aux2\\n\\n final_layers = self._make_final_classifiers(network, name_generator)\\n layers += final_layers\\n\\n connections = [\\n (conv1.name, (conv1.top, relu1.bottom, relu1.top, maxpool1.bottom)),\\n (maxpool1.name, (maxpool1.top, lrn1.bottom)),\\n (lrn1.name, (lrn1.top, conv2.bottom)),\\n (conv2.name, (conv2.top, relu2.bottom, relu2.top, conv3.bottom)),\\n (conv3.name, (conv3.top, relu3.bottom, relu3.top, lrn2.bottom)),\\n (lrn2.name, (lrn2.top, maxpool2.bottom)),\\n (maxpool2.name, (maxpool2.top, inception1[0].bottom)),\\n (inception1[-1].name, (inception1[-1].top, inception2[0].bottom)),\\n (inception2[-1].name, (inception2[-1].top, maxpool3.bottom)),\\n (maxpool3.name, (maxpool3.top, inception3[0].bottom)),\\n (inception3[-1].name, (inception3[-1].top, inception4[0].bottom, aux1[0].bottom)),\\n (inception4[-1].name, (inception4[-1].top, inception5[0].bottom)),\\n (inception5[-1].name, (inception5[-1].top, inception6[0].bottom)),\\n (inception6[-1].name, (inception6[-1].top, inception7[0].bottom, aux2[0].bottom)),\\n (inception7[-1].name, (inception7[-1].top, maxpool4.bottom)),\\n (maxpool4.name, (maxpool4.top, inception8[0].bottom)),\\n (inception8[-1].name, (inception8[-1].top, inception9[0].bottom)),\\n (inception9[-1].name, (inception9[-1].top, avgpool.bottom)),\\n (avgpool.name, (avgpool.top, dropout.bottom)),\\n (dropout.name, (dropout.top, final_layers[0].bottom)),\\n ]\\n\\n for connection in connections:\\n self._tie(connection, name_generator)\\n\\n conv1.bottom.append('data')\\n\\n for pos, layer in enumerate(layers):\\n print layer\\n layer.name += '_%d' % pos\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235fa73f887c8f20d1e0a381adab5d40\",\n \"score\": \"0.66376126\",\n \"text\": \"def setup_layers(self, params):\\n\\n # Input and encoding layers\\n in_shape = [None]\\n if isinstance(params.in_size, (list, tuple)):\\n in_shape.extend(params.in_size)\\n else:\\n in_shape.extend([params.in_size, 1])\\n\\n encode_layers = [\\n tf.placeholder(\\n \\\"float\\\",\\n name=\\\"input\\\",\\n shape=in_shape\\n )\\n ]\\n\\n for idx, num_nodes in enumerate(params.encode_nodes):\\n encode_layers.append(\\n self.make_conv_layer(\\n encode_layers[-1],\\n num_nodes,\\n 'encode_{}'.format(idx),\\n params.encode_params\\n )\\n )\\n\\n # Bottleneck and decoding layers\\n decode_layers = [\\n self.make_conv_layer(\\n encode_layers[-1],\\n params.bottleneck_dim,\\n 'bottleneck',\\n params.bottleneck_params\\n )\\n ]\\n\\n for idx, num_nodes in enumerate(params.decode_nodes):\\n decode_layers.append(\\n self.make_deconv_layer(\\n decode_layers[-1],\\n num_nodes,\\n 'decode_{}'.format(idx),\\n params.decode_params\\n )\\n )\\n\\n # Standardizing the input/output layer names\\n in_layer = encode_layers[0]\\n\\n target_layer = tf.placeholder(\\n \\\"float\\\",\\n name=\\\"target\\\",\\n shape=in_layer.get_shape().as_list()\\n )\\n\\n out_layer = tops.fit_to_shape(\\n self.make_deconv_layer(\\n decode_layers[-1],\\n 1,\\n 'output_layer',\\n params.output_params\\n ),\\n target_layer.get_shape().as_list()\\n )\\n\\n loss = tops.loss_mse(\\n target_layer,\\n out_layer\\n )\\n\\n return in_layer, out_layer, target_layer, loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c15dd36695bb54001dbdecefa20caa6\",\n \"score\": \"0.65856797\",\n \"text\": \"def generate_net(self):\\n span = 1\\n #Input Layer\\n inputs = np.zeros(self.layer_sizes[0])\\n biases = np.random.uniform(-span,span,self.layer_sizes[0])\\n self.net.add_node_layer(inputs, biases)\\n #Hidden Layer and Output Layer\\n for i in range(1,self.length):\\n data = np.zeros(self.layer_sizes[i])\\n biases = np.random.uniform(-span,span,self.layer_sizes[i])\\n connections = np.random.uniform(-span,span,(self.layer_sizes[i],self.layer_sizes[i-1]))\\n self.net.add_node_layer(data, biases)\\n self.net.add_connection_layer(connections)\\n return self.net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77481307e8ee6b679b8bbc8f7c15802c\",\n \"score\": \"0.6505242\",\n \"text\": \"def init_network_from_config(self):\\n nn_modules = []\\n orig_net = self.config.get_value('network')\\n net = copy.deepcopy(orig_net)\\n for layer_name, layer_desc in net.items():\\n assert isinstance(layer_desc, dict)\\n assert 'class' in layer_desc, 'class is not defined for layer: {}'.format(layer_name)\\n layer_class = layer_desc['class']\\n layer_module = nn_module.get_module(layer_class)\\n del layer_desc['class']\\n nn_modules.append((layer_module, layer_desc))\\n self.net_model = model.NNModel(nn_modules)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2dd1312c1633b1334eaa190707e729\",\n \"score\": \"0.6453071\",\n \"text\": \"def buildNetwork(mode='train'):\\n \\n if mode == 'train':\\n batch_size = 360# this is n rows expected by placeholder - files produce multiple rows\\n \\n elif mode == 'test':\\n batch_size = 50\\n \\n \\n \\n nBatch = batch_size\\n patch_size = 5\\n # depth = 1\\n num_hidden = 560\\n nTest = 1216#testFiles.shape[0]\\n nTrainBatch = round(nBatch*(1-validProp))\\n nValidBatch = round(nBatch*validProp)\\n nValidBatch = 160\\n # nValid = cValid.shape[0]*0.5\\n nValid = 200\\n graph = tf.Graph()\\n \\n \\n with graph.as_default():\\n\\n # Input data.\\n trainSubset = tf.placeholder(tf.float32, shape=(nTrainBatch, \\n zSize, ySize, xSize, num_channels), \\n name='PHTrain')\\n \\n trainSubsetLabels = tf.placeholder(tf.float32, shape=(nTrainBatch,2), \\n name='PHTrainLabs')\\n \\n validSubset = tf.placeholder(tf.float32, shape=(nValidBatch, \\n zSize, ySize, xSize, num_channels), \\n name='PHValid')\\n \\n validSubsetLabels = tf.placeholder(tf.float32, shape=(nValidBatch,2), \\n name='PHValidLabs')\\n \\n testDataset = tf.placeholder(tf.float32, shape = (nTest, \\n zSize, ySize, xSize, num_channels), \\n name='PHTest')\\n \\n LR = tf.Variable(1)\\n outChans = 80\\n \\n # Layer sizes not defined in functions\\n layer4_weights = tf.Variable(tf.truncated_normal(\\n [num_hidden, num_labels], stddev=0.1))\\n \\n layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\\n \\n # Model.\\n def model(smData, outChans=80, patchSize=5,\\n fcNum = num_hidden, mName=''):\\n \\n act1 = convLayer(smData, chanIn=1, chanOut=outChans,\\n strides=[1, 2, 2, 2, 1], \\n patchSize=patchSize, \\n layerName=mName+'conv1', act='tanh')\\n \\n act2 = convLayer(act1, chanIn=outChans, chanOut=outChans, \\n strides=[1, 2, 2, 2, 1],\\n patchSize=patchSize, \\n layerName=mName+'conv2', act='drop')\\n \\n # Reshape for FC\\n shape = act2.get_shape().as_list()\\n linSize = shape[1] * shape[2] * shape[3] * shape[4]\\n reshape = tf.reshape(act2, [shape[0], linSize])\\n \\n act3 = fcLayer(reshape, chanIn=linSize, chanOut=fcNum, \\n layerName=mName+'fc1', act='drop')\\n \\n output = tf.matmul(act3, layer4_weights) + layer4_biases\\n \\n return output\\n \\n # Training computation.\\n with tf.name_scope(\\\"Models\\\"): \\n logits = model(trainSubset, outChans=outChans, patchSize=patch_size, \\n fcNum=num_hidden, mName='train') \\n logitsV = model(validSubset, outChans=outChans, patchSize=patch_size, \\n fcNum=num_hidden, mName='valid')\\n \\n with tf.name_scope(\\\"xent\\\"): \\n \\n loss = tf.reduce_mean(\\n tf.nn.softmax_cross_entropy_with_logits(labels=trainSubsetLabels, \\n logits=logits))\\n \\n lossV = tf.reduce_mean(\\n tf.nn.softmax_cross_entropy_with_logits(labels=validSubsetLabels, \\n logits=logitsV))\\n \\n # Optimizer.\\n with tf.name_scope(\\\"train\\\"):\\n optimizer = tf.train.GradientDescentOptimizer(0.00001).minimize(loss)\\n \\n # Predictions for the training, validation, and test data.\\n with tf.name_scope(\\\"Preds\\\"):\\n train_prediction = tf.nn.softmax(logits)\\n valid_prediction = tf.nn.softmax(logitsV)\\n \\n test_prediction = tf.nn.softmax(model(testDataset, outChans=outChans, \\n patchSize=patch_size, \\n fcNum=num_hidden))\\n \\n with tf.name_scope(\\\"Accuracy\\\"):\\n \\n cp = tf.equal(tf.argmax(logits,1), tf.argmax(trainSubsetLabels,1))\\n trAcc2 = tf.reduce_mean(tf.cast(cp, tf.float32))\\n \\n cp2 = tf.equal(tf.argmax(logitsV,1), tf.argmax(validSubsetLabels,1))\\n vAcc2 = tf.reduce_mean(tf.cast(cp2, tf.float32))\\n \\n # Add ops to save and restore all the variables.\\n saver = tf.train.Saver()\\n # Add summary writer\\n \\n tf.summary.scalar(\\\"va\\\", vAcc2)\\n # Create a summary to monitor accuracy tensor\\n tf.summary.scalar(\\\"ta\\\", trAcc2)\\n variable_summaries(trAcc2, 'TrainingAcc')\\n variable_summaries(vAcc2, 'ValidAcc')\\n variable_summaries(lossV, 'Trainingloss')\\n variable_summaries(loss, 'Validloss')\\n merged_summary = tf.summary.merge_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df47338cc7a85c92b0e2ba989985737d\",\n \"score\": \"0.64382416\",\n \"text\": \"def _setUpNetwork(self):\\n\\n #so initally we want 50 subclasses so 50 neurons amongst 784 features\\n\\n # s is 50 R is 784\\n\\n W_1_0 = np.matrix(np.random.uniform(low = -1, high = 1,size = (50,784))) # 50 X 784\\n layer1 = {'weights': W_1_0.T,\\n 'transFunc':'Compet',\\n 'bias':0}\\n W_2_0 = layer2Weight()\\n layer2 = {'weights': W_2_0,\\n 'transFunc':'Purlin',\\n 'bias':0}\\n\\n #popualte the layers\\n self.layers.append(layer1)\\n self.layers.append(layer2)\\n\\n\\n print(W_1_0.shape)\\n print(W_2_0.shape)\\n #continue to append\\n #self.plotWeights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488f5e06129a538997961dca3b61d727\",\n \"score\": \"0.6435688\",\n \"text\": \"def _build_layers(self):\\n layers = []\\n n_layers = len(self._topology)\\n for i, (name, params) in enumerate(self._topology):\\n # Construct Keras layer to be built from string 'name' which is\\n # part of topology list.\\n layer_cls = getattr(keras.layers, name)\\n\\n # In order to get the accepteble arguments for each layer we need\\n # to use inspect because of keras's legacy support decorators.\\n try:\\n args = getargspec(layer_cls.__init__._original_function)[0]\\n except AttributeError:\\n args = getargspec(layer_cls)[0]\\n\\n # Add activation and other input arguments to params dict when\\n # necessary. For example, MaxPooling1D does not\\n # accept the argument 'kernel_initializer'.\\n if 'kernel_initializer' in args:\\n params['kernel_initializer'] = self._rnd_init\\n\\n # The 'return_squences' argument is necessary for stacking RNNs.\\n if 'return_sequences' in args and i < n_layers - 1:\\n params['return_sequences'] = True\\n\\n # Create Keras layer, but make sure that the input shape is\\n # passed to the first later.\\n if i == 0:\\n layer = layer_cls(**params, input_shape=self._input_shape)\\n else:\\n layer = layer_cls(**params)\\n layers.append(layer)\\n\\n # Add at least a dense layer for output with output shape of the\\n # time series dimensionality.\\n params = {}\\n params['units'] = self._input_shape[1]\\n if not self._topology:\\n params['input_shape'] = self._input_shape\\n layers.append(keras.layers.Dense(**params))\\n\\n return layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601f9ed2dba692e7915c5361305b55b5\",\n \"score\": \"0.64308953\",\n \"text\": \"def setup_networks(self):\\n\\t\\tself.encoder = Encoder(self.image_dim, self.label_dim, self.z_dim)\\n\\t\\tself.decoder = Decoder(self.image_dim, self.label_dim, self.z_dim)\\n\\t\\t# using GPUs for faster training of the networks\\n\\t\\tif self.use_cuda:\\n\\t\\t self.cuda()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d6ff6920c26c44f8d0fdc14914272f\",\n \"score\": \"0.63918954\",\n \"text\": \"def _create_network(self):\\n\\n #1st convolution layer w/ 64 5x5 kernels and 2x2 maxpool\\n layer1_conv_W = weight_variable([5,5,1,64], name=\\\"layer1_W\\\")\\n layer1_conv_b = bias_variable([64], name=\\\"layer1_b\\\")\\n layer1_conv = tf.nn.relu(conv2d(self.x, layer1_conv_W, name='layer1_conv') + layer1_conv_b, name='layer1_relu')\\n tf.histogram_summary('layer1_relu', layer1_conv)\\n layer1_pool = max_pool_2x2(layer1_conv, name='layer1_pool')\\n # layer1_drop = tf.nn.dropout(layer1_pool, self.keep_prob, name='layer1_drop')\\n\\n #2nd conv layer w/ 128 5x5 kernels and 2x2 maxpool\\n layer2_conv_W = weight_variable([5,5,64,128], name='layer2_W')\\n layer2_conv_b = bias_variable([128], name='layer2_b')\\n layer2_conv = tf.nn.relu(conv2d(layer1_pool, layer2_conv_W) + layer2_conv_b, name='layer2_relu')\\n tf.histogram_summary('layer2_relu', layer2_conv)\\n layer2_pool = max_pool_2x2(layer2_conv, name='layer2_pool')\\n\\n #3rd conv layer w/ 256 kernels and 2x2 maxpool\\n image_size_8 = self.image_size / 8\\n layer3_conv_W = weight_variable([5,5,128,256], name='layer3_W')\\n layer3_conv_b = bias_variable([256], name='layer3_b')\\n layer3_conv = tf.nn.relu(conv2d(layer2_pool, layer3_conv_W) + layer3_conv_b, name='layer3_relu')\\n tf.histogram_summary('layer3_relu', layer3_conv)\\n layer3_pool = max_pool_2x2(layer3_conv, name='layer3_pool')\\n # layer3_drop = tf.nn.dropout(layer3_pool, self.keep_prob, name='layer3_drop')\\n\\n #Flatten output into 1D tensor for input into fully connected layer\\n layer3_flat = tf.reshape(layer3_pool, [-1, image_size_8 * image_size_8 * 256], name='layer2_pool_flat')\\n\\n #Fully connected layer; output is 300 neurons\\n layer4_full_W = weight_variable(shape=[image_size_8 * image_size_8 * 256, 300], name='layer4_W')\\n layer4_full_b = bias_variable([300], name='layer4_b')\\n layer4_full = tf.nn.relu(tf.matmul(layer3_flat, layer4_full_W, name='layer4_matmull') + layer4_full_b, name='layer4_full')\\n layer4_drop = tf.nn.dropout(layer4_full, self.keep_prob, name='layer4_drop')\\n\\n #Output layer of 2 neurons \\n layer5_soft_W = weight_variable(shape=[300, OUTPUT_SIZE], name='layer5_W')\\n layer5_soft_b = bias_variable([OUTPUT_SIZE], name='layer5_b')\\n layer5_soft = tf.nn.relu(tf.matmul(layer4_drop, layer5_soft_W) + layer5_soft_b)\\n\\n #Cost and training calculations\\n cross_entropy = -tf.reduce_sum(self.y_*tf.log(layer5_soft + 1e50))\\n tf.scalar_summary = ('cost', cross_entropy)\\n train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cross_entropy)\\n init_ops = tf.initialize_all_variables()\\n\\n return train_step, layer5_soft, init_ops\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de342aecfb9bccfe506148aa43550ac4\",\n \"score\": \"0.6364454\",\n \"text\": \"def _build_net(self):\\n output = tf.layers.conv2d(\\n inputs=self.input_ph,\\n filters=self.config.filters[0],\\n strides=self.config.strides[0],\\n kernel_size=self.config.kernel[0],\\n activation=tf.nn.relu,\\n name='c0')\\n\\n for i, layer in enumerate(self.config.filters[1:]):\\n output = tf.layers.conv2d(\\n inputs=output,\\n filters=layer,\\n strides=self.config.strides[i+1],\\n kernel_size=self.config.kernel_size[i+1],\\n activation=tf.nn.relu,\\n name='c{}'.format(i+1))\\n\\n # Fully connected layers\\n output = tf.layers.flatten(output)\\n for i, layer in enumerate(self.config.fc_layers):\\n output = tf.layers.dense(\\n output,\\n layer,\\n activation=tf.nn.relu,\\n name='fc{}'.format(i))\\n\\n self.output = tf.layers.dense(\\n output,\\n self.dataset.n_labels,\\n activation=tf.nn.softmax,\\n name='output')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c212e665ea681e4161bad558ff93564\",\n \"score\": \"0.63355863\",\n \"text\": \"def _build_networks(self):\\n\\n # set up the optimizers\\n d_opt, g_opt = self._build_optimizers()\\n\\n # now build all of the networks\\n for i in range(self.num_levels):\\n Gz, d_loss, g_loss = self._build_network(self.X, self.Z, self.alpha)\\n\\n d_vars, g_vars = self.get_training_variables(i)\\n d_solver = d_opt.minimize(d_loss, var_list=d_vars)\\n g_solver = g_opt.minimize(g_loss, var_list=g_vars,\\n global_step=self.global_step)\\n\\n # self._build_summaries(Gz, d_loss, g_loss)\\n\\n self.networks.append((Gz, d_loss, g_loss, d_solver, g_solver))\\n\\n self.expand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ed391ea6babc8c6b7209079286b2ee8\",\n \"score\": \"0.6328495\",\n \"text\": \"def build_network(self):\\n\\n # Create output neurons\\n for ii in range(self.output_size):\\n label = self.output_labels[ii]\\n\\n self.g_dict[self.node_id] = {\\n 'type': 'output',\\n 'label': label,\\n 'connections': None,\\n 'function': ActivationFunction(function=self.o_function)\\n }\\n\\n self.node_id += 1\\n\\n # Create input neurons and connect them to the output neurons\\n for ii in range(self.input_size):\\n label = self.input_labels[ii]\\n cons = {}\\n\\n for nn in self.g_dict.keys():\\n if self.g_dict[nn]['type'] == 'output':\\n cons[nn] = {\\n 'weight': random.gauss(0, 1),\\n 'enabled': True\\n }\\n\\n self.g_dict[self.node_id] = {\\n 'type': 'input',\\n 'label': label,\\n 'connections': cons,\\n 'function': ActivationFunction(function='identity')\\n }\\n\\n self.node_id += 1\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e9b1f67706cf0b6460d7ca3c4f6e88\",\n \"score\": \"0.6288556\",\n \"text\": \"def generate_netlist(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aac05f0370cbb446ad214cd12274ba0\",\n \"score\": \"0.62730634\",\n \"text\": \"def _write_create_layers(nw, of):\\n to_write = \\\"\\\"\\\"std::vector<Layer *> create_layers(void)\\n{\\n std::cout << \\\"Creating the layers...\\\" << std::endl;\\n\\n std::vector<Layer *> layers;\\\"\\\"\\\" + os.linesep + os.linesep\\n\\n for i, layer in enumerate(nw.layers):\\n layer_id = \\\"layer_\\\" + str(i)\\n to_write += \\\" Layer *\\\" + layer_id + \\\" = new Layer();\\\" + os.linesep\\n\\n to_write += \\\" for (unsigned int i = 0; i < \\\" + str(len(layer))\\n to_write += \\\"; i++)\\\" + os.linesep\\n to_write += \\\" {\\\" + os.linesep\\n\\n # neuron_id is like: neuron_0_1\\n neuron_id = \\\"\\\\\\\"neuron_\\\" + str(i) + \\\"_\\\\\\\"\\\" + \\\" + std::to_string(i)\\\"\\n is_input = i == 0\\n is_output = i == len(nw.layers) - 1\\n to_write += \\\" Neuron n(\\\" + neuron_id + \\\", \\\" + str(is_input).lower()\\n to_write += \\\", \\\" + str(is_output).lower() + \\\");\\\" + os.linesep\\n\\n to_write += \\\" \\\" + layer_id + \\\"->add_neuron(n);\\\" + os.linesep\\n to_write += \\\" }\\\" + os.linesep\\n to_write += \\\" layers.push_back(\\\" + layer_id + \\\");\\\" + os.linesep\\n to_write += os.linesep\\n to_write += \\\" return layers;\\\" + os.linesep\\n to_write += \\\"}\\\" + os.linesep\\n\\n of.write(to_write)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01618beda122392316035b947672416\",\n \"score\": \"0.6265136\",\n \"text\": \"def build_network():\\n if FLAGS.model_architecture == 'mobilenet_v2':\\n network_func = functools.partial(\\n mobilenetv2_model.mobilenet_v2,\\n expansion_factor=FLAGS.expansion_factor)\\n elif FLAGS.model_architecture == 'mobilenet_v1':\\n network_func = functools.partial(mobilenetv1_model.mobilenet_v1)\\n elif FLAGS.model_architecture == 'resnet':\\n prune_first_layer = FLAGS.first_layer_sparsity != 0.\\n network_func = functools.partial(\\n resnet_model.resnet_v1_,\\n resnet_depth=FLAGS.resnet_depth,\\n init_method=FLAGS.init_method,\\n end_sparsity=FLAGS.end_sparsity,\\n prune_first_layer=prune_first_layer)\\n elif FLAGS.model_architecture.startswith('vgg'):\\n network_func = functools.partial(\\n vgg.vgg,\\n vgg_type=FLAGS.model_architecture,\\n init_method=FLAGS.init_method,\\n end_sparsity=FLAGS.end_sparsity)\\n else:\\n raise ValueError('Unknown archiecture ' + FLAGS.archiecture)\\n prune_last_layer = FLAGS.last_layer_sparsity != 0.\\n network = network_func(\\n num_classes=FLAGS.num_label_classes,\\n # TODO remove the pruning_method option.\\n pruning_method='threshold',\\n width=width,\\n prune_last_layer=prune_last_layer,\\n data_format=FLAGS.data_format,\\n weight_decay=FLAGS.weight_decay)\\n\\n is_training = (mode == tf_estimator.ModeKeys.TRAIN)\\n if FLAGS.use_batch_statistics:\\n is_training = True\\n return network(inputs=features, is_training=is_training)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4d1d775ef70676043620b9daeeddfd\",\n \"score\": \"0.62396497\",\n \"text\": \"def train_comparison_networks_CD(conv_layers,dense_layers,train_generator,validation_generator,nbr_epochs=80,network_name=\\\"CD_normal\\\",path=\\\"\\\",input_shape=(150,150,3)):\\n for i in range(len(conv_layers)):\\n nbr_layers_added = i+1\\n model = get_normal_network_CD(conv_layers[0:nbr_layers_added],dense_layers=[],input_shape=input_shape)\\n model.compile(loss='binary_crossentropy',\\n optimizer=optimizers.RMSprop(lr=1e-4),\\n metrics=['acc'])\\n model.summary()\\n history = model.fit_generator(\\n train_generator,\\n steps_per_epoch=100,\\n epochs=nbr_epochs,\\n validation_data=validation_generator,\\n validation_steps=50)\\n model_name = network_name+\\\"_conv_\\\"+str(nbr_epochs)+\\\"e_\\\"+str(nbr_layers_added)+\\\"L\\\"\\n file_name = model_name+\\\"results.txt\\\"#change so that this\\n print_results_to_file(model,history,file_name,model_name,path=path)\\n model_name = path+model_name+\\\".h5\\\"\\n model.save(model_name)\\n ### Then add the dense layers\\n for i in range(len(dense_layers)):\\n nbr_layers_added = i+1\\n model = get_normal_network_CD(conv_layers,dense_layers[0:nbr_layers_added],input_shape=input_shape)\\n model.compile(loss='binary_crossentropy',\\n optimizer=optimizers.RMSprop(lr=1e-4),\\n metrics=['acc'])\\n model.summary()\\n history = model.fit_generator(\\n train_generator,\\n steps_per_epoch=100,\\n epochs=nbr_epochs,\\n validation_data=validation_generator,\\n validation_steps=50)\\n model_name = network_name+\\\"_dense_\\\"+str(nbr_epochs)+\\\"e_\\\"+str(nbr_layers_added)+\\\"L\\\"\\n file_name = model_name+\\\"results.txt\\\"#change so that this\\n print_results_to_file(model,history,file_name,model_name,path=path)\\n model_name = path+model_name+\\\".h5\\\"\\n model.save(model_name)\\n\\n return \\\"Success\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f3ed5c0bc25c70218d6fc482398f0b\",\n \"score\": \"0.623451\",\n \"text\": \"def network(self):\\n nbr_patches=cfg.nbr_patches\\n weights = []\\n tensor = None\\n padding='SAME'\\n\\n #### with one image as input\\n for i in range(cfg.nbr_layers):\\n ## Parameters for first layer\\n if i==0:\\n filter,bias_size=[3,3,cfg.channel,nbr_patches],[nbr_patches]\\n entry=self.LR\\n ## Parameters for last layer\\n elif i==cfg.nbr_layers-1:\\n filter,bias_size= [3,3,nbr_patches,3],[3]\\n entry=tensor\\n ## Parameters for hidden layers\\n else:\\n filter,bias_size= [3,3,nbr_patches,nbr_patches],[nbr_patches]\\n entry=tensor\\n\\n ## Construction of the CNN based on parameters above\\n conv_w = tf.get_variable(\\\"conv_w_%s\\\" % (i), filter, initializer=tf.random_normal_initializer(stddev=np.sqrt(2.0/9/64)))\\n conv_b = tf.get_variable(\\\"conv_b_%s\\\" % (i), bias_size, initializer=tf.constant_initializer(0))\\n weights.append(conv_w)\\n weights.append(conv_b)\\n if i==cfg.nbr_layers-1:\\n ## We don't put the ReLu layer at the end of the network\\n tensor = tf.nn.bias_add(tf.nn.conv2d(entry, conv_w, strides=[1,1,1,1], padding=padding), conv_b)\\n else:\\n tensor = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(entry, conv_w, strides=[1,1,1,1], padding=padding), conv_b)) #, strides=[1,1,1,1]\\n\\n ## Reconstruction of the image (adding low-res and residual)\\n tensor_out=tf.add(tensor,self.LR_losses)\\n\\n ## output generated image, weights of the network and the residuals image\\n return tensor_out,weights,tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f956fb1cc3be4e990a976b02454d26c4\",\n \"score\": \"0.6225239\",\n \"text\": \"def gen_net_conf(self,args):\\n\\t\\t# Ref: http://docker-py.readthedocs.io/en/stable/api.html#docker.api.container.ContainerApiMixin.create_networking_config\\n\\t\\tnetwork_dict=self.handle.create_networking_config({args['network_name']: self.gen_ep_conf(args)})\\n\\t\\treturn network_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d087d1fabe22fcbfc6d9802ff11001f5\",\n \"score\": \"0.6212829\",\n \"text\": \"def create_layer_map(self):\\n\\t\\tself._layer_map[0]=\\t{'name':'data',\\t\\t\\t\\t\\t'inputs':[],\\t\\t\\t\\t\\t'fl':7,'type':'data'} \\n\\t\\tself._layer_map[1]=\\t{'name':'conv1',\\t\\t\\t\\t'inputs':[0],\\t\\t\\t\\t\\t'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\tself._layer_map[2]=\\t{'name':'conv2',\\t\\t\\t\\t'inputs':[1],\\t\\t\\t\\t\\t'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[3]=\\t{'name':'conv3',\\t\\t\\t\\t'inputs':[2],\\t\\t\\t\\t\\t'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\t# Inception 3a ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[4]=\\t{'name':'inc3a/pool1',\\t\\t\\t'inputs':[3],\\t\\t\\t\\t\\t 'fl':6,'type':'plmx'}\\n\\t\\tself._layer_map[5]=\\t{'name':'inc3a/conv1',\\t\\t\\t'inputs':[4],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[6]=\\t{'name':'inc3a/conv3_1',\\t\\t'inputs':[3],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[7]=\\t{'name':'inc3a/conv3_2',\\t\\t'inputs':[6],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[8]=\\t{'name':'inc3a/conv5_1',\\t\\t'inputs':[3],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[9]=\\t{'name':'inc3a/conv5_2',\\t\\t'inputs':[8],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[10]={'name':'inc3a/conv5_3',\\t\\t'inputs':[9],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 3b ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[11]={'name':'inc3b/conv1',\\t\\t\\t'inputs':[5,7,10], \\t\\t\\t\\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[12]={'name':'inc3b/conv3_1',\\t\\t'inputs':[5,7,10], \\t\\t\\t\\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[13]={'name':'inc3b/conv3_2',\\t\\t'inputs':[12], \\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[14]={'name':'inc3b/conv5_1',\\t\\t'inputs':[5,7,10], \\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[15]={'name':'inc3b/conv5_2',\\t\\t'inputs':[14], \\t\\t\\t\\t\\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[16]={'name':'inc3b/conv5_3',\\t\\t'inputs':[15],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 3c ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[17]={'name':'inc3c/conv1',\\t\\t\\t'inputs':[11,13,16],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[18]={'name':'inc3c/conv3_1',\\t\\t'inputs':[11,13,16],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[19]={'name':'inc3c/conv3_2',\\t\\t'inputs':[18], \\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[20]={'name':'inc3c/conv5_1',\\t\\t'inputs':[11,13,16],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[21]={'name':'inc3c/conv5_2',\\t\\t'inputs':[20], \\t\\t\\t\\t \\t 'fl':6, 'fl_w':13,'type':'conv'}\\n\\t\\tself._layer_map[22]={'name':'inc3c/conv5_3',\\t\\t'inputs':[21],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 3d ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[23]={'name':'inc3d/conv1',\\t\\t\\t'inputs':[17,19,22],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[24]={'name':'inc3d/conv3_1',\\t\\t'inputs':[17,19,22],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[25]={'name':'inc3d/conv3_2',\\t\\t'inputs':[24],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[26]={'name':'inc3d/conv5_1',\\t\\t'inputs':[17,19,22],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[27]={'name':'inc3d/conv5_2',\\t\\t'inputs':[26],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[28]={'name':'inc3d/conv5_3',\\t\\t'inputs':[27],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\t# Inception 3e ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[29]={'name':'inc3e/conv1',\\t\\t\\t'inputs':[23,25,28],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[30]={'name':'inc3e/conv3_1',\\t\\t'inputs':[23,25,28],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[31]={'name':'inc3e/conv3_2',\\t\\t'inputs':[30],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[32]={'name':'inc3e/conv5_1',\\t\\t'inputs':[23,25,28],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[33]={'name':'inc3e/conv5_2',\\t\\t'inputs':[32],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[34]={'name':'inc3e/conv5_3',\\t\\t'inputs':[33],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 4a ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[35]={'name':'inc4a/pool1',\\t\\t\\t'inputs':[29,31,34],\\t\\t \\t 'fl':6,'type':'plmx'}\\n\\t\\tself._layer_map[36]={'name':'inc4a/conv1',\\t\\t\\t'inputs':[35], \\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[37]={'name':'inc4a/conv3_1',\\t\\t'inputs':[29,31,34],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[38]={'name':'inc4a/conv3_2',\\t\\t'inputs':[37],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[39]={'name':'inc4a/conv5_1',\\t\\t'inputs':[29,31,34],\\t\\t \\t 'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\tself._layer_map[40]={'name':'inc4a/conv5_2',\\t\\t'inputs':[39],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[41]={'name':'inc4a/conv5_3',\\t\\t'inputs':[40],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\t# Inception 4b ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[42]={'name':'inc4b/conv1',\\t\\t\\t'inputs':[36,38,41],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[43]={'name':'inc4b/conv3_1',\\t\\t'inputs':[36,38,41],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[44]={'name':'inc4b/conv3_2',\\t\\t'inputs':[43],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[45]={'name':'inc4b/conv5_1',\\t\\t'inputs':[36,38,41],\\t\\t \\t 'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\tself._layer_map[46]={'name':'inc4b/conv5_2',\\t\\t'inputs':[45],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[47]={'name':'inc4b/conv5_3',\\t\\t'inputs':[46],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 4c ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[48]={'name':'inc4c/conv1',\\t\\t\\t'inputs':[42,44,47],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[49]={'name':'inc4c/conv3_1',\\t\\t'inputs':[42,44,47],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[50]={'name':'inc4c/conv3_2',\\t\\t'inputs':[49],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[51]={'name':'inc4c/conv5_1',\\t\\t'inputs':[42,44,47],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[52]={'name':'inc4c/conv5_2',\\t\\t'inputs':[51],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[53]={'name':'inc4c/conv5_3',\\t\\t'inputs':[52],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\t# Inception 4d ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[54]={'name':'inc4d/conv1',\\t\\t\\t'inputs':[48,50,53],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[55]={'name':'inc4d/conv3_1',\\t\\t'inputs':[48,50,53],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[56]={'name':'inc4d/conv3_2',\\t\\t'inputs':[55], \\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[57]={'name':'inc4d/conv5_1',\\t\\t'inputs':[48,50,53],\\t\\t \\t 'fl':6, 'fl_w':16,'type':'conv'}\\n\\t\\tself._layer_map[58]={'name':'inc4d/conv5_2',\\t\\t'inputs':[57],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[59]={'name':'inc4d/conv5_3',\\t\\t'inputs':[58],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Inception 4e ~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[60]={'name':'inc4e/conv1',\\t\\t\\t'inputs':[54,56,59],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[61]={'name':'inc4e/conv3_1',\\t\\t'inputs':[54,56,59],\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[62]={'name':'inc4e/conv3_2',\\t\\t'inputs':[61],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\tself._layer_map[63]={'name':'inc4e/conv5_1',\\t\\t'inputs':[54,56,59],\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[64]={'name':'inc4e/conv5_2',\\t\\t'inputs':[63],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':14,'type':'conv'}\\n\\t\\tself._layer_map[65]={'name':'inc4e/conv5_3',\\t\\t'inputs':[64],\\t\\t\\t\\t \\t 'fl':6, 'fl_w':15,'type':'conv'}\\n\\t\\t# Downsample ~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\\t\\tself._layer_map[66]={'name':'downsample', \\t\\t\\t'inputs':[3], \\t\\t\\t\\t\\t 'fl':6,'type':'plmx'}\\n\\t\\tself._layer_map[67]={'name':'convf', \\t\\t\\t\\t'inputs':[66,29,31,34,60,62,65], 'fl':8, 'fl_w':16,'type':'conv'}\\t\\t\\t\\t\\t\\n\\t\\tself._layer_map[68]={'name':'convf_up2',\\t\\t\\t'inputs':[67],\\t\\t\\t\\t\\t 'fl':8, 'fl_w':16,'type':'decv'}\\t# TODO: fl and fl_w values\\n\\t\\tself._layer_map[69]={'name':'rpn_conv1', \\t\\t\\t'inputs':[67], \\t\\t\\t\\t\\t 'fl':10, 'fl_w':17,'type':'conv'}\\n\\n\\t\\tself._layer_map[70]={'name':'rpn_cls_score_fabu',\\t'inputs':[69], \\t\\t\\t\\t\\t 'fl':10, 'fl_w':17,'type':'conv'}\\n\\t\\tself._layer_map[71]={'name':'rpn_bbox_pred_fabu', \\t'inputs':[69], \\t\\t\\t\\t\\t 'fl':13, 'fl_w':18,'type':'conv'}\\n\\t\\tself._layer_map[72]={'name':'proposal', \\t\\t\\t'inputs':[70,71], \\t\\t\\t\\t 'fl':4,'type':'prop'}\\t\\t\\t\\t\\t\\t\\t\\n\\t\\tself._layer_map[73]={'name':'roi_pool_conv5', \\t\\t'inputs':[68,72], \\t\\t\\t\\t 'fl':8,'type':'roip'}\\t\\t\\t\\t\\t\\t\\t\\n\\t\\tself._layer_map[74]={'name':'fc6_L', \\t\\t\\t\\t'inputs':[73],\\t\\t\\t\\t\\t 'fl':6, 'fl_w':17,'type':'fcon'}\\n\\t\\tself._layer_map[75]={'name':'fc6_U', \\t\\t\\t\\t'inputs':[74],\\t\\t\\t\\t\\t 'fl':7, 'fl_w':17,'type':'fcon'}\\n\\t\\tself._layer_map[76]={'name':'fc7_L', \\t\\t\\t\\t'inputs':[75],\\t\\t\\t\\t\\t 'fl':7, 'fl_w':17,'type':'fcon'}\\n\\t\\tself._layer_map[77]={'name':'fc7_U', \\t\\t\\t\\t'inputs':[76],\\t\\t\\t\\t\\t 'fl':9, 'fl_w':17,'type':'fcon'}\\n\\t\\tself._layer_map[78]={'name':'cls_score_fabu', \\t\\t'inputs':[77],\\t\\t\\t\\t\\t 'fl':9, 'fl_w':17,'type':'fcon'}\\n\\t\\tself._layer_map[79]={'name':'bbox_pred_fabu', \\t\\t'inputs':[77],\\t\\t\\t\\t\\t 'fl':9, 'fl_w':17,'type':'fcon'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c34c195ab1048c43e4d02e501b03d4\",\n \"score\": \"0.6130811\",\n \"text\": \"def _build_net(self):\\n\\n np.random.seed(self.rand_seed)\\n\\n model = Sequential()\\n\\n for layer in self.layers:\\n model = self._add_layer(model, layer)\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2630326f3dc226a08fca03ebb840968b\",\n \"score\": \"0.6126979\",\n \"text\": \"def test_create_base(self):\\n drive.convert_nets(networks, 'weight')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"599de5985ddb9f6be495c27aae7dc31e\",\n \"score\": \"0.61083186\",\n \"text\": \"def generator_network(z, filters, start_shape=(4,4)):\\n\\n with tf.variable_scope('latent'):\\n\\n # pop the first filter, this ensures we make the correct number of\\n # convolutional layers later on...\\n\\n # calculate the number of features\\n initial_shape = start_shape+(filters[0],)\\n num_units = np.prod(initial_shape)\\n\\n dense = tf.layers.dense(inputs=pixel_norm(z),\\n units=num_units,\\n activation=k_leaky_relu_alpha,\\n name='dense1',\\n reuse=tf.AUTO_REUSE)\\n\\n # pixel norm and reshape to an image\\n reshaped = pixel_norm(tf.reshape(dense, (-1,)+initial_shape))\\n\\n # # first convolutional layer\\n conv0 = weighted_conv2d(inputs=reshaped,\\n filters=filters[0],\\n kernel_size=[3, 3],\\n padding=\\\"same\\\",\\n activation=k_leaky_relu_alpha,\\n name='conv',\\n reuse=tf.AUTO_REUSE,\\n norm=True)\\n\\n # now set the value of x (the output of the previous layer)\\n conv_layers = [conv0]\\n\\n # now build the layers\\n for l, f in enumerate(filters[1:]):\\n with tf.variable_scope('layer_{0:d}'.format(l)):\\n\\n upscale = double_size(conv_layers[-1])\\n\\n # convolutional layers\\n conv1 = weighted_conv2d(inputs=upscale,\\n filters=f,\\n kernel_size=[3, 3],\\n padding=\\\"same\\\",\\n activation=k_leaky_relu_alpha,\\n name='conv1',\\n reuse=tf.AUTO_REUSE,\\n norm=True)\\n\\n conv2 = weighted_conv2d(inputs=conv1,\\n filters=f,\\n kernel_size=[3, 3],\\n padding=\\\"same\\\",\\n activation=k_leaky_relu_alpha,\\n name='conv2',\\n reuse=tf.AUTO_REUSE,\\n norm=True)\\n\\n # convolutional layer\\n conv_layers.append(conv2)\\n\\n outputs = []\\n with tf.variable_scope(\\\"to_image\\\", auxiliary_name_scope=True):\\n for l, conv in enumerate(conv_layers):\\n output = to_image(conv, filters=2, n=l)\\n outputs.append(output)\\n\\n return outputs, output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b773034ec95a72beed9756d3032a9827\",\n \"score\": \"0.61083186\",\n \"text\": \"def add_onenet_config(cfg):\\n cfg.MODEL.OneNet = CN()\\n cfg.MODEL.OneNet.NUM_CLASSES = 80\\n cfg.MODEL.OneNet.NMS = False # for ablation\\n cfg.MODEL.OneNet.HEAD = \\\"CenterNet\\\" # or FCOS or RetinaNet\\n cfg.MODEL.OneNet.PRE_DEFINE = False\\n \\n # CenterNet Head.\\n cfg.MODEL.OneNet.IN_FEATURES = [\\\"res2\\\", \\\"res3\\\", \\\"res4\\\", \\\"res5\\\"]\\n cfg.MODEL.OneNet.ACTIVATION = 'relu'\\n cfg.MODEL.OneNet.DECONV_CHANNEL= [2048, 256, 128, 64]\\n cfg.MODEL.OneNet.DECONV_KERNEL = [4, 4, 4]\\n cfg.MODEL.OneNet.DCN = True\\n cfg.MODEL.OneNet.MODULATE_DEFORM = True\\n \\n # FCOS or RetinaNet Head.\\n # cfg.MODEL.OneNet.IN_FEATURES = [\\\"p3\\\", \\\"p4\\\", \\\"p5\\\", \\\"p6\\\", \\\"p7\\\"]\\n cfg.MODEL.OneNet.FEATURES_STRIDE = [8, 16, 32, 64, 128]\\n cfg.MODEL.OneNet.OBJECT_SIZES_OF_INTEREST=[\\n [-1, 64],\\n [64, 128],\\n [128, 256],\\n [256, 512],\\n [512, float(\\\"inf\\\")],]\\n cfg.MODEL.OneNet.NUM_CONV = 4\\n cfg.MODEL.OneNet.CONV_NORM = \\\"GN\\\"\\n cfg.MODEL.OneNet.CONV_CHANNELS = 256 \\n \\n # Cost.\\n cfg.MODEL.OneNet.CLASS_COST = 2.0\\n cfg.MODEL.OneNet.GIOU_COST = 2.0\\n cfg.MODEL.OneNet.L1_COST = 5.0\\n \\n # Loss.\\n cfg.MODEL.OneNet.CLASS_WEIGHT = 2.0\\n cfg.MODEL.OneNet.GIOU_WEIGHT = 2.0\\n cfg.MODEL.OneNet.L1_WEIGHT = 5.0\\n \\n # Focal Loss.\\n cfg.MODEL.OneNet.ALPHA = 0.25\\n cfg.MODEL.OneNet.GAMMA = 2.0\\n cfg.MODEL.OneNet.PRIOR_PROB = 0.01\\n \\n # Optimizer.\\n cfg.SOLVER.OPTIMIZER = \\\"ADAMW\\\"\\n cfg.SOLVER.BACKBONE_MULTIPLIER = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ec2238dded7c2e5a805fab27c6933c\",\n \"score\": \"0.6086006\",\n \"text\": \"def build_layers(self, input_shape, layer_key='first'):\\n x = torch.zeros(input_shape) # create dummy inputs to be used to infer shapes of layers\\n out = self.process(x)\\n print(\\\"Building basic block of Convolutional Network using input shape {} for layer key {}\\\".format(out.shape, layer_key))\\n context_list = []\\n\\n for i in range(self.num_layers): # for number of layers times\\n if i > 0:\\n # to give every layer access to all prev layers (DenseNet connectivity)\\n out = torch.cat(context_list, dim=1)\\n\\n dilation = int(DILATION_PARAM**i)\\n self.layer_dict['conv_{}_{}'.format(i, layer_key)] = nn.Conv1d(in_channels=out.shape[1],\\n # add a conv layer in the module dict\\n kernel_size=3,\\n out_channels=self.num_filters,\\n padding=dilation,\\n bias=False,\\n dilation=dilation)\\n\\n out = self.layer_dict['conv_{}_{}'.format(i, layer_key)](out) # use layer on inputs to get an output\\n self.layer_dict['batch_norm_{}_{}'.format(i, layer_key)] = nn.BatchNorm1d(num_features=out.shape[1])\\n out = self.layer_dict['batch_norm_{}_{}'.format(i, layer_key)](out)\\n out = F.leaky_relu(out)\\n out = self.drop(out)\\n context_list.append(out)\\n\\n out = torch.cat(context_list, dim=1)\\n out = F.max_pool1d(out, out.shape[-1])\\n out = out.view(out.shape[0], -1)\\n self.layer_dict['fc_layer_{}'.format(layer_key)] = nn.Linear(in_features=out.shape[1], # add a linear layer\\n out_features=self.num_output_classes,\\n bias=self.use_bias)\\n\\n out = self.layer_dict['fc_layer_{}'.format(layer_key)](out) # apply linear layer on flattened inputs\\n print(\\\"Block is built, output volume is {} for layer key {}\\\".format(out.shape, layer_key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f928f8ae4a7c898f0ad9f30338be493f\",\n \"score\": \"0.60796314\",\n \"text\": \"def load_layers():\\r\\n import arcpy\\r\\n net = nx.DiGraph()\\r\\n current_mxd = arcpy.mapping.MapDocument('CURRENT')\\r\\n layer_list = arcpy.mapping.ListLayers(current_mxd)\\r\\n \\r\\n for layer in layer_list:\\r\\n describe_layer = arcpy.Describe(layer)\\r\\n \\r\\n if describe_layer.shapeType == 'Point':\\r\\n desc = arcpy.Describe(layer)\\r\\n shape = desc.ShapeFieldName\\r\\n rows = arcpy.SearchCursor(layer)\\r\\n for row in rows:\\r\\n feat = row.getValue(shape)\\r\\n node = feat.getPart()\\r\\n nodeXY = node.X, node.Y\\r\\n net.add_node(nodeXY) \\r\\n del row, rows\\r\\n\\r\\n if describe_layer.shapeType == 'Polyline':\\r\\n desc = arcpy.Describe(layer)\\r\\n shape = desc.ShapeFieldName\\r\\n rows = arcpy.SearchCursor(layer)\\r\\n for row in rows:\\r\\n feat = row.getValue(shape)\\r\\n edge_pt1 = feat.firstPoint\\r\\n edge_pt2 = feat.lastPoint\\r\\n edge_pt1XY = edge_pt1.X, edge_pt1.Y\\r\\n edge_pt2XY = edge_pt2.X, edge_pt2.Y\\r\\n net.add_edge(edge_pt1XY, edge_pt2XY)\\r\\n del row, rows\\r\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bb701d7603a4b0eabe2a90eb9d88559\",\n \"score\": \"0.607124\",\n \"text\": \"def load_layers():\\n weights, biases = load_pretrained('model_without_order_info_224.mat')\\n\\n model_layers = []\\n\\n # L1\\n model_layers.append(layers.Conv2D(\\n 64,\\n (15, 15),\\n strides=3,\\n padding='valid',\\n kernel_initializer=initializers.Constant(weights['conv1']),\\n bias_initializer=initializers.Constant(biases['conv1']),\\n activation='relu',\\n input_shape=(225, 225, 1),\\n name='L1conv'\\n ))\\n model_layers.append(layers.MaxPooling2D(\\n pool_size=(3, 3),\\n strides=2,\\n padding='valid',\\n name='Pool1'\\n ))\\n\\n # L2\\n model_layers.append(layers.Conv2D(\\n 128,\\n (5, 5),\\n strides=1,\\n padding='valid',\\n kernel_initializer=initializers.Constant(weights['conv2']),\\n bias_initializer=initializers.Constant(biases['conv2']),\\n activation='relu',\\n name='L2conv'\\n ))\\n model_layers.append(layers.MaxPooling2D(\\n pool_size=(3, 3),\\n strides=2,\\n padding='valid',\\n name='Pool2'\\n ))\\n\\n # L3\\n model_layers.append(layers.Conv2D(\\n 256,\\n (3, 3),\\n strides=1,\\n padding='same',\\n kernel_initializer=initializers.Constant(weights['conv3']),\\n bias_initializer=initializers.Constant(biases['conv3']),\\n activation='relu',\\n name='L3conv'\\n ))\\n\\n # L4\\n model_layers.append(layers.Conv2D(\\n 256,\\n (3, 3),\\n strides=1,\\n padding='same',\\n kernel_initializer=initializers.Constant(weights['conv4']),\\n bias_initializer=initializers.Constant(biases['conv4']),\\n activation='relu',\\n name='L4conv'\\n ))\\n\\n # L5\\n model_layers.append(layers.Conv2D(\\n 256,\\n (3, 3),\\n strides=1,\\n padding='same',\\n kernel_initializer=initializers.Constant(weights['conv5']),\\n bias_initializer=initializers.Constant(biases['conv5']),\\n activation='relu',\\n name='L5conv'\\n ))\\n model_layers.append(layers.MaxPooling2D(\\n pool_size=(3, 3),\\n strides=2,\\n padding='valid',\\n name='Pool5'\\n ))\\n\\n # L6\\n model_layers.append(layers.Conv2D(\\n 512,\\n (7, 7),\\n strides=1,\\n padding='valid',\\n kernel_initializer=initializers.Constant(weights['conv6']),\\n bias_initializer=initializers.Constant(biases['conv6']),\\n activation='relu',\\n name='L6conv'\\n ))\\n model_layers.append(layers.Dropout(\\n rate=0.5,\\n name='Dropout8'\\n ))\\n\\n # L7\\n model_layers.append(layers.Conv2D(\\n 512,\\n (1, 1),\\n strides=1,\\n padding='valid',\\n kernel_initializer=initializers.Constant(weights['conv7']),\\n bias_initializer=initializers.Constant(biases['conv7']),\\n activation='relu',\\n name='L7conv'\\n ))\\n model_layers.append(layers.Dropout(\\n rate=0.5,\\n name='Dropout7'\\n ))\\n\\n # L8\\n model_layers.append(layers.Conv2D(\\n 250,\\n (1, 1),\\n strides=1,\\n padding='valid',\\n kernel_initializer=initializers.Constant(weights['conv8']),\\n bias_initializer=initializers.Constant(biases['conv8']),\\n activation='relu',\\n name='L8conv'\\n ))\\n\\n return model_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02fa631837c16af7bda81194f11e4bf9\",\n \"score\": \"0.6062069\",\n \"text\": \"def make_net(bottom, arch_str, is_generator, concat_list=[], norm_layer='BN', reuse=False, is_training=None,\\r\\n do_print=True):\\r\\n\\r\\n assert is_training is not None, \\\"is_training must be set\\\"\\r\\n\\r\\n # Create arch list from arch string\\r\\n arch_list = arch_string2list(arch_str, is_generator=is_generator, norm_layer=norm_layer)\\r\\n\\r\\n # Make sure that the generator is being created if any input is given (other than bottom)\\r\\n assert len(concat_list) == 0 or is_generator, \\\"Discriminator should not have concat_list\\\"\\r\\n\\r\\n # Print network architecture\\r\\n if do_print:\\r\\n print('Creating {} network:'.format('generator' if is_generator else 'discriminator'))\\r\\n print_arch_list(arch_list)\\r\\n\\r\\n # Create dictionary of layer names\\r\\n name2layer = {'bottom': bottom}\\r\\n\\r\\n # Create layers one by one\\r\\n for layer in arch_list:\\r\\n if reuse:\\r\\n tf.get_variable_scope().reuse_variables()\\r\\n\\r\\n # Set batchnorm mode\\r\\n if layer['op'] == 'batchnorm':\\r\\n layer['params'] = {'training': is_training}\\r\\n\\r\\n # If layer has parameters, apply them. For deconvolution perform concatenation.\\r\\n if 'params' in layer.keys():\\r\\n if layer['op'] == 'deconv' and len(concat_list) > 0:\\r\\n tensor_to_concat = concat_list.pop(0) # Pop the first tensor on list\\r\\n # If it's None, don't concat anything. Otherwise, concat it to layer's input.\\r\\n if tensor_to_concat is None:\\r\\n bottom_ = name2layer[layer['bottom']]\\r\\n else:\\r\\n deconv_bottom = name2layer[layer['bottom']]\\r\\n # If there is no bottom, just take tensor_to_concat (e.g. if dim_z_global = dim_z_local = 0)\\r\\n if deconv_bottom is not None:\\r\\n bottom_ = tf.concat([deconv_bottom, tensor_to_concat], axis=3)\\r\\n else:\\r\\n bottom_ = tensor_to_concat\\r\\n else:\\r\\n bottom_ = name2layer[layer['bottom']]\\r\\n\\r\\n layer_tf = op2layer(layer['op'])(bottom_, **layer['params'], name=layer['name'])\\r\\n if do_print:\\r\\n print(\\\"Creating {} layer {} with parameters {}\\\".format(layer['op'], layer['name'], layer['params']))\\r\\n elif layer['op'] == 'instancenorm': # Instace norm layer does not take a name parameter for some reason...\\r\\n layer_tf = op2layer(layer['op'])(name2layer[layer['bottom']], reuse=reuse, scope=layer['name'])\\r\\n if do_print:\\r\\n print(\\\"Creating {} layer {}\\\".format(layer['op'], layer['name']))\\r\\n else:\\r\\n layer_tf = op2layer(layer['op'])(name2layer[layer['bottom']], name=layer['name'])\\r\\n if do_print:\\r\\n print(\\\"Creating {} layer {}\\\".format(layer['op'], layer['name']))\\r\\n\\r\\n name2layer[layer['name']] = layer_tf\\r\\n\\r\\n assert len(concat_list) == 0, \\\"Not all concat tensors were used (length at the end is {})\\\".format(len(concat_list))\\r\\n\\r\\n # For generator, find kernel size (which we assert is constant for all deconv layers)\\r\\n # and crop k - 2 pixels from each of the two dimensions\\r\\n if is_generator:\\r\\n for layer in arch_list:\\r\\n if layer['op'] == 'deconv':\\r\\n kernel_size = layer['params']['kernel_size']\\r\\n break\\r\\n return layer_tf[:, :-(kernel_size-2), :-(kernel_size-2), :]\\r\\n\\r\\n return layer_tf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3458617c39658bbae6388d9d1e022b92\",\n \"score\": \"0.6059142\",\n \"text\": \"def build_architecture(opts, sample):\\n opts.architecture = {}\\n if opts.network_type == 'heading_network':\\n opts.architecture['nlayers'] = opts.nlayers\\n opts.architecture['filters'] = [ 2**(6+i) for i in range(opts.nlayers) ]\\n opts.architecture['strides'] = [ 2 ] * opts.nlayers\\n opts.architecture['kernel_sizes'] = [ 3 ] * opts.nlayers\\n opts.architecture['paddings'] = [ 'VALID' ] * opts.nlayers\\n opts.architecture['activations'] = [ 'relu' ] * opts.nlayers\\n elif opts.network_type == 'heading_resnet':\\n opts.architecture['nlayers'] = opts.nlayers\\n opts.architecture['filters'] = [ [2**(6+i)]*2 for i in range(opts.nlayers) ]\\n opts.architecture['strides'] = [ 2 ] * opts.nlayers\\n opts.architecture['kernel_sizes'] = [ 3 ] * opts.nlayers\\n opts.architecture['activations'] = [ 'relu' ] * opts.nlayers\\n # TODO: Add back polymix\\n # if opts['activation-type'] == \\\"polymix\\\":\\n # opts['activations'][0] = \\\"relusq\\\"\\n elif opts.network_type == 'hourglass':\\n opts.architecture['nb_stack'] = 1\\n opts.architecture['nlayers'] = opts.nlayers\\n opts.architecture['width'] = 256\\n opts.architecture['bn_decay'] = 0.9\\n opts.architecture['epsilon'] = 1e-5\\n\\n opts.architecture['noutputs'] = opts.noutputs\\n\\n regularizer = tf.contrib.layers.l1_l2_regularizer(scale_l1=opts.weight_l1_decay,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tscale_l2=opts.weight_decay)\\n if opts.network_type == 'heading_network':\\n return build_heading_network(opts, sample[\\\"flow\\\"], regularizer=regularizer)\\n elif opts.network_type == 'heading_resnet':\\n return build_heading_resnet(opts, sample[\\\"flow\\\"], regularizer=regularizer)\\n elif opts.network_type == 'heading_resnet_2':\\n return build_heading_resnet_2(opts, sample[\\\"flow\\\"], regularizer=regularizer)\\n elif opts.network_type == 'hourglass':\\n hg = hourglass.single_hourglass(opts,\\n verbose=False,\\n name='network',\\n regularizer=regularizer)\\n return hg(sample[\\\"image\\\"])\\n else:\\n return None # TODO: Proper error handling\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7bacb3fbbcd159a029e50adac60332\",\n \"score\": \"0.6037738\",\n \"text\": \"def __generate_network(\\n self\\n ) -> None:\\n for i in range(0, self.__nb_network):\\n net = self.__random_network()\\n local = True if i == 0 else False\\n if net not in self.__hosts.keys():\\n self.__hosts[net.replace('/24','')] = list()\\n self.__networks.append((net, local))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c38b5688b2147bf69801a20eef6382\",\n \"score\": \"0.60345113\",\n \"text\": \"def build_network(self):\\n self.network = input_data(shape=[None, 48, 48, 1])\\n print(\\\"Input data \\\", self.network.shape[1:])\\n\\n self.network = conv_2d(self.network, 64, 5, activation='relu')\\n print(\\\"Conv1 \\\", self.network.shape[1:])\\n\\n self.network = max_pool_2d(self.network, 3, strides=2)\\n print(\\\"Maxpool1 \\\", self.network.shape[1:])\\n\\n self.network = conv_2d(self.network, 64, 5, activation='relu')\\n print(\\\"Conv2 \\\", self.network.shape[1:])\\n\\n self.network = max_pool_2d(self.network, 3, strides=2)\\n print(\\\"Maxpool2 \\\", self.network.shape[1:])\\n\\n self.network = conv_2d(self.network, 128, 4, activation='relu')\\n print(\\\"Conv3 \\\", self.network.shape[1:])\\n\\n self.network = fully_connected(self.network, 3072, activation='relu')\\n print(\\\"Fully connected\\\", self.network.shape[1:])\\n\\n self.network = fully_connected(self.network, len(\\n self.target_classes), activation='softmax')\\n print(\\\"Output \\\", self.network.shape[1:])\\n # Generates a TrainOp which contains the information about optimization process - optimizer, loss function, etc\\n\\n self.network = regression(\\n self.network, optimizer='momentum', metric='accuracy', loss='categorical_crossentropy')\\n\\n # Creates a model instance.\\n self.model = tflearn.DNN(\\n self.network, checkpoint_path='./utility/emotionDetection/model_1_atul', max_checkpoints=1, tensorboard_verbose=2)\\n\\n # Loads the model weights from the checkpoint\\n self.load_model()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a0f3e4b98cfb04930da262fe66d03b\",\n \"score\": \"0.60299116\",\n \"text\": \"def connect_layers(self):\\n logging.info('Converted layer info')\\n logging.info(\\\"{:22s} {:22s} {:12s} {:5s} {:18s} {:8s} {:s}\\\".format(\\n 'Input Node', 'Output Node', 'Node Type', 'Live Range',\\n 'Output Dimension', 'Addr', 'Size'))\\n\\n class LayerLiveRange(object):\\n def __init__(self, layer, index):\\n self.layer = layer\\n self.birth_index = index\\n self.death_index = index\\n self.output_concat_lr = None\\n self.allocated = False\\n live_ranges = []\\n weight_size = 0\\n for index, layer in enumerate(self.layer):\\n # calc weight and increment the counter\\n if layer.type is LayerType.Convolution:\\n for run in layer.run:\\n weight_size += get_weight_size(run.conv, self.quantization)\\n\\n # add to output_layer\\n if layer.is_output:\\n self.num_output_layers += 1\\n self.output_layer.append(layer)\\n\\n # create LayerLiveRange\\n lr = LayerLiveRange(layer, index)\\n for node_in, lr_in in itertools.product(layer.node_in.input_nodes,\\n live_ranges):\\n if node_in is lr_in.layer.node_out:\\n lr.layer.layer_in.append(lr_in.layer)\\n if lr_in.death_index < index:\\n lr_in.death_index = index\\n if lr.layer.is_output:\\n lr.death_index = len(self.layer) - 1\\n live_ranges.append(lr)\\n\\n # handle concat layer and update live_ranges\\n def _lr_is_partof_concat(lr, concat_lr):\\n if concat_lr.layer.type is not LayerType.Concatenate:\\n return False\\n while lr:\\n if lr is concat_lr:\\n return True\\n lr = lr.output_concat_lr\\n return False\\n\\n for index, lr in enumerate(reversed(live_ranges)):\\n index = len(live_ranges) - index\\n if lr.layer.type is LayerType.Concatenate:\\n for prev_lr in live_ranges[:index]:\\n if prev_lr.layer.node_out in lr.layer.node_in.input_nodes:\\n prev_lr.output_concat_lr = lr\\n\\n for index, lr in enumerate(reversed(live_ranges)):\\n index = len(live_ranges) - index\\n if lr.layer.type is LayerType.Concatenate:\\n for prev_lr in live_ranges[:index]:\\n if _lr_is_partof_concat(prev_lr, lr):\\n max_di = max(prev_lr.death_index, lr.death_index)\\n prev_lr.death_index = max_di\\n lr.death_index = max_di\\n min_di = min(prev_lr.birth_index, lr.birth_index)\\n prev_lr.birth_index = min_di\\n lr.birth_index = min_di\\n\\n def get_dst_concat_lr(lr):\\n \\\"\\\"\\\"\\n search final concat node Life Range\\n If lr.output_concat_lr is None, lr is returned.\\n \\\"\\\"\\\"\\n _lr = lr\\n while _lr.output_concat_lr:\\n _lr = _lr.output_concat_lr\\n return _lr\\n\\n allocated_size = 0 # size to be allocated\\n for lr in sorted(reversed(live_ranges),\\n key=(lambda x : x.death_index),\\n reverse=True):\\n if lr.output_concat_lr:\\n offset = 0\\n for node in lr.output_concat_lr.layer.node_in.input_nodes:\\n if node == lr.layer.node_out:\\n break\\n offset += node.output_size\\n lr.layer.output_addr_offset = lr.output_concat_lr.layer.output_addr_offset + offset\\n lr.allocated = True\\n\\n necessary_size = make_align_size(lr.layer.node_out.output_size)\\n if not lr.allocated:\\n current_live_ranges = [_lr for _lr in live_ranges\\n if not\\n (_lr.birth_index > lr.death_index or\\n _lr.death_index < lr.birth_index)]\\n current_live_ranges = sorted(\\n current_live_ranges,\\n key=(lambda x: x.layer.output_addr_offset))\\n\\n # find if can re-use empty spaces in current live ranges\\n empty_found = False\\n current_offset = 0\\n for clr in current_live_ranges:\\n if not clr.allocated:\\n continue\\n\\n empty_found = (clr.layer.output_addr_offset\\n - current_offset >= necessary_size)\\n if empty_found:\\n lr.layer.output_addr_offset = current_offset\\n break\\n else:\\n # to next, update current_offset\\n increment_size = make_align_size(\\n clr.layer.node_out.output_size)\\n if clr.output_concat_lr:\\n increment_size = make_align_size(\\n clr.output_concat_lr.layer.node_out.output_size)\\n current_offset = make_align_size(\\n clr.layer.output_addr_offset + increment_size)\\n\\n # if not, put it in the end of current buffer\\n if not empty_found:\\n lr.layer.output_addr_offset = current_offset\\n if current_offset + necessary_size > allocated_size:\\n allocated_size = current_offset + necessary_size\\n lr.allocated = True\\n\\n for lr in live_ranges:\\n logging.info(\\\"{:22s} {:22s} {:12s} {:02d} {:02d} {:18s} {:08X} {:08X}{:s}\\\".format(\\n lr.layer.node_in.name, lr.layer.node_out.name,\\n str(lr.layer.type),\\n lr.birth_index, lr.death_index,\\n str(lr.layer.node_out.output_dim),\\n lr.layer.output_addr_offset,\\n lr.layer.node_out.output_size,\\n ('*' if lr.layer.is_output else '')))\\n logging.info('allocated size:{:d}'.format(allocated_size))\\n self.weight_size = weight_size\\n self.buffer_size = allocated_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d5d82de158387791533ca7fb8876e47\",\n \"score\": \"0.6023222\",\n \"text\": \"def build(self,inputs):\\n assert inputs.get_shape().as_list()[1:]==[224,224,3], 'the size of inputs is incorrect!'\\n \\n # start to build the densenet model\\n net = inputs\\n print \\\"start-shape：\\\"+str(inputs.shape)\\n \\n net = self.tf_op.conv_layer(net, kernel_num=2*cfg.growth_rate, kernel_size=7, stride=2, layer_name=\\\"conv1\\\",padding='SAME')\\n net = self.tf_op.batch_normalization(net,scope_name=\\\"bn1\\\")\\n net = tf.nn.relu(net) \\n net = self.tf_op.max_pool(net,layer_name=\\\"pool1\\\", kernel_size=3, stride=2, padding='SAME')\\n print \\\"the 1th stage-shape：\\\"+str(net.shape)\\n \\n net = self.dense_block(net,6,\\\"denseblock1\\\") \\n net = self.compression_transitionLayer(net,\\\"transition1\\\")\\n print \\\"the 2th stage-shape：\\\"+str(net.shape)\\n \\n net = self.dense_block(net,12,\\\"denseblock2\\\") \\n net = self.compression_transitionLayer(net,\\\"transition2\\\")\\n print \\\"the 3th stage-shape：\\\"+str(net.shape)\\n \\n net = self.dense_block(net,24,\\\"denseblock3\\\") \\n net = self.compression_transitionLayer(net,\\\"transition3\\\")\\n print \\\"the 4th stage-shape：\\\"+str(net.shape)\\n \\n net = self.dense_block(net,16,\\\"denseblock4\\\") \\n net = self.tf_op.batch_normalization(net,scope_name=\\\"bn4\\\")\\n net = tf.nn.relu(net) \\n net = self.tf_op.global_avg_pool(net, \\\"pool4\\\", stride=1)\\n print \\\"the 5th stage-shape：\\\"+str(net.shape)\\n \\n shape = net.get_shape()\\n in_size = shape[1]*shape[2]*shape[3]\\n net = self.tf_op.fc_layer(net, int(in_size), self.class_num, \\\"fc5\\\") \\n net = self.tf_op.drop_out(net,dropout_rate=self.dropout_rate)\\n result = tf.nn.softmax(net,name=\\\"prob\\\")\\n print \\\"the 6th stage-shape：\\\"+str(result.shape)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"736963c08b4a158106b6d70d5e595380\",\n \"score\": \"0.6016884\",\n \"text\": \"def _build_net(x_placeholder, shape, weight_generator, bias_generator, activator, activate_output):\\n a_set = [x_placeholder]\\n for i in range(len(shape) - 1):\\n up_size = shape[i]\\n down_size = shape[i + 1]\\n\\n if verbose:\\n print('mid-layer [{0}, {1}]'.format(up_size, down_size))\\n\\n w = tf.Variable(weight_generator([up_size, down_size]))\\n b = tf.Variable(bias_generator([down_size]))\\n if i == len(shape) - 2: # output\\n if activate_output:\\n a_set.append(activator(tf.matmul(a_set[-1], w) + b, name='y'))\\n else:\\n a_set.append(tf.add(tf.matmul(a_set[-1], w), b, name='y'))\\n else: # upstream\\n a_set.append(tf.matmul(a_set[-1], w) + b)\\n return a_set[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8614955959bc981d30d2e8dacee80b\",\n \"score\": \"0.60113466\",\n \"text\": \"def create_neural_network(blocks):\\n\\n previous_filters = 3 # starts out as 3, since we have a RGB image with 3 colour channels\\n\\n for block in blocks:\\n module=nn.Sequential()\\n if block[type]==\\\"net\\\":\\n continue\\n # Adding convolutional layer\\n elif block[type]==\\\"convolutional\\\":\\n activation=block[\\\"activation\\\"]\\n kernel_size=block[\\\"size\\\"]\\n pad=block[\\\"pad\\\"]\\n stride=block[\\\"stride\\\"]\\n filters=block[\\\"filter\\\"]\\n\\n # Generally, bias does not exist in layers that have batch normalization.\\n # This is so because batch normalization inherently takes care of the necessary bias factor.\\n # Thus, we check below if the block defined has batch normalization and make a decision on bias accordingly.\\n\\n if \\\"batch_normalize\\\" in block.keys():\\n bias=False\\n batch_normalization=block[\\\"batch_normalize\\\"]\\n else:\\n bias=True\\n batch_normalization=0\\n\\n # Adding layers now\\n conv_layer=nn.Conv2d(previous_filters, filters, kernel_size, stride, pad, bias=bias)\\n module.add_module(conv_layer)\\n\\n # Add Batch Normalization\\n if batch_normalization:\\n bn_layer=nn.BatchNorm2d(filters)\\n module.add_module(bn_layer)\\n\\n # Add Activation layer\\n # Activation layer can be either a Leaky-Relu layer or a linear activation layer.\\n if activation==\\\"leaky\\\":\\n activation_layer=nn.LeakyReLU(0.1, inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65134cb50461f59b00cd5d20f39ab9f\",\n \"score\": \"0.6006831\",\n \"text\": \"def make_stages(cfg_dict):\\n layers = []\\n for i in range(len(cfg_dict) - 1):\\n one_ = cfg_dict[i]\\n for k, v in one_.items():\\n if 'pool' in k:\\n layers += [nn.MaxPool2d(kernel_size=v[0], stride=v[1],\\n padding=v[2])]\\n else:\\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\\n kernel_size=v[2], stride=v[3],\\n padding=v[4])\\n layers += [conv2d, nn.ReLU(inplace=True)]\\n one_ = list(cfg_dict[-1].keys())\\n k = one_[0]\\n v = cfg_dict[-1][k]\\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\\n kernel_size=v[2], stride=v[3], padding=v[4])\\n layers += [conv2d]\\n return nn.Sequential(*layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65134cb50461f59b00cd5d20f39ab9f\",\n \"score\": \"0.6006831\",\n \"text\": \"def make_stages(cfg_dict):\\n layers = []\\n for i in range(len(cfg_dict) - 1):\\n one_ = cfg_dict[i]\\n for k, v in one_.items():\\n if 'pool' in k:\\n layers += [nn.MaxPool2d(kernel_size=v[0], stride=v[1],\\n padding=v[2])]\\n else:\\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\\n kernel_size=v[2], stride=v[3],\\n padding=v[4])\\n layers += [conv2d, nn.ReLU(inplace=True)]\\n one_ = list(cfg_dict[-1].keys())\\n k = one_[0]\\n v = cfg_dict[-1][k]\\n conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],\\n kernel_size=v[2], stride=v[3], padding=v[4])\\n layers += [conv2d]\\n return nn.Sequential(*layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc01e7dda44cf781d93b855033d2337\",\n \"score\": \"0.60021186\",\n \"text\": \"def create_network_deployment(context):\\n network_config = {}\\n network_config_array = []\\n for nics in range(context.properties['numNics']):\\n if nics == 0:\\n net_name = 'mgmt'\\n subnet_name = 'mgmt'\\n subnet_description = 'Subnetwork used for management traffic'\\n elif context.properties['numNics'] != 1 and nics == 1:\\n net_name = 'external'\\n subnet_name = 'external'\\n subnet_description = 'Subnetwork used for external traffic'\\n else:\\n net_name = 'internal' + str(nics)\\n subnet_name = 'internal' + str(nics)\\n subnet_description = 'Subnetwork used for internal traffic'\\n subnet_config = [{\\n 'description': subnet_description,\\n 'name': subnet_name,\\n 'region': context.properties['region'],\\n 'ipCidrRange': '10.0.' + str(nics) + '.0/24'\\n }]\\n if nics + 1 == context.properties['numNics']:\\n app_subnet_config = {\\n 'description': 'Subnetwork used for POC application.',\\n 'name': 'app',\\n 'region': context.properties['region'],\\n 'ipCidrRange': '10.0.' + str(nics + 1) + '.0/24'\\n }\\n subnet_config.append(app_subnet_config)\\n network_config = {\\n 'name': 'network' + str(nics),\\n 'type': '../modules/network/network.py',\\n 'properties': {\\n 'name': net_name,\\n 'provisionPublicIp': context.properties['provisionPublicIp'],\\n 'region': context.properties['region'],\\n 'uniqueString': context.properties['uniqueString'],\\n 'subnets': subnet_config\\n }\\n }\\n network_config_array.append(network_config)\\n return network_config_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875c8aac86890ccc4aa054da06b681dd\",\n \"score\": \"0.599502\",\n \"text\": \"def init_nets():\\n global pnet, rnet, onet\\n pnet_model = td.Model('pnet.pkl')\\n rnet_model = td.Model('rnet.pkl')\\n onet_model = td.Model('onet.pkl')\\n print pnet_model.roots\\n print rnet_model.roots\\n print onet_model.roots\\n\\n # define all nets i/o\\n pnet_input, pnet_output1, pnet_output2 = pnet_model.get('pnet/input:0', 'pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0')\\n print pnet_input, pnet_output1, pnet_output2\\n rnet_input, rnet_output1, rnet_output2 = rnet_model.get('rnet/input:0', 'rnet/conv5-2/conv5-2:0', 'rnet/prob1:0')\\n print rnet_input, rnet_output1, rnet_output2\\n onet_input, onet_output1, onet_output2, onet_output3 = onet_model.get('onet/input:0', 'onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0')\\n print onet_input, onet_output1, onet_output2, onet_output3\\n\\n # define all nets processing function\\n pnet = lambda img: (pnet_output1.eval({pnet_input: img}), pnet_output2.eval({pnet_input: img}))\\n rnet = lambda img: (rnet_output1.eval({rnet_input: img}), rnet_output2.eval({rnet_input: img}))\\n onet = lambda img: (onet_output1.eval({onet_input: img}), onet_output2.eval({onet_input: img}), onet_output3.eval({onet_input: img}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e875f7fb3bec50459b0a5273bfb47fc9\",\n \"score\": \"0.59947395\",\n \"text\": \"def nnet_builder(self):\\n \\n # encoder \\n with tf.variable_scope('encoder'):\\n # input to hidden layer 1\\n self.layer_enc_hidden1, self.params['layer_enc_hidden1_weights'], self.params['layer_enc_hidden1_biases'] = layer_fc(self.x,\\n self.n_hidden,0.5,'layer_enc_hidden1',self.initializer,self.nonlinearity)\\n\\n self.layer_enc_hidden2, self.params['layer_enc_hidden2_weights'], self.params['layer_enc_hidden2_biases'] = layer_fc(self.layer_enc_hidden1,\\n self.n_hidden,0.5,'layer_enc_hidden2',self.initializer,self.nonlinearity)\\n\\n\\n # latent sampler\\n with tf.variable_scope('latent'): \\n self.layer_z_mu, self.params['layer_z_mu_weights'], self.params['layer_z_mu_biases'] = layer_fc(self.layer_enc_hidden1,self.n_latent,0.5,'z_mu',self.initializer)\\n\\n self.layer_z_logvar, self.params['layer_z_logvar_weights'], self.params['layer_z_logvar_biases'] = layer_fc(self.layer_enc_hidden1,self.n_latent,0.5,'z_logvar',self.initializer)\\n\\n self.epsilon = tf.random_normal(tf.stack([tf.shape(self.layer_enc_hidden1)[0], self.n_latent]),mean=0.0,stddev=1.0, dtype = tf.float32)\\n self.layer_z = tf.add(self.layer_z_mu,tf.multiply(tf.sqrt(tf.exp(self.layer_z_logvar)),self.epsilon)) \\n\\n \\n # decoder \\n with tf.variable_scope('decoder'): \\n self.layer_dec_hidden1, self.params['layer_dec_hidden1_weights'], self.params['layer_dec_hidden1_biases'] = layer_fc(self.layer_z,\\n self.n_hidden,0.5,'layer_dec_hidden1',self.initializer,self.nonlinearity)\\n \\n self.y_hat, self.params['y_hat_weights'], self.params['y_hat_biases'] = layer_fc(self.layer_dec_hidden1,self.dim_outputs[1],0.5,'y_hat',nonlinearity=tf.nn.sigmoid)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5e6d4576736831467812c7dd1be397\",\n \"score\": \"0.5990629\",\n \"text\": \"def makeNet_fullyFeatured(self, n, training_boards=[]):\\n # We seperate the features of the input \\n input_A = Input(shape=(4,))\\n input_PAR = Input(shape=(2*n+2,))\\n input_B = Input(shape=(n**2,))\\n input_C = Input(shape=(n**2,))\\n input_D = Input(shape=(n**2,))\\n\\n tempo_layer = Dense(4, activation=self.layer1actfn)(input_A)\\n parity_layer = Dense(2*n+2, activation=self.layer1actfn)(input_PAR)\\n taken_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_B)\\n dead_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_C)\\n available_squares_layer = Dense(n**2, activation=self.layer1actfn)(input_D)\\n\\n # We merge the features, and then include layers for high-level feature extraction and output.\\n merged_layer1 = Concatenate()([taken_squares_layer, dead_squares_layer, available_squares_layer])\\n abstract_layer1 = Dense(2*(n**2), activation=self.layer2actfn)(merged_layer1)\\n \\n merged_layer2 = Concatenate()([abstract_layer1, parity_layer, tempo_layer])\\n abstract_layer2 = Dense(n**2, activation=self.layer2actfn)(merged_layer2)\\n \\n output_layer = Dense(1, activation=self.layer_out_actfn)(abstract_layer2)\\n\\n self.model = tf.keras.Model(inputs=[input_A, input_PAR, input_B, input_C, input_D], outputs=output_layer)\\n\\n # Sanity checks\\n #self.model.summary()\\n #tf.keras.utils.plot_model(self.model, show_shapes=True)\\n\\n if training_boards != []:\\n input_A = []\\n input_PAR = []\\n input_B = []\\n input_C = []\\n input_D = []\\n y_train = []\\n for board, value in training_boards:\\n x_train = self.parseBoardAsInput(board)\\n input_A.append(x_train[0])\\n input_PAR.append(x_train[1])\\n input_B.append(x_train[2])\\n input_C.append(x_train[3])\\n input_D.append(x_train[4])\\n y_train.append(value)\\n\\n self.train([\\n numpy.asarray(input_A), \\n numpy.asarray(input_PAR),\\n numpy.asarray(input_B), \\n numpy.asarray(input_C), \\n numpy.asarray(input_D)\\n ], \\n numpy.asarray([numpy.asarray([y]) for y in y_train]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464643318e214218996d524a8e99fc37\",\n \"score\": \"0.5988742\",\n \"text\": \"def create_network():\\n network = {}\\n dim0 = 784 # 28x28 (pixels in an image)\\n dim1 = 100 # chosen arbitrarily\\n dim2 = 50 # chosen arbitrarily\\n dim3 = 10 # the number of kinds of labels (0, 1, .. , 9)\\n\\n # layer-1: (dim0, 1)-matrix -> (dim1, 1)-matrix\\n network['W1'] = np.random.rand(dim0, dim1)\\n network['b1'] = np.random.rand(dim1)\\n\\n # layer-2: (dim1, 1)-matrix -> (dim2, 1)-matrix\\n network['W2'] = np.random.rand(dim1, dim2)\\n network['b2'] = np.random.rand(dim2)\\n\\n # layer-3: (dim2, 1)-matrix -> (dim3, 1)-matrix\\n network['W3'] = np.random.rand(dim2, dim3)\\n network['b3'] = np.random.rand(dim3)\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0c731528835b837e6734c99bee0afc\",\n \"score\": \"0.59876484\",\n \"text\": \"def build(self):\\n for i in range(1, len(self.layers)):\\n self.layers[i](self.layers[i - 1].n_neuron)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732605c4d2212be0ac417ee595748970\",\n \"score\": \"0.59767497\",\n \"text\": \"def _make_final_classifiers(self, network, name_generator):\\n\\n layers = []\\n\\n split = self._make_split_layer(network)\\n layers.append(split)\\n\\n connections = []\\n\\n softmaxes = []\\n for label, num_output in self.other_args['labels']:\\n inner_product = self._make_inner_product_layer(\\n network, num_output=num_output)\\n layers.append(inner_product)\\n\\n softmax_loss = self._make_softmax_loss_layer(network, loss_id=label)\\n layers.append(softmax_loss)\\n\\n accuracy = self._make_accuracy_layer(network, accuracy_id=label)\\n layers.append(accuracy)\\n\\n connections.append((split.name, (split.top, inner_product.bottom)))\\n connections.append((inner_product.name, (inner_product.top, softmax_loss.bottom, accuracy.bottom)))\\n\\n softmaxes.append((label, softmax_loss))\\n softmaxes.append((label, accuracy))\\n\\n for connection in connections:\\n self._tie(connection, name_generator)\\n\\n for label, loss in softmaxes:\\n loss.bottom.append(label)\\n\\n return layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dd09df62c77ae1b336079615cfe3754\",\n \"score\": \"0.59734416\",\n \"text\": \"def create_layers(self, defaultExtension=''):\\n # Make sure that the layer listing exists\\n if self.metadata is None:\\n self.list_layers()\\n # no raster data availabile?\\n if self.metadata is not None:\\n for fileName, layerMeta in self.metadata.items():\\n self.create_layer(fileName, layerMeta, defaultExtension=defaultExtension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3616cc1c154ff53e7c2bd5a16a5694d5\",\n \"score\": \"0.5964093\",\n \"text\": \"def __init__( self, layers_def):\\n\\t\\tprint \\\"=> NET:\\\"\\n\\t\\tself.layers = []\\n\\t\\tfor layer_def in layers_def:\\n\\t\\t\\t# Convolutional layer\\n\\t\\t\\tif layer_def['layer'] == 'conv':\\n\\t\\t\\t\\tlayer = ConvLayer(input_size, layer_def['n'], (layer_def['kern_size'], layer_def['kern_size']), layer_def['pad'], layer_def['stride'])\\n\\t\\t\\t\\tinput_size = layer.output_shape\\n\\t\\t\\t# Fully connected layer\\n\\t\\t\\telif layer_def['layer'] == 'fc':\\n\\t\\t\\t\\tlayer = FCLayer(input_size, layer_def['n'], layer_def['act_type'])\\n\\t\\t\\t\\tinput_size = (layer.n_neurons,)\\n\\t\\t\\t# Maxpool layer\\n\\t\\t\\telif layer_def['layer'] == 'maxpool':\\n\\t\\t\\t\\tlayer = MaxPoolLayer(input_size, (layer_def['kern_size'], layer_def['kern_size']), layer_def['stride'])\\n\\t\\t\\t\\tinput_size = layer.output_shape\\n\\t\\t\\t# Input size definition\\n\\t\\t\\telif layer_def['layer'] == 'input':\\n\\t\\t\\t\\tinput_size = (layer_def['c'], layer_def['h'], layer_def['w'])\\n\\t\\t\\t\\tself.input_size = input_size\\n\\t\\t\\t\\tprint \\\"* Input -> Size: \\\", input_size\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t# Error layer\\n\\t\\t\\telif layer_def['layer'] == 'error':\\n\\t\\t\\t\\tlayer = ErrorLayer(layer_def['type'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise ValueError(\\\"*** UNKOWN LAYER TYPE (\\\"+layer_def['layer']+\\\")\\\")\\n\\t\\t\\tself.layers.append(layer)\\n\\n\\t\\tself.n_classes = int(input_size[0])\\n\\t\\tself.layers_def = layers_def\\n\\t\\tprint \\\"* NET: done initializing net!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2336c5091d72636e0ff84eca534e62a8\",\n \"score\": \"0.5960152\",\n \"text\": \"def create_model_from_cfg(cfg):\\n\\n # create the model\\n model = tf.keras.models.Sequential()\\n block_index = 0\\n \\n def add_conv_or_local(is_conv, section):\\n \\n filters = section['filters']\\n kernel_size = section['size']\\n strides = section['stride']\\n\\n # add padding\\n padding = section['pad']\\n if padding == 1:\\n pad = (kernel_size - 1) // 2\\n else:\\n pad = 0\\n model.add(ZeroPadding2D(pad, name=f'pad_{block_index}'))\\n\\n # check batch norm\\n try:\\n batch_normalize = (section['batch_normalize'] == 1)\\n except:\\n batch_normalize = False\\n use_bias = not batch_normalize\\n\\n # add the 2d convolution or locally connected layer\\n if is_conv:\\n layer_class = Conv2D\\n layer_name = f'conv_{block_index}'\\n else:\\n layer_class = LocallyConnected2D\\n layer_name = f'local_{block_index}'\\n model.add(layer_class(filters, kernel_size, strides, use_bias=use_bias, name=layer_name))\\n\\n # add batch norm layer\\n if batch_normalize:\\n model.add(BatchNormalization(name=f'batchnorm_{block_index}'))\\n\\n # add the activation\\n activation = section['activation']\\n if activation == 'leaky':\\n model.add(LeakyReLU(alpha=0.1, name=f'leaky_{block_index}'))\\n\\n for name, section in cfg:\\n if name == 'net':\\n input_shape = (section['height'], section['width'], section['channels'])\\n model.add(InputLayer(input_shape=input_shape, name='input_0'))\\n elif name == 'convolutional':\\n add_conv_or_local(True, section)\\n elif name == 'maxpool':\\n pool_size = section['size']\\n strides = section['stride']\\n model.add(MaxPooling2D(pool_size, strides, name=f'maxpool_{block_index}'))\\n elif name == 'local':\\n add_conv_or_local(False, section)\\n elif name == 'dropout':\\n rate = section['probability']\\n model.add(Dropout(rate, name=f'dropout_{block_index}'))\\n elif name == 'connected':\\n units = section['output']\\n activation = section['activation']\\n \\n model.add(Flatten(name=f'flatten_{block_index}'))\\n model.add(Dense(units, name=f'connected_{block_index}'))\\n \\n if activation == 'leaky':\\n model.add(LeakyReLU(alpha=0.1, name=f'leaky_{block_index}'))\\n elif name == 'detection':\\n s = section['side']\\n b = section['num']\\n c = section['classes']\\n\\n model.add(YOLODetection(s, b, c, name=f'detection_{block_index}'))\\n \\n block_index += 1\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07261d07ed5de244fe048c0088a0cd1\",\n \"score\": \"0.59579855\",\n \"text\": \"def make_net(in_channels, conf, include_last_relu=True):\\r\\n def make_layer(layer_cfg):\\r\\n nonlocal in_channels\\r\\n \\r\\n # Possible patterns:\\r\\n # ( 256, 3, {}) -> conv\\r\\n # ( 256,-2, {}) -> deconv\\r\\n # (None,-2, {}) -> bilinear interpolate\\r\\n # ('cat',[],{}) -> concat the subnetworks in the list\\r\\n #\\r\\n # You know it would have probably been simpler just to adopt a 'c' 'd' 'u' naming scheme.\\r\\n # Whatever, it's too late now.\\r\\n if isinstance(layer_cfg[0], str):\\r\\n layer_name = layer_cfg[0]\\r\\n\\r\\n if layer_name == 'cat':\\r\\n nets = [make_net(in_channels, x) for x in layer_cfg[1]]\\r\\n layer = Concat([net[0] for net in nets], layer_cfg[2])\\r\\n num_channels = sum([net[1] for net in nets])\\r\\n else:\\r\\n num_channels = layer_cfg[0]\\r\\n kernel_size = layer_cfg[1]\\r\\n\\r\\n if kernel_size > 0:\\r\\n layer = nn.Conv2d(in_channels, num_channels, kernel_size, **layer_cfg[2])\\r\\n else:\\r\\n if num_channels is None:\\r\\n layer = InterpolateModule(scale_factor=-kernel_size, mode='bilinear', align_corners=False, **layer_cfg[2])\\r\\n else:\\r\\n layer = nn.ConvTranspose2d(in_channels, num_channels, -kernel_size, **layer_cfg[2])\\r\\n \\r\\n in_channels = num_channels if num_channels is not None else in_channels\\r\\n\\r\\n # Don't return a ReLU layer if we're doing an upsample. This probably doesn't affect anything\\r\\n # output-wise, but there's no need to go through a ReLU here.\\r\\n # Commented out for backwards compatibility with previous models\\r\\n # if num_channels is None:\\r\\n # return [layer]\\r\\n # else:\\r\\n return [layer, nn.ReLU(inplace=True)]\\r\\n\\r\\n # Use sum to concat together all the component layer lists\\r\\n net = sum([make_layer(x) for x in conf], [])\\r\\n if not include_last_relu:\\r\\n net = net[:-1]\\r\\n\\r\\n return nn.Sequential(*(net)), in_channels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963218937b4ac07330b34e4597ce447b\",\n \"score\": \"0.59327227\",\n \"text\": \"def _build_network(self, X, Z, alpha):\\n\\n num_layers = self.current_level\\n filters = self.filters[:(num_layers+1)]\\n d_filters_crop = filters[::-1] # reversed\\n g_filters_crop = filters\\n\\n print num_layers, d_filters_crop, g_filters_crop\\n\\n print \\\"Building new graph with {0:d} layers...\\\".format(num_layers)\\n with tf.variable_scope('GAN', reuse=tf.AUTO_REUSE):\\n\\n # generator discriminator pair\\n g_layers, Gz_raw = self.generator(Z, g_filters_crop)\\n\\n # resize the real input\\n X_resized = tf.image.resize_images(X,\\n self.current_size,\\n align_corners=True)\\n\\n # make a mixed output if we have more than one layer...\\n if num_layers > 0:\\n\\n prev_Gz = double_size(g_layers[-2])\\n Gz = tf.add(alpha*Gz_raw, (1.-alpha)*prev_Gz)\\n\\n # do the same for the real data\\n prev_X = double_size(half_size(X_resized))\\n X_resized = tf.add(alpha*X_resized, (1.-alpha)*prev_X)\\n\\n else:\\n Gz = Gz_raw\\n\\n # discriminator for arbitrary inputs\\n d_layers, Dz = self.discriminator(Gz, d_filters_crop)\\n _, Dx = self.discriminator(X_resized, d_filters_crop)\\n\\n print X_resized.shape, Gz.shape\\n\\n # sanity check, make sure the same number of layers in each network\\n assert(len(g_layers) == len(d_layers))\\n\\n # operations to mix the input and generated\\n r = tf.random_uniform(shape=[self.batch_size, 1, 1, 1],\\n minval=0.0,\\n maxval=1.0,\\n dtype=tf.float32)\\n mix = tf.add(r*X_resized, (1-r)*Gz)\\n _, Dmix = self.discriminator(mix, d_filters_crop)\\n\\n\\n # now make the Wasserstein Loss Function\\n with tf.variable_scope('WGAN-GP_loss'):\\n loss_lambda = 10.\\n\\n grad = tf.gradients(Dmix, [mix])[0]\\n grad_normed = tf.sqrt(tf.reduce_sum(tf.square(grad),[1,2,3]))\\n # grad_penalty = tf.square((grad_normed-1.0))\\n lipschitz_penalty = tf.square(tf.maximum((grad_normed-1.0), 0))\\n scaled_penalty = loss_lambda*lipschitz_penalty\\n eps_penalty = 0.001 * tf.square(Dx)\\n\\n g_loss = tf.reduce_mean(-Dz)\\n d_loss = tf.reduce_mean(-Dx + Dz + scaled_penalty + eps_penalty)\\n\\n # hand back the un-mixed Gz\\n return Gz_raw, d_loss, g_loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a37ea7d431362e80342f0469e913cb\",\n \"score\": \"0.59243923\",\n \"text\": \"def create_output_layers(self):\\n output_layers = nn.ModuleList([])\\n input_dim = self.input_dim_into_final_layer\\n if not isinstance(self.layers_info[-1][0], list) : self.layers_info[-1] = [self.layers_info[-1]]\\n for output_layer in self.layers_info[-1]:\\n self.create_and_append_layer(input_dim, output_layer, output_layers)\\n return output_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d17603f0f93f50b14273661ec943e9e\",\n \"score\": \"0.59229696\",\n \"text\": \"def spawn_network(patch_size: int):\\n name = \\\"tprime_psize{}\\\".format(patch_size)\\n inputs = keras.layers.Input(shape=(patch_size, patch_size, 3))\\n if (patch_size == 17):\\n conv1 = _conv2d(inputs, n_filter=128, ksize=5)\\n conv2 = _conv2d(conv1, n_filter=256, ksize=5)\\n conv3 = _conv2d(conv2, n_filter=256, ksize=5)\\n conv4 = _conv2d(conv3, n_filter=128, ksize=5)\\n decode = _conv2d(conv4, n_filter=512, ksize=1)\\n elif (patch_size == 33):\\n conv1 = _conv2d(inputs, n_filter=128, ksize=3)\\n maxp1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv1)\\n conv2 = _conv2d(maxp1, n_filter=256, ksize=5)\\n maxp2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv2)\\n conv3 = _conv2d(maxp2, n_filter=256, ksize=2)\\n conv4 = _conv2d(conv3, n_filter=128, ksize=4)\\n decode = _conv2d(conv4, n_filter=512, ksize=1)\\n elif (patch_size==65):\\n conv1 = _conv2d(inputs, n_filter=128, ksize=5)\\n maxp1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv1)\\n conv2 = _conv2d(maxp1, n_filter=128, ksize=5)\\n maxp2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv2)\\n conv3 = _conv2d(maxp2, n_filter=128, ksize=5)\\n maxp3 = MaxPool2D(pool_size=(2, 2), strides=2)(conv3)\\n conv4 = _conv2d(maxp3, n_filter=256, ksize=4)\\n conv5 = _conv2d(conv4, n_filter=128, ksize=1)\\n decode = _conv2d(conv5, n_filter=512, ksize=1)\\n else:\\n raise ValueError(\\\"Unspported size: {}\\\".format(patch_size))\\n return keras.Model(inputs=inputs, outputs=decode, name=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc9e116a4ced17639773b46c2eec06f\",\n \"score\": \"0.5920516\",\n \"text\": \"def ghostnet_base(inputs,\\r\\n\\t\\t\\t\\t final_endpoint=None,\\r\\n\\t\\t\\t\\t min_depth=8,\\r\\n\\t\\t\\t\\t depth_multiplier=1.0,\\r\\n\\t\\t\\t\\t depth=1.0,\\r\\n\\t\\t\\t\\t conv_defs=None,\\r\\n\\t\\t\\t\\t output_stride=None,\\r\\n\\t\\t\\t\\t dw_code=None,\\r\\n\\t\\t\\t\\t ratio_code=None,\\r\\n\\t\\t\\t\\t se=1,\\r\\n\\t\\t\\t\\t scope=None,\\r\\n\\t\\t\\t\\t is_training=False):\\r\\n\\r\\n\\tdef depth(d):\\r\\n\\t\\td = max(int(d * depth_multiplier), min_depth)\\r\\n\\t\\td = round(d / 4) * 4\\r\\n\\t\\treturn d\\r\\n\\r\\n\\tend_points = {}\\r\\n\\r\\n\\t# Used to find thinned depths for each layer.\\r\\n\\tif depth_multiplier <= 0:\\r\\n\\t\\traise ValueError('depth_multiplier is not greater than zero.')\\r\\n\\r\\n\\tif conv_defs is None:\\r\\n\\t\\tconv_defs = _CONV_DEFS_0\\r\\n\\r\\n\\tif dw_code is None or len(dw_code) < len(conv_defs):\\r\\n\\t\\tdw_code = [3] * len(conv_defs)\\r\\n\\tprint('dw_code', dw_code)\\r\\n\\r\\n\\tif ratio_code is None or len(ratio_code) < len(conv_defs):\\r\\n\\t\\tratio_code = [2] * len(conv_defs)\\r\\n\\tprint('ratio_code', ratio_code)\\r\\n\\r\\n\\tse_code = [x.se for x in conv_defs]\\r\\n\\tprint('se_code', se_code)\\r\\n\\r\\n\\tif final_endpoint is None:\\r\\n\\t\\tfinal_endpoint = 'Conv2d_%d' % (len(conv_defs) - 1)\\r\\n\\r\\n\\tif output_stride is not None and output_stride not in [8, 16, 32]:\\r\\n\\t\\traise ValueError('Only allowed output_stride values are 8, 16, 32.')\\r\\n\\r\\n\\twith tf.variable_scope(scope, 'MobilenetV2', [inputs]):\\r\\n\\r\\n\\t\\t# The current_stride variable keeps track of the output stride of the\\r\\n\\t\\t# activations, i.e., the running product of convolution strides up to the\\r\\n\\t\\t# current network layer. This allows us to invoke atrous convolution\\r\\n\\t\\t# whenever applying the next convolution would result in the activations\\r\\n\\t\\t# having output stride larger than the target output_stride.\\r\\n\\t\\tcurrent_stride = 1\\r\\n\\r\\n\\t\\t# The atrous convolution rate parameter.\\r\\n\\t\\trate = 1\\r\\n\\t\\tnet = inputs\\r\\n\\t\\tin_depth = 3\\r\\n\\t\\tgi = 0\\r\\n\\t\\tfor i, conv_def in enumerate(conv_defs):\\r\\n\\t\\t\\tprint('---')\\r\\n\\t\\t\\tend_point_base = 'Conv2d_%d' % i\\r\\n\\t\\t\\tif output_stride is not None and current_stride == output_stride:\\r\\n\\t\\t\\t\\t# If we have reached the target output_stride, then we need to employ\\r\\n\\t\\t\\t\\t# atrous convolution with stride=1 and multiply the atrous rate by the\\r\\n\\t\\t\\t\\t# current unit's stride for use in subsequent layers.\\r\\n\\t\\t\\t\\tlayer_stride = 1\\r\\n\\t\\t\\t\\tlayer_rate = rate\\r\\n\\t\\t\\t\\trate *= conv_def.stride\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tlayer_stride = conv_def.stride\\r\\n\\t\\t\\t\\tlayer_rate = 1\\r\\n\\t\\t\\t\\tcurrent_stride *= conv_def.stride\\r\\n\\r\\n\\t\\t\\t# change last bottleneck\\r\\n\\t\\t\\tif i + 2 == len(conv_defs):\\t\\t\\t# remove last two head layers\\r\\n\\t\\t\\t\\treturn [\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_1_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_3_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_5_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_11_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_16_residual']\\r\\n\\t\\t\\t\\t]\\r\\n\\r\\n\\t\\t\\telif isinstance(conv_def, Conv):\\r\\n\\t\\t\\t\\tend_point = end_point_base\\r\\n\\t\\t\\t\\tnet = ConvBlock(net, depth(conv_def.depth), conv_def.kernel, stride=conv_def.stride,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tname='ConvBlock_{}'.format(i), is_training=is_training)\\r\\n\\t\\t\\t\\tend_points[end_point] = net\\r\\n\\r\\n\\t\\t\\t# Bottleneck block.\\r\\n\\t\\t\\telif isinstance(conv_def, Bottleneck):\\r\\n\\t\\t\\t\\t# Stride > 1 or different depth: no residual part.\\r\\n\\t\\t\\t\\tif layer_stride == 1 and in_depth == conv_def.depth:\\r\\n\\t\\t\\t\\t\\tres = net\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tres = DepthConv(net, conv_def.kernel, stride=layer_stride, name='Bottleneck_block_{}_shortcut_dw'.format(i))\\r\\n\\t\\t\\t\\t\\tres = tf.layers.batch_normalization(res, training=is_training, name='Bottleneck_block_{}_shortcut_dw_BN'.format(i), axis=1)\\r\\n\\t\\t\\t\\t\\tres = ConvBlock(res, depth(conv_def.depth), (1, 1), (1, 1),\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tname='Bottleneck_block_{}_shortcut_1x1'.format(i), is_training=is_training)\\r\\n\\r\\n\\t\\t\\t\\t# Increase depth with 1x1 conv.\\r\\n\\t\\t\\t\\tend_point = end_point_base + '_up_pointwise'\\r\\n\\t\\t\\t\\tnet = MyConv('Bottleneck_block_{}_up_pointwise'.format(i), net, depth(in_depth * conv_def.factor), [1, 1],\\r\\n\\t\\t\\t\\t\\t\\t\\t dw_code[gi],\\r\\n\\t\\t\\t\\t\\t\\t\\t ratio_code[gi], strides=1, use_bias=False, is_training=is_training, activation=True)\\r\\n\\r\\n\\t\\t\\t\\tend_points[end_point] = net\\r\\n\\r\\n\\t\\t\\t\\t# Depthwise conv2d.\\r\\n\\t\\t\\t\\tif layer_stride > 1:\\r\\n\\t\\t\\t\\t\\tend_point = end_point_base + '_depthwise'\\r\\n\\t\\t\\t\\t\\tnet = DepthConv(net, conv_def.kernel, stride=layer_stride, name='Bottleneck_block_{}_depthwise'.format(i))\\r\\n\\t\\t\\t\\t\\tnet = tf.layers.batch_normalization(net, training=is_training, name='Bottleneck_block_{}_depthwise_BN'.format(i), axis=1)\\r\\n\\t\\t\\t\\t\\tend_points[end_point] = net\\r\\n\\t\\t\\t\\t# SE\\r\\n\\t\\t\\t\\tif se_code[i] > 0 and se > 0:\\r\\n\\t\\t\\t\\t\\tend_point = end_point_base + '_se'\\r\\n\\t\\t\\t\\t\\tnet = SELayer(net, depth(in_depth * conv_def.factor), 4)\\r\\n\\t\\t\\t\\t\\tend_points[end_point] = net\\r\\n\\r\\n\\t\\t\\t\\t# Downscale 1x1 conv.\\r\\n\\t\\t\\t\\tnet = MyConv('Bottleneck_block_{}_down_pointwise'.format(i), net, depth(conv_def.depth), [1, 1], dw_code[gi],\\r\\n\\t\\t\\t\\t\\t\\t\\t ratio_code[gi], strides=1, use_bias=False, is_training=is_training, activation=False)\\r\\n\\t\\t\\t\\tnet = tf.layers.batch_normalization(net, training=is_training, name='Bottleneck_block_{}_down_pointwise_BN'.format(i), axis=1)\\r\\n\\r\\n\\t\\t\\t\\tgi += 1\\r\\n\\r\\n\\t\\t\\t\\t# Residual connection?\\r\\n\\t\\t\\t\\tend_point = end_point_base + '_residual'\\r\\n\\t\\t\\t\\tnet = tf.add(res, net, name='Bottleneck_block_{}_Add'.format(i)) if res is not None else net\\r\\n\\t\\t\\t\\tend_points[end_point] = net\\r\\n\\r\\n\\t\\t\\tin_depth = conv_def.depth\\r\\n\\t\\t\\t# Final end point?\\r\\n\\r\\n\\t\\t\\tif final_endpoint in end_points:\\r\\n\\r\\n\\t\\t\\t\\treturn [\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_1_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_3_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_5_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_11_residual'],\\r\\n\\t\\t\\t\\t\\tend_points['Conv2d_16_residual']\\r\\n\\t\\t\\t\\t]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08d97a73b33ce6e858e877fa8ba5024\",\n \"score\": \"0.5914832\",\n \"text\": \"def setup(self, layer):\\n\\n layers = OrderedDict()\\n self.layer_map = self.CFG_MAP[self.cnn_type]\\n\\n if self.cnn_type.startswith('vgg'):\\n assert len(self._base_cnn.features) == len(self.layer_map)\\n\\n # There's no named modules inside VGG, all integers\\n for module, params in zip(self.layer_map, self._base_cnn.features):\\n layers[module] = params\\n # 'penultimate' takes all conv layers by default\\n if layer != 'penultimate' and module == layer:\\n break\\n\\n if layer == 'penultimate':\\n # Add flatten layer\\n layers['flatten'] = Flatten()\\n\\n # Exclude final classification layer\\n for i in range(len(self._base_cnn.classifier) - 1):\\n mod = self._base_cnn.classifier[i]\\n name = \\\"{}{}\\\".format(mod.__class__.__name__, i)\\n layers[name] = mod\\n\\n elif self.cnn_type.startswith('resnet'):\\n assert layer in self.layer_map.values(), \\\\\\n \\\"The given layer {} is not known.\\\".format(layer)\\n for module, params in self._base_cnn.named_children():\\n # Add the layer with our naming scheme\\n layers[self.layer_map[module]] = params\\n # If we've hit the extraction point, break the loop\\n if self.layer_map[module] == layer:\\n break\\n\\n self.cnn = torch.nn.Sequential(layers)\\n\\n # Disable requires_grad by default\\n self.set_requires_grad(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c88cd32e853b1a69a3bbdde80d98a1bb\",\n \"score\": \"0.59111285\",\n \"text\": \"def FCDenseNet_103Layers(input_shape, \\n class_num, \\n growth_rate,\\n layer_depths,\\n model_name,\\n probes=[[],[]], \\n **kwargs):\\n \\n # growth rate\\n k = growth_rate\\n\\n # dimension\\n dim = len(input_shape) - 1\\n\\n # first layer\\n # dim=2 : [48, [3,3]]\\n # dim=3 : [48, [3,3,3]]\\n pw_dict = {'first': ['conv', [48, [3]*dim], 'MA']}\\n\\n \\n ''' DB+TD '''\\n # number of Dense block layers in the downward path\\n Ls = layer_depths[:5]\\n for i in range(len(Ls)):\\n # dense block\\n DB = {'DB%d_%d'%(i,j): ['conv', [k, [3]*dim], 'BAM']\\n for j in range(Ls[i])}\\n pw_dict.update(DB)\\n # transition down\\n nfmap = 48+np.sum(Ls[:i+1])*k\\n TD = {'T_%d'%i: ['conv', [nfmap, [1]*dim], 'BMA'],\\n 'pool_%d'%i: ['pool', [2]*dim]}\\n pw_dict.update(TD)\\n\\n ''' Bottleneck Dense Block''' \\n L = 15\\n BT = {'BottleDB_%d'%j: ['conv', [k, [3]*dim], 'BAM']\\n for j in range(L)}\\n pw_dict.update(BT)\\n\\n ''' TU+DB '''\\n Ls = np.flip(Ls+[layer_depths[5]], 0)\\n for i in range(1,len(Ls)):\\n # transition up\\n nfmap = Ls[i-1]*k\\n TU = {'TU_%d'%(i-1): ['conv_transpose', \\n [nfmap, [3]*dim, [2]*dim], 'M']}\\n pw_dict.update(TU)\\n # dense block\\n DB = {'DB%d_%d'%(5+i-1,j): ['conv', [k, [3]*dim], 'BAM']\\n for j in range(Ls[i])}\\n pw_dict.update(DB)\\n\\n # last layer\\n pw_dict.update({'last': ['conv', [class_num,[1]*dim], 'M']})\\n\\n\\n ''' Establishing the Skip Connections '''\\n layer_names = np.array(list(pw_dict.keys()))\\n stype = 'con' # concatenation\\n skips = []\\n\\n # intra-DB skip connections of downward path\\n for i in range(5):\\n # starting index\\n start_ind = np.where('DB%d_0'%i==layer_names)[0][0]\\n # number of layers\\n L = np.sum(['DB%d'%i in name for \\n name in layer_names])\\n for j in range(L):\\n skips += [[start_ind-1+j,\\n list(np.arange(start_ind+1+j,\\n start_ind+L+1)),\\n stype]]\\n # bottleneck DB\\n start_ind = np.where('BottleDB_0'==layer_names)[0][0]\\n L = np.sum(['BottleDB' in name for name in layer_names])\\n # for bottleneck, the input of the first layer (or output\\n # of pool_4) is connected to all intermediate layers\\n # but the last output (input of TU_0)\\n skips += [[start_ind-1, \\n list(np.arange(start_ind+1,\\n start_ind+L)), stype]]\\n # but the rest will be connected to the last one too\\n for j in range(1,L):\\n skips += [[start_ind-1+j, \\n list(np.arange(start_ind+1+j,\\n start_ind+L+1)),\\n stype]]\\n\\n # inter-DB skip connections\\n skipped_nodes = np.array([skips[i][0] for i in\\n range(len(skips))])\\n # NOTE1: we know that output nodes of DBs in downward\\n # path are not already skipped somewhere else, hence\\n # we don't need to check that with an if\\n #\\n # NOTE2: more importantly, for source nodes, we only \\n # save their outuputs before combining them with other\\n # sources. For example, in the first connection below\\n # the output of DB4_11 (end node of DB4) has to be\\n # connected to the input of DB5. However, the output\\n # node of DB4 (which is the same as input node of\\n # T_4) is also combined with many other previous\\n # nodes. Hence, what we actually have to do here is\\n # to combine output node of DB4_11 together with \\n # outputs of all other previous nodes that have\\n # already combined with input of T_4. In other words,\\n # input of DB5_0 should be the destination of (output\\n # of) DB4_11 and all connections of T_4\\n # \\n # output of DB4_11 + connections to T_4 \\n # --> \\n # output of TU_0 (input of DB5_0)\\n DB4_end_node = np.where(layer_names=='DB4_{}'.format(layer_depths[4]-1))[0][0]\\n DB5_start_node = np.where(layer_names=='DB5_0')[0][0]\\n T4_node = np.where(layer_names=='T_4')[0][0]\\n # go through all nodes, and if T_4 was in their\\n # destination, put DB5_0 as a destination too\\n for i in range(len(skips)):\\n if T4_node in skips[i][1]:\\n skips[i][1] += [DB5_start_node]\\n skips += [[DB4_end_node,[DB5_start_node], stype]]\\n\\n # output of DB3 + connections to T_3\\n # --> \\n # output of TU_1 (input of DB6_0)\\n DB3_end_node = np.where(layer_names=='DB3_{}'.format(layer_depths[3]-1))[0][0]\\n DB6_start_node = np.where(layer_names=='DB6_0')[0][0]\\n T3_node = np.where(layer_names=='T_3')[0][0]\\n for i in range(len(skips)):\\n if T3_node in skips[i][1]:\\n skips[i][1] += [DB6_start_node]\\n skips += [[DB3_end_node, [DB6_start_node], stype]]\\n\\n # output of DB2 + connections to T_2 \\n # --> \\n # output of TU_2 (input of DB7_0)\\n DB2_end_node = np.where(layer_names=='DB2_{}'.format(layer_depths[2]-1))[0][0]\\n DB7_start_node = np.where(layer_names=='DB7_0')[0][0]\\n T2_node = np.where(layer_names=='T_2')[0][0]\\n for i in range(len(skips)):\\n if T2_node in skips[i][1]:\\n skips[i][1] += [DB7_start_node]\\n skips += [[DB2_end_node, [DB7_start_node], stype]]\\n\\n # output of DB1 + connections to T_1 \\n # -->\\n # output of TU_3 (input of DB8_0)\\n DB1_end_node = np.where(layer_names=='DB1_{}'.format(layer_depths[1]-1))[0][0]\\n DB8_start_node = np.where(layer_names=='DB8_0')[0][0]\\n T1_node = np.where(layer_names=='T_1')[0][0]\\n for i in range(len(skips)):\\n if T1_node in skips[i][1]:\\n skips[i][1] += [DB8_start_node]\\n skips += [[DB1_end_node, [DB8_start_node], stype]]\\n\\n # output of DB0 + connections to T_0\\n # --> \\n # output of TU_4 (input of DB9_0)\\n DB0_end_node = np.where(layer_names=='DB0_{}'.format(layer_depths[0]-1))[0][0]\\n DB9_start_node = np.where(layer_names=='DB9_0')[0][0]\\n T0_node = np.where(layer_names=='T_0')[0][0]\\n for i in range(len(skips)):\\n if T0_node in skips[i][1]:\\n skips[i][1] += [DB9_start_node]\\n skips += [[DB0_end_node, [DB9_start_node], stype]]\\n\\n # After estalishing the skip connections between the \\n # downward and upward paths, for the intra-DB skips of\\n # the upward path. It is different than the intra-DB skips\\n # of downward path, because here for connecting the \\n # layers inside a DB, the first layer itself has \\n # combination of outputs of many other layers from\\n # the downward path that should be taken into account.\\n # For instance, skips of DB5, we should also consider layers\\n # of DB4, which hare connected to the input of DB5_0\\n for i in range(5,9):\\n # starting index\\n start_ind = np.where('DB%d_0'%i==layer_names)[0][0]\\n # first do the connections to the last layer\\n # because this one does not receive the first layer\\n # number of layers\\n L = np.sum(['DB%d'%i in name for \\n name in layer_names])\\n skips += [[start_ind-1, \\n list(np.arange(start_ind+1,\\n start_ind+L)), stype]]\\n for j in range(1,L):\\n skips += [[start_ind-1+j,\\n list(np.arange(start_ind+1+j,\\n start_ind+L+1)),\\n stype]]\\n # also add the intermediate layers as destination\\n # of the skipped connections (i.e. those layers\\n # that already had the start_ind as their destination)\\n for j in range(len(skips)):\\n if start_ind in skips[j][1]:\\n skips[j][1] += list(np.arange(start_ind+1,\\n start_ind+L))\\n\\n # for DB9 (last DB) do the same thing, except its input\\n # should also be connected to the last layer\\n start_ind = np.where('DB9_0'==layer_names)[0][0]\\n L = np.sum(['DB%d'%i in name for name in layer_names])\\n for j in range(L):\\n skips += [[start_ind-1+j,\\n list(np.arange(start_ind+1+j,\\n start_ind+L+1)),stype]]\\n for j in range(len(skips)):\\n if start_ind in skips[j][1]:\\n skips[j][1] += list(np.arange(start_ind+1,\\n start_ind+L+1))\\n\\n # sorting the skips in terms of the sources\\n sort_inds = np.argsort([skips[i][0] for i in range(len(skips))])\\n sorted_skips = []\\n for ind in sort_inds:\\n sorted_skips += [skips[ind]]\\n\\n\\n ''' Specifying Drop-out Layers '''\\n # including all the layers, except max-pooling and\\n # upscaling layers\\n dp_layers = np.arange(len(pw_dict)).tolist()\\n no_dp = ['pool_0', 'pool_1', 'pool_2', 'pool_3',\\n 'pool_4', 'TU_0', 'TU_1', 'TU_2', 'TU_3',\\n 'TU_4']\\n for name in no_dp:\\n loc = np.where(layer_names==name)[0][0]\\n dp_layers.remove(loc)\\n dp_rate = 0.1\\n\\n \\n ''' Creating the Model '''\\n x = tf.placeholder(tf.float32, [None,]+input_shape)\\n model = NN_extended.CNN(x, pw_dict, model_name, \\n sorted_skips,None,\\n [dp_layers, dp_rate],\\n probes,\\n **kwargs)\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b75396319b93aa5a18b1ec2f280840\",\n \"score\": \"0.5904189\",\n \"text\": \"def _build_net(self):\\n # network define\\n x = constant_op.constant(1.0, shape=[1, 28, 28, 3], dtype=dtypes.float32)\\n is_training = ops.convert_to_tensor(True)\\n\\n # conv1\\n x = self._conv2d(x, 'conv1', self.conv1_kernel, self.conv1_bias)\\n x = self._batchnorm(x, 'bn1', self.bn1_gamma, self.bn1_beta, \\\\\\n self.bn1_moving_mean, self.bn1_moving_variance, \\\\\\n is_training)\\n x = nn_ops.relu(x)\\n\\n # conv2a\\n residual_x = self._conv2d(x,\\n 'conv2a_1',\\n self.conv2a_1_kernel,\\n self.conv2a_1_bias)\\n residual_x = self._batchnorm(residual_x,\\n 'bn2a_1',\\n self.bn2a_1_gamma,\\n self.bn2a_1_beta,\\n self.bn2a_1_moving_mean,\\n self.bn2a_1_moving_variance,\\n is_training)\\n residual_x = nn_ops.relu(residual_x)\\n\\n card_in = gap_finetune.split(residual_x,\\n num_or_size_splits=4,\\n axis=-1,\\n gap=self.gap,\\n gap_vars=self.gap_vars)\\n card_out = []\\n for i in range(4):\\n out = self._conv2d(card_in[i],\\n 'conv2a_2_%d'%i,\\n self.conv2a_2_kernel[i],\\n self.conv2a_2_bias[i])\\n card_out.append(out)\\n residual_x = array_ops.concat(card_out, axis=-1)\\n residual_x = self._batchnorm(residual_x,\\n 'bn2a_2',\\n self.bn2a_2_gamma,\\n self.bn2a_2_beta,\\n self.bn2a_2_moving_mean,\\n self.bn2a_2_moving_variance,\\n is_training)\\n residual_x = nn_ops.relu(residual_x)\\n\\n residual_x = self._conv2d(residual_x,\\n 'conv2a_3',\\n self.conv2a_3_kernel,\\n self.conv2a_3_bias)\\n residual_x = self._batchnorm(residual_x,\\n 'bn2a_3',\\n self.bn2a_3_gamma,\\n self.bn2a_3_beta,\\n self.bn2a_3_moving_mean,\\n self.bn2a_3_moving_variance,\\n is_training)\\n\\n # conv2b\\n shortcut_x = self._conv2d(x, 'conv2b', self.conv2b_kernel, self.conv2b_bias)\\n shortcut_x = self._batchnorm(shortcut_x,\\n 'bn2b',\\n self.bn2b_gamma,\\n self.bn2b_beta,\\n self.bn2b_moving_mean,\\n self.bn2b_moving_variance,\\n is_training)\\n\\n x = nn_ops.relu(residual_x + shortcut_x)\\n\\n # conv3\\n x = self._conv2d(x, 'conv3', self.conv3_kernel, self.conv3_bias)\\n x = self._batchnorm(x, 'bn3', self.bn3_gamma, self.bn3_beta, \\\\\\n self.bn3_moving_mean, self.bn3_moving_variance,\\\\\\n is_training)\\n conv_output = nn_ops.relu(x)\\n\\n # loss\\n self.loss = nn_ops.l2_loss(conv_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fabd55903fc34d6d876270b74b58a2e\",\n \"score\": \"0.58965385\",\n \"text\": \"def get_net(batch_size, num_actions=18, image_shape=(4, 84, 84), dtype=\\\"float32\\\", layout=\\\"NCHW\\\"):\\n data_shape = (batch_size,) + image_shape\\n data = relay.var(\\\"data\\\", shape=data_shape, dtype=dtype)\\n\\n bias_axis = layout.index(\\\"C\\\")\\n\\n conv1_bias = relay.var(\\\"conv1_bias\\\")\\n conv1 = layers.conv2d(\\n data,\\n kernel_size=(8, 8),\\n strides=(4, 4),\\n padding=(0, 0),\\n channels=32,\\n name=\\\"conv1\\\",\\n data_layout=layout,\\n kernel_layout=layers.conv_kernel_layout(layout),\\n )\\n conv1 = relay.nn.bias_add(conv1, conv1_bias, bias_axis)\\n relu1 = relay.nn.relu(conv1)\\n\\n conv2_bias = relay.var(\\\"conv2_bias\\\")\\n conv2 = layers.conv2d(\\n relu1,\\n kernel_size=(4, 4),\\n strides=(2, 2),\\n padding=(0, 0),\\n channels=64,\\n name=\\\"conv2\\\",\\n data_layout=layout,\\n kernel_layout=layers.conv_kernel_layout(layout),\\n )\\n conv2 = relay.nn.bias_add(conv2, conv2_bias, bias_axis)\\n relu2 = relay.nn.relu(conv2)\\n\\n conv3_bias = relay.var(\\\"conv3_bias\\\")\\n conv3 = layers.conv2d(\\n relu2,\\n kernel_size=(3, 3),\\n strides=(1, 1),\\n padding=(0, 0),\\n channels=64,\\n name=\\\"conv3\\\",\\n data_layout=layout,\\n kernel_layout=layers.conv_kernel_layout(layout),\\n )\\n conv3 = relay.nn.bias_add(conv3, conv3_bias, bias_axis)\\n relu3 = relay.nn.relu(conv3)\\n\\n bf1 = relay.nn.batch_flatten(relu3)\\n dense1 = layers.dense_add_bias(bf1, units=512, name=\\\"dense1\\\")\\n relu4 = relay.nn.relu(dense1)\\n dense2 = layers.dense_add_bias(relu4, units=num_actions, name=\\\"dense2\\\")\\n\\n args = relay.analysis.free_vars(dense2)\\n return relay.Function(args, dense2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd113f9b6e77dd2c6ddde8a5f37119d\",\n \"score\": \"0.58961946\",\n \"text\": \"def _buildNetwork(self):\\n\\n #Core Network (reusable)\\n conv1 = self._convLayer(self.obsPH, filterDims = 8, outputFilters = 16, strides = 4,name=\\\"conv1\\\", activation = tf.nn.relu)\\n conv2 = self._convLayer(conv1 , filterDims = 4, outputFilters = 32, strides = 2,name=\\\"conv2\\\", activation = tf.nn.relu)\\n fc1 = self._fcLayer (conv2 , numOutputs = 256,name=\\\"fc1\\\", activation = tf.nn.relu )\\n\\n #Outputs\\n self.valueEst = tf.squeeze(self._fcLayer(fc1, numOutputs = 1,name=\\\"valueEst\\\", activation = lambda x: x), axis=[1])\\n self.policyProb = self._fcLayer(fc1, numOutputs = self.actionSpace,name=\\\"policyProb\\\", activation = tf.nn.softmax)\\n \\n #Log results\\n self.log.addHist(\\\"valueEst\\\", self.valueEst)\\n self.log.addHist(\\\"policyProb\\\", self.policyProb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7668b1092f2c12bddc3d1c9bf25763bd\",\n \"score\": \"0.5895739\",\n \"text\": \"def layers(self):\\n if self._layers is not None:\\n return self._layers\\n\\n neurons = self._config['neurons']\\n\\n self._layers = [\\n partial(self.convolutional_layer, [5, 5, 1, 32], [32]),\\n partial(self.convolutional_layer, [5, 5, 32, 64], [64]),\\n partial(self.connected_layer,\\n [7 * 7 * 64, neurons], [neurons], [-1, 7 * 7 * 64]),\\n partial(self.dropout_layer, self.keep_prob),\\n partial(self.readout_layer, [neurons, self.DIGITS], [self.DIGITS])\\n ]\\n\\n return self._layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3452bd6fa78532fe6451c643a87efd15\",\n \"score\": \"0.5890459\",\n \"text\": \"def buildNN(architecture,training_params,input_dim):\\n current_units = input_dim\\n \\n model = Sequential()\\n is_first_layer = True\\n \\n for layer in architecture:\\n layer_name = layer.keys()[0]\\n layer_params = layer[layer_name]\\n if layer_name == 'Activation':\\n model.add(Activation(layer_params['type']))\\n if layer_name == 'PReLU':\\n model.add(PReLU())\\n if layer_name == 'Dropout':\\n if is_first_layer:\\n model.add(Dropout(layer_params['p'], input_shape=(None, current_units)))\\n else:\\n model.add(Dropout(layer_params['p']))\\n if layer_name == 'Dense':\\n model.add(Dense(layer_params['num_units']))\\n current_units = layer_params['num_units']\\n if layer_name == 'Conv':\\n # a filter covers all predictions for a single time sample\\n # strides are made between time samples\\n model.add(Conv2D(layer_params['nb_filters'], 1))\\n current_units = layer_params['nb_filters']*training_params['num_strides']\\n if layer_name == 'GRU':\\n model.add(GRU(layer_params['num_units'], \\n input_shape=((None, input_dim) if is_first_layer else None),\\n dropout=(layer_params['dropout'] if 'dropout' in layer_params else 0.0),\\n return_sequences=layer_params['next_GRU']))\\n current_units = layer_params['num_units']\\n if layer_name == 'LSTM':\\n model.add(LSTM(layer_params['num_units'], return_sequences=layer_params['next_GRU']))\\n current_units = layer_params['num_units']\\n if layer_name == 'BatchNormalization':\\n model.add(BatchNormalization())\\n if layer_name == 'Flatten':\\n model.add(Flatten())\\n if layer_name == 'Output':\\n model.add(Dense(N_EVENTS))\\n model.add(Activation('sigmoid'))\\n is_first_layer = False\\n\\n if not training_params.has_key('optim') or training_params['optim'] == 'sgd':\\n optim = SGD(lr=training_params['lr'], decay=float(training_params['decay']), momentum=training_params['momentum'], nesterov=True) \\n elif training_params['optim'] == 'adam':\\n optim = Adam(lr=training_params['lr'])\\n \\n model.compile(loss='binary_crossentropy', optimizer=optim)\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c9126f9b64fa6d7ca79a84bd0643df\",\n \"score\": \"0.5889604\",\n \"text\": \"def load_networks(self, epoch):\\n for model_name in self.model_names:\\n if isinstance(model_name, str):\\n load_filename = '{:s}_net_{:s}.pth'.format(epoch, model_name)\\n load_path = os.path.join(self.load_net_dir, load_filename)\\n # Use the str to get the attribute aka the network (self.netG / self.netD)\\n net = getattr(self, 'net' + model_name)\\n # If we use multi GPUs and apply the data parallel\\n if isinstance(net, torch.nn.DataParallel):\\n net = net.module\\n print('Loading the model from %s' % load_path)\\n state_dict = torch.load(load_path, map_location=self.device)\\n if hasattr(state_dict, '_metadata'):\\n del state_dict._metadata\\n\\n net.load_state_dict(state_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde20fbb7ed82e712438ff8a6867f4ff\",\n \"score\": \"0.5888223\",\n \"text\": \"def build(self, input_shape):\\n params = self.get_config()\\n for lid in range(params[\\\"num_layers\\\"]):\\n self._stacking_layers.append([\\n build_transformer_component({\\n \\\"base_layer.class\\\": LightConvolutionLayer.__name__,\\n \\\"base_layer.params\\\": dict(\\n kernel_size=params[\\\"conv_kernel_size_list\\\"][lid],\\n num_heads=params[\\\"num_conv_heads\\\"],\\n conv_type=params[\\\"conv_type\\\"],\\n conv_dim=params[\\\"conv_hidden_size\\\"],\\n use_glu=params[\\\"glu_after_proj\\\"],\\n weight_dropout_rate=params[\\\"conv_weight_dropout_rate\\\"],\\n name=\\\"light_conv\\\"\\n )},\\n dropout_rate=params[\\\"layer_postprocess_dropout_rate\\\"],\\n epsilon=params[\\\"layer_postprocess_epsilon\\\"]),\\n build_transformer_component({\\n \\\"base_layer.class\\\": MultiHeadAttention.__name__,\\n \\\"base_layer.params\\\": dict(\\n num_heads=params[\\\"num_attention_heads\\\"],\\n num_units=input_shape[-1],\\n attention_dropout_rate=params[\\\"attention_dropout_rate\\\"],\\n attention_type=params[\\\"attention_type\\\"],\\n name=\\\"encdec_attention\\\")},\\n dropout_rate=params[\\\"layer_postprocess_dropout_rate\\\"],\\n epsilon=params[\\\"layer_postprocess_epsilon\\\"]),\\n build_transformer_component({\\n \\\"base_layer.class\\\": TransformerFFN.__name__,\\n \\\"base_layer.params\\\": dict(\\n filter_size=params[\\\"filter_size\\\"],\\n output_size=input_shape[-1],\\n dropout_rate=params[\\\"ffn_dropout_rate\\\"],\\n activation=params[\\\"ffn_activation\\\"],\\n name=\\\"ffn\\\")},\\n dropout_rate=params[\\\"layer_postprocess_dropout_rate\\\"],\\n epsilon=params[\\\"layer_postprocess_epsilon\\\"])])\\n self._output_norm_layer = tf.keras.layers.LayerNormalization(\\n epsilon=params[\\\"layer_postprocess_epsilon\\\"],\\n dtype=\\\"float32\\\", name=\\\"output_ln\\\")\\n super(LightConvolutionDecoder, self).build(input_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0da8e992d5e67856dbd98a496a61121\",\n \"score\": \"0.58795035\",\n \"text\": \"def network(configs):\\n tf.reset_default_graph()\\n # input: historical talent flow\\n flow_input = tf.placeholder(tf.float32,\\n [configs[\\\"batch_size\\\"],\\n configs[\\\"en_steps\\\"],\\n configs[\\\"flow_features\\\"]],\\n name=\\\"flow_input\\\")\\n # input: stock series\\n stock_input = tf.placeholder(tf.float32,\\n [configs[\\\"batch_size\\\"],\\n configs[\\\"en_steps\\\"],\\n configs[\\\"stock_features\\\"]],\\n name=\\\"stock_input\\\")\\n # input: profiles\\n profile_input = tf.placeholder(tf.float32,\\n [configs[\\\"batch_size\\\"],\\n configs[\\\"profile_features\\\"]],\\n name=\\\"profile_input\\\")\\n # result\\n decoder_targets = tf.placeholder(tf.float32,\\n [configs[\\\"batch_size\\\"],\\n configs[\\\"de_steps\\\"]],\\n name=\\\"decoder_targets\\\")\\n ###########\\n\\n # profile encoder\\n profile_vec = ann_layer(profile_input, configs[\\\"profile_cells\\\"],\\n scope=\\\"profile_vec\\\")\\n\\n # flow encoder\\n flow_outputs, flow_final_state = encoder(flow_input, scope=\\\"flow_encoder\\\",\\n cell_units=configs[\\\"en_flow_units\\\"])\\n # stock encoder\\n stock_outputs, stock_final_state = encoder(stock_input, scope=\\\"stock_encoder\\\",\\n cell_units=configs[\\\"en_stock_units\\\"])\\n\\n en_outputs = tf.concat([flow_outputs, stock_outputs], 2)\\n en_final_state = tf.concat([flow_final_state, stock_final_state], 1)\\n\\n # decoder\\n train_res, train_alphas, test_res, test_alphas = decoder(en_final_state,\\n en_outputs, profile_vec,\\n decoder_targets,\\n configs[\\\"de_cell_units\\\"],\\n configs[\\\"de_out_units\\\"],\\n configs[\\\"de_steps\\\"],\\n configs[\\\"attention_units\\\"])\\n\\n # loss function\\n vars = tf.trainable_variables()\\n lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars]) * configs[\\\"l2e\\\"]\\n loss = lossL2 + tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(train_res, decoder_targets)), axis=1)))\\n opt = tf.train.AdamOptimizer(configs[\\\"learning_rate\\\"]).minimize(loss)\\n return dict(loss=loss, opt=opt, predictions=test_res,\\n train_res=train_res,\\n stock_input=stock_input,\\n profile_input=profile_input,\\n alphas=test_alphas,\\n flow_input=flow_input,\\n decoder_targets=decoder_targets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd0508b891a48d88c249932d51fc43a1\",\n \"score\": \"0.5876715\",\n \"text\": \"def build_layers(inputs):\\n resolution = inputs.shape[1]\\n if resolution > 2:\\n kernel_shapes = ((1, 1), (3, 3), (3, 3), (1, 1))\\n else:\\n kernel_shapes = ((1, 1), (1, 1), (1, 1), (1, 1))\\n\\n conv_layers = []\\n aux_conv_layers = []\\n for layer_idx, kernel_shape in enumerate(kernel_shapes):\\n is_last = layer_idx == _NUM_CONV_LAYER_PER_BLOCK - 1\\n num_channels = self.output_channels if is_last else self.internal_channels\\n weights_scale = self.last_weights_scale if is_last else 1.\\n conv_layers.append(\\n get_vdvae_convolution(\\n num_channels,\\n kernel_shape,\\n weights_scale,\\n name=f'c{layer_idx}',\\n precision=self.precision))\\n aux_conv_layers.append(\\n get_vdvae_convolution(\\n num_channels, (1, 1),\\n 0.,\\n name=f'aux_c{layer_idx}',\\n precision=self.precision))\\n\\n return conv_layers, aux_conv_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"920bc82978b87b89c0f2838f919415b8\",\n \"score\": \"0.5874086\",\n \"text\": \"def build_standard_net(layer_sizes, normalize, L2_reg=0.0, L1_reg=0.0, activation_function=relu,\\n nll_func=mean_squared_error, num_outputs=1):\\n # How to change the output size here?\\n layer_sizes = layer_sizes + [num_outputs]\\n # Add number of outputs here\\n\\n parser = WeightsParser()\\n for i, shape in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\\n parser.add_weights(('weights', i), shape)\\n parser.add_weights(('biases', i), (1, shape[1]))\\n\\n def predictions(W_vect, X):\\n cur_units = X\\n for layer in range(len(layer_sizes) - 1):\\n cur_W = parser.get(W_vect, ('weights', layer))\\n cur_B = parser.get(W_vect, ('biases', layer))\\n cur_units = np.dot(cur_units, cur_W) + cur_B\\n if layer < len(layer_sizes) - 2:\\n if normalize:\\n cur_units = batch_normalize(cur_units)\\n cur_units = activation_function(cur_units)\\n # Note, cur_units will not be normalized for the categorical_nll case.\\n # will let categorical_nll handle it.\\n return cur_units\\n \\n\\n def loss(w, X, targets):\\n assert len(w) > 0\\n log_prior = -L2_reg * np.dot(w, w) / len(w) - L1_reg * np.mean(np.abs(w))\\n preds = predictions(w, X)\\n if nll_func == categorical_nll:\\n return nll_func(preds, targets, num_outputs) - log_prior\\n return nll_func(preds, targets) - log_prior\\n\\n return loss, predictions, parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdab737468a42818f73a91fdb9c8255c\",\n \"score\": \"0.58727676\",\n \"text\": \"def create_network_topology(n_layers, init_n_neurons, n_neurons_decay_factor, n_decaying_layers):\\n\\n random_network_topology = [init_n_neurons]*n_layers\\n for decaying_layer in range(n_decaying_layers, 0, -1):\\n random_network_topology[n_layers-decaying_layer] = \\\\\\n int(random_network_topology[n_layers-decaying_layer-1] / n_neurons_decay_factor)\\n\\n return random_network_topology\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc3bb415e909723549d2dde2b144fb4f\",\n \"score\": \"0.5865945\",\n \"text\": \"def build_yolonet(module_params):\\n # get the hyperparameters of the neural net.\\n net_param = module_params.pop(0)\\n channels = [int(net_param['channels'])]\\n modules = nn.ModuleList()\\n detect_param = module_params.pop(-1)\\n\\n # hyperparameters:\\n momentum = float(net_param['momentum'])\\n\\n # detection layer\\n\\n for i, layer in enumerate(module_params):\\n module = nn.Sequential()\\n layer_type = layer['type']\\n # convolutional layer\\n if layer_type == \\\"convolutional\\\":\\n kernel = int(layer['size'])\\n out_channel = int(layer['filters'])\\n stride = int(layer['stride'])\\n # pad = int(layer['pad'])\\n pad = (kernel - 1) // 2\\n bn = int(layer['batch_normalize']\\n ) if \\\"batch_normalize\\\" in layer else 0\\n # print(type(kernel), type(out_channel), type(stride), type(pad), type(bn))\\n\\n conv_layer = nn.Conv2d(in_channels=channels[-1], out_channels=out_channel,\\n kernel_size=kernel, stride=stride, padding=pad, bias=not bn)\\n module.add_module(\\\"conv_layer_{}\\\".format(i), conv_layer)\\n if bn:\\n batch_norm = nn.BatchNorm2d(\\n num_features=out_channel, momentum=momentum)\\n module.add_module(\\\"batch_norm_{}\\\".format(i), batch_norm)\\n if \\\"activation\\\" in layer and layer[\\\"activation\\\"] == \\\"leaky\\\":\\n leaky = nn.LeakyReLU(negative_slope=0.1, inplace=False)\\n module.add_module(\\\"leaky_relu_{}\\\".format(i), leaky)\\n # update channels\\n channels.append(out_channel)\\n\\n # max-pooling layer\\n elif layer_type == \\\"maxpool\\\":\\n kernel = int(layer['size'])\\n stride = int(layer['stride'])\\n maxpool = nn.MaxPool2d(kernel_size=kernel, stride=stride)\\n module.add_module(\\\"max_pool_{}\\\".format(i), maxpool)\\n\\n # local\\n elif layer_type == \\\"local\\\":\\n kernel = int(layer['size'])\\n stride = int(layer['stride'])\\n pad = int(layer['pad'])\\n out_channel = int(layer['filters']) * \\\\\\n (kernel + pad) * (kernel + pad)\\n gird_size = int(2 * kernel + stride)\\n\\n # local_layer = Local(in_channels=channels[-1], out_channels=out_channel,\\n # kernel_size=kernel, stride=stride, padding=pad)\\n module.add_module(\\\"Flatten_{}\\\".format(i), Flatten())\\n local_layer = nn.Linear(in_features=(\\n gird_size * gird_size * channels[-1]), out_features=out_channel)\\n module.add_module(\\\"local_layer_{}\\\".format(i), local_layer)\\n if \\\"activation\\\" in layer and layer[\\\"activation\\\"] == \\\"leaky\\\":\\n leaky = nn.LeakyReLU(negative_slope=0.1, inplace=False)\\n module.add_module(\\\"leaky_relu_{}\\\".format(i), leaky)\\n # update channels\\n channels.append(out_channel)\\n\\n # dropout\\n elif layer_type == \\\"dropout\\\":\\n prob = float(layer['probability'])\\n dropout = nn.Dropout(p=prob, inplace=False)\\n module.add_module(\\\"dropout_{}\\\".format(i), dropout)\\n\\n # connection layer\\n elif layer_type == \\\"connected\\\":\\n out_channel = int(layer['output'])\\n if \\\"activation\\\" in layer and layer[\\\"activation\\\"] == \\\"linear\\\":\\n linear = nn.Linear(\\n in_features=channels[-1], out_features=out_channel)\\n module.add_module(\\\"linear_{}\\\".format(i), linear)\\n sigmoid_layer = nn.Sigmoid()\\n module.add_module(\\\"sigmoid_{}\\\".format(i), sigmoid_layer)\\n\\n # skip the detection layer (detection_cuda will do detection)\\n elif layer_type == \\\"detection\\\": # like yolo layer in yolo-v3\\n continue\\n\\n # add module\\n modules.append(module)\\n # print(type(modules))\\n return net_param, detect_param, modules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2237214673b4f3082246952a89dd4d6\",\n \"score\": \"0.58587134\",\n \"text\": \"def create_layer(self, in_shape: list):\\n if len(in_shape) == 4:\\n batch, channels, h, w = in_shape\\n elif len(in_shape) == 2:\\n batch, channels = in_shape\\n else:\\n raise Exception('Invalid input with shape: ' + str(in_shape))\\n\\n reshape_layer: Optional[Reshape] = None\\n img_flat_size = int(reduce(lambda x, y: x * y, in_shape) / batch)\\n\\n # Calculating out feature size, creating deep layer and reshaping if necessary\\n if self.module_node.layer_type.value == nn.Conv2d:\\n if len(in_shape) == 2:\\n h = w = int(math.sqrt(img_flat_size / channels))\\n reshape_layer = Reshape(batch, channels, h, w)\\n\\n # TODO make kernel size and stride a tuple\\n window_size = self.module_node.layer_type.get_sub_value('conv_window_size')\\n stride = self.module_node.layer_type.get_sub_value('conv_stride')\\n padding = math.ceil((window_size - h) / 2)\\n padding = padding if padding >= 0 else 0\\n # dilation = 1\\n\\n # h_out = math.floor((h + 2 * padding - dilation * (window_size - 1) - 1) / stride + 1)\\n # w_out = math.floor((w + 2 * padding - dilation * (window_size - 1) - 1) / stride + 1) # same cause square\\n #\\n # # If using max pooling out feature size changes\\n # neat_reduction = self.module_node.layer_type.get_sub_value('reduction', return_mutagen=True)\\n # if neat_reduction is not None and neat_reduction.value is not None: # Using max pooling\\n # pool_size = neat_reduction.get_sub_value('pool_size')\\n # h_out = math.ceil((h_out - pool_size) / pool_size + 1)\\n # w_out = math.ceil((w_out - pool_size) / pool_size + 1)\\n\\n deep_layer = nn.Conv2d(channels, self.out_features, window_size, stride, padding)\\n else: # self.module_node.layer_type.value == nn.Linear:\\n if len(in_shape) != 2 or channels != img_flat_size:\\n reshape_layer = Reshape(batch, img_flat_size)\\n\\n deep_layer = nn.Linear(img_flat_size, self.out_features)\\n\\n self.sequential = nn.Sequential(*[module for module in\\n [reshape_layer, deep_layer, *self._create_regularisers()]\\n if module is not None])\\n self.out_shape = list(self.forward(zeros(in_shape)).size())\\n\\n return self.out_shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e68455b6c8d5a18992d4cc3de075f2a\",\n \"score\": \"0.5857208\",\n \"text\": \"def _network_setup(\\n max_iteration: int,\\n ak_raw: ArrayKey,\\n ak_mask: ArrayKey,\\n labels: List[CNNectome.utils.label.Label],\\n) -> Tuple[\\n Dict[str, str],\\n int,\\n Dict[str, gunpowder.array.ArrayKey],\\n Dict[str, gunpowder.array.ArrayKey],\\n]:\\n # load net_io_names.json\\n with open(\\\"net_io_names.json\\\", \\\"r\\\") as f:\\n net_io_names = json.load(f)\\n\\n # find checkpoint from previous training, start a new one if not found\\n if tf.train.latest_checkpoint(\\\".\\\"):\\n start_iteration = int(tf.train.latest_checkpoint(\\\".\\\").split(\\\"_\\\")[-1])\\n if start_iteration >= max_iteration:\\n logging.info(\\n \\\"Network has already been trained for {0:} iterations\\\".format(\\n start_iteration\\n )\\n )\\n else:\\n logging.info(\\\"Resuming training from {0:}\\\".format(start_iteration))\\n else:\\n start_iteration = 0\\n logging.info(\\\"Starting fresh training\\\")\\n\\n # define network inputs\\n inputs = dict()\\n inputs[net_io_names[\\\"raw\\\"]] = ak_raw\\n inputs[net_io_names[\\\"mask\\\"]] = ak_mask\\n for label in labels:\\n inputs[net_io_names[\\\"mask_\\\" + label.labelname]] = label.mask_key\\n inputs[net_io_names[\\\"gt_\\\" + label.labelname]] = label.gt_dist_key\\n if label.scale_loss or label.scale_key is not None:\\n inputs[net_io_names[\\\"w_\\\" + label.labelname]] = label.scale_key\\n\\n # define network outputs\\n outputs = dict()\\n for label in labels:\\n outputs[net_io_names[label.labelname]] = label.pred_dist_key\\n return net_io_names, start_iteration, inputs, outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d70dfee50d60fa68232f6ba7919ff95\",\n \"score\": \"0.585505\",\n \"text\": \"def build(width, height, depth, reg=0.0005):\\r\\n # initialize the input shape to be \\\"channels last\\\" and the\\r\\n # channels dimension itself\\r\\n input_shape = height, width, depth\\r\\n cha_dim = -1\\r\\n # if we are using \\\"channels first\\\", update the input shape\\r\\n # and channels dimension\\r\\n if K.image_data_format() == \\\"channels_first\\\":\\r\\n input_shape = (depth, height, width)\\r\\n cha_dim = 1\\r\\n\\r\\n inputs = Input(shape=input_shape)\\r\\n x = GoogLeNet.conv_module(inputs, 32, (3, 3), (1, 1), cha_dim, reg=reg, name=\\\"block1\\\")\\r\\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\\\"pool1\\\")(x)\\r\\n x = GoogLeNet.conv_module(x, 32, (1, 1), (1, 1), cha_dim, reg=reg, name=\\\"block2\\\")\\r\\n x = GoogLeNet.conv_module(x, 32, (3, 3), (1, 1), cha_dim, reg=reg, name=\\\"block3\\\")\\r\\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\\\"pool2\\\")(x)\\r\\n\\r\\n # apply two Inception modules followed by a POOL\\r\\n x = GoogLeNet.inception_module(x, 64, 32, 64, 16, 32, 64, 64, cha_dim, \\\"3\\\", reg=reg)\\r\\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\\\"pool3\\\")(x)\\r\\n\\r\\n x = GoogLeNet.inception_module(x, 128, 32, 128, 24, 48, 64, 64, cha_dim, \\\"4\\\", reg=reg)\\r\\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\\\"pool4\\\")(x)\\r\\n\\r\\n x = GoogLeNet.inception_module(x, 128, 32, 128, 24, 48, 64, 64, cha_dim, \\\"5\\\", reg=reg)\\r\\n x = MaxPooling2D((2, 2), strides=(2, 2), name=\\\"pool5\\\")(x)\\r\\n\\r\\n x = GoogLeNet.inception_module(x, 256, 112, 256, 32, 64, 128, 128, cha_dim, \\\"6\\\", reg=reg)\\r\\n # apply a POOL layer (average) followed by dropout\\r\\n x = GlobalAveragePooling2D(K.image_data_format())(x)\\r\\n x = Dropout(0.5, name=\\\"do\\\")(x)\\r\\n\\r\\n # outputs\\r\\n model = Model(inputs=inputs, outputs=x)\\r\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac00d191387b7f41368aea8fda054f8\",\n \"score\": \"0.58516353\",\n \"text\": \"def make_configs(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac00d191387b7f41368aea8fda054f8\",\n \"score\": \"0.58516353\",\n \"text\": \"def make_configs(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b8a7f184b69eeb43964b95f618af60\",\n \"score\": \"0.58484864\",\n \"text\": \"def _configure_layers(self):\\n nvis = self.Train[0].meta.shape[1]\\n layers = []\\n layer_sizes = get_layer_size_iterator(self.SizeHiddenLayers.value)\\n for _ in range(self.NumHiddenLayers.value):\\n nhid = layer_sizes.next()\\n layer = rbm.RBM(nvis=nvis, nhid=nhid,\\n irange=4, init_bias_hid=4,\\n init_bias_vis=0,\\n monitor_reconstruction=True)\\n nvis = nhid\\n layers.append(layer)\\n\\n n_out = self.Train[1].meta.shape[1]\\n output = mlp.Softmax(n_out, 'output', irange=.1)\\n layers.append(output)\\n self._layers = layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a50163fb3cd09bca48462922d0e4edf\",\n \"score\": \"0.5844027\",\n \"text\": \"def create_dnn(first_output_dim, second_output_dim, input_dim, first_layer_count, second_layer_count, interm_fraction, neuron_count):\\n def dnn_model_builder(layer_count, inp_dim, out_dim, loss, act):\\n model = Sequential()\\n for inx in range(int(layer_count)):\\n if inx == 0:\\n model.add(Dense(neuron_count, activation='relu', input_dim=inp_dim, name='dense-{}'.format(inx+1)))\\n elif inx == layer_count - 1:\\n model.add(Dense(out_dim, activation=act, name='output-{}'.format(act)))\\n model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])\\n return model\\n else:\\n model.add(Dense(neuron_count, activation='relu', name='dense-{}'.format(inx+1)))\\n\\n # Create first model with specified number of layers\\n first_model = dnn_model_builder(first_layer_count, input_dim, first_output_dim, loss='categorical_crossentropy', act='softmax')\\n\\n # Create intermediate layers with interm_fraction of first_model layers\\n intermediate = Sequential()\\n for inx in range(int(Decimal(first_layer_count*interm_fraction).quantize(0, ROUND_HALF_UP))):\\n if inx == 0:\\n intermediate.add(Dense(neuron_count, activation='relu', input_dim=input_dim, name='dense-{}'.format(inx+1)))\\n else:\\n intermediate.add(Dense(neuron_count, activation='relu', name='dense-{}'.format(inx+1)))\\n intermediate.compile(optimizer=\\\"adam\\\", loss=\\\"binary_crossentropy\\\", metrics=[\\\"accuracy\\\"])\\n\\n\\n second_model = dnn_model_builder(second_layer_count, input_dim, second_output_dim, loss='binary_crossentropy', act='sigmoid')\\n latent_model = dnn_model_builder(second_layer_count, neuron_count, second_output_dim, loss='binary_crossentropy', act='sigmoid')\\n\\n return first_model, intermediate, second_model, latent_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ec85ecdaa6dd5d21ea5a998d924133\",\n \"score\": \"0.5823734\",\n \"text\": \"def networks_build_mlp(\\n hid_sizes: Iterable[int],\\n name: Optional[str] = None,\\n activation: Optional[Callable] = tf.nn.relu,\\n initializer: Optional[Callable] = None,\\n) -> LayersDict:\\n layers = collections.OrderedDict()\\n\\n # Hidden layers\\n for i, size in enumerate(hid_sizes):\\n key = f\\\"{name}_dense{i}\\\"\\n layer = tf.layers.Dense(\\n size, activation=activation, kernel_initializer=initializer, name=key\\n ) # type: tf.layers.Layer\\n layers[key] = layer\\n\\n # Final layer\\n layer = tf.layers.Dense(\\n 1, kernel_initializer=initializer, name=f\\\"{name}_dense_final\\\"\\n ) # type: tf.layers.Layer\\n layers[f\\\"{name}_dense_final\\\"] = layer\\n\\n return layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a40cdbbbf3f2b1d20ae445cecb37e31\",\n \"score\": \"0.5820634\",\n \"text\": \"def _make_aux(self, network, aux_id, name_generator):\\n\\n layers = []\\n\\n avgpool = self._make_avgpool_layer(network, kernel_size=5, stride=3)\\n self._set_mode(avgpool, 'TRAIN')\\n layers.append(avgpool)\\n\\n conv = self._make_conv_layer(\\n network, kernel_size=1, stride=1, num_output=128, bias_value=0)\\n self._set_mode(conv, 'TRAIN')\\n layers.append(conv)\\n\\n relu1 = self._make_relu_layer(network)\\n self._set_mode(relu1, 'TRAIN')\\n layers.append(relu1)\\n\\n inner_product1 = self._make_inner_product_layer(network, num_output=1024)\\n self._set_mode(inner_product1, 'TRAIN')\\n layers.append(inner_product1)\\n\\n relu2 = self._make_relu_layer(network)\\n self._set_mode(relu2, 'TRAIN')\\n layers.append(relu2)\\n\\n dropout = self._make_dropout_layer(network, dropout_ratio=0.7)\\n self._set_mode(dropout, 'TRAIN')\\n layers.append(dropout)\\n\\n final_layers = self._make_aux_classifiers(\\n network, aux_id, name_generator, loss_weight=0.3)\\n layers += final_layers\\n\\n connections = [\\n (avgpool.name, (avgpool.top, conv.bottom)),\\n (conv.name, (conv.top, relu1.bottom, relu1.top, inner_product1.bottom)),\\n (inner_product1.name, (inner_product1.top, relu2.bottom, relu2.top, dropout.bottom)),\\n (dropout.name, (dropout.top, final_layers[0].bottom)),\\n ]\\n\\n for connection in connections:\\n self._tie(connection, name_generator)\\n\\n return layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc5d89b8e3fd982bd0fd3daeb5b12354\",\n \"score\": \"0.58150876\",\n \"text\": \"def initialize_networks(self, opt):\\n\\n generator = co_mod_gan.Generator()\\n\\n return generator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8aab13475e9ed18715b9fa84704d68\",\n \"score\": \"0.5810496\",\n \"text\": \"def createRMCfg(model, remoteIns, deviceOuts, index):\\n deviceOuts = [i.name for i in deviceOuts]\\n# remoteIns = [i.name for i in remoteIns]\\n modelCfg = model.get_config()\\n\\n remoteIns = [createInpCfg(i) for i in remoteIns]\\n\\n modelLayers = modelCfg['layers'][index+1:]\\n\\n for i in remoteIns:\\n modelLayers.insert(0, i)\\n modelCfg['layers'] = modelLayers\\n\\n return modelCfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bfa3e91d5389e105babc54afa3b6fc7\",\n \"score\": \"0.5808745\",\n \"text\": \"def build_model(opt):\\n # TODO stage 1.2 train mapping network\\n print(\\\"build 2 vae and a transfer network\\\")\\n model = mapping_model.Pix2PixHDModel_Mapping()\\n model.initialize(opt)\\n\\n ##### define networks\\n print(\\\"build vae1 and vae2 ...\\\")\\n vae1 = networks.GlobalGenerator_DCDCv2(\\n opt.input_nc,\\n opt.output_nc,\\n opt.ngf,\\n opt.k_size,\\n opt.n_downsample_global,\\n networks.get_norm_layer(norm_type=opt.norm),\\n opt=opt,\\n )\\n vae2 = networks.GlobalGenerator_DCDCv2(\\n opt.input_nc,\\n opt.output_nc,\\n opt.ngf,\\n opt.k_size,\\n opt.n_downsample_global,\\n networks.get_norm_layer(norm_type=opt.norm),\\n opt=opt,\\n )\\n vae1.apply(networks.weights_init)\\n vae2.apply(networks.weights_init)\\n print(\\\"build vae1 and vae2 finish!\\\")\\n print(\\\"build D ...\\\")\\n xr_recon_d = networks.Z_xr_Discriminator(input_nc=3, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\\n z_xr_d = networks.Z_xr_Discriminator(input_nc=opt.feat_dim, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\\n y_recon_d =networks.Z_xr_Discriminator(input_nc=3, ndf=opt.disc_ch, n_layers=opt.disc_layers).to(opt.device)\\n print(\\\"build D finish\\\")\\n \\\"\\\"\\\" Optim \\\"\\\"\\\"\\n optimizer_vae1 = torch.optim.Adam(vae1.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\\n optimizer_d1 = torch.optim.Adam(itertools.chain(xr_recon_d.parameters(), z_xr_d.parameters()),lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\\n optimizer_vae2 = torch.optim.Adam(vae2.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\\n optimizer_d2 = torch.optim.Adam(y_recon_d.parameters(), lr=opt.lr, betas=(0.5, 0.999), weight_decay=0, eps=1e-6)\\n return vae1, xr_recon_d, z_xr_d, vae2, y_recon_d, optimizer_vae1, optimizer_d1, optimizer_vae2, optimizer_d2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c148e704b5f8f0bf3cdb36eebcebb4\",\n \"score\": \"0.5782471\",\n \"text\": \"def generate_config(self, indent_level: int = 0) -> str:\\n idl: int = indent_level\\n sp: int = indent_level * 4\\n conf: str = self._generate_config(indent_level, skip = \\\"layers\\\")\\n\\n conf += \\\" \\\"*sp + \\\"'layers': [\\\\n\\\"\\n for layer in self.layers:\\n conf += \\\" \\\"*(idl+1)*4 + \\\"(\\\" + type(layer).__name__ + \\\", \\\"\\n conf += \\\"'\\\" + layer.name + \\\"',\\\\n\\\"\\n conf += \\\" \\\"*(idl+2)*4 + \\\"{\\\\n\\\"\\n conf += layer.generate_config(idl+3)\\n #for key in layer[2]:\\n # conf += \\\" \\\"*16 + \\\"'\\\" + key + \\\"': \\\" + get_str(layer[2][key])\\n # conf += \\\",\\\\n\\\"\\n conf += \\\" \\\"*(idl+2)*4 + \\\"}\\\\n\\\"\\n conf += \\\" \\\"*(idl+1)*4 + \\\"),\\\\n\\\"\\n\\n conf += \\\" \\\"*sp + \\\"]\\\\n\\\"\\n return conf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2c345014e70e990639ec6993a85f27\",\n \"score\": \"0.57783914\",\n \"text\": \"def create_lenet(self, data, train=True):\\n\\n # Creating the architecture\\n # First convolutional\\n with tf.name_scope('conv_1') as scope:\\n conv1 = create_conv2d(data, self.W_conv1)\\n # relu1 = create_relu(conv1, self.b_conv1)\\n # relu1 = create_sigmoid(conv1, self.b_conv1)\\n\\n with tf.name_scope('tanh_1') as scope:\\n tanh_1 = create_tanh(conv1, self.b_conv1)\\n\\n\\n # Pooling\\n # pool1 = create_max_pool(relu1)\\n # pool1 = create_max_pool(relu1)\\n with tf.name_scope('pool_1') as scope:\\n pool1 = create_max_pool(tanh_1)\\n\\n\\n # Second convolutional\\n with tf.name_scope('conv_2') as scope:\\n conv2 = create_conv2d(pool1, self.W_conv2)\\n # relu2 = create_relu(conv2, self.b_conv2)\\n # relu2 = create_sigmoid(conv2, self.b_conv2)\\n\\n\\n with tf.name_scope('tanh_2') as scope:\\n # pool2 = create_max_pool(relu2)\\n #tanh_2 = create_relu(conv2, self.b_conv2)\\n # pool2 = create_max_pool(conv2)\\n tanh_2 = create_tanh(conv2, self.b_conv2)\\n\\n # Pooling\\n with tf.name_scope('pool_2') as scope:\\n pool2 = create_max_pool(tanh_2)\\n\\n\\n #if train:\\n #pool2 = tf.nn.dropout(pool2, 0.5, seed=self.seed)\\n\\n # Reshaping all the convolved images to 2D to feed the FC layers\\n # FC1\\n with tf.name_scope('fc_1') as scope:\\n pool_shape = pool2.get_shape().as_list()\\n reshape = tf.reshape(pool2, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\\n #fc1 = tf.nn.relu(tf.matmul(reshape, self.W_fc1) + self.b_fc1)\\n fc1 = tf.nn.tanh(tf.matmul(reshape, self.W_fc1) + self.b_fc1)\\n\\n #if train:\\n #fc1 = tf.nn.dropout(fc1, 0.5, seed=self.seed)\\n\\n # FC2\\n with tf.name_scope('fc_2') as scope:\\n fc2 = tf.matmul(fc1, self.W_fc2) + self.b_fc2\\n #fc2 = tf.nn.softmax(tf.matmul(fc1, self.W_fc2) + self.b_fc2)\\n\\n return fc2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"155d961763c65f921e915574c0ac40ea\",\n \"score\": \"0.5775039\",\n \"text\": \"def build_rpn(cfg, in_channels):\\n # add by hui ###############################\\n if cfg.MODEL.LOC_ON:\\n return build_location_net(cfg, in_channels)\\n if cfg.MODEL.FCOS_ON:\\n if cfg.MODEL.FCOS.CASCADE_ON:\\n return build_cascade_fcos(cfg, in_channels)\\n else:\\n return build_fcos(cfg, in_channels)\\n if cfg.MODEL.GAU_ON:\\n return build_gaussian_net(cfg, in_channels)\\n ############################################################\\n if cfg.MODEL.RETINANET_ON:\\n # add by hui ###############################\\n if cfg.FREEANCHOR.FREEANCHOR_ON:\\n return retinanet_fa.build_retinanet(cfg, in_channels)\\n ############################################################\\n return build_retinanet(cfg, in_channels)\\n\\n return RPNModule(cfg, in_channels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd08270219ac74700a0ae31cd7264ff8\",\n \"score\": \"0.57739896\",\n \"text\": \"def generate_net(hidden_layers=2, n_neurons=128):\\n # Initialize Sequential model\\n model = tf.keras.models.Sequential()\\n\\n # Add input layer\\n model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))\\n\\n # Add hidden layers\\n for i in range(hidden_layers):\\n model.add(tf.keras.layers.Dense(n_neurons, activation='relu'))\\n # Add output layer\\n model.add(tf.keras.layers.Dense(10))\\n\\n model.summary()\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0dfa91c85e3280a80b3c821a0b8b2d\",\n \"score\": \"0.5769726\",\n \"text\": \"def _build(self, inputs, is_training, dropout_keep_prob=1.0):\\n\\n target_layers = []\\n\\n if self._final_endpoint not in self.VALID_ENDPOINTS:\\n raise ValueError('Unknown final endpoint %s' % self._final_endpoint)\\n\\n net = inputs\\n end_points = {}\\n end_point = 'Conv3d_1a_7x7'\\n net = Unit3D(output_channels=64, kernel_shape=[7, 7, 7],\\n stride=[2, 2, 2], name=end_point)(net, is_training=is_training)\\n end_points[end_point] = net\\n\\n target_layers.append(net)\\n net = rank_layer(net)\\n\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n end_point = 'MaxPool3d_2a_3x3'\\n net = tf.nn.max_pool3d(net, ksize=[1, 1, 3, 3, 1], strides=[1, 1, 2, 2, 1],\\n padding=snt.SAME, name=end_point)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n end_point = 'Conv3d_2b_1x1'\\n net = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name=end_point)(net, is_training=is_training)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n end_point = 'Conv3d_2c_3x3'\\n net = Unit3D(output_channels=192, kernel_shape=[3, 3, 3],\\n name=end_point)(net, is_training=is_training)\\n end_points[end_point] = net\\n\\n target_layers.append(net)\\n net = rank_layer(net)\\n\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n end_point = 'MaxPool3d_3a_3x3'\\n net = tf.nn.max_pool3d(net, ksize=[1, 1, 3, 3, 1], strides=[1, 1, 2, 2, 1],\\n padding=snt.SAME, name=end_point)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_3b'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=96, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=16, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=32, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_3c'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=192, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=96, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n\\n target_layers.append(net)\\n net = rank_layer(net)\\n\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'MaxPool3d_4a_3x3'\\n net = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1],\\n padding=snt.SAME, name=end_point)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_4b'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=192, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=96, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=208, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=16, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=48, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_4c'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=112, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=224, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=24, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_4d'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=256, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=24, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_4e'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=112, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=144, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=288, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=64, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=64, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_4f'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=256, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=320, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n\\n target_layers.append(net)\\n net = rank_layer(net)\\n\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'MaxPool3d_5a_2x2'\\n net = tf.nn.max_pool3d(net, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1],\\n padding=snt.SAME, name=end_point)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_5b'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=256, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=160, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=320, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=32, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\\n name='Conv3d_0a_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Mixed_5c'\\n with tf.variable_scope(end_point):\\n with tf.variable_scope('Branch_0'):\\n branch_0 = Unit3D(output_channels=384, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n with tf.variable_scope('Branch_1'):\\n branch_1 = Unit3D(output_channels=192, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_1 = Unit3D(output_channels=384, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_1,\\n is_training=is_training)\\n with tf.variable_scope('Branch_2'):\\n branch_2 = Unit3D(output_channels=48, kernel_shape=[1, 1, 1],\\n name='Conv3d_0a_1x1')(net, is_training=is_training)\\n branch_2 = Unit3D(output_channels=128, kernel_shape=[3, 3, 3],\\n name='Conv3d_0b_3x3')(branch_2,\\n is_training=is_training)\\n with tf.variable_scope('Branch_3'):\\n branch_3 = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.SAME,\\n name='MaxPool3d_0a_3x3')\\n branch_3 = Unit3D(output_channels=128, kernel_shape=[1, 1, 1],\\n name='Conv3d_0b_1x1')(branch_3,\\n is_training=is_training)\\n net = tf.concat([branch_0, branch_1, branch_2, branch_3], 4)\\n end_points[end_point] = net\\n\\n target_layers.append(net)\\n net = rank_layer(net)\\n\\n if self._final_endpoint == end_point: return net, end_points, target_layers\\n\\n end_point = 'Logits'\\n with tf.variable_scope(end_point):\\n net = tf.nn.avg_pool3d(net, ksize=[1, 2, 7, 7, 1],\\n strides=[1, 1, 1, 1, 1], padding=snt.VALID)\\n net = tf.nn.dropout(net, dropout_keep_prob)\\n logits = Unit3D(output_channels=self._num_classes,\\n kernel_shape=[1, 1, 1],\\n activation_fn=None,\\n use_batch_norm=False,\\n use_bias=True,\\n name='Conv3d_0c_1x1')(net, is_training=is_training)\\n if self._spatial_squeeze:\\n logits = tf.squeeze(logits, [2, 3], name='SpatialSqueeze')\\n averaged_logits = tf.reduce_mean(logits, axis=1)\\n end_points[end_point] = averaged_logits\\n if self._final_endpoint == end_point: return averaged_logits, end_points, target_layers\\n\\n end_point = 'Predictions'\\n predictions = tf.nn.softmax(averaged_logits)\\n end_points[end_point] = predictions\\n return predictions, end_points, target_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f066d639318df1e83b4d3a48228d173e\",\n \"score\": \"0.5768032\",\n \"text\": \"def convert_to_layers(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16685cf3dcd6c8a80d8b271ec2cf1a10\",\n \"score\": \"0.5765233\",\n \"text\": \"def build_module(self):\\n print(\\\"Building basic block of ConvolutionalNetwork using input shape\\\", self.input_shape)\\n x = torch.zeros((self.input_shape)) # create dummy inputs to be used to infer shapes of layers\\n out = x\\n#------------------------conv layer-----------------------------------------------------------------\\n out = self.conv_layer(out.shape[1], 0, 64, out)\\n out = self.conv_layer(out.shape[1], 1, 64, out)\\n self.layer_dict['dim_reduction_max_pool_{}'.format(1)] = nn.MaxPool2d(3, stride=2)\\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(1)](out)\\n self.dim_reduction_layer[1] = 1\\n print(out.shape)\\n\\n out = self.conv_layer(out.shape[1], 2, 128, out)\\n out = self.conv_layer(out.shape[1], 3, 128, out)\\n out = self.conv_layer(out.shape[1], 4, 128, out)\\n self.layer_dict['dim_reduction_max_pool_{}'.format(4)] = nn.MaxPool2d(3, stride=2)\\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(4)](out)\\n self.dim_reduction_layer[4] = 1\\n print(out.shape)\\n\\n\\n out = self.conv_layer(out.shape[1], 5, 256, out)\\n self.layer_dict['dim_reduction_max_pool_{}'.format(5)] = nn.MaxPool2d(3, stride=2)\\n out = self.layer_dict['dim_reduction_max_pool_{}'.format(5)](out)\\n self.dim_reduction_layer[5] = 1\\n print(out.shape)\\n\\n if out.shape[-1] != 2:\\n out = F.adaptive_avg_pool2d(out,\\n 2) # apply adaptive pooling to make sure output of conv layers is always (2, 2) spacially (helps with comparisons).\\n print('shape before final linear layer', out.shape)\\n out = out.view(out.shape[0], -1)\\n\\n#-----------------------fully connected layer-------------------------------------------------------\\n self.layer_dict['fcc_{}'.format(6)] = nn.Linear(in_features=out.shape[1], # add a linear layer\\n out_features=128,\\n bias=self.use_bias)\\n out = self.layer_dict['fcc_{}'.format(6)](out) # apply ith fcc layer to the previous layers outputs\\n out = F.relu(out) # apply a ReLU on the outputs\\n\\n self.layer_dict['fcc_{}'.format(7)] = nn.Linear(in_features=out.shape[1], # add a linear layer\\n out_features=64,\\n bias=self.use_bias)\\n out = self.layer_dict['fcc_{}'.format(7)](out) # apply ith fcc layer to the previous layers outputs\\n out = F.relu(out) # apply a ReLU on the outputs\\n\\n self.softmax_layer = nn.Linear(in_features=out.shape[1], # add a linear layer\\n out_features=self.num_output_classes,\\n bias=self.use_bias)\\n out = self.softmax_layer(out) # apply ith fcc layer to the previous layers outputs\\n out = F.softmax(out, dim=0) # apply a softmax on the outputs\\n print(\\\"Block is built, output volume is\\\", out.shape)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7deb0b30bbb63ce4fa92d72f91aae25\",\n \"score\": \"0.5764015\",\n \"text\": \"def generate_output_layer_info(self):\\n output_layers = []\\n\\n for i in range(len(self.layer_info['layer_names'])):\\n start = 1.5\\n anchor_size = self.layer_info['anchor_sizes'][i]\\n layer_name = self.layer_info['layer_names'][i]\\n window_scale = self.layer_info['window_scales'][i]\\n window_length = self.layer_info['window_lengths'][i]\\n if anchor_size % 3 == 0:\\n start = -anchor_size / 3.0\\n elif anchor_size % 2 == 0:\\n start = -anchor_size / 2.0 + 0.5\\n k = 1\\n for row in range(anchor_size):\\n for col in range(anchor_size):\\n out_layer = FaceDetectionLayerOutput(\\n prob_name=layer_name + '/prob',\\n reg_name=layer_name + '/bb',\\n anchor_index=k - 1,\\n anchor_size=anchor_size * anchor_size,\\n win_scale=window_scale,\\n win_length=window_length,\\n win_trans_x=float((start + col) / anchor_size),\\n win_trans_y=float((start + row) / anchor_size)\\n )\\n output_layers.append(out_layer)\\n k += 1\\n return output_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f64ebaba1565ff266c9169007223eec\",\n \"score\": \"0.5761241\",\n \"text\": \"def set_train_layers(self, layer_names=[]):\\n\\n for k, p in self.day_encoder.named_parameters():\\n bool_list = []\\n for layer in layer_names:\\n bool_list.append(k.startswith(layer))\\n if not any(bool_list):\\n p.requires_grad = False\\n for k, p in self.night_encoder.named_parameters():\\n bool_list = []\\n for layer in layer_names:\\n bool_list.append(k.startswith(layer))\\n if not any(bool_list):\\n p.requires_grad = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17180b415ace1d755d252be79b853349\",\n \"score\": \"0.5747913\",\n \"text\": \"def build_model(is_training, inputs, params):\\n images = inputs['images']\\n \\n assert images.get_shape().as_list() == [None, params.image_size, params.image_size, 3]\\n\\n out = images\\n \\n conv1 = convLayer(out, 3, 3, 1, 1, 32, \\\"conv1\\\")\\n pool1 = maxPoolLayer(conv1, 2, 2, 2, 2, \\\"pool1\\\")\\n #dropout1 = dropout(pool1, 0.5)\\n \\n conv2 = convLayer(pool1, 3, 3, 1, 1, 32, \\\"conv2\\\")\\n pool2 = maxPoolLayer(conv2, 2, 2, 2, 2, \\\"pool2\\\")\\n dropout1 = dropout(pool2, 0.5)\\n\\n conv3 = convLayer(dropout1, 3, 3, 1, 1, 64, \\\"conv3\\\")\\n pool3 = maxPoolLayer(conv3, 2, 2, 2, 2, \\\"pool3\\\")\\n #dropout3 = dropout(pool3, 0.5)\\n \\n conv4 = convLayer(pool3, 3, 3, 1, 1, 64, \\\"conv4\\\")\\n pool4 = maxPoolLayer(conv4, 2, 2, 2, 2, \\\"pool4\\\")\\n #dropout4 = dropout(pool4, 0.5)\\n \\n conv5 = convLayer(pool4, 3, 3, 1, 1, 128, \\\"conv5\\\")\\n pool5 = maxPoolLayer(conv5, 2, 2, 2, 2, \\\"pool5\\\")\\n #dropout5 = dropout(pool5, 0.5)\\n \\n fcIn = tf.reshape(pool5, [-1, 2 * 2 * 128])\\n dense1 = tf.layers.dense(fcIn, 128, name = 'dense1', kernel_regularizer=regularizers.l2(0.005))\\n dense1r = tf.nn.relu(dense1)\\n \\n dropout2 = dropout(dense1r, 0.5)\\n dense2 = tf.layers.dense(dropout2, params.num_labels, name = 'dense2', kernel_regularizer=regularizers.l2(0.005))\\n \\n #fc7 = fcLayer(dropout1, 4096, 4096, True, \\\"fc7\\\")\\n #dropout2 = dropout(fc7, 0.5)\\n\\n #fc8 = fcLayer(dropout2, 4096, params.num_labels, True, \\\"fc8\\\")\\n #print(fc8.shape)\\n return dense2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a078e0a28428953cd3c5b024b505f9\",\n \"score\": \"0.57444566\",\n \"text\": \"def _get_network_config(self):\\n return {\\n 'default_category': 'front_panel',\\n 'mapping': {\\n 'oobm': {\\n 'netns': None,\\n 'managed_by': 'docker',\\n 'prefix': ''\\n },\\n 'front_panel': {\\n 'netns': 'front_panel',\\n 'managed_by': 'platform',\\n 'prefix': ''\\n }\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b7c2d62d8996948168cc09c0bc6086\",\n \"score\": \"0.5740701\",\n \"text\": \"def build_network(net_name, rep_dim=64, bias_terms=False):\\n\\n implemented_networks = ('mnist_LeNet', 'cifar10_LeNet', 'imagenet_WideResNet', 'toy_Net',\\n 'mnist_LeNet_classifier', 'cifar10_LeNet_classifier', 'imagenet_WideResNet_classifier',\\n 'toy_Net_classifier')\\n assert net_name in implemented_networks\\n\\n net = None\\n\\n if net_name == 'mnist_LeNet':\\n net = MNIST_LeNet(rep_dim=rep_dim, bias_terms=bias_terms)\\n\\n if net_name == 'cifar10_LeNet':\\n net = CIFAR10_LeNet(rep_dim=rep_dim, bias_terms=bias_terms)\\n\\n if net_name == 'imagenet_WideResNet':\\n net = ImageNet_WideResNet(rep_dim=rep_dim)\\n\\n if net_name == 'toy_Net':\\n net = Toy_Net(rep_dim=rep_dim)\\n\\n if net_name in ['mnist_LeNet_classifier', 'cifar10_LeNet_classifier', 'imagenet_WideResNet_classifier',\\n 'toy_Net_classifier']:\\n net = ClassifierNet(net_name, rep_dim=rep_dim, bias_terms=bias_terms)\\n\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eae1c470391de479bc7d02b5cee839b\",\n \"score\": \"0.5740444\",\n \"text\": \"def _init_layers(self):\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.retina_cls = nn.Conv2d(\\n self.feat_channels,\\n self.num_anchors * self.cls_out_channels,\\n 3,\\n padding=1)\\n self.retina_reg = nn.Conv2d(\\n self.feat_channels, self.num_anchors * 4, 3, padding=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c26aebb8fecc1bb7a85e42294f6c933\",\n \"score\": \"0.57372904\",\n \"text\": \"def build_layers(self, model_pre, seq_len, conv_feats, fc_feats, conv_k_size,\\n conv_stride, LOGDIR, gamma=0, batch_size=0, learning_rate=0):\\n\\n for i in range(0, len(conv_feats)):\\n layer = tf.keras.layers.Conv2D(filters=conv_feats[i],\\n kernel_size=conv_k_size[i],\\n strides=conv_stride[i],\\n data_format=\\\"channels_last\\\",\\n activation=tf.nn.relu,\\n use_bias=True,\\n kernel_initializer='glorot_normal',\\n bias_initializer='zeros')\\n self.layer_dict[model_pre+\\\"_cnn_layer\\\"+str(i)] = layer\\n\\n self.layer_dict[model_pre+\\\"_flatten\\\"] = tf.keras.layers.Flatten()\\n\\n for i in range(0, len(fc_feats)):\\n layer = tf.keras.layers.Dense(fc_feats[i],\\n activation=tf.nn.relu,\\n use_bias=True,\\n kernel_initializer='glorot_normal',\\n bias_initializer='zeros')\\n self.layer_dict[model_pre+\\\"_dense\\\"+str(i)] = layer\\n\\n self.layer_dict[model_pre+\\\"_fdense\\\"] = tf.keras.layers.Dense(3, activation=None, use_bias=True,\\n kernel_initializer='glorot_normal',\\n bias_initializer='zeros')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36111726233655887c728adc6cdf6953\",\n \"score\": \"0.57331663\",\n \"text\": \"def build_inference_network(self, model):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626cf80ea79cdc7ebc8293d4347e2344\",\n \"score\": \"0.5733042\",\n \"text\": \"def create(self):\\n # 1st Layer: Conv (w ReLu) -> Lrn -> Pool\\n conv1 = conv(self.X, 11, 11, 96, 4, 4, padding='VALID', name=self.scope_initial + 'conv1')\\n norm1 = lrn(conv1, 2, 1e-04, 0.75, name=self.scope_initial + 'norm1')\\n pool1 = max_pool(norm1, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool1')\\n \\n # 2nd Layer: Conv (w ReLu) -> Lrn -> Pool with 2 groups\\n conv2 = conv(pool1, 5, 5, 256, 1, 1, groups=2, name=self.scope_initial + 'conv2')\\n norm2 = lrn(conv2, 2, 1e-04, 0.75, name=self.scope_initial + 'norm2')\\n pool2 = max_pool(norm2, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool2')\\n \\n # 3rd Layer: Conv (w ReLu)\\n conv3 = conv(pool2, 3, 3, 384, 1, 1, name=self.scope_initial + 'conv3')\\n\\n # 4th Layer: Conv (w ReLu) splitted into two groups\\n conv4 = conv(conv3, 3, 3, 384, 1, 1, groups=2, name=self.scope_initial + 'conv4')\\n\\n # 5th Layer: Conv (w ReLu) -> Pool splitted into two groups\\n conv5 = conv(conv4, 3, 3, 256, 1, 1, groups=2, name=self.scope_initial + 'conv5')\\n pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name=self.scope_initial + 'pool5')\\n\\n # 6th Layer: Flatten -> FC (w ReLu) -> Dropout\\n flattened = tf.reshape(pool5, [-1, 6*6*256])\\n fc6 = fc(flattened, 6*6*256, 4096, name=self.scope_initial + 'fc6')\\n dropout6 = dropout(fc6, self.KEEP_PROB)\\n\\n # 7th Layer: FC (w ReLu) -> Dropout\\n fc7 = fc(dropout6, 4096, 4096, name=self.scope_initial + 'fc7')\\n dropout7 = dropout(fc7, self.KEEP_PROB)\\n\\n # 8th Layer: FC and return unscaled activations\\n self.fc8 = fc_8(dropout7, 4096, self.NUM_CLASSES, relu=False, name=self.scope_initial + 'fc8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6adf73ed9376313b198d1d6eddb65e\",\n \"score\": \"0.5732038\",\n \"text\": \"def layers_list(self, spectral_norm, shared_activation=False, shared_channels=False, depth=7, n_channels=64, image_channels=1, kernel_size=3, padding=1):\\n if spectral_norm == \\\"Chen\\\":\\n if self.bias is False:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n shared_activations = self.init_activation(('conv', 1))\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n shared_activations = self.init_activation(('conv', n_channels))\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n for _ in range(depth - 2):\\n if self.bias is False:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\\n if self.batchnorm is True:\\n self.layers.append(nn.BatchNorm2d(n_channels))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n if self.bias is False:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(utils.Spectral_Normalize_chen.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding)))\\n\\n elif spectral_norm == \\\"Normal\\\":\\n\\n if self.bias is False:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n shared_activations = self.init_activation(('conv', 1))\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n shared_activations = self.init_activation(('conv', n_channels))\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n for _ in range(depth - 2):\\n if self.bias is False:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding)))\\n if self.batchnorm is True:\\n self.layers.append(nn.BatchNorm2d(n_channels))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n if self.bias is False:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False)))\\n elif self.bias is True:\\n self.layers.append(nn.utils.spectral_norm(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding)))\\n\\n elif spectral_norm == \\\"None\\\":\\n\\n if self.bias is False:\\n self.layers.append(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n shared_activations = self.init_activation(('conv', 1))\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n shared_activations = self.init_activation(('conv', n_channels))\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n for _ in range(depth - 2):\\n if self.bias is False:\\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding))\\n if self.batchnorm is True:\\n self.layers.append(nn.BatchNorm2d(n_channels))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n if self.bias is False:\\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding))\\n\\n elif spectral_norm == \\\"Parseval\\\":\\n\\n if self.bias is False:\\n self.layers.append(PersevalConv(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(PersevalConv(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=True))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n shared_activations = self.init_activation(('conv', 1))\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n shared_activations = self.init_activation(('conv', n_channels))\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n for _ in range(depth - 2):\\n if self.bias is False:\\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size,padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size,padding=padding, bias=True))\\n if self.batchnorm is True:\\n self.layers.append(nn.BatchNorm2d(n_channels))\\n if self.activation == \\\"relu\\\":\\n self.layers.append(nn.ReLU(inplace=True))\\n elif self.activation == \\\"leaky_relu\\\":\\n self.layers.append(nn.LeakyReLU(inplace=True))\\n elif self.activation == \\\"prelu\\\":\\n self.layers.append(nn.PReLU(num_parameters=n_channels))\\n else:\\n if (shared_activation is True) and (shared_channels is True):\\n self.layers.append(shared_activations)\\n elif (shared_activation is True) and (shared_channels is False):\\n self.layers.append(shared_activations)\\n elif (shared_activation is False) and (shared_channels is True):\\n self.layers.append(self.init_activation(('conv', 1)))\\n elif (shared_activation is False) and (shared_channels is False):\\n self.layers.append(self.init_activation(('conv', n_channels)))\\n if self.bias is False:\\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False))\\n elif self.bias is True:\\n self.layers.append(PersevalConv(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=True))\\n\\n else:\\n raise ValueError(\\\"The normalization specified is not provided choose : None, Normal, Chen or Perseval\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2626959a70c557c902d08b1148e5174b\",\n \"score\": \"0.57236356\",\n \"text\": \"def _build_models(output_c_dim, df_dim, gf_dim, latent_dim, f_size, is_batchnorm, is_bicycle):\\n\\n discr = Discriminator(df_dim, f_size, 3, is_batchnorm, is_bicycle)\\n discr2 = Discriminator(df_dim, f_size, 4, is_batchnorm, is_bicycle)\\n\\n \\\"\\\"\\\"\\n #Generator used for adding latent values only at the geerator input\\n gen = Generator(output_c_dim, gf_dim, f_size, latent_dim, is_batchnorm, is_bicycle)\\n \\n \\\"\\\"\\\"\\n # Generator used for adding latent values at all the input layers\\n gen = Generator_add_all(output_c_dim, gf_dim, f_size, latent_dim, is_batchnorm, is_bicycle)\\n\\n enc = ResNet18(df_dim, latent_dim, f_size, is_batchnorm)\\n\\n # A separate discriminator for latent values\\n #discr_z = Discriminator_zz()\\n\\n return discr, discr2, gen, enc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324592ae4bacdb03d130c6a40466ba2e\",\n \"score\": \"0.5720984\",\n \"text\": \"def create_output_layers(self):\\n output_layers = nn.ModuleList([])\\n if len(self.layers_info) >= 2: input_dim = self.layers_info[-2]\\n else: input_dim = self.input_dim\\n if not isinstance(self.layers_info[-1], list): output_layer = [self.layers_info[-1]]\\n else: output_layer = self.layers_info[-1]\\n for output_dim in output_layer:\\n output_layers.extend([nn.Linear(input_dim, output_dim)])\\n return output_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b7b36b15eeae9bd95816aaf685d8d7\",\n \"score\": \"0.57189214\",\n \"text\": \"def build_resnet152_model(self,inputs):\\n eps = 1.1e-5\\n #img_rows = self.x_shape[0]\\n #img_cols = self.x_shape[1]\\n #color_type = self.x_shape[2]\\n\\n # Handle Dimension Ordering for different backends\\n global bn_axis\\n bn_axis = 3\\n img_input = inputs\\n\\n x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)\\n x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)\\n x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x)\\n x = Scale(axis=bn_axis, name='scale_conv1')(x)\\n x = Activation('relu', name='conv1_relu')(x)\\n x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)\\n\\n x = self.conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\\n x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='b')\\n x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='c')\\n\\n x = self.conv_block(x, 3, [128, 128, 512], stage=3, block='a')\\n for i in range(1,8):\\n x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i))\\n\\n x = self.conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\\n for i in range(1,36):\\n x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i))\\n\\n x = self.conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\\n x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\\n x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\\n\\n x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)\\n x_fc = Flatten()(x_fc)\\n x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)\\n\\n model = Model(img_input, x_fc)\\n return [model, x]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993682,"cells":{"query_id":{"kind":"string","value":"c9a569b56de5dbd8f8539da9ecd33b6d"},"query":{"kind":"string","value":"Save data in dic to a hd5 file."},"positive_passages":{"kind":"list like","value":[{"docid":"bff8019ca851b8d1a94fed35da0c33ba","score":"0.75301886","text":"def _save_h5(fp, dic):\n import warnings\n warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning)\n\n jutil.create_dir(fp)\n h5 = pd.HDFStore(fp, complevel=9, complib='blosc')\n for key, value in dic.items():\n h5[key] = value\n h5.close()","title":""}],"string":"[\n {\n \"docid\": \"bff8019ca851b8d1a94fed35da0c33ba\",\n \"score\": \"0.75301886\",\n \"text\": \"def _save_h5(fp, dic):\\n import warnings\\n warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning)\\n\\n jutil.create_dir(fp)\\n h5 = pd.HDFStore(fp, complevel=9, complib='blosc')\\n for key, value in dic.items():\\n h5[key] = value\\n h5.close()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e613c24abca0917289bc6962b1238918","score":"0.71980035","text":"def save_hdf5(file_path, dictionary, compression=False):\n f = h5py.File(file_path, 'a')\n for key in dictionary:\n if key in f:\n del f[key]\n if compression:\n f.create_dataset(key, data=dictionary[key], compression='gzip')\n else:\n f[key] = dictionary[key]\n f.close()","title":""},{"docid":"3268743dc5240a6d8c7ffdb6ec2aac94","score":"0.7166863","text":"def saveh5(self):\n\n filename=pyu.getlong(self.filename+'.h5',pstruc['DIRSIG'])\n f=h5py.File(filename,'w')\n\n # try/except to avoid loosing the h5 file if\n # read/write error\n try:\n f.attrs['L']=self.L._filename\n f.attrs['source']=self.source\n f.attrs['target']=self.target\n f.attrs['cutoff']=self.cutoff\n for k in self.keys():\n f.create_dataset(str(k),shape=np.shape(self[k]),data=self[k])\n f.close()\n except:\n f.close()\n raise NameError('Signature: issue when writting h5py file')","title":""},{"docid":"6c41336abd845b84697130029351f8a2","score":"0.70859015","text":"def hdf5_fileWRITER(filE_NAME,HE53_dict):\n filE_NAME = filE_NAME[:-4]\n with h5py.File(filE_NAME + '.h5','w') as hf: # Create an open hdf5 file for writing.\n for k in HE53_dict:\n # for-loop disects the m-file dictionary and writes data and dictionary elements\n # into a hdf5 file.\n k1 = k.replace('/','-') # replace the forward slash with a hyphen\n hf.create_group(k1) # Create a key/element in hdf5 file based on nested python dictionary\n for l in HE53_dict[k]:\n # Within the python dictionary, take all elements (keys and data) and incorporate them \n # into hdf5 file\n hf[k1][l] = HE53_dict[k][l] # Create new nested element with data in hdf5 file","title":""},{"docid":"ba7bf6672231fb4c2ba8aef4affc874d","score":"0.7072902","text":"def save_to_h5(self, fname):\n dicttoh5(self.__dict__)\n self._logger.info(f\"RixsData saved to {fname}\")","title":""},{"docid":"9e275f6834b09b037229deb9a866ba4a","score":"0.7065984","text":"def save_dict_to_h5(h5file, dic, path='/'):\n for key, item in dic.items():\n if isinstance(item, (np.int64, np.float64, str, bytes, tuple, float, int)):\n h5file[path + key] = item\n elif isinstance(item, np.ndarray):\n if item.ndim==1 and isinstance(item[0], np.ndarray): #pragma: no cover\n # this chunk of code is only ever used by the unit testing suite.\n dt = h5py.special_dtype(vlen=np.float64)\n h5file.create_dataset(path+key, data=item, dtype=dt)\n elif 'U' in str(item.dtype):\n temp = [i.encode() for i in item]\n h5file[path + key] = temp \n else:\n h5file[path + key] = item\n elif isinstance(item, dict):\n save_dict_to_h5(h5file, item, path + key + '/')\n elif isinstance(item, list):\n if isinstance(item[0], dict):\n for i in range(len(item)):\n try:\n save_dict_to_h5(h5file, item[i], path + key + '/')\n except AttributeError:\n h5file[path + key + '/' + str(i)] = item[i]\n elif isinstance(item[0], str):\n temp = [i.encode() for i in item]\n h5file[path + key] = temp\n else:\n h5file[path + key] = item \n elif item is None:\n continue\n\n else:\n raise ValueError('Cannot save %s type'%type(item))","title":""},{"docid":"1207fb4e5930272b1aa50435172ac3d5","score":"0.704466","text":"def save(self):\n with open(self.data_folder + self.name + '.pkl', 'wb') as data_folder:\n pickle.dump(self.chardict, data_folder, pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"f3a5a8d03d8af18993a836beae9a0636","score":"0.69632137","text":"def save(self, filename: str):\n filename = filename.rsplit('.', 1)[0] + '.hdf'\n my_dict = self.to_dict()\n try:\n import h5py\n from os.path import exists\n except ModuleNotFoundError: # pragma: no cover\n raise ModuleNotFoundError(\"To save and load objects, the h5py module is required\") # pragma: no cover\n if exists(filename):\n raise ValueError(f\"The file {filename} already exists, please delete it or change the input filename.\")\n with h5py.File(filename, 'w') as h5file:\n _dict_to_group(h5file, '/', my_dict)","title":""},{"docid":"8909dd99142b716f8d618eee000b8d86","score":"0.6872224","text":"def save_to_file(self, file_object: BinaryIO) -> None:\n with h5py.File(file_object, \"w\") as write_file:\n for key, data in self.items():\n write_file.create_dataset(\n self._encode_key(key), data=data, dtype=\"f\", compression=\"gzip\"\n )","title":""},{"docid":"fd6c5382a2c69bed3267e8454041d523","score":"0.6857693","text":"def save(self, filename, items={}):\n import h5py\n with h5py.File(filename, 'w') as hf:\n for key, val in zip(self.keys, self.vals):\n hf.create_dataset(key, data=val)\n\n for key, val in items.iteritems():\n hf.create_dataset(key, data=val)","title":""},{"docid":"fd6c5382a2c69bed3267e8454041d523","score":"0.6857693","text":"def save(self, filename, items={}):\n import h5py\n with h5py.File(filename, 'w') as hf:\n for key, val in zip(self.keys, self.vals):\n hf.create_dataset(key, data=val)\n\n for key, val in items.iteritems():\n hf.create_dataset(key, data=val)","title":""},{"docid":"4a66f7e54e3f1bca38119ff11014e400","score":"0.68258893","text":"def save_dict(filepath,dic):\n import pickle\n with open(filepath, 'wb') as file_pi:\n pickle.dump(dic, file_pi)","title":""},{"docid":"b4493957c6e811d5b21cb902a98f8999","score":"0.68176967","text":"def save_dict_h5py(array_dict, fname):\n\n # Ensure directory exists\n directory = os.path.dirname(fname)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n with h5py.File(fname, 'w') as hf:\n for key in array_dict.keys():\n hf.create_dataset(key, data=np.array(array_dict[key]))","title":""},{"docid":"073e09da64b8dc95b1cc4ade2f720050","score":"0.67233795","text":"def save(self,dataFile):\r\n print 'Saving data to', dataFile\r\n store = HDFStore(dataFile)\r\n for symbol in self.wp.items:\r\n store[symbol] = self.wp[symbol]\r\n \r\n store.close()","title":""},{"docid":"a20a4932e5724d2e8250cfb2f7d9ec2e","score":"0.6721466","text":"def save(self, filename):\r\n\r\n inputData = {}\r\n inputData[\"version\"] = self.version\r\n inputData[\"t\"] = self.t\r\n inputData[\"ep\"] = self.ep.tolist()\r\n inputData[\"w\"] = self.w\r\n inputData[\"a\"] = self.a\r\n inputData[\"b\"] = self.b\r\n inputData[\"h\"] = self.h\r\n inputData[\"v\"] = self.v\r\n inputData[\"E\"] = self.E\r\n \r\n\r\n ofile = open(filename, \"w\")\r\n json.dump(inputData, ofile, sort_keys = True, indent = 4)\r\n ofile.close()","title":""},{"docid":"c4fedf0d82149ee308fdf9f65c8b0550","score":"0.6710869","text":"def write_dict_to_hdf5_file(data, file, keys_to_write=['all']):\n if not isinstance(data, dict) or not isinstance(file, basestring):\n raise TypeError('passed data/file need to be dict/str. Passed type are: %s/%s'%(type(data),type(file)))\n if 'all' in keys_to_write:\n keys_to_write = data.keys()\n\n fOUT = h5py.File(file, \"w\")\n for key in keys_to_write:\n print 'writing', key\n if key not in data.keys():\n print keys_to_write, '\\n', data.keys()\n raise ValueError('%s not in dict!'%(str(key)))\n fOUT.create_dataset(key, data=np.asarray(data[key]), dtype=np.float32)\n fOUT.close()\n return","title":""},{"docid":"4fc801d45daf262e616520f8773aeeb6","score":"0.6590534","text":"def save(self, data, out_file):","title":""},{"docid":"5ce9ba39e92b016b30a13ece3a7a18ea","score":"0.65700823","text":"def save_file(self):\n z_profile = self.local_to_global_average(self.de_domain_IVP.z.flatten())\n profiles = OrderedDict()\n for k, item in self.avg_profiles.items():\n profiles[k] = self.local_to_global_average(item/self.elapsed_avg_time)\n\n if self.rank == 0:\n file_name = self.file_dir + \"profile_dict_file_{:04d}.h5\".format(self.n_files_saved+1)\n with h5py.File(file_name, 'w') as f:\n for k, item in profiles.items():\n f[k] = item\n f['z'] = z_profile \n self.n_files_saved += 1","title":""},{"docid":"7121927c4d4816bfae3c3182391d27da","score":"0.65468943","text":"def save_h5(self,dataframe):\n dataframe['key']=pd.Series(np.arange(0,dataframe.shape[0],dtype=int))\n print( self.filename)\n dataframe.to_hdf(self.filename,key='key')\n print ('saved ')","title":""},{"docid":"bbb008930e371dd3acdfc406f74e81c9","score":"0.6543284","text":"def save(self):\n if not \"dih_input\" in self.params.keys():\n blobDump( self.dihedrals, self.blobFilename(\"dihedrals\") )\n else:\n blobDump( self.dihedrals, self.blobFilename(self.params[\"dih_input\"]) )","title":""},{"docid":"21f9fb2d05cfcc00d8dead24d97d5408","score":"0.6536651","text":"def save(self, filename):\n # check correct file ending\n if filename[-5:] == '.hdf5':\n f = h5py.File(filename, 'w')\n else:\n f = h5py.File(filename + \".hdf5\", 'w')\n\n # set up header group\n header = f.create_group(\"Header\")\n header.attrs['NDiscs'] = self.ndiscs\n header.attrs['NSpheroids'] = self.nspheroids\n header.attrs['SatelliteMass'] = self.sat_mass\n header.attrs['SatelliteRadius'] = self.sat_radius\n header.attrs['SatelliteX0'] = self.sat_x0\n header.attrs['SatelliteV0'] = self.sat_v0\n header.attrs['ModGrav'] = self.modgrav\n if self.modgrav:\n header.attrs['Beta'] = self.beta\n header.attrs['MW_RScreen'] = self.MW_r_screen\n header.attrs['Satellite_RScreen'] = self.sat_r_screen\n header.attrs['Tracers'] = self.tracers\n if self.tracers:\n header.attrs['NPart_Type1'] = self.N1\n header.attrs['NPart_Type2'] = self.N2\n header.attrs['NSnapshots'] = self.N_snapshots\n header.attrs['TimeMax'] = self.t_max\n header.attrs['TimestepSize'] = self.dt\n\n # create subgroups to store MW disc and spheroid parameters\n for i in range(self.ndiscs):\n grp = f.create_group(\"Header/Disc_\" + str(i))\n for k, v in self.dpars[i].items():\n grp.attrs[k] = v\n for i in range(self.nspheroids):\n grp = f.create_group(\"Header/Spheroid_\" + str(i))\n for k, v in self.spars[i].items():\n grp.attrs[k] = v\n\n # array of times\n f.create_dataset('SnapshotTimes', data=self.times)\n\n # satellite group and data\n grp0 = f.create_group(\"Satellite\")\n grp0.create_dataset(\"Position\", data=self.p0_positions)\n grp0.create_dataset(\"Velocity\", data=self.p0_velocities)\n\n # groups and data for tracer particles\n if self.tracers:\n grp1 = f.create_group(\"PartType1\")\n grp2 = f.create_group(\"PartType2\")\n\n grp1.create_dataset(\"Position\", data=self.p1_positions)\n grp1.create_dataset(\"Velocity\", data=self.p1_velocities)\n grp1.create_dataset(\"Disrupted\", data=self.p1_disrupted)\n grp1.create_dataset(\"DisruptionTime\", data=self.p1_disruption_time)\n\n grp2.create_dataset(\"Position\", data=self.p2_positions)\n grp2.create_dataset(\"Velocity\", data=self.p2_velocities)\n grp2.create_dataset(\"Disrupted\", data=self.p2_disrupted)\n grp2.create_dataset(\"DisruptionTime\", data=self.p2_disruption_time)\n\n f.close()\n return","title":""},{"docid":"3426eea54f018656e6406de80e9c1003","score":"0.6536348","text":"def _saveh5(self,filenameh5,grpname):\n\n\n filename=pyu.getlong(filenameh5,pstruc['DIRLNK'])\n # if grpname == '':\n # grpname = str(self.source) +'_'+str(self.target) +'_'+ str(self.cutoff)\n try:\n # file management\n fh5=h5py.File(filename,'a')\n if not grpname in fh5['sig'].keys():\n fh5['sig'].create_group(grpname)\n else :\n raise NameError('sig/'+grpname +'already exists in '+filenameh5)\n f=fh5['sig/'+grpname]\n\n # write data\n f.attrs['L']=self.L._filename\n f.attrs['source']=self.source\n f.attrs['target']=self.target\n f.attrs['cutoff']=self.cutoff\n f.attrs['threshold']=self.threshold\n f.create_group('ratio')\n f.create_group('sig')\n for k in self.keys():\n f['sig'].create_dataset(str(k),shape=np.shape(self[k]),data=self[k])\n f['ratio'].create_dataset(str(k),shape=np.shape(self.ratio[k]),data=self.ratio[k])\n fh5.close()\n except:\n fh5.close()\n raise NameError('Signature: issue when writting h5py file')","title":""},{"docid":"ce9765e9d030bbbc47d8cac05198a437","score":"0.65214115","text":"def save_data_to_file(file_name, list_of_data):\r\n # Create a File Object by opening and appending the input dictionary object into the StudentData.dat file\r\n # the \"ab+\" binary access mode is used below to first create the binary file if needed, otherwise just append\r\n # the data to the end of the existing file\r\n file = open(file_name, \"ab+\")\r\n # Use Pickle here to write list (dictionary) row to the binary file\r\n pickle.dump(list_of_data, file)\r\n # Close the file object here\r\n file.close()","title":""},{"docid":"dbcc72467cb9ad5a461029b38bd9aa4f","score":"0.6520215","text":"def save_dict_to_file(self):\n file = open(self.file_path, \"wb\")\n pickle.dump(self.item_dict, file)\n file.close()","title":""},{"docid":"894b14e7794c287e5af98c54d0d15264","score":"0.64962465","text":"def write_dic(name, dic, path_localdata):\n\n with open('%s/%s.pkl' % (path_localdata, name), 'w') as f:\n pickle.dump(dic, f)","title":""},{"docid":"6afd1f1db95fd40671a23964d987a22e","score":"0.6450912","text":"def save_recorded_data(data, filename):\n deepdish.io.save(filename, data)","title":""},{"docid":"73d01c57abb91e2a85e65bd220f0e2c8","score":"0.64484227","text":"def save(self):\n with open(self.filename, 'w') as fp:\n json.dump(self._dict,fp)","title":""},{"docid":"86786a20e841e830b5607b5386eb3e95","score":"0.6447914","text":"def _save_to_txtfile(dic_data):\n with open('result.txt', 'w') as wf:\n for key in dic_data:\n line = str(unicode(key))+\":\"+str(dic_data[key])+\"\\n\"\n wf.write(line)","title":""},{"docid":"81e60c7372b94de79cab468ca11c550d","score":"0.6435995","text":"def store_dic(dic, name):\r\n with open(name, 'w') as file:\r\n json.dump(dic, file)","title":""},{"docid":"38f6b9919b09e7b85d017dd1251f1d2c","score":"0.6435874","text":"def save_data_to_file(file_name, dict_weather):\n obj_file = open(file_name, \"wb\") # The \"wb\" = write to binary. If file exists, contents is overwritten. If not, it is created.\n pickle.dump(dict_weather, obj_file) # Dump function requires 2 arguments--> Data to dump, and file\n obj_file.close() # Always remember to close file!!","title":""},{"docid":"272aae9d946a24ac8389e01bd895c99e","score":"0.64083713","text":"def Save_Data(Input_dict, Input_file):\r\n with open(Input_file, \"w\") as file: # open a file handle\r\n for task in Input_dict: # loop through key, value and write to file\r\n file.write(\"{}, {}\\n\".format(task, Input_dict[task]))\r\n print(\"\\nData saved to the file\")","title":""},{"docid":"8749b30a6b2859f85b10a8539a004bd9","score":"0.638854","text":"def save(self, name):\n with h5py.File(name + \".h5\", \"w\") as h5:\n for key, kernel in self.weights.items():\n h5.create_dataset(name=key, shape=kernel.shape, data=kernel)\n for key, bias in self.bias.items():\n h5.create_dataset(name=key, shape=bias.shape, data=bias)","title":""},{"docid":"f1c3ccea1d654b8589a6766d233e3ccd","score":"0.63860416","text":"def _save(self):\n # w+ flag adds file if not in directory\n with self.data_file.open('w+') as f:\n json.dump(self.astro_dict, f)","title":""},{"docid":"d7b5a27ff8eb4246427f78f1b6e65706","score":"0.6371683","text":"def save(self, path):\n print (\"Storing sate dict in file: \",path )\n torch.save(self.state_dict(), path)","title":""},{"docid":"3648b664670243331c99d85a595e65ff","score":"0.6370827","text":"def save_list_dict_h5py(array_dict, fname):\n\n # Ensure directory exists\n directory = os.path.dirname(fname)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n with h5py.File(fname, 'w') as hf:\n for i in range(len(array_dict)):\n grp = hf.create_group(str(i))\n for key in array_dict[i].keys():\n grp.create_dataset(key, data=array_dict[i][key])","title":""},{"docid":"b0187ee6c15b00820e85789e14ff63c7","score":"0.6369403","text":"def to_ekpdat(self, file):\n\t\twith open(file, 'wb') as f:\n\t\t\tpickle.dump(self._dict, f)","title":""},{"docid":"2c3cb042ed56f8da528b0ceec3ba470d","score":"0.6366328","text":"def save(self, dict, save_hash):\r\n pass","title":""},{"docid":"d2ca2e8b30d4297cd746a9cef6f30e44","score":"0.63608205","text":"def write_json(self,d):\n reef_path = os.path.dirname(self.h5_dir)\n metadata_path = os.path.join(reef_path, 'ICESAT_metadata.json')\n with open(metadata_path, 'w') as f:\n f.write(json.dumps(d, sort_keys=True, indent=4, separators=(',', ': ')))","title":""},{"docid":"33010f33876c9969e36f75f1f992b033","score":"0.6349521","text":"def save_HDF5(self,filename):\n if filename == \"\": return\n self.makedir(filename)\n import tables\n h5file = tables.openFile(filename,mode='w')\n for column in self.columns:\n h5file.createArray(h5file.root,column,self[column])\n h5file.close()\n self.filename = filename\n self.format = \"HDF5\"\n self.file_timestamp = file_timestamp(self.filename)\n self.filesize = filesize(self.filename)","title":""},{"docid":"3dd3b8046376caca643b8569bd4bec6d","score":"0.6333551","text":"def save(self, path: str):\n file = open(path, 'wb')\n dump(self._dict, file)\n file.close()","title":""},{"docid":"ec4cc3072e915e6f8350a3b0d93e9d12","score":"0.6331431","text":"def save_dataview(self, folder_path):\n abs_folder = os.path.abspath(folder_path)\n meta_path = os.path.join(folder_path, 'meta_data.json')\n data_path = os.path.join(folder_path, 'data.hd5')\n\n data_to_store = {'data_d': self.data_d,\n 'data_q': self.data_q,\n 'data_benchmark': self.data_benchmark,\n 'data_inst': self.data_inst,\n 'factor_df': self._factor_df\n }\n for symbol in self.index_weights.keys():\n data_to_store['index_weight/' + symbol] = self.index_weights[symbol]\n\n for group in self.industry_groups.keys():\n data_to_store['industry_group/' + group] = self.industry_groups[group]\n\n data_to_store = {k: v for k, v in data_to_store.items() if v is not None}\n meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}\n\n\n print(\"\\nStore data...\")\n jutil.save_json(meta_data_to_store, meta_path)\n self._save_h5(data_path, data_to_store)\n\n print(\"Dataview has been successfully saved to:\\n\"\n + abs_folder + \"\\n\\n\"\n + \"You can load it with load_dataview('{:s}')\".format(abs_folder))","title":""},{"docid":"0f92bbeed54541eca3530af3ca6124e0","score":"0.63221645","text":"def save_dict(self, path):\n\t\twith open(path, 'wb') as handle:\n\t\t\tpickle.dump(self.__dict__, handle, protocol=pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"abe8781074fd401156209537aff4fc85","score":"0.6298063","text":"def write_binary_file(self,filename,dic):\n import cPickle\n with open(filename,'w') as file:\n my_pickler = cPickle.Pickler(file)\n my_pickler.dump(dic)\n file.close()","title":""},{"docid":"7314bba7f0682d7bb57b3eed04f318c5","score":"0.6290563","text":"def save(self):\n all_hands_json = json.dumps([hand.to_json() for hand in self._hands.values()])\n with open(self._filename, 'w') as f:\n f.write(all_hands_json)","title":""},{"docid":"7d7907ccfd15a02a460efbad0fca85db","score":"0.6282119","text":"def save_data_to_hdf5_file(fname,orbitals,ns,N_e,occ,grid,ee_coefs):\n\n # Create hdf5 file\n hf = h5py.File(fname+'.hdf5','w')\n\n # Add the datasets\n hf.create_dataset('orbitals', data=orbitals,dtype='f')\n hf.create_dataset('ns', data=ns,dtype='f')\n hf.create_dataset('N_e', data=N_e,dtype='i8')\n hf.create_dataset('occ', data=occ,dtype='i8')\n hf.create_dataset('grid', data=grid,dtype='f')\n hf.create_dataset('ee_coefs', data=ee_coefs,dtype='f')\n hf.close()","title":""},{"docid":"d2f61005a85928daff4d3125e351ca4a","score":"0.6280122","text":"def write_hdf5_correlators(path, filename, data, key, verbose=False):\n\n ensure_dir(path)\n data.to_hdf(path+filename, key, mode='w')\n \n if verbose:\n print '\\tfinished writing', filename","title":""},{"docid":"9acc3dc7dc22b91b6b53104041102223","score":"0.62568045","text":"def save(self, path):\r\n with open(path, 'wb') as fout:\r\n pkl.dump(self.idf, fout)","title":""},{"docid":"e936217dddbfe8a5d87790d4b4c60b95","score":"0.6256244","text":"def dump_to_hdf5(self, file):\n # if (self.isglobal and (len(self.data_hist) > 0)): # Global field.\n if (self.isglobal): # Global field.\n file['fields/' + self.name].resize((len(self.data_hist),))\n file['fields/' + self.name][:] = np.array(self.data_hist).flatten();","title":""},{"docid":"85f38b64bc49a746b0e6ed2c8e4ce1cd","score":"0.62508833","text":"def save(self):\n with open(\"physics_metadata.txt\", \"w\") as fout:\n fout.write(str(self.data1) + str(';'))\n fout.write(str(self.data2) + str(';'))\n fout.write(str(self.data3) + str(';'))\n fout.write(str(self.data4) + str(';'))","title":""},{"docid":"a1e42c473ef11f332f43d095743b6bec","score":"0.6248119","text":"def save(self,\n *,\n filename,\n path='./',\n data_type='pickle'):\n if not data_type == \"hdf5\":\n # Have a new dict to save everything to:\n dict_to_save = {}\n # Iterate through dict and replace Variables by strings of\n # Variables and Entities/taxa as strings with their uid attached.\n for key, item in self.items():\n # Go to lower level, if item is indeed another dictionary. This\n # is the case for all Variables except the time 't'!\n # One could also check for isinstance(item, Variable)\n if isinstance(item, dict):\n new_key = str(key)\n # print('new_key', new_key, type(new_key))\n dict_to_save[new_key] = {}\n for key_2, value in item.items():\n # Here, key_2 are entities or taxa. Value are lists with\n # the values the variable took during the run.\n\n # First find out if key_2 is Taxon or Entity:\n if isinstance(key_2, _AbstractEntityMixin):\n # Entity:\n new_key_2 = str(key_2)\n # print('new_key_2', new_key_2, type(new_key_2))\n # If values are not numbers, they have to be\n # transformed into strings, too:\n if all(isinstance(val, (float, int)) for val in value):\n dict_to_save[new_key][new_key_2] = value\n # Maybe value is a list of list with floats/ints?\n elif (all(isinstance(val, (float, int)) for val in\n list(self.traverse(value)))\n ):\n dict_to_save[new_key][new_key_2] = value\n # Networks:\n elif all(isinstance(val, nx.Graph) for val in value):\n # save as dict of dicts\n new_value = [nx.to_dict_of_dicts(val) for val in value]\n # print(new_value)\n # iterate through timesteps in list, where for every\n # timestep there is a dictionary di in the list\n for di in new_value:\n # Rewrite keys (the nodes), as they are\n # objects, to strings of objects:\n for node_key, connections in di.copy().items():\n di[str(node_key)] = di.pop(node_key)\n # Also, connections for all nodes need to\n # be replaced by strings:\n for node_key_2, connections_2 in connections.copy().items():\n connections[str(node_key_2)] = connections.pop(node_key_2)\n dict_to_save[new_key][new_key_2] = new_value\n else:\n new_val = [str(val) for val in value]\n dict_to_save[new_key][new_key_2] = new_val\n\n elif isinstance(key_2, _AbstractProcessTaxonMixin):\n # Taxon\n new_key_2 = str(key_2)\n # If values are not numbers, they have to be\n # transformed into strings, too:\n if all(isinstance(val, (float, int)) for val in value):\n dict_to_save[new_key][new_key_2] = value\n # Maybe value is a list of list with floats/ints?\n elif (all(isinstance(val, (float, int)) for val in\n list(self.traverse(value)))\n ):\n dict_to_save[new_key][new_key_2] = value\n # Networks:\n elif all(isinstance(val, nx.Graph) for val in value):\n # save as dict of dicts\n new_value = [nx.to_dict_of_dicts(val) for val in value]\n # print(new_value)\n # iterate through timesteps in list, where for every\n # timestep there is a dictionary di in the list\n for di in new_value:\n # Rewrite keys (the nodes), as they are\n # objects, to strings of objects:\n for node_key, connections in di.copy().items():\n di[str(node_key)] = di.pop(node_key)\n # Also, connections for all nodes need to\n # be replaced by strings:\n for node_key_2, connections_2 in connections.copy().items():\n connections[str(node_key_2)] = connections.pop(node_key_2)\n dict_to_save[new_key][new_key_2] = new_value\n else:\n new_val = [str(val) for val in value]\n dict_to_save[new_key][new_key_2] = new_val\n else:\n raise Exception('neither taxon nor entity')\n elif key == 't':\n # here we don't need to do anyhing\n dict_to_save['t'] = item\n else:\n raise Exception('neither variable nor time!')\n\n # checking if all entries have the same length as time:\n # tlen = len(dict_to_save['t'])\n # for var, item in dict_to_save.items():\n # if isinstance(item, dict):\n # for instance, val in item.items():\n # assert len(dict_to_save[var][instance]) == tlen, (\n # tlen, len(dict_to_save[var][instance]),\n # dict_to_save[var][instance], var, instance\n # )\n\n # Add a file versio:\n dict_to_save['file-version'] = 0.1\n # Fuse Filename and path:\n # add \"/\" to paths if missing\n save_path = path + \"/\" if not path.endswith(\"/\") else path\n # Now save as datatype:\n if data_type == 'pickle':\n save_name = save_path + filename + '.pickle'\n with open(save_name, 'wb') as dumpfile:\n pickle.dump(dict_to_save, dumpfile, pickle.HIGHEST_PROTOCOL)\n if data_type == 'json':\n save_name = save_path + filename + '.json'\n with open(save_name, 'w') as dumpfile:\n json.dump(dict_to_save, dumpfile)\n\n else:\n # HDF5 mode\n # If we want to implement this, we need to solve the following\n # problem: HDF5 and Pytables only support same datatype entries in\n # a table. If, however, an entity is deactivated during a run, all\n # its values when being idel are None (dtype:None)! Earlier values\n # might include floats, booleans, or integers. This is not possible\n # with HDF5 at all.\n raise Exception('Not implemented yet')","title":""},{"docid":"314a0f0666af34c4867b4e6018cdf945","score":"0.62413794","text":"def save_h5(data, labels, filename):\n\twith h5py.File('{}.h5'.format(filename), 'w') as f:\n\t\tfor i in range(len(data)):\n\t\t\tf.create_dataset(labels[i], data=np.array(data[i]))\n\tprint('Data saved as '+ filename)\n\treturn","title":""},{"docid":"d0754b1b6c985b2a4163ab70f7cc0eae","score":"0.6240954","text":"def save_to_disk(data,file_name):\n fileObject = open(file_name,'wb')\n pickle.dump(data,fileObject)\n fileObject.close()","title":""},{"docid":"b8b20ee525df2dbd3d69a6fe533ab7ea","score":"0.6235207","text":"def save(name, data):\n filename = get_full_pathname(name)\n print('..... saving to: {}'.format(filename))\n \n with open(filename, 'w') as fout:\n for entry in data:\n fout.write(entry + '\\n')","title":""},{"docid":"589f0a10c8f8783a59c4a2f5c32d6789","score":"0.6219106","text":"def save(self, savefile, overwrite=False):\n with hdf5_savehandle(savefile, overwrite) as hf:\n\n meta = hf.create_dataset(\"meta\", shape=(0,))\n meta.attrs[\"economize\"] = int(self.economize)\n if self.r is not None:\n meta.attrs[\"r\"] = self.r\n\n if self.entries is not None:\n hf.create_dataset(\"entries\", data=self.__entries)\n hf.create_dataset(\"svdvals\", data=self.__svdvals)\n hf.create_dataset(\"dual\", data=self.__dual)","title":""},{"docid":"1e82fb89bb71e574cf21226163c870be","score":"0.6211481","text":"def h5save(filename, data, name=None, mode='w', mkdir=True, **kwargs):\n if mkdir:\n target_dir = osp.dirname(filename)\n if target_dir and not osp.exists(target_dir):\n os.makedirs(target_dir)\n hdf = h5py.File(filename, mode)\n hdf.attrs.create('__pymvpa_hdf5_version__', '2')\n hdf.attrs.create('__pymvpa_version__', mvpa2.__version__)\n try:\n obj2hdf(hdf, data, name, **kwargs)\n finally:\n hdf.close()","title":""},{"docid":"0de63e613104ae6a568a46233cc07bfd","score":"0.61892146","text":"def save_fitdata_to_hdf(fpath, data_dict,\n datapath='xrfmap/detsum',\n data_saveas='xrf_fit',\n dataname_saveas='xrf_fit_name'):\n f = h5py.File(fpath, 'a')\n try:\n dataGrp = f.create_group(datapath)\n except ValueError:\n dataGrp=f[datapath]\n\n data = []\n namelist = []\n for k, v in six.iteritems(data_dict):\n if not isinstance(k, six.string_types):\n k = k.decode()\n namelist.append(k)\n data.append(v)\n\n if data_saveas in dataGrp:\n del dataGrp[data_saveas]\n\n data = np.asarray(data)\n ds_data = dataGrp.create_dataset(data_saveas, data=data)\n ds_data.attrs['comments'] = ' '\n\n if dataname_saveas in dataGrp:\n del dataGrp[dataname_saveas]\n\n if not isinstance(dataname_saveas, six.string_types):\n dataname_saveas = dataname_saveas.decode()\n namelist = np.array(namelist).astype('|S9')\n name_data = dataGrp.create_dataset(dataname_saveas, data=namelist)\n name_data.attrs['comments'] = ' '\n\n f.close()","title":""},{"docid":"a42bb07161c06213a8e1c1911779459f","score":"0.61760247","text":"def save_files(self):\n print 'Image output shape: ', self.image_output_data.shape\n f = h5py.File(self.save_folder_loc + 'db_' +\n self.country_code + '_' + str(self.resolution) + '.hdf5', 'w')\n f.create_dataset('features', \n data = self.image_output_data, \n compression=\"gzip\")\n f.create_dataset('targets',\n data = self.pop_output_data.tolist(), \n compression=\"gzip\")\n f.close()","title":""},{"docid":"d3e0c6e1db7f70d17ca65ce19a0c7207","score":"0.61756647","text":"def save(self):\n\t\tfile = open(\"/home/joseph/bin/spotify/data/tracks/\"+self.id+'.txt','wb')\n\t\tfile.write(self.cPickle.dumps(self.__dict__))\n\t\tfile.close()","title":""},{"docid":"2ab3363f5b041bfef0c45f1c5144a738","score":"0.61735415","text":"def write(self):\n packed_dict = {key: (d._type, d.value) for key, d in self.__dict__.items()}\n path = path_data/(str(self.__ID__) + \".\" + file_extension)\n with open(path, 'w+') as file:\n yaml.dump(packed_dict, file, default_flow_style=False)","title":""},{"docid":"941660235f91204e45e200f467c612c5","score":"0.61685336","text":"def save_dictionary(self):\n w_dict = codecs.open(\n os.path.join(os.path.dirname(__file__), 'slowa.txt'), 'w', encoding='utf-8')\n\n for word in self.w_dict:\n w_dict.write('%s\\n' % word)","title":""},{"docid":"42f77c89dc63a4a12f35a938cb67ecd1","score":"0.6153445","text":"def write_hdf5_datasets(hdf5_fname='info.hdf5'):\n\n # HDF5 complains if file exists. 'w'' write option didn't seem to deal with it\n\n if Path(hdf5_fname).is_file():\n os.system(\"rm \" + hdf5_fname)\n\n hdf5_file = h5py.File(hdf5_fname, 'w')\n\n # Cannot pass python dictionary directly to hdf5\n # hdf5_file.create_dataset('volumes', data=volumes)\n\n hdf5_file.close()\n return","title":""},{"docid":"43c27c05cac286f361cab6a55e7dd171","score":"0.61526406","text":"def save(self):\n with open(FileStorage.__file_path, \"w\", encoding=\"utf-8\") as f:\n d = {k: v.to_dict() for k, v in FileStorage.__objects.items()}\n json.dump(d, f)","title":""},{"docid":"aef5f52c9a7f61ec2c686c793196fcb1","score":"0.6152404","text":"def save(self,filename):\n data = {key:self.__dict__[key] for key in self.fields}\n with open(filename,'w') as fp:\n yaml.safe_dump(data,fp)","title":""},{"docid":"c39ec9d1ca41bfd8af7b7f858ae51d65","score":"0.6148742","text":"def save_on_disk(self):\n with open(self.path, 'wb') as file:\n pickle.dump(self.data, file)","title":""},{"docid":"d13835bf6fcda87f9b1f52a1491693b2","score":"0.6141411","text":"def saveData(actData, dp):\r\n fp = open(dp,'w')\r\n \r\n for key in actData.keys():\r\n fp.write('AccountNumber:%s%s' % (key , os.linesep))\r\n fp.write('name:%s%s' % (actData[key].name(), os.linesep))\r\n fp.write('balance:%s%s' % (actData[key].balance(), os.linesep))\r\n for trans in actData[key].transactionList():\r\n fp.write('trans:%s%s' % (trans, os.linesep))","title":""},{"docid":"f71243b697e7faad5ee6bb0df6f5e5a5","score":"0.6137899","text":"def save_data(self):\n\n data = self.get('1.0', 'end')\n data = data.encode('utf-8')\n fd = open(self.filename, 'w')\n fd.write(data)\n fd.close()\n self.event_generate('<<SaveData>>')\n\n type, _ = mimetypes.guess_type(self.filename)\n self.event_generate('<<Save-%s>>' % type)","title":""},{"docid":"58b320916bef4b383cf26015238f5000","score":"0.61376846","text":"def save(filename, arr):\n data = {\"stored\": arr}\n sio.savemat(filename, data)","title":""},{"docid":"46bdbf9ab1e2af54a483c43db0f65309","score":"0.61291707","text":"def save(self):\n obj = {}\n for key, value in FileStorage.__objects.items():\n obj[key] = value.to_dict()\n with open(FileStorage.__file_path, 'w') as fd:\n json.dump(obj, fd)","title":""},{"docid":"9a985cf394a42ba422ade1e1136e84f6","score":"0.61257744","text":"def save(self):\n\n if os.path.isfile(self.fileName):\n #clear any previous text\n out = open(self.fileName, \"r+\")\n out.truncate(0)\n out.close()\n\n with open(self.fileName, 'wb') as f:\n pickle.dump(self.dictionary, f)","title":""},{"docid":"1f12daa65b078ddf9134e22244ce2293","score":"0.6112673","text":"def save_data_to_disk(data, filename):\n\n GameData._delete_game_data_file(filename)\n\n # open new stream\n stream = open(filename, 'w')\n\n # dump the data to file stream\n yaml.dump(data, stream)\n file.close(stream)","title":""},{"docid":"58a3db0b5ce840f4c49e87681aaee5b4","score":"0.61061203","text":"def save_dataview(self, folder_path):\n abs_folder = os.path.abspath(folder_path)\n meta_path = os.path.join(folder_path, 'meta_data.json')\n data_path = os.path.join(folder_path, 'data.hd5')\n\n data_to_store = {'data_d': self.data_d, 'data_q': self.data_q,\n 'data_benchmark': self.data_benchmark, 'data_inst': self.data_inst,\n 'data_custom': self.data_custom}\n data_to_store = {k: v for k, v in data_to_store.items() if v is not None}\n meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}\n\n print(\"\\nStore data...\")\n jutil.save_json(meta_data_to_store, meta_path)\n self._save_h5(data_path, data_to_store)\n\n print(\"Dataview has been successfully saved to:\\n\"\n + abs_folder + \"\\n\\n\"\n + \"You can load it with load_dataview('{:s}')\".format(abs_folder))","title":""},{"docid":"31c845a3cc0f00bea0384390cd8c67f6","score":"0.6102145","text":"def save_dictionary(filename, dic):\n logger.info('Saving dictionary at: {}'.format(filename))\n sorted_dic = sorted(dic.items(), key=operator.itemgetter(1))\n with open(filename, 'w') as fout:\n for key, id in sorted_dic:\n fout.write('{}\\n'.format(key))","title":""},{"docid":"ead7c9a3066d4298ce08e81ff7a7d953","score":"0.60964227","text":"def save(self,filename,data):\n with open(filename,'wb') as fp:\n pickle.dump(data,fp)","title":""},{"docid":"144dfbb6d4e477c3d9da2e1c1186fd24","score":"0.6082891","text":"def save(self, filename):\n self.header['DETPOS'] = self.detpos\n self.header['CCDNUM'] = decaminfo.ccdnums[self.detpos]\n fitsio.write(filename, self.d, extname=self.extname, clobber=True, header=self.header)","title":""},{"docid":"bda6392d6ec1824fd113761eb085e0b6","score":"0.60765517","text":"def save_conf(self):\n remove(self.filename)\n with open(self.filename, 'x') as f:\n for key in self.data:\n f.write('{0} = {1}\\n'.format(key, self.data[key]))","title":""},{"docid":"f0923ed1c69f6ea0945ebeebf4b963b5","score":"0.6074103","text":"def save(fh, cards):\n ki = KeyIndexer()\n end_key = ki.getIndex(\" \")\n assert end_key == 0\n data = \"\"\n for card in cards:\n for k,v in card:\n i = ki.getIndex(k.encode(\"utf8\"))\n data += chr(i)\n data += v.encode(\"utf8\")\n data += \"\\0\"\n data += chr(end_key)\n contacts = as_chunk(\"MCRD\", data)\n keys = as_chunk(\"KEYS\", ki.binary())\n full = as_chunk(\"FORM\", \"NTCT\"+keys+contacts)\n fh.write(full)","title":""},{"docid":"54a0e21730216a3c60a00bfb8929d02d","score":"0.6068611","text":"def save(self):\n\n\t\tfile = open(self.get_save_path(), 'w')\n\t\tfile.write(cPickle.dumps(self.__dict__))\n\t\tfile.close()","title":""},{"docid":"129b8e6903c0d4e01d18c36412898851","score":"0.6064931","text":"def save(self):\n\n print(\"\" \"\", self.fnm)\n with open(self.fnm, 'w') as f:\n json.dump(self.__dict__, f)","title":""},{"docid":"6ce76e58f45b33d2c5a4f753482e53b5","score":"0.6056807","text":"def save(self, file_name=\"word_dict.p\"):\n pickle.dump(self, open(file_name, \"wb\"))","title":""},{"docid":"d646a343c4b2484cd2f279e62a1e5e22","score":"0.6056027","text":"def h5write(fil, fa):\n attrs = dict([('nx', fa.nx), ('ny', fa.ny), ('nz', fa.nz), \n ('dx', fa.dx), ('dy', fa.dy), ('dz', fa.dz),\n ('x0', fa.x0), ('y0', fa.y0), ('z0', fa.z0)])\n with h5py.File(fil, 'w') as dfil:\n dfil.create_dataset('electrode', data=fa.electrode)\n dfil.create_dataset('pa', data=fa.pa)\n for key, val in attrs.items():\n dfil.attrs[key] = val\n dfil.close()","title":""},{"docid":"77179807c1513e5fbd92cf9c72da8f51","score":"0.604648","text":"def save_files_postestimation(self):\n print 'Image output shape: ', self.image_output_data.shape\n f = h5py.File(self.save_folder_loc + 'db_post_' +\n self.country_code + '_' + str(self.resolution) + '.hdf5', 'w')\n f.create_dataset('features', \n data = self.image_output_data, \n compression=\"gzip\")\n f.create_dataset('locations', \n data = self.post_location_series, \n compression=\"gzip\")\n\n f.close()","title":""},{"docid":"241b270538d8309447a9bd937a3c3792","score":"0.604644","text":"def save(self):\n\n json_data = json.dumps(self.entries)\n encrypted_data = self.cipher.encrypt(json_data.encode())\n self.data_file.seek(0)\n self.data_file.truncate()\n self.data_file.write(encrypted_data)","title":""},{"docid":"9a129fad1edf6d734531a06c9ceb3662","score":"0.6033172","text":"def _write_hdf_dataset(self, fp, key: str = 'data'):\n dataset = fp.create_dataset(key, data=self.data)\n dataset.attrs['class'] = self.__class__.__name__\n if self.label: \n dataset.attrs['label'] = str(self.label) \n dataset.attrs['grid'] = self.grid.state_serialized","title":""},{"docid":"52fe780d14e3b89f92a4c9595f061902","score":"0.60307133","text":"def save(self):\n\t\twith open(self.__path, \"w\") as f:\n\t\t\tjson.dump(self.__data, f, indent=\"\\t\")","title":""},{"docid":"749636d46797156f974541d4e9c5602b","score":"0.601693","text":"def dict2h5(filename, d):\r\n import h5py # must be installed\r\n if isinstance(filename, str):\r\n h = h5py.File(filename,'w') # create or truncate\r\n elif isinstance(filename, h5py.File) or isinstance(filename, h5py.Group):\r\n h = filename\r\n else:\r\n raise TypeError\r\n for k, v in d.items():\r\n if v is None:\r\n continue\r\n elif isinstance(v, list):\r\n h.create_dataset(k, data=np.array(v), compression=\"gzip\")\r\n elif isinstance(v, (np.ndarray, np.int64, np.float64, str, bytes, np.float, float, np.float32,int)):\r\n try:\r\n h.create_dataset(k, data=v, compression=\"gzip\")\r\n except TypeError:\r\n h.create_dataset(k, data=v)\r\n elif isinstance(v, dict):\r\n grp = h.create_group(k)\r\n dict2h5(v, filename=grp)","title":""},{"docid":"660c99bd267df7991e0f966425f92ecc","score":"0.60155416","text":"def __call__(self, data_dict: xr.DataArray):\n for key in data_dict:\n with open(self.get_path(f\"{self.prefix}_{key}.npy\"), \"wb\") as file:\n np.save(file, data_dict[key].values)","title":""},{"docid":"2c3db249d4cedd1ab1f0f0cdb1102c42","score":"0.60086435","text":"def save(self):\n with open(self.settingsfile, 'wb') as outfile:\n for key in self:\n line = \"%s=%s\\n\" % (str(key), str(self[key]))\n outfile.write(line)","title":""},{"docid":"e3407b9417f8840a6d4043a6aa8652b7","score":"0.6003839","text":"def save(contents, filename, wavelength_unit='', flux_unit=''):\n with h5py.File(filename, \"w\") as file_obj:\n for key in contents.keys():\n flux = contents[key]['fluxes']\n wavelength = contents[key]['wavelengths']\n\n # If units are astropy.units Units objects, change to strings\n # If wavelengths are not a Quantity, fall back onto wavelength_unit\n # and flux_unit\n if isinstance(wavelength, u.quantity.Quantity):\n wavelength_units = units_to_string(wavelength.unit)\n wavelength_values = wavelength.value\n else:\n wavelength_units = wavelength_unit\n wavelength_values = wavelength\n if isinstance(flux, u.quantity.Quantity):\n flux_units = units_to_string(flux.unit)\n flux_values = flux.value\n else:\n flux_units = flux_unit\n flux_values = flux\n\n dset = file_obj.create_dataset(str(key), data=[wavelength_values, flux_values], dtype='f',\n compression=\"gzip\", compression_opts=9)\n\n # Set dataset units. Not currently inspected by mirage.\n if wavelength_units != '':\n dset.attrs[u'wavelength_units'] = wavelength_units\n if flux_units != '':\n dset.attrs[u'flux_units'] = flux_units","title":""},{"docid":"a2fe69533823a5b10819c003c3dae504","score":"0.60004896","text":"def saveData(self, plus_subfiles=False):\n try:\n contents = self._getPrintableContents()\n filetools.writeFile(contents, self.path_holder.absolutePath())\n except IOError:\n logger.error('Could not write contents for %s file to %s' %\n (self.path_holder.extension, self.path_holder.absolutePath()))\n raise IOError('Unable to write file to disc')\n\n if self.subfiles and plus_subfiles:\n for subfile in self.subfiles:\n try:\n contents = subfile._getPrintableContents()\n filetools.writeFile(contents, subfile.path_holder.absolutePath())\n except IOError:\n logger.error('Could not write contents for %s file to %s' %\n (subfile.path_holder.absolutePath()))\n raise","title":""},{"docid":"84742515393c58cf98d13b92a9395b03","score":"0.5999583","text":"def save(self, data): \n\n with open(self.filePath, 'a') as f:\n f.write(data)","title":""},{"docid":"4d26d991a14d2f966e02361521e54a30","score":"0.59983355","text":"def writeSave(self):\n\n #store required data in savegame\n self.savegame.currentMap = self.player.map.id\n self.savegame.currentPosition = self.player.position\n self.savegame.currentLevel = self.player.level\n self.savegame.currentDirection = self.player.direction\n self.savegame.party = self.party\n self.savegame.bag = self.bag\n\n #open the file for writing, dump the save game, and close\n save.writeSave(self.savegame)\n\n print(\"Game saved to \" + self.savegame.fn)","title":""},{"docid":"4a8cb05ff4b90de302387f479dc2578e","score":"0.599769","text":"def save(self):\n new_dic = {}\n for key in self.__objects:\n new_dic[key] = self.__objects[key].to_dict()\n with open(self.__file_path, \"w\") as file:\n json.dump(new_dic, file)","title":""},{"docid":"9019821d0739a79c4da781fbb0556b31","score":"0.5996704","text":"def pickle_data(self,filename):\n headername = filename + '_header.na'\n filename = filename + '.na'\n fileObject = open(filename,'wb')\n fileObject2 = open(headername,'wb')\n\n pickle.dump(self.data_dictionary,fileObject)\n pickle.dump(self.data_dictionary_header,fileObject2)\n \n fileObject.close()\n fileObject2.close()","title":""},{"docid":"87824be8369fb86d60533ae80005e1d7","score":"0.5994846","text":"def save_session(self):\n with open(self.path, \"wb\") as file:\n pk.dump(self, file)","title":""},{"docid":"e19fbd943d67089be29fa5fda140c114","score":"0.5987675","text":"def write_to_file(self):\n # Serialize the items via YAML\n data = {k: v.to_dict() for k, v in self.data.items()}\n encoded_str = self.encoder.dump(data)\n\n # Save the changes to file\n with open(self.filename, \"w+\") as f:\n f.write(encoded_str)\n\n # Update our hash\n self.save_to_cache(file_hash=_hash(encoded_str), data=self.data)\n\n self.is_dirty = False","title":""},{"docid":"b1fd3cbe60896f11b115734ded232342","score":"0.5983551","text":"def save_dataclass_to_disk(data, path):\n\n data_dict = dataclasses.asdict(data)\n with open(path, 'w') as file:\n json.dump(data_dict, file, indent=2)","title":""},{"docid":"e752a9dc02cded0fdfd60ab4a1faf22f","score":"0.5978557","text":"def save(self):\n serial = {}\n for key in self.__objects:\n serial[key] = self.__objects[key].to_dict()\n with open(self.__file_path, 'w', encoding='utf-8') as f:\n json.dump(serial, f)","title":""},{"docid":"4643034061c523056c1cc7bcd92f0c8a","score":"0.59693456","text":"def saveDataset(self, filename):\n\n with open(os.path.join(filename), 'wb') as handle:\n data = { # 警告:如果在这里添加内容，也要修改loadDataset\n 'word2id': self.word2id,\n 'id2word': self.id2word,\n 'idCount': self.idCount,\n 'trainingSamples': self.trainingSamples\n }\n pickle.dump(data, handle, -1) # Using the highest protocol available","title":""},{"docid":"2e564bb54db8b54a56fff8d4598b3795","score":"0.59624255","text":"def save(self):\n\t\tfile = open('spotify.txt','wb')\n\t\tfile.write(self.cPickle.dumps(self.__dict__))\n\t\tfile.close()","title":""},{"docid":"f9187f847885be6fc6694d74eee61735","score":"0.59606695","text":"def save(self, fname, overwrite=False, verbose=None):\n ending = '-%s.h5' % (self['kind'].lower(),)\n check_fname(fname, self['kind'], (ending,))\n csd_orig = None\n try:\n if 'csd' in self:\n csd_orig = self['csd']\n self['csd'] = self['csd'].__getstate__()\n write_hdf5(fname, self, overwrite=overwrite, title='mnepython')\n finally:\n if csd_orig is not None:\n self['csd'] = csd_orig","title":""}],"string":"[\n {\n \"docid\": \"e613c24abca0917289bc6962b1238918\",\n \"score\": \"0.71980035\",\n \"text\": \"def save_hdf5(file_path, dictionary, compression=False):\\n f = h5py.File(file_path, 'a')\\n for key in dictionary:\\n if key in f:\\n del f[key]\\n if compression:\\n f.create_dataset(key, data=dictionary[key], compression='gzip')\\n else:\\n f[key] = dictionary[key]\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3268743dc5240a6d8c7ffdb6ec2aac94\",\n \"score\": \"0.7166863\",\n \"text\": \"def saveh5(self):\\n\\n filename=pyu.getlong(self.filename+'.h5',pstruc['DIRSIG'])\\n f=h5py.File(filename,'w')\\n\\n # try/except to avoid loosing the h5 file if\\n # read/write error\\n try:\\n f.attrs['L']=self.L._filename\\n f.attrs['source']=self.source\\n f.attrs['target']=self.target\\n f.attrs['cutoff']=self.cutoff\\n for k in self.keys():\\n f.create_dataset(str(k),shape=np.shape(self[k]),data=self[k])\\n f.close()\\n except:\\n f.close()\\n raise NameError('Signature: issue when writting h5py file')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c41336abd845b84697130029351f8a2\",\n \"score\": \"0.70859015\",\n \"text\": \"def hdf5_fileWRITER(filE_NAME,HE53_dict):\\n filE_NAME = filE_NAME[:-4]\\n with h5py.File(filE_NAME + '.h5','w') as hf: # Create an open hdf5 file for writing.\\n for k in HE53_dict:\\n # for-loop disects the m-file dictionary and writes data and dictionary elements\\n # into a hdf5 file.\\n k1 = k.replace('/','-') # replace the forward slash with a hyphen\\n hf.create_group(k1) # Create a key/element in hdf5 file based on nested python dictionary\\n for l in HE53_dict[k]:\\n # Within the python dictionary, take all elements (keys and data) and incorporate them \\n # into hdf5 file\\n hf[k1][l] = HE53_dict[k][l] # Create new nested element with data in hdf5 file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba7bf6672231fb4c2ba8aef4affc874d\",\n \"score\": \"0.7072902\",\n \"text\": \"def save_to_h5(self, fname):\\n dicttoh5(self.__dict__)\\n self._logger.info(f\\\"RixsData saved to {fname}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e275f6834b09b037229deb9a866ba4a\",\n \"score\": \"0.7065984\",\n \"text\": \"def save_dict_to_h5(h5file, dic, path='/'):\\n for key, item in dic.items():\\n if isinstance(item, (np.int64, np.float64, str, bytes, tuple, float, int)):\\n h5file[path + key] = item\\n elif isinstance(item, np.ndarray):\\n if item.ndim==1 and isinstance(item[0], np.ndarray): #pragma: no cover\\n # this chunk of code is only ever used by the unit testing suite.\\n dt = h5py.special_dtype(vlen=np.float64)\\n h5file.create_dataset(path+key, data=item, dtype=dt)\\n elif 'U' in str(item.dtype):\\n temp = [i.encode() for i in item]\\n h5file[path + key] = temp \\n else:\\n h5file[path + key] = item\\n elif isinstance(item, dict):\\n save_dict_to_h5(h5file, item, path + key + '/')\\n elif isinstance(item, list):\\n if isinstance(item[0], dict):\\n for i in range(len(item)):\\n try:\\n save_dict_to_h5(h5file, item[i], path + key + '/')\\n except AttributeError:\\n h5file[path + key + '/' + str(i)] = item[i]\\n elif isinstance(item[0], str):\\n temp = [i.encode() for i in item]\\n h5file[path + key] = temp\\n else:\\n h5file[path + key] = item \\n elif item is None:\\n continue\\n\\n else:\\n raise ValueError('Cannot save %s type'%type(item))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1207fb4e5930272b1aa50435172ac3d5\",\n \"score\": \"0.704466\",\n \"text\": \"def save(self):\\n with open(self.data_folder + self.name + '.pkl', 'wb') as data_folder:\\n pickle.dump(self.chardict, data_folder, pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a5a8d03d8af18993a836beae9a0636\",\n \"score\": \"0.69632137\",\n \"text\": \"def save(self, filename: str):\\n filename = filename.rsplit('.', 1)[0] + '.hdf'\\n my_dict = self.to_dict()\\n try:\\n import h5py\\n from os.path import exists\\n except ModuleNotFoundError: # pragma: no cover\\n raise ModuleNotFoundError(\\\"To save and load objects, the h5py module is required\\\") # pragma: no cover\\n if exists(filename):\\n raise ValueError(f\\\"The file {filename} already exists, please delete it or change the input filename.\\\")\\n with h5py.File(filename, 'w') as h5file:\\n _dict_to_group(h5file, '/', my_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8909dd99142b716f8d618eee000b8d86\",\n \"score\": \"0.6872224\",\n \"text\": \"def save_to_file(self, file_object: BinaryIO) -> None:\\n with h5py.File(file_object, \\\"w\\\") as write_file:\\n for key, data in self.items():\\n write_file.create_dataset(\\n self._encode_key(key), data=data, dtype=\\\"f\\\", compression=\\\"gzip\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6c5382a2c69bed3267e8454041d523\",\n \"score\": \"0.6857693\",\n \"text\": \"def save(self, filename, items={}):\\n import h5py\\n with h5py.File(filename, 'w') as hf:\\n for key, val in zip(self.keys, self.vals):\\n hf.create_dataset(key, data=val)\\n\\n for key, val in items.iteritems():\\n hf.create_dataset(key, data=val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6c5382a2c69bed3267e8454041d523\",\n \"score\": \"0.6857693\",\n \"text\": \"def save(self, filename, items={}):\\n import h5py\\n with h5py.File(filename, 'w') as hf:\\n for key, val in zip(self.keys, self.vals):\\n hf.create_dataset(key, data=val)\\n\\n for key, val in items.iteritems():\\n hf.create_dataset(key, data=val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a66f7e54e3f1bca38119ff11014e400\",\n \"score\": \"0.68258893\",\n \"text\": \"def save_dict(filepath,dic):\\n import pickle\\n with open(filepath, 'wb') as file_pi:\\n pickle.dump(dic, file_pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4493957c6e811d5b21cb902a98f8999\",\n \"score\": \"0.68176967\",\n \"text\": \"def save_dict_h5py(array_dict, fname):\\n\\n # Ensure directory exists\\n directory = os.path.dirname(fname)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n with h5py.File(fname, 'w') as hf:\\n for key in array_dict.keys():\\n hf.create_dataset(key, data=np.array(array_dict[key]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"073e09da64b8dc95b1cc4ade2f720050\",\n \"score\": \"0.67233795\",\n \"text\": \"def save(self,dataFile):\\r\\n print 'Saving data to', dataFile\\r\\n store = HDFStore(dataFile)\\r\\n for symbol in self.wp.items:\\r\\n store[symbol] = self.wp[symbol]\\r\\n \\r\\n store.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20a4932e5724d2e8250cfb2f7d9ec2e\",\n \"score\": \"0.6721466\",\n \"text\": \"def save(self, filename):\\r\\n\\r\\n inputData = {}\\r\\n inputData[\\\"version\\\"] = self.version\\r\\n inputData[\\\"t\\\"] = self.t\\r\\n inputData[\\\"ep\\\"] = self.ep.tolist()\\r\\n inputData[\\\"w\\\"] = self.w\\r\\n inputData[\\\"a\\\"] = self.a\\r\\n inputData[\\\"b\\\"] = self.b\\r\\n inputData[\\\"h\\\"] = self.h\\r\\n inputData[\\\"v\\\"] = self.v\\r\\n inputData[\\\"E\\\"] = self.E\\r\\n \\r\\n\\r\\n ofile = open(filename, \\\"w\\\")\\r\\n json.dump(inputData, ofile, sort_keys = True, indent = 4)\\r\\n ofile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4fedf0d82149ee308fdf9f65c8b0550\",\n \"score\": \"0.6710869\",\n \"text\": \"def write_dict_to_hdf5_file(data, file, keys_to_write=['all']):\\n if not isinstance(data, dict) or not isinstance(file, basestring):\\n raise TypeError('passed data/file need to be dict/str. Passed type are: %s/%s'%(type(data),type(file)))\\n if 'all' in keys_to_write:\\n keys_to_write = data.keys()\\n\\n fOUT = h5py.File(file, \\\"w\\\")\\n for key in keys_to_write:\\n print 'writing', key\\n if key not in data.keys():\\n print keys_to_write, '\\\\n', data.keys()\\n raise ValueError('%s not in dict!'%(str(key)))\\n fOUT.create_dataset(key, data=np.asarray(data[key]), dtype=np.float32)\\n fOUT.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc801d45daf262e616520f8773aeeb6\",\n \"score\": \"0.6590534\",\n \"text\": \"def save(self, data, out_file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce9ba39e92b016b30a13ece3a7a18ea\",\n \"score\": \"0.65700823\",\n \"text\": \"def save_file(self):\\n z_profile = self.local_to_global_average(self.de_domain_IVP.z.flatten())\\n profiles = OrderedDict()\\n for k, item in self.avg_profiles.items():\\n profiles[k] = self.local_to_global_average(item/self.elapsed_avg_time)\\n\\n if self.rank == 0:\\n file_name = self.file_dir + \\\"profile_dict_file_{:04d}.h5\\\".format(self.n_files_saved+1)\\n with h5py.File(file_name, 'w') as f:\\n for k, item in profiles.items():\\n f[k] = item\\n f['z'] = z_profile \\n self.n_files_saved += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7121927c4d4816bfae3c3182391d27da\",\n \"score\": \"0.65468943\",\n \"text\": \"def save_h5(self,dataframe):\\n dataframe['key']=pd.Series(np.arange(0,dataframe.shape[0],dtype=int))\\n print( self.filename)\\n dataframe.to_hdf(self.filename,key='key')\\n print ('saved ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb008930e371dd3acdfc406f74e81c9\",\n \"score\": \"0.6543284\",\n \"text\": \"def save(self):\\n if not \\\"dih_input\\\" in self.params.keys():\\n blobDump( self.dihedrals, self.blobFilename(\\\"dihedrals\\\") )\\n else:\\n blobDump( self.dihedrals, self.blobFilename(self.params[\\\"dih_input\\\"]) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f9fb2d05cfcc00d8dead24d97d5408\",\n \"score\": \"0.6536651\",\n \"text\": \"def save(self, filename):\\n # check correct file ending\\n if filename[-5:] == '.hdf5':\\n f = h5py.File(filename, 'w')\\n else:\\n f = h5py.File(filename + \\\".hdf5\\\", 'w')\\n\\n # set up header group\\n header = f.create_group(\\\"Header\\\")\\n header.attrs['NDiscs'] = self.ndiscs\\n header.attrs['NSpheroids'] = self.nspheroids\\n header.attrs['SatelliteMass'] = self.sat_mass\\n header.attrs['SatelliteRadius'] = self.sat_radius\\n header.attrs['SatelliteX0'] = self.sat_x0\\n header.attrs['SatelliteV0'] = self.sat_v0\\n header.attrs['ModGrav'] = self.modgrav\\n if self.modgrav:\\n header.attrs['Beta'] = self.beta\\n header.attrs['MW_RScreen'] = self.MW_r_screen\\n header.attrs['Satellite_RScreen'] = self.sat_r_screen\\n header.attrs['Tracers'] = self.tracers\\n if self.tracers:\\n header.attrs['NPart_Type1'] = self.N1\\n header.attrs['NPart_Type2'] = self.N2\\n header.attrs['NSnapshots'] = self.N_snapshots\\n header.attrs['TimeMax'] = self.t_max\\n header.attrs['TimestepSize'] = self.dt\\n\\n # create subgroups to store MW disc and spheroid parameters\\n for i in range(self.ndiscs):\\n grp = f.create_group(\\\"Header/Disc_\\\" + str(i))\\n for k, v in self.dpars[i].items():\\n grp.attrs[k] = v\\n for i in range(self.nspheroids):\\n grp = f.create_group(\\\"Header/Spheroid_\\\" + str(i))\\n for k, v in self.spars[i].items():\\n grp.attrs[k] = v\\n\\n # array of times\\n f.create_dataset('SnapshotTimes', data=self.times)\\n\\n # satellite group and data\\n grp0 = f.create_group(\\\"Satellite\\\")\\n grp0.create_dataset(\\\"Position\\\", data=self.p0_positions)\\n grp0.create_dataset(\\\"Velocity\\\", data=self.p0_velocities)\\n\\n # groups and data for tracer particles\\n if self.tracers:\\n grp1 = f.create_group(\\\"PartType1\\\")\\n grp2 = f.create_group(\\\"PartType2\\\")\\n\\n grp1.create_dataset(\\\"Position\\\", data=self.p1_positions)\\n grp1.create_dataset(\\\"Velocity\\\", data=self.p1_velocities)\\n grp1.create_dataset(\\\"Disrupted\\\", data=self.p1_disrupted)\\n grp1.create_dataset(\\\"DisruptionTime\\\", data=self.p1_disruption_time)\\n\\n grp2.create_dataset(\\\"Position\\\", data=self.p2_positions)\\n grp2.create_dataset(\\\"Velocity\\\", data=self.p2_velocities)\\n grp2.create_dataset(\\\"Disrupted\\\", data=self.p2_disrupted)\\n grp2.create_dataset(\\\"DisruptionTime\\\", data=self.p2_disruption_time)\\n\\n f.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3426eea54f018656e6406de80e9c1003\",\n \"score\": \"0.6536348\",\n \"text\": \"def _saveh5(self,filenameh5,grpname):\\n\\n\\n filename=pyu.getlong(filenameh5,pstruc['DIRLNK'])\\n # if grpname == '':\\n # grpname = str(self.source) +'_'+str(self.target) +'_'+ str(self.cutoff)\\n try:\\n # file management\\n fh5=h5py.File(filename,'a')\\n if not grpname in fh5['sig'].keys():\\n fh5['sig'].create_group(grpname)\\n else :\\n raise NameError('sig/'+grpname +'already exists in '+filenameh5)\\n f=fh5['sig/'+grpname]\\n\\n # write data\\n f.attrs['L']=self.L._filename\\n f.attrs['source']=self.source\\n f.attrs['target']=self.target\\n f.attrs['cutoff']=self.cutoff\\n f.attrs['threshold']=self.threshold\\n f.create_group('ratio')\\n f.create_group('sig')\\n for k in self.keys():\\n f['sig'].create_dataset(str(k),shape=np.shape(self[k]),data=self[k])\\n f['ratio'].create_dataset(str(k),shape=np.shape(self.ratio[k]),data=self.ratio[k])\\n fh5.close()\\n except:\\n fh5.close()\\n raise NameError('Signature: issue when writting h5py file')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce9765e9d030bbbc47d8cac05198a437\",\n \"score\": \"0.65214115\",\n \"text\": \"def save_data_to_file(file_name, list_of_data):\\r\\n # Create a File Object by opening and appending the input dictionary object into the StudentData.dat file\\r\\n # the \\\"ab+\\\" binary access mode is used below to first create the binary file if needed, otherwise just append\\r\\n # the data to the end of the existing file\\r\\n file = open(file_name, \\\"ab+\\\")\\r\\n # Use Pickle here to write list (dictionary) row to the binary file\\r\\n pickle.dump(list_of_data, file)\\r\\n # Close the file object here\\r\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcc72467cb9ad5a461029b38bd9aa4f\",\n \"score\": \"0.6520215\",\n \"text\": \"def save_dict_to_file(self):\\n file = open(self.file_path, \\\"wb\\\")\\n pickle.dump(self.item_dict, file)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894b14e7794c287e5af98c54d0d15264\",\n \"score\": \"0.64962465\",\n \"text\": \"def write_dic(name, dic, path_localdata):\\n\\n with open('%s/%s.pkl' % (path_localdata, name), 'w') as f:\\n pickle.dump(dic, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afd1f1db95fd40671a23964d987a22e\",\n \"score\": \"0.6450912\",\n \"text\": \"def save_recorded_data(data, filename):\\n deepdish.io.save(filename, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d01c57abb91e2a85e65bd220f0e2c8\",\n \"score\": \"0.64484227\",\n \"text\": \"def save(self):\\n with open(self.filename, 'w') as fp:\\n json.dump(self._dict,fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86786a20e841e830b5607b5386eb3e95\",\n \"score\": \"0.6447914\",\n \"text\": \"def _save_to_txtfile(dic_data):\\n with open('result.txt', 'w') as wf:\\n for key in dic_data:\\n line = str(unicode(key))+\\\":\\\"+str(dic_data[key])+\\\"\\\\n\\\"\\n wf.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e60c7372b94de79cab468ca11c550d\",\n \"score\": \"0.6435995\",\n \"text\": \"def store_dic(dic, name):\\r\\n with open(name, 'w') as file:\\r\\n json.dump(dic, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f6b9919b09e7b85d017dd1251f1d2c\",\n \"score\": \"0.6435874\",\n \"text\": \"def save_data_to_file(file_name, dict_weather):\\n obj_file = open(file_name, \\\"wb\\\") # The \\\"wb\\\" = write to binary. If file exists, contents is overwritten. If not, it is created.\\n pickle.dump(dict_weather, obj_file) # Dump function requires 2 arguments--> Data to dump, and file\\n obj_file.close() # Always remember to close file!!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272aae9d946a24ac8389e01bd895c99e\",\n \"score\": \"0.64083713\",\n \"text\": \"def Save_Data(Input_dict, Input_file):\\r\\n with open(Input_file, \\\"w\\\") as file: # open a file handle\\r\\n for task in Input_dict: # loop through key, value and write to file\\r\\n file.write(\\\"{}, {}\\\\n\\\".format(task, Input_dict[task]))\\r\\n print(\\\"\\\\nData saved to the file\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8749b30a6b2859f85b10a8539a004bd9\",\n \"score\": \"0.638854\",\n \"text\": \"def save(self, name):\\n with h5py.File(name + \\\".h5\\\", \\\"w\\\") as h5:\\n for key, kernel in self.weights.items():\\n h5.create_dataset(name=key, shape=kernel.shape, data=kernel)\\n for key, bias in self.bias.items():\\n h5.create_dataset(name=key, shape=bias.shape, data=bias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c3ccea1d654b8589a6766d233e3ccd\",\n \"score\": \"0.63860416\",\n \"text\": \"def _save(self):\\n # w+ flag adds file if not in directory\\n with self.data_file.open('w+') as f:\\n json.dump(self.astro_dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b5a27ff8eb4246427f78f1b6e65706\",\n \"score\": \"0.6371683\",\n \"text\": \"def save(self, path):\\n print (\\\"Storing sate dict in file: \\\",path )\\n torch.save(self.state_dict(), path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3648b664670243331c99d85a595e65ff\",\n \"score\": \"0.6370827\",\n \"text\": \"def save_list_dict_h5py(array_dict, fname):\\n\\n # Ensure directory exists\\n directory = os.path.dirname(fname)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n with h5py.File(fname, 'w') as hf:\\n for i in range(len(array_dict)):\\n grp = hf.create_group(str(i))\\n for key in array_dict[i].keys():\\n grp.create_dataset(key, data=array_dict[i][key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0187ee6c15b00820e85789e14ff63c7\",\n \"score\": \"0.6369403\",\n \"text\": \"def to_ekpdat(self, file):\\n\\t\\twith open(file, 'wb') as f:\\n\\t\\t\\tpickle.dump(self._dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3cb042ed56f8da528b0ceec3ba470d\",\n \"score\": \"0.6366328\",\n \"text\": \"def save(self, dict, save_hash):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ca2e8b30d4297cd746a9cef6f30e44\",\n \"score\": \"0.63608205\",\n \"text\": \"def write_json(self,d):\\n reef_path = os.path.dirname(self.h5_dir)\\n metadata_path = os.path.join(reef_path, 'ICESAT_metadata.json')\\n with open(metadata_path, 'w') as f:\\n f.write(json.dumps(d, sort_keys=True, indent=4, separators=(',', ': ')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33010f33876c9969e36f75f1f992b033\",\n \"score\": \"0.6349521\",\n \"text\": \"def save_HDF5(self,filename):\\n if filename == \\\"\\\": return\\n self.makedir(filename)\\n import tables\\n h5file = tables.openFile(filename,mode='w')\\n for column in self.columns:\\n h5file.createArray(h5file.root,column,self[column])\\n h5file.close()\\n self.filename = filename\\n self.format = \\\"HDF5\\\"\\n self.file_timestamp = file_timestamp(self.filename)\\n self.filesize = filesize(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd3b8046376caca643b8569bd4bec6d\",\n \"score\": \"0.6333551\",\n \"text\": \"def save(self, path: str):\\n file = open(path, 'wb')\\n dump(self._dict, file)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4cc3072e915e6f8350a3b0d93e9d12\",\n \"score\": \"0.6331431\",\n \"text\": \"def save_dataview(self, folder_path):\\n abs_folder = os.path.abspath(folder_path)\\n meta_path = os.path.join(folder_path, 'meta_data.json')\\n data_path = os.path.join(folder_path, 'data.hd5')\\n\\n data_to_store = {'data_d': self.data_d,\\n 'data_q': self.data_q,\\n 'data_benchmark': self.data_benchmark,\\n 'data_inst': self.data_inst,\\n 'factor_df': self._factor_df\\n }\\n for symbol in self.index_weights.keys():\\n data_to_store['index_weight/' + symbol] = self.index_weights[symbol]\\n\\n for group in self.industry_groups.keys():\\n data_to_store['industry_group/' + group] = self.industry_groups[group]\\n\\n data_to_store = {k: v for k, v in data_to_store.items() if v is not None}\\n meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}\\n\\n\\n print(\\\"\\\\nStore data...\\\")\\n jutil.save_json(meta_data_to_store, meta_path)\\n self._save_h5(data_path, data_to_store)\\n\\n print(\\\"Dataview has been successfully saved to:\\\\n\\\"\\n + abs_folder + \\\"\\\\n\\\\n\\\"\\n + \\\"You can load it with load_dataview('{:s}')\\\".format(abs_folder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f92bbeed54541eca3530af3ca6124e0\",\n \"score\": \"0.63221645\",\n \"text\": \"def save_dict(self, path):\\n\\t\\twith open(path, 'wb') as handle:\\n\\t\\t\\tpickle.dump(self.__dict__, handle, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe8781074fd401156209537aff4fc85\",\n \"score\": \"0.6298063\",\n \"text\": \"def write_binary_file(self,filename,dic):\\n import cPickle\\n with open(filename,'w') as file:\\n my_pickler = cPickle.Pickler(file)\\n my_pickler.dump(dic)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7314bba7f0682d7bb57b3eed04f318c5\",\n \"score\": \"0.6290563\",\n \"text\": \"def save(self):\\n all_hands_json = json.dumps([hand.to_json() for hand in self._hands.values()])\\n with open(self._filename, 'w') as f:\\n f.write(all_hands_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7907ccfd15a02a460efbad0fca85db\",\n \"score\": \"0.6282119\",\n \"text\": \"def save_data_to_hdf5_file(fname,orbitals,ns,N_e,occ,grid,ee_coefs):\\n\\n # Create hdf5 file\\n hf = h5py.File(fname+'.hdf5','w')\\n\\n # Add the datasets\\n hf.create_dataset('orbitals', data=orbitals,dtype='f')\\n hf.create_dataset('ns', data=ns,dtype='f')\\n hf.create_dataset('N_e', data=N_e,dtype='i8')\\n hf.create_dataset('occ', data=occ,dtype='i8')\\n hf.create_dataset('grid', data=grid,dtype='f')\\n hf.create_dataset('ee_coefs', data=ee_coefs,dtype='f')\\n hf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f61005a85928daff4d3125e351ca4a\",\n \"score\": \"0.6280122\",\n \"text\": \"def write_hdf5_correlators(path, filename, data, key, verbose=False):\\n\\n ensure_dir(path)\\n data.to_hdf(path+filename, key, mode='w')\\n \\n if verbose:\\n print '\\\\tfinished writing', filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acc3dc7dc22b91b6b53104041102223\",\n \"score\": \"0.62568045\",\n \"text\": \"def save(self, path):\\r\\n with open(path, 'wb') as fout:\\r\\n pkl.dump(self.idf, fout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e936217dddbfe8a5d87790d4b4c60b95\",\n \"score\": \"0.6256244\",\n \"text\": \"def dump_to_hdf5(self, file):\\n # if (self.isglobal and (len(self.data_hist) > 0)): # Global field.\\n if (self.isglobal): # Global field.\\n file['fields/' + self.name].resize((len(self.data_hist),))\\n file['fields/' + self.name][:] = np.array(self.data_hist).flatten();\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f38b64bc49a746b0e6ed2c8e4ce1cd\",\n \"score\": \"0.62508833\",\n \"text\": \"def save(self):\\n with open(\\\"physics_metadata.txt\\\", \\\"w\\\") as fout:\\n fout.write(str(self.data1) + str(';'))\\n fout.write(str(self.data2) + str(';'))\\n fout.write(str(self.data3) + str(';'))\\n fout.write(str(self.data4) + str(';'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e42c473ef11f332f43d095743b6bec\",\n \"score\": \"0.6248119\",\n \"text\": \"def save(self,\\n *,\\n filename,\\n path='./',\\n data_type='pickle'):\\n if not data_type == \\\"hdf5\\\":\\n # Have a new dict to save everything to:\\n dict_to_save = {}\\n # Iterate through dict and replace Variables by strings of\\n # Variables and Entities/taxa as strings with their uid attached.\\n for key, item in self.items():\\n # Go to lower level, if item is indeed another dictionary. This\\n # is the case for all Variables except the time 't'!\\n # One could also check for isinstance(item, Variable)\\n if isinstance(item, dict):\\n new_key = str(key)\\n # print('new_key', new_key, type(new_key))\\n dict_to_save[new_key] = {}\\n for key_2, value in item.items():\\n # Here, key_2 are entities or taxa. Value are lists with\\n # the values the variable took during the run.\\n\\n # First find out if key_2 is Taxon or Entity:\\n if isinstance(key_2, _AbstractEntityMixin):\\n # Entity:\\n new_key_2 = str(key_2)\\n # print('new_key_2', new_key_2, type(new_key_2))\\n # If values are not numbers, they have to be\\n # transformed into strings, too:\\n if all(isinstance(val, (float, int)) for val in value):\\n dict_to_save[new_key][new_key_2] = value\\n # Maybe value is a list of list with floats/ints?\\n elif (all(isinstance(val, (float, int)) for val in\\n list(self.traverse(value)))\\n ):\\n dict_to_save[new_key][new_key_2] = value\\n # Networks:\\n elif all(isinstance(val, nx.Graph) for val in value):\\n # save as dict of dicts\\n new_value = [nx.to_dict_of_dicts(val) for val in value]\\n # print(new_value)\\n # iterate through timesteps in list, where for every\\n # timestep there is a dictionary di in the list\\n for di in new_value:\\n # Rewrite keys (the nodes), as they are\\n # objects, to strings of objects:\\n for node_key, connections in di.copy().items():\\n di[str(node_key)] = di.pop(node_key)\\n # Also, connections for all nodes need to\\n # be replaced by strings:\\n for node_key_2, connections_2 in connections.copy().items():\\n connections[str(node_key_2)] = connections.pop(node_key_2)\\n dict_to_save[new_key][new_key_2] = new_value\\n else:\\n new_val = [str(val) for val in value]\\n dict_to_save[new_key][new_key_2] = new_val\\n\\n elif isinstance(key_2, _AbstractProcessTaxonMixin):\\n # Taxon\\n new_key_2 = str(key_2)\\n # If values are not numbers, they have to be\\n # transformed into strings, too:\\n if all(isinstance(val, (float, int)) for val in value):\\n dict_to_save[new_key][new_key_2] = value\\n # Maybe value is a list of list with floats/ints?\\n elif (all(isinstance(val, (float, int)) for val in\\n list(self.traverse(value)))\\n ):\\n dict_to_save[new_key][new_key_2] = value\\n # Networks:\\n elif all(isinstance(val, nx.Graph) for val in value):\\n # save as dict of dicts\\n new_value = [nx.to_dict_of_dicts(val) for val in value]\\n # print(new_value)\\n # iterate through timesteps in list, where for every\\n # timestep there is a dictionary di in the list\\n for di in new_value:\\n # Rewrite keys (the nodes), as they are\\n # objects, to strings of objects:\\n for node_key, connections in di.copy().items():\\n di[str(node_key)] = di.pop(node_key)\\n # Also, connections for all nodes need to\\n # be replaced by strings:\\n for node_key_2, connections_2 in connections.copy().items():\\n connections[str(node_key_2)] = connections.pop(node_key_2)\\n dict_to_save[new_key][new_key_2] = new_value\\n else:\\n new_val = [str(val) for val in value]\\n dict_to_save[new_key][new_key_2] = new_val\\n else:\\n raise Exception('neither taxon nor entity')\\n elif key == 't':\\n # here we don't need to do anyhing\\n dict_to_save['t'] = item\\n else:\\n raise Exception('neither variable nor time!')\\n\\n # checking if all entries have the same length as time:\\n # tlen = len(dict_to_save['t'])\\n # for var, item in dict_to_save.items():\\n # if isinstance(item, dict):\\n # for instance, val in item.items():\\n # assert len(dict_to_save[var][instance]) == tlen, (\\n # tlen, len(dict_to_save[var][instance]),\\n # dict_to_save[var][instance], var, instance\\n # )\\n\\n # Add a file versio:\\n dict_to_save['file-version'] = 0.1\\n # Fuse Filename and path:\\n # add \\\"/\\\" to paths if missing\\n save_path = path + \\\"/\\\" if not path.endswith(\\\"/\\\") else path\\n # Now save as datatype:\\n if data_type == 'pickle':\\n save_name = save_path + filename + '.pickle'\\n with open(save_name, 'wb') as dumpfile:\\n pickle.dump(dict_to_save, dumpfile, pickle.HIGHEST_PROTOCOL)\\n if data_type == 'json':\\n save_name = save_path + filename + '.json'\\n with open(save_name, 'w') as dumpfile:\\n json.dump(dict_to_save, dumpfile)\\n\\n else:\\n # HDF5 mode\\n # If we want to implement this, we need to solve the following\\n # problem: HDF5 and Pytables only support same datatype entries in\\n # a table. If, however, an entity is deactivated during a run, all\\n # its values when being idel are None (dtype:None)! Earlier values\\n # might include floats, booleans, or integers. This is not possible\\n # with HDF5 at all.\\n raise Exception('Not implemented yet')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314a0f0666af34c4867b4e6018cdf945\",\n \"score\": \"0.62413794\",\n \"text\": \"def save_h5(data, labels, filename):\\n\\twith h5py.File('{}.h5'.format(filename), 'w') as f:\\n\\t\\tfor i in range(len(data)):\\n\\t\\t\\tf.create_dataset(labels[i], data=np.array(data[i]))\\n\\tprint('Data saved as '+ filename)\\n\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0754b1b6c985b2a4163ab70f7cc0eae\",\n \"score\": \"0.6240954\",\n \"text\": \"def save_to_disk(data,file_name):\\n fileObject = open(file_name,'wb')\\n pickle.dump(data,fileObject)\\n fileObject.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b20ee525df2dbd3d69a6fe533ab7ea\",\n \"score\": \"0.6235207\",\n \"text\": \"def save(name, data):\\n filename = get_full_pathname(name)\\n print('..... saving to: {}'.format(filename))\\n \\n with open(filename, 'w') as fout:\\n for entry in data:\\n fout.write(entry + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589f0a10c8f8783a59c4a2f5c32d6789\",\n \"score\": \"0.6219106\",\n \"text\": \"def save(self, savefile, overwrite=False):\\n with hdf5_savehandle(savefile, overwrite) as hf:\\n\\n meta = hf.create_dataset(\\\"meta\\\", shape=(0,))\\n meta.attrs[\\\"economize\\\"] = int(self.economize)\\n if self.r is not None:\\n meta.attrs[\\\"r\\\"] = self.r\\n\\n if self.entries is not None:\\n hf.create_dataset(\\\"entries\\\", data=self.__entries)\\n hf.create_dataset(\\\"svdvals\\\", data=self.__svdvals)\\n hf.create_dataset(\\\"dual\\\", data=self.__dual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e82fb89bb71e574cf21226163c870be\",\n \"score\": \"0.6211481\",\n \"text\": \"def h5save(filename, data, name=None, mode='w', mkdir=True, **kwargs):\\n if mkdir:\\n target_dir = osp.dirname(filename)\\n if target_dir and not osp.exists(target_dir):\\n os.makedirs(target_dir)\\n hdf = h5py.File(filename, mode)\\n hdf.attrs.create('__pymvpa_hdf5_version__', '2')\\n hdf.attrs.create('__pymvpa_version__', mvpa2.__version__)\\n try:\\n obj2hdf(hdf, data, name, **kwargs)\\n finally:\\n hdf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de63e613104ae6a568a46233cc07bfd\",\n \"score\": \"0.61892146\",\n \"text\": \"def save_fitdata_to_hdf(fpath, data_dict,\\n datapath='xrfmap/detsum',\\n data_saveas='xrf_fit',\\n dataname_saveas='xrf_fit_name'):\\n f = h5py.File(fpath, 'a')\\n try:\\n dataGrp = f.create_group(datapath)\\n except ValueError:\\n dataGrp=f[datapath]\\n\\n data = []\\n namelist = []\\n for k, v in six.iteritems(data_dict):\\n if not isinstance(k, six.string_types):\\n k = k.decode()\\n namelist.append(k)\\n data.append(v)\\n\\n if data_saveas in dataGrp:\\n del dataGrp[data_saveas]\\n\\n data = np.asarray(data)\\n ds_data = dataGrp.create_dataset(data_saveas, data=data)\\n ds_data.attrs['comments'] = ' '\\n\\n if dataname_saveas in dataGrp:\\n del dataGrp[dataname_saveas]\\n\\n if not isinstance(dataname_saveas, six.string_types):\\n dataname_saveas = dataname_saveas.decode()\\n namelist = np.array(namelist).astype('|S9')\\n name_data = dataGrp.create_dataset(dataname_saveas, data=namelist)\\n name_data.attrs['comments'] = ' '\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a42bb07161c06213a8e1c1911779459f\",\n \"score\": \"0.61760247\",\n \"text\": \"def save_files(self):\\n print 'Image output shape: ', self.image_output_data.shape\\n f = h5py.File(self.save_folder_loc + 'db_' +\\n self.country_code + '_' + str(self.resolution) + '.hdf5', 'w')\\n f.create_dataset('features', \\n data = self.image_output_data, \\n compression=\\\"gzip\\\")\\n f.create_dataset('targets',\\n data = self.pop_output_data.tolist(), \\n compression=\\\"gzip\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e0c6e1db7f70d17ca65ce19a0c7207\",\n \"score\": \"0.61756647\",\n \"text\": \"def save(self):\\n\\t\\tfile = open(\\\"/home/joseph/bin/spotify/data/tracks/\\\"+self.id+'.txt','wb')\\n\\t\\tfile.write(self.cPickle.dumps(self.__dict__))\\n\\t\\tfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab3363f5b041bfef0c45f1c5144a738\",\n \"score\": \"0.61735415\",\n \"text\": \"def write(self):\\n packed_dict = {key: (d._type, d.value) for key, d in self.__dict__.items()}\\n path = path_data/(str(self.__ID__) + \\\".\\\" + file_extension)\\n with open(path, 'w+') as file:\\n yaml.dump(packed_dict, file, default_flow_style=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"941660235f91204e45e200f467c612c5\",\n \"score\": \"0.61685336\",\n \"text\": \"def save_dictionary(self):\\n w_dict = codecs.open(\\n os.path.join(os.path.dirname(__file__), 'slowa.txt'), 'w', encoding='utf-8')\\n\\n for word in self.w_dict:\\n w_dict.write('%s\\\\n' % word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f77c89dc63a4a12f35a938cb67ecd1\",\n \"score\": \"0.6153445\",\n \"text\": \"def write_hdf5_datasets(hdf5_fname='info.hdf5'):\\n\\n # HDF5 complains if file exists. 'w'' write option didn't seem to deal with it\\n\\n if Path(hdf5_fname).is_file():\\n os.system(\\\"rm \\\" + hdf5_fname)\\n\\n hdf5_file = h5py.File(hdf5_fname, 'w')\\n\\n # Cannot pass python dictionary directly to hdf5\\n # hdf5_file.create_dataset('volumes', data=volumes)\\n\\n hdf5_file.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c27c05cac286f361cab6a55e7dd171\",\n \"score\": \"0.61526406\",\n \"text\": \"def save(self):\\n with open(FileStorage.__file_path, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n d = {k: v.to_dict() for k, v in FileStorage.__objects.items()}\\n json.dump(d, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef5f52c9a7f61ec2c686c793196fcb1\",\n \"score\": \"0.6152404\",\n \"text\": \"def save(self,filename):\\n data = {key:self.__dict__[key] for key in self.fields}\\n with open(filename,'w') as fp:\\n yaml.safe_dump(data,fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39ec9d1ca41bfd8af7b7f858ae51d65\",\n \"score\": \"0.6148742\",\n \"text\": \"def save_on_disk(self):\\n with open(self.path, 'wb') as file:\\n pickle.dump(self.data, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13835bf6fcda87f9b1f52a1491693b2\",\n \"score\": \"0.6141411\",\n \"text\": \"def saveData(actData, dp):\\r\\n fp = open(dp,'w')\\r\\n \\r\\n for key in actData.keys():\\r\\n fp.write('AccountNumber:%s%s' % (key , os.linesep))\\r\\n fp.write('name:%s%s' % (actData[key].name(), os.linesep))\\r\\n fp.write('balance:%s%s' % (actData[key].balance(), os.linesep))\\r\\n for trans in actData[key].transactionList():\\r\\n fp.write('trans:%s%s' % (trans, os.linesep))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71243b697e7faad5ee6bb0df6f5e5a5\",\n \"score\": \"0.6137899\",\n \"text\": \"def save_data(self):\\n\\n data = self.get('1.0', 'end')\\n data = data.encode('utf-8')\\n fd = open(self.filename, 'w')\\n fd.write(data)\\n fd.close()\\n self.event_generate('<<SaveData>>')\\n\\n type, _ = mimetypes.guess_type(self.filename)\\n self.event_generate('<<Save-%s>>' % type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58b320916bef4b383cf26015238f5000\",\n \"score\": \"0.61376846\",\n \"text\": \"def save(filename, arr):\\n data = {\\\"stored\\\": arr}\\n sio.savemat(filename, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46bdbf9ab1e2af54a483c43db0f65309\",\n \"score\": \"0.61291707\",\n \"text\": \"def save(self):\\n obj = {}\\n for key, value in FileStorage.__objects.items():\\n obj[key] = value.to_dict()\\n with open(FileStorage.__file_path, 'w') as fd:\\n json.dump(obj, fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a985cf394a42ba422ade1e1136e84f6\",\n \"score\": \"0.61257744\",\n \"text\": \"def save(self):\\n\\n if os.path.isfile(self.fileName):\\n #clear any previous text\\n out = open(self.fileName, \\\"r+\\\")\\n out.truncate(0)\\n out.close()\\n\\n with open(self.fileName, 'wb') as f:\\n pickle.dump(self.dictionary, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f12daa65b078ddf9134e22244ce2293\",\n \"score\": \"0.6112673\",\n \"text\": \"def save_data_to_disk(data, filename):\\n\\n GameData._delete_game_data_file(filename)\\n\\n # open new stream\\n stream = open(filename, 'w')\\n\\n # dump the data to file stream\\n yaml.dump(data, stream)\\n file.close(stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a3db0b5ce840f4c49e87681aaee5b4\",\n \"score\": \"0.61061203\",\n \"text\": \"def save_dataview(self, folder_path):\\n abs_folder = os.path.abspath(folder_path)\\n meta_path = os.path.join(folder_path, 'meta_data.json')\\n data_path = os.path.join(folder_path, 'data.hd5')\\n\\n data_to_store = {'data_d': self.data_d, 'data_q': self.data_q,\\n 'data_benchmark': self.data_benchmark, 'data_inst': self.data_inst,\\n 'data_custom': self.data_custom}\\n data_to_store = {k: v for k, v in data_to_store.items() if v is not None}\\n meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}\\n\\n print(\\\"\\\\nStore data...\\\")\\n jutil.save_json(meta_data_to_store, meta_path)\\n self._save_h5(data_path, data_to_store)\\n\\n print(\\\"Dataview has been successfully saved to:\\\\n\\\"\\n + abs_folder + \\\"\\\\n\\\\n\\\"\\n + \\\"You can load it with load_dataview('{:s}')\\\".format(abs_folder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c845a3cc0f00bea0384390cd8c67f6\",\n \"score\": \"0.6102145\",\n \"text\": \"def save_dictionary(filename, dic):\\n logger.info('Saving dictionary at: {}'.format(filename))\\n sorted_dic = sorted(dic.items(), key=operator.itemgetter(1))\\n with open(filename, 'w') as fout:\\n for key, id in sorted_dic:\\n fout.write('{}\\\\n'.format(key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ead7c9a3066d4298ce08e81ff7a7d953\",\n \"score\": \"0.60964227\",\n \"text\": \"def save(self,filename,data):\\n with open(filename,'wb') as fp:\\n pickle.dump(data,fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144dfbb6d4e477c3d9da2e1c1186fd24\",\n \"score\": \"0.6082891\",\n \"text\": \"def save(self, filename):\\n self.header['DETPOS'] = self.detpos\\n self.header['CCDNUM'] = decaminfo.ccdnums[self.detpos]\\n fitsio.write(filename, self.d, extname=self.extname, clobber=True, header=self.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda6392d6ec1824fd113761eb085e0b6\",\n \"score\": \"0.60765517\",\n \"text\": \"def save_conf(self):\\n remove(self.filename)\\n with open(self.filename, 'x') as f:\\n for key in self.data:\\n f.write('{0} = {1}\\\\n'.format(key, self.data[key]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0923ed1c69f6ea0945ebeebf4b963b5\",\n \"score\": \"0.6074103\",\n \"text\": \"def save(fh, cards):\\n ki = KeyIndexer()\\n end_key = ki.getIndex(\\\" \\\")\\n assert end_key == 0\\n data = \\\"\\\"\\n for card in cards:\\n for k,v in card:\\n i = ki.getIndex(k.encode(\\\"utf8\\\"))\\n data += chr(i)\\n data += v.encode(\\\"utf8\\\")\\n data += \\\"\\\\0\\\"\\n data += chr(end_key)\\n contacts = as_chunk(\\\"MCRD\\\", data)\\n keys = as_chunk(\\\"KEYS\\\", ki.binary())\\n full = as_chunk(\\\"FORM\\\", \\\"NTCT\\\"+keys+contacts)\\n fh.write(full)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a0e21730216a3c60a00bfb8929d02d\",\n \"score\": \"0.6068611\",\n \"text\": \"def save(self):\\n\\n\\t\\tfile = open(self.get_save_path(), 'w')\\n\\t\\tfile.write(cPickle.dumps(self.__dict__))\\n\\t\\tfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129b8e6903c0d4e01d18c36412898851\",\n \"score\": \"0.6064931\",\n \"text\": \"def save(self):\\n\\n print(\\\"\\\" \\\"\\\", self.fnm)\\n with open(self.fnm, 'w') as f:\\n json.dump(self.__dict__, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce76e58f45b33d2c5a4f753482e53b5\",\n \"score\": \"0.6056807\",\n \"text\": \"def save(self, file_name=\\\"word_dict.p\\\"):\\n pickle.dump(self, open(file_name, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d646a343c4b2484cd2f279e62a1e5e22\",\n \"score\": \"0.6056027\",\n \"text\": \"def h5write(fil, fa):\\n attrs = dict([('nx', fa.nx), ('ny', fa.ny), ('nz', fa.nz), \\n ('dx', fa.dx), ('dy', fa.dy), ('dz', fa.dz),\\n ('x0', fa.x0), ('y0', fa.y0), ('z0', fa.z0)])\\n with h5py.File(fil, 'w') as dfil:\\n dfil.create_dataset('electrode', data=fa.electrode)\\n dfil.create_dataset('pa', data=fa.pa)\\n for key, val in attrs.items():\\n dfil.attrs[key] = val\\n dfil.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77179807c1513e5fbd92cf9c72da8f51\",\n \"score\": \"0.604648\",\n \"text\": \"def save_files_postestimation(self):\\n print 'Image output shape: ', self.image_output_data.shape\\n f = h5py.File(self.save_folder_loc + 'db_post_' +\\n self.country_code + '_' + str(self.resolution) + '.hdf5', 'w')\\n f.create_dataset('features', \\n data = self.image_output_data, \\n compression=\\\"gzip\\\")\\n f.create_dataset('locations', \\n data = self.post_location_series, \\n compression=\\\"gzip\\\")\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241b270538d8309447a9bd937a3c3792\",\n \"score\": \"0.604644\",\n \"text\": \"def save(self):\\n\\n json_data = json.dumps(self.entries)\\n encrypted_data = self.cipher.encrypt(json_data.encode())\\n self.data_file.seek(0)\\n self.data_file.truncate()\\n self.data_file.write(encrypted_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a129fad1edf6d734531a06c9ceb3662\",\n \"score\": \"0.6033172\",\n \"text\": \"def _write_hdf_dataset(self, fp, key: str = 'data'):\\n dataset = fp.create_dataset(key, data=self.data)\\n dataset.attrs['class'] = self.__class__.__name__\\n if self.label: \\n dataset.attrs['label'] = str(self.label) \\n dataset.attrs['grid'] = self.grid.state_serialized\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52fe780d14e3b89f92a4c9595f061902\",\n \"score\": \"0.60307133\",\n \"text\": \"def save(self):\\n\\t\\twith open(self.__path, \\\"w\\\") as f:\\n\\t\\t\\tjson.dump(self.__data, f, indent=\\\"\\\\t\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749636d46797156f974541d4e9c5602b\",\n \"score\": \"0.601693\",\n \"text\": \"def dict2h5(filename, d):\\r\\n import h5py # must be installed\\r\\n if isinstance(filename, str):\\r\\n h = h5py.File(filename,'w') # create or truncate\\r\\n elif isinstance(filename, h5py.File) or isinstance(filename, h5py.Group):\\r\\n h = filename\\r\\n else:\\r\\n raise TypeError\\r\\n for k, v in d.items():\\r\\n if v is None:\\r\\n continue\\r\\n elif isinstance(v, list):\\r\\n h.create_dataset(k, data=np.array(v), compression=\\\"gzip\\\")\\r\\n elif isinstance(v, (np.ndarray, np.int64, np.float64, str, bytes, np.float, float, np.float32,int)):\\r\\n try:\\r\\n h.create_dataset(k, data=v, compression=\\\"gzip\\\")\\r\\n except TypeError:\\r\\n h.create_dataset(k, data=v)\\r\\n elif isinstance(v, dict):\\r\\n grp = h.create_group(k)\\r\\n dict2h5(v, filename=grp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"660c99bd267df7991e0f966425f92ecc\",\n \"score\": \"0.60155416\",\n \"text\": \"def __call__(self, data_dict: xr.DataArray):\\n for key in data_dict:\\n with open(self.get_path(f\\\"{self.prefix}_{key}.npy\\\"), \\\"wb\\\") as file:\\n np.save(file, data_dict[key].values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3db249d4cedd1ab1f0f0cdb1102c42\",\n \"score\": \"0.60086435\",\n \"text\": \"def save(self):\\n with open(self.settingsfile, 'wb') as outfile:\\n for key in self:\\n line = \\\"%s=%s\\\\n\\\" % (str(key), str(self[key]))\\n outfile.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3407b9417f8840a6d4043a6aa8652b7\",\n \"score\": \"0.6003839\",\n \"text\": \"def save(contents, filename, wavelength_unit='', flux_unit=''):\\n with h5py.File(filename, \\\"w\\\") as file_obj:\\n for key in contents.keys():\\n flux = contents[key]['fluxes']\\n wavelength = contents[key]['wavelengths']\\n\\n # If units are astropy.units Units objects, change to strings\\n # If wavelengths are not a Quantity, fall back onto wavelength_unit\\n # and flux_unit\\n if isinstance(wavelength, u.quantity.Quantity):\\n wavelength_units = units_to_string(wavelength.unit)\\n wavelength_values = wavelength.value\\n else:\\n wavelength_units = wavelength_unit\\n wavelength_values = wavelength\\n if isinstance(flux, u.quantity.Quantity):\\n flux_units = units_to_string(flux.unit)\\n flux_values = flux.value\\n else:\\n flux_units = flux_unit\\n flux_values = flux\\n\\n dset = file_obj.create_dataset(str(key), data=[wavelength_values, flux_values], dtype='f',\\n compression=\\\"gzip\\\", compression_opts=9)\\n\\n # Set dataset units. Not currently inspected by mirage.\\n if wavelength_units != '':\\n dset.attrs[u'wavelength_units'] = wavelength_units\\n if flux_units != '':\\n dset.attrs[u'flux_units'] = flux_units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2fe69533823a5b10819c003c3dae504\",\n \"score\": \"0.60004896\",\n \"text\": \"def saveData(self, plus_subfiles=False):\\n try:\\n contents = self._getPrintableContents()\\n filetools.writeFile(contents, self.path_holder.absolutePath())\\n except IOError:\\n logger.error('Could not write contents for %s file to %s' %\\n (self.path_holder.extension, self.path_holder.absolutePath()))\\n raise IOError('Unable to write file to disc')\\n\\n if self.subfiles and plus_subfiles:\\n for subfile in self.subfiles:\\n try:\\n contents = subfile._getPrintableContents()\\n filetools.writeFile(contents, subfile.path_holder.absolutePath())\\n except IOError:\\n logger.error('Could not write contents for %s file to %s' %\\n (subfile.path_holder.absolutePath()))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84742515393c58cf98d13b92a9395b03\",\n \"score\": \"0.5999583\",\n \"text\": \"def save(self, data): \\n\\n with open(self.filePath, 'a') as f:\\n f.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d26d991a14d2f966e02361521e54a30\",\n \"score\": \"0.59983355\",\n \"text\": \"def writeSave(self):\\n\\n #store required data in savegame\\n self.savegame.currentMap = self.player.map.id\\n self.savegame.currentPosition = self.player.position\\n self.savegame.currentLevel = self.player.level\\n self.savegame.currentDirection = self.player.direction\\n self.savegame.party = self.party\\n self.savegame.bag = self.bag\\n\\n #open the file for writing, dump the save game, and close\\n save.writeSave(self.savegame)\\n\\n print(\\\"Game saved to \\\" + self.savegame.fn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8cb05ff4b90de302387f479dc2578e\",\n \"score\": \"0.599769\",\n \"text\": \"def save(self):\\n new_dic = {}\\n for key in self.__objects:\\n new_dic[key] = self.__objects[key].to_dict()\\n with open(self.__file_path, \\\"w\\\") as file:\\n json.dump(new_dic, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9019821d0739a79c4da781fbb0556b31\",\n \"score\": \"0.5996704\",\n \"text\": \"def pickle_data(self,filename):\\n headername = filename + '_header.na'\\n filename = filename + '.na'\\n fileObject = open(filename,'wb')\\n fileObject2 = open(headername,'wb')\\n\\n pickle.dump(self.data_dictionary,fileObject)\\n pickle.dump(self.data_dictionary_header,fileObject2)\\n \\n fileObject.close()\\n fileObject2.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87824be8369fb86d60533ae80005e1d7\",\n \"score\": \"0.5994846\",\n \"text\": \"def save_session(self):\\n with open(self.path, \\\"wb\\\") as file:\\n pk.dump(self, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19fbd943d67089be29fa5fda140c114\",\n \"score\": \"0.5987675\",\n \"text\": \"def write_to_file(self):\\n # Serialize the items via YAML\\n data = {k: v.to_dict() for k, v in self.data.items()}\\n encoded_str = self.encoder.dump(data)\\n\\n # Save the changes to file\\n with open(self.filename, \\\"w+\\\") as f:\\n f.write(encoded_str)\\n\\n # Update our hash\\n self.save_to_cache(file_hash=_hash(encoded_str), data=self.data)\\n\\n self.is_dirty = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fd3cbe60896f11b115734ded232342\",\n \"score\": \"0.5983551\",\n \"text\": \"def save_dataclass_to_disk(data, path):\\n\\n data_dict = dataclasses.asdict(data)\\n with open(path, 'w') as file:\\n json.dump(data_dict, file, indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e752a9dc02cded0fdfd60ab4a1faf22f\",\n \"score\": \"0.5978557\",\n \"text\": \"def save(self):\\n serial = {}\\n for key in self.__objects:\\n serial[key] = self.__objects[key].to_dict()\\n with open(self.__file_path, 'w', encoding='utf-8') as f:\\n json.dump(serial, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4643034061c523056c1cc7bcd92f0c8a\",\n \"score\": \"0.59693456\",\n \"text\": \"def saveDataset(self, filename):\\n\\n with open(os.path.join(filename), 'wb') as handle:\\n data = { # 警告:如果在这里添加内容，也要修改loadDataset\\n 'word2id': self.word2id,\\n 'id2word': self.id2word,\\n 'idCount': self.idCount,\\n 'trainingSamples': self.trainingSamples\\n }\\n pickle.dump(data, handle, -1) # Using the highest protocol available\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e564bb54db8b54a56fff8d4598b3795\",\n \"score\": \"0.59624255\",\n \"text\": \"def save(self):\\n\\t\\tfile = open('spotify.txt','wb')\\n\\t\\tfile.write(self.cPickle.dumps(self.__dict__))\\n\\t\\tfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9187f847885be6fc6694d74eee61735\",\n \"score\": \"0.59606695\",\n \"text\": \"def save(self, fname, overwrite=False, verbose=None):\\n ending = '-%s.h5' % (self['kind'].lower(),)\\n check_fname(fname, self['kind'], (ending,))\\n csd_orig = None\\n try:\\n if 'csd' in self:\\n csd_orig = self['csd']\\n self['csd'] = self['csd'].__getstate__()\\n write_hdf5(fname, self, overwrite=overwrite, title='mnepython')\\n finally:\\n if csd_orig is not None:\\n self['csd'] = csd_orig\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993683,"cells":{"query_id":{"kind":"string","value":"0bc4addec4b5366a097d76a134a5529d"},"query":{"kind":"string","value":"Insert a row with values into table 'tname'. When the row exists it will be replaced with the new values. vallist must be in the same sequence as the table cols"},"positive_passages":{"kind":"list like","value":[{"docid":"6aa4a825926fef1adda63853720b384d","score":"0.76797056","text":"def insert_or_replace(self, tname, vallist):\n s = u'REPLACE INTO {0:s} values'.format(tname)\n qs = ['?'] * len(vallist)\n qs = \",\".join(qs)\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\n self.commit()","title":""}],"string":"[\n {\n \"docid\": \"6aa4a825926fef1adda63853720b384d\",\n \"score\": \"0.76797056\",\n \"text\": \"def insert_or_replace(self, tname, vallist):\\n s = u'REPLACE INTO {0:s} values'.format(tname)\\n qs = ['?'] * len(vallist)\\n qs = \\\",\\\".join(qs)\\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\\n self.commit()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bdcc00bfd47dbcffa0f28bd39ea7408b","score":"0.7562238","text":"def add_row(self, tname, vallist, commit=True):\n s = u'INSERT INTO {0:s} values'.format(tname)\n qs = ['?'] * len(vallist)\n qs = \",\".join(qs)\n\n try:\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\n except Exception, e:\n self.logger.error(\"Failed to add row: %s\" % e)\n # print '''%s (%s)''' % (s, qs)\n # print \"vallist\", vallist\n return {'mesg': \"Failed to add row, rollback: %s\" % e, 'id': None}\n else:\n if commit:\n self.commit()\n return {'mesg': \"ok\", 'id': self.c.lastrowid}","title":""},{"docid":"9b367ba6088587989613e396e56ffb6a","score":"0.65765333","text":"def insert(self, table, cols, vals):\n # lets assume we will only have 2 columns, for simplification\n assert len(cols) == len(vals) == 2\n cursor = self.connection.cursor()\n sql = \"CREATE TABLE IF NOT EXISTS %s (%s, %s, added)\" \\\n % (table, cols[0], cols[1])\n cursor.execute(sql)\n sql = \"INSERT INTO %s (%s, %s, added) VALUES \" \\\n \"('%s','%s',CURRENT_TIMESTAMP)\" \\\n % (table, cols[0], cols[1], vals[0], vals[1])\n cursor.execute(sql)\n\n self.connection.commit()","title":""},{"docid":"ff56a06893378da18860c189859891dd","score":"0.6538034","text":"def insert_row_to_tbl(table_row_values, table):\n if table == 'Medical':\n new_row = Medical(**table_row_values)\n db.session.add(new_row)\n db.session.commit()\n return new_row","title":""},{"docid":"c7ff3d6e8b01e7431c8037cb045951c8","score":"0.64920574","text":"def addRow(self, listCols, listValues):\n colNames = \", \".join(listCols)\n colNames = \"(\" + colNames + \")\"\n values = re.sub(\"\\[\", \"\", str(listValues))\n values = re.sub(\"\\]\", \"\", values)\n values = \"(\" + values + \")\"\n request = str(\"INSERT INTO \" + self.DBManNew.getNameTable() +\n \" \" + colNames + \" VALUES \" + values + \";\")\n return request","title":""},{"docid":"b443c36a9fdbacaad788a7f7a24afb94","score":"0.64618","text":"def insert_values(self, values):\r\n try:\r\n line = \"INSERT INTO {} VALUES ({},\\'{}\\')\".format(self.table_name, self.last_row, '\\',\\''.join(values))\r\n self.last_row = self.cursor.execute(line).lastrowid\r\n self.do_commit()\r\n print(f'{values} insert to the table {self.table_name}')\r\n except:\r\n print(\"Error insert_value!\")","title":""},{"docid":"6360cfc4db5daa8fa9d92f34b5d78630","score":"0.64482224","text":"def insertRow(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"da61b3e3de75817ba25831889c575a5c","score":"0.64472973","text":"def add_row(tablename, data, conn):\n sql = \"INSERT INTO %s\" % tablename\n columns, vals = zip(*data)\n colstr =\"(\"\n valstr = \"(\"\n for i, column in enumerate(columns):\n if i > 0:\n colstr += \", \"\n valstr += \", \"\n colstr += \"%s\" % column\n valstr += \"?\" \n sql = sql + colstr +\") VALUES\" + valstr + \");\"\n cur = conn.cursor()\n try:\n res = cur.execute(sql, tuple(vals))\n except Error as e:\n print \"Error in add_row:\", (e), \". SQL:\", sql, vals\n return None\n print \"SQL:\", sql, vals\n rowid = cur.lastrowid \n conn.commit()\n return rowid","title":""},{"docid":"eec8206284703aa01c33672c7ab8575d","score":"0.63869905","text":"def update_row(self, tname, col, val, valtuple):\n i = len(valtuple) / 2\n if i == 0:\n i = 1\n qs = ['%s = \"%s\"'] * i\n qs = \",\".join(qs)\n s0 = 'UPDATE %s SET' % tname\n s1 = ' %s WHERE ' % qs\n s2 = '%s=\"%s\"' % (col, val)\n s1 = s1 % valtuple\n #print s0 + s1 + s2\n self.c.execute(s0 + s1 + s2)\n self.commit()","title":""},{"docid":"2a1398621baabdbae59f2ac1d89d7ae1","score":"0.63759947","text":"def insert(self, table, values):\n return","title":""},{"docid":"66f06878b058a44b6b160a9d53228005","score":"0.6347618","text":"def insertMany(self, tablename, vals, transaction=None):\n colnames = \",\".join(self.escapeColNames(vals[0].keys()))\n params = \",\".join([self.insertArgsToString(val) for val in vals])\n args = []\n for val in vals:\n args = args + val.values()\n q = \"INSERT INTO %s (%s) VALUES %s\" % (tablename, colnames, params)\n if transaction is not None:\n return self.executeTxn(transaction, q, args)\n return self.executeOperation(q, args)","title":""},{"docid":"3b8babfec9e69b069ce302d89ab672dc","score":"0.63271517","text":"def bulk_row_insert(self, table_name: str, columns: List[Column],\n rows: List[List]):\n pass","title":""},{"docid":"05bfe2c79b5a0b0ff85d9da4264ef2ce","score":"0.6324573","text":"def insert_row(self, table_name, values, columns=\"\"):\n input_values = ', '.join(map(lambda x: \"'%s'\", values))\n input_values = input_values % tuple(values)\n sql_query = \"INSERT INTO %s %s \" \\\n \"VALUES (%s)\"\n sql_query = sql_query % (table_name, columns, input_values)\n sql_query = sql_query.encode('utf-8')\n try:\n self.cur.execute(sql_query)\n self.con.commit()\n except Exception as ex:\n print(ex)\n self.con.rollback()","title":""},{"docid":"079f9bc36e58e0cd541eaf5a7292e74a","score":"0.62759054","text":"def insertfromdata(tablename, records, multiple=True):\n if multiple:\n lst = records[0]\n else:\n lst = records\n s = 'INSERT INTO ' + str(tablename) + ' VALUES '\n s += \"( \" + \", \".join([\"?\"]*len(lst)) + \")\"\n return s","title":""},{"docid":"0ea12a87bef8d902889981416dee5f6c","score":"0.6269725","text":"def bulk_insert(self, table_spec, rows):\n extras.execute_values(\n self.cursor,\n 'INSERT INTO {0:s} VALUES %s ON CONFLICT DO NOTHING'.format(table_spec),\n rows)","title":""},{"docid":"676a3833bc5289c8253c4eb330ee1f1b","score":"0.6252999","text":"def insert(self, row: List):\n pass","title":""},{"docid":"3a888580bddbcae539609f3db535fb1e","score":"0.624624","text":"def insert_into_table(table, data):\r\n\r\n for line in data:\r\n cols = ', '.join(\"'{}'\".format(col) for col in line.keys())\r\n vals = ', '.join(':{}'.format(col) for col in line.keys())\r\n sql = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(table, cols, vals)\r\n try:\r\n cursor.execute(sql, line)\r\n except sqlite3.IntegrityError:\r\n # ignore duplicate\r\n pass\r\n except sqlite3.DatabaseError as err:\r\n raise err\r\n connect.commit()","title":""},{"docid":"591a90d12855e484fd6cdd7c6b1f40c5","score":"0.6210337","text":"def insert(self, tablename, vals, transaction=None):\n params = self.insertArgsToString(vals)\n colnames = \"\"\n if len(vals) > 0:\n ecolnames = self.escapeColNames(vals.keys())\n colnames = \"(\" + \",\".join(ecolnames) + \")\"\n params = \"VALUES %s\" % params\n q = \"INSERT INTO %s %s %s\" % (tablename, colnames, params)\n if transaction:\n return self.executeTxn(transaction, q, vals.values())\n return self.executeOperation(q, vals.values())","title":""},{"docid":"c262462e4f1c734b4752a96a7793e254","score":"0.62078285","text":"def insert_data(self, conn, cur, row: pd.DataFrame, table_name: str) -> None:\n try:\n columns = self.tables[table_name][\"headers\"]\n except KeyError as e:\n logging.error(\n f\"You forgot to specify the 'headers' on table {table_name}.\")\n raise e\n\n # Check if the element is a nested list and extract it.\n if isinstance(columns[0], list):\n for more_columns in columns:\n # Generate enough parameters for query string.\n parameter_string = ','.join(['%s']*len(more_columns))\n\n # Extract relevant columns from dataframe and cast it to a list.\n row_list = row[more_columns].values.tolist()\n\n # Do not perform insert if the first value is empty (which is always the primary key).\n if not row_list[0]:\n continue\n\n # Cast empty values to None for DB adapter to work.\n row_list = [None if not x else x for x in row_list]\n # print(f\"--- {row_list}\")\n\n try:\n attribute_string = ','.join(\n self.tables[table_name][\"attributes\"])\n unique_string = ','.join(\n self.tables[table_name][\"uniques\"])\n except KeyError as e:\n logging.error(\n f\"Your forgot to specify either the 'attributes' or the 'uniques' ont the table {table_name}\")\n raise e\n\n # Catch uniqueness constraint with \"UPSERT\" method\n if not unique_string:\n insert_string = f\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string})\"\n else:\n insert_string = f\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string}) ON CONFLICT ({unique_string}) DO NOTHING\"\n\n try:\n cur.execute(insert_string, row_list)\n except UniqueViolation as e:\n logging.error(\n f\"Something went wrong during inserting into: {table_name}\")\n logging.info(e)\n except InFailedSqlTransaction as e:\n logging.error(\n f\"Transaction stopped. Rolling back...: {table_name}\")\n logging.info(e)\n # conn.rollback()\n pass\n\n else:\n # Generate enough parameters for query string.\n parameter_string = ','.join(['%s']*len(columns))\n\n # Extract relevant columns from dataframe and cast it to a list.\n # print(row)\n # _ = input(\"Pause...\")\n row_list = row[columns].values.tolist()\n\n # Do not perform insert if the first value is empty (which is always the primary key).\n if not row_list[0]:\n return\n\n # Cast empty values to None for DB adapter to work.\n row_list = [None if not x else x for x in row_list]\n # print(f\"--- {row_list}\")\n\n try:\n attribute_string = ','.join(\n self.tables[table_name][\"attributes\"])\n unique_string = ','.join(\n self.tables[table_name][\"uniques\"])\n except KeyError as e:\n logging.error(\n f\"Your forgot to specify either the 'attributes' or the 'uniques' ont the table {table_name}\")\n raise e\n\n # Catch uniqueness constraint with \"UPSERT\" method\n if not unique_string:\n insert_string = f\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string})\"\n else:\n insert_string = f\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string}) ON CONFLICT ({unique_string}) DO NOTHING\"\n\n try:\n cur.execute(insert_string, row_list)\n except UniqueViolation as e:\n logging.error(\n f\"Something went wrong during inserting data into: {table_name}\")\n logging.info(e)\n except InFailedSqlTransaction as e:\n logging.error(\n f\"Transaction stopped. Rolling back...: {table_name}\")\n logging.info(e)\n pass","title":""},{"docid":"251b607fe8ed9e4e8a5ff4a3ea373578","score":"0.61890787","text":"def insert(self, table_name, values):\n\n self.cursor.execute('INSERT INTO ' + table_name + ' VALUES(' + '\"' + ('\"' + \", \" + '\"').join(values) + '\"' +\n ');')\n\n self.connector.commit()","title":""},{"docid":"354618b78044ea5428a6601a6def3fc4","score":"0.6181655","text":"def single_row_insert(self, table_name: str, columns: List[Column],\n row: List):\n pass","title":""},{"docid":"b5d88e415e007fde848059375fddcf96","score":"0.61611545","text":"def insert(self, table, names, values):\n names_tpl = sql_tuple(names)\n values_tpl = sql_tuple(values)\n cmd = \"insert into {} {} values {};\".format(table, names_tpl,\n values_tpl)\n self.execute(cmd)\n rowid = str(self.cursor.lastrowid)\n return rowid","title":""},{"docid":"ce5cda637786aa76693be3f8a903ac6c","score":"0.6129226","text":"def insert_row(conn, value):\n\n sql = ''' INSERT or REPLACE into SCORES(name,score) VALUES (?,?) '''\n cur = conn.cursor()\n cur.execute(sql, value)\n conn.commit()","title":""},{"docid":"197abd2c1e43c2cccd475bb8aa8987bf","score":"0.6097784","text":"def insert_Data(table_name, values):\r\n conn = mysql.connect()\r\n cursor = conn.cursor(pymysql.cursors.DictCursor)\r\n cols = values.keys()\r\n vals = values.values()\r\n strComma = \"','\"\r\n newVals = \"'\" + strComma.join(vals) + \"'\"\r\n qs_insert = \"INSERT INTO %s (%s) VALUES(%s)\" % (table_name, \",\".join(cols), \"\" + newVals)\r\n try:\r\n cursor.execute(qs_insert)\r\n conn.commit()\r\n last_insert_id = cursor.lastrowid\r\n cursor.close()\r\n conn.close()\r\n return True\r\n except Exception as err:\r\n print(err, '-- occurred during adding data')\r\n return False","title":""},{"docid":"9a151c2f7f55d67959dca50ec3006af2","score":"0.6080456","text":"def autoexec_insert(self, table, keyvals, literals=[], optype=\"INSERT\", **kwargs):\n optype = optype.upper()\n if optype not in [\"INSERT\", \"REPLACE\", \"INSERT IGNORE\", \"ON DUPLICATE\"]:\n raise ProgrammingError(\n \"MySQL operation must be 'INSERT', 'REPLACE', 'INSERT IGNORE', 'ON DUPLICATE',\" \\\n \"got '%s'\" % optype)\n if type(keyvals) is not dict:\n raise Exception(\"Data must be a dictionary\")\n if type(literals) is not list:\n raise Exception(\"Literals must be a list\")\n table = self.__fix_table(table)\n parsed_literals = self.__process_literals(optype, keyvals, literals)\n (column_names, column_substituion, column_values, literal_values, ondupe_out) = parsed_literals\n ondupe_values = []\n inital_type = optype\n if optype == \"ON DUPLICATE\":\n inital_type = \"INSERT\"\n if inital_type in [\"INSERT\", \"REPLACE\"]:\n inital_type += \" INTO\"\n rownames = \",\".join(\"`%s`\" % k for k in column_names)\n rowvalues = \", \".join(k for k in column_substituion)\n query = \"%s %s\\n\" \\\n \"(%s)\\n\" \\\n \"VALUES(%s)\" % (inital_type, table, rownames, rowvalues) % tuple(literal_values)\n if optype == \"ON DUPLICATE\":\n dupe_out = \",\\n\".join(\"%s\" % k for k in ondupe_out)\n query += \"\\nON DUPLICATE KEY UPDATE\\n\" \\\n \"%s\" % dupe_out\n column_values += ondupe_values\n return self.execute(query, args=tuple(column_values), commit=True, get_id=True, raise_exc=True, **kwargs)","title":""},{"docid":"3a67e5076bb473284a8bde68375a44dd","score":"0.60337764","text":"def insertValues(table, values):\n query = \"INSERT INTO \" + table + \" VALUES (\"\n for i in values : query += DataBase.toDBString(i) + \", \"\n query = query[0:-2] + \");\"\n DataBase.DBdriver.execute(query)","title":""},{"docid":"e9c1bb0aaef2ef4cfc7c6b4305a7bd52","score":"0.60129875","text":"def insert(self, tableName, rows):\n if isinstance(rows, list):\n return self._jsparkSession.insert(tableName, [rows, ])\n elif isinstance(rows, tuple):\n return self._jsparkSession.insert(tableName, rows)\n else:\n raise TypeError(\"rows should be tuple or a list\")","title":""},{"docid":"45cc42dfb06731a75bd23c3ff4082757","score":"0.6006435","text":"def sqlite_insert(conn, table, row):\n # validate table, row before\n cols = ', '.join('\"{}\"'.format(col) for col in row.keys())\n vals = ', '.join(':{}'.format(col) for col in row.keys())\n sql = 'INSERT INTO \"{0}\" ({1}) VALUES ({2})'.format(table, cols, vals)\n conn.cursor().execute(sql, row)\n conn.commit()\n conn.cursor().close()\n\n return","title":""},{"docid":"167b50e6930123e9021e23a4a3f69560","score":"0.6002454","text":"def bulk_insert(self, rows: List[List]):\n pass","title":""},{"docid":"166b3303a4db26caa40f98efa77e46b5","score":"0.5991085","text":"def _execute_insert_query(self, cursor, value_list, column_names):\n\n num_cols = len(self.columns)\n num_rows = len(value_list)\n\n # Check data squareness. There should be no rows with missing or extra columns.\n for elem in value_list:\n if len(elem) != num_cols:\n raise Exception(\"Misaligned data in mysql_load: \"\n \"row '{row}' does not match columns '{columns}'\".format(\n row=elem, columns=column_names\n ))\n\n # The \"%s\" placeholder is used by the mysql-connector library\n # to execute the prepared statement, it is not used with a\n # traditional python \"%\" operator.\n parameters = \"(\" + \",\".join([\"%s\"] * num_cols) + \")\"\n all_parameters = \",\".join([parameters] * num_rows)\n\n table_name = self.table\n if self.overwrite and self.use_temp_table_for_overwrite:\n table_name = self.table + '_temp'\n\n query = \"INSERT INTO {table} ({column_names}) VALUES {values}\".format(\n table=table_name, column_names=column_names, values=all_parameters\n )\n cursor.execute(query, list(chain.from_iterable(value_list)))\n log.debug(\"Wrote %d rows to table %s\", num_rows, self.table)","title":""},{"docid":"f26d509e63db2101f0b6444f1dcae57a","score":"0.5951947","text":"def lst_to_field(table, field, lst):\n if len(lst) == 0:\n message(\"No values to add to '{}'.\".format(field))\n elif field_exists(table, field): \n with arcpy.da.UpdateCursor(table, [field]) as cursor:\n # For row in cursor:\n for i, row in enumerate(cursor):\n row[0] = lst[i]\n cursor.updateRow(row)\n else:\n message(\"{} field not found in {}\".format(field, table))","title":""},{"docid":"d1f5263ef268a15960a073cbf134f5a9","score":"0.5942753","text":"def insert_data(self, table_name: str, values: Tuple) -> None:\n # Need to parse the columns in 'values` differently depending on what table we are inserting into\n if table_name == 'User':\n query = \"INSERT INTO %s VALUES ('%s', %s)\"\n elif table_name == 'Activity':\n query = \"INSERT INTO %s (user_id, transportation_mode, start_date_time, end_date_time) VALUES ('%s', %s, '%s', '%s')\"\n try:\n self.cursor.execute(query % (table_name, *values))\n self.db_connection.commit()\n except Exception as e:\n print(\"Unable to add values to table:\", e)","title":""},{"docid":"186c39a3d65c11b6749639841c310d4c","score":"0.5940869","text":"def insert(self, row):\n values = None\n if isinstance(row, dict):\n keys = row.keys()\n values = [list(row.values())]\n elif isinstance(row, (list, tuple)):\n keys = row[0].keys()\n values = [list(r.values()) for r in row]\n q = [\"?\"] * len(keys)\n q = \",\".join(q)\n sql = \"\"\"INSERT INTO {table} ({fields})\n VALUES({q})\"\"\".format(\n table=self._table_name, fields=\",\".join(keys), q=q\n )\n inserts = []\n cur = self._con.cursor()\n cur.execute(sql, list(values[0]))\n self._con.commit()\n return True","title":""},{"docid":"c9ed46778e5e2c51e7bb24a4bb966970","score":"0.5935122","text":"def insert_row(dbconn, tablename, *args):\n cur = dbconn.cursor()\n cur.execute(\"INSERT INTO '{name}' VALUES{args}\".format(name=tablename, args=args))\n dbconn.commit()","title":""},{"docid":"20a5c287ec52513e72baae9d6aedc99a","score":"0.5901541","text":"def sqlInsert(sql_connection, table, col_list, values):\n\n with sql_connection.cursor() as cursor:\n sub_set = ','.join(['%s' for _ in range(len(values))])\n command = \"INSERT INTO \" + table + \"(\" + ','.join(col_list) + \") VALUES (\" + sub_set + \");\"\n cursor.execute(command, values)\n\n sql_connection.commit()\n\n return 0 # return code","title":""},{"docid":"0a408572eb79479b6ce2781d2488c038","score":"0.58750194","text":"def insert_row(cur, data):\n # Build INSERT statement\n q = \"INSERT INTO facebook (\" + ','.join(_db_cols) + \") \\\n VALUES (\" + ','.join(['?']*len(_db_cols)) + \");\"\n # Build record to insert\n row = tuple(data.get(c) for c in _db_cols)\n cur.execute(q, row)","title":""},{"docid":"60cde2561de53ed5a0232d41171252cf","score":"0.58744645","text":"def insert(self, idx, val):\n if idx is None:\n idx=self.nrows()\n if self._add_item(idx,val) is not self:\n raise ValueError(\"can't insert items while preserving column type\")","title":""},{"docid":"fd2fbba6cb3c73bf382879f0b6239110","score":"0.58527756","text":"def populate_table(conn, row):\n sql = ''' INSERT INTO data_points(n,p,data)\n VALUES(?,?,?) '''\n cur = conn.cursor()\n cur.execute(sql, row)\n conn.commit()\n return cur.lastrowid","title":""},{"docid":"15780a00f538cbdaee5252df5a9aa13e","score":"0.5827503","text":"def insert(self, tablename, data):\n cols = []\n values = []\n for col, val in data.items():\n cols.append(col)\n values.append(val)\n query = \"INSERT INTO {tblName} ({columns}) VALUES ({questionmarks})\"\n query = query.format(\n tblName=tablename,\n columns=', '.join(cols),\n questionmarks=', '.join(len(values)*'?'))\n self.cursor.execute(query, tuple(values))\n self.connection.commit()\n if tablename in ['tblProject', 'tblWorkflow']:\n return self.cursor.lastrowid","title":""},{"docid":"235bac13b403c38570ff40d41983a82f","score":"0.581869","text":"def insert(db, schema, table, data):\n engine = get_engine(db)\n tablename = tablepattern.format(**locals())\n columns = []\n values = []\n for column, value in data.items():\n columns.append(column)\n values.append(\"'%s'\" % str(value))\n columns = ', '.join(columns)\n values = ', '.join(values)\n sql = \"\"\"INSERT INTO {tablename} ({columns}) VALUES ({values})\n \"\"\".format(**locals())\n with engine.begin() as con:\n con.execute(sql)","title":""},{"docid":"e308b495278d69acfaa4c0f2b61ed844","score":"0.57868624","text":"def insert_data(self, keys, values):\n\n t = range(self.num_tables)\n self.tables = [[[] for _ in range(self.table_size)] for _ in t]\n rows = self.put(keys)\n\n # can parallelize outer loop for each table?\n\n for ti in torch.arange(0, self.num_tables).long():\n #for val in values:#torch.arange(0, len(values)):\n for row, val in zip(rows[ti], values):\n self.tables[ti][row].append(int(val))","title":""},{"docid":"469b937b93fefb4035a234851fd5a533","score":"0.5773728","text":"def insert(self):\n self.cursor.executemany(self.sql_insert, self.val)","title":""},{"docid":"6ebad24fb8141669d8d01ec463d1b340","score":"0.57394326","text":"def insert_adjustment_values(c):\n adjustment_values = load_adjustment_values()\n # insert into table ------------------------------------\n q_insert = (\n 'INSERT INTO adjustment_values\\n'\n ' (prst, seq_nmbr, name, val)\\n'\n ' VALUES (?, ?, ?, ?)\\n'\n )\n c.executemany(q_insert, adjustment_values)","title":""},{"docid":"9824dbbaf1f99700bcf6d2e5c3703f49","score":"0.5738879","text":"def insert_into_table(values, connection):\n cursor_obj = connection.cursor()\n entities = (values['id'], values['name'], values['rating'], values['date'])\n cursor_obj.execute('INSERT INTO games(id, name, rating, launchDate) VALUES(?, ?, ?, ?)', entities)\n connection.commit()","title":""},{"docid":"9699f9557b110348e95cfb7267ef929c","score":"0.5735011","text":"def insert ( self, object, trait, row, value ):\r\n getattr( object, trait ) [ row: row ] = [ value ]","title":""},{"docid":"b2ba35c56f0bdd0d9be7460a5c0894a4","score":"0.57133347","text":"def _insert(self):\n columns = []\n\n self._match(tokens.INSERT_KW)\n self._match(tokens.INTO_KW)\n\n table_name = self._lookahead_token(0).text\n self._match(tokens.ID)\n\n self._match(tokens.SET_KW)\n column_name = self._lookahead_token(0).text\n self._match(tokens.ID)\n self._match(tokens.EQUALS)\n\n column_val = self._expr()\n columns.append((column_name, column_val))\n\n while self._lookahead_type(0) == tokens.COMMA:\n self._match(tokens.COMMA)\n column_name = self._lookahead_token(0).text\n self._match(tokens.ID)\n self._match(tokens.EQUALS)\n column_val = self._expr()\n columns.append((column_name, column_val))\n\n self._match(tokens.SEMICOLON)\n\n # invoke action\n self.interpreter.insert_into(table_name, columns)","title":""},{"docid":"dbe82a6308ed16769e0e2eaa79cfeea4","score":"0.5684314","text":"def insert_row(self, table, variablen):\n # Get the keys\n keys = list(variablen.keys())\n\n # Go tru all variables\n for index in enumerate(variablen[keys[0]]):\n\n # Create the list where all variables wil be collected in\n var_lijst = []\n\n # Get all variables\n for key in keys:\n var_lijst.append(\"{}\".format(variablen[key][index[0]]))\n\n # Create the SQL Insert statement\n insert = \"INSERT INTO {}({}) VALUES({}) \".format(table,\n \", \".join(keys),\n \", \".join(var_lijst))\n\n # Execute the insert statement\n self.cursor.execute(insert)","title":""},{"docid":"d4137488f26546e32173b22df6a09653","score":"0.56783444","text":"def insert(self, table, data):\n names = ''\n values = ''\n for i in data.items():\n n, v = i\n names += f\"{n}, \"\n values += f\"$${v}$$, \"\n names = names[:-2]\n values = values[:-2]\n query = f'INSERT INTO {table} ({names}) VALUES ({values})'\n self.do_query(query)","title":""},{"docid":"93bfa505a67a8ef198570ddfdac44580","score":"0.5675854","text":"def insert(self, table, name, row):\n\n # Build insert prepared statement\n columns = [name for name, _ in table.items()]\n insert = SQLite.INSERT_ROW.format(table=name,\n columns=\", \".join(columns),\n values=(\"?, \" * len(columns))[:-2])\n\n try:\n # Execute insert statement\n self.cur.execute(insert, self.values(table, row, columns))\n # pylint: disable=W0703\n except Exception as ex:\n print(\"Error inserting row: {}\".format(row), ex)","title":""},{"docid":"973b2d4b2858439c2436ef3680f9d921","score":"0.5673136","text":"def insert(self, table_name, column_names, values):\n data = []\n values_template = ''\n # If a list of records is received\n if type(values) == list:\n data = [tuple([value[column] for column in column_names]) for value in values]\n values_template = ','.join(['%s'] * len(values))\n elif type(values) == dict:\n data = tuple([values[column] for column in column_names])\n values_template = '({})'.format(','.join(['%s'] * len(column_names)))\n # Create sql query\n sql_q = 'INSERT INTO {} ({}) VALUES {} RETURNING id;'.format(table_name, ','.join(column_names),\n values_template)\n result = self.__execute_sql(query=sql_q, values=data)\n return result","title":""},{"docid":"e91aacb75f77c827ec5b77f0f5da45ba","score":"0.56700295","text":"def populate_table(self, table, values):\r\n\t\t# to be conpleted according to sqlite3 requirements\r\n\t\tif self.platform == STATIC.PLATFORM_WINDOWS:\r\n\t\t\tfor value in values:\r\n\t\t\t\t#print (str(value.MONTH) + \" \" + value.FLOW + \" \" + value.CONTRIB + \" \")\r\n\t\t\t\t# rev ex rox\r\n\t\t\t\ttable[STATIC.equivData[\"Rev\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.REV_EX_ROX\r\n\t\t\t\t# rpk\r\n\t\t\t\ttable[STATIC.equivData[\"RPK\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.RPK\r\n\t\t\t\t# ask\r\n\t\t\t\ttable[STATIC.equivData[\"ASK\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.ASK\r\n\t\telse:\r\n\t\t\tfor value in values:\r\n\t\t\t\t# rev ex rox\r\n\t\t\t\ttable[STATIC.equivData[\"Rev\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[4]\r\n\t\t\t\t# rpk\r\n\t\t\t\ttable[STATIC.equivData[\"RPK\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[5]\r\n\t\t\t\t# ask\r\n\t\t\t\ttable[STATIC.equivData[\"ASK\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[6]","title":""},{"docid":"7fb6a1e67c0a46f56e00fe83b8b5d1de","score":"0.56561756","text":"def add_row(self, vals, names=None, units=None):\n if isinstance(vals, Row):\n vals = DataClass.from_table(vals)\n else:\n if isinstance(vals, Mapping):\n keys_list = list(vals.keys())\n vals_list = [vals[k] for k in keys_list]\n vals = vals_list\n if names is None:\n names = keys_list\n else:\n # assume it's an iterable that can be taken as columns\n if names is None:\n # if names of columns are not specified, default to the\n # existing names and orders\n names = self.field_names\n # check if any astropy Time columns\n for i, k in enumerate(names):\n if k in self and isinstance(self[k], Time):\n vals[i] = Time(vals[i], scale=self[k].scale,\n format='isot' if isinstance(vals[i], str)\n else 'jd')\n vals = DataClass.from_rows(vals, names, units=units)\n self.vstack(vals)","title":""},{"docid":"bdddfdc4382d2033674278a9ffd1f2b5","score":"0.5614611","text":"def sql_update_table(table_title, columns, data): \r\n col = \"\"\r\n val = \"\"\r\n for i, j in zip(columns, data):\r\n col+=f'{i},' \r\n val+=f\"\"\"?,\"\"\" \r\n sql = f\"\"\"INSERT INTO {table_title} ({col[:-1]}) VALUES ({val[:-1]});\"\"\" \r\n return sql","title":""},{"docid":"97bd5386ba8ee77a24e3aaf9b6ac2295","score":"0.5603458","text":"def test_insert_dictlist(self):\n self.tbl.insert([\n {\n 'name': 'Alyssa P. Hacker',\n 'age': 25,\n 'height': 66.25\n },\n {\n 'name': 'Ben Bitdiddle',\n 'age': 24,\n 'height': 70.1\n }])\n data = self.tbl.select()\n idata = np.array([\n ['Alyssa P. Hacker', 25, 66.25],\n ['Ben Bitdiddle', 24, 70.1]],\n dtype='object')\n assert self.check(idata, data)","title":""},{"docid":"304f8cdcc3cedcf12bae43d20c71f10f","score":"0.55888283","text":"def add_table_row(self, list_row_entries):\r\n if INVALID_COLUMN_NUMBER == self.__num_of_columns:\r\n raise TypeError(\"Before filling the table, set the names and datatypes.(\"+\r\n self.__file_name+\")\")\r\n else:\r\n if self.__num_of_columns != len(list_row_entries):\r\n raise TypeError(\"Number of entries does not match the column number of the table.(\"+\r\n self.__file_name+\")\")\r\n \r\n self.__table[I_TABLE_LINES].append(list_row_entries)","title":""},{"docid":"2150b6b3b340c0e47b1c36e16e49f6f7","score":"0.5588518","text":"def put(self, tableName, rows):\n if isinstance(rows, list):\n return self._jsparkSession.put(tableName, [rows, ])\n elif isinstance(rows, tuple):\n return self._jsparkSession.put(tableName, rows)\n else:\n raise TypeError(\"rows should be tuple or a list\")","title":""},{"docid":"72361edc87e774709a4589d73950238b","score":"0.55853707","text":"def updateValues(self, vald, table):\n self.logger.warning(\"updateValues: not allowed for %s\", self.name)","title":""},{"docid":"9209d31ac6f708126303487acc9b1661","score":"0.5577928","text":"def insert_batch(self, table_name: str, values: Tuple, batchSize: int) -> None:\n if len(self.batchList) < batchSize:\n self.batchList.append(values)\n else:\n for val in self.batchList:\n self.cursor.excecute(\"INSERT INTO %s VALUES (%s, %s, %s, %s,'%s','%s')\" % (table_name, *val))\n self.db_connection.commit()\n self.batchList = [] # Resets temp storage of queries to be excecuted","title":""},{"docid":"9083a0a1505bae43e92bbe97cfdd23ae","score":"0.556624","text":"def addRowToTable_IDL(self, domain_id, table_id, row):\n pass","title":""},{"docid":"7f841f20025903e488e6bbc491c80282","score":"0.554408","text":"def add_row_to_table(self, table: str, row: dict):\n with sqlite3.connect(self.db) as conn:\n match = re.compile(\"P[0-9]+\")\n headers = []\n values = []\n for key in row:\n if match.match(key) or \"id\" == key:\n headers.append(key)\n values.append(row[key])\n # valuesQ MUST come before headers\n valuesQ = \", \".join([\"?\" for _ in headers])\n headers = \", \".join(headers)\n values = [self.convert(value) for value in values]\n rtuple = tuple(values)\n current = conn.execute(\n f\"SELECT * FROM {table} WHERE id=?\", (row[\"id\"],)\n ).fetchone()\n if current:\n conn.execute(f\"DELETE FROM {table} WHERE id=?\", (row[\"id\"],))\n try:\n conn.execute(\n \"INSERT INTO {0} ({1}) VALUES ({2})\".format(\n table, headers, valuesQ\n ),\n rtuple,\n )\n except sqlite3.IntegrityError as exception:\n logging.error(\n f\"INSERT INTO {table} ({headers}) VALUES ({rtuple})\"\n )\n if \"P19\" in row:\n print(row[\"P19\"])\n logging.error(\n \"Duplicates: \"\n + str(\n conn.execute(\n f\"SELECT id FROM {table} WHERE P19=?\",\n (row[\"P19\"],),\n ).fetchall()\n )\n )\n if \"P16\" in row:\n logging.error(\n \"Duplicates: \"\n + str(\n conn.execute(\n f\"SELECT id FROM {table} WHERE P16=?\",\n (row[\"P16\"],),\n )\n )\n )\n raise exception\n if current:\n cursor = conn.execute(\n f\"SELECT * FROM {table} WHERE id=?\", (row[\"id\"],)\n )\n names = [desc[0] for desc in cursor.description]\n new = cursor.fetchone()\n logging.info(new)\n entries = len(new)\n entry = 0\n while entry < entries:\n if new[entry] != current[entry]:\n message = \" \".join(\n [\n row[\"id\"],\n \"changed\",\n str(names[entry]),\n str(current[entry]),\n \"to\",\n str(new[entry]),\n os.linesep,\n ]\n )\n logging.error(message)\n with open(CHANGED_ENTRIES, \"a\") as log:\n log.write(message)\n entry = entry + 1\n conn.commit()","title":""},{"docid":"ff889cb0413cf4ebad9f4bf3beaf60e9","score":"0.55277216","text":"def insert(table: str, **values_dict):\n columns = ', '.join(values_dict.keys())\n values = [_ for _ in values_dict.values()]\n placeholders = \", \".join(\"?\" * len(values_dict.keys()))\n query = f\"INSERT INTO {table} ({columns}) VALUES ({placeholders})\"\n cursor.execute(query, values)\n conn.commit()","title":""},{"docid":"c2c3384284f38bde5d84bcba95b278ec","score":"0.5526922","text":"def sql_fill_table(table_title, data):\r\n \r\n sql = f\"\"\"INSERT INTO {table_title} VALUES (\"\"\"\r\n \r\n for i in data[0]:\r\n sql+='?,'\r\n sql = sql[:-1] +');' \r\n return sql","title":""},{"docid":"48b390a0f7dec1c5db00088cc94ceac5","score":"0.5525141","text":"def insert_to_table(table, columns, values):\n conn = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (HOST, DB_NAME, USER, PASS))\n cur = conn.cursor()\n insert_query = \"INSERT INTO %s %s VALUES %s;\" % (table, columns, values)\n cur.execute(insert_query)\n conn.commit()\n conn.close()","title":""},{"docid":"fa9fbf4ef5e6af69ec94780dd71359b4","score":"0.5524008","text":"def insertData(tableName, dbFileName, dataList):\n conn = sqlite3.connect(dbFileName)\n try:\n cols = len(dataList[0])\n query = f\"insert into {tableName} values ({'?, '*(cols - 1)} ?)\"\n for row in dataList:\n try:\n with conn:\n conn.execute(query, row)\n except sqlite3.IntegrityError as e:\n print ('Error code: ', e)\n conn.close()\n except Exception as e:\n print('Неправильный список!!', e)","title":""},{"docid":"f2e1a435a2bf3c029007633eea90aed5","score":"0.5523307","text":"def patch_table() :\n global TSUGI_DB_TO_ROW_FIELDS\n for tc in range(len(TSUGI_DB_TO_ROW_FIELDS)) :\n table = TSUGI_DB_TO_ROW_FIELDS[tc]\n for fc in range(len(table)) :\n if fc == 0 : continue; # skip table name\n if fc == 1 : continue; # skip PK/FK row\n if type(table[fc]) == type([]) and len(table[fc]) == 3 : continue\n\n # When there are two fields, duplicate the second as last\n if type(table[fc]) == type([]) and len(table[fc]) == 2 :\n TSUGI_DB_TO_ROW_FIELDS[tc][fc].append(table[fc][1])\n continue\n\n # When there is one field, use it as second and third\n field = table[fc]\n TSUGI_DB_TO_ROW_FIELDS[tc][fc] = [field,field,field]","title":""},{"docid":"6d8d3298524d4dac04c73a9bf994fbb5","score":"0.5522204","text":"def insert_row(batch, row):\n row = row.rstrip()\n line = row.split(\"\\t\");\n word = line[0].split(\"#\"); \n #print(word[0],line[1])\n batch.put(word[0], {\"data:polarity\":line[1]})","title":""},{"docid":"5fe27a17a8593d6c1577a45edbbacc15","score":"0.5507124","text":"def insert(self, entries, table_name=None):\n\t\tif not table_name:\n\t\t\ttable_name = self.default_table\n\t\tinsert_str = 'INSERT INTO \"{0}\" ({1}) VALUES ({2})'\n\t\tkeys = [key for key in entries]\n\t\trows = ','.join('\"{0}\"'.format(key) for key in keys) + ',RST_ID'\n\t\tvals = (\n\t\t\t','.join('\"{0}\"'.format(entries[key]) for key in keys) +\n\t\t\t',{0}'.format(self.rst_id)\n\t\t)\n\t\tinsert_cmd = insert_str.format(table_name, rows, vals)\n\t\treturn self.run_cmd(insert_cmd)","title":""},{"docid":"5731ce3702777cd5b5235b268fef1db4","score":"0.54837114","text":"def _validate_insert_values(self, values: ROW_TYPE) -> Result[None, str]:\n columns = self.get_columns()\n\n if len(values) != len(columns):\n return Err(f'number of values ({len(values)} != number of columns ({len(columns)})')\n\n for i, (column, value) in enumerate(zip(columns, values)):\n ty = column.ty.primitive()\n if not isinstance(value, ty):\n return Err(f'value #{i} has type {type(value).__name__}, expected: {ty.__name__}')\n\n return Ok(None)","title":""},{"docid":"ee53c5ae726d08078bd371aa640ceba5","score":"0.54828006","text":"def _insert_rows(self, table_name: str, users: list):\n conn, crsr = self.init()\n\n values = \"(\" + \"%s ,\" * 5 + \")\"\n values = values[:-3] + values[-1] + \",\"\n values = values * (len(users) // 5)\n values = values[:-1]\n\n crsr.execute(\n f\"INSERT INTO {table_name} (user_id, first_name, last_name, birthdate, img_path) VALUES {values}\"\n , users)\n\n conn.commit()\n crsr.close()\n conn.close()","title":""},{"docid":"49741f39d6da0636bc9e6309e5573d02","score":"0.5481272","text":"def insertEntries(db, cursor, entries):\n\t# Get the columns to use in the correct order\n\tcolumns = \", \".join(sorted(DATA_TO_CSV_COLUMN, key = lambda k: DATA_TO_CSV_COLUMN[k]))\n\n\t# Create the prepared statement\n\tinsertSql = \"INSERT INTO housepricehistory_soldproperty (%s) VALUES %s;\" % (columns,\n\t\t(\"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s), \" * len(entries))[:-2])\n\n\t# Execute with all entry data unpacked\n\tcursor.execute(insertSql, tuple(chain(*entries)))\n\tdb.commit()","title":""},{"docid":"f2da17aa82570b69c5a6d3a64b054b7d","score":"0.54812175","text":"def test_insert_longlist(self):\n self.tbl.insert([1, 2, 'Alyssa P. Hacker', 25, 66.25])","title":""},{"docid":"a69c645f935b1aa7250ca89925b8f543","score":"0.5477853","text":"def insert_sql(table, dict_list):\n\n if not dict_list:\n return\n\n # Names of fields\n fields = dict_list[0].keys()\n field_string = \"%s\" % \", \".join(['\"%s\"' % e for e in fields])\n\n # Placeholders for values.\n values = [list(e.values()) for e in dict_list]\n value_string = ', '.join(['%s'] * len(values[0]))\n\n cursor = connection.cursor()\n command = \"INSERT INTO {} ({}) VALUES ({});\".format(table, field_string, value_string)\n cursor.executemany(command, values)","title":""},{"docid":"a1a531c123234d9c5504a89264146bf8","score":"0.54774004","text":"def insert_csv_row(csv_row, cursor):\r\n liste_row = csv_row.strip().split(\";\")\r\n new_row = [liste_row[3], liste_row[4], liste_row[5], liste_row[7], liste_row[9]]\r\n cursor.execute(\"\"\"INSERT INTO infoarret VALUES (?,?,?,?,?) \"\"\",\r\n new_row)","title":""},{"docid":"1e766ef5427b0762d581f8fe169dbe55","score":"0.5474849","text":"def upsert(transaction: spanner_transaction.Transaction, table_name: str,\n columns: Iterable[str], values: Iterable[Iterable[Any]]) -> None:\n _logger.debug('Upsert table=%s columns=%s values=%s', table_name, columns,\n values)\n transaction.insert_or_update(table=table_name, columns=columns, values=values)","title":""},{"docid":"ea4e21db8610fa83aad8c42b95cb8357","score":"0.5468907","text":"def addRowToDataFrame(self, df, values):\n\n\t\tlistLength = len(values)\n\n\t\tnewRow = pd.DataFrame(np.array(values).reshape(1,listLength), columns = list(df.columns))\n\n\t\tdf = df.append(newRow, ignore_index=True)\n\n\t\treturn df","title":""},{"docid":"cd788d6652beb36bbd769237887afba2","score":"0.54623276","text":"def insert_csv_row(csv_row, cursor):\n cursor.execute(\"\"\"INSERT INTO infoarret VALUES (?,?,?,?,?,?,?,?,?,?,?) \"\"\",\n csv_row.strip().split(\";\"))","title":""},{"docid":"dfd99530feededdd47d33e8020108322","score":"0.54536813","text":"def insert_data(session, insert_query, df, df_cols):\n for i, row in df.iterrows():\n data = tuple([row[col] for col in df_cols])\n # print(data)\n session.execute(insert_query, data)","title":""},{"docid":"28ad15e03a7fc3ded7d842ffa28d0088","score":"0.54524577","text":"def insert(transaction: spanner_transaction.Transaction, table_name: str,\n columns: Iterable[str], values: Iterable[Iterable[Any]]) -> None:\n _logger.debug('Insert table=%s columns=%s values=%s', table_name, columns,\n values)\n transaction.insert(table=table_name, columns=columns, values=values)","title":""},{"docid":"502205e832668143e23fa33e7d87ffec","score":"0.54490775","text":"def test_insert_row(self):\n vizual.insert_row(\n dataset_name='ABC',\n position=1,\n validate=True\n )","title":""},{"docid":"2eff4f18c3f198653ccb81f34ac13cf8","score":"0.54478765","text":"def insert(tokens):\n def get_comma_seperated_contents(tokens):\n contents = []\n pop_and_check(tokens, \"(\")\n while True:\n item = tokens.pop(0)\n contents.append(item)\n comma_or_close = tokens.pop(0)\n if comma_or_close == \")\":\n return contents\n assert comma_or_close == ',', comma_or_close\n\n pop_and_check(tokens, \"INSERT\")\n pop_and_check(tokens, \"INTO\")\n table_name = tokens.pop(0)\n if tokens[0] == \"(\":\n col_names = get_comma_seperated_contents(tokens)\n qual_col_names = [QualifiedColumnName(col_name, table_name)\n for col_name in col_names]\n else:\n qual_col_names = None\n pop_and_check(tokens, \"VALUES\")\n while tokens:\n row_contents = get_comma_seperated_contents(tokens)\n if qual_col_names:\n assert len(row_contents) == len(qual_col_names)\n\n if self.database.transaction.get_open_transactions():\n self.database.locking_state_add(\"RESERVED\")\n db_copy = self.get_db_copy()\n db_copy.insert_into(table_name, row_contents, qual_col_names=qual_col_names)\n\n else:\n self.database.insert_into(table_name, row_contents, qual_col_names=qual_col_names)\n if tokens:\n pop_and_check(tokens, \",\")","title":""},{"docid":"a3623a7acc61b75a394fe5e486fa1cdc","score":"0.54473454","text":"def add_row(self, values, index=None, **kwargs):\r\n for i in self.frame.values():\r\n i.destroy()\r\n self.frame = {}\r\n if index is None:\r\n index = len(self.values) \r\n try:\r\n self.values.insert(index, values)\r\n self.rows+=1\r\n except IndexError: pass\r\n \r\n self.draw_table(**kwargs)\r\n self.update_data()","title":""},{"docid":"40c974886f7ac6939494b3f2cd4b385c","score":"0.54312664","text":"def insert(self, table_id, values):\n stringValues = \"\"\n count = 1\n cols = values.keys()\n values = values.values()\n for value in values:\n if type(value).__name__=='int':\n stringValues = '%s%d' % (stringValues, value)\n elif type(value).__name__=='float':\n stringValues = '%s%f' % (stringValues, value)\n else:\n stringValues = \"%s'%s'\" % (stringValues,\n value.encode('string-escape'))\n if count < len(values): stringValues = \"%s,\" % (stringValues)\n count += 1\n\n return 'INSERT INTO %d (%s) VALUES (%s)' % \\\n (int(table_id), ','.join([\"'%s'\" % col for col in cols]), stringValues)","title":""},{"docid":"f33fbe553d59e3fe3c4e980e285b1a3f","score":"0.5426162","text":"def Insert_Values_To_Table(self, df, table_name):\n conn = self.__connect_to_db()\n if conn is not None:\n cur = conn.cursor()\n\n db_columns = self.__get_column_names_from_db_table(cur, table_name)\n try:\n df[db_columns].to_sql(name=table_name, con=conn, if_exists='append', index=False)\n print('SQL insert process finished')\n except sqlite3.Error as err:\n print(f'Value Insersion Failed due to {err}')\n finally:\n conn.close() \n else:\n print('Connection to database failed')\n return None","title":""},{"docid":"34c9c5b30383b2655b9499aa301af322","score":"0.5423953","text":"def insert(query, records, cur):\n for record in records:\n cur.execute(query, record)","title":""},{"docid":"b5fdaf2d08612ef0e125cbcfe51a9aeb","score":"0.54170585","text":"def insert(self, keys, values):\n self.id2value.insert(self._get_key(keys), values)","title":""},{"docid":"c55cb40536dfd697797bfe2c023d9cfa","score":"0.54154116","text":"def addRow(self, *value):\n if len(value) == 1 and isinstance(value[0], (tuple, list)):\n value = value[0]\n assert len(value) == self.getNumCols()\n self._impl.addRow(Tuple(value)._impl)","title":""},{"docid":"fb4d41c697bc9879d4b5dc8da7c277a5","score":"0.5410713","text":"def append_new_entry(self, connection, values, column_names):\n self.logger.info('New data gathered:\\n\\t names: %s\\n\\t values: %s' % (values, column_names))\n unique_id = db_api.add_new_row(connection=connection,\n table=self.table)\n values.append(unique_id)\n db_api.update_item(connection=connection,\n table=self.table,\n value_tuple=tuple(values),\n column_names=column_names)\n # not sure why this is needed but a unit test was failing because the nutrition get_new_data result somehow had\n # the unique_id appended even though nothing is returned in this function. No other tests showing this.\n values.pop(-1)","title":""},{"docid":"28565c0aa7012cefe14bd5965bde5308","score":"0.5400337","text":"def insert(self, key, value): #still need to be checked \n if type(key) == tuple:\n for check in key: \n if type(check) == dict or type(check) == list:\n print(\"key is ILLEGAL!\")\n return None\n num_key = len(key)\n elif type(key)== int or type(key)== float:\n num_key = key\n elif type(key) == str:\n num_key = superord(key)\n else: print(\"key is ILLEGAL!\"); return None\n i = self.hash_mod(num_key)\n \n for k in self.table[i]:\n if k[0] == key:\n #print(\"yeaaaa!!!\")\n k[1] = value #why when i tried to change the whole list it failed?\n if [key,value] not in self.table[i]:\n self.table[i].append([key,value])","title":""},{"docid":"27afdc3d4c1144e08da2be1f6f1a018e","score":"0.5400116","text":"def insert_from_list(company_id, data, table, columns):\n\n v = ' VALUES '\n query = 'INSERT INTO ' + table + columns\n length = len(data)\n for index, item in enumerate(data):\n v += '(\"' + str(item) + '\",' + '\"' + str(company_id) + '\")'\n if index != length - 1:\n v += ','\n query += v\n return query","title":""},{"docid":"c6b17cedf06cfff597aa295244c0613b","score":"0.5399158","text":"def save_match_to_psql(self, table_name, column_list, values_insert):\n all_val_list_or_tuple = all(isinstance(\n value, (list, tuple)) for value in values_insert)\n all_len_match = all(len(value) == len(column_list)\n for value in values_insert)\n column_string = \", \".join(column_list)\n if (all_len_match and all_val_list_or_tuple):\n for value in values_insert:\n value_string = \", \".join(value)\n self._cursor.execute(self._insert_query.format(\n table_name, column_string, value_string))\n self._pg_db.commit()\n records = self._cursor.fetchall()\n print(\"Output: \", records)\n elif (all_val_list_or_tuple == False and (len(column_list) == len(values_insert))):\n value_string = \", \".join(values_insert)\n self._cursor.execute(self._insert_query.format(\n table_name, column_string, value_string))\n self._pg_db.commit()\n records = self._cursor.fetchall()\n print(\"Output: \", records)\n else:\n print(\"The length of columns and values don't match at least for one element\")","title":""},{"docid":"d849c7d1001673e752419eb7aff5752e","score":"0.5398647","text":"def insert(self, table_name, data):\n self._ensure_writeable()\n\n # Turn the input into a dataframe\n df = self._framify(data)\n\n # Turn the dataframe into a record list\n recs = self._listify(df)\n\n # Insert the record list\n with self.connector.connection as connection:\n connection[table_name].insert_many(recs)\n return self","title":""},{"docid":"0787b7d1cd76a105e5a634653cf0b834","score":"0.53985065","text":"def insert_value_in_table(server, table, db, cursor, column, value):\n insert = \"\"\"REPLACE INTO %s (hostname, %s)\n VALUES ('%s', '%s')\n \"\"\" % (table, column, server, value)\n try:\n cursor.execute(insert)\n db.commit()\n\n except e:\n logger.error(\"%s: %s\" % (server, str(e)))\n db.rollback()\n\n return","title":""},{"docid":"f1177033fc41ff63240a3bcf3848068d","score":"0.5393329","text":"def test_insert_lists(self):\n self.insert()\n data = self.tbl.select()\n assert self.check(self.idata, data)","title":""},{"docid":"fc9c6e39317abd18cdfa7eb231fbc37c","score":"0.539308","text":"def register_query(values, table):\n cols = '('\n v = ' VALUES ('\n length = len(values)\n for index, (key, value) in enumerate(values.items()):\n cols += key + ''\n v += '\"' + str(value) + '\"'\n if index != length - 1:\n cols += ','\n v += ','\n query = 'INSERT INTO ' + table + cols + ')' + v + ')'\n return query","title":""},{"docid":"d0e6a9ea32bdd991ccae0337b82c4157","score":"0.5389561","text":"def insert_sample(cursor,sample_list):\n cursor.execute(\"BEGIN TRANSACTION\")\n cursor.execute('''insert into samples values \\\n (?,?,?,?,?,?,?,?)''', sample_list)\n cursor.execute(\"END\")","title":""},{"docid":"3c35f3a1405eea52b373e83ed9b8ac11","score":"0.5383944","text":"def synapse_table(cur,data):\n sql = (\"insert into synapse \"\n \"(idsynapse,idcontin,idpre,idpost) \"\n \"values (%s,%s,%s,%s)\")\n cur.executemany(sql,data)","title":""},{"docid":"456d46a6e193b28c84713fe2ea50c32c","score":"0.5380284","text":"def insert(self, table_name=None, repeat=1, **attr):\n assert(attr and table_name != None)\n SQL_FMT = \"insert into %s(%s) values(%s);\"\n keys = attr.keys()\n values = attr.values()\n cols = ','.join(i for i,j in zip(keys,values) if j != None)\n vals = ','.join(Mydb.process_value(i) for i in values if i != None)\n SQL = SQL_FMT%(table_name,cols,vals)\n for i in xrange(repeat):\n db_debug(SQL)\n self.cursor.execute(SQL)\n self.db.commit()","title":""},{"docid":"96d37db698060910bedda48aec6dbd19","score":"0.5368154","text":"def test_insert_dictlist(self):\n self.tbl.insert([\n {\n 'id': 1,\n 'name': 'Alyssa P. Hacker',\n 'age': 25,\n 'height': 66.25\n },\n {\n 'id': 2,\n 'name': 'Ben Bitdiddle',\n 'age': 24,\n 'height': 70.1\n }])\n data = self.tbl.select()\n idata = np.array([\n [1, 'Alyssa P. Hacker', 25, 66.25],\n [2, 'Ben Bitdiddle', 24, 70.1]],\n dtype='object')\n assert self.check(idata, data)","title":""},{"docid":"aebe13e239bf0b15725f21891d8c4265","score":"0.53632635","text":"def insert_data(data_list, db):\n\t\tcursor = db.cursor()\n\t\tfor data_row in data_list:\n\t\t\ttry:\n\t\t\t\tcursor.execute('insert into celebrities values (?, ?, ?, ?, ?)',data_row)\n\t\t\texcept Exception, e:\n\t\t\t\t# When it fails integrity error or null data is tried to be inserted\n\t\t\t\tcontinue\n\t\tdb.commit()","title":""},{"docid":"d2cddcf278111eb778d3d97260585872","score":"0.5351996","text":"def insert(self, key, value):\n rows = self.pre(key)\n for ti in torch.arange(0, self.num_tables):\n self.tables[ti][rows[ti]].append(value)","title":""},{"docid":"c64780800e147f907648a36a243bff19","score":"0.5349216","text":"def insert_rows(self, cursor):\n if isinstance(self.columns[0], basestring):\n column_names = ','.join([name for name in self.columns])\n elif len(self.columns[0]) == 2:\n column_names = ','.join([name for name, _type in self.columns])\n else:\n raise Exception('columns must consist of column strings or '\n '(column string, type string) tuples (was %r ...)'\n % (self.columns[0],))\n\n value_list = []\n row_count = 0\n for row_count, row in enumerate(self.rows(), start=1):\n entry = tuple([coerce_for_mysql_connect(elem) for elem in row])\n value_list.append(entry)\n if row_count % self.insert_chunk_size == 0:\n self._execute_insert_query(cursor, value_list, column_names)\n value_list = []\n\n if self.overwrite and not self.allow_empty_insert and row_count == 0:\n raise Exception('Cannot overwrite a table with an empty result set.')\n\n if len(value_list) > 0:\n self._execute_insert_query(cursor, value_list, column_names)","title":""}],"string":"[\n {\n \"docid\": \"bdcc00bfd47dbcffa0f28bd39ea7408b\",\n \"score\": \"0.7562238\",\n \"text\": \"def add_row(self, tname, vallist, commit=True):\\n s = u'INSERT INTO {0:s} values'.format(tname)\\n qs = ['?'] * len(vallist)\\n qs = \\\",\\\".join(qs)\\n\\n try:\\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\\n except Exception, e:\\n self.logger.error(\\\"Failed to add row: %s\\\" % e)\\n # print '''%s (%s)''' % (s, qs)\\n # print \\\"vallist\\\", vallist\\n return {'mesg': \\\"Failed to add row, rollback: %s\\\" % e, 'id': None}\\n else:\\n if commit:\\n self.commit()\\n return {'mesg': \\\"ok\\\", 'id': self.c.lastrowid}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b367ba6088587989613e396e56ffb6a\",\n \"score\": \"0.65765333\",\n \"text\": \"def insert(self, table, cols, vals):\\n # lets assume we will only have 2 columns, for simplification\\n assert len(cols) == len(vals) == 2\\n cursor = self.connection.cursor()\\n sql = \\\"CREATE TABLE IF NOT EXISTS %s (%s, %s, added)\\\" \\\\\\n % (table, cols[0], cols[1])\\n cursor.execute(sql)\\n sql = \\\"INSERT INTO %s (%s, %s, added) VALUES \\\" \\\\\\n \\\"('%s','%s',CURRENT_TIMESTAMP)\\\" \\\\\\n % (table, cols[0], cols[1], vals[0], vals[1])\\n cursor.execute(sql)\\n\\n self.connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff56a06893378da18860c189859891dd\",\n \"score\": \"0.6538034\",\n \"text\": \"def insert_row_to_tbl(table_row_values, table):\\n if table == 'Medical':\\n new_row = Medical(**table_row_values)\\n db.session.add(new_row)\\n db.session.commit()\\n return new_row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ff3d6e8b01e7431c8037cb045951c8\",\n \"score\": \"0.64920574\",\n \"text\": \"def addRow(self, listCols, listValues):\\n colNames = \\\", \\\".join(listCols)\\n colNames = \\\"(\\\" + colNames + \\\")\\\"\\n values = re.sub(\\\"\\\\[\\\", \\\"\\\", str(listValues))\\n values = re.sub(\\\"\\\\]\\\", \\\"\\\", values)\\n values = \\\"(\\\" + values + \\\")\\\"\\n request = str(\\\"INSERT INTO \\\" + self.DBManNew.getNameTable() +\\n \\\" \\\" + colNames + \\\" VALUES \\\" + values + \\\";\\\")\\n return request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b443c36a9fdbacaad788a7f7a24afb94\",\n \"score\": \"0.64618\",\n \"text\": \"def insert_values(self, values):\\r\\n try:\\r\\n line = \\\"INSERT INTO {} VALUES ({},\\\\'{}\\\\')\\\".format(self.table_name, self.last_row, '\\\\',\\\\''.join(values))\\r\\n self.last_row = self.cursor.execute(line).lastrowid\\r\\n self.do_commit()\\r\\n print(f'{values} insert to the table {self.table_name}')\\r\\n except:\\r\\n print(\\\"Error insert_value!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6360cfc4db5daa8fa9d92f34b5d78630\",\n \"score\": \"0.64482224\",\n \"text\": \"def insertRow(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da61b3e3de75817ba25831889c575a5c\",\n \"score\": \"0.64472973\",\n \"text\": \"def add_row(tablename, data, conn):\\n sql = \\\"INSERT INTO %s\\\" % tablename\\n columns, vals = zip(*data)\\n colstr =\\\"(\\\"\\n valstr = \\\"(\\\"\\n for i, column in enumerate(columns):\\n if i > 0:\\n colstr += \\\", \\\"\\n valstr += \\\", \\\"\\n colstr += \\\"%s\\\" % column\\n valstr += \\\"?\\\" \\n sql = sql + colstr +\\\") VALUES\\\" + valstr + \\\");\\\"\\n cur = conn.cursor()\\n try:\\n res = cur.execute(sql, tuple(vals))\\n except Error as e:\\n print \\\"Error in add_row:\\\", (e), \\\". SQL:\\\", sql, vals\\n return None\\n print \\\"SQL:\\\", sql, vals\\n rowid = cur.lastrowid \\n conn.commit()\\n return rowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec8206284703aa01c33672c7ab8575d\",\n \"score\": \"0.63869905\",\n \"text\": \"def update_row(self, tname, col, val, valtuple):\\n i = len(valtuple) / 2\\n if i == 0:\\n i = 1\\n qs = ['%s = \\\"%s\\\"'] * i\\n qs = \\\",\\\".join(qs)\\n s0 = 'UPDATE %s SET' % tname\\n s1 = ' %s WHERE ' % qs\\n s2 = '%s=\\\"%s\\\"' % (col, val)\\n s1 = s1 % valtuple\\n #print s0 + s1 + s2\\n self.c.execute(s0 + s1 + s2)\\n self.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a1398621baabdbae59f2ac1d89d7ae1\",\n \"score\": \"0.63759947\",\n \"text\": \"def insert(self, table, values):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f06878b058a44b6b160a9d53228005\",\n \"score\": \"0.6347618\",\n \"text\": \"def insertMany(self, tablename, vals, transaction=None):\\n colnames = \\\",\\\".join(self.escapeColNames(vals[0].keys()))\\n params = \\\",\\\".join([self.insertArgsToString(val) for val in vals])\\n args = []\\n for val in vals:\\n args = args + val.values()\\n q = \\\"INSERT INTO %s (%s) VALUES %s\\\" % (tablename, colnames, params)\\n if transaction is not None:\\n return self.executeTxn(transaction, q, args)\\n return self.executeOperation(q, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b8babfec9e69b069ce302d89ab672dc\",\n \"score\": \"0.63271517\",\n \"text\": \"def bulk_row_insert(self, table_name: str, columns: List[Column],\\n rows: List[List]):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05bfe2c79b5a0b0ff85d9da4264ef2ce\",\n \"score\": \"0.6324573\",\n \"text\": \"def insert_row(self, table_name, values, columns=\\\"\\\"):\\n input_values = ', '.join(map(lambda x: \\\"'%s'\\\", values))\\n input_values = input_values % tuple(values)\\n sql_query = \\\"INSERT INTO %s %s \\\" \\\\\\n \\\"VALUES (%s)\\\"\\n sql_query = sql_query % (table_name, columns, input_values)\\n sql_query = sql_query.encode('utf-8')\\n try:\\n self.cur.execute(sql_query)\\n self.con.commit()\\n except Exception as ex:\\n print(ex)\\n self.con.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"079f9bc36e58e0cd541eaf5a7292e74a\",\n \"score\": \"0.62759054\",\n \"text\": \"def insertfromdata(tablename, records, multiple=True):\\n if multiple:\\n lst = records[0]\\n else:\\n lst = records\\n s = 'INSERT INTO ' + str(tablename) + ' VALUES '\\n s += \\\"( \\\" + \\\", \\\".join([\\\"?\\\"]*len(lst)) + \\\")\\\"\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea12a87bef8d902889981416dee5f6c\",\n \"score\": \"0.6269725\",\n \"text\": \"def bulk_insert(self, table_spec, rows):\\n extras.execute_values(\\n self.cursor,\\n 'INSERT INTO {0:s} VALUES %s ON CONFLICT DO NOTHING'.format(table_spec),\\n rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"676a3833bc5289c8253c4eb330ee1f1b\",\n \"score\": \"0.6252999\",\n \"text\": \"def insert(self, row: List):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a888580bddbcae539609f3db535fb1e\",\n \"score\": \"0.624624\",\n \"text\": \"def insert_into_table(table, data):\\r\\n\\r\\n for line in data:\\r\\n cols = ', '.join(\\\"'{}'\\\".format(col) for col in line.keys())\\r\\n vals = ', '.join(':{}'.format(col) for col in line.keys())\\r\\n sql = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(table, cols, vals)\\r\\n try:\\r\\n cursor.execute(sql, line)\\r\\n except sqlite3.IntegrityError:\\r\\n # ignore duplicate\\r\\n pass\\r\\n except sqlite3.DatabaseError as err:\\r\\n raise err\\r\\n connect.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591a90d12855e484fd6cdd7c6b1f40c5\",\n \"score\": \"0.6210337\",\n \"text\": \"def insert(self, tablename, vals, transaction=None):\\n params = self.insertArgsToString(vals)\\n colnames = \\\"\\\"\\n if len(vals) > 0:\\n ecolnames = self.escapeColNames(vals.keys())\\n colnames = \\\"(\\\" + \\\",\\\".join(ecolnames) + \\\")\\\"\\n params = \\\"VALUES %s\\\" % params\\n q = \\\"INSERT INTO %s %s %s\\\" % (tablename, colnames, params)\\n if transaction:\\n return self.executeTxn(transaction, q, vals.values())\\n return self.executeOperation(q, vals.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c262462e4f1c734b4752a96a7793e254\",\n \"score\": \"0.62078285\",\n \"text\": \"def insert_data(self, conn, cur, row: pd.DataFrame, table_name: str) -> None:\\n try:\\n columns = self.tables[table_name][\\\"headers\\\"]\\n except KeyError as e:\\n logging.error(\\n f\\\"You forgot to specify the 'headers' on table {table_name}.\\\")\\n raise e\\n\\n # Check if the element is a nested list and extract it.\\n if isinstance(columns[0], list):\\n for more_columns in columns:\\n # Generate enough parameters for query string.\\n parameter_string = ','.join(['%s']*len(more_columns))\\n\\n # Extract relevant columns from dataframe and cast it to a list.\\n row_list = row[more_columns].values.tolist()\\n\\n # Do not perform insert if the first value is empty (which is always the primary key).\\n if not row_list[0]:\\n continue\\n\\n # Cast empty values to None for DB adapter to work.\\n row_list = [None if not x else x for x in row_list]\\n # print(f\\\"--- {row_list}\\\")\\n\\n try:\\n attribute_string = ','.join(\\n self.tables[table_name][\\\"attributes\\\"])\\n unique_string = ','.join(\\n self.tables[table_name][\\\"uniques\\\"])\\n except KeyError as e:\\n logging.error(\\n f\\\"Your forgot to specify either the 'attributes' or the 'uniques' ont the table {table_name}\\\")\\n raise e\\n\\n # Catch uniqueness constraint with \\\"UPSERT\\\" method\\n if not unique_string:\\n insert_string = f\\\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string})\\\"\\n else:\\n insert_string = f\\\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string}) ON CONFLICT ({unique_string}) DO NOTHING\\\"\\n\\n try:\\n cur.execute(insert_string, row_list)\\n except UniqueViolation as e:\\n logging.error(\\n f\\\"Something went wrong during inserting into: {table_name}\\\")\\n logging.info(e)\\n except InFailedSqlTransaction as e:\\n logging.error(\\n f\\\"Transaction stopped. Rolling back...: {table_name}\\\")\\n logging.info(e)\\n # conn.rollback()\\n pass\\n\\n else:\\n # Generate enough parameters for query string.\\n parameter_string = ','.join(['%s']*len(columns))\\n\\n # Extract relevant columns from dataframe and cast it to a list.\\n # print(row)\\n # _ = input(\\\"Pause...\\\")\\n row_list = row[columns].values.tolist()\\n\\n # Do not perform insert if the first value is empty (which is always the primary key).\\n if not row_list[0]:\\n return\\n\\n # Cast empty values to None for DB adapter to work.\\n row_list = [None if not x else x for x in row_list]\\n # print(f\\\"--- {row_list}\\\")\\n\\n try:\\n attribute_string = ','.join(\\n self.tables[table_name][\\\"attributes\\\"])\\n unique_string = ','.join(\\n self.tables[table_name][\\\"uniques\\\"])\\n except KeyError as e:\\n logging.error(\\n f\\\"Your forgot to specify either the 'attributes' or the 'uniques' ont the table {table_name}\\\")\\n raise e\\n\\n # Catch uniqueness constraint with \\\"UPSERT\\\" method\\n if not unique_string:\\n insert_string = f\\\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string})\\\"\\n else:\\n insert_string = f\\\"INSERT INTO {table_name} ({attribute_string}) VALUES ({parameter_string}) ON CONFLICT ({unique_string}) DO NOTHING\\\"\\n\\n try:\\n cur.execute(insert_string, row_list)\\n except UniqueViolation as e:\\n logging.error(\\n f\\\"Something went wrong during inserting data into: {table_name}\\\")\\n logging.info(e)\\n except InFailedSqlTransaction as e:\\n logging.error(\\n f\\\"Transaction stopped. Rolling back...: {table_name}\\\")\\n logging.info(e)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251b607fe8ed9e4e8a5ff4a3ea373578\",\n \"score\": \"0.61890787\",\n \"text\": \"def insert(self, table_name, values):\\n\\n self.cursor.execute('INSERT INTO ' + table_name + ' VALUES(' + '\\\"' + ('\\\"' + \\\", \\\" + '\\\"').join(values) + '\\\"' +\\n ');')\\n\\n self.connector.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"354618b78044ea5428a6601a6def3fc4\",\n \"score\": \"0.6181655\",\n \"text\": \"def single_row_insert(self, table_name: str, columns: List[Column],\\n row: List):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d88e415e007fde848059375fddcf96\",\n \"score\": \"0.61611545\",\n \"text\": \"def insert(self, table, names, values):\\n names_tpl = sql_tuple(names)\\n values_tpl = sql_tuple(values)\\n cmd = \\\"insert into {} {} values {};\\\".format(table, names_tpl,\\n values_tpl)\\n self.execute(cmd)\\n rowid = str(self.cursor.lastrowid)\\n return rowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5cda637786aa76693be3f8a903ac6c\",\n \"score\": \"0.6129226\",\n \"text\": \"def insert_row(conn, value):\\n\\n sql = ''' INSERT or REPLACE into SCORES(name,score) VALUES (?,?) '''\\n cur = conn.cursor()\\n cur.execute(sql, value)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197abd2c1e43c2cccd475bb8aa8987bf\",\n \"score\": \"0.6097784\",\n \"text\": \"def insert_Data(table_name, values):\\r\\n conn = mysql.connect()\\r\\n cursor = conn.cursor(pymysql.cursors.DictCursor)\\r\\n cols = values.keys()\\r\\n vals = values.values()\\r\\n strComma = \\\"','\\\"\\r\\n newVals = \\\"'\\\" + strComma.join(vals) + \\\"'\\\"\\r\\n qs_insert = \\\"INSERT INTO %s (%s) VALUES(%s)\\\" % (table_name, \\\",\\\".join(cols), \\\"\\\" + newVals)\\r\\n try:\\r\\n cursor.execute(qs_insert)\\r\\n conn.commit()\\r\\n last_insert_id = cursor.lastrowid\\r\\n cursor.close()\\r\\n conn.close()\\r\\n return True\\r\\n except Exception as err:\\r\\n print(err, '-- occurred during adding data')\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a151c2f7f55d67959dca50ec3006af2\",\n \"score\": \"0.6080456\",\n \"text\": \"def autoexec_insert(self, table, keyvals, literals=[], optype=\\\"INSERT\\\", **kwargs):\\n optype = optype.upper()\\n if optype not in [\\\"INSERT\\\", \\\"REPLACE\\\", \\\"INSERT IGNORE\\\", \\\"ON DUPLICATE\\\"]:\\n raise ProgrammingError(\\n \\\"MySQL operation must be 'INSERT', 'REPLACE', 'INSERT IGNORE', 'ON DUPLICATE',\\\" \\\\\\n \\\"got '%s'\\\" % optype)\\n if type(keyvals) is not dict:\\n raise Exception(\\\"Data must be a dictionary\\\")\\n if type(literals) is not list:\\n raise Exception(\\\"Literals must be a list\\\")\\n table = self.__fix_table(table)\\n parsed_literals = self.__process_literals(optype, keyvals, literals)\\n (column_names, column_substituion, column_values, literal_values, ondupe_out) = parsed_literals\\n ondupe_values = []\\n inital_type = optype\\n if optype == \\\"ON DUPLICATE\\\":\\n inital_type = \\\"INSERT\\\"\\n if inital_type in [\\\"INSERT\\\", \\\"REPLACE\\\"]:\\n inital_type += \\\" INTO\\\"\\n rownames = \\\",\\\".join(\\\"`%s`\\\" % k for k in column_names)\\n rowvalues = \\\", \\\".join(k for k in column_substituion)\\n query = \\\"%s %s\\\\n\\\" \\\\\\n \\\"(%s)\\\\n\\\" \\\\\\n \\\"VALUES(%s)\\\" % (inital_type, table, rownames, rowvalues) % tuple(literal_values)\\n if optype == \\\"ON DUPLICATE\\\":\\n dupe_out = \\\",\\\\n\\\".join(\\\"%s\\\" % k for k in ondupe_out)\\n query += \\\"\\\\nON DUPLICATE KEY UPDATE\\\\n\\\" \\\\\\n \\\"%s\\\" % dupe_out\\n column_values += ondupe_values\\n return self.execute(query, args=tuple(column_values), commit=True, get_id=True, raise_exc=True, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a67e5076bb473284a8bde68375a44dd\",\n \"score\": \"0.60337764\",\n \"text\": \"def insertValues(table, values):\\n query = \\\"INSERT INTO \\\" + table + \\\" VALUES (\\\"\\n for i in values : query += DataBase.toDBString(i) + \\\", \\\"\\n query = query[0:-2] + \\\");\\\"\\n DataBase.DBdriver.execute(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c1bb0aaef2ef4cfc7c6b4305a7bd52\",\n \"score\": \"0.60129875\",\n \"text\": \"def insert(self, tableName, rows):\\n if isinstance(rows, list):\\n return self._jsparkSession.insert(tableName, [rows, ])\\n elif isinstance(rows, tuple):\\n return self._jsparkSession.insert(tableName, rows)\\n else:\\n raise TypeError(\\\"rows should be tuple or a list\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45cc42dfb06731a75bd23c3ff4082757\",\n \"score\": \"0.6006435\",\n \"text\": \"def sqlite_insert(conn, table, row):\\n # validate table, row before\\n cols = ', '.join('\\\"{}\\\"'.format(col) for col in row.keys())\\n vals = ', '.join(':{}'.format(col) for col in row.keys())\\n sql = 'INSERT INTO \\\"{0}\\\" ({1}) VALUES ({2})'.format(table, cols, vals)\\n conn.cursor().execute(sql, row)\\n conn.commit()\\n conn.cursor().close()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167b50e6930123e9021e23a4a3f69560\",\n \"score\": \"0.6002454\",\n \"text\": \"def bulk_insert(self, rows: List[List]):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166b3303a4db26caa40f98efa77e46b5\",\n \"score\": \"0.5991085\",\n \"text\": \"def _execute_insert_query(self, cursor, value_list, column_names):\\n\\n num_cols = len(self.columns)\\n num_rows = len(value_list)\\n\\n # Check data squareness. There should be no rows with missing or extra columns.\\n for elem in value_list:\\n if len(elem) != num_cols:\\n raise Exception(\\\"Misaligned data in mysql_load: \\\"\\n \\\"row '{row}' does not match columns '{columns}'\\\".format(\\n row=elem, columns=column_names\\n ))\\n\\n # The \\\"%s\\\" placeholder is used by the mysql-connector library\\n # to execute the prepared statement, it is not used with a\\n # traditional python \\\"%\\\" operator.\\n parameters = \\\"(\\\" + \\\",\\\".join([\\\"%s\\\"] * num_cols) + \\\")\\\"\\n all_parameters = \\\",\\\".join([parameters] * num_rows)\\n\\n table_name = self.table\\n if self.overwrite and self.use_temp_table_for_overwrite:\\n table_name = self.table + '_temp'\\n\\n query = \\\"INSERT INTO {table} ({column_names}) VALUES {values}\\\".format(\\n table=table_name, column_names=column_names, values=all_parameters\\n )\\n cursor.execute(query, list(chain.from_iterable(value_list)))\\n log.debug(\\\"Wrote %d rows to table %s\\\", num_rows, self.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26d509e63db2101f0b6444f1dcae57a\",\n \"score\": \"0.5951947\",\n \"text\": \"def lst_to_field(table, field, lst):\\n if len(lst) == 0:\\n message(\\\"No values to add to '{}'.\\\".format(field))\\n elif field_exists(table, field): \\n with arcpy.da.UpdateCursor(table, [field]) as cursor:\\n # For row in cursor:\\n for i, row in enumerate(cursor):\\n row[0] = lst[i]\\n cursor.updateRow(row)\\n else:\\n message(\\\"{} field not found in {}\\\".format(field, table))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f5263ef268a15960a073cbf134f5a9\",\n \"score\": \"0.5942753\",\n \"text\": \"def insert_data(self, table_name: str, values: Tuple) -> None:\\n # Need to parse the columns in 'values` differently depending on what table we are inserting into\\n if table_name == 'User':\\n query = \\\"INSERT INTO %s VALUES ('%s', %s)\\\"\\n elif table_name == 'Activity':\\n query = \\\"INSERT INTO %s (user_id, transportation_mode, start_date_time, end_date_time) VALUES ('%s', %s, '%s', '%s')\\\"\\n try:\\n self.cursor.execute(query % (table_name, *values))\\n self.db_connection.commit()\\n except Exception as e:\\n print(\\\"Unable to add values to table:\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"186c39a3d65c11b6749639841c310d4c\",\n \"score\": \"0.5940869\",\n \"text\": \"def insert(self, row):\\n values = None\\n if isinstance(row, dict):\\n keys = row.keys()\\n values = [list(row.values())]\\n elif isinstance(row, (list, tuple)):\\n keys = row[0].keys()\\n values = [list(r.values()) for r in row]\\n q = [\\\"?\\\"] * len(keys)\\n q = \\\",\\\".join(q)\\n sql = \\\"\\\"\\\"INSERT INTO {table} ({fields})\\n VALUES({q})\\\"\\\"\\\".format(\\n table=self._table_name, fields=\\\",\\\".join(keys), q=q\\n )\\n inserts = []\\n cur = self._con.cursor()\\n cur.execute(sql, list(values[0]))\\n self._con.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ed46778e5e2c51e7bb24a4bb966970\",\n \"score\": \"0.5935122\",\n \"text\": \"def insert_row(dbconn, tablename, *args):\\n cur = dbconn.cursor()\\n cur.execute(\\\"INSERT INTO '{name}' VALUES{args}\\\".format(name=tablename, args=args))\\n dbconn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a5c287ec52513e72baae9d6aedc99a\",\n \"score\": \"0.5901541\",\n \"text\": \"def sqlInsert(sql_connection, table, col_list, values):\\n\\n with sql_connection.cursor() as cursor:\\n sub_set = ','.join(['%s' for _ in range(len(values))])\\n command = \\\"INSERT INTO \\\" + table + \\\"(\\\" + ','.join(col_list) + \\\") VALUES (\\\" + sub_set + \\\");\\\"\\n cursor.execute(command, values)\\n\\n sql_connection.commit()\\n\\n return 0 # return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a408572eb79479b6ce2781d2488c038\",\n \"score\": \"0.58750194\",\n \"text\": \"def insert_row(cur, data):\\n # Build INSERT statement\\n q = \\\"INSERT INTO facebook (\\\" + ','.join(_db_cols) + \\\") \\\\\\n VALUES (\\\" + ','.join(['?']*len(_db_cols)) + \\\");\\\"\\n # Build record to insert\\n row = tuple(data.get(c) for c in _db_cols)\\n cur.execute(q, row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cde2561de53ed5a0232d41171252cf\",\n \"score\": \"0.58744645\",\n \"text\": \"def insert(self, idx, val):\\n if idx is None:\\n idx=self.nrows()\\n if self._add_item(idx,val) is not self:\\n raise ValueError(\\\"can't insert items while preserving column type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2fbba6cb3c73bf382879f0b6239110\",\n \"score\": \"0.58527756\",\n \"text\": \"def populate_table(conn, row):\\n sql = ''' INSERT INTO data_points(n,p,data)\\n VALUES(?,?,?) '''\\n cur = conn.cursor()\\n cur.execute(sql, row)\\n conn.commit()\\n return cur.lastrowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15780a00f538cbdaee5252df5a9aa13e\",\n \"score\": \"0.5827503\",\n \"text\": \"def insert(self, tablename, data):\\n cols = []\\n values = []\\n for col, val in data.items():\\n cols.append(col)\\n values.append(val)\\n query = \\\"INSERT INTO {tblName} ({columns}) VALUES ({questionmarks})\\\"\\n query = query.format(\\n tblName=tablename,\\n columns=', '.join(cols),\\n questionmarks=', '.join(len(values)*'?'))\\n self.cursor.execute(query, tuple(values))\\n self.connection.commit()\\n if tablename in ['tblProject', 'tblWorkflow']:\\n return self.cursor.lastrowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235bac13b403c38570ff40d41983a82f\",\n \"score\": \"0.581869\",\n \"text\": \"def insert(db, schema, table, data):\\n engine = get_engine(db)\\n tablename = tablepattern.format(**locals())\\n columns = []\\n values = []\\n for column, value in data.items():\\n columns.append(column)\\n values.append(\\\"'%s'\\\" % str(value))\\n columns = ', '.join(columns)\\n values = ', '.join(values)\\n sql = \\\"\\\"\\\"INSERT INTO {tablename} ({columns}) VALUES ({values})\\n \\\"\\\"\\\".format(**locals())\\n with engine.begin() as con:\\n con.execute(sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e308b495278d69acfaa4c0f2b61ed844\",\n \"score\": \"0.57868624\",\n \"text\": \"def insert_data(self, keys, values):\\n\\n t = range(self.num_tables)\\n self.tables = [[[] for _ in range(self.table_size)] for _ in t]\\n rows = self.put(keys)\\n\\n # can parallelize outer loop for each table?\\n\\n for ti in torch.arange(0, self.num_tables).long():\\n #for val in values:#torch.arange(0, len(values)):\\n for row, val in zip(rows[ti], values):\\n self.tables[ti][row].append(int(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469b937b93fefb4035a234851fd5a533\",\n \"score\": \"0.5773728\",\n \"text\": \"def insert(self):\\n self.cursor.executemany(self.sql_insert, self.val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebad24fb8141669d8d01ec463d1b340\",\n \"score\": \"0.57394326\",\n \"text\": \"def insert_adjustment_values(c):\\n adjustment_values = load_adjustment_values()\\n # insert into table ------------------------------------\\n q_insert = (\\n 'INSERT INTO adjustment_values\\\\n'\\n ' (prst, seq_nmbr, name, val)\\\\n'\\n ' VALUES (?, ?, ?, ?)\\\\n'\\n )\\n c.executemany(q_insert, adjustment_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9824dbbaf1f99700bcf6d2e5c3703f49\",\n \"score\": \"0.5738879\",\n \"text\": \"def insert_into_table(values, connection):\\n cursor_obj = connection.cursor()\\n entities = (values['id'], values['name'], values['rating'], values['date'])\\n cursor_obj.execute('INSERT INTO games(id, name, rating, launchDate) VALUES(?, ?, ?, ?)', entities)\\n connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9699f9557b110348e95cfb7267ef929c\",\n \"score\": \"0.5735011\",\n \"text\": \"def insert ( self, object, trait, row, value ):\\r\\n getattr( object, trait ) [ row: row ] = [ value ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ba35c56f0bdd0d9be7460a5c0894a4\",\n \"score\": \"0.57133347\",\n \"text\": \"def _insert(self):\\n columns = []\\n\\n self._match(tokens.INSERT_KW)\\n self._match(tokens.INTO_KW)\\n\\n table_name = self._lookahead_token(0).text\\n self._match(tokens.ID)\\n\\n self._match(tokens.SET_KW)\\n column_name = self._lookahead_token(0).text\\n self._match(tokens.ID)\\n self._match(tokens.EQUALS)\\n\\n column_val = self._expr()\\n columns.append((column_name, column_val))\\n\\n while self._lookahead_type(0) == tokens.COMMA:\\n self._match(tokens.COMMA)\\n column_name = self._lookahead_token(0).text\\n self._match(tokens.ID)\\n self._match(tokens.EQUALS)\\n column_val = self._expr()\\n columns.append((column_name, column_val))\\n\\n self._match(tokens.SEMICOLON)\\n\\n # invoke action\\n self.interpreter.insert_into(table_name, columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe82a6308ed16769e0e2eaa79cfeea4\",\n \"score\": \"0.5684314\",\n \"text\": \"def insert_row(self, table, variablen):\\n # Get the keys\\n keys = list(variablen.keys())\\n\\n # Go tru all variables\\n for index in enumerate(variablen[keys[0]]):\\n\\n # Create the list where all variables wil be collected in\\n var_lijst = []\\n\\n # Get all variables\\n for key in keys:\\n var_lijst.append(\\\"{}\\\".format(variablen[key][index[0]]))\\n\\n # Create the SQL Insert statement\\n insert = \\\"INSERT INTO {}({}) VALUES({}) \\\".format(table,\\n \\\", \\\".join(keys),\\n \\\", \\\".join(var_lijst))\\n\\n # Execute the insert statement\\n self.cursor.execute(insert)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4137488f26546e32173b22df6a09653\",\n \"score\": \"0.56783444\",\n \"text\": \"def insert(self, table, data):\\n names = ''\\n values = ''\\n for i in data.items():\\n n, v = i\\n names += f\\\"{n}, \\\"\\n values += f\\\"$${v}$$, \\\"\\n names = names[:-2]\\n values = values[:-2]\\n query = f'INSERT INTO {table} ({names}) VALUES ({values})'\\n self.do_query(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bfa505a67a8ef198570ddfdac44580\",\n \"score\": \"0.5675854\",\n \"text\": \"def insert(self, table, name, row):\\n\\n # Build insert prepared statement\\n columns = [name for name, _ in table.items()]\\n insert = SQLite.INSERT_ROW.format(table=name,\\n columns=\\\", \\\".join(columns),\\n values=(\\\"?, \\\" * len(columns))[:-2])\\n\\n try:\\n # Execute insert statement\\n self.cur.execute(insert, self.values(table, row, columns))\\n # pylint: disable=W0703\\n except Exception as ex:\\n print(\\\"Error inserting row: {}\\\".format(row), ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973b2d4b2858439c2436ef3680f9d921\",\n \"score\": \"0.5673136\",\n \"text\": \"def insert(self, table_name, column_names, values):\\n data = []\\n values_template = ''\\n # If a list of records is received\\n if type(values) == list:\\n data = [tuple([value[column] for column in column_names]) for value in values]\\n values_template = ','.join(['%s'] * len(values))\\n elif type(values) == dict:\\n data = tuple([values[column] for column in column_names])\\n values_template = '({})'.format(','.join(['%s'] * len(column_names)))\\n # Create sql query\\n sql_q = 'INSERT INTO {} ({}) VALUES {} RETURNING id;'.format(table_name, ','.join(column_names),\\n values_template)\\n result = self.__execute_sql(query=sql_q, values=data)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91aacb75f77c827ec5b77f0f5da45ba\",\n \"score\": \"0.56700295\",\n \"text\": \"def populate_table(self, table, values):\\r\\n\\t\\t# to be conpleted according to sqlite3 requirements\\r\\n\\t\\tif self.platform == STATIC.PLATFORM_WINDOWS:\\r\\n\\t\\t\\tfor value in values:\\r\\n\\t\\t\\t\\t#print (str(value.MONTH) + \\\" \\\" + value.FLOW + \\\" \\\" + value.CONTRIB + \\\" \\\")\\r\\n\\t\\t\\t\\t# rev ex rox\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"Rev\\\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.REV_EX_ROX\\r\\n\\t\\t\\t\\t# rpk\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"RPK\\\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.RPK\\r\\n\\t\\t\\t\\t# ask\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"ASK\\\"]][STATIC.equivFlow[value.FLOW]][STATIC.equivYield[value.CONTRIB]][value.MONTH] = value.ASK\\r\\n\\t\\telse:\\r\\n\\t\\t\\tfor value in values:\\r\\n\\t\\t\\t\\t# rev ex rox\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"Rev\\\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[4]\\r\\n\\t\\t\\t\\t# rpk\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"RPK\\\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[5]\\r\\n\\t\\t\\t\\t# ask\\r\\n\\t\\t\\t\\ttable[STATIC.equivData[\\\"ASK\\\"]][STATIC.equivFlow[value[2]]][STATIC.equivYield[value[1]]][value[0]] = value[6]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb6a1e67c0a46f56e00fe83b8b5d1de\",\n \"score\": \"0.56561756\",\n \"text\": \"def add_row(self, vals, names=None, units=None):\\n if isinstance(vals, Row):\\n vals = DataClass.from_table(vals)\\n else:\\n if isinstance(vals, Mapping):\\n keys_list = list(vals.keys())\\n vals_list = [vals[k] for k in keys_list]\\n vals = vals_list\\n if names is None:\\n names = keys_list\\n else:\\n # assume it's an iterable that can be taken as columns\\n if names is None:\\n # if names of columns are not specified, default to the\\n # existing names and orders\\n names = self.field_names\\n # check if any astropy Time columns\\n for i, k in enumerate(names):\\n if k in self and isinstance(self[k], Time):\\n vals[i] = Time(vals[i], scale=self[k].scale,\\n format='isot' if isinstance(vals[i], str)\\n else 'jd')\\n vals = DataClass.from_rows(vals, names, units=units)\\n self.vstack(vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdddfdc4382d2033674278a9ffd1f2b5\",\n \"score\": \"0.5614611\",\n \"text\": \"def sql_update_table(table_title, columns, data): \\r\\n col = \\\"\\\"\\r\\n val = \\\"\\\"\\r\\n for i, j in zip(columns, data):\\r\\n col+=f'{i},' \\r\\n val+=f\\\"\\\"\\\"?,\\\"\\\"\\\" \\r\\n sql = f\\\"\\\"\\\"INSERT INTO {table_title} ({col[:-1]}) VALUES ({val[:-1]});\\\"\\\"\\\" \\r\\n return sql\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bd5386ba8ee77a24e3aaf9b6ac2295\",\n \"score\": \"0.5603458\",\n \"text\": \"def test_insert_dictlist(self):\\n self.tbl.insert([\\n {\\n 'name': 'Alyssa P. Hacker',\\n 'age': 25,\\n 'height': 66.25\\n },\\n {\\n 'name': 'Ben Bitdiddle',\\n 'age': 24,\\n 'height': 70.1\\n }])\\n data = self.tbl.select()\\n idata = np.array([\\n ['Alyssa P. Hacker', 25, 66.25],\\n ['Ben Bitdiddle', 24, 70.1]],\\n dtype='object')\\n assert self.check(idata, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304f8cdcc3cedcf12bae43d20c71f10f\",\n \"score\": \"0.55888283\",\n \"text\": \"def add_table_row(self, list_row_entries):\\r\\n if INVALID_COLUMN_NUMBER == self.__num_of_columns:\\r\\n raise TypeError(\\\"Before filling the table, set the names and datatypes.(\\\"+\\r\\n self.__file_name+\\\")\\\")\\r\\n else:\\r\\n if self.__num_of_columns != len(list_row_entries):\\r\\n raise TypeError(\\\"Number of entries does not match the column number of the table.(\\\"+\\r\\n self.__file_name+\\\")\\\")\\r\\n \\r\\n self.__table[I_TABLE_LINES].append(list_row_entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2150b6b3b340c0e47b1c36e16e49f6f7\",\n \"score\": \"0.5588518\",\n \"text\": \"def put(self, tableName, rows):\\n if isinstance(rows, list):\\n return self._jsparkSession.put(tableName, [rows, ])\\n elif isinstance(rows, tuple):\\n return self._jsparkSession.put(tableName, rows)\\n else:\\n raise TypeError(\\\"rows should be tuple or a list\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72361edc87e774709a4589d73950238b\",\n \"score\": \"0.55853707\",\n \"text\": \"def updateValues(self, vald, table):\\n self.logger.warning(\\\"updateValues: not allowed for %s\\\", self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9209d31ac6f708126303487acc9b1661\",\n \"score\": \"0.5577928\",\n \"text\": \"def insert_batch(self, table_name: str, values: Tuple, batchSize: int) -> None:\\n if len(self.batchList) < batchSize:\\n self.batchList.append(values)\\n else:\\n for val in self.batchList:\\n self.cursor.excecute(\\\"INSERT INTO %s VALUES (%s, %s, %s, %s,'%s','%s')\\\" % (table_name, *val))\\n self.db_connection.commit()\\n self.batchList = [] # Resets temp storage of queries to be excecuted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9083a0a1505bae43e92bbe97cfdd23ae\",\n \"score\": \"0.556624\",\n \"text\": \"def addRowToTable_IDL(self, domain_id, table_id, row):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f841f20025903e488e6bbc491c80282\",\n \"score\": \"0.554408\",\n \"text\": \"def add_row_to_table(self, table: str, row: dict):\\n with sqlite3.connect(self.db) as conn:\\n match = re.compile(\\\"P[0-9]+\\\")\\n headers = []\\n values = []\\n for key in row:\\n if match.match(key) or \\\"id\\\" == key:\\n headers.append(key)\\n values.append(row[key])\\n # valuesQ MUST come before headers\\n valuesQ = \\\", \\\".join([\\\"?\\\" for _ in headers])\\n headers = \\\", \\\".join(headers)\\n values = [self.convert(value) for value in values]\\n rtuple = tuple(values)\\n current = conn.execute(\\n f\\\"SELECT * FROM {table} WHERE id=?\\\", (row[\\\"id\\\"],)\\n ).fetchone()\\n if current:\\n conn.execute(f\\\"DELETE FROM {table} WHERE id=?\\\", (row[\\\"id\\\"],))\\n try:\\n conn.execute(\\n \\\"INSERT INTO {0} ({1}) VALUES ({2})\\\".format(\\n table, headers, valuesQ\\n ),\\n rtuple,\\n )\\n except sqlite3.IntegrityError as exception:\\n logging.error(\\n f\\\"INSERT INTO {table} ({headers}) VALUES ({rtuple})\\\"\\n )\\n if \\\"P19\\\" in row:\\n print(row[\\\"P19\\\"])\\n logging.error(\\n \\\"Duplicates: \\\"\\n + str(\\n conn.execute(\\n f\\\"SELECT id FROM {table} WHERE P19=?\\\",\\n (row[\\\"P19\\\"],),\\n ).fetchall()\\n )\\n )\\n if \\\"P16\\\" in row:\\n logging.error(\\n \\\"Duplicates: \\\"\\n + str(\\n conn.execute(\\n f\\\"SELECT id FROM {table} WHERE P16=?\\\",\\n (row[\\\"P16\\\"],),\\n )\\n )\\n )\\n raise exception\\n if current:\\n cursor = conn.execute(\\n f\\\"SELECT * FROM {table} WHERE id=?\\\", (row[\\\"id\\\"],)\\n )\\n names = [desc[0] for desc in cursor.description]\\n new = cursor.fetchone()\\n logging.info(new)\\n entries = len(new)\\n entry = 0\\n while entry < entries:\\n if new[entry] != current[entry]:\\n message = \\\" \\\".join(\\n [\\n row[\\\"id\\\"],\\n \\\"changed\\\",\\n str(names[entry]),\\n str(current[entry]),\\n \\\"to\\\",\\n str(new[entry]),\\n os.linesep,\\n ]\\n )\\n logging.error(message)\\n with open(CHANGED_ENTRIES, \\\"a\\\") as log:\\n log.write(message)\\n entry = entry + 1\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff889cb0413cf4ebad9f4bf3beaf60e9\",\n \"score\": \"0.55277216\",\n \"text\": \"def insert(table: str, **values_dict):\\n columns = ', '.join(values_dict.keys())\\n values = [_ for _ in values_dict.values()]\\n placeholders = \\\", \\\".join(\\\"?\\\" * len(values_dict.keys()))\\n query = f\\\"INSERT INTO {table} ({columns}) VALUES ({placeholders})\\\"\\n cursor.execute(query, values)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c3384284f38bde5d84bcba95b278ec\",\n \"score\": \"0.5526922\",\n \"text\": \"def sql_fill_table(table_title, data):\\r\\n \\r\\n sql = f\\\"\\\"\\\"INSERT INTO {table_title} VALUES (\\\"\\\"\\\"\\r\\n \\r\\n for i in data[0]:\\r\\n sql+='?,'\\r\\n sql = sql[:-1] +');' \\r\\n return sql\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48b390a0f7dec1c5db00088cc94ceac5\",\n \"score\": \"0.5525141\",\n \"text\": \"def insert_to_table(table, columns, values):\\n conn = psycopg2.connect(\\\"host=%s dbname=%s user=%s password=%s\\\" % (HOST, DB_NAME, USER, PASS))\\n cur = conn.cursor()\\n insert_query = \\\"INSERT INTO %s %s VALUES %s;\\\" % (table, columns, values)\\n cur.execute(insert_query)\\n conn.commit()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9fbf4ef5e6af69ec94780dd71359b4\",\n \"score\": \"0.5524008\",\n \"text\": \"def insertData(tableName, dbFileName, dataList):\\n conn = sqlite3.connect(dbFileName)\\n try:\\n cols = len(dataList[0])\\n query = f\\\"insert into {tableName} values ({'?, '*(cols - 1)} ?)\\\"\\n for row in dataList:\\n try:\\n with conn:\\n conn.execute(query, row)\\n except sqlite3.IntegrityError as e:\\n print ('Error code: ', e)\\n conn.close()\\n except Exception as e:\\n print('Неправильный список!!', e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e1a435a2bf3c029007633eea90aed5\",\n \"score\": \"0.5523307\",\n \"text\": \"def patch_table() :\\n global TSUGI_DB_TO_ROW_FIELDS\\n for tc in range(len(TSUGI_DB_TO_ROW_FIELDS)) :\\n table = TSUGI_DB_TO_ROW_FIELDS[tc]\\n for fc in range(len(table)) :\\n if fc == 0 : continue; # skip table name\\n if fc == 1 : continue; # skip PK/FK row\\n if type(table[fc]) == type([]) and len(table[fc]) == 3 : continue\\n\\n # When there are two fields, duplicate the second as last\\n if type(table[fc]) == type([]) and len(table[fc]) == 2 :\\n TSUGI_DB_TO_ROW_FIELDS[tc][fc].append(table[fc][1])\\n continue\\n\\n # When there is one field, use it as second and third\\n field = table[fc]\\n TSUGI_DB_TO_ROW_FIELDS[tc][fc] = [field,field,field]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8d3298524d4dac04c73a9bf994fbb5\",\n \"score\": \"0.5522204\",\n \"text\": \"def insert_row(batch, row):\\n row = row.rstrip()\\n line = row.split(\\\"\\\\t\\\");\\n word = line[0].split(\\\"#\\\"); \\n #print(word[0],line[1])\\n batch.put(word[0], {\\\"data:polarity\\\":line[1]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fe27a17a8593d6c1577a45edbbacc15\",\n \"score\": \"0.5507124\",\n \"text\": \"def insert(self, entries, table_name=None):\\n\\t\\tif not table_name:\\n\\t\\t\\ttable_name = self.default_table\\n\\t\\tinsert_str = 'INSERT INTO \\\"{0}\\\" ({1}) VALUES ({2})'\\n\\t\\tkeys = [key for key in entries]\\n\\t\\trows = ','.join('\\\"{0}\\\"'.format(key) for key in keys) + ',RST_ID'\\n\\t\\tvals = (\\n\\t\\t\\t','.join('\\\"{0}\\\"'.format(entries[key]) for key in keys) +\\n\\t\\t\\t',{0}'.format(self.rst_id)\\n\\t\\t)\\n\\t\\tinsert_cmd = insert_str.format(table_name, rows, vals)\\n\\t\\treturn self.run_cmd(insert_cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5731ce3702777cd5b5235b268fef1db4\",\n \"score\": \"0.54837114\",\n \"text\": \"def _validate_insert_values(self, values: ROW_TYPE) -> Result[None, str]:\\n columns = self.get_columns()\\n\\n if len(values) != len(columns):\\n return Err(f'number of values ({len(values)} != number of columns ({len(columns)})')\\n\\n for i, (column, value) in enumerate(zip(columns, values)):\\n ty = column.ty.primitive()\\n if not isinstance(value, ty):\\n return Err(f'value #{i} has type {type(value).__name__}, expected: {ty.__name__}')\\n\\n return Ok(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee53c5ae726d08078bd371aa640ceba5\",\n \"score\": \"0.54828006\",\n \"text\": \"def _insert_rows(self, table_name: str, users: list):\\n conn, crsr = self.init()\\n\\n values = \\\"(\\\" + \\\"%s ,\\\" * 5 + \\\")\\\"\\n values = values[:-3] + values[-1] + \\\",\\\"\\n values = values * (len(users) // 5)\\n values = values[:-1]\\n\\n crsr.execute(\\n f\\\"INSERT INTO {table_name} (user_id, first_name, last_name, birthdate, img_path) VALUES {values}\\\"\\n , users)\\n\\n conn.commit()\\n crsr.close()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49741f39d6da0636bc9e6309e5573d02\",\n \"score\": \"0.5481272\",\n \"text\": \"def insertEntries(db, cursor, entries):\\n\\t# Get the columns to use in the correct order\\n\\tcolumns = \\\", \\\".join(sorted(DATA_TO_CSV_COLUMN, key = lambda k: DATA_TO_CSV_COLUMN[k]))\\n\\n\\t# Create the prepared statement\\n\\tinsertSql = \\\"INSERT INTO housepricehistory_soldproperty (%s) VALUES %s;\\\" % (columns,\\n\\t\\t(\\\"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s), \\\" * len(entries))[:-2])\\n\\n\\t# Execute with all entry data unpacked\\n\\tcursor.execute(insertSql, tuple(chain(*entries)))\\n\\tdb.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2da17aa82570b69c5a6d3a64b054b7d\",\n \"score\": \"0.54812175\",\n \"text\": \"def test_insert_longlist(self):\\n self.tbl.insert([1, 2, 'Alyssa P. Hacker', 25, 66.25])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a69c645f935b1aa7250ca89925b8f543\",\n \"score\": \"0.5477853\",\n \"text\": \"def insert_sql(table, dict_list):\\n\\n if not dict_list:\\n return\\n\\n # Names of fields\\n fields = dict_list[0].keys()\\n field_string = \\\"%s\\\" % \\\", \\\".join(['\\\"%s\\\"' % e for e in fields])\\n\\n # Placeholders for values.\\n values = [list(e.values()) for e in dict_list]\\n value_string = ', '.join(['%s'] * len(values[0]))\\n\\n cursor = connection.cursor()\\n command = \\\"INSERT INTO {} ({}) VALUES ({});\\\".format(table, field_string, value_string)\\n cursor.executemany(command, values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a531c123234d9c5504a89264146bf8\",\n \"score\": \"0.54774004\",\n \"text\": \"def insert_csv_row(csv_row, cursor):\\r\\n liste_row = csv_row.strip().split(\\\";\\\")\\r\\n new_row = [liste_row[3], liste_row[4], liste_row[5], liste_row[7], liste_row[9]]\\r\\n cursor.execute(\\\"\\\"\\\"INSERT INTO infoarret VALUES (?,?,?,?,?) \\\"\\\"\\\",\\r\\n new_row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e766ef5427b0762d581f8fe169dbe55\",\n \"score\": \"0.5474849\",\n \"text\": \"def upsert(transaction: spanner_transaction.Transaction, table_name: str,\\n columns: Iterable[str], values: Iterable[Iterable[Any]]) -> None:\\n _logger.debug('Upsert table=%s columns=%s values=%s', table_name, columns,\\n values)\\n transaction.insert_or_update(table=table_name, columns=columns, values=values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4e21db8610fa83aad8c42b95cb8357\",\n \"score\": \"0.5468907\",\n \"text\": \"def addRowToDataFrame(self, df, values):\\n\\n\\t\\tlistLength = len(values)\\n\\n\\t\\tnewRow = pd.DataFrame(np.array(values).reshape(1,listLength), columns = list(df.columns))\\n\\n\\t\\tdf = df.append(newRow, ignore_index=True)\\n\\n\\t\\treturn df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd788d6652beb36bbd769237887afba2\",\n \"score\": \"0.54623276\",\n \"text\": \"def insert_csv_row(csv_row, cursor):\\n cursor.execute(\\\"\\\"\\\"INSERT INTO infoarret VALUES (?,?,?,?,?,?,?,?,?,?,?) \\\"\\\"\\\",\\n csv_row.strip().split(\\\";\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd99530feededdd47d33e8020108322\",\n \"score\": \"0.54536813\",\n \"text\": \"def insert_data(session, insert_query, df, df_cols):\\n for i, row in df.iterrows():\\n data = tuple([row[col] for col in df_cols])\\n # print(data)\\n session.execute(insert_query, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ad15e03a7fc3ded7d842ffa28d0088\",\n \"score\": \"0.54524577\",\n \"text\": \"def insert(transaction: spanner_transaction.Transaction, table_name: str,\\n columns: Iterable[str], values: Iterable[Iterable[Any]]) -> None:\\n _logger.debug('Insert table=%s columns=%s values=%s', table_name, columns,\\n values)\\n transaction.insert(table=table_name, columns=columns, values=values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"502205e832668143e23fa33e7d87ffec\",\n \"score\": \"0.54490775\",\n \"text\": \"def test_insert_row(self):\\n vizual.insert_row(\\n dataset_name='ABC',\\n position=1,\\n validate=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eff4f18c3f198653ccb81f34ac13cf8\",\n \"score\": \"0.54478765\",\n \"text\": \"def insert(tokens):\\n def get_comma_seperated_contents(tokens):\\n contents = []\\n pop_and_check(tokens, \\\"(\\\")\\n while True:\\n item = tokens.pop(0)\\n contents.append(item)\\n comma_or_close = tokens.pop(0)\\n if comma_or_close == \\\")\\\":\\n return contents\\n assert comma_or_close == ',', comma_or_close\\n\\n pop_and_check(tokens, \\\"INSERT\\\")\\n pop_and_check(tokens, \\\"INTO\\\")\\n table_name = tokens.pop(0)\\n if tokens[0] == \\\"(\\\":\\n col_names = get_comma_seperated_contents(tokens)\\n qual_col_names = [QualifiedColumnName(col_name, table_name)\\n for col_name in col_names]\\n else:\\n qual_col_names = None\\n pop_and_check(tokens, \\\"VALUES\\\")\\n while tokens:\\n row_contents = get_comma_seperated_contents(tokens)\\n if qual_col_names:\\n assert len(row_contents) == len(qual_col_names)\\n\\n if self.database.transaction.get_open_transactions():\\n self.database.locking_state_add(\\\"RESERVED\\\")\\n db_copy = self.get_db_copy()\\n db_copy.insert_into(table_name, row_contents, qual_col_names=qual_col_names)\\n\\n else:\\n self.database.insert_into(table_name, row_contents, qual_col_names=qual_col_names)\\n if tokens:\\n pop_and_check(tokens, \\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3623a7acc61b75a394fe5e486fa1cdc\",\n \"score\": \"0.54473454\",\n \"text\": \"def add_row(self, values, index=None, **kwargs):\\r\\n for i in self.frame.values():\\r\\n i.destroy()\\r\\n self.frame = {}\\r\\n if index is None:\\r\\n index = len(self.values) \\r\\n try:\\r\\n self.values.insert(index, values)\\r\\n self.rows+=1\\r\\n except IndexError: pass\\r\\n \\r\\n self.draw_table(**kwargs)\\r\\n self.update_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c974886f7ac6939494b3f2cd4b385c\",\n \"score\": \"0.54312664\",\n \"text\": \"def insert(self, table_id, values):\\n stringValues = \\\"\\\"\\n count = 1\\n cols = values.keys()\\n values = values.values()\\n for value in values:\\n if type(value).__name__=='int':\\n stringValues = '%s%d' % (stringValues, value)\\n elif type(value).__name__=='float':\\n stringValues = '%s%f' % (stringValues, value)\\n else:\\n stringValues = \\\"%s'%s'\\\" % (stringValues,\\n value.encode('string-escape'))\\n if count < len(values): stringValues = \\\"%s,\\\" % (stringValues)\\n count += 1\\n\\n return 'INSERT INTO %d (%s) VALUES (%s)' % \\\\\\n (int(table_id), ','.join([\\\"'%s'\\\" % col for col in cols]), stringValues)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f33fbe553d59e3fe3c4e980e285b1a3f\",\n \"score\": \"0.5426162\",\n \"text\": \"def Insert_Values_To_Table(self, df, table_name):\\n conn = self.__connect_to_db()\\n if conn is not None:\\n cur = conn.cursor()\\n\\n db_columns = self.__get_column_names_from_db_table(cur, table_name)\\n try:\\n df[db_columns].to_sql(name=table_name, con=conn, if_exists='append', index=False)\\n print('SQL insert process finished')\\n except sqlite3.Error as err:\\n print(f'Value Insersion Failed due to {err}')\\n finally:\\n conn.close() \\n else:\\n print('Connection to database failed')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c9c5b30383b2655b9499aa301af322\",\n \"score\": \"0.5423953\",\n \"text\": \"def insert(query, records, cur):\\n for record in records:\\n cur.execute(query, record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fdaf2d08612ef0e125cbcfe51a9aeb\",\n \"score\": \"0.54170585\",\n \"text\": \"def insert(self, keys, values):\\n self.id2value.insert(self._get_key(keys), values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c55cb40536dfd697797bfe2c023d9cfa\",\n \"score\": \"0.54154116\",\n \"text\": \"def addRow(self, *value):\\n if len(value) == 1 and isinstance(value[0], (tuple, list)):\\n value = value[0]\\n assert len(value) == self.getNumCols()\\n self._impl.addRow(Tuple(value)._impl)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4d41c697bc9879d4b5dc8da7c277a5\",\n \"score\": \"0.5410713\",\n \"text\": \"def append_new_entry(self, connection, values, column_names):\\n self.logger.info('New data gathered:\\\\n\\\\t names: %s\\\\n\\\\t values: %s' % (values, column_names))\\n unique_id = db_api.add_new_row(connection=connection,\\n table=self.table)\\n values.append(unique_id)\\n db_api.update_item(connection=connection,\\n table=self.table,\\n value_tuple=tuple(values),\\n column_names=column_names)\\n # not sure why this is needed but a unit test was failing because the nutrition get_new_data result somehow had\\n # the unique_id appended even though nothing is returned in this function. No other tests showing this.\\n values.pop(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28565c0aa7012cefe14bd5965bde5308\",\n \"score\": \"0.5400337\",\n \"text\": \"def insert(self, key, value): #still need to be checked \\n if type(key) == tuple:\\n for check in key: \\n if type(check) == dict or type(check) == list:\\n print(\\\"key is ILLEGAL!\\\")\\n return None\\n num_key = len(key)\\n elif type(key)== int or type(key)== float:\\n num_key = key\\n elif type(key) == str:\\n num_key = superord(key)\\n else: print(\\\"key is ILLEGAL!\\\"); return None\\n i = self.hash_mod(num_key)\\n \\n for k in self.table[i]:\\n if k[0] == key:\\n #print(\\\"yeaaaa!!!\\\")\\n k[1] = value #why when i tried to change the whole list it failed?\\n if [key,value] not in self.table[i]:\\n self.table[i].append([key,value])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27afdc3d4c1144e08da2be1f6f1a018e\",\n \"score\": \"0.5400116\",\n \"text\": \"def insert_from_list(company_id, data, table, columns):\\n\\n v = ' VALUES '\\n query = 'INSERT INTO ' + table + columns\\n length = len(data)\\n for index, item in enumerate(data):\\n v += '(\\\"' + str(item) + '\\\",' + '\\\"' + str(company_id) + '\\\")'\\n if index != length - 1:\\n v += ','\\n query += v\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b17cedf06cfff597aa295244c0613b\",\n \"score\": \"0.5399158\",\n \"text\": \"def save_match_to_psql(self, table_name, column_list, values_insert):\\n all_val_list_or_tuple = all(isinstance(\\n value, (list, tuple)) for value in values_insert)\\n all_len_match = all(len(value) == len(column_list)\\n for value in values_insert)\\n column_string = \\\", \\\".join(column_list)\\n if (all_len_match and all_val_list_or_tuple):\\n for value in values_insert:\\n value_string = \\\", \\\".join(value)\\n self._cursor.execute(self._insert_query.format(\\n table_name, column_string, value_string))\\n self._pg_db.commit()\\n records = self._cursor.fetchall()\\n print(\\\"Output: \\\", records)\\n elif (all_val_list_or_tuple == False and (len(column_list) == len(values_insert))):\\n value_string = \\\", \\\".join(values_insert)\\n self._cursor.execute(self._insert_query.format(\\n table_name, column_string, value_string))\\n self._pg_db.commit()\\n records = self._cursor.fetchall()\\n print(\\\"Output: \\\", records)\\n else:\\n print(\\\"The length of columns and values don't match at least for one element\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d849c7d1001673e752419eb7aff5752e\",\n \"score\": \"0.5398647\",\n \"text\": \"def insert(self, table_name, data):\\n self._ensure_writeable()\\n\\n # Turn the input into a dataframe\\n df = self._framify(data)\\n\\n # Turn the dataframe into a record list\\n recs = self._listify(df)\\n\\n # Insert the record list\\n with self.connector.connection as connection:\\n connection[table_name].insert_many(recs)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0787b7d1cd76a105e5a634653cf0b834\",\n \"score\": \"0.53985065\",\n \"text\": \"def insert_value_in_table(server, table, db, cursor, column, value):\\n insert = \\\"\\\"\\\"REPLACE INTO %s (hostname, %s)\\n VALUES ('%s', '%s')\\n \\\"\\\"\\\" % (table, column, server, value)\\n try:\\n cursor.execute(insert)\\n db.commit()\\n\\n except e:\\n logger.error(\\\"%s: %s\\\" % (server, str(e)))\\n db.rollback()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1177033fc41ff63240a3bcf3848068d\",\n \"score\": \"0.5393329\",\n \"text\": \"def test_insert_lists(self):\\n self.insert()\\n data = self.tbl.select()\\n assert self.check(self.idata, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc9c6e39317abd18cdfa7eb231fbc37c\",\n \"score\": \"0.539308\",\n \"text\": \"def register_query(values, table):\\n cols = '('\\n v = ' VALUES ('\\n length = len(values)\\n for index, (key, value) in enumerate(values.items()):\\n cols += key + ''\\n v += '\\\"' + str(value) + '\\\"'\\n if index != length - 1:\\n cols += ','\\n v += ','\\n query = 'INSERT INTO ' + table + cols + ')' + v + ')'\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e6a9ea32bdd991ccae0337b82c4157\",\n \"score\": \"0.5389561\",\n \"text\": \"def insert_sample(cursor,sample_list):\\n cursor.execute(\\\"BEGIN TRANSACTION\\\")\\n cursor.execute('''insert into samples values \\\\\\n (?,?,?,?,?,?,?,?)''', sample_list)\\n cursor.execute(\\\"END\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c35f3a1405eea52b373e83ed9b8ac11\",\n \"score\": \"0.5383944\",\n \"text\": \"def synapse_table(cur,data):\\n sql = (\\\"insert into synapse \\\"\\n \\\"(idsynapse,idcontin,idpre,idpost) \\\"\\n \\\"values (%s,%s,%s,%s)\\\")\\n cur.executemany(sql,data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"456d46a6e193b28c84713fe2ea50c32c\",\n \"score\": \"0.5380284\",\n \"text\": \"def insert(self, table_name=None, repeat=1, **attr):\\n assert(attr and table_name != None)\\n SQL_FMT = \\\"insert into %s(%s) values(%s);\\\"\\n keys = attr.keys()\\n values = attr.values()\\n cols = ','.join(i for i,j in zip(keys,values) if j != None)\\n vals = ','.join(Mydb.process_value(i) for i in values if i != None)\\n SQL = SQL_FMT%(table_name,cols,vals)\\n for i in xrange(repeat):\\n db_debug(SQL)\\n self.cursor.execute(SQL)\\n self.db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d37db698060910bedda48aec6dbd19\",\n \"score\": \"0.5368154\",\n \"text\": \"def test_insert_dictlist(self):\\n self.tbl.insert([\\n {\\n 'id': 1,\\n 'name': 'Alyssa P. Hacker',\\n 'age': 25,\\n 'height': 66.25\\n },\\n {\\n 'id': 2,\\n 'name': 'Ben Bitdiddle',\\n 'age': 24,\\n 'height': 70.1\\n }])\\n data = self.tbl.select()\\n idata = np.array([\\n [1, 'Alyssa P. Hacker', 25, 66.25],\\n [2, 'Ben Bitdiddle', 24, 70.1]],\\n dtype='object')\\n assert self.check(idata, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aebe13e239bf0b15725f21891d8c4265\",\n \"score\": \"0.53632635\",\n \"text\": \"def insert_data(data_list, db):\\n\\t\\tcursor = db.cursor()\\n\\t\\tfor data_row in data_list:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcursor.execute('insert into celebrities values (?, ?, ?, ?, ?)',data_row)\\n\\t\\t\\texcept Exception, e:\\n\\t\\t\\t\\t# When it fails integrity error or null data is tried to be inserted\\n\\t\\t\\t\\tcontinue\\n\\t\\tdb.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2cddcf278111eb778d3d97260585872\",\n \"score\": \"0.5351996\",\n \"text\": \"def insert(self, key, value):\\n rows = self.pre(key)\\n for ti in torch.arange(0, self.num_tables):\\n self.tables[ti][rows[ti]].append(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64780800e147f907648a36a243bff19\",\n \"score\": \"0.5349216\",\n \"text\": \"def insert_rows(self, cursor):\\n if isinstance(self.columns[0], basestring):\\n column_names = ','.join([name for name in self.columns])\\n elif len(self.columns[0]) == 2:\\n column_names = ','.join([name for name, _type in self.columns])\\n else:\\n raise Exception('columns must consist of column strings or '\\n '(column string, type string) tuples (was %r ...)'\\n % (self.columns[0],))\\n\\n value_list = []\\n row_count = 0\\n for row_count, row in enumerate(self.rows(), start=1):\\n entry = tuple([coerce_for_mysql_connect(elem) for elem in row])\\n value_list.append(entry)\\n if row_count % self.insert_chunk_size == 0:\\n self._execute_insert_query(cursor, value_list, column_names)\\n value_list = []\\n\\n if self.overwrite and not self.allow_empty_insert and row_count == 0:\\n raise Exception('Cannot overwrite a table with an empty result set.')\\n\\n if len(value_list) > 0:\\n self._execute_insert_query(cursor, value_list, column_names)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993684,"cells":{"query_id":{"kind":"string","value":"594bfddb6d92d27142ddf9e2757fb099"},"query":{"kind":"string","value":"Hyperbolic Tangent Nonlinear Activation Function"},"positive_passages":{"kind":"list like","value":[{"docid":"8a8d72a1dcac6398226531aa510de7b4","score":"0.709707","text":"def tangent_hyperbolic(z):\n return np.tanh(z)","title":""}],"string":"[\n {\n \"docid\": \"8a8d72a1dcac6398226531aa510de7b4\",\n \"score\": \"0.709707\",\n \"text\": \"def tangent_hyperbolic(z):\\n return np.tanh(z)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0768039b3e11f67b65f2e77de62fb04c","score":"0.8281349","text":"def HiperbolicTangensActivationFunction( x ):\n return tanh( x )","title":""},{"docid":"3f12af731c1234fe21563b14525f2d64","score":"0.8131882","text":"def HiperbolicTangensDerivActivationFunction( x ):\n return ( 1.0 - pow( tanh( x ), 2.0 ) )","title":""},{"docid":"f723ed0b4680c2c84dcbb56f7fead226","score":"0.76846695","text":"def tanh():\n return function.Function(lambda x: math.tanh(x),\n 'activation.tanh',\n 'Tanh activation function')","title":""},{"docid":"3e744f65fc3c6c4d39d372c6b2e390cd","score":"0.74650675","text":"def _activation(self, z, tanh=True):\n if tanh:\n return np.tanh(z)\n else:\n return 1 / (1 + np.exp(-z))","title":""},{"docid":"9963ea4ce5021aaa6baf979ae3121c38","score":"0.7432859","text":"def activation_function(self, x):\n return np.tanh(x)","title":""},{"docid":"97542b2437b98d094305bda14d128d69","score":"0.73241377","text":"def LogisticDerivActivationFunction( x ):\n return LogisticActivationFunction( x ) * ( 1 - LogisticActivationFunction( x ) )","title":""},{"docid":"b8b7331732656fb95fb772caccfa2eb2","score":"0.72636825","text":"def _activation_function(self, x):\n return 1 / (1 + exp(-x))","title":""},{"docid":"c59f863cbb91dba0c319ef955848ad20","score":"0.72101736","text":"def activation(x):\r\n return 1/(1+torch.exp(-x))","title":""},{"docid":"0a6b62c5c75cd8827013ca50c85371c6","score":"0.7134875","text":"def activation(x):\n\treturn 1/(1 + torch.exp(-x))","title":""},{"docid":"9957afc2f945d8ae8e1559cff48d7a11","score":"0.71344924","text":"def tanh(x):\n return tfe.tanh(x)","title":""},{"docid":"0345a150f9778a571882ff03a6907c10","score":"0.71143526","text":"def activation_function(x):\n return 1/(1+np.exp(-x))","title":""},{"docid":"56f08b035f498943b84d9277bd580965","score":"0.7090844","text":"def activation(x):\n return 1/(1+torch.exp(-x))","title":""},{"docid":"56f08b035f498943b84d9277bd580965","score":"0.7090844","text":"def activation(x):\n return 1/(1+torch.exp(-x))","title":""},{"docid":"f3ea2e6707a227b422d43b86ac2fb423","score":"0.70439166","text":"def tanh(x):","title":""},{"docid":"f3ea2e6707a227b422d43b86ac2fb423","score":"0.70439166","text":"def tanh(x):","title":""},{"docid":"5bef71e737fdcbe8015f5a2694b218d9","score":"0.69575846","text":"def sigmoid_activation_deriv(self, in_acts):\n \"\"\"YOUR CODE\"\"\"","title":""},{"docid":"f5a52ceb1708b0998cbf81ab89d83c0e","score":"0.69570386","text":"def activation(x, w):\n z = x.dot(w);\n # print(\"\\nz\")\n # print(z)\n H = 1/(1+exp(-z))\n # print(\"\\nH\")\n # print(H)\n return H","title":""},{"docid":"63f3de9ff2fd2063b3b9314ecde803ac","score":"0.6908077","text":"def activation_deriv(self):\n assert self.output.size > 0\n return 1.0 / (1.0 + np.exp(-self.linear_output))","title":""},{"docid":"deed66aa60c7ca66be5617341e41479a","score":"0.690227","text":"def Kenue1DerivActivationFunction( x ):\n return ( 2.0 / pi ) * pow( cosh( x ), -1.0 );","title":""},{"docid":"b3f4787d5583e6e80a70719ead53f2e4","score":"0.68727994","text":"def activation_gradient(activation, X):\n z = 1.0\n w = 1.0\n if activation.__name__ == 'tanh':\n z = 2.0\n w = 4.0\n elif activation.__name__ != 'expit':\n raise NotImplementedError\n\n return(w*sp.special.expit(z*X)*(1-sp.special.expit(z*X)))","title":""},{"docid":"49cedcb20d1352189cd4bd87cf741e08","score":"0.6868825","text":"def sigmoid(t):\n \n return 1.0 / (1 + np.exp(-t))","title":""},{"docid":"988b7d367891906f3f06088b17797a08","score":"0.6833743","text":"def activate(self):\n def gelu(x):\n cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n return x * cdf\n\n return gelu","title":""},{"docid":"6c42857af2af6001e0e41c2156b97fff","score":"0.6832049","text":"def Kenue2DerivActivationFunction( x ):\n return ( 4.0 / pi ) * pow( pow( cosh( x ), -1.0 ), 3.0 );","title":""},{"docid":"c2c3bc332610bbc98328e05b4568e048","score":"0.6819093","text":"def activation(self, z):\n return 1. / (1. + np.exp(-z))","title":""},{"docid":"c1c9a6257eacf9c4371d8b735247cf83","score":"0.68157375","text":"def sigmoid(t):\n return 1 / (1 + np.exp(-t))","title":""},{"docid":"0a2b0661040d146b41029e8b69478463","score":"0.68133634","text":"def sigmoid(t):\n return 1. / (1. + np.exp(-t))","title":""},{"docid":"53814da52902cc86aaa767642ce18fce","score":"0.6802573","text":"def _activation(self, z):\r\n return 1 / (1 + np.exp(-np.clip(z, -250, 250)))","title":""},{"docid":"c196d2335778c45cd64cbb2ddb068cc5","score":"0.6799323","text":"def sigmoid(t):\n\n return 1. / (1 + np.exp(-t))","title":""},{"docid":"3deea9a2fa7964a30c92c37f7f20a1d2","score":"0.67923594","text":"def activation(self, z):\n return 1.0 / (1.0 + np.exp(-z))","title":""},{"docid":"fb7ce06995bc32254a8a78111881550f","score":"0.6785471","text":"def sigmoid(t):\n return 1.0 / (1 + np.exp(-t))","title":""},{"docid":"ff231c6eac7f8f1966c45412a6e4e73c","score":"0.6780622","text":"def tanh(x):\n return (1 - exp(-2*x)) / (1 + exp(-2*x))","title":""},{"docid":"4a1b7c17c641e9a4a116838ec1a9a018","score":"0.67682654","text":"def sigmoid(alpha):\n return 1 / (1 + np.exp(-alpha))","title":""},{"docid":"85019a0d556a9e56ae15d35d6af4fcf1","score":"0.6748394","text":"def tanh(x):\n return K.tanh(x)","title":""},{"docid":"62c6a15997b29d7e5ad7749015d36c2e","score":"0.67449754","text":"def xtanh(x, alpha=.1):\n return x.tanh() + alpha * x","title":""},{"docid":"d110ffbd4f77a74acaed8979a05b7e57","score":"0.6743973","text":"def sigmoid(t):\n exp_inv = np.exp(-1.0*t)\n return np.divide(1,1+exp_inv)","title":""},{"docid":"f69e031f3ec0eeabe4408312e2a373d6","score":"0.673918","text":"def sigmoid(x):\n # Could experiment with more activation functions: \n #return np.tanh(x)\n return logistic.cdf(x)","title":""},{"docid":"82e0c95764156110a8419e9ef399bc64","score":"0.67387015","text":"def sigmoid(x):\n return np.tanh(x)","title":""},{"docid":"b7281330547b87f7cfa60a7a3f17c2c7","score":"0.6730346","text":"def sigmoid(t):\n\n return np.exp(t)/(1+np.exp(t))","title":""},{"docid":"d413e9fd375b33db7486905dcc79dfc1","score":"0.67262965","text":"def grad_activation(self,input):\n\n if self.activ_func == 'relu':\n return input>0\n \n if self.activ_func == 'sigmoid':\n return np.exp(input)/(1+np.exp(input)) * (1. - np.exp(input)/(1+np.exp(input)))\n \n if self.activ_func == 'tanh':\n return 1-(((np.exp(2*input)-1)/(np.exp(2*input)+1))**2)","title":""},{"docid":"f3f9fff5ad38ce4f8d12da56d95d1e2f","score":"0.6725492","text":"def _tanh(self):\n X = self._input_nodes[0]._value # First argument\n return np.tanh(X)","title":""},{"docid":"20b405b43d70da5c161cbeb136137e3e","score":"0.67251056","text":"def hsigmoid_activating(x):\n return tf.nn.relu6(x+3.)*0.16667","title":""},{"docid":"4b266077fd9fd3429ebeaab04301ca55","score":"0.67132413","text":"def activation(z):\n return 1. / (1. + np.exp(-np.clip(z, -250, 250)))","title":""},{"docid":"f76b4eb21a1a621354475d69ec9c51a7","score":"0.6697952","text":"def dtanh(x):\n return 1 - np.tanh(x)**2","title":""},{"docid":"c2c8c893799ac28e3faee958373a40d0","score":"0.6688739","text":"def sigmoid_activation(self, in_acts):\n \"\"\"YOUR CODE\"\"\"","title":""},{"docid":"45352557aeb7976aaaed29ceaa16cff8","score":"0.66840404","text":"def sigmoid(t):\n exp = np.exp(-t)\n \n return 1.0/(1+exp)","title":""},{"docid":"0b7d4cb777fcbc3c8879a2d601e6b9a2","score":"0.66834897","text":"def activate(self, z, function):\n if function == Functions.SIGMOID:\n return sigmoid(z)\n elif function == Functions.SOFTMAX:\n return softmax(z)\n else:\n print(\"Unrecognized activation function\")\n sys.exit(1)","title":""},{"docid":"afb11b53d13f1ce63390f7a24179b2a5","score":"0.66762996","text":"def tanh_forward(z):\n return np.tanh(z)","title":""},{"docid":"62792c625ba954163de74c3180da2d91","score":"0.6672956","text":"def __sigmoid_grad(x):\n return ActivationFunctions.sigmoid(x)*(1-ActivationFunctions.sigmoid(x))","title":""},{"docid":"e06ae18e2a9e9ff1038a33699e4a8eb1","score":"0.66710925","text":"def sigmoid(t):\n return np.exp(t) / (1+np.exp(t))","title":""},{"docid":"ec892bf230d1f35035451d6e8e83fe2f","score":"0.6664264","text":"def d_tanh(y):\n return 1 - y**2","title":""},{"docid":"c59df80411f6e83b0d1e5f23e0e65eed","score":"0.66529334","text":"def f(cls, x):\n return numx.tanh(x)","title":""},{"docid":"4b6b5f771b157fd9da344bdfb4d6a686","score":"0.6642599","text":"def activation(self, z):\n return 1. / (1. + np.exp(-np.clip(z, -250, 250)))","title":""},{"docid":"f875f3bd7f5f70c6671908a3226433fc","score":"0.6638346","text":"def sigmoid(t):\n return np.exp(-np.logaddexp(0, -t))","title":""},{"docid":"f77f51aec82e755b495582682113f91e","score":"0.66352564","text":"def derivative_tanh(x):\n return 1 - torch.tanh(x)**2","title":""},{"docid":"3b798243fb1d8795c929718f2c19957a","score":"0.66315585","text":"def activation(self, z):\n return 1./(1+np.exp(-np.clip(z, -250, 250)))","title":""},{"docid":"f3085b322eab228a4b10cacc1c42aa84","score":"0.6628338","text":"def tanh(self, x):\n self.x = x\n return np.tanh(x)","title":""},{"docid":"aaa25bd2b6eb78e997ba21fa9d8ca155","score":"0.66264516","text":"def lisht(x):\n return tf.multiply(x, tf.nn.tanh(x))","title":""},{"docid":"800fc8a5c50addc04be4a2d3777b8688","score":"0.6625754","text":"def tanh(m):\n return m._ufunc_tanh()","title":""},{"docid":"07ca5534d5173b0c42bd15bf49d81a77","score":"0.6619132","text":"def sigmoid(x):","title":""},{"docid":"07ca5534d5173b0c42bd15bf49d81a77","score":"0.6619132","text":"def sigmoid(x):","title":""},{"docid":"07ca5534d5173b0c42bd15bf49d81a77","score":"0.6619132","text":"def sigmoid(x):","title":""},{"docid":"c7568a5c90caefa03c9cdcf04552ba3f","score":"0.6615469","text":"def tanh_deriv(x):\n return 1.0 - np.power(np.tanh(x), 2)","title":""},{"docid":"1d5f58b9dc201c4dce33e9d459a091ce","score":"0.66071075","text":"def activation(z, method=\"tanh\"):\n if method == \"tanh\":\n return tanh(z)\n elif method == \"linear\":\n return z\n elif method == \"sigmoid\":\n return 1./(1.+np.exp(-z))","title":""},{"docid":"751138e26a9fceef8c85eb92990107ce","score":"0.6603773","text":"def activation(self,input):\n\n if self.activ_func == 'relu':\n return (input>0)*input\n \n if self.activ_func == 'sigmoid':\n return np.exp(input)/(1+np.exp(input))\n \n if self.activ_func == 'tanh':\n return (np.exp(2*input)-1)/(np.exp(2*input)+1)","title":""},{"docid":"5b6c963ecd1d9496cd4a4bee8a13296b","score":"0.66011584","text":"def tanh(x):\n return np.tanh(x)","title":""},{"docid":"5b6c963ecd1d9496cd4a4bee8a13296b","score":"0.66011584","text":"def tanh(x):\n return np.tanh(x)","title":""},{"docid":"e750994965583a4b3adaeb1921899cd5","score":"0.6591646","text":"def sigmoid(t):\n t_exp = np.exp(t)\n\n return t_exp/(1 + t_exp)","title":""},{"docid":"2f05153765afedd8be3b5f828a1a9671","score":"0.6581308","text":"def grad_tanh(self):\n return np.subtract(1, np.power(self.tanh(self.x), 2))","title":""},{"docid":"1f5adcb5d1defe4a2854bdfbbce84c4b","score":"0.65725887","text":"def tanhplus(inpt):\n return T.tanh(inpt) + inpt","title":""},{"docid":"1c60ab56fdc88d7f180c567e2c15bcd9","score":"0.65694565","text":"def tanh(a):","title":""},{"docid":"639b32d0f0d0041775aa26df8e4fc1a2","score":"0.65662307","text":"def scaled_tanh(self,x):\r\n vmax = 10.0\r\n return backend.tanh(x)*vmax*0.5","title":""},{"docid":"994705df8b6049fb8969658426682032","score":"0.6564388","text":"def tanh(x):\n return np.tanh(x);","title":""},{"docid":"9fdce1c33180f75d4b4711537df72b54","score":"0.65217257","text":"def apply_deriv(cls, raw_activation):\n sig = cls._sigmoid(raw_activation)\n return sig * (1 - sig)","title":""},{"docid":"09cf17cee36d2108905facb50c8647bd","score":"0.65196973","text":"def tan_sigmoid(z):\n return (2 / (1 + K.exp(-2.0 * z))) - 1","title":""},{"docid":"cd9d4b9d6b925d93431627dcafdce254","score":"0.6514894","text":"def sigmoid_forward(z):\n return 1 / (1 + np.exp(-z)), z","title":""},{"docid":"9ef03bcba50e82aa3a58e7fc1f7f5b69","score":"0.65084577","text":"def reg_logistic_grad(y, tx, w, lambda_):\n e=tx.dot(w)\n sig_e=sigmoid(e)\n return (tx.T).dot(sig_e-y)+lambda_*w","title":""},{"docid":"e6eb04b1e330b2510aea458b5f6b49c9","score":"0.64992666","text":"def _activation_function(self, x):\n return np.where(x >= 0., 1, -1)","title":""},{"docid":"5515758ecccb26c69329f1edb3a2472e","score":"0.6496716","text":"def hθ(theta, X):\n return sigmoid(np.dot(X, theta))","title":""},{"docid":"a1acae46f0e3f2e6fadd3fe010791270","score":"0.6495682","text":"def grad_tanh(self):\n return 1 - np.tanh(self.x)**2","title":""},{"docid":"b42a0aaf2916173699dca0f28c086950","score":"0.6494281","text":"def activation_fn(self, r):\n a = activate(self.activation, r)\n return a\n\n # if self.activation == None or self.activation == \"linear\":\n # return r\n # if self.activation == \"relu\":\n # r[r<0] = 0\n # return r\n\n # # tanh\n # if self.activation == 'tanh':\n # return np.tanh(r)\n\n # # sigmoid\n # if self.activation == 'sigmoid':\n\n # return 1 / (1 + np.exp(-r))\n\n # if self.activation == \"softmax\":\n # # stable softmax\n # r = r - np.max(r)\n # s = np.exp(r)\n # return s / np.sum(s)","title":""},{"docid":"ae46457f0165bbdaf17de0034f848ffb","score":"0.6485403","text":"def tanhDerivative(x):\n return 1.0 - x**2","title":""},{"docid":"05ea57adea2111e7c4a20eca951f6169","score":"0.6484352","text":"def tanh(x):\n\n y = (np_exp(x) - np_exp(-x)) / (np_exp(x) + np_exp(-x))\n\n return y","title":""},{"docid":"f8c5a72e722511784df73bba4593da1e","score":"0.64772713","text":"def activate(act):\n if act == 'sigmoid': return (sigmoid, d_sigmoid)\n if act == 'relu': return (relu, d_relu)\n if act == 'tanh': return (tanh, d_tanh)","title":""},{"docid":"0fdd8437f2af1c11116ea48b6411e6f5","score":"0.64739877","text":"def Kenue2ActivationFunction( x ):\n return ( 2.0 / pi ) * ( tanh( x ) / cosh( x ) + atan( sinh( x ) ) );","title":""},{"docid":"3774fd36c8e7aaeed51d3317c0255b59","score":"0.6473774","text":"def sigmoid(t):\n sigmoid=1/(1+np.exp(-t))\n return sigmoid","title":""},{"docid":"69093be568c2dfef52dcc3e24fb987fa","score":"0.64718205","text":"def activation_fn(self, r):\n a = activate(self.activation, r)\n return a\n\n # if self.activation == None or self.activation == \"linear\":\n # return r\n\n # # tanh\n # if self.activation == 'tanh':\n # return np.tanh(r)\n\n # # sigmoid\n # if self.activation == 'sigmoid':\n\n # return 1 / (1 + np.exp(-r))\n\n # if self.activation == \"softmax\":\n # # stable softmax\n # r = r - np.max(r)\n # s = np.exp(r)\n # return s / np.sum(s)\n # if self.activation == \"relu\":\n # r[r<0] = 0\n # return r","title":""},{"docid":"bc84a25bec8d70a419ee84ccef7840de","score":"0.64711595","text":"def tanh():\n def tanh_forward(z):\n \"\"\"Returns the element-wise tanh of z\"\"\"\n return np.tanh(z)\n\n def tanh_backward(z):\n \"\"\"Applies the element-wise derivative of tanh on z\"\"\"\n return 1-np.power(tanh_forward(z), 2)\n\n return tanh_forward, tanh_backward","title":""},{"docid":"8fbeeaf973b8a21466d94e1801788fc8","score":"0.64569604","text":"def topo_eta_b(x):\r\n return ((H-0.0004)/2)*(1+np.tanh(alpha*(x-x_0)))","title":""},{"docid":"2d71a4f58e60c52e15737cfb27da2620","score":"0.6456309","text":"def Sigmoid(a):\r\n # calculate h\r\n h = 1 / (1 + np.exp(-a))\r\n\r\n # return h\r\n return h","title":""},{"docid":"2556156d0acef9c512e47af0772eac31","score":"0.64503455","text":"def __act_f(self, p):\n # linear activation if no function is given\n if self.activation is None:\n return p\n # tanh\n if self.activation == \"tanh\":\n return np.tanh(p)\n # sigmoid\n if self.activation == \"sigmoid\":\n return 1 / (1 + np.exp(-p))\n # relu\n if self.activation == \"relu\":\n return p * (p > 0)","title":""},{"docid":"22dc9ade24e3f319d37e2335ea0c5b4d","score":"0.64486057","text":"def activation_deriv(self):\n assert self.output.size > 0\n return np.multiply(self.output, (1 - self.output))","title":""},{"docid":"31d8461b3949ec8162c8e25414093ad0","score":"0.6446108","text":"def activation(self, z):\n return np.log(1 + np.exp(z))","title":""},{"docid":"dc45bebbebb25ebc996dec6d5f2770a8","score":"0.6440318","text":"def grad_tanh(self):\n return (1 - np.power(np.tanh(self.x), 2))","title":""},{"docid":"74a1e9cefd45aee889586a240697f1ee","score":"0.6439361","text":"def swish_activation(x):\n return K.sigmoid(x) * x","title":""},{"docid":"10d5203dd3e7f3f5bdb0467c59affe26","score":"0.6436083","text":"def tanh(x):\n value = np.tanh(x)\n gradient = 1.0 - value**2\n return value, gradient","title":""},{"docid":"3da91345065112d33ed4b89c334d87d1","score":"0.64289105","text":"def get_activation_gradient(self, h, activation_type):\n if(activation_type == 'tanh'):\n return 1 - np.power(h, 2)\n elif(activation_type == 'ReLU'):\n h[h > 0] = 1\n h[h < 0] = 0\n h[h == 0] = 0\n return h\n elif(activation_type == 'sigmoid'):\n return h * (1 - h)","title":""},{"docid":"2076a0ece31630e309087fbae1ffacdc","score":"0.64234185","text":"def sigmoid(t):\n t = np.clip(t,a_min=-40,a_max=None)\n return 1/(1+np.exp(-t))","title":""},{"docid":"4db1605e2a075c3f4e04c64841da67ef","score":"0.6413243","text":"def coth(x):\r\n return 1 / math.tanh(x)","title":""},{"docid":"3f718bb5025a3612942956fd332437b0","score":"0.6412491","text":"def tanh(z):\r\n\r\n return np.tanh(z)","title":""},{"docid":"d2a579db1aa0797615354a4fef7fd3a1","score":"0.64118046","text":"def sigmoid_activation(self, num):\r\n result = 1/(1 + np.exp(-num))\r\n return result","title":""}],"string":"[\n {\n \"docid\": \"0768039b3e11f67b65f2e77de62fb04c\",\n \"score\": \"0.8281349\",\n \"text\": \"def HiperbolicTangensActivationFunction( x ):\\n return tanh( x )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f12af731c1234fe21563b14525f2d64\",\n \"score\": \"0.8131882\",\n \"text\": \"def HiperbolicTangensDerivActivationFunction( x ):\\n return ( 1.0 - pow( tanh( x ), 2.0 ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f723ed0b4680c2c84dcbb56f7fead226\",\n \"score\": \"0.76846695\",\n \"text\": \"def tanh():\\n return function.Function(lambda x: math.tanh(x),\\n 'activation.tanh',\\n 'Tanh activation function')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e744f65fc3c6c4d39d372c6b2e390cd\",\n \"score\": \"0.74650675\",\n \"text\": \"def _activation(self, z, tanh=True):\\n if tanh:\\n return np.tanh(z)\\n else:\\n return 1 / (1 + np.exp(-z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9963ea4ce5021aaa6baf979ae3121c38\",\n \"score\": \"0.7432859\",\n \"text\": \"def activation_function(self, x):\\n return np.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97542b2437b98d094305bda14d128d69\",\n \"score\": \"0.73241377\",\n \"text\": \"def LogisticDerivActivationFunction( x ):\\n return LogisticActivationFunction( x ) * ( 1 - LogisticActivationFunction( x ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b7331732656fb95fb772caccfa2eb2\",\n \"score\": \"0.72636825\",\n \"text\": \"def _activation_function(self, x):\\n return 1 / (1 + exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59f863cbb91dba0c319ef955848ad20\",\n \"score\": \"0.72101736\",\n \"text\": \"def activation(x):\\r\\n return 1/(1+torch.exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6b62c5c75cd8827013ca50c85371c6\",\n \"score\": \"0.7134875\",\n \"text\": \"def activation(x):\\n\\treturn 1/(1 + torch.exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9957afc2f945d8ae8e1559cff48d7a11\",\n \"score\": \"0.71344924\",\n \"text\": \"def tanh(x):\\n return tfe.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0345a150f9778a571882ff03a6907c10\",\n \"score\": \"0.71143526\",\n \"text\": \"def activation_function(x):\\n return 1/(1+np.exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f08b035f498943b84d9277bd580965\",\n \"score\": \"0.7090844\",\n \"text\": \"def activation(x):\\n return 1/(1+torch.exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f08b035f498943b84d9277bd580965\",\n \"score\": \"0.7090844\",\n \"text\": \"def activation(x):\\n return 1/(1+torch.exp(-x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ea2e6707a227b422d43b86ac2fb423\",\n \"score\": \"0.70439166\",\n \"text\": \"def tanh(x):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ea2e6707a227b422d43b86ac2fb423\",\n \"score\": \"0.70439166\",\n \"text\": \"def tanh(x):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bef71e737fdcbe8015f5a2694b218d9\",\n \"score\": \"0.69575846\",\n \"text\": \"def sigmoid_activation_deriv(self, in_acts):\\n \\\"\\\"\\\"YOUR CODE\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a52ceb1708b0998cbf81ab89d83c0e\",\n \"score\": \"0.69570386\",\n \"text\": \"def activation(x, w):\\n z = x.dot(w);\\n # print(\\\"\\\\nz\\\")\\n # print(z)\\n H = 1/(1+exp(-z))\\n # print(\\\"\\\\nH\\\")\\n # print(H)\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f3de9ff2fd2063b3b9314ecde803ac\",\n \"score\": \"0.6908077\",\n \"text\": \"def activation_deriv(self):\\n assert self.output.size > 0\\n return 1.0 / (1.0 + np.exp(-self.linear_output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deed66aa60c7ca66be5617341e41479a\",\n \"score\": \"0.690227\",\n \"text\": \"def Kenue1DerivActivationFunction( x ):\\n return ( 2.0 / pi ) * pow( cosh( x ), -1.0 );\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f4787d5583e6e80a70719ead53f2e4\",\n \"score\": \"0.68727994\",\n \"text\": \"def activation_gradient(activation, X):\\n z = 1.0\\n w = 1.0\\n if activation.__name__ == 'tanh':\\n z = 2.0\\n w = 4.0\\n elif activation.__name__ != 'expit':\\n raise NotImplementedError\\n\\n return(w*sp.special.expit(z*X)*(1-sp.special.expit(z*X)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49cedcb20d1352189cd4bd87cf741e08\",\n \"score\": \"0.6868825\",\n \"text\": \"def sigmoid(t):\\n \\n return 1.0 / (1 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988b7d367891906f3f06088b17797a08\",\n \"score\": \"0.6833743\",\n \"text\": \"def activate(self):\\n def gelu(x):\\n cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\\n return x * cdf\\n\\n return gelu\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c42857af2af6001e0e41c2156b97fff\",\n \"score\": \"0.6832049\",\n \"text\": \"def Kenue2DerivActivationFunction( x ):\\n return ( 4.0 / pi ) * pow( pow( cosh( x ), -1.0 ), 3.0 );\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c3bc332610bbc98328e05b4568e048\",\n \"score\": \"0.6819093\",\n \"text\": \"def activation(self, z):\\n return 1. / (1. + np.exp(-z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c9a6257eacf9c4371d8b735247cf83\",\n \"score\": \"0.68157375\",\n \"text\": \"def sigmoid(t):\\n return 1 / (1 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2b0661040d146b41029e8b69478463\",\n \"score\": \"0.68133634\",\n \"text\": \"def sigmoid(t):\\n return 1. / (1. + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53814da52902cc86aaa767642ce18fce\",\n \"score\": \"0.6802573\",\n \"text\": \"def _activation(self, z):\\r\\n return 1 / (1 + np.exp(-np.clip(z, -250, 250)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c196d2335778c45cd64cbb2ddb068cc5\",\n \"score\": \"0.6799323\",\n \"text\": \"def sigmoid(t):\\n\\n return 1. / (1 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3deea9a2fa7964a30c92c37f7f20a1d2\",\n \"score\": \"0.67923594\",\n \"text\": \"def activation(self, z):\\n return 1.0 / (1.0 + np.exp(-z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7ce06995bc32254a8a78111881550f\",\n \"score\": \"0.6785471\",\n \"text\": \"def sigmoid(t):\\n return 1.0 / (1 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff231c6eac7f8f1966c45412a6e4e73c\",\n \"score\": \"0.6780622\",\n \"text\": \"def tanh(x):\\n return (1 - exp(-2*x)) / (1 + exp(-2*x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1b7c17c641e9a4a116838ec1a9a018\",\n \"score\": \"0.67682654\",\n \"text\": \"def sigmoid(alpha):\\n return 1 / (1 + np.exp(-alpha))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85019a0d556a9e56ae15d35d6af4fcf1\",\n \"score\": \"0.6748394\",\n \"text\": \"def tanh(x):\\n return K.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c6a15997b29d7e5ad7749015d36c2e\",\n \"score\": \"0.67449754\",\n \"text\": \"def xtanh(x, alpha=.1):\\n return x.tanh() + alpha * x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d110ffbd4f77a74acaed8979a05b7e57\",\n \"score\": \"0.6743973\",\n \"text\": \"def sigmoid(t):\\n exp_inv = np.exp(-1.0*t)\\n return np.divide(1,1+exp_inv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69e031f3ec0eeabe4408312e2a373d6\",\n \"score\": \"0.673918\",\n \"text\": \"def sigmoid(x):\\n # Could experiment with more activation functions: \\n #return np.tanh(x)\\n return logistic.cdf(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e0c95764156110a8419e9ef399bc64\",\n \"score\": \"0.67387015\",\n \"text\": \"def sigmoid(x):\\n return np.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7281330547b87f7cfa60a7a3f17c2c7\",\n \"score\": \"0.6730346\",\n \"text\": \"def sigmoid(t):\\n\\n return np.exp(t)/(1+np.exp(t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d413e9fd375b33db7486905dcc79dfc1\",\n \"score\": \"0.67262965\",\n \"text\": \"def grad_activation(self,input):\\n\\n if self.activ_func == 'relu':\\n return input>0\\n \\n if self.activ_func == 'sigmoid':\\n return np.exp(input)/(1+np.exp(input)) * (1. - np.exp(input)/(1+np.exp(input)))\\n \\n if self.activ_func == 'tanh':\\n return 1-(((np.exp(2*input)-1)/(np.exp(2*input)+1))**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f9fff5ad38ce4f8d12da56d95d1e2f\",\n \"score\": \"0.6725492\",\n \"text\": \"def _tanh(self):\\n X = self._input_nodes[0]._value # First argument\\n return np.tanh(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b405b43d70da5c161cbeb136137e3e\",\n \"score\": \"0.67251056\",\n \"text\": \"def hsigmoid_activating(x):\\n return tf.nn.relu6(x+3.)*0.16667\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b266077fd9fd3429ebeaab04301ca55\",\n \"score\": \"0.67132413\",\n \"text\": \"def activation(z):\\n return 1. / (1. + np.exp(-np.clip(z, -250, 250)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76b4eb21a1a621354475d69ec9c51a7\",\n \"score\": \"0.6697952\",\n \"text\": \"def dtanh(x):\\n return 1 - np.tanh(x)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c8c893799ac28e3faee958373a40d0\",\n \"score\": \"0.6688739\",\n \"text\": \"def sigmoid_activation(self, in_acts):\\n \\\"\\\"\\\"YOUR CODE\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45352557aeb7976aaaed29ceaa16cff8\",\n \"score\": \"0.66840404\",\n \"text\": \"def sigmoid(t):\\n exp = np.exp(-t)\\n \\n return 1.0/(1+exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7d4cb777fcbc3c8879a2d601e6b9a2\",\n \"score\": \"0.66834897\",\n \"text\": \"def activate(self, z, function):\\n if function == Functions.SIGMOID:\\n return sigmoid(z)\\n elif function == Functions.SOFTMAX:\\n return softmax(z)\\n else:\\n print(\\\"Unrecognized activation function\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb11b53d13f1ce63390f7a24179b2a5\",\n \"score\": \"0.66762996\",\n \"text\": \"def tanh_forward(z):\\n return np.tanh(z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62792c625ba954163de74c3180da2d91\",\n \"score\": \"0.6672956\",\n \"text\": \"def __sigmoid_grad(x):\\n return ActivationFunctions.sigmoid(x)*(1-ActivationFunctions.sigmoid(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06ae18e2a9e9ff1038a33699e4a8eb1\",\n \"score\": \"0.66710925\",\n \"text\": \"def sigmoid(t):\\n return np.exp(t) / (1+np.exp(t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec892bf230d1f35035451d6e8e83fe2f\",\n \"score\": \"0.6664264\",\n \"text\": \"def d_tanh(y):\\n return 1 - y**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59df80411f6e83b0d1e5f23e0e65eed\",\n \"score\": \"0.66529334\",\n \"text\": \"def f(cls, x):\\n return numx.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6b5f771b157fd9da344bdfb4d6a686\",\n \"score\": \"0.6642599\",\n \"text\": \"def activation(self, z):\\n return 1. / (1. + np.exp(-np.clip(z, -250, 250)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f875f3bd7f5f70c6671908a3226433fc\",\n \"score\": \"0.6638346\",\n \"text\": \"def sigmoid(t):\\n return np.exp(-np.logaddexp(0, -t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77f51aec82e755b495582682113f91e\",\n \"score\": \"0.66352564\",\n \"text\": \"def derivative_tanh(x):\\n return 1 - torch.tanh(x)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b798243fb1d8795c929718f2c19957a\",\n \"score\": \"0.66315585\",\n \"text\": \"def activation(self, z):\\n return 1./(1+np.exp(-np.clip(z, -250, 250)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3085b322eab228a4b10cacc1c42aa84\",\n \"score\": \"0.6628338\",\n \"text\": \"def tanh(self, x):\\n self.x = x\\n return np.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa25bd2b6eb78e997ba21fa9d8ca155\",\n \"score\": \"0.66264516\",\n \"text\": \"def lisht(x):\\n return tf.multiply(x, tf.nn.tanh(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800fc8a5c50addc04be4a2d3777b8688\",\n \"score\": \"0.6625754\",\n \"text\": \"def tanh(m):\\n return m._ufunc_tanh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ca5534d5173b0c42bd15bf49d81a77\",\n \"score\": \"0.6619132\",\n \"text\": \"def sigmoid(x):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ca5534d5173b0c42bd15bf49d81a77\",\n \"score\": \"0.6619132\",\n \"text\": \"def sigmoid(x):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ca5534d5173b0c42bd15bf49d81a77\",\n \"score\": \"0.6619132\",\n \"text\": \"def sigmoid(x):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7568a5c90caefa03c9cdcf04552ba3f\",\n \"score\": \"0.6615469\",\n \"text\": \"def tanh_deriv(x):\\n return 1.0 - np.power(np.tanh(x), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5f58b9dc201c4dce33e9d459a091ce\",\n \"score\": \"0.66071075\",\n \"text\": \"def activation(z, method=\\\"tanh\\\"):\\n if method == \\\"tanh\\\":\\n return tanh(z)\\n elif method == \\\"linear\\\":\\n return z\\n elif method == \\\"sigmoid\\\":\\n return 1./(1.+np.exp(-z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751138e26a9fceef8c85eb92990107ce\",\n \"score\": \"0.6603773\",\n \"text\": \"def activation(self,input):\\n\\n if self.activ_func == 'relu':\\n return (input>0)*input\\n \\n if self.activ_func == 'sigmoid':\\n return np.exp(input)/(1+np.exp(input))\\n \\n if self.activ_func == 'tanh':\\n return (np.exp(2*input)-1)/(np.exp(2*input)+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6c963ecd1d9496cd4a4bee8a13296b\",\n \"score\": \"0.66011584\",\n \"text\": \"def tanh(x):\\n return np.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6c963ecd1d9496cd4a4bee8a13296b\",\n \"score\": \"0.66011584\",\n \"text\": \"def tanh(x):\\n return np.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e750994965583a4b3adaeb1921899cd5\",\n \"score\": \"0.6591646\",\n \"text\": \"def sigmoid(t):\\n t_exp = np.exp(t)\\n\\n return t_exp/(1 + t_exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f05153765afedd8be3b5f828a1a9671\",\n \"score\": \"0.6581308\",\n \"text\": \"def grad_tanh(self):\\n return np.subtract(1, np.power(self.tanh(self.x), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5adcb5d1defe4a2854bdfbbce84c4b\",\n \"score\": \"0.65725887\",\n \"text\": \"def tanhplus(inpt):\\n return T.tanh(inpt) + inpt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c60ab56fdc88d7f180c567e2c15bcd9\",\n \"score\": \"0.65694565\",\n \"text\": \"def tanh(a):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639b32d0f0d0041775aa26df8e4fc1a2\",\n \"score\": \"0.65662307\",\n \"text\": \"def scaled_tanh(self,x):\\r\\n vmax = 10.0\\r\\n return backend.tanh(x)*vmax*0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994705df8b6049fb8969658426682032\",\n \"score\": \"0.6564388\",\n \"text\": \"def tanh(x):\\n return np.tanh(x);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fdce1c33180f75d4b4711537df72b54\",\n \"score\": \"0.65217257\",\n \"text\": \"def apply_deriv(cls, raw_activation):\\n sig = cls._sigmoid(raw_activation)\\n return sig * (1 - sig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09cf17cee36d2108905facb50c8647bd\",\n \"score\": \"0.65196973\",\n \"text\": \"def tan_sigmoid(z):\\n return (2 / (1 + K.exp(-2.0 * z))) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9d4b9d6b925d93431627dcafdce254\",\n \"score\": \"0.6514894\",\n \"text\": \"def sigmoid_forward(z):\\n return 1 / (1 + np.exp(-z)), z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef03bcba50e82aa3a58e7fc1f7f5b69\",\n \"score\": \"0.65084577\",\n \"text\": \"def reg_logistic_grad(y, tx, w, lambda_):\\n e=tx.dot(w)\\n sig_e=sigmoid(e)\\n return (tx.T).dot(sig_e-y)+lambda_*w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6eb04b1e330b2510aea458b5f6b49c9\",\n \"score\": \"0.64992666\",\n \"text\": \"def _activation_function(self, x):\\n return np.where(x >= 0., 1, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5515758ecccb26c69329f1edb3a2472e\",\n \"score\": \"0.6496716\",\n \"text\": \"def hθ(theta, X):\\n return sigmoid(np.dot(X, theta))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1acae46f0e3f2e6fadd3fe010791270\",\n \"score\": \"0.6495682\",\n \"text\": \"def grad_tanh(self):\\n return 1 - np.tanh(self.x)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b42a0aaf2916173699dca0f28c086950\",\n \"score\": \"0.6494281\",\n \"text\": \"def activation_fn(self, r):\\n a = activate(self.activation, r)\\n return a\\n\\n # if self.activation == None or self.activation == \\\"linear\\\":\\n # return r\\n # if self.activation == \\\"relu\\\":\\n # r[r<0] = 0\\n # return r\\n\\n # # tanh\\n # if self.activation == 'tanh':\\n # return np.tanh(r)\\n\\n # # sigmoid\\n # if self.activation == 'sigmoid':\\n\\n # return 1 / (1 + np.exp(-r))\\n\\n # if self.activation == \\\"softmax\\\":\\n # # stable softmax\\n # r = r - np.max(r)\\n # s = np.exp(r)\\n # return s / np.sum(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae46457f0165bbdaf17de0034f848ffb\",\n \"score\": \"0.6485403\",\n \"text\": \"def tanhDerivative(x):\\n return 1.0 - x**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05ea57adea2111e7c4a20eca951f6169\",\n \"score\": \"0.6484352\",\n \"text\": \"def tanh(x):\\n\\n y = (np_exp(x) - np_exp(-x)) / (np_exp(x) + np_exp(-x))\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c5a72e722511784df73bba4593da1e\",\n \"score\": \"0.64772713\",\n \"text\": \"def activate(act):\\n if act == 'sigmoid': return (sigmoid, d_sigmoid)\\n if act == 'relu': return (relu, d_relu)\\n if act == 'tanh': return (tanh, d_tanh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdd8437f2af1c11116ea48b6411e6f5\",\n \"score\": \"0.64739877\",\n \"text\": \"def Kenue2ActivationFunction( x ):\\n return ( 2.0 / pi ) * ( tanh( x ) / cosh( x ) + atan( sinh( x ) ) );\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3774fd36c8e7aaeed51d3317c0255b59\",\n \"score\": \"0.6473774\",\n \"text\": \"def sigmoid(t):\\n sigmoid=1/(1+np.exp(-t))\\n return sigmoid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69093be568c2dfef52dcc3e24fb987fa\",\n \"score\": \"0.64718205\",\n \"text\": \"def activation_fn(self, r):\\n a = activate(self.activation, r)\\n return a\\n\\n # if self.activation == None or self.activation == \\\"linear\\\":\\n # return r\\n\\n # # tanh\\n # if self.activation == 'tanh':\\n # return np.tanh(r)\\n\\n # # sigmoid\\n # if self.activation == 'sigmoid':\\n\\n # return 1 / (1 + np.exp(-r))\\n\\n # if self.activation == \\\"softmax\\\":\\n # # stable softmax\\n # r = r - np.max(r)\\n # s = np.exp(r)\\n # return s / np.sum(s)\\n # if self.activation == \\\"relu\\\":\\n # r[r<0] = 0\\n # return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc84a25bec8d70a419ee84ccef7840de\",\n \"score\": \"0.64711595\",\n \"text\": \"def tanh():\\n def tanh_forward(z):\\n \\\"\\\"\\\"Returns the element-wise tanh of z\\\"\\\"\\\"\\n return np.tanh(z)\\n\\n def tanh_backward(z):\\n \\\"\\\"\\\"Applies the element-wise derivative of tanh on z\\\"\\\"\\\"\\n return 1-np.power(tanh_forward(z), 2)\\n\\n return tanh_forward, tanh_backward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbeeaf973b8a21466d94e1801788fc8\",\n \"score\": \"0.64569604\",\n \"text\": \"def topo_eta_b(x):\\r\\n return ((H-0.0004)/2)*(1+np.tanh(alpha*(x-x_0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d71a4f58e60c52e15737cfb27da2620\",\n \"score\": \"0.6456309\",\n \"text\": \"def Sigmoid(a):\\r\\n # calculate h\\r\\n h = 1 / (1 + np.exp(-a))\\r\\n\\r\\n # return h\\r\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2556156d0acef9c512e47af0772eac31\",\n \"score\": \"0.64503455\",\n \"text\": \"def __act_f(self, p):\\n # linear activation if no function is given\\n if self.activation is None:\\n return p\\n # tanh\\n if self.activation == \\\"tanh\\\":\\n return np.tanh(p)\\n # sigmoid\\n if self.activation == \\\"sigmoid\\\":\\n return 1 / (1 + np.exp(-p))\\n # relu\\n if self.activation == \\\"relu\\\":\\n return p * (p > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22dc9ade24e3f319d37e2335ea0c5b4d\",\n \"score\": \"0.64486057\",\n \"text\": \"def activation_deriv(self):\\n assert self.output.size > 0\\n return np.multiply(self.output, (1 - self.output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d8461b3949ec8162c8e25414093ad0\",\n \"score\": \"0.6446108\",\n \"text\": \"def activation(self, z):\\n return np.log(1 + np.exp(z))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc45bebbebb25ebc996dec6d5f2770a8\",\n \"score\": \"0.6440318\",\n \"text\": \"def grad_tanh(self):\\n return (1 - np.power(np.tanh(self.x), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a1e9cefd45aee889586a240697f1ee\",\n \"score\": \"0.6439361\",\n \"text\": \"def swish_activation(x):\\n return K.sigmoid(x) * x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d5203dd3e7f3f5bdb0467c59affe26\",\n \"score\": \"0.6436083\",\n \"text\": \"def tanh(x):\\n value = np.tanh(x)\\n gradient = 1.0 - value**2\\n return value, gradient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da91345065112d33ed4b89c334d87d1\",\n \"score\": \"0.64289105\",\n \"text\": \"def get_activation_gradient(self, h, activation_type):\\n if(activation_type == 'tanh'):\\n return 1 - np.power(h, 2)\\n elif(activation_type == 'ReLU'):\\n h[h > 0] = 1\\n h[h < 0] = 0\\n h[h == 0] = 0\\n return h\\n elif(activation_type == 'sigmoid'):\\n return h * (1 - h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2076a0ece31630e309087fbae1ffacdc\",\n \"score\": \"0.64234185\",\n \"text\": \"def sigmoid(t):\\n t = np.clip(t,a_min=-40,a_max=None)\\n return 1/(1+np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db1605e2a075c3f4e04c64841da67ef\",\n \"score\": \"0.6413243\",\n \"text\": \"def coth(x):\\r\\n return 1 / math.tanh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f718bb5025a3612942956fd332437b0\",\n \"score\": \"0.6412491\",\n \"text\": \"def tanh(z):\\r\\n\\r\\n return np.tanh(z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a579db1aa0797615354a4fef7fd3a1\",\n \"score\": \"0.64118046\",\n \"text\": \"def sigmoid_activation(self, num):\\r\\n result = 1/(1 + np.exp(-num))\\r\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993685,"cells":{"query_id":{"kind":"string","value":"677e5819b96ac20217667fc0c75fed70"},"query":{"kind":"string","value":"Print a menu recursively. Raises HandinError if a menu type is unhandled. Raises KeyError if a necessary menu value did not exist. This includes 'type' for all menus, 'choices' for type=menu, and 'id' for type=selection."},"positive_passages":{"kind":"list like","value":[{"docid":"08922252a5f03f19c356b91b826bce7e","score":"0.766687","text":"def _print_menu_choices(menu, key, choices, level=0):\n indent = \" \" * level\n if menu['type'] == 'selection':\n print \"%s [%s] %s\" % (\n indent,\n choices[menu['id']],\n menu.get('display_name', key),\n )\n elif menu['type'] == 'menu':\n print \"%s%s\" % (indent, menu.get('display_name', key))\n for k in menu['choices'].keys():\n _print_menu_choices(menu['choices'][k], k, choices, level + 1)\n else:\n raise HandinError(\"Unknown item type: %s\" % menu['type'])","title":""}],"string":"[\n {\n \"docid\": \"08922252a5f03f19c356b91b826bce7e\",\n \"score\": \"0.766687\",\n \"text\": \"def _print_menu_choices(menu, key, choices, level=0):\\n indent = \\\" \\\" * level\\n if menu['type'] == 'selection':\\n print \\\"%s [%s] %s\\\" % (\\n indent,\\n choices[menu['id']],\\n menu.get('display_name', key),\\n )\\n elif menu['type'] == 'menu':\\n print \\\"%s%s\\\" % (indent, menu.get('display_name', key))\\n for k in menu['choices'].keys():\\n _print_menu_choices(menu['choices'][k], k, choices, level + 1)\\n else:\\n raise HandinError(\\\"Unknown item type: %s\\\" % menu['type'])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f529799e4495690bc0e73382ebad7e33","score":"0.6648107","text":"def print_menu():\n # your code goes here!\n print(\"Our menu:\")\n print(\"*******************\")\n for k, v in menu.items():\n print(k, \": \", v)\n print(\"*******************\\n\")","title":""},{"docid":"43b94f196729d58cb3feb5e03769aede","score":"0.6577854","text":"def _print_menu(menu_dict: Dict[str, Dict[str, list]]) -> None:\n for key, value in menu_dict.items():\n print(colored(key+\":\\n\", \"blue\", attrs=['bold']))\n for dining_hall, dishes in value.items():\n print(colored(dining_hall+\":\", \"white\", attrs=['bold']))\n for dish in dishes:\n print(\"\\t\"+dish)\n print(\"\")","title":""},{"docid":"a888ac0636175c05a464bd532097e6a9","score":"0.6495988","text":"def display_menu(self):\n print(\"Menu Choices:\")\n for key in self.choices:\n choice = self.choices[key].__name__\n choice = choice.replace(\"_\", \" \").title()\n print(f\"{key}: {choice}\")","title":""},{"docid":"73645607b5dbdac107f1afbd66ea1cc8","score":"0.6495514","text":"def show_menu(self):\n # print('debug: calling show_menu' + self.name)\n print(self.name)\n for choice in self.choices:\n print(choice)","title":""},{"docid":"7b1476615f97a7fdd7a9a599e7b367c5","score":"0.6351823","text":"def print_menu_detail_all(type: Period) -> None:\n _print_menu(_format_output(_get_html(MENU_URL+\"/\"+type)))","title":""},{"docid":"60a062237a1d187513c27b360117cc99","score":"0.6334381","text":"def print_menu():\n logger.info(\"### PrintMenu ###\")\n print('1 Einstellungen')\n print('2 Wörter üben')\n print('3 Wörter hinzufügen')\n print('4 Wörter anzeigen')\n print('9 Ende')","title":""},{"docid":"efc0585c2d3f0618dcaad1d7d81ffa13","score":"0.6162404","text":"def submenus(menu: RofiMenu) -> None:\n if menu is None:\n return\n\n for key, value in menu:\n # Convert dict to RofiMenu\n if isinstance(value, dict):\n menu[key] = RofiMenu(value)\n submenus(menu[key])\n\n if isinstance(value, list):\n for i in range(len(value)): # For each item on the list\n if isinstance(value[i], dict):\n menu[key][i] = RofiMenu(menu[key][i])\n submenus(menu[key][i])","title":""},{"docid":"e61f85b070b16bbc0afa78c2fad87d11","score":"0.6161514","text":"def get_menu(cls, menu):\n output = \"\\n==== Menu ====\\n\"\n for k, v in menu.items():\n output += f\"{k}. {v}\\n\"\n return output","title":""},{"docid":"9a778a4671ae2bc7b55729c4aa106c27","score":"0.61242706","text":"def print_menu():\r\n\r\n print('Menu\\n\\n[l] load Inventory from file\\n[a] Add CD\\n[i] Display Current Inventory')\r\n print('[d] delete CD from Inventory\\n[s] Save Inventory to file\\n[x] exit\\n')","title":""},{"docid":"9a778a4671ae2bc7b55729c4aa106c27","score":"0.61242706","text":"def print_menu():\r\n\r\n print('Menu\\n\\n[l] load Inventory from file\\n[a] Add CD\\n[i] Display Current Inventory')\r\n print('[d] delete CD from Inventory\\n[s] Save Inventory to file\\n[x] exit\\n')","title":""},{"docid":"e285216ab39ad51d04314f3aa00cea67","score":"0.60845155","text":"def print_menu():\r\n\r\n print('Menu\\n\\n[l] load Inventory from file\\n[a] Add CD\\n[i] Display Current Inventory')\r\n print('[s] Save Inventory to file\\n[x] exit\\n')","title":""},{"docid":"f08bc2367791d2c70211e999ae730ea0","score":"0.60730404","text":"def print_menu():\n\n print(\"==============================================\")\n print(\"What do you want to do now? \")\n print(\"==============================================\")\n print(\"Available options:\")\n i = 1\n for a in available_actions:\n if current_state in a[\"valid_states\"]:\n # Only hint about the action if the current state allows it\n print(\" %i) %s\" % (i, a[\"description\"]))\n i += 1\n print()","title":""},{"docid":"a1bb8b561248db6358f15a0559b1c49e","score":"0.6001676","text":"def present_menus(menu_dict):\n for key, d in menu_dict.items():\n print(f'{key}) {d.get(\"info\", \"no description\")}')\n print(\"-\" * 20)","title":""},{"docid":"5e234877826e44d4e26b4366d63f7f44","score":"0.59529275","text":"def populate_menu(self, menu, dictionary):\n\n def add_mode_to_menu(menu_obj, mode):\n action = menu_obj.addAction(mode.mode_name_formatted)\n action.triggered.connect(lambda: self.select_mode(mode))\n\n for menu_key, menu_val in dictionary.items():\n if isinstance(menu_val, list):\n sub_menu = menu.addMenu(menu_key)\n for value in menu_val:\n if isinstance(value, GameMode):\n add_mode_to_menu(sub_menu, value)\n if isinstance(menu_val, dict):\n sub_menu = menu.addMenu(menu_key)\n self.populate_menu(sub_menu, menu_val)\n if isinstance(menu_val, GameMode):\n add_mode_to_menu(menu, menu_val)","title":""},{"docid":"79a81bbbb09732666b76359681c1bf8f","score":"0.59387755","text":"def print_menu():\n str_result = ''\n for i in actions_dictionary:\n str_result += (\"{}) {}\".format(i, actions_dictionary[i]))\n str_result += '\\n'\n str_result += 'Select the corresponding number for the action you want to take action: '\n return str_result","title":""},{"docid":"d6f1f7effcfb52c87e8ccd2f65598102","score":"0.5921677","text":"def PrintMenuItems():\n print('''\n Menu of Options\n 1) Show current data\n 2) Add a new product.\n 3) Save Data to File\n 4) Exit Program\n ''')\n print() # Add an extra line for looks","title":""},{"docid":"f2bd55edaa3b719e879a74ea115d92b6","score":"0.58480316","text":"def _get_menu_choices(menu):\n import operator\n if menu['type'] == 'selection':\n return (menu['id'],)\n elif menu['type'] == 'menu':\n choices = [_get_menu_choices(submenu) for submenu in\n menu['choices'].values()]\n return reduce(operator.add, choices, ())\n else:\n raise HandinError(\"Unknown item type: %s\" % menu['type'])","title":""},{"docid":"43d95264ed8a3506d9566b949706e254","score":"0.5846854","text":"def menuAction(self, *args, **kwargs) :\r\n success = False\r\n\r\n #####\r\n # If the action parameter\r\n if self.action:\r\n success = self.action()\r\n\r\n self.logger.log(lp.DEBUG, \"Action returns success: \" + str(success))\r\n\r\n #####\r\n # Print the menu and act on the menu selection\r\n while True :\r\n try:\r\n quit = self.getValue(\"quit\")\r\n except KeyError:\r\n quit = False\r\n\r\n #####\r\n # Write out the ANSI code to clear the screen - might not work\r\n # if a user's terminal is set to unicode\r\n print(\"\\033c\")\r\n sys.stdout.write(\"\\033c\")\r\n #####\r\n # Start menu logic\r\n print(\"\\n\" + self.name + \" Menu\\n\")\r\n i = 1\r\n for item in self.child_nodes :\r\n print(\"[\" + str(i) + \"] \" + self.child_nodes[(i-1)].name)\r\n i = i + 1\r\n if not self.anchor :\r\n if not self.anchor:\r\n print(\"[\" + str(i) + \"] Return to \" + \\\r\n self.previous.name + \\\r\n \" Menu\")\r\n if self.anchor:\r\n print(\"[\" + str(i+1) + \"] Quit\")\r\n elif self.previous.anchor:\r\n print(\"[\" + str(i+1) + \"] Quit\")\r\n elif not self.anchor and not self.previous.anchor:\r\n print(\"[\" + str(i+1) + \"] Main menu\")\r\n print(\"[\" + str(i+2) + \"] Quit\")\r\n else :\r\n print(\"[\" + str(i) + \"] Quit\")\r\n\r\n print(\"\\nSelect an option and hit Enter\")\r\n\r\n # get input from the command line\r\n enter = sys.stdin.readline()\r\n\r\n # Figure out which number (or [Q|q] they hit\r\n if re.match(\"^\\d+$\", enter) :\r\n enter = int(enter)\r\n elif re.match(\"^[Qq]$\", enter) and not quit:\r\n break\r\n elif re.match(\"^[Qq]$\", enter) and quit:\r\n try :\r\n sys.exit()\r\n except OSError as err :\r\n self.logger.log(lp.DEBUG, \"OSError on attempt to exit: \" + \\\r\n str(err))\r\n elif re.match(\"^[Mm]$\", enter):\r\n self.goToMainMenu()\r\n else :\r\n continue\r\n \r\n if enter == (len(self.child_nodes)+1) and self.anchor and not quit:\r\n while not self.anchor:\r\n self = self.previous\r\n break\r\n if enter == (len(self.child_nodes)+1) and self.anchor and quit:\r\n try :\r\n sys.exit()\r\n except OSError as err :\r\n self.logger.log(lp.DEBUG, \"OSError on attempt to exit: \" + \\\r\n str(err))\r\n # go back to the previous menu \r\n if enter == (len(self.child_nodes)+1) and self.previous.anchor :\r\n while not self.anchor:\r\n self = self.previous\r\n break\r\n if enter == (len(self.child_nodes)+1) and not self.anchor and not self.previous.anchor :\r\n break\r\n\r\n # Either quit, or execute the \"execute\" method of the MenuItem, or\r\n # MenuComposite\r\n if enter == (len(self.child_nodes)+ 1) and self.anchor and not quit:\r\n while not self.anchor:\r\n self = self.previous\r\n break\r\n if enter == (len(self.child_nodes)+ 1) and self.anchor and quit:\r\n try :\r\n sys.exit()\r\n except OSError as err :\r\n self.logger.log(lp.DEBUG, \"OSError on attempt to exit: \" + str(err))\r\n elif enter == (len(self.child_nodes)+ 2) and self.previous.anchor and not quit:\r\n while not self.anchor:\r\n self = self.previous\r\n break\r\n elif enter == (len(self.child_nodes)+ 2) and self.previous.anchor and quit:\r\n try :\r\n sys.exit()\r\n except OSError as err :\r\n self.logger.log(lp.DEBUG, \"OSError on attempt to exit: \" + str(err))\r\n elif enter == (len(self.child_nodes)+ 2) and not self.previous.anchor:\r\n self.goToMainMenu()\r\n elif enter == (len(self.child_nodes)+ 3) and not self.previous.anchor:\r\n while not self.anchor:\r\n self = self.previous\r\n break\r\n elif enter == (len(self.child_nodes)+ 3) and not self.previous.anchor:\r\n try :\r\n sys.exit()\r\n except OSError as err :\r\n self.logger.log(lp.DEBUG, \"OSError on attempt to exit: \" + str(err))\r\n \r\n elif enter >= 0 and enter <= len(self.child_nodes) :\r\n #####\r\n # If the action parameter\r\n if self.action:\r\n print(\"Action: \" + str(self.action))\r\n success = self.action(self, *args, **kwargs)\r\n\r\n self.logger.log(lp.DEBUG, \"Action returns success: \" + str(success))\r\n\r\n self.child_nodes[(enter-1)].menuAction()\r\n else :\r\n continue","title":""},{"docid":"7f494e0e84678ccc3d371cd3262663ab","score":"0.5811621","text":"def print_menu_choice():\r\n print('''\r\nMenu of Options\r\n 1) View current product data\r\n 2) Add new product data\r\n 3) Save Data to File\r\n 4) Exit Program \r\n ''')","title":""},{"docid":"efe80b4abd58e45d51be43ec791a9832","score":"0.5784042","text":"def menu(self):\n # Print header and selection name.\n print('\\nMain menu', end='')\n if self.selected is None:\n print(' (no entry selected):')\n else:\n print(' ({} selected): '.format(self.selected))\n\n # Print menu entries.\n for entry in self.menu_items:\n print(entry[1])\n\n # Print the data of the selected entry.\n if self.selected is not None:\n print('\\nSelected: ')\n self.printEntry(self.selected, False)","title":""},{"docid":"c298c5425c4dab8177a5a7c89113c135","score":"0.5778603","text":"def menu(donor_collection_obj, menu_dict):\n user_option = \"\"\n while user_option.lower() != 'e':\n displayMenu()\n user_option = input(\"Select an option from the menu: \")\n user_option = user_option.lower().strip()\n #user_option = 't' #for testing purposes\n #return user_option #used to test menu produces correct user input\n try:\n menu_dict[user_option](donor_collection_obj)\n except KeyError as e:\n print(e)\n print(\"Invalid option selected. Please try again\")","title":""},{"docid":"42f22e51cf283efe6e7566e4a607d90e","score":"0.5730634","text":"def menu(d,prompt='true'): \n keys = []\n try:\n title = d.pop('TITLE')\n section(title)\n except KeyError:\n section()\n sorted_d = sorted(d.items())\n for key in sorted_d:\n row = [C_LB,' {0:10}'.format(key[0]),' : ',C_LG,'{}\\n'.format(key[1])]\n keys.extend(row)\n cprint(*keys)\n if prompt:\n return raw_input('Enter Selection: ')","title":""},{"docid":"f4e5b208275ce6fff67c7d75ace16d77","score":"0.57220185","text":"def display(self, node=None, level=0):\n node = self.root if node is None else self.__get_node(node)\n\n t = \"\\t\" * level\n header = t + \"#{}, {} keys, {} children\"\n keys = t + \"\\tKeys: {}\"\n children = t + \"\\tChildren: {}\"\n\n pos = [self.__get_node(child).pos for child in node.children]\n\n if level == 0:\n print(\"Order: {}\".format(self.order))\n\n print(header.format(node.pos, node.n_keys, node.n_children))\n print(keys.format(str(node.keys)))\n print(children.format(str(pos)))\n print('-' * 60)\n\n for child in node.children:\n self.display(child, level + 1)","title":""},{"docid":"747af905c3efd1848017c01658ed3181","score":"0.5710187","text":"def print_menu():\n print(\"Elija lo que quiera hacer.\")\n print(\"Las opciones son:\")\n print(\"*****************************************************************\")\n print(\"*\\tz: Mostrar el tamaño del diccionario en uso. \\t*\")\n print(\"*\\ts: Buscar una definicion para una palabra dada. \\t*\")\n print(\"*\\ta: Agregar una palabra con su definicion. \\t*\")\n print(\"*\\td: Borrar una palabra. \\t*\")\n print(\"*\\te: Vaciar el diccionario. \\t*\")\n print(\"*\\th: Mostrar el diccionario actual. \\t*\")\n print(\"*\\tc: Duplicar el diccionario actual. \\t*\")\n print(\"*\\tl: Cargar un nuevo diccionario desde un archivo.\\t*\")\n print(\"*\\tu: Guardar el diccionario actual en un archivo. \\t*\")\n print(\"*\\tq: Finalizar \\t*\")\n print(\"*****************************************************************\")\n\n opcion = raw_input(\"Introduzca su elección: \")\n\n return opcion","title":""},{"docid":"cfe290c1b628bae394a1e300c105595f","score":"0.5696634","text":"def show_menu():\n print('\\nMenu:')\n print('1: Load patterns')\n print('2: Calc output')\n print('3: Educate')\n print('4: Set structure')\n print('5: Exit')\n key = input('Select menu item: ')\n return key","title":""},{"docid":"64cc607bfddb9084f0e5aad451846232","score":"0.5685386","text":"def print_menu(exits):\n print_inventory_items(inventory)\n for direction in exits:\n print_exit(direction, exit_leads_to(exits, direction))\n if current_room[\"rapperbeat\"] == False:\n if len(current_room[\"rapper\"]) != 0:\n slow_type(\"\\n\"+\"Battle \" + current_room[\"rapper\"], 250)\n else:\n print(\"\")\n print(\"\")","title":""},{"docid":"09c9878333452f912e2e6eae26d56b79","score":"0.56323445","text":"def run(self):\r\n option_fx = None\r\n opt_exit = None\r\n opt_parent = None\r\n options = self.get_options()\r\n HAS_EXIT = False\r\n HAS_PARENT = False\r\n\r\n if not options:\r\n raise MenuRuntimeError(\"Called menu has no options\")\r\n\r\n # Print options to the screen\r\n for option in options:\r\n if option.get_id() == OPT_ID_NAVP:\r\n HAS_PARENT = True\r\n opt_parent = option\r\n continue\r\n if option.get_id() == OPT_ID_NAVE:\r\n HAS_EXIT = True\r\n opt_exit = option\r\n continue\r\n else:\r\n option.print_opt()\r\n if HAS_PARENT:\r\n opt_parent.print_opt()\r\n if HAS_EXIT:\r\n opt_exit.print_opt()\r\n print(\"\")\r\n\r\n while True:\r\n user_sel = self.get_selection()\r\n\r\n # Combine all option selector lists into one list\r\n sel_subs = [s.get_selectors() for s in options]\r\n\r\n # Then flatten the sublists into a list\r\n sel_all = [item for sublist in sel_subs for item in sublist]\r\n\r\n if user_sel in sel_all:\r\n break\r\n\r\n # print a blank line if success\r\n print(\"\")\r\n\r\n # for every option\r\n for option in options:\r\n if option_fx:\r\n break\r\n\r\n if user_sel in option.get_selectors():\r\n option_fx = option.get_fx()\r\n break\r\n\r\n if option_fx:\r\n option_fx()\r\n else:\r\n raise MenuRuntimeError(\"Called menu option has no function\")","title":""},{"docid":"ba20840cc201d9da782d754b8c66dd8d","score":"0.56109405","text":"def __str__(self):\n ret = NO_MENU_MSG\n if len(self._menu_items) != 0:\n ret = MENU_GREETING_MSG\n current_type = self._menu_items[0].get_type()\n ret += current_type + \":\\n\"\n for item in self._menu_items:\n if item.get_type() != current_type:\n current_type = item.get_type()\n ret += current_type + \":\\n\"\n ret += \"\\t\" + item.__str__()\n return ret","title":""},{"docid":"c4b4e965edf426ba3367bc1578fcacd8","score":"0.5584861","text":"def start_menu(self):\n\n self.menu: dict = {}\n self.menu['1'] = \"1) Emitir ordem de compra ou venda;\" \n self.menu['2'] = \"2) Visualizar minha lista de interesse de cotacao;\"\n self.menu['3'] = \"3) Atualizar minha lista de interesse de cotacao;\"\n self.menu['4'] = \"4) Visualizar minha carteira de acoes;\"\n self.menu['5'] = \"5) Visualizar minha lista de limite de ganho/perca;\"\n self.menu['6'] = \"6) Atualizar minha lista de limite de ganho/perca;\"\n self.menu['7'] = \"0) Sair;\"\n\n print(\"Welcomed {}\".format(self.nome_de_usuario))\n\n while True: \n print()\n options=sorted(self.menu.keys())\n for entry in options: \n print (self.menu[entry])\n\n selection=input(\"Digite o numero da opcao desejada: \") \n \n if selection =='1': \n print (self.menu[selection])\n try:\n construtor_de_ordem = ConstrutorDeOrdem(self.nome_de_usuario)\n ordem: Ordem = construtor_de_ordem.obter_ordem_do_usuario()\n self.cliente_http.emitir_ordem(ordem)\n except:\n self.log_error(\"Nao foi possivel emitir a ordem\", exc=sys.exc_info()[0])\n \n elif selection == '2': \n print (self.menu[selection])\n try:\n self.cliente_http.obter_interesses()\n except:\n self.log_error(\"Nao foi possivel obter cotacoes da lista de interesse\", exc=sys.exc_info()[0])\n \n elif selection == '3': \n print (self.menu[selection])\n try:\n construtor_de_interesse = ConstrutorDeInteresse(self.nome_de_usuario)\n interesse = construtor_de_interesse.obter_interesse_do_usuario()\n self.cliente_http.enviar_interesse(interesse)\n except:\n self.log_error(\"Nao foi possivel enviar novo interesse\", exc=sys.exc_info()[0])\n\n elif selection == '4': \n print (self.menu[selection])\n try:\n self.cliente_http.obter_carteira()\n except:\n self.log_error(\"Nao foi possivel obter carteira\", exc=sys.exc_info()[0])\n \n elif selection == '5': \n print (self.menu[selection])\n try:\n self.cliente_http.obter_limites()\n except:\n self.log_error(\"Nao foi possivel obter limites\", exc=sys.exc_info()[0])\n\n elif selection == '6': \n print (self.menu[selection])\n try:\n construtor_de_limite = ConstrutorDeLimite(self.nome_de_usuario)\n limite = construtor_de_limite.obter_limite_do_usuario()\n self.cliente_http.enviar_limite(limite)\n except:\n self.log_error(\"Nao foi possivel enviar novo limite\", exc=sys.exc_info()[0]) \n\n elif selection == '0': \n os._exit(1)\n break\n else: \n print (\"Opcao invalida\")","title":""},{"docid":"ccf20c283ae76a2eee86689a3f89757a","score":"0.5582814","text":"def print_levels(self):\n if (self.left != None):\n self.left.print_levels()\n print(self.key,\" \", self.node_level())\n if (self.right != None):\n self.right.print_levels()","title":""},{"docid":"b67d8c4875d6a110ad3702f74f2e6629","score":"0.5578455","text":"def scrape_menu(self):\n try:\n if self.menu_link[1] == 'urbanspoon':\n self.urbanspoon_scraper(self.menu_link[0])\n elif self.menu_link[1] == 'singleplatform':\n self.singleplatform_scraper(self.menu_link[0])\n elif self.menu_link[1] == 'custom':\n self.scrape_custom_menu(self.menu_link[0])\n except TypeError:\n return","title":""},{"docid":"b5030edfa8b87fd5f2fa0d1a72a4a7aa","score":"0.5555626","text":"def menu(self):\n ## This is a DICTIONARY, it's a list with custom index values\n # You may change the menu if you'd like to add an experimental method\n menu = {\"n\": (\"Navigate forward\", self.nav),\n \"d\": (\"Dance\", self.dance),\n \"o\": (\"Obstacle count\", self.obstacle_count),\n \"c\": (\"Calibrate\", self.calibrate),\n \"s\": (\"Check status\", self.status),\n \"h\": (\"Open House\", self.open_house),\n \"q\": (\"Quit\", quit_now),\n \"f\": (\"Forward\", self.move_ahead),\n \"l\": (\"Turn left\", self.left_turn),\n \"r\": (\"Turn right\", self.right_turn),\n \"t\": (\"Test left or right\", self.skill_test)}\n # loop and print the menu...\n for key in sorted(menu.keys()):\n print(key + \":\" + menu[key][0])\n # store the user's answer\n ans = raw_input(\"Your selection: \")\n # activate the item selected\n menu.get(ans, [None, error])[1]()","title":""},{"docid":"b2c13928ed9ebc53fddae9096c88bf83","score":"0.5545239","text":"def menu(menu_dict):\n running = True\n while running:\n present_menus(menu_dict)\n answer = input('>')\n if answer in menu_dict:\n func = menu_dict[answer].get(\"func\", lambda: None)\n func()\n else:\n print(\"exiting menu\")\n running = False","title":""},{"docid":"50bc8d0c46150a28eae873a1c1fa75c9","score":"0.55271715","text":"def display_start_menu(manager):\n while True:\n output = Catalogue.get_menu(Catalogue.start_menu)\n print(output)\n try:\n user_input = int(input(\"Enter selection: \"))\n if user_input > len(Catalogue.start_menu):\n raise ValueError\n except ValueError as e:\n print(\"Invalid input. Enter the number of your selected \"\n \"action.\")\n else:\n choices = {\n 1: manager.view_all_cards,\n 2: manager.view_cards_by_type,\n 3: manager.add_card,\n 4: manager.search_card,\n 5: manager.delete_card,\n 6: manager.backup_data,\n 7: sys.exit\n }\n\n if 0 < user_input < 8:\n choices.get(user_input)()","title":""},{"docid":"fad8d46fbae27abc83693f51783bef2b","score":"0.55262345","text":"def menu(self):\n ## This is a DICTIONARY, it's a list with custom index values. Python is cool.\n # Please feel free to change the menu and add options.\n print(\"\\n *** MENU ***\") \n menu = {\"n\": (\"Navigate\", self.nav),\n \"d\": (\"Dance\", self.dance),\n \"o\": (\"Obstacle count\", self.obstacle_count),\n \"c\": (\"Calibrate\", self.calibrate),\n \"q\": (\"Quit\", self.quit),\n \"h\": (\"Return to start\", self.ret_to_st),\n \"s\": (\"Slither\", self.slither)\n }\n # loop and print the menu...\n for key in sorted(menu.keys()):\n print(key + \":\" + menu[key][0])\n # store the user's answer\n ans = str.lower(input(\"Your selection: \"))\n # activate the item selected\n menu.get(ans, [None, self.quit])[1]()","title":""},{"docid":"42043ec9d9a018ed9e3ff84b2c5f3289","score":"0.5513636","text":"def menu(self):\n ## This is a DICTIONARY, it's a list with custom index values\n # You may change the menu if you'd like to add an experimental method\n menu = {\"n\": (\"Navigate forward\", self.nav),\n \"d\": (\"Dance\", self.dance),\n \"o\": (\"Obstacle count\", self.obstacle_count),\n \"c\": (\"Calibrate\", self.calibrate),\n \"s\": (\"Check status\", self.status),\n \"h\": (\"Open House\", self.open_house),\n \"q\": (\"Quit\", quit_now),\n \"t\": (\"Test\", self.skill_test)}\n # loop and print the menu...\n for key in sorted(menu.keys()):\n print(key + \":\" + menu[key][0])\n # store the user's answers\n ans = raw_input(\"Your selection: \")\n # activate the item selected\n menu.get(ans, [None, error])[1]()","title":""},{"docid":"88bb1a363ca09786d067dafb602d2a21","score":"0.54982567","text":"def run(self):\n while True:\n\n sys.stdout.write(self.title + os.linesep) # print title\n for x in self.entries:\n sys.stdout.write(self.prefix + x['key'] + self.postfix + x['text'] + os.linesep)\n\n try: # do safe/smart prompt\n answer = '' # safe\n answer = input(self.prompt)\n\n except EOFError: # user pressed ^D\n self.myerr.write('you pressed ^D' + os.linesep)\n return False\n\n except KeyboardInterrupt:\n self.myerr.write('you pressed ^C' + os.linesep)\n return False\n\n # self.myerr.write(('you pressed %s\\n' % answer) + os.linesep) #DEBUG\n\n # validate answer\n if len(answer) != 1:\n self.myerr.write('invalid menu entry' + os.linesep)\n self.myerr.write(os.linesep)\n continue\n\n # look for answer\n found = False\n for x in self.entries:\n if x['key'] == answer:\n found = True\n if type(x['sub']) in [type(None)]:\n return x['key']\n\n elif type(x['sub']) in [type(lambda: True)]:\n # lambda functions that run each time\n # if they return true, you exit menu\n # if they return false, stay in menu\n if x['sub']():\n return x['key']\n else:\n continue\n\n elif type(x['sub']) in [type(menu_system())]:\n # sub menu system\n self.myout.write(os.linesep)\n recurse = x['sub'].run()\n if not (type(recurse) is str): recurse = '0'\n return x['key'] + recurse\n\n if not found:\n self.myerr.write('invalid menu entry' + os.linesep)\n self.myerr.write(os.linesep)\n continue","title":""},{"docid":"18ad572d1ca1467e30ce1d01a1ca8155","score":"0.54892653","text":"def print_menu():\n print('1. Print Users')\n print('2. Add a User')\n print('3. Remove a User')\n print('4. Lookup a User')\n print('5. Quit')\n print()","title":""},{"docid":"70088a7ffc97329505c13892dfba80f2","score":"0.54825145","text":"def main_menu():\n build_menu()\n choice = input(\">> \\n\")\n\n if choice == \"1\":\n annex_habitat(keahua)\n\n if choice == \"2\":\n release_animal(keahua)\n\n if choice == \"3\":\n feed_animal_menu()\n\n if choice == \"4\":\n cultivate_plant(keahua)\n\n if choice == \"5\":\n build_facility_report(keahua)\n pass\n\n if choice != \"6\":\n main_menu()","title":""},{"docid":"5e3c08a8352f68a10507185d8d45b05a","score":"0.5475939","text":"def display_menu(header, menu_list):\n while True:\n print()\n print(header)\n for ind, menu_item in enumerate(menu_list):\n print(\"{}: {}\".format(ind, menu_item[0]))\n \n user_input = raw_input(\"Enter a selection index: \")\n # Handle the case where the user input something non-integer\n try:\n user_input_int = int(user_input)\n except:\n print(\"ERROR: input an integer\")\n continue\n \n # Handle the case where the user input something out of range\n if user_input_int not in range(len(menu_list)):\n print(\"ERROR: select something on the menu\")\n continue\n\n # Handle the case where the user did something else invalid\n try:\n selected_entry = menu_list[user_input_int]\n except:\n print(\"ERROR: bad selection, you bad mother\")\n continue\n\n # This might break if the programmer doesn't provide proper input\n # Let it break - how else would you handle programmer stupidity?\n return selected_entry[1]","title":""},{"docid":"a01a73c2791ae6eeea7932b5e9dc2402","score":"0.5468891","text":"def print_menu_options(self):\n print('1. Exit')\n for i, sub_program in enumerate(self.program_list):\n print(f'{i + SUB_PROGRAM_MENU_OFFSET}. {sub_program}')\n print('\\n')","title":""},{"docid":"14bdce21eb23f288be693b3900598353","score":"0.5468833","text":"def displayMenu():\n str_menu = \"\\n1. Type 't' to Send a Thank You to add a name, donation and thank you card\\n\"+\\\n \"2. Type 'r' to Create a Report and see a list of the donors\\n\"+\\\n \"3. Type 's' to Send letters to everyone\\n\"+\\\n \"4. Type 'e' to exit the program\\n\"\n #for testing purposes to determine menu is returned correctly\n #return str_menu\n print(str_menu)","title":""},{"docid":"33b263f30d8ba604c807b1dca969233e","score":"0.5464424","text":"def menu(self):\n ## This is a DICTIONARY, it's a list with custom index values\n # You may change the menu if you'd like to add an experimental method\n menu = {\"n\": (\"Navigate forward\", self.nav),\n \"d\": (\"Dance\", self.dance),\n \"c\": (\"Calibrate\", self.calibrate),\n \"o\": (\"Obstacle count\", self.obstacle_count),\n \"co\": (\"Circle count\", self.circle_counting),\n \"s\": (\"Check status\", self.status),\n \"t\": (\"Test restore heading\", self.test_restore_heading),\n \"q\": (\"Quit\", quit_now)\n }\n # loop and print the menu...\n for key in sorted(menu.keys()):\n print(key + \":\" + menu[key][0])\n # store the user's answer\n ans = raw_input(\"Your selection: \")\n # activate the item selected\n menu.get(ans, [None, error])[1]()","title":""},{"docid":"1755c3cb5569760ee2db5fdbb39775a3","score":"0.5459167","text":"def add_menu_options(self, menu):\n ##################\n category_name = _(\"Tree Options\")\n\n if self.name.split(\",\")[0] == _RPT_NAME:\n self.__pid = PersonOption(_(\"Report for\"))\n self.__pid.set_help(_(\"The main person for the report\"))\n menu.add_option(category_name, \"pid\", self.__pid)\n else: #if self.name == \"familial_descend_tree\":\n self.__pid = FamilyOption(_(\"Report for\"))\n self.__pid.set_help(_(\"The main family for the report\"))\n menu.add_option(category_name, \"pid\", self.__pid)\n\n self.showparents = BooleanOption(\n _('Start with the parent(s) of the selected first'),\n False)\n self.showparents.set_help(\n _(\"Will show the parents, brother and sisters of the \"\n \"selected person.\")\n )\n menu.add_option(category_name, \"show_parents\", self.showparents)\n\n max_gen = NumberOption(_(\"Generations\"), 10, 1, 50)\n max_gen.set_help(_(\"The number of generations to include in the tree\"))\n menu.add_option(category_name, \"maxgen\", max_gen)\n\n max_spouse = NumberOption(_(\"Level of Spouses\"), 1, 0, 10)\n max_spouse.set_help(_(\"0=no Spouses, 1=include Spouses, 2=include \"\n \"Spouses of the spouse, etc\"))\n menu.add_option(category_name, \"maxspouse\", max_spouse)\n\n compresst = BooleanOption(_('Compress tree'), False)\n compresst.set_help(_(\"Whether to move people up, where possible, \"\n \"resulting in a smaller tree\"))\n menu.add_option(category_name, \"compress_tree\", compresst)\n\n stdoptions.add_localization_option(menu, category_name)\n\n ##################\n category_name = _(\"Display\")\n\n disp = TextOption(_(\"Descendant\\nDisplay Format\"),\n [\"$n\",\n \"%s $b\" %_BORN,\n \"-{%s $d}\" %_DIED])\n disp.set_help(_(\"Display format for a descendant.\"))\n menu.add_option(category_name, \"descend_disp\", disp)\n\n bold = BooleanOption(_('Bold direct descendants'), True)\n bold.set_help(\n _(\"Whether to bold those people that are direct \"\n \"(not step or half) descendants.\")\n )\n menu.add_option(category_name, \"bolddirect\", bold)\n\n #bug 4767\n #diffspouse = BooleanOption(\n # _(\"Use separate display format for spouses\"),\n # True)\n #diffspouse.set_help(_(\"Whether spouses can have a different format.\"))\n #menu.add_option(category_name, \"diffspouse\", diffspouse)\n\n indspouce = BooleanOption(_('Indent Spouses'), True)\n indspouce.set_help(_(\"Whether to indent the spouses in the tree.\"))\n menu.add_option(category_name, \"ind_spouse\", indspouce)\n\n sdisp = TextOption(_(\"Spousal\\nDisplay Format\"),\n [\"$n\",\n \"%s $b\" %_BORN,\n \"-{%s $d}\" %_DIED])\n sdisp.set_help(_(\"Display format for a spouse.\"))\n menu.add_option(category_name, \"spouse_disp\", sdisp)\n\n incmarr = BooleanOption(_('Include Marriage box'), True)\n incmarr.set_help(\n _(\"Whether to include a separate marital box in the report\"))\n menu.add_option(category_name, \"inc_marr\", incmarr)\n\n marrdisp = StringOption(_(\"Marriage\\nDisplay Format\"), \"%s $m\" % _MARR) \n marrdisp.set_help(_(\"Display format for the marital box.\"))\n menu.add_option(category_name, \"marr_disp\", marrdisp)\n\n ##################\n category_name = _(\"Replace\")\n\n repldisp = TextOption(\n _(\"Replace Display Format:\\n'Replace this'/' with this'\"),\n [])\n repldisp.set_help(_(\"i.e.\\nUnited States of America/U.S.A\"))\n menu.add_option(category_name, \"replace_list\", repldisp)\n\n ##################\n category_name = _(\"Size\")\n\n self.scale = EnumeratedListOption(_(\"Scale tree to fit\"), 0)\n self.scale.add_item( 0, _(\"Do not scale tree\"))\n self.scale.add_item( 1, _(\"Scale tree to fit page width only\"))\n self.scale.add_item( 2, _(\"Scale tree to fit the size of the page\"))\n self.scale.set_help(\n _(\"Whether to scale the tree to fit a specific paper size\")\n )\n menu.add_option(category_name, \"scale_tree\", self.scale)\n self.scale.connect('value-changed', self.__check_blank)\n\n if \"BKI\" not in self.name.split(\",\"):\n self.__onepage = BooleanOption(_(\"Resize Page to Fit Tree size\\n\"\n \"\\n\"\n \"Note: Overrides options in the 'Paper Option' tab\"\n ), \n False)\n self.__onepage.set_help(\n _(\"Whether to resize the page to fit the size \\n\"\n \"of the tree. Note: the page will have a \\n\"\n \"non standard size.\\n\"\n \"\\n\"\n \"With this option selected, the following will happen:\\n\"\n \"\\n\"\n \"With the 'Do not scale tree' option the page\\n\"\n \" is resized to the height/width of the tree\\n\"\n \"\\n\"\n \"With 'Scale tree to fit page width only' the height of\\n\"\n \" the page is resized to the height of the tree\\n\"\n \"\\n\"\n \"With 'Scale tree to fit the size of the page' the page\\n\"\n \" is resized to remove any gap in either height or width\"\n ))\n menu.add_option(category_name, \"resize_page\", self.__onepage)\n self.__onepage.connect('value-changed', self.__check_blank)\n else:\n self.__onepage = None\n \n self.box_Y_sf = NumberOption(_(\"inter-box Y scale factor\"),\n 1.00, 0.10, 2.00, 0.01)\n self.box_Y_sf.set_help(_(\"Make the inter-box Y bigger or smaller\"))\n menu.add_option(category_name, \"box_Yscale\", self.box_Y_sf)\n \n self.box_shadow_sf = NumberOption(_(\"box shadow scale factor\"),\n 1.00, 0.00, 2.00, 0.01) # down to 0\n self.box_shadow_sf.set_help(_(\"Make the box shadow bigger or smaller\"))\n menu.add_option(category_name, \"shadowscale\", self.box_shadow_sf)\n \n \n ##################\n category_name = _(\"Include\")\n\n self.title = EnumeratedListOption(_(\"Report Title\"), 0)\n self.title.add_item( 0, _(\"Do not include a title\"))\n self.title.add_item( 1, _(\"Descendant Chart for [selected person(s)]\"))\n if self.name.split(\",\")[0] != _RPT_NAME:\n self.title.add_item(2,\n _(\"Family Chart for [names of chosen family]\"))\n if self.showparents.get_value():\n self.title.add_item(3,\n _(\"Cousin Chart for [names of children]\"))\n self.title.set_help(_(\"Choose a title for the report\"))\n menu.add_option(category_name, \"report_title\", self.title)\n self.showparents.connect('value-changed', self.__Title_enum)\n\n border = BooleanOption(_('Include a border'), False)\n border.set_help(_(\"Whether to make a border around the report.\"))\n menu.add_option(category_name, \"inc_border\", border)\n\n prnnum = BooleanOption(_('Include Page Numbers'), False)\n prnnum.set_help(_(\"Whether to include page numbers on each page.\"))\n menu.add_option(category_name, \"inc_pagenum\", prnnum)\n\n self.__blank = BooleanOption(_('Include Blank Pages'), True)\n self.__blank.set_help(_(\"Whether to include pages that are blank.\"))\n menu.add_option(category_name, \"inc_blank\", self.__blank)\n \n #category_name = _(\"Notes\")\n\n self.usenote = BooleanOption(_('Include a note'), False)\n self.usenote.set_help(\n _(\"Whether to include a note on the report.\")\n )\n menu.add_option(category_name, \"inc_note\", self.usenote)\n\n self.notedisp = TextOption(_(\"Note\"),[])\n self.notedisp.set_help(_(\"Add a note\"\n \"\\n\\n$T inserts today's date\"))\n menu.add_option(category_name, \"note_disp\", self.notedisp)\n\n locals = NoteType(0)\n notelocal = EnumeratedListOption(_(\"Note Location\"), 2)\n for num, text in locals.note_locals():\n notelocal.add_item( num, text )\n notelocal.set_help(_(\"Where to place the note.\"))\n menu.add_option(category_name, \"note_place\", notelocal)","title":""},{"docid":"be5fc8cd7b966d59506d17ad558f7fa6","score":"0.5433564","text":"def printMenu():\n print(\"\\nBienvenido\")\n print(\"1- Cargar Datos\")\n print(\"2- Ranking de peliculas\")\n print(\"3- Conocer un director\")\n print(\"4- Conocer un actor\")\n print(\"5- Entender un genero\")\n print(\"6- Crear ranking\")\n print(\"0- Salir\")","title":""},{"docid":"101293013c58d8146c0773ff2ec62dfd","score":"0.54217243","text":"def generic_menu(diccionario): \n while(1):\n for valor in diccionario.items():\n print(valor[0])\n respuesta = input(\"Opción: \")\n if respuesta in diccionario:\n pasar = diccionario[respuesta]\n print(operacion(respuesta, pasar[0], pasar[1]))\n elif respuesta == \" \":\n break\n else:\n print(\"Opción no válida.\")","title":""},{"docid":"8634c37ff377db2fd7d83e4255518c85","score":"0.53807473","text":"def main_menu():\n build_menu()\n choice = input(\">> \")\n\n if choice == \"1\":\n annex_habitat(keahua)\n\n if choice == \"2\":\n release_animal(keahua)\n\n if choice == \"3\":\n feed_animal(keahua)\n\n if choice == \"4\":\n add_plant(keahua)\n\n if choice == \"5\":\n build_facility_report(keahua)\n pass\n\n if choice != \"6\":\n main_menu()","title":""},{"docid":"e86d3df9bb99b279629580c6dffdc7ef","score":"0.5380118","text":"def display_menu_option():\r\n print(\"\\n---- MENU ----\\n\")\r\n print(\"Here are your choices:\")\r\n print(\"1) Display Team Stats\")\r\n print(\"2) Quit\\n\")","title":""},{"docid":"d6a7a33771f1a1b95eebf569a2160a5f","score":"0.53780174","text":"def offerMenuItems(choices):\n\n print(\"Choose From The Following Options: \")\n # prints out a numbered list of choices\n for i in range(len(choices)):\n print(('%s, %s' %(i, choices[i])))\n\n # prompts the user to select a number from the menu list\n enter = int(input(\"Enter your desired choice number: \"))\n choice = (\"You entered %d (for %s)\" % (enter, choices[enter]))\n #print(choices)\n\n # returns choice which prints out the number you chose and the associated menu item\n return choices[enter]","title":""},{"docid":"dfb7e58aacb40e4ebbe5eb290ec0f8aa","score":"0.5372298","text":"def print_menu():\n\n os.system('clear') # For Linux/OS X\n os.system('cls') # For Windows\n print(30 * \"-\", \"MENU\", 30 * \"-\")\n print(\"1. raj2jar \")\n print(\"2. collect_sources \")\n print(\"3. rebuild_packages \")\n print(\"4. download_tests \")\n print(\"\\n0. Exit\")\n print(67 * \"-\")","title":""},{"docid":"fbe683ea9d91a187e121db21cbe71339","score":"0.53706336","text":"def sub_menu():\n menu_selection(sub_prompt, sub_dispatch)","title":""},{"docid":"4ebde36be32f4a5feba81fe16b0ac963","score":"0.53574353","text":"def user_menu(outlet_num):\n while True:\n print('===Taking from Outlet ', outlet_num, '===')\n i = 1\n # Use this dictionary to store the item number and the name, to identify the item when user inputs a number\n user_dict = {}\n # User Menu\n for k in recipes:\n a, low, miss = quantity_check(k)\n if not a:\n print_deficiency(k, low, miss)\n else:\n print(i, \".\", k)\n user_dict[i] = k\n i += 1\n print(i, \". Refill\")\n\n sel = take_input(outlet_num, int, i)\n\n if sel is i:\n # User selects to refill\n refill_item(outlet_num)\n else:\n if dispense(user_dict[sel]):\n print(user_dict[sel], \" Dispensed from outlet\", outlet_num)","title":""},{"docid":"3db9d2c01c95d37efb25dcaefcb92526","score":"0.53563076","text":"def print_menu(title, available_options, exit_message='Exit'):\n compilated_message = available_options + [exit_message]\n\n print(title + ':')\n\n for i, option in enumerate(compilated_message):\n print('{}{}. {}'.format('\\t', (i+1) % len(compilated_message), option))\n\n print()","title":""},{"docid":"c9ca9dbb446a5847e1ceef9b9715aa6c","score":"0.53472716","text":"def format_menus(menus):\n menu = ''\n idx = 0\n for name, items in menus.items():\n menu += f\"\\n*{name}*\\n\"\n menu += \"\".join(f\":{poll_opt}: {item}\\n\"\n for poll_opt, item in zip(DIGITS[idx:], items))\n idx += len(items)\n\n # menu contains a leading and trailing new line, but slack doesn't care\n\n return menu, DIGITS[:idx]","title":""},{"docid":"3971ee4d29aa7c040b21f1b10e74727f","score":"0.5345263","text":"def menu(menu_data):\n print(\"\\nPlease choose one of the following options:\")\n\n for index, menu_item in enumerate(menu_data): # Prints the menu user text\n print(f\"{index + 1}) {menu_item[0]}\")\n\n choice = int(input(\"> \")) - 1\n\n if choice in range(len(menu_data)): # Ensure that option chosen is within menu range, this\n return menu_data[choice][1], menu_data[choice][2] # handles choosing 0, which would return menu_data[-1][1]\n\n return None","title":""},{"docid":"21d1948db83f8aaaafa180499c2fa81e","score":"0.53317946","text":"def __repr__(self):\n return \"<MenuItem %s for %s at %s>\" % (\n self.name,\n self.course.name,\n self.course.meal.menu.hall.name,\n )","title":""},{"docid":"7aba16dbdedb6bdb910a21d5c1c3963d","score":"0.5313438","text":"def print_menu():\n print('''\n Options\n 1 - Display reading list\n 2 - Add title to list\n 3 - Remove title from list\n 4 - Save list to file\n 5 - Load list from file\n 6 - Exit\n ''')","title":""},{"docid":"c96a540f89acb813fc467ab2126f6e50","score":"0.53124","text":"def display_menu():\n print()\n print(\"Commands:\")\n print(\" quit - Quit\")\n print(\" new - Create new account\")\n print(\" display - Display account information\")\n print(\" deposit - Desposit money\")\n print(\" check - Write a check\")","title":""},{"docid":"2c7ebca75d742313412a47a0ea8d542f","score":"0.5304529","text":"def main_menu(self):\n\n while True:\n print(\"\\n----- MAIN MENU ----- \")\n print(\"\\n1. Create Account\")\n print(\"\\n2. Account Details\")\n print('\\n3. Close Account')\n print('\\n4. Transaction Menu')\n print('\\n5. Close application')\n print('\\n\\n')\n try:\n option = int(input('Enter your option ...: '))\n\n if option == 1:\n self.create_account()\n elif option == 2:\n self.show_details()\n elif option == 3:\n self.close_account()\n elif option == 4:\n self.transaction_menu()\n elif option == 5:\n print(\"\\n*****************\\nSee You Next Time!\\n*****************\\n\")\n self.exit_menu()\n\n except TypeError as err:\n print(\"\\n*************\\nInvalid input!\\n*************\\nPlease try again!\\n\")\n # Log\n logger.error(\"Invalid input: '{}' - ValueError message: {} from main_menu() at\".format(option, err))\n # Back to main menu\n self.main_menu()\n except ValueError as err:\n print(\"\\n*************\\nInvalid input!\\n*************\\nPlease try again!\\n\")\n # Log\n logger.error(\"Invalid input: '{}' - ValueError message: {} from main_menu() at\".format(option, err))\n # Back to main menu\n self.main_menu()","title":""},{"docid":"48f51d9719992a222896017895d8ee7c","score":"0.5282117","text":"def print_menu():\n print(\"\"\"\n 1. Print Pokedex\n 2. Print Pokemon by Name\n 3. Print Pokemon by Number\n 4. Count Pokemon with Type\n 5. Print Average Pokemon Combat Points\n 6. Quit\n \"\"\")","title":""},{"docid":"b2e8ea60b55d24d7758fc3bca0c99ccc","score":"0.5273748","text":"def sub_menu():\n\n ccTremb = db.connect()\n cStation = db.stations(ccTremb)\n sSub_menu = \"\"\"\n\nHOUSING SUB-MENU (H):\n.: Exit\n1: Add a housing\n4: Pretty print\n\n\"\"\" # Closes the multi=line txt\n\n bExit = False\n while bExit == False: # loop until the user exits\n print(sSub_menu)\n sInput = input().upper()\n\n # User has made their choice. Now, process it.\n if sInput == \".\": # Exit\n bExit = True\n elif sInput == \"1\": # New\n add_housing(ccTremb)\n# elif sInput == \"2\": # All the stations\n# view_all(ccTremb)\n# elif sInput == \"3\": # View single\n# view_single(ccTremb)\n elif sInput == \"4\": # Formats the little bit of data\n pretty_print_single(ccTremb)\n else:\n bExit = True","title":""},{"docid":"aa39b5b022aab92998b87f021379f013","score":"0.52665216","text":"def print_menu():\n print(\"Calculator menu:\")\n print(\" + for adding a rational number\")\n print(\" c to clear the calculator\")\n print(\" u to undo the last operation\")\n print(\" x to close the calculator\")","title":""},{"docid":"4dca98229e7ab89b9b4677a31d8eaee3","score":"0.5254829","text":"def print_menu(self):\n print(\"1. Youtube\")\n print(\"2. ott\")\n print(\"3. imdb\")\n print(\"4. Exit\")","title":""},{"docid":"0693428135c9a68e033575c89ebf67e8","score":"0.525311","text":"def actions(self):\n menu = {\"title\": _(\"menu_actions\", default=\"Actions\")}\n children = menu[\"children\"] = []\n submenu = self.factories()\n if submenu:\n children.append(submenu)\n submenu = self.organise()\n if submenu:\n children.append(submenu)\n\n if children:\n return menu\n else:\n return None","title":""},{"docid":"111f16002e664206ebb4c9800f47ccf7","score":"0.5242372","text":"def ask_questions(sub_menu):\r\n good_answer = False\r\n while not good_answer:\r\n print(\"\\nYour options are:\\n\")\r\n\r\n for key, value in sub_menu.items():\r\n # print the options looking pretty\r\n value = value.__name__\r\n option_list = value.split(\"_\")\r\n option_string = \" \".join(option_list)\r\n option_string = option_string.title()\r\n s = f\"{key}.) {option_string}\"\r\n print(s)\r\n\r\n answer = input(\"\\nEnter letter according to your choice: \")\r\n\r\n # check to see if the \"answer\" letter is one of the keys in the sub_menu dictionary\r\n good_answer = sub_menu.get(answer, False)\r\n\r\n return sub_menu[answer]()","title":""},{"docid":"1b8b5e3cfa086930adc41319ae8bc84d","score":"0.524088","text":"def display_main_menu(self) -> int:\n\n # display main menu until user makes acceptable choice\n user_choice=0\n while(True):\n # clear screen\n if os.name == 'nt':\n _ = os.system('cls')\n else:\n _ = os.system('clear')\n # display menu\n print(\"Main menu\",\"=========\",sep='\\n')\n for k in self.menu_items.keys():\n print(str.format(\"{0}) {1}\",k,self.menu_items[k][0]))\n user_input=input(\"\\nEnter choice: \")\n try:\n user_choice = int(user_input)\n if user_choice in self.menu_items.keys():\n return user_choice\n except ValueError:\n pass","title":""},{"docid":"d0c37eaaafb23ec481589ba04cffd3e3","score":"0.52383924","text":"def input_message_menu(self):\n print()\n choice = \"\\n\".join(\n [\n f\"{colored(index, 'yellow')}. {item}\"\n for index, item in enumerate(self.menu_choices, 1)\n ]\n )\n print(choice)\n print(f\"{s.CR}{self.footer}\")\n if s.ERROR:\n print(self.msg_error)\n print(self.quit)","title":""},{"docid":"5a72abb6eb158b8e363352f49fc0af7e","score":"0.5236311","text":"def show_menu_pan():\n MENU_STRING = ''\n for key in MENU_DICT.keys(): # use .keys() .values() .items()\n MENU_STRING += '{:>2}. {:<16} {:.^10} {:5,} won'.format(\n key,\n MENU_DICT[key][0],\n '.',\n MENU_DICT[key][1]) + '\\n'\n print(MENU_PAN_FORMAT %MENU_STRING)\n print(NOW)","title":""},{"docid":"2b2468dc26b7755515f6fc999ad2338d","score":"0.5230493","text":"def menu_options() -> None:\n print(\"================== MENU ==================\")\n print(\"0 to exit the application\")\n print(\"[1] Add a new product\")\n print(\"[2] Get all product from the store\")\n print(\"[3] Get a product details by product id\")\n print(\"[4] Remove a product by product id\")\n print(\"[5] Remove all product by their name\")\n print(\"[6] Increase the product in the store\")\n print(\"[7] Decrease the product from the store\")\n print(\"[8] Change a product name\")\n print(\"[9] Change a product price\")\n print(\"==========================================\")","title":""},{"docid":"ca42442a44ce5bd0bb826f5aa2456904","score":"0.52237767","text":"def inner_menu(user: User):\n clear()\n print(\"Welcome back {name}!\".format(name=user.username))\n while True:\n print(\"Choose an option\",\n \"================\",\n \"1. Recipe Book\",\n \"2. View Pantry\",\n \"3. Edit User Info\", # TODO\n \"4. Log Out\", sep=\"\\n\") # TODO\n\n try:\n op = int(input(\"> \"))\n\n if op == 1:\n clear()\n if recipe_menu(user):\n clear()\n continue\n break\n elif op == 2:\n clear()\n if pantry_menu(user):\n clear()\n continue\n break\n elif op == 3:\n clear()\n if user_edit_menu(user):\n continue\n elif op == 4:\n\n clear()\n const = \". . . \"\n message = \"Logging you out\"\n message = message + const\n print(message)\n\n for i in range(3):\n time.sleep(0.5)\n # remove a dot form message\n message = message[:-2]\n # clear and reprint message\n clear()\n print(message)\n time.sleep(0.5)\n # at the end clear and return\n clear()\n return True\n\n except ValueError:\n clear()\n print(\"Incorrect option. Try again.\\n\")\n continue","title":""},{"docid":"4c8402e65680eedb52c4fe0547979343","score":"0.5221359","text":"def menu_categories(scorepad):\n\n # available_choices = []\n menu_list = []\n score_status = dict\n\n score_status = scorepad_available_scores(scorepad)\n\n for _ in score_status['AVAILABLE']:\n menu_list.append(menu_items[_])\n\n # Append bonus counter since it does not have track prefix\n menu_list.append([14, 'H - Five of a Kind Bonus'])\n\n print('=' * 50)\n\n print('MENU LIST')\n\n for idx, _ in enumerate(sorted(menu_list)):\n\n if (idx + 1) % 4 == 0:\n print(_[1])\n else:\n pad = 25 - len(_[1])\n print(_[1], end=(' ' * pad))\n\n print()","title":""},{"docid":"aab0244bb5b1b273e9b344e4d70dcd35","score":"0.5221142","text":"def makeMenuList(self,menu):\n try:\n l = list()\n \n #index of current line < len(menu)\n line = 0\n \n #keep in mind the last folder name and the line index, when this name firt occurred\n lastValue = None\n startLine = 0\n \n # as long as we have elements in the loop\n while line < len(menu):\n #access current line\n parts = menu[line]\n \n # if we encounter a tree leaf and last node was also a tree leaf\n # we just add this leaf and don't care for children\n if len(menu[line]) == 1 and line == startLine:\n #append leaf to list\n lastValue = parts[0]\n l.append({lastValue: [\"end\"]})\n \n #assume that line is not the last line, if so, ignore the exception, while loop will terminate afterwards\n try:\n lastValue = menu[line+1][0]\n except:\n pass\n \n #for further processing: ignore leaf, as it has already been appended to the list\n line += 1\n startLine = line\n #continue while loop\n continue \n # if we encounter a tree leaf and last node was not a tree leaf\n # we first care for the prior nodes and then just add this leaf\n elif len(menu[line]) == 1 and line != startLine:\n # look now into the children of a\n # recursion would get in the example\n # /b\n # /c\n # because /a is thrown away each iteration\n children = self.makeMenuList(menu[startLine:line])\n #assuming that there is an entry in the list corresponding to last value\n #why try?\n #because \n #/1\n #/a/b\n #would go wrong\n try:\n l[len(l) -1][lastValue].extend(children)\n #in case this entry is not there: add it!\n except:\n l.append({lastValue: children})\n \n #append leaf to list\n lastValue = parts[0]\n l.append({lastValue: [\"end\"]})\n \n #assume that line is not the last line, if so, ignore the exception, while loop will terminate afterwards\n try:\n lastValue = menu[line+1][0]\n except:\n pass\n \n #for further processing: ignore leaf, as it has already been appended to the list\n line += 1\n startLine = line\n #continue while loop\n continue\n \n \n # if we are in the first loop, initialise variables\n elif lastValue == None:\n #save folder name\n lastValue = parts[0]\n startLine = 0\n #start with root entry\n dictionary = {lastValue: list()}\n l.append(dictionary)\n #we have encountered a folder name, deviating from the folder name we had before\n #example\n #/a/b\n #/a/c\n #/e/d\n #remark: / is no more present due to split(\"/\")!\n elif lastValue != parts[0]:\n # look now into the children of a\n # recursion would get in the example\n # /b\n # /c\n # because /a is thrown away each iteration\n children = self.makeMenuList(menu[startLine:line])\n #assuming that there is an entry in the list corresponding to last value\n #why try?\n #because \n #/1\n #/a/b\n #would go wrong\n try:\n l[len(l) -1][lastValue].extend(children)\n #in case this entry is not there: add it!\n except:\n l.append({lastValue: children})\n \n #start remembering the new folder\n lastValue = parts[0]\n startLine = line\n dictionaryNew = {lastValue: list()}\n l.append(dictionaryNew)\n \n lastWasLeafFlag = False\n \n #throw away first column of current line\n menu[line] = menu[line][1:]\n #increase line index\n line += 1\n \n #do the same as in loop, because the last line might not have been added yet.\n if startLine < line:\n children = self.makeMenuList(menu[startLine:line])\n try:\n l[len(l) -1][lastValue].extend(children)\n except:\n l.append({lastValue: children})\n \n return l\n except Exception as inst:\n# self.err.rethrowException(inst)\n return None","title":""},{"docid":"1068a8104b776ad5c4494e4c01501f99","score":"0.5214295","text":"def display(self, menu):\n\n self.runner.setCurrentMenu(menu)","title":""},{"docid":"1f34b5b3c69547cc63728ec60580471c","score":"0.5212197","text":"def show_menu():\n print(\"User Menu \\n\"\n \"1. Create Account \\n\"\n \"2. Login \\n\"\n \"3. Search \\n\"\n \"4. Insert \\n\"\n \"5. Update \\n\"\n \"6. Delete \\n\"\n \"7. Exit \\n\")","title":""},{"docid":"7daa17bdabb4fa1a23eab5f017938e6a","score":"0.5207583","text":"def print_menu_tasks():\n print(\"\"\"\n Choice Menu\n 1) Display Current Product List\n 2) Add Product to List\n 3) Save Product List to File and Exit Program\n \"\"\")","title":""},{"docid":"5c95a11f5b112bb44bf86e663a3a2983","score":"0.51888365","text":"def transaction_menu(self):\n\n while True:\n print(\"\\n ----- TRANSACTION MENU ----- \")\n print(\"\\n1. Deposit Amount\")\n print('\\n2. Withdraw Amount')\n print('\\n3. Back to Main Menu')\n print('\\n\\n')\n try:\n option2 = int(input('Enter your option ...: '))\n if option2 == 1:\n self.deposit_amount()\n elif option2 == 2:\n self.withdraw_amount()\n elif option2 == 3:\n self.main_menu()\n\n except TypeError as err:\n print(\"\\n*************\\nInvalid input!\\n*************\\nPlease try again!\\n\")\n # Log\n logger.error(\"Invalid input: '{}' - ValueError message: {} from transaction_menu()\".format(option2, err))\n # Back to transaction menu\n self.transaction_menu()\n except ValueError as err:\n print(\"\\n*************\\nInvalid input!\\n*************\\nPlease try again!\\n\")\n # Log\n logger.error(\"Invalid input: '{}' - ValueError message: {} from transaction_menu()\".format(option2, err))\n # Back to transaction menu\n self.transaction_menu()","title":""},{"docid":"8bd87277dc8160ff45c5eb0c8b014bb5","score":"0.5184358","text":"def printMainMenu():\n print \"\"\"\n1.주소정보를 입력한다.\n2.주소정보를 삭제한다.\n3.주소정보를 수정한다.\n4.전체 주소정보를 출력한다.\n5.전체 주소정보를 파일에 저장한다.\n6.파일에서 전체 주소정보를 불러온다.\n7.전체 주소정보를 DB에 저장한다.\n9.DB에서 전체 주소정보를 불러온다.\n0.종료한다.\n \"\"\"","title":""},{"docid":"664b1fca060e70c381d8dbf25997ab02","score":"0.5183275","text":"def get_menu(self):\n pass","title":""},{"docid":"6d45488ab7455c4dcf745bf7ec9f7850","score":"0.5180017","text":"def show_menu():\n if not pf.GUI.menu.item(_menu):\n create_menu()","title":""},{"docid":"be38f5e9518e72d0ddc9263bdea8e2db","score":"0.51734394","text":"def __repr__(self):\n return (\"MenuItem('{type}', '{user_name}', '{selector}', '{host}', \"\n \"'{port}')\").format(**self.__dict__)","title":""},{"docid":"fa9d58882a86fdb9de7a200470c42a97","score":"0.51710016","text":"def show_menu(entries, **kwargs):\n global _AUTO\n hdr = kwargs.get('hdr', \"\")\n note = kwargs.get('note', \"\")\n dft = kwargs.get('dft', \"\")\n fzf = kwargs.pop('fzf', True)\n compact = kwargs.get('compact', False)\n returns = kwargs.get('returns', \"name\")\n limit = kwargs.get('limit', None)\n dft = kwargs.get('dft', None)\n msg = []\n if limit:\n return show_limit(entries, **kwargs)\n def show_banner():\n banner = \"-- MENU\"\n if hdr:\n banner += \": \" + hdr\n banner += \" --\"\n msg.append(banner)\n if _AUTO:\n return\n for i in entries:\n msg.append(\" (%s) %s\" % (i.name, i.desc))\n valid = [i.name for i in entries]\n if type(dft) == int:\n dft = str(dft)\n if dft not in valid:\n dft = None\n if not compact:\n show_banner()\n if note and not _AUTO:\n msg.append(\"[!] \" + note)\n if fzf:\n valid.append(FCHR)\n if _AUTO:\n valid.append(\"-d\")\n msg.append(QSTR + kwargs.get('msg', \"Enter menu selection\"))\n msg = os.linesep.join(msg)\n entry = None\n while entry not in entries:\n choice = ask(msg, vld=valid, dft=dft, qstr=False)\n if _AUTO and choice == \"-d\":\n choice = dft\n if choice == FCHR and fzf:\n try:\n from iterfzf import iterfzf\n choice = iterfzf(reversed([\"%s\\t%s\" % (i.name, i.desc) for i in entries])).strip(\"\\0\").split(\"\\t\", 1)[0]\n except:\n warn(\"Issue encountered during fzf search.\")\n match = [i for i in entries if i.name == choice]\n if match:\n entry = match[0]\n if entry.func:\n fresult = run_func(entry)\n if \"func\" == returns:\n return fresult\n try:\n return getattr(entry, returns)\n except:\n return getattr(entry, \"name\")","title":""},{"docid":"991ab7ca22a43c53b5f00d790ef60408","score":"0.51524115","text":"def print_level(self):\n\n # First grab information about our snakes\n disp_snake_coords = {}\n for sb in self.snakebirds_l:\n sb.compute_display_chars()\n for (idx, coords) in enumerate(sb.cells):\n disp_snake_coords[coords] = (sb, idx)\n\n # Grab information about pushables and pushable decorations\n disp_pushable_coords = {}\n disp_pushable_decorations = {}\n for (push_num, pushable) in self.pushables.items():\n for coords in pushable.cells:\n disp_pushable_coords[coords] = '{}{}{}'.format(\n color_pushables[push_num],\n PUSH_CHARS[push_num],\n color_reset,\n )\n disp_pushable_decorations.update(pushable.get_decoration_chars())\n\n # Find out what color our exit should be\n if len(self.fruits) == 0:\n color_wall[TYPE_EXIT] = color_wall_exit_open\n else:\n color_wall[TYPE_EXIT] = color_wall_exit_closed\n\n # Now loop through and print the level\n print(self.desc)\n for (y, row) in enumerate(self.cells):\n for (x, col) in enumerate(row):\n if (x,y) in disp_snake_coords:\n (sb, idx) = disp_snake_coords[(x,y)]\n sys.stdout.write(sb.get_print_char(idx))\n elif (x,y) in disp_pushable_coords:\n sys.stdout.write(disp_pushable_coords[(x,y)])\n elif (x,y) in self.fruits:\n sys.stdout.write('{}{}'.format(color_fruit, 'F'))\n elif col == TYPE_EMPTY and (x, y) in disp_pushable_decorations:\n sys.stdout.write(disp_pushable_decorations[(x,y)])\n else:\n sys.stdout.write('{}{}'.format(color_wall[col], TYPE_DISP_MAP[col]))\n sys.stdout.write(\"{}\\n\".format(color_reset));","title":""},{"docid":"3f0c6b0f90f3c29cb105fd61996957cc","score":"0.51320636","text":"def menu_report(self):\n\n print(\"*********** Report Menu ***********\")\n print(\"Welcome, please select your action and press Enter\")\n print(\"List of all players:\")\n print(\" 1. Alphabetical order\")\n print(\" 2. Ranking order\")\n print(\"List of all players for one tournament:\")\n print(\" 3. Alphabetical order\")\n print(\" 4. Ranking order\")\n print(\"5.List of all tournaments\")\n print(\"6.List of all rounds in a tournament\")\n print(\"7.List of all matches in a tournament\")\n print(\"8.Return to start menu\")\n print(\"***********************************\")","title":""},{"docid":"5a374d2aab32cc7851f9332cdb685857","score":"0.5131412","text":"def show_section(section, prefix):\n for key, value in section.items():\n if self._is_section(value):\n # Go to next level\n print(prefix + \"[\" + key + \"]\")\n show_section(section[key], prefix + \" \")\n else:\n print(prefix + str(key) + \":\" + repr(value))","title":""},{"docid":"5ecc54a61d059bad4bdb0963cec571cb","score":"0.5127224","text":"def show_menu():\n print(\"Hello, there! Please choose from the following menu: \" + \"\\n\")\n print(\"(1) Show current products and prices.\" + \"\\n\")\n print(\"(2) Add a product to the list.\" + \"\\n\")\n print(\"(3) Save to file and exit.\" + \"\\n\")","title":""},{"docid":"a1dfa9bfeffae059d4a21b9453786d43","score":"0.5116037","text":"def show_items():\n with open('menu.txt', 'r') as file:\n rest_menu = file.read().strip().split(', ')\n return print('\\n'.join(rest_menu))","title":""},{"docid":"b1c29a97cce026450b081e3b6adbd1d0","score":"0.5096008","text":"def view_restaurant_menus(conn, chosen_restaurant_id):\n cursor = conn.raw_connection().cursor()\n cursor.callproc(\"view_restaurant_menus\", [chosen_restaurant_id])\n results = list(cursor.fetchall())\n print \"------------------------------------------------\"\n print \"Menus for Restaurant \" + str(chosen_restaurant_id)\n\n all_menu_ids = []\n for (menu_id, menu_type) in results:\n all_menu_ids.append(menu_id)\n print \"\\tID: \" + str(menu_id) + \"\\tType: \" + str(menu_type).capitalize()\n\n cursor.close()\n\n print \"------------------------------------------------\"\n\n choose_next_option(conn, chosen_restaurant_id, all_menu_ids)","title":""},{"docid":"df4315853cc8c934968e74b4fa7c60cc","score":"0.5088995","text":"def menu_help(args):\n if not len(args):\n for k in items:\n print('{0:12}\\t{1}'.format(k, items[k][1]))\n else:\n if args in items:\n print('{0:12}\\t{1}'.format(args, items[args][1]))\n else:\n print('No such command: {0}'.format(args))\n return False","title":""},{"docid":"e58e3fa5d18286cf88f0d62f3604fb6b","score":"0.50877094","text":"def show_menu():\n print(\"User Menu \\n\" \"1. Create Account \\n\" \"2. Login \\n\" \"3. EXIT \\n\")","title":""},{"docid":"70690279de56217867ccff06c78d3649","score":"0.5085597","text":"def main(dictionnaire):\n\n menu(dictionnaire)","title":""},{"docid":"a84511d9b2925eacf123e8c90b1fbb65","score":"0.50796336","text":"def get_menu(self, request):\r\n menu_tree = []\r\n for menu_top in self.get_queryset().filter(parent_menu=None):\r\n if auth.has_perm(request=request, perm=menu_top.permission):\r\n menu_temp = {}\r\n menu_temp['id'] = menu_top.id\r\n menu_temp['name'] = menu_top.name\r\n menu_temp['code'] = menu_top.code\r\n menu_temp['icon'] = menu_top.icon\r\n menu_temp['url'] = menu_top.url\r\n menu_temp['sub_menu'] = self._get_child_menu_tree(request=request, menu=menu_top)\r\n menu_tree.append(menu_temp)\r\n return menu_tree","title":""},{"docid":"eac05a9fb264ab547267470f0abf12c3","score":"0.50765556","text":"def type_menu(self, tqualname):\n menu = []\n # Things we can do with tqualname\n menu.append({\n 'text': \"Find sub classes\",\n 'title': \"Find sub classes of this class\",\n 'href': self.search(\"+derived:%s\" % tqualname),\n 'icon': 'type'\n })\n menu.append({\n 'text': \"Find base classes\",\n 'title': \"Find base classes of this class\",\n 'href': self.search(\"+bases:%s\" % tqualname),\n 'icon': 'type'\n })\n menu.append({\n 'text': \"Find members\",\n 'title': \"Find members of this class\",\n 'href': self.search(\"+member:%s\" % tqualname),\n 'icon': 'members'\n })\n menu.append({\n 'text': \"Find references\",\n 'title': \"Find references to this class\",\n 'href': self.search(\"+type-ref:%s\" % tqualname),\n 'icon': 'reference'\n })\n return menu","title":""},{"docid":"3247a66866937788ed76e2f0a2f32430","score":"0.5072173","text":"def _menu(self, menus, finish_menus, game_objects):\n old_menu = None\n current_menu = internal.MenuIdentifiers.MAIN_MENU # The first menu displayed\n self.clock.tick()\n with self.interface.use('menu'):\n while True: # Wait for the user to navigate through the menu system\n if old_menu != current_menu:\n old_menu = current_menu\n self.interface.reset('menu')\n menus[current_menu](game_objects) # Set up the current menu\n self.interface.flush()\n while True: # Wait for input from this menu\n self.clock.tick(config.RENDER_FRAMERATE)\n inputs = self.interface.inp()\n self.interface.flush()\n if inputs:\n if len([c_m for c_m, input_type in inputs if input_type != internal.InputTypes.MENU]):\n # If we have any non-MENU inputs\n raise exceptions.ProgrammingException\n current_menu = next(c_m for c_m, input_type in inputs if input_type == internal.InputTypes.MENU)\n break\n if current_menu in finish_menus:\n break","title":""},{"docid":"4f180e928c286a057cdbfbf3382ed9fb","score":"0.50696373","text":"def sub_menu():\n\n ccTremb = db.connect()\n cDest = db.destinations(ccTremb)\n sSub_menu = \"\"\"\n\nDESTINATIONS SUB-MENU (D):\n.: Exit\n1: Add an area\n1A: Add MUNICIPALITIES semi-automatically\n2: View 'children'\n3: View single element\n4: Pretty print a single element to a file\n\nB: Balance town: Small-scale maps (1:1M) INDUSTRY DRIVEN DEMOGRAPHICS\nC: Balance city: Large-scale maps (1:50k) HOUSEHOLD DRIVEN DEMOGRAPHICS\nD: Balance non-residential: Household demand for commercial/industrial zones.\nE: Edit an entry\nG: Assign geo-codes to 'children'. (Use once a parent has all its children)\nH: Add housing: Adds household supply\nM: Add a map\nT: Types (workforce demand type summary for example 'OMB')\nU: Update parent with children's data (demographics, vehilces, resources)\nW: Add workplace: generates population demand in town.\nX: Remove workplace: Can be used to fix 'mistakes'\n\n!: Reset demographics to zero.\n\"\"\" # Closes the multi=line txt\n\n bExit = False\n while bExit == False: # loop until the user exits\n print(sSub_menu)\n sInput = input().upper()\n\n # User has made their choice. Now, process it.\n if sInput == \".\": # Exit\n bExit = True\n elif sInput == \"1\": # New\n add_area_to_db(ccTremb)\n elif sInput == \"1A\": # Semi-Automatic batch area\n add_semi_auto(ccTremb)\n elif sInput == \"2\": # View children\n view_children(ccTremb)\n elif sInput == \"3\": # Outputs all the data for one geo-code\n view_single(ccTremb)\n elif sInput == \"4\": # Pretty print single element\n pretty_print_single(ccTremb)\n # elif sInput == 5: # Sub-component which has its own entry\n # add_sub_comp(ccTremb)\n elif sInput == \"B\": # Industry driven balancing:\n balance_town(ccTremb)\n elif sInput == \"C\": # City balancing: Driven by available houses\n balance_city(ccTremb)\n elif sInput == \"D\": # Calculates housing demand for non-res area\n household_demands(ccTremb)\n elif sInput == \"E\": # Edit an entry\n edit_entry(ccTremb)\n elif sInput == \"G\": # Assign geocodes to all children\n assign_geocodes(ccTremb)\n elif sInput == \"H\": # Adds supply households\n add_housing(ccTremb)\n elif sInput == \"M\": # Add a map for referencing\n add_map_to_db(ccTremb)\n elif sInput == \"T\": # Outputs total of certain employer types.\n type_summary(ccTremb)\n elif sInput == \"U\": # Update parent with children's data\n update_parent(ccTremb)\n elif sInput == \"W\": # Add workplaces (farms, offices)\n add_workplace(ccTremb)\n elif sInput == \"X\": # Remove worplace\n remove_workplace(ccTremb)\n elif sInput == \"!\": # Remove demographics\n reset_demographics(ccTremb)","title":""},{"docid":"dba5997ceb373c6628945a42691ca262","score":"0.5059759","text":"def display_menu(self, menu):\n self.menu_win.clear()\n self.loc_win.clear()\n\n items_per_col = 12\n menu_len = len(menu.items)\n items = menu.items\n longest_item = max(items, key=len)\n n_cols = math.ceil(menu_len/items_per_col)\n\n loc_str = 37*'_'\n try:\n self.loc_win.addstr(1,self.center_str(loc_str),\n loc_str)\n self.loc_win.addstr(1,self.center_str(f' {menu.parent.name.replace(\"*\",\"\")} '),\n f' {menu.parent.name.replace(\"*\",\"\")} ')\n self.loc_win.addstr(2,self.center_str(loc_str),\n loc_str)\n self.loc_win.addstr(2, self.center_str(f' {menu.name.replace(\"*\",\"\")} '),\n f' {menu.name.replace(\"*\",\"\")} ')\n except AttributeError:\n self.loc_win.addstr(1,self.center_str(loc_str),\n loc_str)\n self.loc_win.addstr(1, self.center_str(f' {menu.name.replace(\"*\",\"\")} '),\n f' {menu.name.replace(\"*\",\"\")} ')\n self.loc_win.refresh()\n\n x_space = len(longest_item) + 3\n if n_cols == 1:\n if len(longest_item) <= 30:\n x_init = self.x_center - 18\n else:\n x_init = self.x_center - len(longest_item)//2 - 3\n else:\n x_init = self.xmax//2 - (x_space*n_cols)//2\n\n for item in menu.items:\n if item.startswith('*'):\n if menu_len > items_per_col:\n box_len = items_per_col + 2\n else:\n box_len = menu_len + 2\n else:\n if menu_len > items_per_col:\n box_len = items_per_col + 1\n else:\n box_len = menu_len + 1\n\n if self.xmax % 2 == 0:\n increment_box_edge = 0\n else:\n increment_box_edge = 0\n rectangle(self.menu_win, 0, x_init-1, box_len+len(menu.instructions)+1,\n self.xmax - x_init +1)\n rectangle(self.menu_win,1, x_init, box_len + 1,\n self.xmax-x_init-increment_box_edge)\n\n if menu.instructions:\n for i, instructions in enumerate(menu.instructions):\n if i == 0:\n self.menu_win.addstr(i,self.center_str(instructions),\n instructions)\n else:\n self.menu_win.addstr(box_len + 2 + (i-1), x_init+1,\n instructions)\n\n self.display_module_info()\n current_i = 0\n while 1:\n for i, item in enumerate(items):\n if menu_len > items_per_col:\n x = x_init + int(x_space*(i//items_per_col)) + 1\n y = i - int((items_per_col)*((i)//items_per_col)) + 2\n else:\n if item.startswith('*'):\n y = i + 3\n else:\n y = i + 2\n x = self.center_str(longest_item) - 1\n\n item = item.strip(\"'\")\n if menu.numbered:\n item = f'{i+1}. {item}'\n\n if i == current_i:\n self.menu_win.attron(curses.color_pair(1))\n self.menu_win.addstr(y, x, item.replace('*',''))\n self.menu_win.attroff(curses.color_pair(1))\n else:\n self.menu_win.addstr(y, x, item.replace('*',''))\n self.menu_win.refresh()\n\n key = self.stdscr.getch()\n if key == curses.KEY_UP:\n if current_i%items_per_col == 0:\n if current_i+items_per_col > menu_len:\n current_i = menu_len - 1\n else:\n current_i += items_per_col-1\n else:\n current_i -= 1\n\n elif key == curses.KEY_DOWN:\n if current_i == menu_len-1:\n current_i = items_per_col*(n_cols-1)\n elif (current_i+1)%items_per_col == 0:\n current_i -= items_per_col-1\n else:\n current_i += 1\n\n elif key == curses.KEY_RIGHT:\n if current_i < menu_len-items_per_col:\n current_i += items_per_col\n elif current_i < items_per_col*(n_cols-1):\n current_i = menu_len - 1\n\n elif key == curses.KEY_LEFT:\n if current_i >= items_per_col:\n current_i -= items_per_col\n\n elif key == curses.KEY_ENTER or key in [10, 13]:\n self.menu_win.clear()\n if isinstance(menu.menues.get(items[current_i]), Menu):\n return menu.menues.get(items[current_i])\n else:\n return items[current_i]\n\n elif key == curses.KEY_BACKSPACE or key in [8, 127]:\n if menu.name not in ['Main Menu','Load Parameterfile']:\n return menu.parent\n\n elif (key == ord('\\t') and\n 'TAB/SPACE: view/edit parameterfile' in menu.instructions):\n self.save(self.tempfile)\n os.chdir(self.run_path)\n curses.endwin()\n subprocess.run(['less', '-S', self.tempfile])\n os.chdir(self.dir_path)\n curses.doupdate()\n\n elif (key == ord(' ') and\n 'TAB/SPACE: view/edit parameterfile' in menu.instructions):\n self.save(self.tempfile)\n os.chdir(self.run_path)\n curses.endwin()\n subprocess.run(['emacs', '-nw', self.tempfile])\n os.chdir(self.dir_path)\n self.config = ConfigureParameterFile(f'{self.run_path}/{self.tempfile}')\n curses.doupdate()\n\n elif key == ord('b') and 'b: previous list' in menu.instructions:\n return 'b'\n elif key == ord('n') and 'n: next list' in menu.instructions:\n return 'n'","title":""},{"docid":"ac4ebcb609e3890188cabe659572d7db","score":"0.50549054","text":"def mainMenu(self):\r\n i = 0\r\n\r\n os.system('cls')\r\n print(\"\\npVault by Max\\n\")\r\n for item in self.__mainmenu:\r\n print(i, \": \\t\", item, sep='')\r\n i = i+1\r\n selection = input()\r\n\r\n if selection == \"0\":\r\n self.accountsMenu()\r\n elif selection == \"1\":\r\n self.findMenu()\r\n elif selection == \"2\":\r\n self.addEntryMenu()\r\n elif selection == \"3\":\r\n #print(\"Are you sure? y/n\")\r\n print(\"notice me senpai\")\r\n time.sleep(0.2)\r\n os.system('cls')\r\n return True\r\n else:\r\n print(\"menu does not exist\")\r\n self.mainMenu()","title":""},{"docid":"482b381afa538bf5cd190c3d055170c3","score":"0.5052712","text":"def yamlmenu(self, name):\n menudata = []\n\n for section in self.tree:\n sec = list(section.values())[0]\n id = sec['id']\n if True:\n menudata.append( sec )\n else:\n cus_sec = {\n 'href' :sec['href'],\n 'id' :sec['id'],\n 'name' :sec['name'],\n 'links' :[],\n }\n self.lg.debug('sec links %s', sec['links'])\n for item in sec['links']:\n href = item['href']\n if self.perm[id][href] == True:\n cus_sec['links'].append(item)\n menudata.append( cus_sec )\n #self.context['menudata_'+name] = menudata\n self.context['menudata'] = menudata","title":""},{"docid":"7e727f1419ef975a74848dd0276c0028","score":"0.50492615","text":"def setMenu(self, menu):\n raise NotImplementedError","title":""},{"docid":"6c8c26e22969d704e06fc3a65ded7370","score":"0.5027003","text":"def show_team_menu():\n team_menu_choice = True\n while team_menu_choice: \n print(\"\\n\")\n menu_index = 1\n for team in teams:\n print(f\"{menu_index}) {team['team']}\")\n menu_index += 1\n try:\n team_menu_choice = int(input(\"\\nEnter an option >\"))\n if team_menu_choice > 0 and team_menu_choice <= len(teams):\n show_team_stats(team_menu_choice - 1)\n break\n else:\n raise ValueError()\n except ValueError:\n print(\"Invalid choice, try again\")","title":""},{"docid":"c4de639eca6bbd4e7a4cf57ba2e626ee","score":"0.5016536","text":"def main_menu():\r\n global menu\r\n option = \"start\"\r\n while True:\r\n sub_menu = menu[option]\r\n option = ask_questions(sub_menu)","title":""}],"string":"[\n {\n \"docid\": \"f529799e4495690bc0e73382ebad7e33\",\n \"score\": \"0.6648107\",\n \"text\": \"def print_menu():\\n # your code goes here!\\n print(\\\"Our menu:\\\")\\n print(\\\"*******************\\\")\\n for k, v in menu.items():\\n print(k, \\\": \\\", v)\\n print(\\\"*******************\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b94f196729d58cb3feb5e03769aede\",\n \"score\": \"0.6577854\",\n \"text\": \"def _print_menu(menu_dict: Dict[str, Dict[str, list]]) -> None:\\n for key, value in menu_dict.items():\\n print(colored(key+\\\":\\\\n\\\", \\\"blue\\\", attrs=['bold']))\\n for dining_hall, dishes in value.items():\\n print(colored(dining_hall+\\\":\\\", \\\"white\\\", attrs=['bold']))\\n for dish in dishes:\\n print(\\\"\\\\t\\\"+dish)\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a888ac0636175c05a464bd532097e6a9\",\n \"score\": \"0.6495988\",\n \"text\": \"def display_menu(self):\\n print(\\\"Menu Choices:\\\")\\n for key in self.choices:\\n choice = self.choices[key].__name__\\n choice = choice.replace(\\\"_\\\", \\\" \\\").title()\\n print(f\\\"{key}: {choice}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73645607b5dbdac107f1afbd66ea1cc8\",\n \"score\": \"0.6495514\",\n \"text\": \"def show_menu(self):\\n # print('debug: calling show_menu' + self.name)\\n print(self.name)\\n for choice in self.choices:\\n print(choice)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b1476615f97a7fdd7a9a599e7b367c5\",\n \"score\": \"0.6351823\",\n \"text\": \"def print_menu_detail_all(type: Period) -> None:\\n _print_menu(_format_output(_get_html(MENU_URL+\\\"/\\\"+type)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a062237a1d187513c27b360117cc99\",\n \"score\": \"0.6334381\",\n \"text\": \"def print_menu():\\n logger.info(\\\"### PrintMenu ###\\\")\\n print('1 Einstellungen')\\n print('2 Wörter üben')\\n print('3 Wörter hinzufügen')\\n print('4 Wörter anzeigen')\\n print('9 Ende')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc0585c2d3f0618dcaad1d7d81ffa13\",\n \"score\": \"0.6162404\",\n \"text\": \"def submenus(menu: RofiMenu) -> None:\\n if menu is None:\\n return\\n\\n for key, value in menu:\\n # Convert dict to RofiMenu\\n if isinstance(value, dict):\\n menu[key] = RofiMenu(value)\\n submenus(menu[key])\\n\\n if isinstance(value, list):\\n for i in range(len(value)): # For each item on the list\\n if isinstance(value[i], dict):\\n menu[key][i] = RofiMenu(menu[key][i])\\n submenus(menu[key][i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61f85b070b16bbc0afa78c2fad87d11\",\n \"score\": \"0.6161514\",\n \"text\": \"def get_menu(cls, menu):\\n output = \\\"\\\\n==== Menu ====\\\\n\\\"\\n for k, v in menu.items():\\n output += f\\\"{k}. {v}\\\\n\\\"\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a778a4671ae2bc7b55729c4aa106c27\",\n \"score\": \"0.61242706\",\n \"text\": \"def print_menu():\\r\\n\\r\\n print('Menu\\\\n\\\\n[l] load Inventory from file\\\\n[a] Add CD\\\\n[i] Display Current Inventory')\\r\\n print('[d] delete CD from Inventory\\\\n[s] Save Inventory to file\\\\n[x] exit\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a778a4671ae2bc7b55729c4aa106c27\",\n \"score\": \"0.61242706\",\n \"text\": \"def print_menu():\\r\\n\\r\\n print('Menu\\\\n\\\\n[l] load Inventory from file\\\\n[a] Add CD\\\\n[i] Display Current Inventory')\\r\\n print('[d] delete CD from Inventory\\\\n[s] Save Inventory to file\\\\n[x] exit\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e285216ab39ad51d04314f3aa00cea67\",\n \"score\": \"0.60845155\",\n \"text\": \"def print_menu():\\r\\n\\r\\n print('Menu\\\\n\\\\n[l] load Inventory from file\\\\n[a] Add CD\\\\n[i] Display Current Inventory')\\r\\n print('[s] Save Inventory to file\\\\n[x] exit\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f08bc2367791d2c70211e999ae730ea0\",\n \"score\": \"0.60730404\",\n \"text\": \"def print_menu():\\n\\n print(\\\"==============================================\\\")\\n print(\\\"What do you want to do now? \\\")\\n print(\\\"==============================================\\\")\\n print(\\\"Available options:\\\")\\n i = 1\\n for a in available_actions:\\n if current_state in a[\\\"valid_states\\\"]:\\n # Only hint about the action if the current state allows it\\n print(\\\" %i) %s\\\" % (i, a[\\\"description\\\"]))\\n i += 1\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1bb8b561248db6358f15a0559b1c49e\",\n \"score\": \"0.6001676\",\n \"text\": \"def present_menus(menu_dict):\\n for key, d in menu_dict.items():\\n print(f'{key}) {d.get(\\\"info\\\", \\\"no description\\\")}')\\n print(\\\"-\\\" * 20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e234877826e44d4e26b4366d63f7f44\",\n \"score\": \"0.59529275\",\n \"text\": \"def populate_menu(self, menu, dictionary):\\n\\n def add_mode_to_menu(menu_obj, mode):\\n action = menu_obj.addAction(mode.mode_name_formatted)\\n action.triggered.connect(lambda: self.select_mode(mode))\\n\\n for menu_key, menu_val in dictionary.items():\\n if isinstance(menu_val, list):\\n sub_menu = menu.addMenu(menu_key)\\n for value in menu_val:\\n if isinstance(value, GameMode):\\n add_mode_to_menu(sub_menu, value)\\n if isinstance(menu_val, dict):\\n sub_menu = menu.addMenu(menu_key)\\n self.populate_menu(sub_menu, menu_val)\\n if isinstance(menu_val, GameMode):\\n add_mode_to_menu(menu, menu_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a81bbbb09732666b76359681c1bf8f\",\n \"score\": \"0.59387755\",\n \"text\": \"def print_menu():\\n str_result = ''\\n for i in actions_dictionary:\\n str_result += (\\\"{}) {}\\\".format(i, actions_dictionary[i]))\\n str_result += '\\\\n'\\n str_result += 'Select the corresponding number for the action you want to take action: '\\n return str_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6f1f7effcfb52c87e8ccd2f65598102\",\n \"score\": \"0.5921677\",\n \"text\": \"def PrintMenuItems():\\n print('''\\n Menu of Options\\n 1) Show current data\\n 2) Add a new product.\\n 3) Save Data to File\\n 4) Exit Program\\n ''')\\n print() # Add an extra line for looks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2bd55edaa3b719e879a74ea115d92b6\",\n \"score\": \"0.58480316\",\n \"text\": \"def _get_menu_choices(menu):\\n import operator\\n if menu['type'] == 'selection':\\n return (menu['id'],)\\n elif menu['type'] == 'menu':\\n choices = [_get_menu_choices(submenu) for submenu in\\n menu['choices'].values()]\\n return reduce(operator.add, choices, ())\\n else:\\n raise HandinError(\\\"Unknown item type: %s\\\" % menu['type'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d95264ed8a3506d9566b949706e254\",\n \"score\": \"0.5846854\",\n \"text\": \"def menuAction(self, *args, **kwargs) :\\r\\n success = False\\r\\n\\r\\n #####\\r\\n # If the action parameter\\r\\n if self.action:\\r\\n success = self.action()\\r\\n\\r\\n self.logger.log(lp.DEBUG, \\\"Action returns success: \\\" + str(success))\\r\\n\\r\\n #####\\r\\n # Print the menu and act on the menu selection\\r\\n while True :\\r\\n try:\\r\\n quit = self.getValue(\\\"quit\\\")\\r\\n except KeyError:\\r\\n quit = False\\r\\n\\r\\n #####\\r\\n # Write out the ANSI code to clear the screen - might not work\\r\\n # if a user's terminal is set to unicode\\r\\n print(\\\"\\\\033c\\\")\\r\\n sys.stdout.write(\\\"\\\\033c\\\")\\r\\n #####\\r\\n # Start menu logic\\r\\n print(\\\"\\\\n\\\" + self.name + \\\" Menu\\\\n\\\")\\r\\n i = 1\\r\\n for item in self.child_nodes :\\r\\n print(\\\"[\\\" + str(i) + \\\"] \\\" + self.child_nodes[(i-1)].name)\\r\\n i = i + 1\\r\\n if not self.anchor :\\r\\n if not self.anchor:\\r\\n print(\\\"[\\\" + str(i) + \\\"] Return to \\\" + \\\\\\r\\n self.previous.name + \\\\\\r\\n \\\" Menu\\\")\\r\\n if self.anchor:\\r\\n print(\\\"[\\\" + str(i+1) + \\\"] Quit\\\")\\r\\n elif self.previous.anchor:\\r\\n print(\\\"[\\\" + str(i+1) + \\\"] Quit\\\")\\r\\n elif not self.anchor and not self.previous.anchor:\\r\\n print(\\\"[\\\" + str(i+1) + \\\"] Main menu\\\")\\r\\n print(\\\"[\\\" + str(i+2) + \\\"] Quit\\\")\\r\\n else :\\r\\n print(\\\"[\\\" + str(i) + \\\"] Quit\\\")\\r\\n\\r\\n print(\\\"\\\\nSelect an option and hit Enter\\\")\\r\\n\\r\\n # get input from the command line\\r\\n enter = sys.stdin.readline()\\r\\n\\r\\n # Figure out which number (or [Q|q] they hit\\r\\n if re.match(\\\"^\\\\d+$\\\", enter) :\\r\\n enter = int(enter)\\r\\n elif re.match(\\\"^[Qq]$\\\", enter) and not quit:\\r\\n break\\r\\n elif re.match(\\\"^[Qq]$\\\", enter) and quit:\\r\\n try :\\r\\n sys.exit()\\r\\n except OSError as err :\\r\\n self.logger.log(lp.DEBUG, \\\"OSError on attempt to exit: \\\" + \\\\\\r\\n str(err))\\r\\n elif re.match(\\\"^[Mm]$\\\", enter):\\r\\n self.goToMainMenu()\\r\\n else :\\r\\n continue\\r\\n \\r\\n if enter == (len(self.child_nodes)+1) and self.anchor and not quit:\\r\\n while not self.anchor:\\r\\n self = self.previous\\r\\n break\\r\\n if enter == (len(self.child_nodes)+1) and self.anchor and quit:\\r\\n try :\\r\\n sys.exit()\\r\\n except OSError as err :\\r\\n self.logger.log(lp.DEBUG, \\\"OSError on attempt to exit: \\\" + \\\\\\r\\n str(err))\\r\\n # go back to the previous menu \\r\\n if enter == (len(self.child_nodes)+1) and self.previous.anchor :\\r\\n while not self.anchor:\\r\\n self = self.previous\\r\\n break\\r\\n if enter == (len(self.child_nodes)+1) and not self.anchor and not self.previous.anchor :\\r\\n break\\r\\n\\r\\n # Either quit, or execute the \\\"execute\\\" method of the MenuItem, or\\r\\n # MenuComposite\\r\\n if enter == (len(self.child_nodes)+ 1) and self.anchor and not quit:\\r\\n while not self.anchor:\\r\\n self = self.previous\\r\\n break\\r\\n if enter == (len(self.child_nodes)+ 1) and self.anchor and quit:\\r\\n try :\\r\\n sys.exit()\\r\\n except OSError as err :\\r\\n self.logger.log(lp.DEBUG, \\\"OSError on attempt to exit: \\\" + str(err))\\r\\n elif enter == (len(self.child_nodes)+ 2) and self.previous.anchor and not quit:\\r\\n while not self.anchor:\\r\\n self = self.previous\\r\\n break\\r\\n elif enter == (len(self.child_nodes)+ 2) and self.previous.anchor and quit:\\r\\n try :\\r\\n sys.exit()\\r\\n except OSError as err :\\r\\n self.logger.log(lp.DEBUG, \\\"OSError on attempt to exit: \\\" + str(err))\\r\\n elif enter == (len(self.child_nodes)+ 2) and not self.previous.anchor:\\r\\n self.goToMainMenu()\\r\\n elif enter == (len(self.child_nodes)+ 3) and not self.previous.anchor:\\r\\n while not self.anchor:\\r\\n self = self.previous\\r\\n break\\r\\n elif enter == (len(self.child_nodes)+ 3) and not self.previous.anchor:\\r\\n try :\\r\\n sys.exit()\\r\\n except OSError as err :\\r\\n self.logger.log(lp.DEBUG, \\\"OSError on attempt to exit: \\\" + str(err))\\r\\n \\r\\n elif enter >= 0 and enter <= len(self.child_nodes) :\\r\\n #####\\r\\n # If the action parameter\\r\\n if self.action:\\r\\n print(\\\"Action: \\\" + str(self.action))\\r\\n success = self.action(self, *args, **kwargs)\\r\\n\\r\\n self.logger.log(lp.DEBUG, \\\"Action returns success: \\\" + str(success))\\r\\n\\r\\n self.child_nodes[(enter-1)].menuAction()\\r\\n else :\\r\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f494e0e84678ccc3d371cd3262663ab\",\n \"score\": \"0.5811621\",\n \"text\": \"def print_menu_choice():\\r\\n print('''\\r\\nMenu of Options\\r\\n 1) View current product data\\r\\n 2) Add new product data\\r\\n 3) Save Data to File\\r\\n 4) Exit Program \\r\\n ''')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe80b4abd58e45d51be43ec791a9832\",\n \"score\": \"0.5784042\",\n \"text\": \"def menu(self):\\n # Print header and selection name.\\n print('\\\\nMain menu', end='')\\n if self.selected is None:\\n print(' (no entry selected):')\\n else:\\n print(' ({} selected): '.format(self.selected))\\n\\n # Print menu entries.\\n for entry in self.menu_items:\\n print(entry[1])\\n\\n # Print the data of the selected entry.\\n if self.selected is not None:\\n print('\\\\nSelected: ')\\n self.printEntry(self.selected, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c298c5425c4dab8177a5a7c89113c135\",\n \"score\": \"0.5778603\",\n \"text\": \"def menu(donor_collection_obj, menu_dict):\\n user_option = \\\"\\\"\\n while user_option.lower() != 'e':\\n displayMenu()\\n user_option = input(\\\"Select an option from the menu: \\\")\\n user_option = user_option.lower().strip()\\n #user_option = 't' #for testing purposes\\n #return user_option #used to test menu produces correct user input\\n try:\\n menu_dict[user_option](donor_collection_obj)\\n except KeyError as e:\\n print(e)\\n print(\\\"Invalid option selected. Please try again\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f22e51cf283efe6e7566e4a607d90e\",\n \"score\": \"0.5730634\",\n \"text\": \"def menu(d,prompt='true'): \\n keys = []\\n try:\\n title = d.pop('TITLE')\\n section(title)\\n except KeyError:\\n section()\\n sorted_d = sorted(d.items())\\n for key in sorted_d:\\n row = [C_LB,' {0:10}'.format(key[0]),' : ',C_LG,'{}\\\\n'.format(key[1])]\\n keys.extend(row)\\n cprint(*keys)\\n if prompt:\\n return raw_input('Enter Selection: ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e5b208275ce6fff67c7d75ace16d77\",\n \"score\": \"0.57220185\",\n \"text\": \"def display(self, node=None, level=0):\\n node = self.root if node is None else self.__get_node(node)\\n\\n t = \\\"\\\\t\\\" * level\\n header = t + \\\"#{}, {} keys, {} children\\\"\\n keys = t + \\\"\\\\tKeys: {}\\\"\\n children = t + \\\"\\\\tChildren: {}\\\"\\n\\n pos = [self.__get_node(child).pos for child in node.children]\\n\\n if level == 0:\\n print(\\\"Order: {}\\\".format(self.order))\\n\\n print(header.format(node.pos, node.n_keys, node.n_children))\\n print(keys.format(str(node.keys)))\\n print(children.format(str(pos)))\\n print('-' * 60)\\n\\n for child in node.children:\\n self.display(child, level + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"747af905c3efd1848017c01658ed3181\",\n \"score\": \"0.5710187\",\n \"text\": \"def print_menu():\\n print(\\\"Elija lo que quiera hacer.\\\")\\n print(\\\"Las opciones son:\\\")\\n print(\\\"*****************************************************************\\\")\\n print(\\\"*\\\\tz: Mostrar el tamaño del diccionario en uso. \\\\t*\\\")\\n print(\\\"*\\\\ts: Buscar una definicion para una palabra dada. \\\\t*\\\")\\n print(\\\"*\\\\ta: Agregar una palabra con su definicion. \\\\t*\\\")\\n print(\\\"*\\\\td: Borrar una palabra. \\\\t*\\\")\\n print(\\\"*\\\\te: Vaciar el diccionario. \\\\t*\\\")\\n print(\\\"*\\\\th: Mostrar el diccionario actual. \\\\t*\\\")\\n print(\\\"*\\\\tc: Duplicar el diccionario actual. \\\\t*\\\")\\n print(\\\"*\\\\tl: Cargar un nuevo diccionario desde un archivo.\\\\t*\\\")\\n print(\\\"*\\\\tu: Guardar el diccionario actual en un archivo. \\\\t*\\\")\\n print(\\\"*\\\\tq: Finalizar \\\\t*\\\")\\n print(\\\"*****************************************************************\\\")\\n\\n opcion = raw_input(\\\"Introduzca su elección: \\\")\\n\\n return opcion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe290c1b628bae394a1e300c105595f\",\n \"score\": \"0.5696634\",\n \"text\": \"def show_menu():\\n print('\\\\nMenu:')\\n print('1: Load patterns')\\n print('2: Calc output')\\n print('3: Educate')\\n print('4: Set structure')\\n print('5: Exit')\\n key = input('Select menu item: ')\\n return key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cc607bfddb9084f0e5aad451846232\",\n \"score\": \"0.5685386\",\n \"text\": \"def print_menu(exits):\\n print_inventory_items(inventory)\\n for direction in exits:\\n print_exit(direction, exit_leads_to(exits, direction))\\n if current_room[\\\"rapperbeat\\\"] == False:\\n if len(current_room[\\\"rapper\\\"]) != 0:\\n slow_type(\\\"\\\\n\\\"+\\\"Battle \\\" + current_room[\\\"rapper\\\"], 250)\\n else:\\n print(\\\"\\\")\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c9878333452f912e2e6eae26d56b79\",\n \"score\": \"0.56323445\",\n \"text\": \"def run(self):\\r\\n option_fx = None\\r\\n opt_exit = None\\r\\n opt_parent = None\\r\\n options = self.get_options()\\r\\n HAS_EXIT = False\\r\\n HAS_PARENT = False\\r\\n\\r\\n if not options:\\r\\n raise MenuRuntimeError(\\\"Called menu has no options\\\")\\r\\n\\r\\n # Print options to the screen\\r\\n for option in options:\\r\\n if option.get_id() == OPT_ID_NAVP:\\r\\n HAS_PARENT = True\\r\\n opt_parent = option\\r\\n continue\\r\\n if option.get_id() == OPT_ID_NAVE:\\r\\n HAS_EXIT = True\\r\\n opt_exit = option\\r\\n continue\\r\\n else:\\r\\n option.print_opt()\\r\\n if HAS_PARENT:\\r\\n opt_parent.print_opt()\\r\\n if HAS_EXIT:\\r\\n opt_exit.print_opt()\\r\\n print(\\\"\\\")\\r\\n\\r\\n while True:\\r\\n user_sel = self.get_selection()\\r\\n\\r\\n # Combine all option selector lists into one list\\r\\n sel_subs = [s.get_selectors() for s in options]\\r\\n\\r\\n # Then flatten the sublists into a list\\r\\n sel_all = [item for sublist in sel_subs for item in sublist]\\r\\n\\r\\n if user_sel in sel_all:\\r\\n break\\r\\n\\r\\n # print a blank line if success\\r\\n print(\\\"\\\")\\r\\n\\r\\n # for every option\\r\\n for option in options:\\r\\n if option_fx:\\r\\n break\\r\\n\\r\\n if user_sel in option.get_selectors():\\r\\n option_fx = option.get_fx()\\r\\n break\\r\\n\\r\\n if option_fx:\\r\\n option_fx()\\r\\n else:\\r\\n raise MenuRuntimeError(\\\"Called menu option has no function\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba20840cc201d9da782d754b8c66dd8d\",\n \"score\": \"0.56109405\",\n \"text\": \"def __str__(self):\\n ret = NO_MENU_MSG\\n if len(self._menu_items) != 0:\\n ret = MENU_GREETING_MSG\\n current_type = self._menu_items[0].get_type()\\n ret += current_type + \\\":\\\\n\\\"\\n for item in self._menu_items:\\n if item.get_type() != current_type:\\n current_type = item.get_type()\\n ret += current_type + \\\":\\\\n\\\"\\n ret += \\\"\\\\t\\\" + item.__str__()\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4b4e965edf426ba3367bc1578fcacd8\",\n \"score\": \"0.5584861\",\n \"text\": \"def start_menu(self):\\n\\n self.menu: dict = {}\\n self.menu['1'] = \\\"1) Emitir ordem de compra ou venda;\\\" \\n self.menu['2'] = \\\"2) Visualizar minha lista de interesse de cotacao;\\\"\\n self.menu['3'] = \\\"3) Atualizar minha lista de interesse de cotacao;\\\"\\n self.menu['4'] = \\\"4) Visualizar minha carteira de acoes;\\\"\\n self.menu['5'] = \\\"5) Visualizar minha lista de limite de ganho/perca;\\\"\\n self.menu['6'] = \\\"6) Atualizar minha lista de limite de ganho/perca;\\\"\\n self.menu['7'] = \\\"0) Sair;\\\"\\n\\n print(\\\"Welcomed {}\\\".format(self.nome_de_usuario))\\n\\n while True: \\n print()\\n options=sorted(self.menu.keys())\\n for entry in options: \\n print (self.menu[entry])\\n\\n selection=input(\\\"Digite o numero da opcao desejada: \\\") \\n \\n if selection =='1': \\n print (self.menu[selection])\\n try:\\n construtor_de_ordem = ConstrutorDeOrdem(self.nome_de_usuario)\\n ordem: Ordem = construtor_de_ordem.obter_ordem_do_usuario()\\n self.cliente_http.emitir_ordem(ordem)\\n except:\\n self.log_error(\\\"Nao foi possivel emitir a ordem\\\", exc=sys.exc_info()[0])\\n \\n elif selection == '2': \\n print (self.menu[selection])\\n try:\\n self.cliente_http.obter_interesses()\\n except:\\n self.log_error(\\\"Nao foi possivel obter cotacoes da lista de interesse\\\", exc=sys.exc_info()[0])\\n \\n elif selection == '3': \\n print (self.menu[selection])\\n try:\\n construtor_de_interesse = ConstrutorDeInteresse(self.nome_de_usuario)\\n interesse = construtor_de_interesse.obter_interesse_do_usuario()\\n self.cliente_http.enviar_interesse(interesse)\\n except:\\n self.log_error(\\\"Nao foi possivel enviar novo interesse\\\", exc=sys.exc_info()[0])\\n\\n elif selection == '4': \\n print (self.menu[selection])\\n try:\\n self.cliente_http.obter_carteira()\\n except:\\n self.log_error(\\\"Nao foi possivel obter carteira\\\", exc=sys.exc_info()[0])\\n \\n elif selection == '5': \\n print (self.menu[selection])\\n try:\\n self.cliente_http.obter_limites()\\n except:\\n self.log_error(\\\"Nao foi possivel obter limites\\\", exc=sys.exc_info()[0])\\n\\n elif selection == '6': \\n print (self.menu[selection])\\n try:\\n construtor_de_limite = ConstrutorDeLimite(self.nome_de_usuario)\\n limite = construtor_de_limite.obter_limite_do_usuario()\\n self.cliente_http.enviar_limite(limite)\\n except:\\n self.log_error(\\\"Nao foi possivel enviar novo limite\\\", exc=sys.exc_info()[0]) \\n\\n elif selection == '0': \\n os._exit(1)\\n break\\n else: \\n print (\\\"Opcao invalida\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf20c283ae76a2eee86689a3f89757a\",\n \"score\": \"0.5582814\",\n \"text\": \"def print_levels(self):\\n if (self.left != None):\\n self.left.print_levels()\\n print(self.key,\\\" \\\", self.node_level())\\n if (self.right != None):\\n self.right.print_levels()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67d8c4875d6a110ad3702f74f2e6629\",\n \"score\": \"0.5578455\",\n \"text\": \"def scrape_menu(self):\\n try:\\n if self.menu_link[1] == 'urbanspoon':\\n self.urbanspoon_scraper(self.menu_link[0])\\n elif self.menu_link[1] == 'singleplatform':\\n self.singleplatform_scraper(self.menu_link[0])\\n elif self.menu_link[1] == 'custom':\\n self.scrape_custom_menu(self.menu_link[0])\\n except TypeError:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5030edfa8b87fd5f2fa0d1a72a4a7aa\",\n \"score\": \"0.5555626\",\n \"text\": \"def menu(self):\\n ## This is a DICTIONARY, it's a list with custom index values\\n # You may change the menu if you'd like to add an experimental method\\n menu = {\\\"n\\\": (\\\"Navigate forward\\\", self.nav),\\n \\\"d\\\": (\\\"Dance\\\", self.dance),\\n \\\"o\\\": (\\\"Obstacle count\\\", self.obstacle_count),\\n \\\"c\\\": (\\\"Calibrate\\\", self.calibrate),\\n \\\"s\\\": (\\\"Check status\\\", self.status),\\n \\\"h\\\": (\\\"Open House\\\", self.open_house),\\n \\\"q\\\": (\\\"Quit\\\", quit_now),\\n \\\"f\\\": (\\\"Forward\\\", self.move_ahead),\\n \\\"l\\\": (\\\"Turn left\\\", self.left_turn),\\n \\\"r\\\": (\\\"Turn right\\\", self.right_turn),\\n \\\"t\\\": (\\\"Test left or right\\\", self.skill_test)}\\n # loop and print the menu...\\n for key in sorted(menu.keys()):\\n print(key + \\\":\\\" + menu[key][0])\\n # store the user's answer\\n ans = raw_input(\\\"Your selection: \\\")\\n # activate the item selected\\n menu.get(ans, [None, error])[1]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c13928ed9ebc53fddae9096c88bf83\",\n \"score\": \"0.5545239\",\n \"text\": \"def menu(menu_dict):\\n running = True\\n while running:\\n present_menus(menu_dict)\\n answer = input('>')\\n if answer in menu_dict:\\n func = menu_dict[answer].get(\\\"func\\\", lambda: None)\\n func()\\n else:\\n print(\\\"exiting menu\\\")\\n running = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50bc8d0c46150a28eae873a1c1fa75c9\",\n \"score\": \"0.55271715\",\n \"text\": \"def display_start_menu(manager):\\n while True:\\n output = Catalogue.get_menu(Catalogue.start_menu)\\n print(output)\\n try:\\n user_input = int(input(\\\"Enter selection: \\\"))\\n if user_input > len(Catalogue.start_menu):\\n raise ValueError\\n except ValueError as e:\\n print(\\\"Invalid input. Enter the number of your selected \\\"\\n \\\"action.\\\")\\n else:\\n choices = {\\n 1: manager.view_all_cards,\\n 2: manager.view_cards_by_type,\\n 3: manager.add_card,\\n 4: manager.search_card,\\n 5: manager.delete_card,\\n 6: manager.backup_data,\\n 7: sys.exit\\n }\\n\\n if 0 < user_input < 8:\\n choices.get(user_input)()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad8d46fbae27abc83693f51783bef2b\",\n \"score\": \"0.55262345\",\n \"text\": \"def menu(self):\\n ## This is a DICTIONARY, it's a list with custom index values. Python is cool.\\n # Please feel free to change the menu and add options.\\n print(\\\"\\\\n *** MENU ***\\\") \\n menu = {\\\"n\\\": (\\\"Navigate\\\", self.nav),\\n \\\"d\\\": (\\\"Dance\\\", self.dance),\\n \\\"o\\\": (\\\"Obstacle count\\\", self.obstacle_count),\\n \\\"c\\\": (\\\"Calibrate\\\", self.calibrate),\\n \\\"q\\\": (\\\"Quit\\\", self.quit),\\n \\\"h\\\": (\\\"Return to start\\\", self.ret_to_st),\\n \\\"s\\\": (\\\"Slither\\\", self.slither)\\n }\\n # loop and print the menu...\\n for key in sorted(menu.keys()):\\n print(key + \\\":\\\" + menu[key][0])\\n # store the user's answer\\n ans = str.lower(input(\\\"Your selection: \\\"))\\n # activate the item selected\\n menu.get(ans, [None, self.quit])[1]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42043ec9d9a018ed9e3ff84b2c5f3289\",\n \"score\": \"0.5513636\",\n \"text\": \"def menu(self):\\n ## This is a DICTIONARY, it's a list with custom index values\\n # You may change the menu if you'd like to add an experimental method\\n menu = {\\\"n\\\": (\\\"Navigate forward\\\", self.nav),\\n \\\"d\\\": (\\\"Dance\\\", self.dance),\\n \\\"o\\\": (\\\"Obstacle count\\\", self.obstacle_count),\\n \\\"c\\\": (\\\"Calibrate\\\", self.calibrate),\\n \\\"s\\\": (\\\"Check status\\\", self.status),\\n \\\"h\\\": (\\\"Open House\\\", self.open_house),\\n \\\"q\\\": (\\\"Quit\\\", quit_now),\\n \\\"t\\\": (\\\"Test\\\", self.skill_test)}\\n # loop and print the menu...\\n for key in sorted(menu.keys()):\\n print(key + \\\":\\\" + menu[key][0])\\n # store the user's answers\\n ans = raw_input(\\\"Your selection: \\\")\\n # activate the item selected\\n menu.get(ans, [None, error])[1]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88bb1a363ca09786d067dafb602d2a21\",\n \"score\": \"0.54982567\",\n \"text\": \"def run(self):\\n while True:\\n\\n sys.stdout.write(self.title + os.linesep) # print title\\n for x in self.entries:\\n sys.stdout.write(self.prefix + x['key'] + self.postfix + x['text'] + os.linesep)\\n\\n try: # do safe/smart prompt\\n answer = '' # safe\\n answer = input(self.prompt)\\n\\n except EOFError: # user pressed ^D\\n self.myerr.write('you pressed ^D' + os.linesep)\\n return False\\n\\n except KeyboardInterrupt:\\n self.myerr.write('you pressed ^C' + os.linesep)\\n return False\\n\\n # self.myerr.write(('you pressed %s\\\\n' % answer) + os.linesep) #DEBUG\\n\\n # validate answer\\n if len(answer) != 1:\\n self.myerr.write('invalid menu entry' + os.linesep)\\n self.myerr.write(os.linesep)\\n continue\\n\\n # look for answer\\n found = False\\n for x in self.entries:\\n if x['key'] == answer:\\n found = True\\n if type(x['sub']) in [type(None)]:\\n return x['key']\\n\\n elif type(x['sub']) in [type(lambda: True)]:\\n # lambda functions that run each time\\n # if they return true, you exit menu\\n # if they return false, stay in menu\\n if x['sub']():\\n return x['key']\\n else:\\n continue\\n\\n elif type(x['sub']) in [type(menu_system())]:\\n # sub menu system\\n self.myout.write(os.linesep)\\n recurse = x['sub'].run()\\n if not (type(recurse) is str): recurse = '0'\\n return x['key'] + recurse\\n\\n if not found:\\n self.myerr.write('invalid menu entry' + os.linesep)\\n self.myerr.write(os.linesep)\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ad572d1ca1467e30ce1d01a1ca8155\",\n \"score\": \"0.54892653\",\n \"text\": \"def print_menu():\\n print('1. Print Users')\\n print('2. Add a User')\\n print('3. Remove a User')\\n print('4. Lookup a User')\\n print('5. Quit')\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70088a7ffc97329505c13892dfba80f2\",\n \"score\": \"0.54825145\",\n \"text\": \"def main_menu():\\n build_menu()\\n choice = input(\\\">> \\\\n\\\")\\n\\n if choice == \\\"1\\\":\\n annex_habitat(keahua)\\n\\n if choice == \\\"2\\\":\\n release_animal(keahua)\\n\\n if choice == \\\"3\\\":\\n feed_animal_menu()\\n\\n if choice == \\\"4\\\":\\n cultivate_plant(keahua)\\n\\n if choice == \\\"5\\\":\\n build_facility_report(keahua)\\n pass\\n\\n if choice != \\\"6\\\":\\n main_menu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3c08a8352f68a10507185d8d45b05a\",\n \"score\": \"0.5475939\",\n \"text\": \"def display_menu(header, menu_list):\\n while True:\\n print()\\n print(header)\\n for ind, menu_item in enumerate(menu_list):\\n print(\\\"{}: {}\\\".format(ind, menu_item[0]))\\n \\n user_input = raw_input(\\\"Enter a selection index: \\\")\\n # Handle the case where the user input something non-integer\\n try:\\n user_input_int = int(user_input)\\n except:\\n print(\\\"ERROR: input an integer\\\")\\n continue\\n \\n # Handle the case where the user input something out of range\\n if user_input_int not in range(len(menu_list)):\\n print(\\\"ERROR: select something on the menu\\\")\\n continue\\n\\n # Handle the case where the user did something else invalid\\n try:\\n selected_entry = menu_list[user_input_int]\\n except:\\n print(\\\"ERROR: bad selection, you bad mother\\\")\\n continue\\n\\n # This might break if the programmer doesn't provide proper input\\n # Let it break - how else would you handle programmer stupidity?\\n return selected_entry[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01a73c2791ae6eeea7932b5e9dc2402\",\n \"score\": \"0.5468891\",\n \"text\": \"def print_menu_options(self):\\n print('1. Exit')\\n for i, sub_program in enumerate(self.program_list):\\n print(f'{i + SUB_PROGRAM_MENU_OFFSET}. {sub_program}')\\n print('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14bdce21eb23f288be693b3900598353\",\n \"score\": \"0.5468833\",\n \"text\": \"def displayMenu():\\n str_menu = \\\"\\\\n1. Type 't' to Send a Thank You to add a name, donation and thank you card\\\\n\\\"+\\\\\\n \\\"2. Type 'r' to Create a Report and see a list of the donors\\\\n\\\"+\\\\\\n \\\"3. Type 's' to Send letters to everyone\\\\n\\\"+\\\\\\n \\\"4. Type 'e' to exit the program\\\\n\\\"\\n #for testing purposes to determine menu is returned correctly\\n #return str_menu\\n print(str_menu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b263f30d8ba604c807b1dca969233e\",\n \"score\": \"0.5464424\",\n \"text\": \"def menu(self):\\n ## This is a DICTIONARY, it's a list with custom index values\\n # You may change the menu if you'd like to add an experimental method\\n menu = {\\\"n\\\": (\\\"Navigate forward\\\", self.nav),\\n \\\"d\\\": (\\\"Dance\\\", self.dance),\\n \\\"c\\\": (\\\"Calibrate\\\", self.calibrate),\\n \\\"o\\\": (\\\"Obstacle count\\\", self.obstacle_count),\\n \\\"co\\\": (\\\"Circle count\\\", self.circle_counting),\\n \\\"s\\\": (\\\"Check status\\\", self.status),\\n \\\"t\\\": (\\\"Test restore heading\\\", self.test_restore_heading),\\n \\\"q\\\": (\\\"Quit\\\", quit_now)\\n }\\n # loop and print the menu...\\n for key in sorted(menu.keys()):\\n print(key + \\\":\\\" + menu[key][0])\\n # store the user's answer\\n ans = raw_input(\\\"Your selection: \\\")\\n # activate the item selected\\n menu.get(ans, [None, error])[1]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1755c3cb5569760ee2db5fdbb39775a3\",\n \"score\": \"0.5459167\",\n \"text\": \"def add_menu_options(self, menu):\\n ##################\\n category_name = _(\\\"Tree Options\\\")\\n\\n if self.name.split(\\\",\\\")[0] == _RPT_NAME:\\n self.__pid = PersonOption(_(\\\"Report for\\\"))\\n self.__pid.set_help(_(\\\"The main person for the report\\\"))\\n menu.add_option(category_name, \\\"pid\\\", self.__pid)\\n else: #if self.name == \\\"familial_descend_tree\\\":\\n self.__pid = FamilyOption(_(\\\"Report for\\\"))\\n self.__pid.set_help(_(\\\"The main family for the report\\\"))\\n menu.add_option(category_name, \\\"pid\\\", self.__pid)\\n\\n self.showparents = BooleanOption(\\n _('Start with the parent(s) of the selected first'),\\n False)\\n self.showparents.set_help(\\n _(\\\"Will show the parents, brother and sisters of the \\\"\\n \\\"selected person.\\\")\\n )\\n menu.add_option(category_name, \\\"show_parents\\\", self.showparents)\\n\\n max_gen = NumberOption(_(\\\"Generations\\\"), 10, 1, 50)\\n max_gen.set_help(_(\\\"The number of generations to include in the tree\\\"))\\n menu.add_option(category_name, \\\"maxgen\\\", max_gen)\\n\\n max_spouse = NumberOption(_(\\\"Level of Spouses\\\"), 1, 0, 10)\\n max_spouse.set_help(_(\\\"0=no Spouses, 1=include Spouses, 2=include \\\"\\n \\\"Spouses of the spouse, etc\\\"))\\n menu.add_option(category_name, \\\"maxspouse\\\", max_spouse)\\n\\n compresst = BooleanOption(_('Compress tree'), False)\\n compresst.set_help(_(\\\"Whether to move people up, where possible, \\\"\\n \\\"resulting in a smaller tree\\\"))\\n menu.add_option(category_name, \\\"compress_tree\\\", compresst)\\n\\n stdoptions.add_localization_option(menu, category_name)\\n\\n ##################\\n category_name = _(\\\"Display\\\")\\n\\n disp = TextOption(_(\\\"Descendant\\\\nDisplay Format\\\"),\\n [\\\"$n\\\",\\n \\\"%s $b\\\" %_BORN,\\n \\\"-{%s $d}\\\" %_DIED])\\n disp.set_help(_(\\\"Display format for a descendant.\\\"))\\n menu.add_option(category_name, \\\"descend_disp\\\", disp)\\n\\n bold = BooleanOption(_('Bold direct descendants'), True)\\n bold.set_help(\\n _(\\\"Whether to bold those people that are direct \\\"\\n \\\"(not step or half) descendants.\\\")\\n )\\n menu.add_option(category_name, \\\"bolddirect\\\", bold)\\n\\n #bug 4767\\n #diffspouse = BooleanOption(\\n # _(\\\"Use separate display format for spouses\\\"),\\n # True)\\n #diffspouse.set_help(_(\\\"Whether spouses can have a different format.\\\"))\\n #menu.add_option(category_name, \\\"diffspouse\\\", diffspouse)\\n\\n indspouce = BooleanOption(_('Indent Spouses'), True)\\n indspouce.set_help(_(\\\"Whether to indent the spouses in the tree.\\\"))\\n menu.add_option(category_name, \\\"ind_spouse\\\", indspouce)\\n\\n sdisp = TextOption(_(\\\"Spousal\\\\nDisplay Format\\\"),\\n [\\\"$n\\\",\\n \\\"%s $b\\\" %_BORN,\\n \\\"-{%s $d}\\\" %_DIED])\\n sdisp.set_help(_(\\\"Display format for a spouse.\\\"))\\n menu.add_option(category_name, \\\"spouse_disp\\\", sdisp)\\n\\n incmarr = BooleanOption(_('Include Marriage box'), True)\\n incmarr.set_help(\\n _(\\\"Whether to include a separate marital box in the report\\\"))\\n menu.add_option(category_name, \\\"inc_marr\\\", incmarr)\\n\\n marrdisp = StringOption(_(\\\"Marriage\\\\nDisplay Format\\\"), \\\"%s $m\\\" % _MARR) \\n marrdisp.set_help(_(\\\"Display format for the marital box.\\\"))\\n menu.add_option(category_name, \\\"marr_disp\\\", marrdisp)\\n\\n ##################\\n category_name = _(\\\"Replace\\\")\\n\\n repldisp = TextOption(\\n _(\\\"Replace Display Format:\\\\n'Replace this'/' with this'\\\"),\\n [])\\n repldisp.set_help(_(\\\"i.e.\\\\nUnited States of America/U.S.A\\\"))\\n menu.add_option(category_name, \\\"replace_list\\\", repldisp)\\n\\n ##################\\n category_name = _(\\\"Size\\\")\\n\\n self.scale = EnumeratedListOption(_(\\\"Scale tree to fit\\\"), 0)\\n self.scale.add_item( 0, _(\\\"Do not scale tree\\\"))\\n self.scale.add_item( 1, _(\\\"Scale tree to fit page width only\\\"))\\n self.scale.add_item( 2, _(\\\"Scale tree to fit the size of the page\\\"))\\n self.scale.set_help(\\n _(\\\"Whether to scale the tree to fit a specific paper size\\\")\\n )\\n menu.add_option(category_name, \\\"scale_tree\\\", self.scale)\\n self.scale.connect('value-changed', self.__check_blank)\\n\\n if \\\"BKI\\\" not in self.name.split(\\\",\\\"):\\n self.__onepage = BooleanOption(_(\\\"Resize Page to Fit Tree size\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"Note: Overrides options in the 'Paper Option' tab\\\"\\n ), \\n False)\\n self.__onepage.set_help(\\n _(\\\"Whether to resize the page to fit the size \\\\n\\\"\\n \\\"of the tree. Note: the page will have a \\\\n\\\"\\n \\\"non standard size.\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"With this option selected, the following will happen:\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"With the 'Do not scale tree' option the page\\\\n\\\"\\n \\\" is resized to the height/width of the tree\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"With 'Scale tree to fit page width only' the height of\\\\n\\\"\\n \\\" the page is resized to the height of the tree\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"With 'Scale tree to fit the size of the page' the page\\\\n\\\"\\n \\\" is resized to remove any gap in either height or width\\\"\\n ))\\n menu.add_option(category_name, \\\"resize_page\\\", self.__onepage)\\n self.__onepage.connect('value-changed', self.__check_blank)\\n else:\\n self.__onepage = None\\n \\n self.box_Y_sf = NumberOption(_(\\\"inter-box Y scale factor\\\"),\\n 1.00, 0.10, 2.00, 0.01)\\n self.box_Y_sf.set_help(_(\\\"Make the inter-box Y bigger or smaller\\\"))\\n menu.add_option(category_name, \\\"box_Yscale\\\", self.box_Y_sf)\\n \\n self.box_shadow_sf = NumberOption(_(\\\"box shadow scale factor\\\"),\\n 1.00, 0.00, 2.00, 0.01) # down to 0\\n self.box_shadow_sf.set_help(_(\\\"Make the box shadow bigger or smaller\\\"))\\n menu.add_option(category_name, \\\"shadowscale\\\", self.box_shadow_sf)\\n \\n \\n ##################\\n category_name = _(\\\"Include\\\")\\n\\n self.title = EnumeratedListOption(_(\\\"Report Title\\\"), 0)\\n self.title.add_item( 0, _(\\\"Do not include a title\\\"))\\n self.title.add_item( 1, _(\\\"Descendant Chart for [selected person(s)]\\\"))\\n if self.name.split(\\\",\\\")[0] != _RPT_NAME:\\n self.title.add_item(2,\\n _(\\\"Family Chart for [names of chosen family]\\\"))\\n if self.showparents.get_value():\\n self.title.add_item(3,\\n _(\\\"Cousin Chart for [names of children]\\\"))\\n self.title.set_help(_(\\\"Choose a title for the report\\\"))\\n menu.add_option(category_name, \\\"report_title\\\", self.title)\\n self.showparents.connect('value-changed', self.__Title_enum)\\n\\n border = BooleanOption(_('Include a border'), False)\\n border.set_help(_(\\\"Whether to make a border around the report.\\\"))\\n menu.add_option(category_name, \\\"inc_border\\\", border)\\n\\n prnnum = BooleanOption(_('Include Page Numbers'), False)\\n prnnum.set_help(_(\\\"Whether to include page numbers on each page.\\\"))\\n menu.add_option(category_name, \\\"inc_pagenum\\\", prnnum)\\n\\n self.__blank = BooleanOption(_('Include Blank Pages'), True)\\n self.__blank.set_help(_(\\\"Whether to include pages that are blank.\\\"))\\n menu.add_option(category_name, \\\"inc_blank\\\", self.__blank)\\n \\n #category_name = _(\\\"Notes\\\")\\n\\n self.usenote = BooleanOption(_('Include a note'), False)\\n self.usenote.set_help(\\n _(\\\"Whether to include a note on the report.\\\")\\n )\\n menu.add_option(category_name, \\\"inc_note\\\", self.usenote)\\n\\n self.notedisp = TextOption(_(\\\"Note\\\"),[])\\n self.notedisp.set_help(_(\\\"Add a note\\\"\\n \\\"\\\\n\\\\n$T inserts today's date\\\"))\\n menu.add_option(category_name, \\\"note_disp\\\", self.notedisp)\\n\\n locals = NoteType(0)\\n notelocal = EnumeratedListOption(_(\\\"Note Location\\\"), 2)\\n for num, text in locals.note_locals():\\n notelocal.add_item( num, text )\\n notelocal.set_help(_(\\\"Where to place the note.\\\"))\\n menu.add_option(category_name, \\\"note_place\\\", notelocal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be5fc8cd7b966d59506d17ad558f7fa6\",\n \"score\": \"0.5433564\",\n \"text\": \"def printMenu():\\n print(\\\"\\\\nBienvenido\\\")\\n print(\\\"1- Cargar Datos\\\")\\n print(\\\"2- Ranking de peliculas\\\")\\n print(\\\"3- Conocer un director\\\")\\n print(\\\"4- Conocer un actor\\\")\\n print(\\\"5- Entender un genero\\\")\\n print(\\\"6- Crear ranking\\\")\\n print(\\\"0- Salir\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101293013c58d8146c0773ff2ec62dfd\",\n \"score\": \"0.54217243\",\n \"text\": \"def generic_menu(diccionario): \\n while(1):\\n for valor in diccionario.items():\\n print(valor[0])\\n respuesta = input(\\\"Opción: \\\")\\n if respuesta in diccionario:\\n pasar = diccionario[respuesta]\\n print(operacion(respuesta, pasar[0], pasar[1]))\\n elif respuesta == \\\" \\\":\\n break\\n else:\\n print(\\\"Opción no válida.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8634c37ff377db2fd7d83e4255518c85\",\n \"score\": \"0.53807473\",\n \"text\": \"def main_menu():\\n build_menu()\\n choice = input(\\\">> \\\")\\n\\n if choice == \\\"1\\\":\\n annex_habitat(keahua)\\n\\n if choice == \\\"2\\\":\\n release_animal(keahua)\\n\\n if choice == \\\"3\\\":\\n feed_animal(keahua)\\n\\n if choice == \\\"4\\\":\\n add_plant(keahua)\\n\\n if choice == \\\"5\\\":\\n build_facility_report(keahua)\\n pass\\n\\n if choice != \\\"6\\\":\\n main_menu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86d3df9bb99b279629580c6dffdc7ef\",\n \"score\": \"0.5380118\",\n \"text\": \"def display_menu_option():\\r\\n print(\\\"\\\\n---- MENU ----\\\\n\\\")\\r\\n print(\\\"Here are your choices:\\\")\\r\\n print(\\\"1) Display Team Stats\\\")\\r\\n print(\\\"2) Quit\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a7a33771f1a1b95eebf569a2160a5f\",\n \"score\": \"0.53780174\",\n \"text\": \"def offerMenuItems(choices):\\n\\n print(\\\"Choose From The Following Options: \\\")\\n # prints out a numbered list of choices\\n for i in range(len(choices)):\\n print(('%s, %s' %(i, choices[i])))\\n\\n # prompts the user to select a number from the menu list\\n enter = int(input(\\\"Enter your desired choice number: \\\"))\\n choice = (\\\"You entered %d (for %s)\\\" % (enter, choices[enter]))\\n #print(choices)\\n\\n # returns choice which prints out the number you chose and the associated menu item\\n return choices[enter]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb7e58aacb40e4ebbe5eb290ec0f8aa\",\n \"score\": \"0.5372298\",\n \"text\": \"def print_menu():\\n\\n os.system('clear') # For Linux/OS X\\n os.system('cls') # For Windows\\n print(30 * \\\"-\\\", \\\"MENU\\\", 30 * \\\"-\\\")\\n print(\\\"1. raj2jar \\\")\\n print(\\\"2. collect_sources \\\")\\n print(\\\"3. rebuild_packages \\\")\\n print(\\\"4. download_tests \\\")\\n print(\\\"\\\\n0. Exit\\\")\\n print(67 * \\\"-\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe683ea9d91a187e121db21cbe71339\",\n \"score\": \"0.53706336\",\n \"text\": \"def sub_menu():\\n menu_selection(sub_prompt, sub_dispatch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ebde36be32f4a5feba81fe16b0ac963\",\n \"score\": \"0.53574353\",\n \"text\": \"def user_menu(outlet_num):\\n while True:\\n print('===Taking from Outlet ', outlet_num, '===')\\n i = 1\\n # Use this dictionary to store the item number and the name, to identify the item when user inputs a number\\n user_dict = {}\\n # User Menu\\n for k in recipes:\\n a, low, miss = quantity_check(k)\\n if not a:\\n print_deficiency(k, low, miss)\\n else:\\n print(i, \\\".\\\", k)\\n user_dict[i] = k\\n i += 1\\n print(i, \\\". Refill\\\")\\n\\n sel = take_input(outlet_num, int, i)\\n\\n if sel is i:\\n # User selects to refill\\n refill_item(outlet_num)\\n else:\\n if dispense(user_dict[sel]):\\n print(user_dict[sel], \\\" Dispensed from outlet\\\", outlet_num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3db9d2c01c95d37efb25dcaefcb92526\",\n \"score\": \"0.53563076\",\n \"text\": \"def print_menu(title, available_options, exit_message='Exit'):\\n compilated_message = available_options + [exit_message]\\n\\n print(title + ':')\\n\\n for i, option in enumerate(compilated_message):\\n print('{}{}. {}'.format('\\\\t', (i+1) % len(compilated_message), option))\\n\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ca9dbb446a5847e1ceef9b9715aa6c\",\n \"score\": \"0.53472716\",\n \"text\": \"def format_menus(menus):\\n menu = ''\\n idx = 0\\n for name, items in menus.items():\\n menu += f\\\"\\\\n*{name}*\\\\n\\\"\\n menu += \\\"\\\".join(f\\\":{poll_opt}: {item}\\\\n\\\"\\n for poll_opt, item in zip(DIGITS[idx:], items))\\n idx += len(items)\\n\\n # menu contains a leading and trailing new line, but slack doesn't care\\n\\n return menu, DIGITS[:idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3971ee4d29aa7c040b21f1b10e74727f\",\n \"score\": \"0.5345263\",\n \"text\": \"def menu(menu_data):\\n print(\\\"\\\\nPlease choose one of the following options:\\\")\\n\\n for index, menu_item in enumerate(menu_data): # Prints the menu user text\\n print(f\\\"{index + 1}) {menu_item[0]}\\\")\\n\\n choice = int(input(\\\"> \\\")) - 1\\n\\n if choice in range(len(menu_data)): # Ensure that option chosen is within menu range, this\\n return menu_data[choice][1], menu_data[choice][2] # handles choosing 0, which would return menu_data[-1][1]\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21d1948db83f8aaaafa180499c2fa81e\",\n \"score\": \"0.53317946\",\n \"text\": \"def __repr__(self):\\n return \\\"<MenuItem %s for %s at %s>\\\" % (\\n self.name,\\n self.course.name,\\n self.course.meal.menu.hall.name,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aba16dbdedb6bdb910a21d5c1c3963d\",\n \"score\": \"0.5313438\",\n \"text\": \"def print_menu():\\n print('''\\n Options\\n 1 - Display reading list\\n 2 - Add title to list\\n 3 - Remove title from list\\n 4 - Save list to file\\n 5 - Load list from file\\n 6 - Exit\\n ''')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96a540f89acb813fc467ab2126f6e50\",\n \"score\": \"0.53124\",\n \"text\": \"def display_menu():\\n print()\\n print(\\\"Commands:\\\")\\n print(\\\" quit - Quit\\\")\\n print(\\\" new - Create new account\\\")\\n print(\\\" display - Display account information\\\")\\n print(\\\" deposit - Desposit money\\\")\\n print(\\\" check - Write a check\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7ebca75d742313412a47a0ea8d542f\",\n \"score\": \"0.5304529\",\n \"text\": \"def main_menu(self):\\n\\n while True:\\n print(\\\"\\\\n----- MAIN MENU ----- \\\")\\n print(\\\"\\\\n1. Create Account\\\")\\n print(\\\"\\\\n2. Account Details\\\")\\n print('\\\\n3. Close Account')\\n print('\\\\n4. Transaction Menu')\\n print('\\\\n5. Close application')\\n print('\\\\n\\\\n')\\n try:\\n option = int(input('Enter your option ...: '))\\n\\n if option == 1:\\n self.create_account()\\n elif option == 2:\\n self.show_details()\\n elif option == 3:\\n self.close_account()\\n elif option == 4:\\n self.transaction_menu()\\n elif option == 5:\\n print(\\\"\\\\n*****************\\\\nSee You Next Time!\\\\n*****************\\\\n\\\")\\n self.exit_menu()\\n\\n except TypeError as err:\\n print(\\\"\\\\n*************\\\\nInvalid input!\\\\n*************\\\\nPlease try again!\\\\n\\\")\\n # Log\\n logger.error(\\\"Invalid input: '{}' - ValueError message: {} from main_menu() at\\\".format(option, err))\\n # Back to main menu\\n self.main_menu()\\n except ValueError as err:\\n print(\\\"\\\\n*************\\\\nInvalid input!\\\\n*************\\\\nPlease try again!\\\\n\\\")\\n # Log\\n logger.error(\\\"Invalid input: '{}' - ValueError message: {} from main_menu() at\\\".format(option, err))\\n # Back to main menu\\n self.main_menu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f51d9719992a222896017895d8ee7c\",\n \"score\": \"0.5282117\",\n \"text\": \"def print_menu():\\n print(\\\"\\\"\\\"\\n 1. Print Pokedex\\n 2. Print Pokemon by Name\\n 3. Print Pokemon by Number\\n 4. Count Pokemon with Type\\n 5. Print Average Pokemon Combat Points\\n 6. Quit\\n \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e8ea60b55d24d7758fc3bca0c99ccc\",\n \"score\": \"0.5273748\",\n \"text\": \"def sub_menu():\\n\\n ccTremb = db.connect()\\n cStation = db.stations(ccTremb)\\n sSub_menu = \\\"\\\"\\\"\\n\\nHOUSING SUB-MENU (H):\\n.: Exit\\n1: Add a housing\\n4: Pretty print\\n\\n\\\"\\\"\\\" # Closes the multi=line txt\\n\\n bExit = False\\n while bExit == False: # loop until the user exits\\n print(sSub_menu)\\n sInput = input().upper()\\n\\n # User has made their choice. Now, process it.\\n if sInput == \\\".\\\": # Exit\\n bExit = True\\n elif sInput == \\\"1\\\": # New\\n add_housing(ccTremb)\\n# elif sInput == \\\"2\\\": # All the stations\\n# view_all(ccTremb)\\n# elif sInput == \\\"3\\\": # View single\\n# view_single(ccTremb)\\n elif sInput == \\\"4\\\": # Formats the little bit of data\\n pretty_print_single(ccTremb)\\n else:\\n bExit = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa39b5b022aab92998b87f021379f013\",\n \"score\": \"0.52665216\",\n \"text\": \"def print_menu():\\n print(\\\"Calculator menu:\\\")\\n print(\\\" + for adding a rational number\\\")\\n print(\\\" c to clear the calculator\\\")\\n print(\\\" u to undo the last operation\\\")\\n print(\\\" x to close the calculator\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dca98229e7ab89b9b4677a31d8eaee3\",\n \"score\": \"0.5254829\",\n \"text\": \"def print_menu(self):\\n print(\\\"1. Youtube\\\")\\n print(\\\"2. ott\\\")\\n print(\\\"3. imdb\\\")\\n print(\\\"4. Exit\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0693428135c9a68e033575c89ebf67e8\",\n \"score\": \"0.525311\",\n \"text\": \"def actions(self):\\n menu = {\\\"title\\\": _(\\\"menu_actions\\\", default=\\\"Actions\\\")}\\n children = menu[\\\"children\\\"] = []\\n submenu = self.factories()\\n if submenu:\\n children.append(submenu)\\n submenu = self.organise()\\n if submenu:\\n children.append(submenu)\\n\\n if children:\\n return menu\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111f16002e664206ebb4c9800f47ccf7\",\n \"score\": \"0.5242372\",\n \"text\": \"def ask_questions(sub_menu):\\r\\n good_answer = False\\r\\n while not good_answer:\\r\\n print(\\\"\\\\nYour options are:\\\\n\\\")\\r\\n\\r\\n for key, value in sub_menu.items():\\r\\n # print the options looking pretty\\r\\n value = value.__name__\\r\\n option_list = value.split(\\\"_\\\")\\r\\n option_string = \\\" \\\".join(option_list)\\r\\n option_string = option_string.title()\\r\\n s = f\\\"{key}.) {option_string}\\\"\\r\\n print(s)\\r\\n\\r\\n answer = input(\\\"\\\\nEnter letter according to your choice: \\\")\\r\\n\\r\\n # check to see if the \\\"answer\\\" letter is one of the keys in the sub_menu dictionary\\r\\n good_answer = sub_menu.get(answer, False)\\r\\n\\r\\n return sub_menu[answer]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8b5e3cfa086930adc41319ae8bc84d\",\n \"score\": \"0.524088\",\n \"text\": \"def display_main_menu(self) -> int:\\n\\n # display main menu until user makes acceptable choice\\n user_choice=0\\n while(True):\\n # clear screen\\n if os.name == 'nt':\\n _ = os.system('cls')\\n else:\\n _ = os.system('clear')\\n # display menu\\n print(\\\"Main menu\\\",\\\"=========\\\",sep='\\\\n')\\n for k in self.menu_items.keys():\\n print(str.format(\\\"{0}) {1}\\\",k,self.menu_items[k][0]))\\n user_input=input(\\\"\\\\nEnter choice: \\\")\\n try:\\n user_choice = int(user_input)\\n if user_choice in self.menu_items.keys():\\n return user_choice\\n except ValueError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0c37eaaafb23ec481589ba04cffd3e3\",\n \"score\": \"0.52383924\",\n \"text\": \"def input_message_menu(self):\\n print()\\n choice = \\\"\\\\n\\\".join(\\n [\\n f\\\"{colored(index, 'yellow')}. {item}\\\"\\n for index, item in enumerate(self.menu_choices, 1)\\n ]\\n )\\n print(choice)\\n print(f\\\"{s.CR}{self.footer}\\\")\\n if s.ERROR:\\n print(self.msg_error)\\n print(self.quit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a72abb6eb158b8e363352f49fc0af7e\",\n \"score\": \"0.5236311\",\n \"text\": \"def show_menu_pan():\\n MENU_STRING = ''\\n for key in MENU_DICT.keys(): # use .keys() .values() .items()\\n MENU_STRING += '{:>2}. {:<16} {:.^10} {:5,} won'.format(\\n key,\\n MENU_DICT[key][0],\\n '.',\\n MENU_DICT[key][1]) + '\\\\n'\\n print(MENU_PAN_FORMAT %MENU_STRING)\\n print(NOW)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b2468dc26b7755515f6fc999ad2338d\",\n \"score\": \"0.5230493\",\n \"text\": \"def menu_options() -> None:\\n print(\\\"================== MENU ==================\\\")\\n print(\\\"0 to exit the application\\\")\\n print(\\\"[1] Add a new product\\\")\\n print(\\\"[2] Get all product from the store\\\")\\n print(\\\"[3] Get a product details by product id\\\")\\n print(\\\"[4] Remove a product by product id\\\")\\n print(\\\"[5] Remove all product by their name\\\")\\n print(\\\"[6] Increase the product in the store\\\")\\n print(\\\"[7] Decrease the product from the store\\\")\\n print(\\\"[8] Change a product name\\\")\\n print(\\\"[9] Change a product price\\\")\\n print(\\\"==========================================\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca42442a44ce5bd0bb826f5aa2456904\",\n \"score\": \"0.52237767\",\n \"text\": \"def inner_menu(user: User):\\n clear()\\n print(\\\"Welcome back {name}!\\\".format(name=user.username))\\n while True:\\n print(\\\"Choose an option\\\",\\n \\\"================\\\",\\n \\\"1. Recipe Book\\\",\\n \\\"2. View Pantry\\\",\\n \\\"3. Edit User Info\\\", # TODO\\n \\\"4. Log Out\\\", sep=\\\"\\\\n\\\") # TODO\\n\\n try:\\n op = int(input(\\\"> \\\"))\\n\\n if op == 1:\\n clear()\\n if recipe_menu(user):\\n clear()\\n continue\\n break\\n elif op == 2:\\n clear()\\n if pantry_menu(user):\\n clear()\\n continue\\n break\\n elif op == 3:\\n clear()\\n if user_edit_menu(user):\\n continue\\n elif op == 4:\\n\\n clear()\\n const = \\\". . . \\\"\\n message = \\\"Logging you out\\\"\\n message = message + const\\n print(message)\\n\\n for i in range(3):\\n time.sleep(0.5)\\n # remove a dot form message\\n message = message[:-2]\\n # clear and reprint message\\n clear()\\n print(message)\\n time.sleep(0.5)\\n # at the end clear and return\\n clear()\\n return True\\n\\n except ValueError:\\n clear()\\n print(\\\"Incorrect option. Try again.\\\\n\\\")\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c8402e65680eedb52c4fe0547979343\",\n \"score\": \"0.5221359\",\n \"text\": \"def menu_categories(scorepad):\\n\\n # available_choices = []\\n menu_list = []\\n score_status = dict\\n\\n score_status = scorepad_available_scores(scorepad)\\n\\n for _ in score_status['AVAILABLE']:\\n menu_list.append(menu_items[_])\\n\\n # Append bonus counter since it does not have track prefix\\n menu_list.append([14, 'H - Five of a Kind Bonus'])\\n\\n print('=' * 50)\\n\\n print('MENU LIST')\\n\\n for idx, _ in enumerate(sorted(menu_list)):\\n\\n if (idx + 1) % 4 == 0:\\n print(_[1])\\n else:\\n pad = 25 - len(_[1])\\n print(_[1], end=(' ' * pad))\\n\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab0244bb5b1b273e9b344e4d70dcd35\",\n \"score\": \"0.5221142\",\n \"text\": \"def makeMenuList(self,menu):\\n try:\\n l = list()\\n \\n #index of current line < len(menu)\\n line = 0\\n \\n #keep in mind the last folder name and the line index, when this name firt occurred\\n lastValue = None\\n startLine = 0\\n \\n # as long as we have elements in the loop\\n while line < len(menu):\\n #access current line\\n parts = menu[line]\\n \\n # if we encounter a tree leaf and last node was also a tree leaf\\n # we just add this leaf and don't care for children\\n if len(menu[line]) == 1 and line == startLine:\\n #append leaf to list\\n lastValue = parts[0]\\n l.append({lastValue: [\\\"end\\\"]})\\n \\n #assume that line is not the last line, if so, ignore the exception, while loop will terminate afterwards\\n try:\\n lastValue = menu[line+1][0]\\n except:\\n pass\\n \\n #for further processing: ignore leaf, as it has already been appended to the list\\n line += 1\\n startLine = line\\n #continue while loop\\n continue \\n # if we encounter a tree leaf and last node was not a tree leaf\\n # we first care for the prior nodes and then just add this leaf\\n elif len(menu[line]) == 1 and line != startLine:\\n # look now into the children of a\\n # recursion would get in the example\\n # /b\\n # /c\\n # because /a is thrown away each iteration\\n children = self.makeMenuList(menu[startLine:line])\\n #assuming that there is an entry in the list corresponding to last value\\n #why try?\\n #because \\n #/1\\n #/a/b\\n #would go wrong\\n try:\\n l[len(l) -1][lastValue].extend(children)\\n #in case this entry is not there: add it!\\n except:\\n l.append({lastValue: children})\\n \\n #append leaf to list\\n lastValue = parts[0]\\n l.append({lastValue: [\\\"end\\\"]})\\n \\n #assume that line is not the last line, if so, ignore the exception, while loop will terminate afterwards\\n try:\\n lastValue = menu[line+1][0]\\n except:\\n pass\\n \\n #for further processing: ignore leaf, as it has already been appended to the list\\n line += 1\\n startLine = line\\n #continue while loop\\n continue\\n \\n \\n # if we are in the first loop, initialise variables\\n elif lastValue == None:\\n #save folder name\\n lastValue = parts[0]\\n startLine = 0\\n #start with root entry\\n dictionary = {lastValue: list()}\\n l.append(dictionary)\\n #we have encountered a folder name, deviating from the folder name we had before\\n #example\\n #/a/b\\n #/a/c\\n #/e/d\\n #remark: / is no more present due to split(\\\"/\\\")!\\n elif lastValue != parts[0]:\\n # look now into the children of a\\n # recursion would get in the example\\n # /b\\n # /c\\n # because /a is thrown away each iteration\\n children = self.makeMenuList(menu[startLine:line])\\n #assuming that there is an entry in the list corresponding to last value\\n #why try?\\n #because \\n #/1\\n #/a/b\\n #would go wrong\\n try:\\n l[len(l) -1][lastValue].extend(children)\\n #in case this entry is not there: add it!\\n except:\\n l.append({lastValue: children})\\n \\n #start remembering the new folder\\n lastValue = parts[0]\\n startLine = line\\n dictionaryNew = {lastValue: list()}\\n l.append(dictionaryNew)\\n \\n lastWasLeafFlag = False\\n \\n #throw away first column of current line\\n menu[line] = menu[line][1:]\\n #increase line index\\n line += 1\\n \\n #do the same as in loop, because the last line might not have been added yet.\\n if startLine < line:\\n children = self.makeMenuList(menu[startLine:line])\\n try:\\n l[len(l) -1][lastValue].extend(children)\\n except:\\n l.append({lastValue: children})\\n \\n return l\\n except Exception as inst:\\n# self.err.rethrowException(inst)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1068a8104b776ad5c4494e4c01501f99\",\n \"score\": \"0.5214295\",\n \"text\": \"def display(self, menu):\\n\\n self.runner.setCurrentMenu(menu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f34b5b3c69547cc63728ec60580471c\",\n \"score\": \"0.5212197\",\n \"text\": \"def show_menu():\\n print(\\\"User Menu \\\\n\\\"\\n \\\"1. Create Account \\\\n\\\"\\n \\\"2. Login \\\\n\\\"\\n \\\"3. Search \\\\n\\\"\\n \\\"4. Insert \\\\n\\\"\\n \\\"5. Update \\\\n\\\"\\n \\\"6. Delete \\\\n\\\"\\n \\\"7. Exit \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7daa17bdabb4fa1a23eab5f017938e6a\",\n \"score\": \"0.5207583\",\n \"text\": \"def print_menu_tasks():\\n print(\\\"\\\"\\\"\\n Choice Menu\\n 1) Display Current Product List\\n 2) Add Product to List\\n 3) Save Product List to File and Exit Program\\n \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c95a11f5b112bb44bf86e663a3a2983\",\n \"score\": \"0.51888365\",\n \"text\": \"def transaction_menu(self):\\n\\n while True:\\n print(\\\"\\\\n ----- TRANSACTION MENU ----- \\\")\\n print(\\\"\\\\n1. Deposit Amount\\\")\\n print('\\\\n2. Withdraw Amount')\\n print('\\\\n3. Back to Main Menu')\\n print('\\\\n\\\\n')\\n try:\\n option2 = int(input('Enter your option ...: '))\\n if option2 == 1:\\n self.deposit_amount()\\n elif option2 == 2:\\n self.withdraw_amount()\\n elif option2 == 3:\\n self.main_menu()\\n\\n except TypeError as err:\\n print(\\\"\\\\n*************\\\\nInvalid input!\\\\n*************\\\\nPlease try again!\\\\n\\\")\\n # Log\\n logger.error(\\\"Invalid input: '{}' - ValueError message: {} from transaction_menu()\\\".format(option2, err))\\n # Back to transaction menu\\n self.transaction_menu()\\n except ValueError as err:\\n print(\\\"\\\\n*************\\\\nInvalid input!\\\\n*************\\\\nPlease try again!\\\\n\\\")\\n # Log\\n logger.error(\\\"Invalid input: '{}' - ValueError message: {} from transaction_menu()\\\".format(option2, err))\\n # Back to transaction menu\\n self.transaction_menu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd87277dc8160ff45c5eb0c8b014bb5\",\n \"score\": \"0.5184358\",\n \"text\": \"def printMainMenu():\\n print \\\"\\\"\\\"\\n1.주소정보를 입력한다.\\n2.주소정보를 삭제한다.\\n3.주소정보를 수정한다.\\n4.전체 주소정보를 출력한다.\\n5.전체 주소정보를 파일에 저장한다.\\n6.파일에서 전체 주소정보를 불러온다.\\n7.전체 주소정보를 DB에 저장한다.\\n9.DB에서 전체 주소정보를 불러온다.\\n0.종료한다.\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664b1fca060e70c381d8dbf25997ab02\",\n \"score\": \"0.5183275\",\n \"text\": \"def get_menu(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d45488ab7455c4dcf745bf7ec9f7850\",\n \"score\": \"0.5180017\",\n \"text\": \"def show_menu():\\n if not pf.GUI.menu.item(_menu):\\n create_menu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be38f5e9518e72d0ddc9263bdea8e2db\",\n \"score\": \"0.51734394\",\n \"text\": \"def __repr__(self):\\n return (\\\"MenuItem('{type}', '{user_name}', '{selector}', '{host}', \\\"\\n \\\"'{port}')\\\").format(**self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9d58882a86fdb9de7a200470c42a97\",\n \"score\": \"0.51710016\",\n \"text\": \"def show_menu(entries, **kwargs):\\n global _AUTO\\n hdr = kwargs.get('hdr', \\\"\\\")\\n note = kwargs.get('note', \\\"\\\")\\n dft = kwargs.get('dft', \\\"\\\")\\n fzf = kwargs.pop('fzf', True)\\n compact = kwargs.get('compact', False)\\n returns = kwargs.get('returns', \\\"name\\\")\\n limit = kwargs.get('limit', None)\\n dft = kwargs.get('dft', None)\\n msg = []\\n if limit:\\n return show_limit(entries, **kwargs)\\n def show_banner():\\n banner = \\\"-- MENU\\\"\\n if hdr:\\n banner += \\\": \\\" + hdr\\n banner += \\\" --\\\"\\n msg.append(banner)\\n if _AUTO:\\n return\\n for i in entries:\\n msg.append(\\\" (%s) %s\\\" % (i.name, i.desc))\\n valid = [i.name for i in entries]\\n if type(dft) == int:\\n dft = str(dft)\\n if dft not in valid:\\n dft = None\\n if not compact:\\n show_banner()\\n if note and not _AUTO:\\n msg.append(\\\"[!] \\\" + note)\\n if fzf:\\n valid.append(FCHR)\\n if _AUTO:\\n valid.append(\\\"-d\\\")\\n msg.append(QSTR + kwargs.get('msg', \\\"Enter menu selection\\\"))\\n msg = os.linesep.join(msg)\\n entry = None\\n while entry not in entries:\\n choice = ask(msg, vld=valid, dft=dft, qstr=False)\\n if _AUTO and choice == \\\"-d\\\":\\n choice = dft\\n if choice == FCHR and fzf:\\n try:\\n from iterfzf import iterfzf\\n choice = iterfzf(reversed([\\\"%s\\\\t%s\\\" % (i.name, i.desc) for i in entries])).strip(\\\"\\\\0\\\").split(\\\"\\\\t\\\", 1)[0]\\n except:\\n warn(\\\"Issue encountered during fzf search.\\\")\\n match = [i for i in entries if i.name == choice]\\n if match:\\n entry = match[0]\\n if entry.func:\\n fresult = run_func(entry)\\n if \\\"func\\\" == returns:\\n return fresult\\n try:\\n return getattr(entry, returns)\\n except:\\n return getattr(entry, \\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991ab7ca22a43c53b5f00d790ef60408\",\n \"score\": \"0.51524115\",\n \"text\": \"def print_level(self):\\n\\n # First grab information about our snakes\\n disp_snake_coords = {}\\n for sb in self.snakebirds_l:\\n sb.compute_display_chars()\\n for (idx, coords) in enumerate(sb.cells):\\n disp_snake_coords[coords] = (sb, idx)\\n\\n # Grab information about pushables and pushable decorations\\n disp_pushable_coords = {}\\n disp_pushable_decorations = {}\\n for (push_num, pushable) in self.pushables.items():\\n for coords in pushable.cells:\\n disp_pushable_coords[coords] = '{}{}{}'.format(\\n color_pushables[push_num],\\n PUSH_CHARS[push_num],\\n color_reset,\\n )\\n disp_pushable_decorations.update(pushable.get_decoration_chars())\\n\\n # Find out what color our exit should be\\n if len(self.fruits) == 0:\\n color_wall[TYPE_EXIT] = color_wall_exit_open\\n else:\\n color_wall[TYPE_EXIT] = color_wall_exit_closed\\n\\n # Now loop through and print the level\\n print(self.desc)\\n for (y, row) in enumerate(self.cells):\\n for (x, col) in enumerate(row):\\n if (x,y) in disp_snake_coords:\\n (sb, idx) = disp_snake_coords[(x,y)]\\n sys.stdout.write(sb.get_print_char(idx))\\n elif (x,y) in disp_pushable_coords:\\n sys.stdout.write(disp_pushable_coords[(x,y)])\\n elif (x,y) in self.fruits:\\n sys.stdout.write('{}{}'.format(color_fruit, 'F'))\\n elif col == TYPE_EMPTY and (x, y) in disp_pushable_decorations:\\n sys.stdout.write(disp_pushable_decorations[(x,y)])\\n else:\\n sys.stdout.write('{}{}'.format(color_wall[col], TYPE_DISP_MAP[col]))\\n sys.stdout.write(\\\"{}\\\\n\\\".format(color_reset));\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0c6b0f90f3c29cb105fd61996957cc\",\n \"score\": \"0.51320636\",\n \"text\": \"def menu_report(self):\\n\\n print(\\\"*********** Report Menu ***********\\\")\\n print(\\\"Welcome, please select your action and press Enter\\\")\\n print(\\\"List of all players:\\\")\\n print(\\\" 1. Alphabetical order\\\")\\n print(\\\" 2. Ranking order\\\")\\n print(\\\"List of all players for one tournament:\\\")\\n print(\\\" 3. Alphabetical order\\\")\\n print(\\\" 4. Ranking order\\\")\\n print(\\\"5.List of all tournaments\\\")\\n print(\\\"6.List of all rounds in a tournament\\\")\\n print(\\\"7.List of all matches in a tournament\\\")\\n print(\\\"8.Return to start menu\\\")\\n print(\\\"***********************************\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a374d2aab32cc7851f9332cdb685857\",\n \"score\": \"0.5131412\",\n \"text\": \"def show_section(section, prefix):\\n for key, value in section.items():\\n if self._is_section(value):\\n # Go to next level\\n print(prefix + \\\"[\\\" + key + \\\"]\\\")\\n show_section(section[key], prefix + \\\" \\\")\\n else:\\n print(prefix + str(key) + \\\":\\\" + repr(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecc54a61d059bad4bdb0963cec571cb\",\n \"score\": \"0.5127224\",\n \"text\": \"def show_menu():\\n print(\\\"Hello, there! Please choose from the following menu: \\\" + \\\"\\\\n\\\")\\n print(\\\"(1) Show current products and prices.\\\" + \\\"\\\\n\\\")\\n print(\\\"(2) Add a product to the list.\\\" + \\\"\\\\n\\\")\\n print(\\\"(3) Save to file and exit.\\\" + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1dfa9bfeffae059d4a21b9453786d43\",\n \"score\": \"0.5116037\",\n \"text\": \"def show_items():\\n with open('menu.txt', 'r') as file:\\n rest_menu = file.read().strip().split(', ')\\n return print('\\\\n'.join(rest_menu))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c29a97cce026450b081e3b6adbd1d0\",\n \"score\": \"0.5096008\",\n \"text\": \"def view_restaurant_menus(conn, chosen_restaurant_id):\\n cursor = conn.raw_connection().cursor()\\n cursor.callproc(\\\"view_restaurant_menus\\\", [chosen_restaurant_id])\\n results = list(cursor.fetchall())\\n print \\\"------------------------------------------------\\\"\\n print \\\"Menus for Restaurant \\\" + str(chosen_restaurant_id)\\n\\n all_menu_ids = []\\n for (menu_id, menu_type) in results:\\n all_menu_ids.append(menu_id)\\n print \\\"\\\\tID: \\\" + str(menu_id) + \\\"\\\\tType: \\\" + str(menu_type).capitalize()\\n\\n cursor.close()\\n\\n print \\\"------------------------------------------------\\\"\\n\\n choose_next_option(conn, chosen_restaurant_id, all_menu_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4315853cc8c934968e74b4fa7c60cc\",\n \"score\": \"0.5088995\",\n \"text\": \"def menu_help(args):\\n if not len(args):\\n for k in items:\\n print('{0:12}\\\\t{1}'.format(k, items[k][1]))\\n else:\\n if args in items:\\n print('{0:12}\\\\t{1}'.format(args, items[args][1]))\\n else:\\n print('No such command: {0}'.format(args))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58e3fa5d18286cf88f0d62f3604fb6b\",\n \"score\": \"0.50877094\",\n \"text\": \"def show_menu():\\n print(\\\"User Menu \\\\n\\\" \\\"1. Create Account \\\\n\\\" \\\"2. Login \\\\n\\\" \\\"3. EXIT \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70690279de56217867ccff06c78d3649\",\n \"score\": \"0.5085597\",\n \"text\": \"def main(dictionnaire):\\n\\n menu(dictionnaire)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84511d9b2925eacf123e8c90b1fbb65\",\n \"score\": \"0.50796336\",\n \"text\": \"def get_menu(self, request):\\r\\n menu_tree = []\\r\\n for menu_top in self.get_queryset().filter(parent_menu=None):\\r\\n if auth.has_perm(request=request, perm=menu_top.permission):\\r\\n menu_temp = {}\\r\\n menu_temp['id'] = menu_top.id\\r\\n menu_temp['name'] = menu_top.name\\r\\n menu_temp['code'] = menu_top.code\\r\\n menu_temp['icon'] = menu_top.icon\\r\\n menu_temp['url'] = menu_top.url\\r\\n menu_temp['sub_menu'] = self._get_child_menu_tree(request=request, menu=menu_top)\\r\\n menu_tree.append(menu_temp)\\r\\n return menu_tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac05a9fb264ab547267470f0abf12c3\",\n \"score\": \"0.50765556\",\n \"text\": \"def type_menu(self, tqualname):\\n menu = []\\n # Things we can do with tqualname\\n menu.append({\\n 'text': \\\"Find sub classes\\\",\\n 'title': \\\"Find sub classes of this class\\\",\\n 'href': self.search(\\\"+derived:%s\\\" % tqualname),\\n 'icon': 'type'\\n })\\n menu.append({\\n 'text': \\\"Find base classes\\\",\\n 'title': \\\"Find base classes of this class\\\",\\n 'href': self.search(\\\"+bases:%s\\\" % tqualname),\\n 'icon': 'type'\\n })\\n menu.append({\\n 'text': \\\"Find members\\\",\\n 'title': \\\"Find members of this class\\\",\\n 'href': self.search(\\\"+member:%s\\\" % tqualname),\\n 'icon': 'members'\\n })\\n menu.append({\\n 'text': \\\"Find references\\\",\\n 'title': \\\"Find references to this class\\\",\\n 'href': self.search(\\\"+type-ref:%s\\\" % tqualname),\\n 'icon': 'reference'\\n })\\n return menu\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3247a66866937788ed76e2f0a2f32430\",\n \"score\": \"0.5072173\",\n \"text\": \"def _menu(self, menus, finish_menus, game_objects):\\n old_menu = None\\n current_menu = internal.MenuIdentifiers.MAIN_MENU # The first menu displayed\\n self.clock.tick()\\n with self.interface.use('menu'):\\n while True: # Wait for the user to navigate through the menu system\\n if old_menu != current_menu:\\n old_menu = current_menu\\n self.interface.reset('menu')\\n menus[current_menu](game_objects) # Set up the current menu\\n self.interface.flush()\\n while True: # Wait for input from this menu\\n self.clock.tick(config.RENDER_FRAMERATE)\\n inputs = self.interface.inp()\\n self.interface.flush()\\n if inputs:\\n if len([c_m for c_m, input_type in inputs if input_type != internal.InputTypes.MENU]):\\n # If we have any non-MENU inputs\\n raise exceptions.ProgrammingException\\n current_menu = next(c_m for c_m, input_type in inputs if input_type == internal.InputTypes.MENU)\\n break\\n if current_menu in finish_menus:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f180e928c286a057cdbfbf3382ed9fb\",\n \"score\": \"0.50696373\",\n \"text\": \"def sub_menu():\\n\\n ccTremb = db.connect()\\n cDest = db.destinations(ccTremb)\\n sSub_menu = \\\"\\\"\\\"\\n\\nDESTINATIONS SUB-MENU (D):\\n.: Exit\\n1: Add an area\\n1A: Add MUNICIPALITIES semi-automatically\\n2: View 'children'\\n3: View single element\\n4: Pretty print a single element to a file\\n\\nB: Balance town: Small-scale maps (1:1M) INDUSTRY DRIVEN DEMOGRAPHICS\\nC: Balance city: Large-scale maps (1:50k) HOUSEHOLD DRIVEN DEMOGRAPHICS\\nD: Balance non-residential: Household demand for commercial/industrial zones.\\nE: Edit an entry\\nG: Assign geo-codes to 'children'. (Use once a parent has all its children)\\nH: Add housing: Adds household supply\\nM: Add a map\\nT: Types (workforce demand type summary for example 'OMB')\\nU: Update parent with children's data (demographics, vehilces, resources)\\nW: Add workplace: generates population demand in town.\\nX: Remove workplace: Can be used to fix 'mistakes'\\n\\n!: Reset demographics to zero.\\n\\\"\\\"\\\" # Closes the multi=line txt\\n\\n bExit = False\\n while bExit == False: # loop until the user exits\\n print(sSub_menu)\\n sInput = input().upper()\\n\\n # User has made their choice. Now, process it.\\n if sInput == \\\".\\\": # Exit\\n bExit = True\\n elif sInput == \\\"1\\\": # New\\n add_area_to_db(ccTremb)\\n elif sInput == \\\"1A\\\": # Semi-Automatic batch area\\n add_semi_auto(ccTremb)\\n elif sInput == \\\"2\\\": # View children\\n view_children(ccTremb)\\n elif sInput == \\\"3\\\": # Outputs all the data for one geo-code\\n view_single(ccTremb)\\n elif sInput == \\\"4\\\": # Pretty print single element\\n pretty_print_single(ccTremb)\\n # elif sInput == 5: # Sub-component which has its own entry\\n # add_sub_comp(ccTremb)\\n elif sInput == \\\"B\\\": # Industry driven balancing:\\n balance_town(ccTremb)\\n elif sInput == \\\"C\\\": # City balancing: Driven by available houses\\n balance_city(ccTremb)\\n elif sInput == \\\"D\\\": # Calculates housing demand for non-res area\\n household_demands(ccTremb)\\n elif sInput == \\\"E\\\": # Edit an entry\\n edit_entry(ccTremb)\\n elif sInput == \\\"G\\\": # Assign geocodes to all children\\n assign_geocodes(ccTremb)\\n elif sInput == \\\"H\\\": # Adds supply households\\n add_housing(ccTremb)\\n elif sInput == \\\"M\\\": # Add a map for referencing\\n add_map_to_db(ccTremb)\\n elif sInput == \\\"T\\\": # Outputs total of certain employer types.\\n type_summary(ccTremb)\\n elif sInput == \\\"U\\\": # Update parent with children's data\\n update_parent(ccTremb)\\n elif sInput == \\\"W\\\": # Add workplaces (farms, offices)\\n add_workplace(ccTremb)\\n elif sInput == \\\"X\\\": # Remove worplace\\n remove_workplace(ccTremb)\\n elif sInput == \\\"!\\\": # Remove demographics\\n reset_demographics(ccTremb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba5997ceb373c6628945a42691ca262\",\n \"score\": \"0.5059759\",\n \"text\": \"def display_menu(self, menu):\\n self.menu_win.clear()\\n self.loc_win.clear()\\n\\n items_per_col = 12\\n menu_len = len(menu.items)\\n items = menu.items\\n longest_item = max(items, key=len)\\n n_cols = math.ceil(menu_len/items_per_col)\\n\\n loc_str = 37*'_'\\n try:\\n self.loc_win.addstr(1,self.center_str(loc_str),\\n loc_str)\\n self.loc_win.addstr(1,self.center_str(f' {menu.parent.name.replace(\\\"*\\\",\\\"\\\")} '),\\n f' {menu.parent.name.replace(\\\"*\\\",\\\"\\\")} ')\\n self.loc_win.addstr(2,self.center_str(loc_str),\\n loc_str)\\n self.loc_win.addstr(2, self.center_str(f' {menu.name.replace(\\\"*\\\",\\\"\\\")} '),\\n f' {menu.name.replace(\\\"*\\\",\\\"\\\")} ')\\n except AttributeError:\\n self.loc_win.addstr(1,self.center_str(loc_str),\\n loc_str)\\n self.loc_win.addstr(1, self.center_str(f' {menu.name.replace(\\\"*\\\",\\\"\\\")} '),\\n f' {menu.name.replace(\\\"*\\\",\\\"\\\")} ')\\n self.loc_win.refresh()\\n\\n x_space = len(longest_item) + 3\\n if n_cols == 1:\\n if len(longest_item) <= 30:\\n x_init = self.x_center - 18\\n else:\\n x_init = self.x_center - len(longest_item)//2 - 3\\n else:\\n x_init = self.xmax//2 - (x_space*n_cols)//2\\n\\n for item in menu.items:\\n if item.startswith('*'):\\n if menu_len > items_per_col:\\n box_len = items_per_col + 2\\n else:\\n box_len = menu_len + 2\\n else:\\n if menu_len > items_per_col:\\n box_len = items_per_col + 1\\n else:\\n box_len = menu_len + 1\\n\\n if self.xmax % 2 == 0:\\n increment_box_edge = 0\\n else:\\n increment_box_edge = 0\\n rectangle(self.menu_win, 0, x_init-1, box_len+len(menu.instructions)+1,\\n self.xmax - x_init +1)\\n rectangle(self.menu_win,1, x_init, box_len + 1,\\n self.xmax-x_init-increment_box_edge)\\n\\n if menu.instructions:\\n for i, instructions in enumerate(menu.instructions):\\n if i == 0:\\n self.menu_win.addstr(i,self.center_str(instructions),\\n instructions)\\n else:\\n self.menu_win.addstr(box_len + 2 + (i-1), x_init+1,\\n instructions)\\n\\n self.display_module_info()\\n current_i = 0\\n while 1:\\n for i, item in enumerate(items):\\n if menu_len > items_per_col:\\n x = x_init + int(x_space*(i//items_per_col)) + 1\\n y = i - int((items_per_col)*((i)//items_per_col)) + 2\\n else:\\n if item.startswith('*'):\\n y = i + 3\\n else:\\n y = i + 2\\n x = self.center_str(longest_item) - 1\\n\\n item = item.strip(\\\"'\\\")\\n if menu.numbered:\\n item = f'{i+1}. {item}'\\n\\n if i == current_i:\\n self.menu_win.attron(curses.color_pair(1))\\n self.menu_win.addstr(y, x, item.replace('*',''))\\n self.menu_win.attroff(curses.color_pair(1))\\n else:\\n self.menu_win.addstr(y, x, item.replace('*',''))\\n self.menu_win.refresh()\\n\\n key = self.stdscr.getch()\\n if key == curses.KEY_UP:\\n if current_i%items_per_col == 0:\\n if current_i+items_per_col > menu_len:\\n current_i = menu_len - 1\\n else:\\n current_i += items_per_col-1\\n else:\\n current_i -= 1\\n\\n elif key == curses.KEY_DOWN:\\n if current_i == menu_len-1:\\n current_i = items_per_col*(n_cols-1)\\n elif (current_i+1)%items_per_col == 0:\\n current_i -= items_per_col-1\\n else:\\n current_i += 1\\n\\n elif key == curses.KEY_RIGHT:\\n if current_i < menu_len-items_per_col:\\n current_i += items_per_col\\n elif current_i < items_per_col*(n_cols-1):\\n current_i = menu_len - 1\\n\\n elif key == curses.KEY_LEFT:\\n if current_i >= items_per_col:\\n current_i -= items_per_col\\n\\n elif key == curses.KEY_ENTER or key in [10, 13]:\\n self.menu_win.clear()\\n if isinstance(menu.menues.get(items[current_i]), Menu):\\n return menu.menues.get(items[current_i])\\n else:\\n return items[current_i]\\n\\n elif key == curses.KEY_BACKSPACE or key in [8, 127]:\\n if menu.name not in ['Main Menu','Load Parameterfile']:\\n return menu.parent\\n\\n elif (key == ord('\\\\t') and\\n 'TAB/SPACE: view/edit parameterfile' in menu.instructions):\\n self.save(self.tempfile)\\n os.chdir(self.run_path)\\n curses.endwin()\\n subprocess.run(['less', '-S', self.tempfile])\\n os.chdir(self.dir_path)\\n curses.doupdate()\\n\\n elif (key == ord(' ') and\\n 'TAB/SPACE: view/edit parameterfile' in menu.instructions):\\n self.save(self.tempfile)\\n os.chdir(self.run_path)\\n curses.endwin()\\n subprocess.run(['emacs', '-nw', self.tempfile])\\n os.chdir(self.dir_path)\\n self.config = ConfigureParameterFile(f'{self.run_path}/{self.tempfile}')\\n curses.doupdate()\\n\\n elif key == ord('b') and 'b: previous list' in menu.instructions:\\n return 'b'\\n elif key == ord('n') and 'n: next list' in menu.instructions:\\n return 'n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac4ebcb609e3890188cabe659572d7db\",\n \"score\": \"0.50549054\",\n \"text\": \"def mainMenu(self):\\r\\n i = 0\\r\\n\\r\\n os.system('cls')\\r\\n print(\\\"\\\\npVault by Max\\\\n\\\")\\r\\n for item in self.__mainmenu:\\r\\n print(i, \\\": \\\\t\\\", item, sep='')\\r\\n i = i+1\\r\\n selection = input()\\r\\n\\r\\n if selection == \\\"0\\\":\\r\\n self.accountsMenu()\\r\\n elif selection == \\\"1\\\":\\r\\n self.findMenu()\\r\\n elif selection == \\\"2\\\":\\r\\n self.addEntryMenu()\\r\\n elif selection == \\\"3\\\":\\r\\n #print(\\\"Are you sure? y/n\\\")\\r\\n print(\\\"notice me senpai\\\")\\r\\n time.sleep(0.2)\\r\\n os.system('cls')\\r\\n return True\\r\\n else:\\r\\n print(\\\"menu does not exist\\\")\\r\\n self.mainMenu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482b381afa538bf5cd190c3d055170c3\",\n \"score\": \"0.5052712\",\n \"text\": \"def yamlmenu(self, name):\\n menudata = []\\n\\n for section in self.tree:\\n sec = list(section.values())[0]\\n id = sec['id']\\n if True:\\n menudata.append( sec )\\n else:\\n cus_sec = {\\n 'href' :sec['href'],\\n 'id' :sec['id'],\\n 'name' :sec['name'],\\n 'links' :[],\\n }\\n self.lg.debug('sec links %s', sec['links'])\\n for item in sec['links']:\\n href = item['href']\\n if self.perm[id][href] == True:\\n cus_sec['links'].append(item)\\n menudata.append( cus_sec )\\n #self.context['menudata_'+name] = menudata\\n self.context['menudata'] = menudata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e727f1419ef975a74848dd0276c0028\",\n \"score\": \"0.50492615\",\n \"text\": \"def setMenu(self, menu):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8c26e22969d704e06fc3a65ded7370\",\n \"score\": \"0.5027003\",\n \"text\": \"def show_team_menu():\\n team_menu_choice = True\\n while team_menu_choice: \\n print(\\\"\\\\n\\\")\\n menu_index = 1\\n for team in teams:\\n print(f\\\"{menu_index}) {team['team']}\\\")\\n menu_index += 1\\n try:\\n team_menu_choice = int(input(\\\"\\\\nEnter an option >\\\"))\\n if team_menu_choice > 0 and team_menu_choice <= len(teams):\\n show_team_stats(team_menu_choice - 1)\\n break\\n else:\\n raise ValueError()\\n except ValueError:\\n print(\\\"Invalid choice, try again\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4de639eca6bbd4e7a4cf57ba2e626ee\",\n \"score\": \"0.5016536\",\n \"text\": \"def main_menu():\\r\\n global menu\\r\\n option = \\\"start\\\"\\r\\n while True:\\r\\n sub_menu = menu[option]\\r\\n option = ask_questions(sub_menu)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993686,"cells":{"query_id":{"kind":"string","value":"66d4561fba67f4ed4ecf2a6249b8e5e1"},"query":{"kind":"string","value":"Initializes an ITS Spot module"},"positive_passages":{"kind":"list like","value":[{"docid":"b1bae47fe8f1a01d318d74babf874e1f","score":"0.0","text":"def __init__(self, RandomGen_seed, CF, SPOT_tree, msg_limit = 800000):\n self.random_generator = Random_generator(RandomGen_seed)\n self.CF = CF\n\n self.Travelmsgs = []\n self.BehaviorMsgs = []\n self.SPOT_tree = SPOT_tree\n self.SPOTBuffer = SPOTBuffer()\n\n self.header = True\n self.Travel_col_order = ['localtime', 'vehicle_ID', 'x', 'y', 'spd']\n self.Behavior_col_order = self.Travel_col_order + ['accel', 'heading', 'yawrate']\n #Limit of message list before printing to a file\n self.msg_limit = msg_limit\n\n self.FT_CONVERSION = 12 * 2.54 / 100 # converts feet to meters\n self.ACCEL_CONVERSION = 1 / 32.1740485564 # converts ft/s2 to g\n self.SPOT_SPD_CONVERSION = 1.609344 # converts mph to km/hr for ITS Spot Output\n self.SPOT_accel_threshold = self.CF.Strategy['SPOTaccelThreshold'] * 32.1740485564 # Convert g to ft/s^2 for ITS Spot Output\n self.SPOT_yawrate_threshold = self.CF.Strategy['SPOTyawrateThreshold'] * 32.1740485564 # Convert g to ft/s^2 for ITS Spot Output","title":""}],"string":"[\n {\n \"docid\": \"b1bae47fe8f1a01d318d74babf874e1f\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, RandomGen_seed, CF, SPOT_tree, msg_limit = 800000):\\n self.random_generator = Random_generator(RandomGen_seed)\\n self.CF = CF\\n\\n self.Travelmsgs = []\\n self.BehaviorMsgs = []\\n self.SPOT_tree = SPOT_tree\\n self.SPOTBuffer = SPOTBuffer()\\n\\n self.header = True\\n self.Travel_col_order = ['localtime', 'vehicle_ID', 'x', 'y', 'spd']\\n self.Behavior_col_order = self.Travel_col_order + ['accel', 'heading', 'yawrate']\\n #Limit of message list before printing to a file\\n self.msg_limit = msg_limit\\n\\n self.FT_CONVERSION = 12 * 2.54 / 100 # converts feet to meters\\n self.ACCEL_CONVERSION = 1 / 32.1740485564 # converts ft/s2 to g\\n self.SPOT_SPD_CONVERSION = 1.609344 # converts mph to km/hr for ITS Spot Output\\n self.SPOT_accel_threshold = self.CF.Strategy['SPOTaccelThreshold'] * 32.1740485564 # Convert g to ft/s^2 for ITS Spot Output\\n self.SPOT_yawrate_threshold = self.CF.Strategy['SPOTyawrateThreshold'] * 32.1740485564 # Convert g to ft/s^2 for ITS Spot Output\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8cb44e717080eb9ee738ac54804b6f6a","score":"0.608191","text":"def __init__(self):\n _libBornAgainCore.SimulationFactory_swiginit(self, _libBornAgainCore.new_SimulationFactory())","title":""},{"docid":"248cebe817b7b1ed68dac8656112abaa","score":"0.6079417","text":"def __init__(self):\n \n # Initialize the components of the turbine\n self.blades = csmBlades()\n self.hub = csmHub() \n self.nac = csmNacelle()\n self.tower = csmTower()\n\n pass","title":""},{"docid":"9d0ffad54fd9700e7a805db6b4aa08f9","score":"0.6074401","text":"def __init__(self):\n _libBornAgainCore.SimulationFactoryTemp_swiginit(self, _libBornAgainCore.new_SimulationFactoryTemp())","title":""},{"docid":"44117a430789f5f3c3a61269afa6954a","score":"0.6052722","text":"def init(self) -> None:\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6036853","text":"def init(self):\n pass","title":""},{"docid":"b78ad45d0267c35a31e084d0063306b6","score":"0.60351825","text":"def Initialize(self):\n self.liSnr = LightSensors()\n self.imuSnr = IMUSensor()\n self.envSnr = EnvironmentalSensor()","title":""},{"docid":"31536638ff58ac6ba4fbae9b1b2276d0","score":"0.6019883","text":"def __init__(self):\n self.playlist_name = 'payloadChunk'\n self.scope = 'user-library-read user-library-modify playlist-modify-private\\\n playlist-read-private'\n if self.check_environment():\n try:\n token = util.prompt_for_user_token(\n self.username, self.scope, self.client_id,\n self.client_secret, self.redirect_uri\n )\n self.spotipy = spotipy.Spotify(auth=token)\n except Exception:\n print(\"[!] Cannot get Spotify API token: {}\".format(Exception))\n sys.exit(0)","title":""},{"docid":"ebd488b8fb56dad51bdd2a61427ca011","score":"0.5971473","text":"def __init__(self):\n _libBornAgainCore.OffSpecSimulation_swiginit(self, _libBornAgainCore.new_OffSpecSimulation())","title":""},{"docid":"0b8d76d1643f434f49b7c1894a1ffe59","score":"0.59540987","text":"def __init__(self):\n\n suitesModule.Suite.__init__( self, [ Particle ] )","title":""},{"docid":"c0c0b779395d7f231ee86572d79918e4","score":"0.5953854","text":"def init() -> None:\n pass","title":""},{"docid":"5b273c4fd1e3b528e6e4e57228b49e15","score":"0.5935448","text":"def init():\n pass","title":""},{"docid":"bde6a4ed4e8539e3a9441ec78d3f994a","score":"0.59236914","text":"def __init__(self, spot, period, matching_request=None):\n if not isinstance(spot, Spot):\n raise AttributeError(\"spot is not of type Spot\")\n self.spot = spot\n self.period = period\n self.__match = matching_request","title":""},{"docid":"7b939b13e1bbb00a33948d5b0d31e644","score":"0.59139943","text":"def __init__(self, spi_stngs):\n # Validate arguments, open and configure SPI peripheral\n assert spi_stngs[0] in (0,1), \"Not a valid SPI port index\"\n assert spi_stngs[1] in (0, 1), \"Not a valid SPI chip select\"\n assert type(spi_stngs[2]) is int and SPI_CLK_MIN <= spi_stngs[2] <= SPI_CLK_MAX, \"Not a valid SPI clock rate\"\n self.spi = spidev.SpiDev()\n self.spi.open(spi_stngs[0], spi_stngs[1])\n self.spi.max_speed_hz = spi_stngs[2]\n self.spi.mode = 0 # phase=0 and polarity=0","title":""},{"docid":"9b30fc65b0b6dbc9a7bcaf7554fcad17","score":"0.590182","text":"def init():","title":""},{"docid":"9b30fc65b0b6dbc9a7bcaf7554fcad17","score":"0.590182","text":"def init():","title":""},{"docid":"9f0df9e185436f9b73534065047335ab","score":"0.5896078","text":"def init(self):\n # initial trajectory, simply holds position\n self.trajectory = Trajectory()\n self.trajectory.start_time = rospy.Time.now()\n self.trajectory.duration = 5\n self.trajectory.ts = 1/self.frequency\n self.trajectory.positions = [[0,0],[0,0]]\n self.trajectory.times = [0,1]\n self.trajectory.updateInterpolation()\n\n self.actor = Obstacle()\n\n self.vel_max = rospy.get_param('trajectory_sampler/velocity/max')\n self.omega_max = rospy.get_param('trajectory_sampler/omega/max')\n self.dist_min = rospy.get_param('trajectory_sampler/distance/min')\n self.target_time = rospy.get_param('trajectory_sampler/target_time')\n self.k_vel = rospy.get_param('trajectory_sampler/k_vel')\n self.k_P = rospy.get_param('trajectory_sampler/k_p')\n self.k_I = rospy.get_param('trajectory_sampler/k_i')\n self.k_D = rospy.get_param('trajectory_sampler/k_d')\n self.err = 0\n self.int = 0","title":""},{"docid":"57c4aae0958c4426453f10d53df0816d","score":"0.58802915","text":"def init(self) -> None:","title":""},{"docid":"b3ebe902c3551e917073fd862e93e8f9","score":"0.5851898","text":"def __init__(self):\n _libBornAgainCore.GISASSimulation_swiginit(self, _libBornAgainCore.new_GISASSimulation())","title":""},{"docid":"ca90b7044d2a65117a39e9521f62172b","score":"0.58383626","text":"def __init__(self):\n # Initialise trackers to None\n self.pipetteTracker = None\n self.cellTracker = None\n self.aspTracker = None","title":""},{"docid":"241b62a55a92249d77af21736b55eddb","score":"0.5831258","text":"def __init__(self):\n _fastjet.PseudoJetStructureBase_swiginit(self, _fastjet.new_PseudoJetStructureBase())","title":""},{"docid":"744be4a15e4ccd558735f1c0d91ccd4f","score":"0.5823807","text":"def init():\n pass","title":""},{"docid":"2cc02228122e69b63d4e29255cba404e","score":"0.581926","text":"def initialize(pts):\n home_robot()\n return","title":""},{"docid":"c15a9a19b7a0ab24399a73b84305706f","score":"0.58076763","text":"def __init__(self, polyglot):\n self.ready = False\n self.discover_thread = None\n self.serverdata = get_server_data(LOGGER)\n self.l_info('init','Initializing VERSION=%s' % (self.serverdata['version']))\n super(wtController, self).__init__(polyglot)\n self.name = 'WirelessTagsController'\n self.address = 'wtcontroller'\n self.primary = self.address","title":""},{"docid":"7ea558100369cd679d453039be2332a8","score":"0.57946557","text":"def __init__(self, gif_token=public_token, sticker_token=public_token):\n self.gif = Giphy(token=gif_token)\n self.sticker = Sticky(token=sticker_token)","title":""},{"docid":"2e9a5c09937c6011385d9e78c2a5275d","score":"0.5784158","text":"def Initialize(self):\n # Set timewarp \n self.Set_time_warp(1)\n \n # Register for desired variables\n self.comms.set_on_connect_callback(self.Register_Vars)\n \n # Connect to the server\n self.comms.run('localhost',9000,'Tides')","title":""},{"docid":"7f6762f92484e366a9e6ec6dd31bdd44","score":"0.5777977","text":"def init(self):\r\n\t\tpass","title":""},{"docid":"cdfb7852bd8a84d02e33fde2a5852782","score":"0.57703704","text":"def __init__(self):\n _libBornAgainCore.SampleBuilderFactory_swiginit(self, _libBornAgainCore.new_SampleBuilderFactory())","title":""},{"docid":"8bdb073461f0708b48cff94013476be4","score":"0.5767229","text":"def __init__(self):\n rospy.loginfo(\"MC: Initializing mission control node...\")\n\n self.beliefs = {\n 'nodesOnline': 1, # Assume nodes are OK.\n 'cameraCalibrated': 1, # Assume camera is calibrated.\n 'crashed': 0, # Roomba crashed into something\n 'targetLocated': 0, # Camera can see the object from far away\n 'atTarget': 0, # Object near the robot\n 'targetReachable': 1, # Arm can grab the object\n 'targetGrasped': 0, # Object in hand \n 'roombaBatteryPerc': 100, # Assume fully charged.\n 'laptopBatteryPerc': 100 # Assume fully charged.\n }\n\n self.currentTask = 0\n\n # Let tuertlebot_node know that we're using a 'roomba' not a 'create'.\n rospy.set_param('~robot_type', 'roomba')\n\n #Linux USB device from which to read the data. Default = /dev/ttyUSB0\n rospy.set_param('~port', '/dev/ttyUSB0')\n\n # Play Mario Bros. theme song.\n rospy.set_param('~bonus', True)","title":""},{"docid":"5f4d2788e4be2a519b5c4507dc4c0a46","score":"0.57625806","text":"def init():\n global D # to hold system state\n\n # we need to give our program a ROS node name\n # the name is not important, so we use \"lab1_node\"\n #rospy.init_node('lab1_node', anonymous=True)\n \n # we obtain the tank service\n rospy.wait_for_service('tank') # wait until the motors are available\n D.tank = rospy.ServiceProxy('tank', Tank) # D.tank is our \"driver\"\n\n # we obtain the stop service\n rospy.wait_for_service('stop') # wait until the stop service is available\n D.stop = rospy.ServiceProxy('stop', Stop)","title":""},{"docid":"5c78342195b24b2f209df6660ed95a69","score":"0.57561165","text":"def __init__(self):\n super().__init__()\n self.set_mount(\"NASMYTH_COROTATING\")\n self.instrument_name = None\n self.data_type = None\n self.instrument_config = None\n self.instrument_mode = None\n self.mccs_mode = None\n self.spectral_element_1 = None\n self.spectral_element_2 = None\n self.slit_id = None\n self.detector_channel = None\n self.spectral_resolution = np.nan\n self.exposure_time = np.nan * units.Unit('s')\n self.total_integration_time = np.nan * units.Unit('s')\n self.wavelength = np.nan * units.Unit('um')","title":""},{"docid":"87d40478e67c6cd19fc3dcba5112c0e8","score":"0.57499415","text":"def __init__(self):\n _libBornAgainCore.SampleBuilderFactoryTemp_swiginit(self, _libBornAgainCore.new_SampleBuilderFactoryTemp())","title":""},{"docid":"ee8181bae7f9057dd337d48c1f3531bc","score":"0.57446814","text":"def __init__(self):\n basicTracker.__init__(self)\n self.MOSSETrack = None","title":""},{"docid":"c65137b914045063b0442d0acae76cac","score":"0.57433623","text":"def init(self):\n\n raise NotImplementedError()","title":""},{"docid":"be57190574652185ccccdf62d4a6b40c","score":"0.573662","text":"def init():\n\n # Placeholder init code. Replace the sleep with check for model files required etc...\n time.sleep(1)","title":""},{"docid":"bb22bcd59b4f883ca4baaf4d8a425975","score":"0.57297486","text":"def __init__(self, api_wrapper: IxTclHalApi) -> None:\n super().__init__(logger, api_wrapper)\n trafficgenerator.tgn_tcl.tcl_interp_g = self.api\n self.session = IxeSession(self.logger, self.api)\n self.chassis_chain: Dict[str, IxeChassis] = {}","title":""},{"docid":"717733d00b2138f6231aefd55dd3f982","score":"0.5722797","text":"def initialize(self):\n self.recon_result = ReconResData()\n self.tao_sipm = TAOSiPM()\n self.sipm_xyz = self.tao_sipm.get_xyz()","title":""},{"docid":"35e2ceaaf53ffe8eb90c20041c5f7d13","score":"0.57205164","text":"def __init__(self, t0):\n\n self.t0 = t0\n self.s2PMAP = []\n self.epPMAP = []\n self.sipmS2P = []","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.57196206","text":"def initialize(self):\n pass","title":""},{"docid":"3bda20d3109670731acd151b4e60f61d","score":"0.57186306","text":"def __init__(self):\n _libBornAgainCore.SpecularSimulation_swiginit(self, _libBornAgainCore.new_SpecularSimulation())","title":""},{"docid":"cc5c7139651e8ae8c82b4b17102fe1b3","score":"0.57130575","text":"def __init__(self):\n self.init_gst()\n self.init_tivo()","title":""},{"docid":"b90ba15659f539d19d1bdc87ed6e1b77","score":"0.57063437","text":"def __init__(self):\n _libBornAgainCore.IterationInfo_swiginit(self, _libBornAgainCore.new_IterationInfo())","title":""},{"docid":"b5e74d88338f607a601029408650f71c","score":"0.56849265","text":"def initialize_sampler():\n\n # initialize sampler\n smp = SAMSSampler(number_of_iterations=burnin_iterations,\n mcmc_moves=propagator(timestep=timestep,\n n_steps=state_update_steps),\n online_analysis_interval=None)\n\n # set sampler states and positions\n smp.from_testsystem(test,\n temperatures=temperatures,\n pressure=pressure,\n stride=checkpoint_iterations,\n storage=ms_container)\n return smp","title":""},{"docid":"b40e18f789fa4dd2454c735c64eb1e0a","score":"0.5673085","text":"def robotInit(self):\n self.pneumaticControlModuleCANID = robotmap.PCM\n self.kDriveTrain = robotmap.DriveTrain\n self.kCubeGrabber = robotmap.CubeGrabber\n self.kElevator = robotmap.Elevator\n self.kSticks = robotmap.Sticks\n self.kClimber = robotmap.Climber\n self.dStick = Joystick(self.kSticks['drive'])\n self.cStick = Joystick(self.kSticks['control'])\n \n self.drive = Drive(self)\n self.cubeGrabber = cubeGrabber(self)\n self.elevator = Elevator(self)\n self.climber = Climber(self)\n \n self.sendableChooser()","title":""},{"docid":"ee602d2d268b4db31e2a1b80343a170c","score":"0.567268","text":"def init(self):\n ResourceTemplate.__init__(self)\n # I have \"session\" as an alias because the specification calls the \"vi\"\n # handle \"session\" for the resource manager.\n self.session = self.vi = vpp43.open_default_resource_manager()","title":""},{"docid":"f522e4426cca006fd2d7aa466ca2c428","score":"0.5672286","text":"def _init(cls):\n if cls.tf is None:\n cls.tf = get_module('tensorflow')\n\n if cls.profiler is None:\n cls.profiler = Profiler()","title":""},{"docid":"913f4e940c1308f0b88361117d7cb208","score":"0.5669934","text":"def initialize ():","title":""},{"docid":"c417f2b609fec52979d806ce6bd7a394","score":"0.56566477","text":"def init(self):\n super(SobjectEditLauncherWdg, self).init()\n self.server = TacticServerStub.get()","title":""},{"docid":"407512adfd279c66ef7eb65257207cb3","score":"0.5651472","text":"def __init__(self, module):\n super().__init__()\n\n self.module = module","title":""},{"docid":"cc4de8dcdc08aedaf3cb84e39abf5cbf","score":"0.56502736","text":"def __init__(self, iceContext):\n self.iceContext = iceContext\n self.params = None","title":""},{"docid":"5dc7d070aac9109c44f55b25da24da57","score":"0.564937","text":"def __init__(self):\n self.initialize()","title":""},{"docid":"c92637668a066945e6c1624fbc4d1976","score":"0.56465846","text":"def initialize():","title":""},{"docid":"811f4a83941b2639efeda42889f9fd6d","score":"0.56448686","text":"def init_instruments(self):\n pass","title":""},{"docid":"c9230cf233a071f9c851a7912cfcbe20","score":"0.5633914","text":"def __init__(self):\n _libBornAgainCore.SimulationOptions_swiginit(self, _libBornAgainCore.new_SimulationOptions())","title":""},{"docid":"7ef1906134b3f53ca79179c3279c3132","score":"0.56321585","text":"def init(self):\n\n warnings.warn(\" \".join([\"The instrument module `jpl_gps` has\",\n \"been deprecated and will be removed in 0.1.0+.\"]),\n DeprecationWarning, stacklevel=2)\n\n pysat.logger.info('')\n self.acknowledgements = mm_gps.ackn_str\n self.references = '\\n'.join((mm_gps.refs['mission'],\n mm_gps.refs['roti15min_jpl']))\n\n return","title":""},{"docid":"cbee975b5fa5bc7b6fc15d9a186e0561","score":"0.56273156","text":"def __init__(self):\n\t\tself.milesTraveled = 0\n\t\tself.gold = 2\n\t\tself.thirst = 0\n\t\tself.location = \"DESERT\"\n\t\tself.inventory = [ \"CANTEEN\" ]\n\t\tself.failed = False\n\n\t\t\"\"\" Camel \"\"\"\n\t\tself.camelTiredness = 0\n\n\t\t\"\"\" Canteen \"\"\"\n\t\tself.canteenSips = 3\n\t\tself.maxCanteenSips = 3\n\n\t\t\"\"\" Their operating system \"\"\"\n\t\tself.usingWindows = False","title":""},{"docid":"392c1e535b43840adab475211ad234ab","score":"0.5621148","text":"def initialize(magnet=False,**kwarg):\n from opentrons import labware, instruments, robot, modules\n import sys\n sys.path.append(\"/data/user_storage/opentrons_data/jupyter/modules_storage\")\n import labware_volume as lv\n global robot, labware, instruments, modules, lv\n robot, labware, instruments, modules, lv = robot, labware, instruments, modules, lv\n robot.connect()\n robot.reset()\n if magnet:\n for module in robot.modules:\n module.disconnect()\n robot.modules = modules.discover_and_connect()","title":""},{"docid":"7a58c121ab57a71319f7f6f318b841c9","score":"0.5620116","text":"def initialize(self):\n HuntingModule.parsing_module = module_manager.get_module('parsing_module')\n # self.database_module = module_manager.get_module('database_module')\n HuntingModule.database_module = module_manager.get_module('database_module')\n HuntingModule.system_commands_module = module_manager.get_module('system_commands_module')\n\n self.load_config_parameters()\n self.create_database_tables_if_not_exist()\n\n self.register_url(HuntingModuleChannelView, '/hunting/channel')\n self.register_url(HuntingModuleHostView, '/hunting/host')\n self.register_url(HuntingModuleHistoricalCsvView, '/hunting/historical')\n self.register_url(HuntingModuleEvidenceOutputsView, '/hunting/evidence_outputs')\n self.register_url(HuntingModuleReportView, '/hunting/report')\n self.register_url(HuntingModuleManualView, '/hunting/session/manual')\n\n self.execute_channel = self.ExecuteChannelTask(HuntingModule.database_module, HuntingModule.parsing_module)\n self.execute_channel.set_interval(HuntingModule.execute_channel_task_interval)\n self.execute_channel.start()\n\n self.agent_task = self.AgentStatusTask(HuntingModule.database_module, HuntingModule.system_commands_module)\n self.agent_task.set_interval(HuntingModule.agent_status_task_interval)\n # self.agent_task.start()\n\n if not os.path.isdir(HuntingModule.hunting_results_dir):\n os.makedirs(HuntingModule.hunting_results_dir)","title":""},{"docid":"5482631b8321c76a95125a83e1f73601","score":"0.56186044","text":"def __init__(self, robot_number):\n self.robot_number = robot_number\n self.is_scouted = False","title":""},{"docid":"21b02c9f5ee23569d3cad9dc6f6f5898","score":"0.56132","text":"def initialize(self, context):\n pass","title":""},{"docid":"21b02c9f5ee23569d3cad9dc6f6f5898","score":"0.56132","text":"def initialize(self, context):\n pass","title":""},{"docid":"d4c84fac7bb0284d8d9a92a6f20889ff","score":"0.5612101","text":"def __init__(self, SPN):\n self.SPNs = SPN.stoichiometry\n # Vector dictionary\n self.vdict = {'p':SPN.places, 't':SPN.transitions, 'c':SPN.capacities,\\\n 'r':SPN.rates, 'm':SPN.initial_marking}\n # Matrices dictionary\n self.mdict = {'pre':self.SPNs.pre_arcs, 'post':self.SPNs.post_arcs, \\\n 'test':SPN.test_arcs, 'inhib':SPN.inhibitory_arcs}","title":""},{"docid":"ea466b21368cf95201156f0545de0b02","score":"0.5601268","text":"def __init__(self):\n self.init_gui()\n self.init_gst()","title":""},{"docid":"a48033a7044fa174a75243d92812e29e","score":"0.55995595","text":"def __init__(self, context: det.TrialContext) -> None:\n pass","title":""},{"docid":"b4900261c9a8f9487c959e7bc8f9680e","score":"0.5596067","text":"def init(self):","title":""},{"docid":"b4900261c9a8f9487c959e7bc8f9680e","score":"0.5596067","text":"def init(self):","title":""},{"docid":"b4900261c9a8f9487c959e7bc8f9680e","score":"0.5596067","text":"def init(self):","title":""},{"docid":"b4900261c9a8f9487c959e7bc8f9680e","score":"0.5596067","text":"def init(self):","title":""},{"docid":"b4900261c9a8f9487c959e7bc8f9680e","score":"0.5596067","text":"def init(self):","title":""},{"docid":"513693f15ffdb840c2977695ecb20a20","score":"0.55940753","text":"def _init(self):\n raise NotImplementedError","title":""},{"docid":"e46df89f13f8c5b46ebff73f80de4e9a","score":"0.55835","text":"def __init__(self):\n\n self._starmap = _load_starmap()","title":""},{"docid":"195cedc6d0b817f162b03cb2d47d59f0","score":"0.5583445","text":"def __init__(self):\n _libBornAgainCore.MultiLayer_swiginit(self, _libBornAgainCore.new_MultiLayer())","title":""},{"docid":"972f821557b2a053761df7a2f0a374e6","score":"0.5580125","text":"def init(self):\n\n pass","title":""},{"docid":"2b44b6154fd2547646f2f78b2dbb0b23","score":"0.5573703","text":"def _initialize(self):\n\t\tpass","title":""},{"docid":"4d78e0d3e028da7d7029e335ba77185e","score":"0.5569636","text":"def initialize():\n\tpass","title":""},{"docid":"0b048a216072f96746095bef046a21ca","score":"0.5568297","text":"def initialize(self):\n self.count_num_points()\n self.generate_IR()\n self.initialize_samples()","title":""},{"docid":"7c6b170b24530b534dc3a4f49fffd8a6","score":"0.55667007","text":"def __init__(self) -> None:\n self.xi_t_T = None\n self.P_t_T = None\n self.xi2_t_T = None\n self.Pcov_t_t1 = None\n self.delta2 = None\n self.chi2 = None\n self.r0_t = None\n self.r1_t = None\n self.N0_t = None\n self.N1_t = None\n self.N2_t = None\n self.is_smoothed = False\n self.is_init_smooth = False","title":""},{"docid":"10772a0330c839a6efd1becdcdc65d58","score":"0.5562055","text":"def initialize(self):\n self.rmt = Remote('test.cfg')\n\n # Turn off SSL cert verification. This is necessary for interacting with\n # developer instances of the Boss.\n self.rmt.project_service.session_send_opts = { 'verify': False }\n self.rmt.metadata_service.session_send_opts = { 'verify': False }\n self.rmt.volume_service.session_send_opts = { 'verify': False }\n requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\n self.coll = CollectionResource('collection2309', API_VER, 'bar')\n\n self.coord = CoordinateFrameResource(\n 'BestFrame', API_VER, 'Test coordinate frame.', 0, 10, -5, 5, 3, 6, \n 1, 1, 1, 'nanometers', 0, 'nanoseconds')\n\n # self.exp.coord_frame must be set with valid id before creating.\n self.exp = ExperimentResource(\n 'exp2309x2', self.coll.name, API_VER, 'my experiment', \n 0, 1, 'iso', 0)\n\n self.chan = ChannelResource(\n 'myChan', self.coll.name, self.exp.name, API_VER, 'test channel', \n 0, 'uint8', 0)\n\n # self.lyr.channels must be set with valid id before creating.\n self.lyr = LayerResource(\n 'topLayer', self.coll.name, self.exp.name, API_VER, 'test layer',\n 0, 'uint64', 0)","title":""},{"docid":"10e9112c432bd66487441c9139ca86af","score":"0.55604273","text":"def __init__(self,\n environment_id: str = 'gym_splendor_code:splendor-v0') -> None:\n self.env = gym_open_ai.make(environment_id)\n self.collect_states_mode = False\n self.collect_states_df = None\n self.dump_probability = 1\n self.n_min_actions = -1","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""},{"docid":"5dd420ba9734f8f9a51b9f962f494872","score":"0.55544496","text":"def initialize(context):","title":""}],"string":"[\n {\n \"docid\": \"8cb44e717080eb9ee738ac54804b6f6a\",\n \"score\": \"0.608191\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SimulationFactory_swiginit(self, _libBornAgainCore.new_SimulationFactory())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248cebe817b7b1ed68dac8656112abaa\",\n \"score\": \"0.6079417\",\n \"text\": \"def __init__(self):\\n \\n # Initialize the components of the turbine\\n self.blades = csmBlades()\\n self.hub = csmHub() \\n self.nac = csmNacelle()\\n self.tower = csmTower()\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0ffad54fd9700e7a805db6b4aa08f9\",\n \"score\": \"0.6074401\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SimulationFactoryTemp_swiginit(self, _libBornAgainCore.new_SimulationFactoryTemp())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44117a430789f5f3c3a61269afa6954a\",\n \"score\": \"0.6052722\",\n \"text\": \"def init(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6036853\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78ad45d0267c35a31e084d0063306b6\",\n \"score\": \"0.60351825\",\n \"text\": \"def Initialize(self):\\n self.liSnr = LightSensors()\\n self.imuSnr = IMUSensor()\\n self.envSnr = EnvironmentalSensor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31536638ff58ac6ba4fbae9b1b2276d0\",\n \"score\": \"0.6019883\",\n \"text\": \"def __init__(self):\\n self.playlist_name = 'payloadChunk'\\n self.scope = 'user-library-read user-library-modify playlist-modify-private\\\\\\n playlist-read-private'\\n if self.check_environment():\\n try:\\n token = util.prompt_for_user_token(\\n self.username, self.scope, self.client_id,\\n self.client_secret, self.redirect_uri\\n )\\n self.spotipy = spotipy.Spotify(auth=token)\\n except Exception:\\n print(\\\"[!] Cannot get Spotify API token: {}\\\".format(Exception))\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd488b8fb56dad51bdd2a61427ca011\",\n \"score\": \"0.5971473\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.OffSpecSimulation_swiginit(self, _libBornAgainCore.new_OffSpecSimulation())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b8d76d1643f434f49b7c1894a1ffe59\",\n \"score\": \"0.59540987\",\n \"text\": \"def __init__(self):\\n\\n suitesModule.Suite.__init__( self, [ Particle ] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c0b779395d7f231ee86572d79918e4\",\n \"score\": \"0.5953854\",\n \"text\": \"def init() -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b273c4fd1e3b528e6e4e57228b49e15\",\n \"score\": \"0.5935448\",\n \"text\": \"def init():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde6a4ed4e8539e3a9441ec78d3f994a\",\n \"score\": \"0.59236914\",\n \"text\": \"def __init__(self, spot, period, matching_request=None):\\n if not isinstance(spot, Spot):\\n raise AttributeError(\\\"spot is not of type Spot\\\")\\n self.spot = spot\\n self.period = period\\n self.__match = matching_request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b939b13e1bbb00a33948d5b0d31e644\",\n \"score\": \"0.59139943\",\n \"text\": \"def __init__(self, spi_stngs):\\n # Validate arguments, open and configure SPI peripheral\\n assert spi_stngs[0] in (0,1), \\\"Not a valid SPI port index\\\"\\n assert spi_stngs[1] in (0, 1), \\\"Not a valid SPI chip select\\\"\\n assert type(spi_stngs[2]) is int and SPI_CLK_MIN <= spi_stngs[2] <= SPI_CLK_MAX, \\\"Not a valid SPI clock rate\\\"\\n self.spi = spidev.SpiDev()\\n self.spi.open(spi_stngs[0], spi_stngs[1])\\n self.spi.max_speed_hz = spi_stngs[2]\\n self.spi.mode = 0 # phase=0 and polarity=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b30fc65b0b6dbc9a7bcaf7554fcad17\",\n \"score\": \"0.590182\",\n \"text\": \"def init():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b30fc65b0b6dbc9a7bcaf7554fcad17\",\n \"score\": \"0.590182\",\n \"text\": \"def init():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f0df9e185436f9b73534065047335ab\",\n \"score\": \"0.5896078\",\n \"text\": \"def init(self):\\n # initial trajectory, simply holds position\\n self.trajectory = Trajectory()\\n self.trajectory.start_time = rospy.Time.now()\\n self.trajectory.duration = 5\\n self.trajectory.ts = 1/self.frequency\\n self.trajectory.positions = [[0,0],[0,0]]\\n self.trajectory.times = [0,1]\\n self.trajectory.updateInterpolation()\\n\\n self.actor = Obstacle()\\n\\n self.vel_max = rospy.get_param('trajectory_sampler/velocity/max')\\n self.omega_max = rospy.get_param('trajectory_sampler/omega/max')\\n self.dist_min = rospy.get_param('trajectory_sampler/distance/min')\\n self.target_time = rospy.get_param('trajectory_sampler/target_time')\\n self.k_vel = rospy.get_param('trajectory_sampler/k_vel')\\n self.k_P = rospy.get_param('trajectory_sampler/k_p')\\n self.k_I = rospy.get_param('trajectory_sampler/k_i')\\n self.k_D = rospy.get_param('trajectory_sampler/k_d')\\n self.err = 0\\n self.int = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57c4aae0958c4426453f10d53df0816d\",\n \"score\": \"0.58802915\",\n \"text\": \"def init(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ebe902c3551e917073fd862e93e8f9\",\n \"score\": \"0.5851898\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.GISASSimulation_swiginit(self, _libBornAgainCore.new_GISASSimulation())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca90b7044d2a65117a39e9521f62172b\",\n \"score\": \"0.58383626\",\n \"text\": \"def __init__(self):\\n # Initialise trackers to None\\n self.pipetteTracker = None\\n self.cellTracker = None\\n self.aspTracker = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241b62a55a92249d77af21736b55eddb\",\n \"score\": \"0.5831258\",\n \"text\": \"def __init__(self):\\n _fastjet.PseudoJetStructureBase_swiginit(self, _fastjet.new_PseudoJetStructureBase())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744be4a15e4ccd558735f1c0d91ccd4f\",\n \"score\": \"0.5823807\",\n \"text\": \"def init():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc02228122e69b63d4e29255cba404e\",\n \"score\": \"0.581926\",\n \"text\": \"def initialize(pts):\\n home_robot()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c15a9a19b7a0ab24399a73b84305706f\",\n \"score\": \"0.58076763\",\n \"text\": \"def __init__(self, polyglot):\\n self.ready = False\\n self.discover_thread = None\\n self.serverdata = get_server_data(LOGGER)\\n self.l_info('init','Initializing VERSION=%s' % (self.serverdata['version']))\\n super(wtController, self).__init__(polyglot)\\n self.name = 'WirelessTagsController'\\n self.address = 'wtcontroller'\\n self.primary = self.address\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea558100369cd679d453039be2332a8\",\n \"score\": \"0.57946557\",\n \"text\": \"def __init__(self, gif_token=public_token, sticker_token=public_token):\\n self.gif = Giphy(token=gif_token)\\n self.sticker = Sticky(token=sticker_token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9a5c09937c6011385d9e78c2a5275d\",\n \"score\": \"0.5784158\",\n \"text\": \"def Initialize(self):\\n # Set timewarp \\n self.Set_time_warp(1)\\n \\n # Register for desired variables\\n self.comms.set_on_connect_callback(self.Register_Vars)\\n \\n # Connect to the server\\n self.comms.run('localhost',9000,'Tides')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6762f92484e366a9e6ec6dd31bdd44\",\n \"score\": \"0.5777977\",\n \"text\": \"def init(self):\\r\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdfb7852bd8a84d02e33fde2a5852782\",\n \"score\": \"0.57703704\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SampleBuilderFactory_swiginit(self, _libBornAgainCore.new_SampleBuilderFactory())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bdb073461f0708b48cff94013476be4\",\n \"score\": \"0.5767229\",\n \"text\": \"def __init__(self):\\n rospy.loginfo(\\\"MC: Initializing mission control node...\\\")\\n\\n self.beliefs = {\\n 'nodesOnline': 1, # Assume nodes are OK.\\n 'cameraCalibrated': 1, # Assume camera is calibrated.\\n 'crashed': 0, # Roomba crashed into something\\n 'targetLocated': 0, # Camera can see the object from far away\\n 'atTarget': 0, # Object near the robot\\n 'targetReachable': 1, # Arm can grab the object\\n 'targetGrasped': 0, # Object in hand \\n 'roombaBatteryPerc': 100, # Assume fully charged.\\n 'laptopBatteryPerc': 100 # Assume fully charged.\\n }\\n\\n self.currentTask = 0\\n\\n # Let tuertlebot_node know that we're using a 'roomba' not a 'create'.\\n rospy.set_param('~robot_type', 'roomba')\\n\\n #Linux USB device from which to read the data. Default = /dev/ttyUSB0\\n rospy.set_param('~port', '/dev/ttyUSB0')\\n\\n # Play Mario Bros. theme song.\\n rospy.set_param('~bonus', True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4d2788e4be2a519b5c4507dc4c0a46\",\n \"score\": \"0.57625806\",\n \"text\": \"def init():\\n global D # to hold system state\\n\\n # we need to give our program a ROS node name\\n # the name is not important, so we use \\\"lab1_node\\\"\\n #rospy.init_node('lab1_node', anonymous=True)\\n \\n # we obtain the tank service\\n rospy.wait_for_service('tank') # wait until the motors are available\\n D.tank = rospy.ServiceProxy('tank', Tank) # D.tank is our \\\"driver\\\"\\n\\n # we obtain the stop service\\n rospy.wait_for_service('stop') # wait until the stop service is available\\n D.stop = rospy.ServiceProxy('stop', Stop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c78342195b24b2f209df6660ed95a69\",\n \"score\": \"0.57561165\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n self.set_mount(\\\"NASMYTH_COROTATING\\\")\\n self.instrument_name = None\\n self.data_type = None\\n self.instrument_config = None\\n self.instrument_mode = None\\n self.mccs_mode = None\\n self.spectral_element_1 = None\\n self.spectral_element_2 = None\\n self.slit_id = None\\n self.detector_channel = None\\n self.spectral_resolution = np.nan\\n self.exposure_time = np.nan * units.Unit('s')\\n self.total_integration_time = np.nan * units.Unit('s')\\n self.wavelength = np.nan * units.Unit('um')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d40478e67c6cd19fc3dcba5112c0e8\",\n \"score\": \"0.57499415\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SampleBuilderFactoryTemp_swiginit(self, _libBornAgainCore.new_SampleBuilderFactoryTemp())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee8181bae7f9057dd337d48c1f3531bc\",\n \"score\": \"0.57446814\",\n \"text\": \"def __init__(self):\\n basicTracker.__init__(self)\\n self.MOSSETrack = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c65137b914045063b0442d0acae76cac\",\n \"score\": \"0.57433623\",\n \"text\": \"def init(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be57190574652185ccccdf62d4a6b40c\",\n \"score\": \"0.573662\",\n \"text\": \"def init():\\n\\n # Placeholder init code. Replace the sleep with check for model files required etc...\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb22bcd59b4f883ca4baaf4d8a425975\",\n \"score\": \"0.57297486\",\n \"text\": \"def __init__(self, api_wrapper: IxTclHalApi) -> None:\\n super().__init__(logger, api_wrapper)\\n trafficgenerator.tgn_tcl.tcl_interp_g = self.api\\n self.session = IxeSession(self.logger, self.api)\\n self.chassis_chain: Dict[str, IxeChassis] = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717733d00b2138f6231aefd55dd3f982\",\n \"score\": \"0.5722797\",\n \"text\": \"def initialize(self):\\n self.recon_result = ReconResData()\\n self.tao_sipm = TAOSiPM()\\n self.sipm_xyz = self.tao_sipm.get_xyz()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e2ceaaf53ffe8eb90c20041c5f7d13\",\n \"score\": \"0.57205164\",\n \"text\": \"def __init__(self, t0):\\n\\n self.t0 = t0\\n self.s2PMAP = []\\n self.epPMAP = []\\n self.sipmS2P = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.57196206\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bda20d3109670731acd151b4e60f61d\",\n \"score\": \"0.57186306\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SpecularSimulation_swiginit(self, _libBornAgainCore.new_SpecularSimulation())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5c7139651e8ae8c82b4b17102fe1b3\",\n \"score\": \"0.57130575\",\n \"text\": \"def __init__(self):\\n self.init_gst()\\n self.init_tivo()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90ba15659f539d19d1bdc87ed6e1b77\",\n \"score\": \"0.57063437\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.IterationInfo_swiginit(self, _libBornAgainCore.new_IterationInfo())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e74d88338f607a601029408650f71c\",\n \"score\": \"0.56849265\",\n \"text\": \"def initialize_sampler():\\n\\n # initialize sampler\\n smp = SAMSSampler(number_of_iterations=burnin_iterations,\\n mcmc_moves=propagator(timestep=timestep,\\n n_steps=state_update_steps),\\n online_analysis_interval=None)\\n\\n # set sampler states and positions\\n smp.from_testsystem(test,\\n temperatures=temperatures,\\n pressure=pressure,\\n stride=checkpoint_iterations,\\n storage=ms_container)\\n return smp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40e18f789fa4dd2454c735c64eb1e0a\",\n \"score\": \"0.5673085\",\n \"text\": \"def robotInit(self):\\n self.pneumaticControlModuleCANID = robotmap.PCM\\n self.kDriveTrain = robotmap.DriveTrain\\n self.kCubeGrabber = robotmap.CubeGrabber\\n self.kElevator = robotmap.Elevator\\n self.kSticks = robotmap.Sticks\\n self.kClimber = robotmap.Climber\\n self.dStick = Joystick(self.kSticks['drive'])\\n self.cStick = Joystick(self.kSticks['control'])\\n \\n self.drive = Drive(self)\\n self.cubeGrabber = cubeGrabber(self)\\n self.elevator = Elevator(self)\\n self.climber = Climber(self)\\n \\n self.sendableChooser()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee602d2d268b4db31e2a1b80343a170c\",\n \"score\": \"0.567268\",\n \"text\": \"def init(self):\\n ResourceTemplate.__init__(self)\\n # I have \\\"session\\\" as an alias because the specification calls the \\\"vi\\\"\\n # handle \\\"session\\\" for the resource manager.\\n self.session = self.vi = vpp43.open_default_resource_manager()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f522e4426cca006fd2d7aa466ca2c428\",\n \"score\": \"0.5672286\",\n \"text\": \"def _init(cls):\\n if cls.tf is None:\\n cls.tf = get_module('tensorflow')\\n\\n if cls.profiler is None:\\n cls.profiler = Profiler()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913f4e940c1308f0b88361117d7cb208\",\n \"score\": \"0.5669934\",\n \"text\": \"def initialize ():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c417f2b609fec52979d806ce6bd7a394\",\n \"score\": \"0.56566477\",\n \"text\": \"def init(self):\\n super(SobjectEditLauncherWdg, self).init()\\n self.server = TacticServerStub.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"407512adfd279c66ef7eb65257207cb3\",\n \"score\": \"0.5651472\",\n \"text\": \"def __init__(self, module):\\n super().__init__()\\n\\n self.module = module\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc4de8dcdc08aedaf3cb84e39abf5cbf\",\n \"score\": \"0.56502736\",\n \"text\": \"def __init__(self, iceContext):\\n self.iceContext = iceContext\\n self.params = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc7d070aac9109c44f55b25da24da57\",\n \"score\": \"0.564937\",\n \"text\": \"def __init__(self):\\n self.initialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92637668a066945e6c1624fbc4d1976\",\n \"score\": \"0.56465846\",\n \"text\": \"def initialize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"811f4a83941b2639efeda42889f9fd6d\",\n \"score\": \"0.56448686\",\n \"text\": \"def init_instruments(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9230cf233a071f9c851a7912cfcbe20\",\n \"score\": \"0.5633914\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.SimulationOptions_swiginit(self, _libBornAgainCore.new_SimulationOptions())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef1906134b3f53ca79179c3279c3132\",\n \"score\": \"0.56321585\",\n \"text\": \"def init(self):\\n\\n warnings.warn(\\\" \\\".join([\\\"The instrument module `jpl_gps` has\\\",\\n \\\"been deprecated and will be removed in 0.1.0+.\\\"]),\\n DeprecationWarning, stacklevel=2)\\n\\n pysat.logger.info('')\\n self.acknowledgements = mm_gps.ackn_str\\n self.references = '\\\\n'.join((mm_gps.refs['mission'],\\n mm_gps.refs['roti15min_jpl']))\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbee975b5fa5bc7b6fc15d9a186e0561\",\n \"score\": \"0.56273156\",\n \"text\": \"def __init__(self):\\n\\t\\tself.milesTraveled = 0\\n\\t\\tself.gold = 2\\n\\t\\tself.thirst = 0\\n\\t\\tself.location = \\\"DESERT\\\"\\n\\t\\tself.inventory = [ \\\"CANTEEN\\\" ]\\n\\t\\tself.failed = False\\n\\n\\t\\t\\\"\\\"\\\" Camel \\\"\\\"\\\"\\n\\t\\tself.camelTiredness = 0\\n\\n\\t\\t\\\"\\\"\\\" Canteen \\\"\\\"\\\"\\n\\t\\tself.canteenSips = 3\\n\\t\\tself.maxCanteenSips = 3\\n\\n\\t\\t\\\"\\\"\\\" Their operating system \\\"\\\"\\\"\\n\\t\\tself.usingWindows = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392c1e535b43840adab475211ad234ab\",\n \"score\": \"0.5621148\",\n \"text\": \"def initialize(magnet=False,**kwarg):\\n from opentrons import labware, instruments, robot, modules\\n import sys\\n sys.path.append(\\\"/data/user_storage/opentrons_data/jupyter/modules_storage\\\")\\n import labware_volume as lv\\n global robot, labware, instruments, modules, lv\\n robot, labware, instruments, modules, lv = robot, labware, instruments, modules, lv\\n robot.connect()\\n robot.reset()\\n if magnet:\\n for module in robot.modules:\\n module.disconnect()\\n robot.modules = modules.discover_and_connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a58c121ab57a71319f7f6f318b841c9\",\n \"score\": \"0.5620116\",\n \"text\": \"def initialize(self):\\n HuntingModule.parsing_module = module_manager.get_module('parsing_module')\\n # self.database_module = module_manager.get_module('database_module')\\n HuntingModule.database_module = module_manager.get_module('database_module')\\n HuntingModule.system_commands_module = module_manager.get_module('system_commands_module')\\n\\n self.load_config_parameters()\\n self.create_database_tables_if_not_exist()\\n\\n self.register_url(HuntingModuleChannelView, '/hunting/channel')\\n self.register_url(HuntingModuleHostView, '/hunting/host')\\n self.register_url(HuntingModuleHistoricalCsvView, '/hunting/historical')\\n self.register_url(HuntingModuleEvidenceOutputsView, '/hunting/evidence_outputs')\\n self.register_url(HuntingModuleReportView, '/hunting/report')\\n self.register_url(HuntingModuleManualView, '/hunting/session/manual')\\n\\n self.execute_channel = self.ExecuteChannelTask(HuntingModule.database_module, HuntingModule.parsing_module)\\n self.execute_channel.set_interval(HuntingModule.execute_channel_task_interval)\\n self.execute_channel.start()\\n\\n self.agent_task = self.AgentStatusTask(HuntingModule.database_module, HuntingModule.system_commands_module)\\n self.agent_task.set_interval(HuntingModule.agent_status_task_interval)\\n # self.agent_task.start()\\n\\n if not os.path.isdir(HuntingModule.hunting_results_dir):\\n os.makedirs(HuntingModule.hunting_results_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5482631b8321c76a95125a83e1f73601\",\n \"score\": \"0.56186044\",\n \"text\": \"def __init__(self, robot_number):\\n self.robot_number = robot_number\\n self.is_scouted = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b02c9f5ee23569d3cad9dc6f6f5898\",\n \"score\": \"0.56132\",\n \"text\": \"def initialize(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b02c9f5ee23569d3cad9dc6f6f5898\",\n \"score\": \"0.56132\",\n \"text\": \"def initialize(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c84fac7bb0284d8d9a92a6f20889ff\",\n \"score\": \"0.5612101\",\n \"text\": \"def __init__(self, SPN):\\n self.SPNs = SPN.stoichiometry\\n # Vector dictionary\\n self.vdict = {'p':SPN.places, 't':SPN.transitions, 'c':SPN.capacities,\\\\\\n 'r':SPN.rates, 'm':SPN.initial_marking}\\n # Matrices dictionary\\n self.mdict = {'pre':self.SPNs.pre_arcs, 'post':self.SPNs.post_arcs, \\\\\\n 'test':SPN.test_arcs, 'inhib':SPN.inhibitory_arcs}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea466b21368cf95201156f0545de0b02\",\n \"score\": \"0.5601268\",\n \"text\": \"def __init__(self):\\n self.init_gui()\\n self.init_gst()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48033a7044fa174a75243d92812e29e\",\n \"score\": \"0.55995595\",\n \"text\": \"def __init__(self, context: det.TrialContext) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4900261c9a8f9487c959e7bc8f9680e\",\n \"score\": \"0.5596067\",\n \"text\": \"def init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4900261c9a8f9487c959e7bc8f9680e\",\n \"score\": \"0.5596067\",\n \"text\": \"def init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4900261c9a8f9487c959e7bc8f9680e\",\n \"score\": \"0.5596067\",\n \"text\": \"def init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4900261c9a8f9487c959e7bc8f9680e\",\n \"score\": \"0.5596067\",\n \"text\": \"def init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4900261c9a8f9487c959e7bc8f9680e\",\n \"score\": \"0.5596067\",\n \"text\": \"def init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513693f15ffdb840c2977695ecb20a20\",\n \"score\": \"0.55940753\",\n \"text\": \"def _init(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46df89f13f8c5b46ebff73f80de4e9a\",\n \"score\": \"0.55835\",\n \"text\": \"def __init__(self):\\n\\n self._starmap = _load_starmap()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"195cedc6d0b817f162b03cb2d47d59f0\",\n \"score\": \"0.5583445\",\n \"text\": \"def __init__(self):\\n _libBornAgainCore.MultiLayer_swiginit(self, _libBornAgainCore.new_MultiLayer())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972f821557b2a053761df7a2f0a374e6\",\n \"score\": \"0.5580125\",\n \"text\": \"def init(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b44b6154fd2547646f2f78b2dbb0b23\",\n \"score\": \"0.5573703\",\n \"text\": \"def _initialize(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d78e0d3e028da7d7029e335ba77185e\",\n \"score\": \"0.5569636\",\n \"text\": \"def initialize():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b048a216072f96746095bef046a21ca\",\n \"score\": \"0.5568297\",\n \"text\": \"def initialize(self):\\n self.count_num_points()\\n self.generate_IR()\\n self.initialize_samples()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c6b170b24530b534dc3a4f49fffd8a6\",\n \"score\": \"0.55667007\",\n \"text\": \"def __init__(self) -> None:\\n self.xi_t_T = None\\n self.P_t_T = None\\n self.xi2_t_T = None\\n self.Pcov_t_t1 = None\\n self.delta2 = None\\n self.chi2 = None\\n self.r0_t = None\\n self.r1_t = None\\n self.N0_t = None\\n self.N1_t = None\\n self.N2_t = None\\n self.is_smoothed = False\\n self.is_init_smooth = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10772a0330c839a6efd1becdcdc65d58\",\n \"score\": \"0.5562055\",\n \"text\": \"def initialize(self):\\n self.rmt = Remote('test.cfg')\\n\\n # Turn off SSL cert verification. This is necessary for interacting with\\n # developer instances of the Boss.\\n self.rmt.project_service.session_send_opts = { 'verify': False }\\n self.rmt.metadata_service.session_send_opts = { 'verify': False }\\n self.rmt.volume_service.session_send_opts = { 'verify': False }\\n requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\\n\\n self.coll = CollectionResource('collection2309', API_VER, 'bar')\\n\\n self.coord = CoordinateFrameResource(\\n 'BestFrame', API_VER, 'Test coordinate frame.', 0, 10, -5, 5, 3, 6, \\n 1, 1, 1, 'nanometers', 0, 'nanoseconds')\\n\\n # self.exp.coord_frame must be set with valid id before creating.\\n self.exp = ExperimentResource(\\n 'exp2309x2', self.coll.name, API_VER, 'my experiment', \\n 0, 1, 'iso', 0)\\n\\n self.chan = ChannelResource(\\n 'myChan', self.coll.name, self.exp.name, API_VER, 'test channel', \\n 0, 'uint8', 0)\\n\\n # self.lyr.channels must be set with valid id before creating.\\n self.lyr = LayerResource(\\n 'topLayer', self.coll.name, self.exp.name, API_VER, 'test layer',\\n 0, 'uint64', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e9112c432bd66487441c9139ca86af\",\n \"score\": \"0.55604273\",\n \"text\": \"def __init__(self,\\n environment_id: str = 'gym_splendor_code:splendor-v0') -> None:\\n self.env = gym_open_ai.make(environment_id)\\n self.collect_states_mode = False\\n self.collect_states_df = None\\n self.dump_probability = 1\\n self.n_min_actions = -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd420ba9734f8f9a51b9f962f494872\",\n \"score\": \"0.55544496\",\n \"text\": \"def initialize(context):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993687,"cells":{"query_id":{"kind":"string","value":"9bb5cf61509db898f75f7d1b7d42d038"},"query":{"kind":"string","value":"Retrieves the body of the email from S3. This requires that you set up a previous action in your SES rules to store the message in S3."},"positive_passages":{"kind":"list like","value":[{"docid":"a06ad3cd3402b630d16da19a4fc388f9","score":"0.81179136","text":"def body(self):\n\n if self._body is None:\n log.debug(\"Downloading message body from s3://{}/{}\".format(\n self.s3_bucket, self.message_id))\n s3 = boto3.client('s3')\n obj = s3.get_object(Bucket=self.s3_bucket, Key=self.message_id)\n self._body = obj['Body'].read().decode('utf-8')\n\n return self._body","title":""}],"string":"[\n {\n \"docid\": \"a06ad3cd3402b630d16da19a4fc388f9\",\n \"score\": \"0.81179136\",\n \"text\": \"def body(self):\\n\\n if self._body is None:\\n log.debug(\\\"Downloading message body from s3://{}/{}\\\".format(\\n self.s3_bucket, self.message_id))\\n s3 = boto3.client('s3')\\n obj = s3.get_object(Bucket=self.s3_bucket, Key=self.message_id)\\n self._body = obj['Body'].read().decode('utf-8')\\n\\n return self._body\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a7e98b7bf32ee40c89ed274de3a937a9","score":"0.6889813","text":"def read(self):\n return self._s3_key.get_contents_as_string()","title":""},{"docid":"dfccaf4e4df65eefb2badef46de88a72","score":"0.6478215","text":"def file_contents(bucket, key):\n s3 = boto3.client('s3')\n return s3.get_object(Bucket=bucket, Key=key)['Body'].read().decode('UTF-8')","title":""},{"docid":"d11fd7cabed5e4038fcd546a6db6d15d","score":"0.63722885","text":"def read(self, document_name):\n # fetch encrypted text from S3 and decrypt\n key = s3.Key(self.bucket)\n key.key = '%s/%s' % (self.gpg_email, document_name)\n\n try:\n encrypted_text = key.get_contents_as_string()\n except boto.exception.S3ResponseError:\n return None\n else:\n return self._decrypt(encrypted_text)","title":""},{"docid":"ebffd67148242b7a656166206e2e50ee","score":"0.61347026","text":"def get_template_body(url):\n url_prefix = os.environ.get(\"GPWM_TEMPLATE_URL_PREFIX\", \"\")\n if url_prefix:\n if url_prefix.endswith(\"/\"):\n url_prefix = url_prefix[:-1]\n if url.startswith(\"/\"):\n url = url[1:]\n url = f\"{url_prefix}/{url}\"\n\n parsed_url = urlparse(url)\n if \"http\" in parsed_url.scheme: # http and https\n try:\n request = requests.get(url)\n request.raise_for_status()\n body = request.text\n except requests.exceptions.RequestException as exc:\n raise SystemExit(exc)\n elif parsed_url.scheme == \"s3\":\n s3 = boto3.resource(\"s3\")\n obj = s3.Object(parsed_url.netloc, parsed_url.path[1:])\n extra_args = {k: v[0] for k, v in parse_qs(parsed_url.query).items()}\n try:\n body = obj.get(**extra_args)[\"Body\"].read()\n except s3.meta.client.exceptions.NoSuchBucket as exc:\n raise SystemExit(\n f\"Error: S3 bucket doesn't exist: {parsed_url.netloc}\"\n )\n except s3.meta.client.exceptions.NoSuchKey as exc:\n raise SystemExit(f\"Error: S3 object doesn't exist: {url}\")\n elif not parsed_url.scheme:\n with open(url) as local_file:\n body = local_file.read()\n else:\n raise SystemExit(f\"URL scheme not supported: {parsed_url.scheme}\")\n\n return parsed_url, body","title":""},{"docid":"1671e29475323fb07424c930306c8645","score":"0.60105187","text":"def get_object(bucket_name, object_name):\n\n # Retrieve the object\n try:\n response = s3.get_object(Bucket=bucket_name, Key=object_name)\n except ClientError as e:\n # AllAccessDisabled error == bucket or object not found\n return None\n # Return an open StreamingBody object\n return response['Body']","title":""},{"docid":"9eca162bbe0dee4842d0a6d58ebcbb0a","score":"0.586785","text":"def get_object(bucket_name, object_name):\n\n # Retrieve the object\n s3 = boto3.client('s3')\n try:\n response = s3.get_object(Bucket=bucket_name, Key=object_name)\n except ClientError as e:\n # AllAccessDisabled error == bucket or object not found\n logging.error(e)\n return None\n # Return an open StreamingBody object\n return response['Body']","title":""},{"docid":"1a45b3ce9aad502cbf74888f92a327d0","score":"0.5792976","text":"def get(self, path):\n file_path = self._client.get_object( Bucket=self._bucket, Key=path)\n return file_path['Body'].read()","title":""},{"docid":"1292bbcc70c588a2b908e7bdbf76e826","score":"0.5783603","text":"def get_email_object(self):\n if self.eml:\n if self.eml.name.endswith('.gz'):\n body = gzip.GzipFile(fileobj=self.eml).read()\n else:\n self.eml.open()\n body = self.eml.file.read()\n self.eml.close()\n else:\n body = self.get_body()\n flat = email.message_from_bytes(body)\n return self._rehydrate(flat)","title":""},{"docid":"a80486a7fb1ceaf98b51f59b27973953","score":"0.5772815","text":"def _get_body(self):\n return self._body","title":""},{"docid":"6217f81cb78e07ce57e3e29e99cb2789","score":"0.57675606","text":"def get_s3_json(bucket, key):\n \n # Load credentials from AWS S3:\n s3 = boto3.client('s3')\n response = s3.get_object(Bucket=bucket, Key=key)\n # Translate S3 object content to a dict:\n string_content = response['Body'].read().decode('utf-8')\n dict_content = json.loads(string_content)\n \n return dict_content","title":""},{"docid":"c5c7156edc935899e487b18f3ff3d91d","score":"0.57497996","text":"def get_data(s3_client, bucket_name: str, object_key: str):\n\n if not re.search(r'^\\S+.json$', object_key):\n raise Exception(\"File format not supported, Only '.json' format is supported as of now.\")\n\n try:\n return s3_client.get_object(Bucket=bucket_name, Key=object_key)\n\n except ClientError as ce:\n if ce.response['Error']['Code'] == 'AccessDenied' or ce.response['Error']['Code'] == 'NoSuchKey':\n return {}\n raise ce","title":""},{"docid":"a2df1baca09969abac0e128bd77bda36","score":"0.5714189","text":"def get_body(self) -> str:\n return self.data[\"body\"]","title":""},{"docid":"a2df1baca09969abac0e128bd77bda36","score":"0.5714189","text":"def get_body(self) -> str:\n return self.data[\"body\"]","title":""},{"docid":"a28aa86756b2ef5982ea806c90533e95","score":"0.5712772","text":"def get_stream(self, rel_path):\n from boto.s3.key import Key\n from boto.exception import S3ResponseError \n \n import StringIO\n \n if self.prefix is not None:\n rel_path = self.prefix+\"/\"+rel_path\n \n k = Key(self.bucket)\n\n k.key = rel_path\n \n b = StringIO.StringIO()\n try:\n k.get_contents_to_file(b)\n b.seek(0)\n return b;\n except S3ResponseError as e:\n if e.status == 404:\n return None\n else:\n raise e","title":""},{"docid":"e381d52a564f25d03f5ec12152ea660d","score":"0.568055","text":"def download(self, s3_bucket, document_s3_path, version_id=None):\n try:\n s3 = boto3.resource(\"s3\", region_name=self._region, **self._credentials)\n if version_id:\n instances_file_content = s3.ObjectVersion(s3_bucket, document_s3_path, version_id).get()[\"Body\"].read()\n else:\n instances_file_content = s3.Object(s3_bucket, document_s3_path).get()[\"Body\"].read()\n return instances_file_content\n except Exception as e:\n self.error(\n \"Failed when downloading file %s from bucket %s in region %s with error %s\".format(\n document_s3_path, s3_bucket, self._region, e\n )\n )\n raise","title":""},{"docid":"1d33ef17b29357c23d141494411eefb3","score":"0.5605963","text":"def s3(self):\n return self._s3","title":""},{"docid":"63687fabdd2601ca958ab45c1d996ee9","score":"0.5541879","text":"def get_s3_response(response: dict) -> dict:\n s3_bucket = response.get(\"s3_bucket\")\n s3_key = response.get(\"s3_key\")\n LOGGER.info(f\"Retrieving data from s3://{s3_bucket}/{s3_key}\")\n\n s3_object = s3_client.get_object(Bucket=s3_bucket, Key=s3_key)[\"Body\"].read().decode(\"utf-8\")\n try:\n return json.loads(s3_object)\n except json.decoder.JSONDecodeError:\n return s3_object","title":""},{"docid":"284fcd652987077b71c6933add0406fa","score":"0.55129445","text":"def read_file_from_s3(self, key):\n downloaded = True\n try:\n bucket = self.S3.Bucket(self.BUCKET)\n file_obj = bucket.Object(key)\n return file_obj\n except botocore.exceptions.ClientError:\n downloaded = False\n return downloaded","title":""},{"docid":"25fc23cd65ec9d02517cb2089d3cd1b7","score":"0.55006593","text":"def get_body(self) -> str:","title":""},{"docid":"48083ceaa2670a041ff949207e57db2a","score":"0.5500529","text":"def get_email_object(self):\n if not hasattr(self, '_email_object'): # Cache fill\n if self.eml:\n if self.eml.name.endswith('.gz'):\n body = gzip.GzipFile(fileobj=self.eml).read()\n else:\n self.eml.open()\n body = self.eml.file.read()\n self.eml.close()\n else:\n body = self.get_body()\n flat = email.message_from_bytes(body)\n self._email_object = self._rehydrate(flat)\n return self._email_object","title":""},{"docid":"32ffa01d82618a47d0cfa1f809d95a0a","score":"0.54829854","text":"def get(self, name):\n obj = self.bucket.Object(name)\n try:\n return (obj.get()['Body'].read(), _extract_metadata(obj))\n except ClientError as e:\n if _is_no_such_key_error(e):\n raise FileNotFound(\"{} not found.\".format(name))\n raise","title":""},{"docid":"e60d33c3cc0da0d98fab71577630c64f","score":"0.54762787","text":"def get_file_as_string(s3, artifact, file_in_zip):\n tmp_file = tempfile.NamedTemporaryFile()\n bucket = artifact['location']['s3Location']['bucketName']\n key = artifact['location']['s3Location']['objectKey']\n\n with tempfile.NamedTemporaryFile() as tmp_file:\n s3.download_file(bucket, key, tmp_file.name)\n with zipfile.ZipFile(tmp_file.name, 'r') as zip:\n return zip.read(file_in_zip)","title":""},{"docid":"fc6bd60db080e620d1c8b70e046855d8","score":"0.54711145","text":"def getmsgBody(self): \n return self.msgBody","title":""},{"docid":"c3e171a010e1bf173fc84fb8c782def4","score":"0.5464676","text":"def _read_message(self):\n line = self._rfile.readline()\n\n if not line:\n return None\n\n content_length = self._content_length(line)\n\n # Blindly consume all header lines\n while line and line.strip():\n line = self._rfile.readline()\n\n if not line:\n return None\n\n # Grab the body\n return self._rfile.read(content_length)","title":""},{"docid":"79ac11f88cd60e23b0cf1c6dbeca2c5e","score":"0.54480714","text":"def process_new_email(event, context):\n x = event['Records'][0]\n bucket = x['s3']['bucket']['name']\n key = x['s3']['object']['key']\n key = parse.unquote_plus(key)\n id = uuid.uuid1()\n local_file = f\"/tmp/{id}.pdf\"\n s3_resource.Bucket(bucket).download_file(Filename=local_file, Key=key)\n parse_send_email(local_file)","title":""},{"docid":"4b95bf0f94458e28796ccf6b5a546bb9","score":"0.5436854","text":"def get_email_content(self, email):\n\n if email.is_multipart():\n self.log('Extracting email contents from multipart message')\n\n magic_type = 'multipart/alternative'\n payload_types = dict((p.get_content_type(), i) for i, p in enumerate(email.get_payload()))\n if magic_type in payload_types.keys():\n self.log('Found magic content type: %s' % magic_type)\n index = payload_types[magic_type]\n payload = email.get_payload()[index].get_payload()\n else:\n payload = email.get_payload()\n\n for pl in payload:\n if pl.get_filename() is not None:\n # it's an attached file\n continue\n\n if pl.get_content_type() in ACCEPTABLE_TYPES:\n return pl.get_payload()\n else:\n return email.get_payload()\n\n return None","title":""},{"docid":"1e6617405363473901f3f12a1b079d55","score":"0.5429134","text":"def send(self):\n if not SesEmail.is_local:\n # Use aws creds for ses if possible, otherwise, use aws_key from config\n s3_client = boto3.client(service_name='ses', region_name=CONFIG_BROKER['ses_aws_region'])\n email_message = {\n \"Subject\": {\n \"Data\": self.subject\n },\n \"Body\": {\n \"Html\": {\n \"Data\": self.content\n }\n }\n }\n email_dest = {\"ToAddresses\": [self.to_address]}\n try:\n return s3_client.send_email(Source=self.from_address, Message=email_message, Destination=email_dest)\n except Exception:\n s3_client = boto3.client(service_name='ses', region_name=CONFIG_BROKER['ses_aws_region'],\n aws_access_key_id=CONFIG_BROKER['ses_access_key_id'],\n aws_secret_access_key=CONFIG_BROKER['ses_secret_access_key'])\n return s3_client.send_email(Source=self.from_address, Message=email_message, Destination=email_dest)\n else:\n new_email_text = \"\\n\\n\".join([\"\", \"Time\", str(datetime.datetime.now()), \"Subject\", self.subject, \"From\",\n self.from_address, \"To\", self.to_address, \"Content\", self.content])\n open(SesEmail.emailLog, \"a\").write(new_email_text)","title":""},{"docid":"2447f3446b37bca22cb5a88a2f3888ef","score":"0.5403479","text":"def read_s3_json(connection, bucket, key):\n\tlog.debug(\"starting read_s3_json for key={}\".format(key) )\n\tout = None\n\tattempts = 1\n\tflag = 1 # 1=had not read file, 0 = file read\n\texc = \"No exceptions recorded\"\n\twhile attempts < 4: # max 3 attempts to read each file, backoff time increases after each attempt\n\t\ttry:\n\t\t\t# keep decode for special characters.\n\t\t\tlog.debug(\"Reading JSON object from S3: bucket={} key={}\".format(bucket, key) )\n\t\t\tobj = connection.get_object(Bucket=bucket, Key=key)\n\t\t\tout = json.load(obj['Body'])\n\t\t\tflag = 0\n\t\t\tlog.debug(\"Reading JSON object complete!\")\n\t\t\tbreak # stop if success reading file\n\t\texcept Exception as e:\n\t\t\texc = e\n\t\t\tattempts += 1\n\t\t\tflag = 1\n\t\t\ttime.sleep((attempts)*5.0)\n\n\tif flag == 1:\n\t\tlog.exception(\"Can't read file {} {} times\\nLast recorded exception: {}\".format(\n\t\t\tkey, attempts, str(exc)))\n\tlog.debug(\"finishing read_s3_json\")\n\treturn out","title":""},{"docid":"5ce9d05084b36ce03885632ee144da4d","score":"0.5395776","text":"def get_cache(key: str) -> dict:\n return json.loads(s3.Object(bucket, os.path.join(bucket_path, key)).get()['Body'].read())","title":""},{"docid":"6b08cabe9a5f15da38cc088901eb8d9b","score":"0.5394642","text":"def import_data(self):\n bucket = s3.Bucket(self.bucket_name)\n obj = bucket.Object(self.key)\n input_data = {}\n body = obj.get()[\"Body\"].read().splitlines()\n for line in body:\n k, v = str(line).strip(\"b'\").split(\"=\")\n input_data[k] = v\n return input_data","title":""},{"docid":"ab055f177dd0f4d1185215d44bdde8c9","score":"0.53824705","text":"def body(self):\n self._body.seek(0)\n return self._body","title":""},{"docid":"68774e53282f28341547bd0e468449a1","score":"0.5381335","text":"def s3():","title":""},{"docid":"9083daf366c94662e6b463a6db6bb19a","score":"0.5374742","text":"def body(self):\r\n self._body.seek(0)\r\n return self._body","title":""},{"docid":"37fd668313cb6871b4ce50b793eaae93","score":"0.5374298","text":"def s3_read_img_extract_text(self):\n extracted_lines = \"\"\n response = self.textract_client.detect_document_text(Document={\n 'S3Object' : {\n \"Bucket\" : self.s3_bucket_path,\n \"Name\" : self.s3_bucket_img_path\n }\n })\n\n blocks = response[\"Blocks\"]\n all_lines = [line for line in blocks if line[\"BlockType\"] == \"LINE\"]\n for line in all_lines:\n extracted_lines = extracted_lines + line[\"Text\"] + \"\\n\"\n return extracted_lines","title":""},{"docid":"cd148f6007b152f8a52343afc4234464","score":"0.5363678","text":"def __read_body(self, r):\n\n self.__files = Storage()\n content_type = r.request.env.get(\"content_type\", None)\n\n if content_type and content_type.startswith(\"multipart/\"):\n\n # Get all attached files from POST\n for p in r.request.post_vars.values():\n if isinstance(p, cgi.FieldStorage) and p.filename:\n self.__files[p.filename] = p.file\n\n # Find the source\n source_name = \"%s.%s\" % (r.name, r.representation)\n post_vars = r.request.post_vars\n source = post_vars.get(source_name, None)\n if isinstance(source, cgi.FieldStorage):\n if source.filename:\n source = source.file\n else:\n source = source.value\n if isinstance(source, basestring):\n source = StringIO.StringIO(source)\n else:\n\n # Body is source\n source = r.request.body\n source.seek(0)\n\n return source","title":""},{"docid":"284eb617f4a48ec768d0866d28970816","score":"0.5345612","text":"def get(self, relative_path):\n if not relative_path:\n raise exc.ImageNotFoundError()\n obj = self.s3.Object(self.bucket_name, relative_path)\n try:\n resp = obj.get()\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == 'NoSuchKey':\n new_exc = exc.ImageNotFoundError(*e.args)\n new_exc.original_exc = e\n raise new_exc\n else:\n raise\n return resp['Body'].read()","title":""},{"docid":"730798d2e7ce486a89bcc494d8f079a6","score":"0.5337997","text":"def _get(path):\n s3 = get_s3_client()\n\n try:\n s3_response = s3.get_object(Bucket=app.config['S3_BUCKET_NAME'],\n Key=path)\n except s3.exceptions.NoSuchKey as e:\n return _ls(path)\n\n def generate_file(result):\n for chunk in iter(lambda: result['Body'].read(int(app.config['CHUNK_SIZE'])), b''):\n yield chunk\n\n _, filename = os.path.split(path)\n response = Response(generate_file(s3_response), mimetype=s3_response['ContentType'])\n response.headers.add('Content-Disposition', 'attachment', filename=filename)\n return response","title":""},{"docid":"a676362b42c3b4ff9ddd2fc631aed68f","score":"0.5329704","text":"def get(self, key):\n \"\"\"This function is to be overwritten by the subclass \"\"\"\n formatted_key = self.__format_key(key)\n try:\n return self.s3_api.read_value(formatted_key)\n except Exception as e:\n print str(e)","title":""},{"docid":"9370d607df0f5083fed7a75fdd466e16","score":"0.5323031","text":"def s3_bucket(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"s3_bucket\")","title":""},{"docid":"d0d386f763ec8b8141cde149c4d4b060","score":"0.5316559","text":"def get_body(self):\n fp = self._environ['wsgi.input']\n return fp.read()","title":""},{"docid":"fcea8617b1843ad9407eed6fa86ebf04","score":"0.5308496","text":"def get_s3_image(bucket, filename):\n key = bucket.get_key(filename)\n tmp = io.BytesIO()\n key.get_contents_to_file(tmp)\n return Image.open(tmp)","title":""},{"docid":"50c982014e8e10255aef9d89d0734fe0","score":"0.529803","text":"def get_content_boto3(iterator):\n\tlog.debug('Entering get_content_boto3')\n\ts3 = get_s3_resource()\n\tresult = []\n\tfor x in iterator:\n\t\tlog.debug('DEBUG: getting contents of file {}'.format(x))\n\t\tx_split = x.split('/') # x is like 's3://data-lake-us-east-2-549323063936-encrypted/Agrian/obfuscated_data/grower/xx.json'\n\t\tbucket = x_split[2] #e.g. 'data-lake-us-east-2-549323063936-encrypted'\n\t\tpath = '/'.join(x_split[3:]) #e.g. 'Agrian/AdHoc/NoETL/list/8.json' or 'Agrian/obfuscated_data/grower/xx.json'\n\t\tobj = s3.Object(bucket, path)\n\t\tattempts = 0\n\t\tmax_attempts = 5\n\t\twhile attempts < max_attempts:\n\t\t\ttry:\n\t\t\t\tcontent = obj.get()['Body'].read().decode('utf-8')\n\t\t\t\tlog.debug('DEBUG: retrieved the body of the file')\n\t\t\t\tresult.append(content) #Note: result is a list of contents of json files, distributed to current worker\n\t\t\t\tlog.debug('DEBUG: appended body to results list')\n\t\t\t\tbreak\n\t\t\texcept Exception as e:\n\t\t\t\tattempts += 1\n\t\t\t\tif attempts == max_attempts - 1: log.debug(\"ERROR: get_content_boto3 got error with {}:\\n{}\".format(x, e))\n\tlog.debug('Exiting get_content_boto3 with {} results.'.format(len(result)))\n\treturn [result]","title":""},{"docid":"401035e830d78f37af6c02835970c5b2","score":"0.52963024","text":"def getBody(self):\n return self.body","title":""},{"docid":"1b1ee6c6601aff5007830ff0f1c3a9c8","score":"0.52927315","text":"def _get_object(self, obj_name: str, size: int, off: int) -> bytes:\n\n try:\n resp = self.s3.get_object(Bucket=cfg.bucket_name, Key=obj_name,\n Range=f'bytes={off}-{off+size-1}')\n except ClientError as exc:\n if exc.response['Error']['Code'] == 'NoSuchKey':\n return bytearray(size)\n raise\n\n body = resp['Body']\n with closing(body):\n buf = body.read()\n\n assert len(buf) == size, f'requested {size} bytes, got {len(buf)}'\n return buf","title":""},{"docid":"6695cacca1ac01b1e0b9b1ec7b5ecf00","score":"0.52920526","text":"def get(self, key):\n fileobj = io.BytesIO()\n self._bucket.download_fileobj(key, fileobj)\n return fileobj.getvalue()","title":""},{"docid":"564907a5b01159b96a47f85f79381e39","score":"0.52790666","text":"def find_body(message):\n\n if message.is_multipart():\n #get the plain text version only\n text_parts = [part for part in typed_subpart_iterator(message,'text','plain')]\n body = []\n for part in text_parts:\n charset = get_charset(part, get_charset(message))\n body.append(str(part.get_payload(decode=True),charset,\"replace\"))\n \n# else: # if it is not multipart, the payload will be a string\n# # representing the message body\n# body = unicode(message.get_payload(decode=True),get_charset(message),\"replace\")\n# return body.splitlines()\n\n body=u''.join(body).splitlines()\n \n bodyy=[]\n for i in body:\n if i is not '':\n bodyy.append(i)\n return bodyy","title":""},{"docid":"1fc6a9bda729bfb2001039a9b73f03bd","score":"0.52726597","text":"def filecontents(self):\n if self.request.files:\n return next(iter(self.request.files.values()))[0].body\n else:\n return self.request.body","title":""},{"docid":"31f405065d8829652c2acc2f5abd5a7e","score":"0.52724797","text":"def body_string(self, charset=None, unicode_errors=\"strict\"):\r\n\r\n if not self.can_read():\r\n raise AlreadyRead()\r\n\r\n\r\n body = self._body.read()\r\n self._already_read = True\r\n\r\n self.connection.release(self.should_close)\r\n\r\n if charset is not None:\r\n try:\r\n body = body.decode(charset, unicode_errors)\r\n except UnicodeDecodeError:\r\n pass\r\n return body","title":""},{"docid":"71ffbe40461bf8d29e69f8fb92bc332f","score":"0.5272454","text":"def get_body(self):\n if self.encoded:\n return base64.b64decode(self.body.encode('ascii'))\n return self.body.encode('utf-8')","title":""},{"docid":"71ffbe40461bf8d29e69f8fb92bc332f","score":"0.5272454","text":"def get_body(self):\n if self.encoded:\n return base64.b64decode(self.body.encode('ascii'))\n return self.body.encode('utf-8')","title":""},{"docid":"cf74ee0e11c33b1cd45e5d7e43ed9b2b","score":"0.52472264","text":"async def get_presigned_s3_url(self, session: aiohttp.ClientSession) -> None:\n # Get the object id from the object path\n _, obj_id = self.object_details.object_path.rsplit('/', 1)\n\n async with session.get(f\"{self.service_root}/{obj_id}\", headers=self.object_service_headers) as response:\n if response.status == 200:\n # Successfully signed the request\n response_data = await response.json()\n self.presigned_s3_url = response_data.get(\"presigned_url\")\n else:\n # Something when wrong while trying to pre-sign the URL.\n body = await response.json()\n raise IOError(f\"Failed to get pre-signed URL for GET at {self.object_details.dataset_path}:{obj_id}.\"\n f\" Status: {response.status}. Response: {body}\")","title":""},{"docid":"415e335072b69dbf70576c9c99dab945","score":"0.5233616","text":"def get_body_from_email(cls, email_path):\n\n english_stopwords = stopwords.words('english')\n wnl = WordNetLemmatizer()\n words = set()\n for word in brown.words():\n words.add(word.lower())\n email_message = email.message_from_file(open(email_path, 'rb'))\n email_bodies = []\n for part in email_message.walk():\n content_encoding = part.get('Content-Transfer-Encoding')\n content_type = part.get_content_type().lower()\n if content_type == 'text/plain':\n part_content = part.get_payload()\n if content_encoding and content_encoding.lower() == 'base64':\n part_content = base64.decodestring(part_content)\n email_bodies.append(part_content)\n elif content_type == 'text/html':\n try:\n part_content = part.get_payload()\n if content_encoding and content_encoding.lower() == 'base64':\n part_content = base64.decodestring(part_content)\n parser = MyHTMLParser()\n parser.feed(part_content)\n email_bodies.append(parser.get_body())\n except Exception as e:\n logging.info(str(e))\n else:\n continue\n email_body_text = ''\n if email_bodies:\n email_body_text = ' '.join(email_bodies)\n email_body_text = re.sub(r'[^a-zA-Z]+', ' ', email_body_text)\n tokens = nltk.word_tokenize(email_body_text)\n filtered_tokens = [wnl.lemmatize(word.lower()) for word in tokens\n if len(word)>1 and word not in english_stopwords and word in words]\n email_body_text = ' '.join(filtered_tokens)\n return email_body_text","title":""},{"docid":"9d85b08376ceeca6b8be843593a3d2f4","score":"0.5222265","text":"def _extract_payload(email_message):\n relevant_content_types = [\"text/plain\", \"text/html\"]\n content = \"\"\n if email_message.is_multipart():\n parts = email_message.get_payload()\n\n # handle alternative parts, e.g. html and text\n # if text/plain is available, than only use this\n if email_message.get_content_subtype == \"alternative\":\n content_types = [part.get_content_type() for part in parts if part.get_content_type() in relevant_content_types]\n if \"text/plain\" in content_types:\n content = content + _extract_payload(parts[content_types.index(\"text/plain\")])\n else:\n # iterate through parts\n for part in parts:\n content = content + _extract_payload(part)\n \n elif email_message.get_content_type() in relevant_content_types:\n payload = email_message.get_payload(decode=True)\\\n .decode(encoding=email_message.get_content_charset(), errors=\"replace\")\n if email_message.get_content_type() == \"text/html\":\n soup = BeautifulSoup(payload, 'html.parser').find(\"body\")\n if soup == None:\n soup = BeautifulSoup(payload, 'html.parser')\n content = content + soup.get_text(separator=\"\\n\")\n else: \n content = content + payload\n return content","title":""},{"docid":"87a3b5a55e4d2dcabfbfd536d782562e","score":"0.5194074","text":"def parse_raw_message(self, consumer, raw_message):\n message_id = raw_message[\"MessageId\"]\n receipt_handle = raw_message[\"ReceiptHandle\"]\n body = raw_message[\"Body\"]\n\n if self.validate_md5:\n self.validate_md5(raw_message, body)\n\n message = loads(body)[\"Message\"]\n media_type, content = self.parse_media_type_and_content(message)\n\n return SQSMessage(\n consumer=consumer,\n content=content,\n media_type=media_type,\n message_id=message_id,\n receipt_handle=receipt_handle,\n )","title":""},{"docid":"8f9d5b0317763fe1cf6cbb14e8d0da0a","score":"0.5193486","text":"def gets3model(s3_bucket, s3_model):\n try:\n #open the file\n logging.info('Connecting to S3 bucket for data %s', s3_bucket)\n s3 = boto3.resource('s3')\n with open(s3_model, 'wb') as data:\n s3.Bucket(s3_bucket).download_fileobj(s3_model, data)\n logging.info('Read file from s3 bucket %s', s3_model)\n # load the pickle model file\n with open(s3_model, 'rb') as data:\n return pickle.load(data)\n except:\n logger.warning(\"Not able to get model from s3\")\n sys.exit(1)","title":""},{"docid":"4886f1768a334783fb67f07b5f5df8fa","score":"0.5181203","text":"def _process_s3_notification(self, message):\n\n try:\n body = json.loads(message.body)\n\n # Previously we checked for body.Subject and body.Type, but this unnecessarily forced us to deliver\n # messages via SNS. When writing tools to mirror the S3 Event Notifications delivered via S3 -> SNS -> SQS\n # this arbitrarily required use of SNS to apply the Subject and Type keys. Lifting these checks allows \n # us to imitate the format with direct programmatic SQS enqueue.\n try:\n # Support messages delivered via both direct S3->SQS and S3->SNS->SQS event notification channels\n if 'Message' in body:\n message = json.loads(body['Message'])\n else:\n message = body\n\n for record in message['Records']:\n if 'eventName' in record and \\\n record['eventName'].startswith('ObjectCreated') and \\\n 'eventVersion' in record and \\\n record['eventVersion'].startswith(self.event_version_supported):\n self._ingest_s3_notification_object(record['s3'])\n else:\n # Log message that didn't match with valid EventName and EventVersion\n raise SQSNotificationError('Unable to process message as it does not match '\n 'EventName and EventVersion: {}'.format(json.dumps(record)))\n except KeyError as ex:\n raise SQSNotificationError(\n 'Exception: {}'\n 'Unable to process message as it does not appear to be an S3 notification: {}'.format(\n ex.message, json.dumps(message)))\n except (TypeError, ValueError) as ex:\n raise SQSNotificationError(\n 'Exception: {}\\nUnable to process message not recognized as valid JSON: {}.'.format(ex.message,\n message))","title":""},{"docid":"d27737878598691ed459c616b805280c","score":"0.5177871","text":"def get(self):\r\n\r\n\t\t# make sure we have a valid queue assignment before executing get\r\n\t\tif not self.queue_url:\r\n\t\t\tlogger.warning(f'{self._describe(\"get\")}: no queue url assigned; get() ignored')\r\n\t\t\treturn None\r\n\r\n\t\tlogger.info(self._describe('get'))\r\n\t\ttry:\r\n\t\t\tresponse = self.client.receive_message(\r\n\t\t\t\tQueueUrl=self.queue_url,\r\n\t\t\t\tMaxNumberOfMessages=1,\r\n\t\t\t\tMessageAttributeNames=['All'],\r\n\t\t\t\tVisibilityTimeout=0,\r\n\t\t\t\tWaitTimeSeconds=0\r\n\t\t\t)\r\n\r\n\t\t# exception handling\r\n\t\texcept ClientError as e:\r\n\t\t\tlogger.error(e)\r\n\t\t\tresponse = None\r\n\t\texcept Exception as e:\r\n\t\t\tlogger.exception(f'client.receive_message() failed: {e}')\r\n\t\t\traise\r\n\r\n\t\t# make sure test messages are filtered out\r\n\t\tif 's3:TestEvent' in str(response):\r\n\t\t\tlogger.info('Deleting AWS S3:SQS linkage test message')\r\n\t\t\tmessage_id = response['Messages'][0]['ReceiptHandle']\r\n\t\t\tself.delete(message_id)\r\n\t\t\treturn None\r\n\r\n\t\t# track seen messages and ignore duplicate messages\r\n\t\tif response and 'Messages' in response:\r\n\t\t\tmessage_id = response['Messages'][0]['ReceiptHandle']\r\n\t\t\tif message_id in self.queue_seen_messages:\r\n\t\t\t\tresponse = None\r\n\t\t\telse:\r\n\t\t\t\tself.queue_seen_messages.add(message_id)\r\n\r\n\t\treturn response","title":""},{"docid":"e4595a3ac6ff8336986a19318e48c3fd","score":"0.51714677","text":"def s3(self) -> s3.S3:\n\n if self._s3:\n return self._s3\n self._s3 = s3.S3(self)\n return self._s3","title":""},{"docid":"72a93922b29323b0a55defa439f809f6","score":"0.5169198","text":"def get_object(self, bucket_name, key, stream=False, extra_get_args={}):\n buffer = None\n try:\n file_path = os.path.join(STORAGE_FOLDER, bucket_name, key)\n with open(file_path, \"rb\") as f:\n if 'Range' in extra_get_args:\n byte_range = extra_get_args['Range'].replace('bytes=', '')\n first_byte, last_byte = map(int, byte_range.split('-'))\n f.seek(first_byte)\n buffer = io.BytesIO(f.read(last_byte-first_byte+1))\n else:\n buffer = io.BytesIO(f.read())\n if stream:\n return buffer\n else:\n return buffer.read()\n except Exception:\n raise StorageNoSuchKeyError(os.path.join(STORAGE_FOLDER, bucket_name), key)","title":""},{"docid":"ba90cba55a49d116969c4ec99d86ca39","score":"0.5168328","text":"def get_body(self):\n return self._segments[1:]","title":""},{"docid":"33cc7b5339ed1b1b50deed926da4c14d","score":"0.51405466","text":"def getMimeMessage(body):\n msg_str = base64.urlsafe_b64decode(body.encode('ASCII'))\n mime_message = email.message_from_bytes(msg_str)\n return mime_message.get_payload(0)","title":""},{"docid":"057214ac7996df738858220069f6026e","score":"0.51254755","text":"def get_content(self):\n return self._message","title":""},{"docid":"0387622c6b9a6a1f674205347a43985c","score":"0.5124639","text":"def aopen(key_name, bucket_name='contrivers-assets'):\n try:\n s3 = boto3.client('s3')\n obj = s3.get_object(Bucket=bucket_name, Key=key_name)\n contents = obj.get('Body').read()\n utf = contents.decode('utf-8')\n logging.debug('boto3 get_object decodes properly from utf-8')\n return utf\n except UnicodeDecodeError:\n logging.debug('boto3 get_object threw an error decoding from utf-8')\n return contents\n except botocore.exceptions.ClientError as err:\n # TODO: handle ClientError\n raise err","title":""},{"docid":"7f620fd222879c857904ff94378a880f","score":"0.5095027","text":"async def read_body(self):\n if self.readystate == self.OPENED:\n self.__read_startline()\n\n if self.readystate == self.IN_HEADERS:\n self.__read_headers()\n\n if self.readystate == self.IN_BODY:\n self.body = await self.reader.read()\n self.readystate = self.DONE\n\n return self.body","title":""},{"docid":"863f2df415be830bfc0e089609b6766c","score":"0.5093462","text":"def _body(self):\n data = self.data()\n return data[data.index(\"\\n\\n\")+2:]","title":""},{"docid":"dee5f89df520509c982017f59f6fdb50","score":"0.5087469","text":"def __get__(self, name: str) -> io.BytesIO:\n obj = self.client.get_object(\n bucket_name=self.name,\n object_name=name\n )\n data = io.BytesIO()\n data.write(obj.read())\n data.seek(0)\n return data","title":""},{"docid":"efa4330237ec8b1d6a7344dd7dcab4f6","score":"0.5076208","text":"def get_message():\n aws_client = boto3.client('sqs', region_name=config.sqs['region'])\n\n response = aws_client.receive_message(\n QueueUrl=config.sqs['repo_url'],\n MessageAttributeNames=['repo'],\n MaxNumberOfMessages=1,\n )\n\n if 'Messages' not in response:\n return []\n\n return response['Messages'][0]","title":""},{"docid":"37f977ee229a153ae1d8f93fccb383e9","score":"0.50674963","text":"def read_from_storage(self, dbname, filename):\n source = \"s3://%s/%s/%s\" % (self.options.bucket, dbname, filename)\n _logger.info('Initiating download from S3 for %s', source)\n tmpdir = tempfile.mkdtemp()\n target = os.path.join(tmpdir, filename)\n command = ['aws', 's3', 'cp', source, target]\n self._exec_s3_cmd(command)\n try:\n with open(target, 'rb') as f:\n yield f\n finally:\n try:\n shutil.rmtree(tmpdir)\n except OSError as err:\n if err.errno != errno.ENOENT: # file does not exist\n raise","title":""},{"docid":"df4355fa65be40ec8a9d71647a9fdcdd","score":"0.5067447","text":"def get(self, request, region, bucket, format=None):\n conn = boto.s3.connect_to_region(region)\n try:\n bucket = conn.get_bucket(bucket)\n try:\n policy = bucket.get_policy()\n except boto.exception.S3ResponseError, e:\n policy = None\n except boto.exception.S3ResponseError, e:\n return Response(e.message, status=e.status)\n return JsonResponse({\n 'name': bucket.name,\n 'keys': bucket.get_all_keys(),\n 'policy': policy,\n }, encoder=serializers.ComplexEncoder)","title":""},{"docid":"d0819b86a8bfe10628bd26a86ad1b531","score":"0.5056976","text":"def list_files(s3, bucket):\n contents = []\n signed_url = {}\n try:\n for item in s3.list_objects(Bucket=bucket)['Contents']:\n object_key = item['Key']\n response = s3.generate_presigned_url('get_object',\n Params={'Bucket': bucket,\n 'Key': object_key},\n ExpiresIn=3600)\n signed_url = {'url': response, 'name': object_key}\n #signed_url.update(signed_url)\n #print(response)\n contents.append(signed_url)\n \n except Exception as e:\n pass\n return contents","title":""},{"docid":"be43e41a1e348f2187b3925499deba54","score":"0.5045635","text":"def extract_email(msg):\n msg_obj = extract_email_headers(msg)\n \n fpPos = msg.fp.tell()\n msg.fp.seek(0)\n mail = email.message_from_string(msg.fp.read())\n contents = []\n for part in mail.walk():\n if part.get_content_type() == 'text/plain':\n charset = part.get_content_charset()\n\n if charset != None:\n payload = quopri.decodestring(part.get_payload()).decode(charset)\n else: # assume ascii\n payload = quopri.decodestring(part.get_payload()).decode('ascii')\n\n payload = format_plaintext_email(payload)\n contents.append(payload)\n\n content = \"\".join(contents)\n msg_obj[\"contents\"] = lxml.html.clean.clean_html(content).encode('utf-8')\n return msg_obj","title":""},{"docid":"2c5f7a5b9f0d0d4b54e857cac14950cb","score":"0.5036696","text":"def s3_resource():\n return boto3.resource('s3')","title":""},{"docid":"999c9b157bab1d25436dffedcda50655","score":"0.50362974","text":"def s3(self) -> Optional['outputs.GetLogDataProtectionPolicyDocumentStatementOperationAuditFindingsDestinationS3Result']:\n return pulumi.get(self, \"s3\")","title":""},{"docid":"3ae8482556abed27ad6c6591a92ae01d","score":"0.50320464","text":"def prepare_json(bucket, bucket_processed_emails, processed_emails_file_path):\n\n key = boto.s3.key.Key(bucket=bucket,\n name=bucket_processed_emails)\n # if the key (json file) exists, download it from s3\n # if it doesn't, create a new file in preparation to upload to S3.\n if key.exists():\n key.get_contents_to_filename(processed_emails_file_path)\n else:\n # creating file\n open(processed_emails_file_path, 'a').close()\n\n # convert to python dict\n # print processed_emails_file_path\n # f = open(processed_emails_file_path, 'r')\n # print f.read()\n processed_emails_json = open(processed_emails_file_path).read()\n processed_emails_dict = {}\n try:\n processed_emails_dict = simplejson.loads(processed_emails_json)\n print \"Processed Emails Dictionary: %s\" % processed_emails_dict\n # this mostly occurs when the file is empty\n except simplejson.scanner.JSONDecodeError:\n print \"\"\n\n return processed_emails_dict","title":""},{"docid":"7cafdc8bed526774c04027f06d441c03","score":"0.50140387","text":"async def get_presigned_s3_url(self, session: aiohttp.ClientSession) -> None:\n # Get the object id from the object path\n _, obj_id = self.object_details.object_path.rsplit('/', 1)\n\n async with session.put(f\"{self.service_root}/{obj_id}\", headers=self.object_service_headers) as response:\n if response.status == 200:\n # Successfully signed the request\n response_data = await response.json()\n self.presigned_s3_url = response_data.get(\"presigned_url\")\n self.set_s3_headers(response_data.get(\"key_id\"))\n elif response.status == 403:\n # Forbidden indicates Object already exists, don't need to re-push since we deduplicate so mark it skip\n self.skip_object = True\n else:\n # Something when wrong while trying to pre-sign the URL.\n body = await response.json()\n raise IOError(f\"Failed to get pre-signed URL for PUT at {self.object_details.dataset_path}:{obj_id}.\"\n f\" Status: {response.status}. Response: {body}\")","title":""},{"docid":"f3f83bd3dc87f6d6409b5a109b223e84","score":"0.50123256","text":"def list_bucket_contents(s3con, bucket):\n bucket = s3con.get_bucket(bucket)\n contents = bucket.list()\n for item in contents:\n print(item)","title":""},{"docid":"0798253ac21bb165ce4a70b630785a8f","score":"0.5008379","text":"def get(self, r=None):\n return self._client.bucket(self._bucket).get(self._key, r)","title":""},{"docid":"a23e7a37026d4e6ddd65c231d2120971","score":"0.5004374","text":"async def get_object(self, session: aiohttp.ClientSession) -> None:\n try:\n decompressor = snappy.StreamDecompressor()\n async with session.get(self.presigned_s3_url) as response:\n if response.status != 200:\n # An error occurred\n body = await response.text()\n raise IOError(f\"Failed to get {self.object_details.dataset_path} to storage backend.\"\n f\" Status: {response.status}. Response: {body}\")\n\n async with aiofiles.open(self.object_details.object_path, 'wb') as fd:\n while True:\n chunk = await response.content.read(self.download_chunk_size)\n if not chunk:\n fd.write(decompressor.flush())\n break\n await fd.write(decompressor.decompress(chunk))\n except Exception as err:\n logger.exception(err)\n raise IOError(f\"Failed to get {self.object_details.dataset_path} to storage backend. {err}\")","title":""},{"docid":"45493e2e0012d789ff0fded64f40c0f7","score":"0.49809808","text":"def _getMetaData(self):\n resp = self.s3_client.head_object(Bucket=self.s3_bucket_name, Key='%s/metadata.json' % self.s3_bucket_key)\n if 'Metadata' not in resp:\n return None\n \n return resp['Metadata']","title":""},{"docid":"f8c0a23e5feae1451826e5ac42e1341a","score":"0.4976314","text":"def extract_message_contents(message: EmailMessage) -> Optional[MessageContent]:\n # Validate required headers are present.\n raw_headers: Optional[Dict[str, str]] = get_message_raw_headers(message=message)\n if not raw_headers:\n print(\"Invalid headers.\")\n return None\n\n # Build message contents\n message_contents = MessageContent(\n subject=get_message_subject(subject_header_str=raw_headers.get(\"subject\")),\n body=get_message_body(message=message),\n )\n\n # Validate final state of message.\n if not message_contents.validate():\n return None\n\n return message_contents","title":""},{"docid":"03e885cecbfdc52bd879f0238863b720","score":"0.49759713","text":"def lambda_handler(event, context) -> None:\r\n for record in event['Records']:\r\n # Get the object from the event and show its content type\r\n bucket = record['s3']['bucket']['name']\r\n key = urllib.parse.unquote_plus(record['s3']['object']['key'], encoding='utf-8')\r\n try:\r\n response = s3.get_object(Bucket=bucket, Key=key)\r\n content = response['Body'].read()\r\n\r\n with gzip.GzipFile(fileobj=io.BytesIO(content), mode='rb') as fh:\r\n event_json = json.load(fh)\r\n output_dict = [record for record in event_json['Records'] if filter_user_events(record)]\r\n if len(output_dict) > 0:\r\n post_to_sns_details(output_dict)\r\n for item in output_dict:\r\n post_to_sns(get_user_email(item['userIdentity']['principalId']), item['eventName'])\r\n\r\n return response['ContentType']\r\n except Exception as e:\r\n print(e)\r\n message = f\"\"\"\r\n Error getting object {key} from bucket {bucket}.\r\n Make sure they exist and your bucket is in the same region as this function.\r\n \"\"\"\r\n print(message)\r\n raise e","title":""},{"docid":"998167e43b7b7d61f0438129e0986f55","score":"0.49743462","text":"def read_io(path):\n note = StringIO()\n if path.startswith(\"s3://\"):\n ss3 = s3(env='prod')\n for line in ss3.read(path):\n note.write(line)\n note.write(\"S3\")\n else:\n with open(path) as local:\n for line in local.readlines():\n note.write(line)\n\n note.seek(0)\n\n return note","title":""},{"docid":"9681ba00918da69e63f9cf8853b42575","score":"0.49625373","text":"def _get_part_contents(self):\n current_io = io.BytesIO()\n current_size = 0\n while current_size < MIN_S3_SIZE * self.part_size_multiplier:\n source_tar_io = self._get_file_from_cache()\n if source_tar_io is None:\n # Must be the end since no more files to add\n break\n source_tar_io.seek(0)\n current_io.write(source_tar_io.read())\n source_tar_io.close() # Cleanup\n # New current size\n current_size = current_io.tell()\n\n return current_io","title":""},{"docid":"571fff5dd6cc79836d3b41af3b934a78","score":"0.49538216","text":"def load_from_s3(self, bucket, prefix=None):\r\n n = 0\r\n if prefix:\r\n prefix = '%s/' % prefix\r\n else:\r\n prefix = '%s/' % self.id[1:]\r\n rs = bucket.list(prefix=prefix)\r\n for key in rs:\r\n n += 1\r\n m = self.new_message(key.get_contents_as_string())\r\n self.write(m)\r\n return n","title":""},{"docid":"828b555b6f332a72eea174e89043f60d","score":"0.49298134","text":"def get_s3(aws_id, aws_secret, region='us-east-1'):\n return boto3.resource(\n service_name='s3',\n region_name=region,\n aws_access_key_id=aws_id,\n aws_secret_access_key=aws_secret\n )","title":""},{"docid":"f66cbfcb843fb3a7a3db3f9843f9aab2","score":"0.4922382","text":"def extract_message_content(msg):\n headers = {str(k).lower():str(v) for k, v in msg.items()}\n\n if not msg.is_multipart():\n body = msg.get_payload()\n else:\n plain, html, other = None, None, None\n for part in [p for p in msg.walk() if not p.is_multipart()]:\n ctype = part.get_content_type()\n\n if 'text/plain' in ctype:\n plain = part.get_payload()\n elif ctype == 'text/html' or ctype == 'mixed/alternative':\n html = part.get_payload()\n else:\n other = part.get_payload()\n if plain:\n body = plain\n elif html:\n body = html\n else:\n body = other\n return headers, body","title":""},{"docid":"c4ac6d0997b4c8f4406dac260fbc63fd","score":"0.49171117","text":"def _get_message():\n raw_msg = self.subscriber_queue.get()\n return json.loads(raw_msg[raw_msg.find('{'):])","title":""},{"docid":"cc953297d66701ac4af0b9e236d6a319","score":"0.49168208","text":"def extract_from_s3(self, s3_url, doc_dir):\n fetch_archive_from_http(url=s3_url, output_dir=doc_dir)","title":""},{"docid":"3fec28e84fcd762afa39d10ad6d184a6","score":"0.4908935","text":"def read_json(self, bucket_name, key, **kwargs):\n try:\n obj = self.s3_client.get_object(Bucket=bucket_name, Key=key)\n return pd.read_json(obj['Body'], **kwargs)\n except Exception as e:\n self.logger.error(e)\n return","title":""},{"docid":"63b1a97c2c8825fe55f8755ce99ce159","score":"0.4904642","text":"def download(cls, bucket, object_key, region, session=None):\n try:\n s3 = boto3.client('s3') # FIXME will fail for cross-account roles\n response = s3.get_object(\n Bucket=bucket,\n Key=object_key\n )\n template_body = response['Body'].read().decode(\"utf-8\")\n return(CFTemplate(template_body, region, s3url=f\"s3://{bucket}/{object_key}\", session=session))\n except ClientError as e:\n logger.error(\"ClientError downloading template: {}\".format(e))\n raise","title":""},{"docid":"f738193f95f753aae5ebba7e8cbc4a9d","score":"0.48943952","text":"def accessor(self, s3_spec: str, *arg) -> object:\n\n bucket_id, key_id = self.toBucketObject(s3_spec)\n session = boto3.session.Session(region_name='us-east-1')\n client = session.client('s3')\n bucket = session.resource('s3').Bucket(bucket_id)\n\n try:\n self._tempFile = tempfile.NamedTemporaryFile('w+b',delete=False)\n with open(self._tempFile.name, 'wb') as f:\n # with open(self._tempFile.name, 'wb') as f:\n bucket.download_fileobj(key_id, f)\n # return self._tempFile.file\n return open(self._tempFile.name,'r')\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == '404':\n return None\n else:\n raise\n except Exception as ei:\n print(type(ei))\n print(ei)\n raise","title":""},{"docid":"18c4d74419a5e29bfc81e484f4d06482","score":"0.48906782","text":"def get_s3(region, KEY, SECRET):\n s3_resource = boto3.resource('s3', \n region_name=region,\n aws_access_key_id=KEY,\n aws_secret_access_key=SECRET)\n \n s3_client = boto3.client('s3', \n region_name=region,\n aws_access_key_id=KEY,\n aws_secret_access_key=SECRET)\n \n return s3_resource, s3_client","title":""},{"docid":"18c4d74419a5e29bfc81e484f4d06482","score":"0.48906782","text":"def get_s3(region, KEY, SECRET):\n s3_resource = boto3.resource('s3', \n region_name=region,\n aws_access_key_id=KEY,\n aws_secret_access_key=SECRET)\n \n s3_client = boto3.client('s3', \n region_name=region,\n aws_access_key_id=KEY,\n aws_secret_access_key=SECRET)\n \n return s3_resource, s3_client","title":""},{"docid":"2958cf0af4aa47c4ab91728a234254af","score":"0.48887345","text":"def sync_s3_text(bucket, key, content_meta):\n if bucket and content_meta:\n bucket.put_object(\n Key=f\"extracts/{key}.json\",\n Body=rapidjson.dumps(content_meta),\n ACL=\"public-read\",\n )","title":""},{"docid":"a6f3d8bd4500dd3180b204c3d5ef4fc3","score":"0.48854437","text":"def get_body_text(self):\r\n return self.get_string(\"getBodyText\", [])","title":""},{"docid":"dc811169e2e0ffcf07c4f8076ac48d14","score":"0.488312","text":"def get_entry(\n s3Uri: str, max_age_in_seconds=60, entry_name=False, check_before_download=True\n):\n\n file_location = get_value(\n argument=s3Uri,\n max_age_in_seconds=max_age_in_seconds,\n entry_name=entry_name,\n miss_function=get_object_from_s3,\n check_before_download=check_before_download,\n send_details=True, # specifies to send all details to miss_function\n s3Uri=s3Uri,\n )\n\n return file_location","title":""},{"docid":"5877572b38aede1f3bb67e39ce3d02ab","score":"0.4882852","text":"def get_request_body(self):","title":""},{"docid":"9ca679ae20a9843e39274b16293c722f","score":"0.48822495","text":"def decode_body(email):\n body = email.get_payload(decode=True)\n default_charset = email.get_charsets()[0]\n if default_charset:\n body = body.decode(default_charset)\n return body","title":""},{"docid":"6dc30ebe2617286248e2464c58240ab7","score":"0.48812604","text":"def get_from_s3(s3_path, local_file_path):\n command = \"sudo s3cmd get {s3_path} {local_file_path}\" \\\n .format(s3_path=s3_path, local_file_path=local_file_path)\n print(command)\n SystemPipeline.run_command(command)","title":""},{"docid":"248af7f14d63d9afab8b83a47cc158d0","score":"0.48798344","text":"def get_s3_resource(): # pragma: no cover\n aws_session = boto3.Session(\n aws_access_key_id=settings.S3_ACCESS_KEY,\n aws_secret_access_key=settings.S3_SECRET,\n region_name=settings.S3_REGION,\n )\n s3_resource = aws_session.resource(\"s3\", endpoint_url=settings.S3_ENDPOINT)\n return s3_resource","title":""}],"string":"[\n {\n \"docid\": \"a7e98b7bf32ee40c89ed274de3a937a9\",\n \"score\": \"0.6889813\",\n \"text\": \"def read(self):\\n return self._s3_key.get_contents_as_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfccaf4e4df65eefb2badef46de88a72\",\n \"score\": \"0.6478215\",\n \"text\": \"def file_contents(bucket, key):\\n s3 = boto3.client('s3')\\n return s3.get_object(Bucket=bucket, Key=key)['Body'].read().decode('UTF-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11fd7cabed5e4038fcd546a6db6d15d\",\n \"score\": \"0.63722885\",\n \"text\": \"def read(self, document_name):\\n # fetch encrypted text from S3 and decrypt\\n key = s3.Key(self.bucket)\\n key.key = '%s/%s' % (self.gpg_email, document_name)\\n\\n try:\\n encrypted_text = key.get_contents_as_string()\\n except boto.exception.S3ResponseError:\\n return None\\n else:\\n return self._decrypt(encrypted_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebffd67148242b7a656166206e2e50ee\",\n \"score\": \"0.61347026\",\n \"text\": \"def get_template_body(url):\\n url_prefix = os.environ.get(\\\"GPWM_TEMPLATE_URL_PREFIX\\\", \\\"\\\")\\n if url_prefix:\\n if url_prefix.endswith(\\\"/\\\"):\\n url_prefix = url_prefix[:-1]\\n if url.startswith(\\\"/\\\"):\\n url = url[1:]\\n url = f\\\"{url_prefix}/{url}\\\"\\n\\n parsed_url = urlparse(url)\\n if \\\"http\\\" in parsed_url.scheme: # http and https\\n try:\\n request = requests.get(url)\\n request.raise_for_status()\\n body = request.text\\n except requests.exceptions.RequestException as exc:\\n raise SystemExit(exc)\\n elif parsed_url.scheme == \\\"s3\\\":\\n s3 = boto3.resource(\\\"s3\\\")\\n obj = s3.Object(parsed_url.netloc, parsed_url.path[1:])\\n extra_args = {k: v[0] for k, v in parse_qs(parsed_url.query).items()}\\n try:\\n body = obj.get(**extra_args)[\\\"Body\\\"].read()\\n except s3.meta.client.exceptions.NoSuchBucket as exc:\\n raise SystemExit(\\n f\\\"Error: S3 bucket doesn't exist: {parsed_url.netloc}\\\"\\n )\\n except s3.meta.client.exceptions.NoSuchKey as exc:\\n raise SystemExit(f\\\"Error: S3 object doesn't exist: {url}\\\")\\n elif not parsed_url.scheme:\\n with open(url) as local_file:\\n body = local_file.read()\\n else:\\n raise SystemExit(f\\\"URL scheme not supported: {parsed_url.scheme}\\\")\\n\\n return parsed_url, body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1671e29475323fb07424c930306c8645\",\n \"score\": \"0.60105187\",\n \"text\": \"def get_object(bucket_name, object_name):\\n\\n # Retrieve the object\\n try:\\n response = s3.get_object(Bucket=bucket_name, Key=object_name)\\n except ClientError as e:\\n # AllAccessDisabled error == bucket or object not found\\n return None\\n # Return an open StreamingBody object\\n return response['Body']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eca162bbe0dee4842d0a6d58ebcbb0a\",\n \"score\": \"0.586785\",\n \"text\": \"def get_object(bucket_name, object_name):\\n\\n # Retrieve the object\\n s3 = boto3.client('s3')\\n try:\\n response = s3.get_object(Bucket=bucket_name, Key=object_name)\\n except ClientError as e:\\n # AllAccessDisabled error == bucket or object not found\\n logging.error(e)\\n return None\\n # Return an open StreamingBody object\\n return response['Body']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a45b3ce9aad502cbf74888f92a327d0\",\n \"score\": \"0.5792976\",\n \"text\": \"def get(self, path):\\n file_path = self._client.get_object( Bucket=self._bucket, Key=path)\\n return file_path['Body'].read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1292bbcc70c588a2b908e7bdbf76e826\",\n \"score\": \"0.5783603\",\n \"text\": \"def get_email_object(self):\\n if self.eml:\\n if self.eml.name.endswith('.gz'):\\n body = gzip.GzipFile(fileobj=self.eml).read()\\n else:\\n self.eml.open()\\n body = self.eml.file.read()\\n self.eml.close()\\n else:\\n body = self.get_body()\\n flat = email.message_from_bytes(body)\\n return self._rehydrate(flat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80486a7fb1ceaf98b51f59b27973953\",\n \"score\": \"0.5772815\",\n \"text\": \"def _get_body(self):\\n return self._body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6217f81cb78e07ce57e3e29e99cb2789\",\n \"score\": \"0.57675606\",\n \"text\": \"def get_s3_json(bucket, key):\\n \\n # Load credentials from AWS S3:\\n s3 = boto3.client('s3')\\n response = s3.get_object(Bucket=bucket, Key=key)\\n # Translate S3 object content to a dict:\\n string_content = response['Body'].read().decode('utf-8')\\n dict_content = json.loads(string_content)\\n \\n return dict_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c7156edc935899e487b18f3ff3d91d\",\n \"score\": \"0.57497996\",\n \"text\": \"def get_data(s3_client, bucket_name: str, object_key: str):\\n\\n if not re.search(r'^\\\\S+.json$', object_key):\\n raise Exception(\\\"File format not supported, Only '.json' format is supported as of now.\\\")\\n\\n try:\\n return s3_client.get_object(Bucket=bucket_name, Key=object_key)\\n\\n except ClientError as ce:\\n if ce.response['Error']['Code'] == 'AccessDenied' or ce.response['Error']['Code'] == 'NoSuchKey':\\n return {}\\n raise ce\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2df1baca09969abac0e128bd77bda36\",\n \"score\": \"0.5714189\",\n \"text\": \"def get_body(self) -> str:\\n return self.data[\\\"body\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2df1baca09969abac0e128bd77bda36\",\n \"score\": \"0.5714189\",\n \"text\": \"def get_body(self) -> str:\\n return self.data[\\\"body\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a28aa86756b2ef5982ea806c90533e95\",\n \"score\": \"0.5712772\",\n \"text\": \"def get_stream(self, rel_path):\\n from boto.s3.key import Key\\n from boto.exception import S3ResponseError \\n \\n import StringIO\\n \\n if self.prefix is not None:\\n rel_path = self.prefix+\\\"/\\\"+rel_path\\n \\n k = Key(self.bucket)\\n\\n k.key = rel_path\\n \\n b = StringIO.StringIO()\\n try:\\n k.get_contents_to_file(b)\\n b.seek(0)\\n return b;\\n except S3ResponseError as e:\\n if e.status == 404:\\n return None\\n else:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e381d52a564f25d03f5ec12152ea660d\",\n \"score\": \"0.568055\",\n \"text\": \"def download(self, s3_bucket, document_s3_path, version_id=None):\\n try:\\n s3 = boto3.resource(\\\"s3\\\", region_name=self._region, **self._credentials)\\n if version_id:\\n instances_file_content = s3.ObjectVersion(s3_bucket, document_s3_path, version_id).get()[\\\"Body\\\"].read()\\n else:\\n instances_file_content = s3.Object(s3_bucket, document_s3_path).get()[\\\"Body\\\"].read()\\n return instances_file_content\\n except Exception as e:\\n self.error(\\n \\\"Failed when downloading file %s from bucket %s in region %s with error %s\\\".format(\\n document_s3_path, s3_bucket, self._region, e\\n )\\n )\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d33ef17b29357c23d141494411eefb3\",\n \"score\": \"0.5605963\",\n \"text\": \"def s3(self):\\n return self._s3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63687fabdd2601ca958ab45c1d996ee9\",\n \"score\": \"0.5541879\",\n \"text\": \"def get_s3_response(response: dict) -> dict:\\n s3_bucket = response.get(\\\"s3_bucket\\\")\\n s3_key = response.get(\\\"s3_key\\\")\\n LOGGER.info(f\\\"Retrieving data from s3://{s3_bucket}/{s3_key}\\\")\\n\\n s3_object = s3_client.get_object(Bucket=s3_bucket, Key=s3_key)[\\\"Body\\\"].read().decode(\\\"utf-8\\\")\\n try:\\n return json.loads(s3_object)\\n except json.decoder.JSONDecodeError:\\n return s3_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284fcd652987077b71c6933add0406fa\",\n \"score\": \"0.55129445\",\n \"text\": \"def read_file_from_s3(self, key):\\n downloaded = True\\n try:\\n bucket = self.S3.Bucket(self.BUCKET)\\n file_obj = bucket.Object(key)\\n return file_obj\\n except botocore.exceptions.ClientError:\\n downloaded = False\\n return downloaded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25fc23cd65ec9d02517cb2089d3cd1b7\",\n \"score\": \"0.55006593\",\n \"text\": \"def get_body(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48083ceaa2670a041ff949207e57db2a\",\n \"score\": \"0.5500529\",\n \"text\": \"def get_email_object(self):\\n if not hasattr(self, '_email_object'): # Cache fill\\n if self.eml:\\n if self.eml.name.endswith('.gz'):\\n body = gzip.GzipFile(fileobj=self.eml).read()\\n else:\\n self.eml.open()\\n body = self.eml.file.read()\\n self.eml.close()\\n else:\\n body = self.get_body()\\n flat = email.message_from_bytes(body)\\n self._email_object = self._rehydrate(flat)\\n return self._email_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ffa01d82618a47d0cfa1f809d95a0a\",\n \"score\": \"0.54829854\",\n \"text\": \"def get(self, name):\\n obj = self.bucket.Object(name)\\n try:\\n return (obj.get()['Body'].read(), _extract_metadata(obj))\\n except ClientError as e:\\n if _is_no_such_key_error(e):\\n raise FileNotFound(\\\"{} not found.\\\".format(name))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60d33c3cc0da0d98fab71577630c64f\",\n \"score\": \"0.54762787\",\n \"text\": \"def get_file_as_string(s3, artifact, file_in_zip):\\n tmp_file = tempfile.NamedTemporaryFile()\\n bucket = artifact['location']['s3Location']['bucketName']\\n key = artifact['location']['s3Location']['objectKey']\\n\\n with tempfile.NamedTemporaryFile() as tmp_file:\\n s3.download_file(bucket, key, tmp_file.name)\\n with zipfile.ZipFile(tmp_file.name, 'r') as zip:\\n return zip.read(file_in_zip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc6bd60db080e620d1c8b70e046855d8\",\n \"score\": \"0.54711145\",\n \"text\": \"def getmsgBody(self): \\n return self.msgBody\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e171a010e1bf173fc84fb8c782def4\",\n \"score\": \"0.5464676\",\n \"text\": \"def _read_message(self):\\n line = self._rfile.readline()\\n\\n if not line:\\n return None\\n\\n content_length = self._content_length(line)\\n\\n # Blindly consume all header lines\\n while line and line.strip():\\n line = self._rfile.readline()\\n\\n if not line:\\n return None\\n\\n # Grab the body\\n return self._rfile.read(content_length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ac11f88cd60e23b0cf1c6dbeca2c5e\",\n \"score\": \"0.54480714\",\n \"text\": \"def process_new_email(event, context):\\n x = event['Records'][0]\\n bucket = x['s3']['bucket']['name']\\n key = x['s3']['object']['key']\\n key = parse.unquote_plus(key)\\n id = uuid.uuid1()\\n local_file = f\\\"/tmp/{id}.pdf\\\"\\n s3_resource.Bucket(bucket).download_file(Filename=local_file, Key=key)\\n parse_send_email(local_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b95bf0f94458e28796ccf6b5a546bb9\",\n \"score\": \"0.5436854\",\n \"text\": \"def get_email_content(self, email):\\n\\n if email.is_multipart():\\n self.log('Extracting email contents from multipart message')\\n\\n magic_type = 'multipart/alternative'\\n payload_types = dict((p.get_content_type(), i) for i, p in enumerate(email.get_payload()))\\n if magic_type in payload_types.keys():\\n self.log('Found magic content type: %s' % magic_type)\\n index = payload_types[magic_type]\\n payload = email.get_payload()[index].get_payload()\\n else:\\n payload = email.get_payload()\\n\\n for pl in payload:\\n if pl.get_filename() is not None:\\n # it's an attached file\\n continue\\n\\n if pl.get_content_type() in ACCEPTABLE_TYPES:\\n return pl.get_payload()\\n else:\\n return email.get_payload()\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6617405363473901f3f12a1b079d55\",\n \"score\": \"0.5429134\",\n \"text\": \"def send(self):\\n if not SesEmail.is_local:\\n # Use aws creds for ses if possible, otherwise, use aws_key from config\\n s3_client = boto3.client(service_name='ses', region_name=CONFIG_BROKER['ses_aws_region'])\\n email_message = {\\n \\\"Subject\\\": {\\n \\\"Data\\\": self.subject\\n },\\n \\\"Body\\\": {\\n \\\"Html\\\": {\\n \\\"Data\\\": self.content\\n }\\n }\\n }\\n email_dest = {\\\"ToAddresses\\\": [self.to_address]}\\n try:\\n return s3_client.send_email(Source=self.from_address, Message=email_message, Destination=email_dest)\\n except Exception:\\n s3_client = boto3.client(service_name='ses', region_name=CONFIG_BROKER['ses_aws_region'],\\n aws_access_key_id=CONFIG_BROKER['ses_access_key_id'],\\n aws_secret_access_key=CONFIG_BROKER['ses_secret_access_key'])\\n return s3_client.send_email(Source=self.from_address, Message=email_message, Destination=email_dest)\\n else:\\n new_email_text = \\\"\\\\n\\\\n\\\".join([\\\"\\\", \\\"Time\\\", str(datetime.datetime.now()), \\\"Subject\\\", self.subject, \\\"From\\\",\\n self.from_address, \\\"To\\\", self.to_address, \\\"Content\\\", self.content])\\n open(SesEmail.emailLog, \\\"a\\\").write(new_email_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2447f3446b37bca22cb5a88a2f3888ef\",\n \"score\": \"0.5403479\",\n \"text\": \"def read_s3_json(connection, bucket, key):\\n\\tlog.debug(\\\"starting read_s3_json for key={}\\\".format(key) )\\n\\tout = None\\n\\tattempts = 1\\n\\tflag = 1 # 1=had not read file, 0 = file read\\n\\texc = \\\"No exceptions recorded\\\"\\n\\twhile attempts < 4: # max 3 attempts to read each file, backoff time increases after each attempt\\n\\t\\ttry:\\n\\t\\t\\t# keep decode for special characters.\\n\\t\\t\\tlog.debug(\\\"Reading JSON object from S3: bucket={} key={}\\\".format(bucket, key) )\\n\\t\\t\\tobj = connection.get_object(Bucket=bucket, Key=key)\\n\\t\\t\\tout = json.load(obj['Body'])\\n\\t\\t\\tflag = 0\\n\\t\\t\\tlog.debug(\\\"Reading JSON object complete!\\\")\\n\\t\\t\\tbreak # stop if success reading file\\n\\t\\texcept Exception as e:\\n\\t\\t\\texc = e\\n\\t\\t\\tattempts += 1\\n\\t\\t\\tflag = 1\\n\\t\\t\\ttime.sleep((attempts)*5.0)\\n\\n\\tif flag == 1:\\n\\t\\tlog.exception(\\\"Can't read file {} {} times\\\\nLast recorded exception: {}\\\".format(\\n\\t\\t\\tkey, attempts, str(exc)))\\n\\tlog.debug(\\\"finishing read_s3_json\\\")\\n\\treturn out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce9d05084b36ce03885632ee144da4d\",\n \"score\": \"0.5395776\",\n \"text\": \"def get_cache(key: str) -> dict:\\n return json.loads(s3.Object(bucket, os.path.join(bucket_path, key)).get()['Body'].read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b08cabe9a5f15da38cc088901eb8d9b\",\n \"score\": \"0.5394642\",\n \"text\": \"def import_data(self):\\n bucket = s3.Bucket(self.bucket_name)\\n obj = bucket.Object(self.key)\\n input_data = {}\\n body = obj.get()[\\\"Body\\\"].read().splitlines()\\n for line in body:\\n k, v = str(line).strip(\\\"b'\\\").split(\\\"=\\\")\\n input_data[k] = v\\n return input_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab055f177dd0f4d1185215d44bdde8c9\",\n \"score\": \"0.53824705\",\n \"text\": \"def body(self):\\n self._body.seek(0)\\n return self._body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68774e53282f28341547bd0e468449a1\",\n \"score\": \"0.5381335\",\n \"text\": \"def s3():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9083daf366c94662e6b463a6db6bb19a\",\n \"score\": \"0.5374742\",\n \"text\": \"def body(self):\\r\\n self._body.seek(0)\\r\\n return self._body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37fd668313cb6871b4ce50b793eaae93\",\n \"score\": \"0.5374298\",\n \"text\": \"def s3_read_img_extract_text(self):\\n extracted_lines = \\\"\\\"\\n response = self.textract_client.detect_document_text(Document={\\n 'S3Object' : {\\n \\\"Bucket\\\" : self.s3_bucket_path,\\n \\\"Name\\\" : self.s3_bucket_img_path\\n }\\n })\\n\\n blocks = response[\\\"Blocks\\\"]\\n all_lines = [line for line in blocks if line[\\\"BlockType\\\"] == \\\"LINE\\\"]\\n for line in all_lines:\\n extracted_lines = extracted_lines + line[\\\"Text\\\"] + \\\"\\\\n\\\"\\n return extracted_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd148f6007b152f8a52343afc4234464\",\n \"score\": \"0.5363678\",\n \"text\": \"def __read_body(self, r):\\n\\n self.__files = Storage()\\n content_type = r.request.env.get(\\\"content_type\\\", None)\\n\\n if content_type and content_type.startswith(\\\"multipart/\\\"):\\n\\n # Get all attached files from POST\\n for p in r.request.post_vars.values():\\n if isinstance(p, cgi.FieldStorage) and p.filename:\\n self.__files[p.filename] = p.file\\n\\n # Find the source\\n source_name = \\\"%s.%s\\\" % (r.name, r.representation)\\n post_vars = r.request.post_vars\\n source = post_vars.get(source_name, None)\\n if isinstance(source, cgi.FieldStorage):\\n if source.filename:\\n source = source.file\\n else:\\n source = source.value\\n if isinstance(source, basestring):\\n source = StringIO.StringIO(source)\\n else:\\n\\n # Body is source\\n source = r.request.body\\n source.seek(0)\\n\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284eb617f4a48ec768d0866d28970816\",\n \"score\": \"0.5345612\",\n \"text\": \"def get(self, relative_path):\\n if not relative_path:\\n raise exc.ImageNotFoundError()\\n obj = self.s3.Object(self.bucket_name, relative_path)\\n try:\\n resp = obj.get()\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == 'NoSuchKey':\\n new_exc = exc.ImageNotFoundError(*e.args)\\n new_exc.original_exc = e\\n raise new_exc\\n else:\\n raise\\n return resp['Body'].read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730798d2e7ce486a89bcc494d8f079a6\",\n \"score\": \"0.5337997\",\n \"text\": \"def _get(path):\\n s3 = get_s3_client()\\n\\n try:\\n s3_response = s3.get_object(Bucket=app.config['S3_BUCKET_NAME'],\\n Key=path)\\n except s3.exceptions.NoSuchKey as e:\\n return _ls(path)\\n\\n def generate_file(result):\\n for chunk in iter(lambda: result['Body'].read(int(app.config['CHUNK_SIZE'])), b''):\\n yield chunk\\n\\n _, filename = os.path.split(path)\\n response = Response(generate_file(s3_response), mimetype=s3_response['ContentType'])\\n response.headers.add('Content-Disposition', 'attachment', filename=filename)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a676362b42c3b4ff9ddd2fc631aed68f\",\n \"score\": \"0.5329704\",\n \"text\": \"def get(self, key):\\n \\\"\\\"\\\"This function is to be overwritten by the subclass \\\"\\\"\\\"\\n formatted_key = self.__format_key(key)\\n try:\\n return self.s3_api.read_value(formatted_key)\\n except Exception as e:\\n print str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9370d607df0f5083fed7a75fdd466e16\",\n \"score\": \"0.5323031\",\n \"text\": \"def s3_bucket(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"s3_bucket\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d386f763ec8b8141cde149c4d4b060\",\n \"score\": \"0.5316559\",\n \"text\": \"def get_body(self):\\n fp = self._environ['wsgi.input']\\n return fp.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcea8617b1843ad9407eed6fa86ebf04\",\n \"score\": \"0.5308496\",\n \"text\": \"def get_s3_image(bucket, filename):\\n key = bucket.get_key(filename)\\n tmp = io.BytesIO()\\n key.get_contents_to_file(tmp)\\n return Image.open(tmp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c982014e8e10255aef9d89d0734fe0\",\n \"score\": \"0.529803\",\n \"text\": \"def get_content_boto3(iterator):\\n\\tlog.debug('Entering get_content_boto3')\\n\\ts3 = get_s3_resource()\\n\\tresult = []\\n\\tfor x in iterator:\\n\\t\\tlog.debug('DEBUG: getting contents of file {}'.format(x))\\n\\t\\tx_split = x.split('/') # x is like 's3://data-lake-us-east-2-549323063936-encrypted/Agrian/obfuscated_data/grower/xx.json'\\n\\t\\tbucket = x_split[2] #e.g. 'data-lake-us-east-2-549323063936-encrypted'\\n\\t\\tpath = '/'.join(x_split[3:]) #e.g. 'Agrian/AdHoc/NoETL/list/8.json' or 'Agrian/obfuscated_data/grower/xx.json'\\n\\t\\tobj = s3.Object(bucket, path)\\n\\t\\tattempts = 0\\n\\t\\tmax_attempts = 5\\n\\t\\twhile attempts < max_attempts:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcontent = obj.get()['Body'].read().decode('utf-8')\\n\\t\\t\\t\\tlog.debug('DEBUG: retrieved the body of the file')\\n\\t\\t\\t\\tresult.append(content) #Note: result is a list of contents of json files, distributed to current worker\\n\\t\\t\\t\\tlog.debug('DEBUG: appended body to results list')\\n\\t\\t\\t\\tbreak\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tattempts += 1\\n\\t\\t\\t\\tif attempts == max_attempts - 1: log.debug(\\\"ERROR: get_content_boto3 got error with {}:\\\\n{}\\\".format(x, e))\\n\\tlog.debug('Exiting get_content_boto3 with {} results.'.format(len(result)))\\n\\treturn [result]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401035e830d78f37af6c02835970c5b2\",\n \"score\": \"0.52963024\",\n \"text\": \"def getBody(self):\\n return self.body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1ee6c6601aff5007830ff0f1c3a9c8\",\n \"score\": \"0.52927315\",\n \"text\": \"def _get_object(self, obj_name: str, size: int, off: int) -> bytes:\\n\\n try:\\n resp = self.s3.get_object(Bucket=cfg.bucket_name, Key=obj_name,\\n Range=f'bytes={off}-{off+size-1}')\\n except ClientError as exc:\\n if exc.response['Error']['Code'] == 'NoSuchKey':\\n return bytearray(size)\\n raise\\n\\n body = resp['Body']\\n with closing(body):\\n buf = body.read()\\n\\n assert len(buf) == size, f'requested {size} bytes, got {len(buf)}'\\n return buf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6695cacca1ac01b1e0b9b1ec7b5ecf00\",\n \"score\": \"0.52920526\",\n \"text\": \"def get(self, key):\\n fileobj = io.BytesIO()\\n self._bucket.download_fileobj(key, fileobj)\\n return fileobj.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564907a5b01159b96a47f85f79381e39\",\n \"score\": \"0.52790666\",\n \"text\": \"def find_body(message):\\n\\n if message.is_multipart():\\n #get the plain text version only\\n text_parts = [part for part in typed_subpart_iterator(message,'text','plain')]\\n body = []\\n for part in text_parts:\\n charset = get_charset(part, get_charset(message))\\n body.append(str(part.get_payload(decode=True),charset,\\\"replace\\\"))\\n \\n# else: # if it is not multipart, the payload will be a string\\n# # representing the message body\\n# body = unicode(message.get_payload(decode=True),get_charset(message),\\\"replace\\\")\\n# return body.splitlines()\\n\\n body=u''.join(body).splitlines()\\n \\n bodyy=[]\\n for i in body:\\n if i is not '':\\n bodyy.append(i)\\n return bodyy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc6a9bda729bfb2001039a9b73f03bd\",\n \"score\": \"0.52726597\",\n \"text\": \"def filecontents(self):\\n if self.request.files:\\n return next(iter(self.request.files.values()))[0].body\\n else:\\n return self.request.body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f405065d8829652c2acc2f5abd5a7e\",\n \"score\": \"0.52724797\",\n \"text\": \"def body_string(self, charset=None, unicode_errors=\\\"strict\\\"):\\r\\n\\r\\n if not self.can_read():\\r\\n raise AlreadyRead()\\r\\n\\r\\n\\r\\n body = self._body.read()\\r\\n self._already_read = True\\r\\n\\r\\n self.connection.release(self.should_close)\\r\\n\\r\\n if charset is not None:\\r\\n try:\\r\\n body = body.decode(charset, unicode_errors)\\r\\n except UnicodeDecodeError:\\r\\n pass\\r\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ffbe40461bf8d29e69f8fb92bc332f\",\n \"score\": \"0.5272454\",\n \"text\": \"def get_body(self):\\n if self.encoded:\\n return base64.b64decode(self.body.encode('ascii'))\\n return self.body.encode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ffbe40461bf8d29e69f8fb92bc332f\",\n \"score\": \"0.5272454\",\n \"text\": \"def get_body(self):\\n if self.encoded:\\n return base64.b64decode(self.body.encode('ascii'))\\n return self.body.encode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf74ee0e11c33b1cd45e5d7e43ed9b2b\",\n \"score\": \"0.52472264\",\n \"text\": \"async def get_presigned_s3_url(self, session: aiohttp.ClientSession) -> None:\\n # Get the object id from the object path\\n _, obj_id = self.object_details.object_path.rsplit('/', 1)\\n\\n async with session.get(f\\\"{self.service_root}/{obj_id}\\\", headers=self.object_service_headers) as response:\\n if response.status == 200:\\n # Successfully signed the request\\n response_data = await response.json()\\n self.presigned_s3_url = response_data.get(\\\"presigned_url\\\")\\n else:\\n # Something when wrong while trying to pre-sign the URL.\\n body = await response.json()\\n raise IOError(f\\\"Failed to get pre-signed URL for GET at {self.object_details.dataset_path}:{obj_id}.\\\"\\n f\\\" Status: {response.status}. Response: {body}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"415e335072b69dbf70576c9c99dab945\",\n \"score\": \"0.5233616\",\n \"text\": \"def get_body_from_email(cls, email_path):\\n\\n english_stopwords = stopwords.words('english')\\n wnl = WordNetLemmatizer()\\n words = set()\\n for word in brown.words():\\n words.add(word.lower())\\n email_message = email.message_from_file(open(email_path, 'rb'))\\n email_bodies = []\\n for part in email_message.walk():\\n content_encoding = part.get('Content-Transfer-Encoding')\\n content_type = part.get_content_type().lower()\\n if content_type == 'text/plain':\\n part_content = part.get_payload()\\n if content_encoding and content_encoding.lower() == 'base64':\\n part_content = base64.decodestring(part_content)\\n email_bodies.append(part_content)\\n elif content_type == 'text/html':\\n try:\\n part_content = part.get_payload()\\n if content_encoding and content_encoding.lower() == 'base64':\\n part_content = base64.decodestring(part_content)\\n parser = MyHTMLParser()\\n parser.feed(part_content)\\n email_bodies.append(parser.get_body())\\n except Exception as e:\\n logging.info(str(e))\\n else:\\n continue\\n email_body_text = ''\\n if email_bodies:\\n email_body_text = ' '.join(email_bodies)\\n email_body_text = re.sub(r'[^a-zA-Z]+', ' ', email_body_text)\\n tokens = nltk.word_tokenize(email_body_text)\\n filtered_tokens = [wnl.lemmatize(word.lower()) for word in tokens\\n if len(word)>1 and word not in english_stopwords and word in words]\\n email_body_text = ' '.join(filtered_tokens)\\n return email_body_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d85b08376ceeca6b8be843593a3d2f4\",\n \"score\": \"0.5222265\",\n \"text\": \"def _extract_payload(email_message):\\n relevant_content_types = [\\\"text/plain\\\", \\\"text/html\\\"]\\n content = \\\"\\\"\\n if email_message.is_multipart():\\n parts = email_message.get_payload()\\n\\n # handle alternative parts, e.g. html and text\\n # if text/plain is available, than only use this\\n if email_message.get_content_subtype == \\\"alternative\\\":\\n content_types = [part.get_content_type() for part in parts if part.get_content_type() in relevant_content_types]\\n if \\\"text/plain\\\" in content_types:\\n content = content + _extract_payload(parts[content_types.index(\\\"text/plain\\\")])\\n else:\\n # iterate through parts\\n for part in parts:\\n content = content + _extract_payload(part)\\n \\n elif email_message.get_content_type() in relevant_content_types:\\n payload = email_message.get_payload(decode=True)\\\\\\n .decode(encoding=email_message.get_content_charset(), errors=\\\"replace\\\")\\n if email_message.get_content_type() == \\\"text/html\\\":\\n soup = BeautifulSoup(payload, 'html.parser').find(\\\"body\\\")\\n if soup == None:\\n soup = BeautifulSoup(payload, 'html.parser')\\n content = content + soup.get_text(separator=\\\"\\\\n\\\")\\n else: \\n content = content + payload\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a3b5a55e4d2dcabfbfd536d782562e\",\n \"score\": \"0.5194074\",\n \"text\": \"def parse_raw_message(self, consumer, raw_message):\\n message_id = raw_message[\\\"MessageId\\\"]\\n receipt_handle = raw_message[\\\"ReceiptHandle\\\"]\\n body = raw_message[\\\"Body\\\"]\\n\\n if self.validate_md5:\\n self.validate_md5(raw_message, body)\\n\\n message = loads(body)[\\\"Message\\\"]\\n media_type, content = self.parse_media_type_and_content(message)\\n\\n return SQSMessage(\\n consumer=consumer,\\n content=content,\\n media_type=media_type,\\n message_id=message_id,\\n receipt_handle=receipt_handle,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9d5b0317763fe1cf6cbb14e8d0da0a\",\n \"score\": \"0.5193486\",\n \"text\": \"def gets3model(s3_bucket, s3_model):\\n try:\\n #open the file\\n logging.info('Connecting to S3 bucket for data %s', s3_bucket)\\n s3 = boto3.resource('s3')\\n with open(s3_model, 'wb') as data:\\n s3.Bucket(s3_bucket).download_fileobj(s3_model, data)\\n logging.info('Read file from s3 bucket %s', s3_model)\\n # load the pickle model file\\n with open(s3_model, 'rb') as data:\\n return pickle.load(data)\\n except:\\n logger.warning(\\\"Not able to get model from s3\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4886f1768a334783fb67f07b5f5df8fa\",\n \"score\": \"0.5181203\",\n \"text\": \"def _process_s3_notification(self, message):\\n\\n try:\\n body = json.loads(message.body)\\n\\n # Previously we checked for body.Subject and body.Type, but this unnecessarily forced us to deliver\\n # messages via SNS. When writing tools to mirror the S3 Event Notifications delivered via S3 -> SNS -> SQS\\n # this arbitrarily required use of SNS to apply the Subject and Type keys. Lifting these checks allows \\n # us to imitate the format with direct programmatic SQS enqueue.\\n try:\\n # Support messages delivered via both direct S3->SQS and S3->SNS->SQS event notification channels\\n if 'Message' in body:\\n message = json.loads(body['Message'])\\n else:\\n message = body\\n\\n for record in message['Records']:\\n if 'eventName' in record and \\\\\\n record['eventName'].startswith('ObjectCreated') and \\\\\\n 'eventVersion' in record and \\\\\\n record['eventVersion'].startswith(self.event_version_supported):\\n self._ingest_s3_notification_object(record['s3'])\\n else:\\n # Log message that didn't match with valid EventName and EventVersion\\n raise SQSNotificationError('Unable to process message as it does not match '\\n 'EventName and EventVersion: {}'.format(json.dumps(record)))\\n except KeyError as ex:\\n raise SQSNotificationError(\\n 'Exception: {}'\\n 'Unable to process message as it does not appear to be an S3 notification: {}'.format(\\n ex.message, json.dumps(message)))\\n except (TypeError, ValueError) as ex:\\n raise SQSNotificationError(\\n 'Exception: {}\\\\nUnable to process message not recognized as valid JSON: {}.'.format(ex.message,\\n message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27737878598691ed459c616b805280c\",\n \"score\": \"0.5177871\",\n \"text\": \"def get(self):\\r\\n\\r\\n\\t\\t# make sure we have a valid queue assignment before executing get\\r\\n\\t\\tif not self.queue_url:\\r\\n\\t\\t\\tlogger.warning(f'{self._describe(\\\"get\\\")}: no queue url assigned; get() ignored')\\r\\n\\t\\t\\treturn None\\r\\n\\r\\n\\t\\tlogger.info(self._describe('get'))\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tresponse = self.client.receive_message(\\r\\n\\t\\t\\t\\tQueueUrl=self.queue_url,\\r\\n\\t\\t\\t\\tMaxNumberOfMessages=1,\\r\\n\\t\\t\\t\\tMessageAttributeNames=['All'],\\r\\n\\t\\t\\t\\tVisibilityTimeout=0,\\r\\n\\t\\t\\t\\tWaitTimeSeconds=0\\r\\n\\t\\t\\t)\\r\\n\\r\\n\\t\\t# exception handling\\r\\n\\t\\texcept ClientError as e:\\r\\n\\t\\t\\tlogger.error(e)\\r\\n\\t\\t\\tresponse = None\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogger.exception(f'client.receive_message() failed: {e}')\\r\\n\\t\\t\\traise\\r\\n\\r\\n\\t\\t# make sure test messages are filtered out\\r\\n\\t\\tif 's3:TestEvent' in str(response):\\r\\n\\t\\t\\tlogger.info('Deleting AWS S3:SQS linkage test message')\\r\\n\\t\\t\\tmessage_id = response['Messages'][0]['ReceiptHandle']\\r\\n\\t\\t\\tself.delete(message_id)\\r\\n\\t\\t\\treturn None\\r\\n\\r\\n\\t\\t# track seen messages and ignore duplicate messages\\r\\n\\t\\tif response and 'Messages' in response:\\r\\n\\t\\t\\tmessage_id = response['Messages'][0]['ReceiptHandle']\\r\\n\\t\\t\\tif message_id in self.queue_seen_messages:\\r\\n\\t\\t\\t\\tresponse = None\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tself.queue_seen_messages.add(message_id)\\r\\n\\r\\n\\t\\treturn response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4595a3ac6ff8336986a19318e48c3fd\",\n \"score\": \"0.51714677\",\n \"text\": \"def s3(self) -> s3.S3:\\n\\n if self._s3:\\n return self._s3\\n self._s3 = s3.S3(self)\\n return self._s3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a93922b29323b0a55defa439f809f6\",\n \"score\": \"0.5169198\",\n \"text\": \"def get_object(self, bucket_name, key, stream=False, extra_get_args={}):\\n buffer = None\\n try:\\n file_path = os.path.join(STORAGE_FOLDER, bucket_name, key)\\n with open(file_path, \\\"rb\\\") as f:\\n if 'Range' in extra_get_args:\\n byte_range = extra_get_args['Range'].replace('bytes=', '')\\n first_byte, last_byte = map(int, byte_range.split('-'))\\n f.seek(first_byte)\\n buffer = io.BytesIO(f.read(last_byte-first_byte+1))\\n else:\\n buffer = io.BytesIO(f.read())\\n if stream:\\n return buffer\\n else:\\n return buffer.read()\\n except Exception:\\n raise StorageNoSuchKeyError(os.path.join(STORAGE_FOLDER, bucket_name), key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba90cba55a49d116969c4ec99d86ca39\",\n \"score\": \"0.5168328\",\n \"text\": \"def get_body(self):\\n return self._segments[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cc7b5339ed1b1b50deed926da4c14d\",\n \"score\": \"0.51405466\",\n \"text\": \"def getMimeMessage(body):\\n msg_str = base64.urlsafe_b64decode(body.encode('ASCII'))\\n mime_message = email.message_from_bytes(msg_str)\\n return mime_message.get_payload(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057214ac7996df738858220069f6026e\",\n \"score\": \"0.51254755\",\n \"text\": \"def get_content(self):\\n return self._message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0387622c6b9a6a1f674205347a43985c\",\n \"score\": \"0.5124639\",\n \"text\": \"def aopen(key_name, bucket_name='contrivers-assets'):\\n try:\\n s3 = boto3.client('s3')\\n obj = s3.get_object(Bucket=bucket_name, Key=key_name)\\n contents = obj.get('Body').read()\\n utf = contents.decode('utf-8')\\n logging.debug('boto3 get_object decodes properly from utf-8')\\n return utf\\n except UnicodeDecodeError:\\n logging.debug('boto3 get_object threw an error decoding from utf-8')\\n return contents\\n except botocore.exceptions.ClientError as err:\\n # TODO: handle ClientError\\n raise err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f620fd222879c857904ff94378a880f\",\n \"score\": \"0.5095027\",\n \"text\": \"async def read_body(self):\\n if self.readystate == self.OPENED:\\n self.__read_startline()\\n\\n if self.readystate == self.IN_HEADERS:\\n self.__read_headers()\\n\\n if self.readystate == self.IN_BODY:\\n self.body = await self.reader.read()\\n self.readystate = self.DONE\\n\\n return self.body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863f2df415be830bfc0e089609b6766c\",\n \"score\": \"0.5093462\",\n \"text\": \"def _body(self):\\n data = self.data()\\n return data[data.index(\\\"\\\\n\\\\n\\\")+2:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee5f89df520509c982017f59f6fdb50\",\n \"score\": \"0.5087469\",\n \"text\": \"def __get__(self, name: str) -> io.BytesIO:\\n obj = self.client.get_object(\\n bucket_name=self.name,\\n object_name=name\\n )\\n data = io.BytesIO()\\n data.write(obj.read())\\n data.seek(0)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa4330237ec8b1d6a7344dd7dcab4f6\",\n \"score\": \"0.5076208\",\n \"text\": \"def get_message():\\n aws_client = boto3.client('sqs', region_name=config.sqs['region'])\\n\\n response = aws_client.receive_message(\\n QueueUrl=config.sqs['repo_url'],\\n MessageAttributeNames=['repo'],\\n MaxNumberOfMessages=1,\\n )\\n\\n if 'Messages' not in response:\\n return []\\n\\n return response['Messages'][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f977ee229a153ae1d8f93fccb383e9\",\n \"score\": \"0.50674963\",\n \"text\": \"def read_from_storage(self, dbname, filename):\\n source = \\\"s3://%s/%s/%s\\\" % (self.options.bucket, dbname, filename)\\n _logger.info('Initiating download from S3 for %s', source)\\n tmpdir = tempfile.mkdtemp()\\n target = os.path.join(tmpdir, filename)\\n command = ['aws', 's3', 'cp', source, target]\\n self._exec_s3_cmd(command)\\n try:\\n with open(target, 'rb') as f:\\n yield f\\n finally:\\n try:\\n shutil.rmtree(tmpdir)\\n except OSError as err:\\n if err.errno != errno.ENOENT: # file does not exist\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4355fa65be40ec8a9d71647a9fdcdd\",\n \"score\": \"0.5067447\",\n \"text\": \"def get(self, request, region, bucket, format=None):\\n conn = boto.s3.connect_to_region(region)\\n try:\\n bucket = conn.get_bucket(bucket)\\n try:\\n policy = bucket.get_policy()\\n except boto.exception.S3ResponseError, e:\\n policy = None\\n except boto.exception.S3ResponseError, e:\\n return Response(e.message, status=e.status)\\n return JsonResponse({\\n 'name': bucket.name,\\n 'keys': bucket.get_all_keys(),\\n 'policy': policy,\\n }, encoder=serializers.ComplexEncoder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0819b86a8bfe10628bd26a86ad1b531\",\n \"score\": \"0.5056976\",\n \"text\": \"def list_files(s3, bucket):\\n contents = []\\n signed_url = {}\\n try:\\n for item in s3.list_objects(Bucket=bucket)['Contents']:\\n object_key = item['Key']\\n response = s3.generate_presigned_url('get_object',\\n Params={'Bucket': bucket,\\n 'Key': object_key},\\n ExpiresIn=3600)\\n signed_url = {'url': response, 'name': object_key}\\n #signed_url.update(signed_url)\\n #print(response)\\n contents.append(signed_url)\\n \\n except Exception as e:\\n pass\\n return contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be43e41a1e348f2187b3925499deba54\",\n \"score\": \"0.5045635\",\n \"text\": \"def extract_email(msg):\\n msg_obj = extract_email_headers(msg)\\n \\n fpPos = msg.fp.tell()\\n msg.fp.seek(0)\\n mail = email.message_from_string(msg.fp.read())\\n contents = []\\n for part in mail.walk():\\n if part.get_content_type() == 'text/plain':\\n charset = part.get_content_charset()\\n\\n if charset != None:\\n payload = quopri.decodestring(part.get_payload()).decode(charset)\\n else: # assume ascii\\n payload = quopri.decodestring(part.get_payload()).decode('ascii')\\n\\n payload = format_plaintext_email(payload)\\n contents.append(payload)\\n\\n content = \\\"\\\".join(contents)\\n msg_obj[\\\"contents\\\"] = lxml.html.clean.clean_html(content).encode('utf-8')\\n return msg_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c5f7a5b9f0d0d4b54e857cac14950cb\",\n \"score\": \"0.5036696\",\n \"text\": \"def s3_resource():\\n return boto3.resource('s3')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"999c9b157bab1d25436dffedcda50655\",\n \"score\": \"0.50362974\",\n \"text\": \"def s3(self) -> Optional['outputs.GetLogDataProtectionPolicyDocumentStatementOperationAuditFindingsDestinationS3Result']:\\n return pulumi.get(self, \\\"s3\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae8482556abed27ad6c6591a92ae01d\",\n \"score\": \"0.50320464\",\n \"text\": \"def prepare_json(bucket, bucket_processed_emails, processed_emails_file_path):\\n\\n key = boto.s3.key.Key(bucket=bucket,\\n name=bucket_processed_emails)\\n # if the key (json file) exists, download it from s3\\n # if it doesn't, create a new file in preparation to upload to S3.\\n if key.exists():\\n key.get_contents_to_filename(processed_emails_file_path)\\n else:\\n # creating file\\n open(processed_emails_file_path, 'a').close()\\n\\n # convert to python dict\\n # print processed_emails_file_path\\n # f = open(processed_emails_file_path, 'r')\\n # print f.read()\\n processed_emails_json = open(processed_emails_file_path).read()\\n processed_emails_dict = {}\\n try:\\n processed_emails_dict = simplejson.loads(processed_emails_json)\\n print \\\"Processed Emails Dictionary: %s\\\" % processed_emails_dict\\n # this mostly occurs when the file is empty\\n except simplejson.scanner.JSONDecodeError:\\n print \\\"\\\"\\n\\n return processed_emails_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cafdc8bed526774c04027f06d441c03\",\n \"score\": \"0.50140387\",\n \"text\": \"async def get_presigned_s3_url(self, session: aiohttp.ClientSession) -> None:\\n # Get the object id from the object path\\n _, obj_id = self.object_details.object_path.rsplit('/', 1)\\n\\n async with session.put(f\\\"{self.service_root}/{obj_id}\\\", headers=self.object_service_headers) as response:\\n if response.status == 200:\\n # Successfully signed the request\\n response_data = await response.json()\\n self.presigned_s3_url = response_data.get(\\\"presigned_url\\\")\\n self.set_s3_headers(response_data.get(\\\"key_id\\\"))\\n elif response.status == 403:\\n # Forbidden indicates Object already exists, don't need to re-push since we deduplicate so mark it skip\\n self.skip_object = True\\n else:\\n # Something when wrong while trying to pre-sign the URL.\\n body = await response.json()\\n raise IOError(f\\\"Failed to get pre-signed URL for PUT at {self.object_details.dataset_path}:{obj_id}.\\\"\\n f\\\" Status: {response.status}. Response: {body}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f83bd3dc87f6d6409b5a109b223e84\",\n \"score\": \"0.50123256\",\n \"text\": \"def list_bucket_contents(s3con, bucket):\\n bucket = s3con.get_bucket(bucket)\\n contents = bucket.list()\\n for item in contents:\\n print(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0798253ac21bb165ce4a70b630785a8f\",\n \"score\": \"0.5008379\",\n \"text\": \"def get(self, r=None):\\n return self._client.bucket(self._bucket).get(self._key, r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a23e7a37026d4e6ddd65c231d2120971\",\n \"score\": \"0.5004374\",\n \"text\": \"async def get_object(self, session: aiohttp.ClientSession) -> None:\\n try:\\n decompressor = snappy.StreamDecompressor()\\n async with session.get(self.presigned_s3_url) as response:\\n if response.status != 200:\\n # An error occurred\\n body = await response.text()\\n raise IOError(f\\\"Failed to get {self.object_details.dataset_path} to storage backend.\\\"\\n f\\\" Status: {response.status}. Response: {body}\\\")\\n\\n async with aiofiles.open(self.object_details.object_path, 'wb') as fd:\\n while True:\\n chunk = await response.content.read(self.download_chunk_size)\\n if not chunk:\\n fd.write(decompressor.flush())\\n break\\n await fd.write(decompressor.decompress(chunk))\\n except Exception as err:\\n logger.exception(err)\\n raise IOError(f\\\"Failed to get {self.object_details.dataset_path} to storage backend. {err}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45493e2e0012d789ff0fded64f40c0f7\",\n \"score\": \"0.49809808\",\n \"text\": \"def _getMetaData(self):\\n resp = self.s3_client.head_object(Bucket=self.s3_bucket_name, Key='%s/metadata.json' % self.s3_bucket_key)\\n if 'Metadata' not in resp:\\n return None\\n \\n return resp['Metadata']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c0a23e5feae1451826e5ac42e1341a\",\n \"score\": \"0.4976314\",\n \"text\": \"def extract_message_contents(message: EmailMessage) -> Optional[MessageContent]:\\n # Validate required headers are present.\\n raw_headers: Optional[Dict[str, str]] = get_message_raw_headers(message=message)\\n if not raw_headers:\\n print(\\\"Invalid headers.\\\")\\n return None\\n\\n # Build message contents\\n message_contents = MessageContent(\\n subject=get_message_subject(subject_header_str=raw_headers.get(\\\"subject\\\")),\\n body=get_message_body(message=message),\\n )\\n\\n # Validate final state of message.\\n if not message_contents.validate():\\n return None\\n\\n return message_contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e885cecbfdc52bd879f0238863b720\",\n \"score\": \"0.49759713\",\n \"text\": \"def lambda_handler(event, context) -> None:\\r\\n for record in event['Records']:\\r\\n # Get the object from the event and show its content type\\r\\n bucket = record['s3']['bucket']['name']\\r\\n key = urllib.parse.unquote_plus(record['s3']['object']['key'], encoding='utf-8')\\r\\n try:\\r\\n response = s3.get_object(Bucket=bucket, Key=key)\\r\\n content = response['Body'].read()\\r\\n\\r\\n with gzip.GzipFile(fileobj=io.BytesIO(content), mode='rb') as fh:\\r\\n event_json = json.load(fh)\\r\\n output_dict = [record for record in event_json['Records'] if filter_user_events(record)]\\r\\n if len(output_dict) > 0:\\r\\n post_to_sns_details(output_dict)\\r\\n for item in output_dict:\\r\\n post_to_sns(get_user_email(item['userIdentity']['principalId']), item['eventName'])\\r\\n\\r\\n return response['ContentType']\\r\\n except Exception as e:\\r\\n print(e)\\r\\n message = f\\\"\\\"\\\"\\r\\n Error getting object {key} from bucket {bucket}.\\r\\n Make sure they exist and your bucket is in the same region as this function.\\r\\n \\\"\\\"\\\"\\r\\n print(message)\\r\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998167e43b7b7d61f0438129e0986f55\",\n \"score\": \"0.49743462\",\n \"text\": \"def read_io(path):\\n note = StringIO()\\n if path.startswith(\\\"s3://\\\"):\\n ss3 = s3(env='prod')\\n for line in ss3.read(path):\\n note.write(line)\\n note.write(\\\"S3\\\")\\n else:\\n with open(path) as local:\\n for line in local.readlines():\\n note.write(line)\\n\\n note.seek(0)\\n\\n return note\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9681ba00918da69e63f9cf8853b42575\",\n \"score\": \"0.49625373\",\n \"text\": \"def _get_part_contents(self):\\n current_io = io.BytesIO()\\n current_size = 0\\n while current_size < MIN_S3_SIZE * self.part_size_multiplier:\\n source_tar_io = self._get_file_from_cache()\\n if source_tar_io is None:\\n # Must be the end since no more files to add\\n break\\n source_tar_io.seek(0)\\n current_io.write(source_tar_io.read())\\n source_tar_io.close() # Cleanup\\n # New current size\\n current_size = current_io.tell()\\n\\n return current_io\",\n \"title\": \"\"\n },\n {\n \"docid\": \"571fff5dd6cc79836d3b41af3b934a78\",\n \"score\": \"0.49538216\",\n \"text\": \"def load_from_s3(self, bucket, prefix=None):\\r\\n n = 0\\r\\n if prefix:\\r\\n prefix = '%s/' % prefix\\r\\n else:\\r\\n prefix = '%s/' % self.id[1:]\\r\\n rs = bucket.list(prefix=prefix)\\r\\n for key in rs:\\r\\n n += 1\\r\\n m = self.new_message(key.get_contents_as_string())\\r\\n self.write(m)\\r\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828b555b6f332a72eea174e89043f60d\",\n \"score\": \"0.49298134\",\n \"text\": \"def get_s3(aws_id, aws_secret, region='us-east-1'):\\n return boto3.resource(\\n service_name='s3',\\n region_name=region,\\n aws_access_key_id=aws_id,\\n aws_secret_access_key=aws_secret\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66cbfcb843fb3a7a3db3f9843f9aab2\",\n \"score\": \"0.4922382\",\n \"text\": \"def extract_message_content(msg):\\n headers = {str(k).lower():str(v) for k, v in msg.items()}\\n\\n if not msg.is_multipart():\\n body = msg.get_payload()\\n else:\\n plain, html, other = None, None, None\\n for part in [p for p in msg.walk() if not p.is_multipart()]:\\n ctype = part.get_content_type()\\n\\n if 'text/plain' in ctype:\\n plain = part.get_payload()\\n elif ctype == 'text/html' or ctype == 'mixed/alternative':\\n html = part.get_payload()\\n else:\\n other = part.get_payload()\\n if plain:\\n body = plain\\n elif html:\\n body = html\\n else:\\n body = other\\n return headers, body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ac6d0997b4c8f4406dac260fbc63fd\",\n \"score\": \"0.49171117\",\n \"text\": \"def _get_message():\\n raw_msg = self.subscriber_queue.get()\\n return json.loads(raw_msg[raw_msg.find('{'):])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc953297d66701ac4af0b9e236d6a319\",\n \"score\": \"0.49168208\",\n \"text\": \"def extract_from_s3(self, s3_url, doc_dir):\\n fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fec28e84fcd762afa39d10ad6d184a6\",\n \"score\": \"0.4908935\",\n \"text\": \"def read_json(self, bucket_name, key, **kwargs):\\n try:\\n obj = self.s3_client.get_object(Bucket=bucket_name, Key=key)\\n return pd.read_json(obj['Body'], **kwargs)\\n except Exception as e:\\n self.logger.error(e)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63b1a97c2c8825fe55f8755ce99ce159\",\n \"score\": \"0.4904642\",\n \"text\": \"def download(cls, bucket, object_key, region, session=None):\\n try:\\n s3 = boto3.client('s3') # FIXME will fail for cross-account roles\\n response = s3.get_object(\\n Bucket=bucket,\\n Key=object_key\\n )\\n template_body = response['Body'].read().decode(\\\"utf-8\\\")\\n return(CFTemplate(template_body, region, s3url=f\\\"s3://{bucket}/{object_key}\\\", session=session))\\n except ClientError as e:\\n logger.error(\\\"ClientError downloading template: {}\\\".format(e))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f738193f95f753aae5ebba7e8cbc4a9d\",\n \"score\": \"0.48943952\",\n \"text\": \"def accessor(self, s3_spec: str, *arg) -> object:\\n\\n bucket_id, key_id = self.toBucketObject(s3_spec)\\n session = boto3.session.Session(region_name='us-east-1')\\n client = session.client('s3')\\n bucket = session.resource('s3').Bucket(bucket_id)\\n\\n try:\\n self._tempFile = tempfile.NamedTemporaryFile('w+b',delete=False)\\n with open(self._tempFile.name, 'wb') as f:\\n # with open(self._tempFile.name, 'wb') as f:\\n bucket.download_fileobj(key_id, f)\\n # return self._tempFile.file\\n return open(self._tempFile.name,'r')\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == '404':\\n return None\\n else:\\n raise\\n except Exception as ei:\\n print(type(ei))\\n print(ei)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c4d74419a5e29bfc81e484f4d06482\",\n \"score\": \"0.48906782\",\n \"text\": \"def get_s3(region, KEY, SECRET):\\n s3_resource = boto3.resource('s3', \\n region_name=region,\\n aws_access_key_id=KEY,\\n aws_secret_access_key=SECRET)\\n \\n s3_client = boto3.client('s3', \\n region_name=region,\\n aws_access_key_id=KEY,\\n aws_secret_access_key=SECRET)\\n \\n return s3_resource, s3_client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c4d74419a5e29bfc81e484f4d06482\",\n \"score\": \"0.48906782\",\n \"text\": \"def get_s3(region, KEY, SECRET):\\n s3_resource = boto3.resource('s3', \\n region_name=region,\\n aws_access_key_id=KEY,\\n aws_secret_access_key=SECRET)\\n \\n s3_client = boto3.client('s3', \\n region_name=region,\\n aws_access_key_id=KEY,\\n aws_secret_access_key=SECRET)\\n \\n return s3_resource, s3_client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2958cf0af4aa47c4ab91728a234254af\",\n \"score\": \"0.48887345\",\n \"text\": \"def sync_s3_text(bucket, key, content_meta):\\n if bucket and content_meta:\\n bucket.put_object(\\n Key=f\\\"extracts/{key}.json\\\",\\n Body=rapidjson.dumps(content_meta),\\n ACL=\\\"public-read\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f3d8bd4500dd3180b204c3d5ef4fc3\",\n \"score\": \"0.48854437\",\n \"text\": \"def get_body_text(self):\\r\\n return self.get_string(\\\"getBodyText\\\", [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc811169e2e0ffcf07c4f8076ac48d14\",\n \"score\": \"0.488312\",\n \"text\": \"def get_entry(\\n s3Uri: str, max_age_in_seconds=60, entry_name=False, check_before_download=True\\n):\\n\\n file_location = get_value(\\n argument=s3Uri,\\n max_age_in_seconds=max_age_in_seconds,\\n entry_name=entry_name,\\n miss_function=get_object_from_s3,\\n check_before_download=check_before_download,\\n send_details=True, # specifies to send all details to miss_function\\n s3Uri=s3Uri,\\n )\\n\\n return file_location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5877572b38aede1f3bb67e39ce3d02ab\",\n \"score\": \"0.4882852\",\n \"text\": \"def get_request_body(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca679ae20a9843e39274b16293c722f\",\n \"score\": \"0.48822495\",\n \"text\": \"def decode_body(email):\\n body = email.get_payload(decode=True)\\n default_charset = email.get_charsets()[0]\\n if default_charset:\\n body = body.decode(default_charset)\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc30ebe2617286248e2464c58240ab7\",\n \"score\": \"0.48812604\",\n \"text\": \"def get_from_s3(s3_path, local_file_path):\\n command = \\\"sudo s3cmd get {s3_path} {local_file_path}\\\" \\\\\\n .format(s3_path=s3_path, local_file_path=local_file_path)\\n print(command)\\n SystemPipeline.run_command(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248af7f14d63d9afab8b83a47cc158d0\",\n \"score\": \"0.48798344\",\n \"text\": \"def get_s3_resource(): # pragma: no cover\\n aws_session = boto3.Session(\\n aws_access_key_id=settings.S3_ACCESS_KEY,\\n aws_secret_access_key=settings.S3_SECRET,\\n region_name=settings.S3_REGION,\\n )\\n s3_resource = aws_session.resource(\\\"s3\\\", endpoint_url=settings.S3_ENDPOINT)\\n return s3_resource\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993688,"cells":{"query_id":{"kind":"string","value":"8953dd3455a97a0169841fcd630a9cb3"},"query":{"kind":"string","value":"Getter for the job id."},"positive_passages":{"kind":"list like","value":[{"docid":"f8ffee80f80151da03d30823017e8ff6","score":"0.8907434","text":"def get_id(self) -> str:\n return self._job.reference.job_id","title":""}],"string":"[\n {\n \"docid\": \"f8ffee80f80151da03d30823017e8ff6\",\n \"score\": \"0.8907434\",\n \"text\": \"def get_id(self) -> str:\\n return self._job.reference.job_id\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7e139d093137b627e99f2305b14524a3","score":"0.90870863","text":"def get_id( self ):\n return self.job_id","title":""},{"docid":"76f0ccaa1b770534f3f4898c3fc7ab37","score":"0.90781033","text":"def job_id(self) -> str:\n return pulumi.get(self, \"job_id\")","title":""},{"docid":"0b5833b47c71c6580a7c4caffdaae616","score":"0.8800562","text":"def job_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"job_id\")","title":""},{"docid":"bc2120bfe3bc011be048cad84bb33fc1","score":"0.8769369","text":"def job_id(self) -> int | None:\n return self._job_id","title":""},{"docid":"e50b5dc54662bf3d8182e902b9cccb58","score":"0.8290635","text":"def get_job_id(job):\n if isinstance(job, threading.Thread):\n return job.ident\n return job.get_id()","title":""},{"docid":"781e7013bfc49ea30c357f49d3814ce7","score":"0.7552397","text":"def get_job_id(self, job_specifier, project=None):\n if project is None:\n project = self._path\n if isinstance(job_specifier, (int, np.integer)):\n return job_specifier # is id\n if len(self._job_table) == 0:\n return None\n job_specifier.replace(\".\", \"_\")\n job_id_lst = self._job_table[\n (self._job_table.project == project)\n & (self._job_table.job == job_specifier)\n ].id.values\n if len(job_id_lst) == 0:\n job_id_lst = self._job_table[\n self._job_table.project.str.contains(project)\n & (self._job_table.job == job_specifier)\n ].id.values\n if len(job_id_lst) == 0:\n return None\n elif len(job_id_lst) == 1:\n return int(job_id_lst[0])\n else:\n raise ValueError(\n \"job name '{0}' in this project is not unique\".format(job_specifier)\n )","title":""},{"docid":"43ad0c40543bb3b4654163757331ea73","score":"0.732742","text":"def job_id(conf):\n # type: (dict) -> str\n return conf['id']","title":""},{"docid":"218d412eee25753a6e1358acf61e4721","score":"0.71127254","text":"def get_job_id():\n return os.environ.get(\"SLURM_JOB_ID\", \"UNKNOWN\")","title":""},{"docid":"b20d76a0cc8eda52cf8aac5c4b505690","score":"0.6962118","text":"def get_id(self):\n return self.data['id']","title":""},{"docid":"866cab3bc6857a9a59136ca5cf27fc1a","score":"0.6961752","text":"def get_id(self):\n \n return self.id","title":""},{"docid":"5e2134f7366dcd29357faf34eca5ddb5","score":"0.69484997","text":"def _get_id(self):\n return self.__id","title":""},{"docid":"5e2134f7366dcd29357faf34eca5ddb5","score":"0.69484997","text":"def _get_id(self):\n return self.__id","title":""},{"docid":"079094ebb39574655ae3d3a0206f6ee5","score":"0.69254583","text":"def _task_id(job: str) -> Union[int, str]:\n maybe_task_id = job.rsplit(\"/\")[-1].rsplit(\":\")[0]\n try:\n return int(maybe_task_id)\n except ValueError:\n return job","title":""},{"docid":"c38b21cddf6195fb56934982ce9458bc","score":"0.6917428","text":"def id(self):\n return self._props[self._id_field()].val","title":""},{"docid":"39be12899f4b1495da7e94e68fdfd64b","score":"0.6869839","text":"def id(self):\n return dict.get(self, 'id', None)","title":""},{"docid":"973b72a1fe0d858ff0a6ae4d610f240e","score":"0.6864465","text":"def get_id(self):\n\n\t\treturn self.__id","title":""},{"docid":"973b72a1fe0d858ff0a6ae4d610f240e","score":"0.6864465","text":"def get_id(self):\n\n\t\treturn self.__id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"47adb8774448e1702ecd622206397edf","score":"0.68641937","text":"def get_id(self):\n return self.id","title":""},{"docid":"5ff0c3db3e3848d036bedd3cf0ec0879","score":"0.6855951","text":"def get_job(self, job_id):\n return self.jobs.get(job_id)","title":""},{"docid":"f39cf8aec741d58b476f53090b558860","score":"0.6847166","text":"def get_id(self):\n return self._id","title":""},{"docid":"f39cf8aec741d58b476f53090b558860","score":"0.6847166","text":"def get_id(self):\n return self._id","title":""},{"docid":"f39cf8aec741d58b476f53090b558860","score":"0.6847166","text":"def get_id(self):\n return self._id","title":""},{"docid":"a90ed738755ccca636291422a5d1e888","score":"0.68403625","text":"def get_id(self):\n\n return self.id","title":""},{"docid":"b29e646e03dea35ae9edbe2aeec8f0b9","score":"0.6823833","text":"def get_id(self):\r\n return self.__id","title":""},{"docid":"1db531b9009c6091cd184f0fba38ca86","score":"0.68208563","text":"def creation_job_id(self):\n with qdb.sql_connection.TRN:\n sql = \"\"\"SELECT creation_job_id\n FROM qiita.prep_template\n WHERE prep_template_id = %s\"\"\"\n qdb.sql_connection.TRN.add(sql, [self.id])\n return qdb.sql_connection.TRN.execute_fetchlast()","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""},{"docid":"2690197262c3fcb4648b3b9e1a3db3e7","score":"0.6814502","text":"def id(self) -> str:\n return pulumi.get(self, \"id\")","title":""}],"string":"[\n {\n \"docid\": \"7e139d093137b627e99f2305b14524a3\",\n \"score\": \"0.90870863\",\n \"text\": \"def get_id( self ):\\n return self.job_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76f0ccaa1b770534f3f4898c3fc7ab37\",\n \"score\": \"0.90781033\",\n \"text\": \"def job_id(self) -> str:\\n return pulumi.get(self, \\\"job_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5833b47c71c6580a7c4caffdaae616\",\n \"score\": \"0.8800562\",\n \"text\": \"def job_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"job_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc2120bfe3bc011be048cad84bb33fc1\",\n \"score\": \"0.8769369\",\n \"text\": \"def job_id(self) -> int | None:\\n return self._job_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e50b5dc54662bf3d8182e902b9cccb58\",\n \"score\": \"0.8290635\",\n \"text\": \"def get_job_id(job):\\n if isinstance(job, threading.Thread):\\n return job.ident\\n return job.get_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781e7013bfc49ea30c357f49d3814ce7\",\n \"score\": \"0.7552397\",\n \"text\": \"def get_job_id(self, job_specifier, project=None):\\n if project is None:\\n project = self._path\\n if isinstance(job_specifier, (int, np.integer)):\\n return job_specifier # is id\\n if len(self._job_table) == 0:\\n return None\\n job_specifier.replace(\\\".\\\", \\\"_\\\")\\n job_id_lst = self._job_table[\\n (self._job_table.project == project)\\n & (self._job_table.job == job_specifier)\\n ].id.values\\n if len(job_id_lst) == 0:\\n job_id_lst = self._job_table[\\n self._job_table.project.str.contains(project)\\n & (self._job_table.job == job_specifier)\\n ].id.values\\n if len(job_id_lst) == 0:\\n return None\\n elif len(job_id_lst) == 1:\\n return int(job_id_lst[0])\\n else:\\n raise ValueError(\\n \\\"job name '{0}' in this project is not unique\\\".format(job_specifier)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ad0c40543bb3b4654163757331ea73\",\n \"score\": \"0.732742\",\n \"text\": \"def job_id(conf):\\n # type: (dict) -> str\\n return conf['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218d412eee25753a6e1358acf61e4721\",\n \"score\": \"0.71127254\",\n \"text\": \"def get_job_id():\\n return os.environ.get(\\\"SLURM_JOB_ID\\\", \\\"UNKNOWN\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20d76a0cc8eda52cf8aac5c4b505690\",\n \"score\": \"0.6962118\",\n \"text\": \"def get_id(self):\\n return self.data['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866cab3bc6857a9a59136ca5cf27fc1a\",\n \"score\": \"0.6961752\",\n \"text\": \"def get_id(self):\\n \\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2134f7366dcd29357faf34eca5ddb5\",\n \"score\": \"0.69484997\",\n \"text\": \"def _get_id(self):\\n return self.__id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2134f7366dcd29357faf34eca5ddb5\",\n \"score\": \"0.69484997\",\n \"text\": \"def _get_id(self):\\n return self.__id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"079094ebb39574655ae3d3a0206f6ee5\",\n \"score\": \"0.69254583\",\n \"text\": \"def _task_id(job: str) -> Union[int, str]:\\n maybe_task_id = job.rsplit(\\\"/\\\")[-1].rsplit(\\\":\\\")[0]\\n try:\\n return int(maybe_task_id)\\n except ValueError:\\n return job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38b21cddf6195fb56934982ce9458bc\",\n \"score\": \"0.6917428\",\n \"text\": \"def id(self):\\n return self._props[self._id_field()].val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39be12899f4b1495da7e94e68fdfd64b\",\n \"score\": \"0.6869839\",\n \"text\": \"def id(self):\\n return dict.get(self, 'id', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973b72a1fe0d858ff0a6ae4d610f240e\",\n \"score\": \"0.6864465\",\n \"text\": \"def get_id(self):\\n\\n\\t\\treturn self.__id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973b72a1fe0d858ff0a6ae4d610f240e\",\n \"score\": \"0.6864465\",\n \"text\": \"def get_id(self):\\n\\n\\t\\treturn self.__id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adb8774448e1702ecd622206397edf\",\n \"score\": \"0.68641937\",\n \"text\": \"def get_id(self):\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff0c3db3e3848d036bedd3cf0ec0879\",\n \"score\": \"0.6855951\",\n \"text\": \"def get_job(self, job_id):\\n return self.jobs.get(job_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39cf8aec741d58b476f53090b558860\",\n \"score\": \"0.6847166\",\n \"text\": \"def get_id(self):\\n return self._id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39cf8aec741d58b476f53090b558860\",\n \"score\": \"0.6847166\",\n \"text\": \"def get_id(self):\\n return self._id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39cf8aec741d58b476f53090b558860\",\n \"score\": \"0.6847166\",\n \"text\": \"def get_id(self):\\n return self._id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a90ed738755ccca636291422a5d1e888\",\n \"score\": \"0.68403625\",\n \"text\": \"def get_id(self):\\n\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b29e646e03dea35ae9edbe2aeec8f0b9\",\n \"score\": \"0.6823833\",\n \"text\": \"def get_id(self):\\r\\n return self.__id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db531b9009c6091cd184f0fba38ca86\",\n \"score\": \"0.68208563\",\n \"text\": \"def creation_job_id(self):\\n with qdb.sql_connection.TRN:\\n sql = \\\"\\\"\\\"SELECT creation_job_id\\n FROM qiita.prep_template\\n WHERE prep_template_id = %s\\\"\\\"\\\"\\n qdb.sql_connection.TRN.add(sql, [self.id])\\n return qdb.sql_connection.TRN.execute_fetchlast()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2690197262c3fcb4648b3b9e1a3db3e7\",\n \"score\": \"0.6814502\",\n \"text\": \"def id(self) -> str:\\n return pulumi.get(self, \\\"id\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993689,"cells":{"query_id":{"kind":"string","value":"168c50cc3ee7c2f854f947842ef62d03"},"query":{"kind":"string","value":"Transcribes the data received via bluetooth to numpy arrays."},"positive_passages":{"kind":"list like","value":[{"docid":"a6df46c8d9d50b9ee7a572a18f274bbd","score":"0.55152607","text":"def bluetoothDataToNPArrays(dataFromBT, numDataPoints, numFeatures):\n\n designMatrix = np.zeros((numDataPoints * 2, numFeatures))\n targetMatrix = np.zeros((numDataPoints * 2, 1))\n\n for i in range(numDataPoints):\n designMatrix[i][0] = dataFromBT[i][0] / 10000000\n designMatrix[i][1:numFeatures] = dataFromBT[i][1:numFeatures]\n targetMatrix[i][0] = dataFromBT[i][numFeatures]\n\n return targetMatrix, designMatrix","title":""}],"string":"[\n {\n \"docid\": \"a6df46c8d9d50b9ee7a572a18f274bbd\",\n \"score\": \"0.55152607\",\n \"text\": \"def bluetoothDataToNPArrays(dataFromBT, numDataPoints, numFeatures):\\n\\n designMatrix = np.zeros((numDataPoints * 2, numFeatures))\\n targetMatrix = np.zeros((numDataPoints * 2, 1))\\n\\n for i in range(numDataPoints):\\n designMatrix[i][0] = dataFromBT[i][0] / 10000000\\n designMatrix[i][1:numFeatures] = dataFromBT[i][1:numFeatures]\\n targetMatrix[i][0] = dataFromBT[i][numFeatures]\\n\\n return targetMatrix, designMatrix\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5e8c673c8e23761577125b46a7aa4813","score":"0.5778601","text":"def __raw_data_to_array(sequence, sampling_rate):\n return sequence['wave']","title":""},{"docid":"6cdd4c901322160893c3d8e9de33a45d","score":"0.576799","text":"def to_array(self):","title":""},{"docid":"68ced1af086ee4f1a72cdd12120246b2","score":"0.5705713","text":"def bytes_to_value(self, byte_array): \n self._logger.debug(\"In Bytes To Value\") \n \n # Array for value bytes (exclude crc check byte)\n bytes_buf = [0]*12 # 2 words for each co2, temperature, humidity\n l = len(bytes_buf)\n ld = len(byte_array)\n # array for word conversion - two words per value\n word_buf = [0]*int(l/2)\n # final data structure - one place per value\n data = [0]*int(len(word_buf)/2)\n\n # Load bytes_buffer, strip crc bytes\n y = 0\n for x in range(0, ld, 6):\n# print(\"x: \" + str(x) + \" y: \" + str(y))\n bytes_buf[y] = byte_array[x]\n bytes_buf[y+1] = byte_array[x+1]\n bytes_buf[y+2] = byte_array[x+3]\n bytes_buf[y+3] = byte_array[x+4] \n y += 4\n self._logger.detail(\"bytes_buf: \" + str(bytes_buf)) \n \n # Convert sensor data reads to physical value per Sensirion specification\n # Load buffer with values\n # Cast 4 bytes to one unsigned 32 bit integer\n # Cast unsigned 32 bit integer to 32 bit float\n\n # Convert bytes to words\n for i in range(len(word_buf)):\n word_buf[i] = (bytes_buf[i*2] << 8) | bytes_buf[i*2+1]\n self._logger.detail(str(hex(bytes_buf[i])) + \" \" + str(hex(bytes_buf[i+1])))\n self._logger.detail(hex(word_buf[i]))\n\n #convert words to int32\n data[self.CO2_DATA_INDEX] = (word_buf[0] << 16) | word_buf[1]\n data[self.TEMP_DATA_INDEX] = (word_buf[2] << 16) | word_buf[3]\n data[self.HUMIDITY_DATA_INDEX] = (word_buf[4] << 16) | word_buf[5]\n\n #Convert int32 data to float32\n floatData = numpy.array(data, dtype=numpy.int32)\n data = floatData.view('float32')\n\n self._logger.detail(\"CO2: \" + str(data[self.CO2_DATA_INDEX]))\n self._logger.detail(\"Temp: \" + str(data[self.TEMP_DATA_INDEX]))\n self._logger.detail(\"RH: \" + str(data[self.HUMIDITY_DATA_INDEX]))\n\n return data","title":""},{"docid":"c874375ec7252367b2475addc13e61e1","score":"0.55983055","text":"def transmission_data(self, thi: float) -> Tuple[NDArray, NDArray]:\n pass","title":""},{"docid":"3104745bed1b3efbfa3318ec20f65305","score":"0.5570566","text":"def to_array(self):\n\n values = []\n values.append(int(self.sec))\n values.append(int(self.nano))\n\n if self.type == 'T': # timestamp\n values.append(0)\n elif self.type == 'S': # system\n values.append(1)\n elif self.type in ('A', 'F'): # adds\n values.append(2)\n elif self.type == 'X': # cancel\n values.append(3)\n elif self.type == 'D': # delete\n values.append(4)\n elif self.type == 'E': # execute\n values.append(5)\n elif self.type == 'C': # execute w/ price\n values.append(6)\n elif self.type == 'U': # replace\n values.append(7)\n else:\n values.append(-1) # other (ignored)\n\n if self.event == 'O': # start messages\n values.append(0)\n elif self.event == 'S': # start system hours\n values.append(1)\n elif self.event == 'Q': # start market hours\n values.append(2)\n elif self.event == 'M': # end market hours\n values.append(3)\n elif self.event == 'E': # end system hours\n values.append(4)\n elif self.event == 'C': # end messages\n values.append(5)\n elif self.event == 'A': # halt trading\n values.append(6)\n elif self.event == 'R': # quotes only\n values.append(7)\n elif self.event == 'B': # resume trading\n values.append(8)\n else:\n values.append(-1) # no event\n\n if self.buysell == 'B': # bid\n values.append(1)\n elif self.buysell == 'S': # ask\n values.append(-1)\n else:\n values.append(0)\n\n values.append(int(self.price))\n values.append(int(self.shares))\n values.append(int(self.refno))\n values.append(int(self.newrefno))\n\n return np.array(values)","title":""},{"docid":"7a0ef17087d0509f9b06982f27ba37ec","score":"0.5542718","text":"def receive_data(self, data: np.ndarray, data_changed: bool):\n raise NotImplementedError","title":""},{"docid":"7c489f8077716efdcb08090c9a7089aa","score":"0.5527125","text":"def get_np_array(self, connection_socket):\n message = self._receiving_subroutine(connection_socket)\n received_weights = pickle.loads(message)\n return received_weights","title":""},{"docid":"3690d71231b39e5a24b1a4def621d368","score":"0.5498337","text":"def get_data(self):\n data = self.board.read(self.sample_no)\n try:\n data = np.array(struct.unpack('b'*self.sample_no, data))\n except struct.error as e:\n print(data)\n raise e\n return data","title":""},{"docid":"fdbc5e9cf1a530cf2b8b2b39d3da7551","score":"0.5458467","text":"def recv_array(socket, flags=0, copy=True, track=False):\n md = socket.recv_json(flags=flags)\n msg = socket.recv(flags=flags, copy=copy, track=track)\n buf = buffer(msg)\n A = np.frombuffer(buf, dtype=md['dtype'])\n return A.reshape(md['shape'])","title":""},{"docid":"0a4f0353f92c1db96e3af050d7c30078","score":"0.5386292","text":"def send_array(self, arr):\n flat = np.array(arr).flatten()\n self.send_raw(flat)","title":""},{"docid":"8f278bd3356724cb1ef2cf526a81cef4","score":"0.53614944","text":"def get_data(sequence, sampling_rate):\n data_type = sequence['type']\n switch = {DataTypes.RAW_DATA: Sequencer.__raw_data_to_array,\n DataTypes.QU_PULSE: Sequencer.__qupulse_template_to_array}\n to_array_function = switch[data_type]\n return to_array_function(sequence, sampling_rate)","title":""},{"docid":"9b06a7c045eb00a1cc8933ea8d8a1531","score":"0.53585905","text":"def read_data(self):\r\n\r\n data = self.serial.read(22)\r\n data_hex = data.hex()\r\n if self.debug: self.vertify_data(data_hex)\r\n pm1_cf = int(data_hex[4]+data_hex[5]+data_hex[6]+data_hex[7],16)\r\n pm25_cf = int(data_hex[8]+data_hex[9]+data_hex[10]+data_hex[11],16)\r\n pm10_cf = int(data_hex[12]+data_hex[13]+data_hex[14]+data_hex[15],16)\r\n pm1 = int(data_hex[16]+data_hex[17]+data_hex[18]+data_hex[19],16)\r\n pm25 = int(data_hex[20]+data_hex[21]+data_hex[22]+data_hex[23],16)\r\n pm10 = int(data_hex[24]+data_hex[25]+data_hex[26]+data_hex[27],16)\r\n if self.debug: print(\"pm1_cf: \"+str(pm1_cf))\r\n if self.debug: print(\"pm25_cf: \"+str(pm25_cf))\r\n if self.debug: print(\"pm10_cf: \"+str(pm10_cf))\r\n if self.debug: print(\"pm1: \"+str(pm1))\r\n if self.debug: print(\"pm25: \"+str(pm25))\r\n if self.debug: print(\"pm10: \"+str(pm10))\r\n data = [pm1_cf, pm10_cf, pm25_cf, pm1, pm10, pm25]\r\n self.serial.close()\r\n\r\n return data","title":""},{"docid":"bca680230fe6bf4476d019580ebebd45","score":"0.5346419","text":"def recv_array(socket: zmq.Socket, flags: int=0, copy: int=True, track: bool=False):\n dictionary = socket.recv_json(flags=flags)\n message = socket.recv(flags=flags, copy=copy, track=track)\n buffer = memoryview(message)\n array = np.frombuffer(buffer, dtype=dictionary['dtype'])\n return array.reshape(dictionary['shape'])","title":""},{"docid":"84ff98d094e10125d03f304c1185b6ac","score":"0.53384554","text":"def _transformDataToArray(self) -> None:\n result = []\n for sequence in self.data:\n arr = np.empty((len(sequence), 2))\n for i, (itemOne, itemTwo) in enumerate(sequence):\n arr[i][0] = itemOne\n arr[i][1] = itemTwo\n result.append(arr)\n self._data = result","title":""},{"docid":"b9a1aadcdfa79c746d1bb37bd1f62155","score":"0.53358775","text":"def data_b(self) -> np.ndarray:\n return self.ch_b","title":""},{"docid":"d520ce82eef85664f1406d6c96a135dc","score":"0.53045624","text":"def prtSensorValuesInBuffer(self):\n\n for i in self.data:\n print i","title":""},{"docid":"2769ee83b6ab0e2a8a3d7ff6b691968e","score":"0.52978307","text":"def to_ndarray(self):","title":""},{"docid":"3d39defa9871fdffb62b6e48db7d72b9","score":"0.5293051","text":"def data(self, data):\n data = data if self._bitmode == 8 else \\\n bytes_to_nibbles(data)\n super(parallel_device, self).data(data)","title":""},{"docid":"abaeb9fb016354a2f06e33ca09267398","score":"0.52670586","text":"def get_ary(self, dbuf):\n hbuf_out = []\n # Initiate the transfer\n for idx, ctx, dbuf, strm in zip(self.device_ids, self.ctxs, dbuf, self.streams):\n ctx.push()\n hbuf_out.append(dbuf.get_async(strm))\n ctx.pop()\n return np.hstack(hbuf_out)","title":""},{"docid":"64a1e59c5ef5811b247bd253db1cd168","score":"0.5233872","text":"def read_input_buffer(self):\r\n\r\n if not self.connected:\r\n return False\r\n\r\n data_changed = False\r\n\r\n # Read all the data...\r\n while self.serial_port.inWaiting() > (self.channels + 1 * 4 + 3):\r\n\r\n # New data is raw, as returned from the microprocessor, but scaled -1 -> +1.\r\n # Here, we scale it -> uV.\r\n new_data = self._read_serial_binary()\r\n\r\n ########## Uncomment this next line for debugging purposes. #######\r\n # new_data[1:] = self._read_mock_data() # overwrites real data with mock data.\r\n\r\n # Now, we scale from -1 -> +1, to uV:\r\n new_data[1:] = new_data[1:] * creare_volts_per_FS * 1.0e6 # now uV.\r\n self.history.append(new_data)\r\n data_changed = True\r\n\r\n if data_changed:\r\n # If the history gets too long, cull it:\r\n if len(self.history) > MAX_HISTORY_LENGTH:\r\n self.history = self.history[-MIN_HISTORY_LENGTH:]\r\n\r\n # Update the numpy timeseries array.\r\n self.timeseries = np.array(self.history[-TIMESERIES_LENGTH:])\r\n\r\n # Infer which channels are on/off... we don't keep track of this\r\n # internally b/c it's safer to infer it from the Arduino output.\r\n [self.channel_1_enabled,\r\n self.channel_2_enabled,\r\n self.channel_3_enabled,\r\n self.channel_4_enabled,\r\n self.channel_5_enabled,\r\n self.channel_6_enabled,\r\n self.channel_7_enabled,\r\n self.channel_8_enabled] = np.logical_not(np.isnan(self.history[-1][1:])).tolist()\r\n self.data_changed = True # fire data_changed event.\r\n\r\n return True","title":""},{"docid":"0053c169e400f30c7530094fd225af43","score":"0.52330863","text":"def read_from_buffer(self):\n for pa in self.arrays:\n pa.read_from_buffer()","title":""},{"docid":"ce7d1ce4d9a7ce9d1dc25058fb07bc83","score":"0.52072465","text":"def to_array(self):\n raise NotImplementedError","title":""},{"docid":"dc2a207a1c67dd4cc9a1da1e2f81f2e6","score":"0.5193455","text":"def data_a(self) -> np.ndarray:\n if self.marker_a is None and self.marker_b is None:\n return self.ch_a\n\n if self.ch_a is None:\n raise NotImplementedError('What data should be used in a?')\n\n # copy channel information\n data = np.array(self.ch_a)\n\n if self.marker_a is not None:\n data.reshape(-1, 8)[1::2, :].flat |= (1 << 14) * self.marker_a.astype(np.uint16)\n\n if self.marker_b is not None:\n data.reshape(-1, 8)[1::2, :].flat |= (1 << 15) * self.marker_b.astype(np.uint16)\n\n return data","title":""},{"docid":"252496336671ef06fa8db59605d219a6","score":"0.5170924","text":"def to_numpy(t):\n return t.data.cpu().numpy()","title":""},{"docid":"89bc6bc755ac2add92cea897b7f6a161","score":"0.51457644","text":"def read_buffer(self, channel, start=0, length=None, format='A'):\n\n cmd = 'TRCA'\n if not length:\n length = self.buffer_length\n self.send('{}? {},{},{}'.format(cmd, channel, start, length))\n if cmd == 'TRCA':\n data = self.recv()\n return np.fromstring(data, sep=',') * ureg.volt\n else:\n raise ValueError('{} transfer format is not implemented'.format(format))","title":""},{"docid":"777a10cdf2c096d719946f7c2f5043a8","score":"0.5137951","text":"def _read_data(self):\n\n # Start the integration\n self._device.ctrl_transfer(StellarNet._OUT_DEVICE, 0xB2, 0, 0)\n\n # Set the timeout to be 500ms longer than the expected integration time\n int_time = self._config['int_time']\n timeout = (int_time + 500)/1000.0\n\n # Sleep for 90% of the integration time to avoid busy waiting\n # for the result and causing unnecessary usb traffic\n start = time.time()\n time.sleep(int_time*.0009)\n \n while True:\n # Check if data is available\n reponse = self._device.ctrl_transfer(StellarNet._IN_DEVICE, 0xB3, 0, 0, 2)\n\n # If device is busy wait for next check else if device is ready wait\n # before reading data. Note: reading data without waiting occasionally\n # results in a right-shifted spectrum which becomes more frequent with\n # shorter integration times..\n time.sleep(.003)\n if reponse[1]:\n break\n \n if time.time() - start > timeout:\n raise TimeoutError('Read spectrum')\n\n # Read the CCD data and convert it to an array of \n # 16-bit integers from the little-endian data buffer\n pixels = StellarNet._PIXEL_MAP[self._config['det_type']]\n return struct.unpack_from('<{}H'.format(pixels),\n self._device.read(usb.util.ENDPOINT_IN | 8, pixels*2))","title":""},{"docid":"79c6c18c9a3b270ad555be4dc010f776","score":"0.5131457","text":"def get_datas(callback, macs=[], run_flag=RunFlag(), bt_device=''):\n\n log.info('Get latest data for sensors. Stop with Ctrl+C.')\n log.info('MACs: %s', macs)\n\n for new_data in RuuviTagSensor._get_ruuvitag_datas(macs, None, run_flag, bt_device):\n callback(new_data)","title":""},{"docid":"cc51cef5f4c8afb2fbdd0a679664decc","score":"0.51259863","text":"def accrue_sensors_data(self):\n pass","title":""},{"docid":"b7b7b4f72a1e94123408b6b58f915aaf","score":"0.5108878","text":"def run(self):\n\n try:\n while not self.__stop_event.isSet():\n samples = self.__serial.get_samples()\n\n if not self.__stop_event.isSet():\n self.__buffer.extend(samples)\n except Exception as e:\n self.__serial.close()\n messagebox.showerror(\"Communication Error\",\n \"Data read timeout occurred!\")\n self.__btn_txt.set(\"START\")","title":""},{"docid":"1a1be313c830064e3f23f31ca00ea082","score":"0.5105884","text":"def convertData():\n\tglobal inputData, npData\n\tLOGGER.info(\"CONV_DATA: Starting to convert data format\")\n\tLOGGER.debug(\"Optimal format is NumPy\")\n\tdata_transformer = NpDataFromRaw(inputData.data)\n\tnpData = data_transformer()\n\tLOGGER.info(\"CONV_DATA: Finished data transformation\")\n\tdel inputData","title":""},{"docid":"08f41e603bbc930a09b0b268e05ba373","score":"0.50931275","text":"def _pack_get_event(evt):\n t = evt.traces\n packed = np.zeros((len(t), 517), dtype='int16')\n packed[:, 0] = t['cobo']\n packed[:, 1] = t['asad']\n packed[:, 2] = t['aget']\n packed[:, 3] = t['channel']\n packed[:, 4] = t['pad']\n packed[:, 5:] = t['data']\n return packed","title":""},{"docid":"9e414747bba685d9f2a62d2cb04a2ca7","score":"0.5089171","text":"def onRecv(self, data):","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"59a1cd65020872c63a44173452e025bd","score":"0.5077538","text":"def array(*args, **kwargs):\n \n pass","title":""},{"docid":"5abb8a543bed9ee589783e67435f73f9","score":"0.5077118","text":"def push_data(x_diode, y_diode, x_diode_t, y_diode_t, buffer):\n \n x_diode_data = pd.Series(x_diode, index=x_diode_t)\n y_diode_data = pd.Series(y_diode, index=y_diode_t)\n zipped = basic_event_builder(x_diode=x_diode_data, y_diode=y_diode_data)\n buffer.send(zipped)","title":""},{"docid":"56ea041c7d0885530531a5e308fb769e","score":"0.50664616","text":"def on_tx_event(self, data):\n # data = array.array('B', [1, 255, 133, 128, 8, 0, 128, 0])\n page_to_send = self._get_next_page()\n data_payload = page_to_send.to_payload() # get new data payload to sent at this TX event.\n # call channel's send_broadcast_data to set the TX buffer to new data.\n self.channel.send_broadcast_data(data_payload)\n print(\"send TX\")","title":""},{"docid":"7fd1deb7f19ad713d7bc9af74feb8f9c","score":"0.5063892","text":"def _put_array(self):\n self.attendance = \\\n bz2.compress(self.attendance_array.tostring())\n self.put()\n return self.attendance_array","title":""},{"docid":"e4714f3b7a5e7af44a82a490a0c1e961","score":"0.5062884","text":"def _read(self):\n\n # Check buffer size and limits\n buffer_size = self.device.in_waiting\n if buffer_size == 1020:\n # The OS serial buffer can hold up to 1020 bytes\n self.logger.warn('Internal buffer saturated. Increase graph rate or decrease device rate.')\n\n # Compute the maximum number of samples we can get\n sample_count = int(buffer_size / self.sample_size)\n\n # Read raw samples from device\n now = clock.now()\n raw = self.device.read(sample_count * self.sample_size)\n\n # Initialize the output matrix\n data = np.full((sample_count, 3), np.nan, np.uint32)\n\n # Parse the raw data\n for sample_number in range(sample_count):\n\n # Extract sample\n start = sample_number * self.sample_size\n stop = start + self.sample_size\n sample = raw[start:stop]\n\n # Check (dummy) CRC\n crc = sample[8]\n if crc != 0:\n self.logger.warn('Corrupted sample.')\n continue\n\n # Parse sample\n data[sample_number, 0] = int.from_bytes(sample[0:2], byteorder='little', signed=False)\n data[sample_number, 1] = int.from_bytes(sample[2:6], byteorder='little', signed=False)\n data[sample_number, 2] = int.from_bytes(sample[6:8], byteorder='little', signed=False)\n\n # Did we miss any sample?\n # Check for discontinuity in the internal sample counter (2 bytes).\n sample_counter = data[sample_number, 0]\n if sample_counter == self._sample_counter + 1:\n pass\n elif sample_counter == 0 and self._sample_counter == 65535:\n pass\n else:\n self.logger.warn('Missed sample.')\n self._sample_counter = sample_counter\n\n # Compute indices\n indices = clock.time_range(self._timestamp, now, len(data))\n self._timestamp = now\n\n return data, indices","title":""},{"docid":"4a3f98594b05870f5a06daa1fc749162","score":"0.50610167","text":"def data(self):\n return self.array","title":""},{"docid":"4a3f98594b05870f5a06daa1fc749162","score":"0.50610167","text":"def data(self):\n return self.array","title":""},{"docid":"7193bdc1e1d7026f25af1697c6f9caed","score":"0.5057166","text":"def getData(self) -> \"void *\":\n return _coin.SoSensor_getData(self)","title":""},{"docid":"fe35628b4a487495c5cbc1e02022937e","score":"0.5038984","text":"def get_data(fb_arr) -> np.ndarray:\n raw_data = fb_arr.DataAsNumpy()\n type_map = {\n DType.uint8: np.uint8,\n DType.int8: np.int8,\n DType.uint16: np.uint16,\n DType.int16: np.int16,\n DType.uint32: np.uint32,\n DType.int32: np.int32,\n DType.uint64: np.uint64,\n DType.int64: np.int64,\n DType.float32: np.float32,\n DType.float64: np.float64,\n }\n return raw_data.view(type_map[fb_arr.DataType()])","title":""},{"docid":"c3ab2ab6a398a3ad13c21ee2d6259f43","score":"0.50361073","text":"def to_np(src_tens: torch.Tensor) -> np.ndarray:\n return src_tens.cpu().detach().numpy()","title":""},{"docid":"7600a74b4389b7d6e17b4855297f4b5e","score":"0.503004","text":"def flush(self, data):\n\n if not self._sacn:\n raise Exception(\"sACN sender not started.\")\n if data.size != self._config[\"channel_count\"]:\n raise Exception(\n f\"Invalid buffer size. {data.size} != {self._config['channel_count']}\"\n )\n\n data = data.flatten()\n current_index = 0\n for universe in range(\n self._config[\"universe\"], self._config[\"universe_end\"] + 1\n ):\n # Calculate offset into the provide input buffer for the channel. There are some\n # cleaner ways this can be done... This is just the quick and dirty\n universe_start = (\n universe - self._config[\"universe\"]\n ) * self._config[\"universe_size\"]\n universe_end = (\n universe - self._config[\"universe\"] + 1\n ) * self._config[\"universe_size\"]\n\n dmx_start = (\n max(universe_start, self._config[\"channel_offset\"])\n % self._config[\"universe_size\"]\n )\n dmx_end = (\n min(\n universe_end,\n self._config[\"channel_offset\"]\n + self._config[\"channel_count\"],\n )\n % self._config[\"universe_size\"]\n )\n if dmx_end == 0:\n dmx_end = self._config[\"universe_size\"]\n\n input_start = current_index\n input_end = current_index + dmx_end - dmx_start\n current_index = input_end\n\n dmx_data = np.array(self._sacn[universe].dmx_data)\n dmx_data[dmx_start:dmx_end] = data[input_start:input_end]\n\n # Because the sACN library checks for data to be of int type, we have to\n # convert the numpy array into a python list of ints using tolist()\n self._sacn[universe].dmx_data = dmx_data.clip(0, 255).tolist()\n # output = dmx_data.clip(0, 255)\n\n # This is ugly - weird race condition where loading on startup from a device with a short ID results in the sACN thread trying to send data to NoneType.\n # No idea how to properly handle it - but this stops it breaking and seems to be reasonably resilient. Sorry to whoever stumbles onto it. -Shaun\n try:\n self._sacn.flush()\n except AttributeError:\n _LOGGER.info(\n \"Attempted to start sACN thread prior to sACN activating. Restarting sACN thread.\"\n )\n self.activate\n\n # # Hack up a manual flush of the E1.31 data vs having a background thread\n # if self._sacn._output_thread._socket:\n # for output in list(self._sacn._output_thread._outputs.values()):\n # self._sacn._output_thread.send_out(output)","title":""},{"docid":"915475475e2adb5986929ad5310ac2c2","score":"0.5025835","text":"def ConvertDataToArrays(the_features):\n\n return np.asarray(the_features)","title":""},{"docid":"9320ee0c79c40beb966dfe6b2b12db22","score":"0.5023859","text":"def Rx(t):\n return np.array(((1,0,0),(0,np.cos(t),-np.sin(t)),(0,np.sin(t),np.cos(t))))","title":""},{"docid":"7a983405ac46867ac1dd863c25f5183a","score":"0.501484","text":"def set_to_array(self, points):","title":""},{"docid":"aa0ddaf3f0146200a25f1fbf75b3a8ee","score":"0.50135607","text":"def serialize_numpy(self, buff, numpy):\n try:\n length = len(self.devices)\n buff.write(_struct_I.pack(length))\n pattern = '<%sI'%length\n buff.write(self.devices.tostring())\n _x = self\n buff.write(_get_struct_B4f2i8f2iB8f4i20fB().pack(_x.params.effectType, _x.params.fixed_color.color.r, _x.params.fixed_color.color.g, _x.params.fixed_color.color.b, _x.params.fixed_color.color.a, _x.params.rainbow.transition_duration.secs, _x.params.rainbow.transition_duration.nsecs, _x.params.fade.first_color.r, _x.params.fade.first_color.g, _x.params.fade.first_color.b, _x.params.fade.first_color.a, _x.params.fade.second_color.r, _x.params.fade.second_color.g, _x.params.fade.second_color.b, _x.params.fade.second_color.a, _x.params.fade.transition_duration.secs, _x.params.fade.transition_duration.nsecs, _x.params.fade.reverse_fade, _x.params.blink.first_color.r, _x.params.blink.first_color.g, _x.params.blink.first_color.b, _x.params.blink.first_color.a, _x.params.blink.second_color.r, _x.params.blink.second_color.g, _x.params.blink.second_color.b, _x.params.blink.second_color.a, _x.params.blink.first_color_duration.secs, _x.params.blink.first_color_duration.nsecs, _x.params.blink.second_color_duration.secs, _x.params.blink.second_color_duration.nsecs, _x.params.progress.first_color.r, _x.params.progress.first_color.g, _x.params.progress.first_color.b, _x.params.progress.first_color.a, _x.params.progress.second_color.r, _x.params.progress.second_color.g, _x.params.progress.second_color.b, _x.params.progress.second_color.a, _x.params.progress.percentage, _x.params.progress.led_offset, _x.params.flow.first_color.r, _x.params.flow.first_color.g, _x.params.flow.first_color.b, _x.params.flow.first_color.a, _x.params.flow.second_color.r, _x.params.flow.second_color.g, _x.params.flow.second_color.b, _x.params.flow.second_color.a, _x.params.flow.percentage, _x.params.flow.velocity, _x.params.preprogrammed.preprogrammed_id))\n _x = self.params.effect_via_topic.topic_name\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n length = len(self.params.data_array.data)\n buff.write(_struct_I.pack(length))\n for val1 in self.params.data_array.data:\n _x = val1\n buff.write(_get_struct_4f().pack(_x.r, _x.g, _x.b, _x.a))\n _x = self\n buff.write(_get_struct_2iB().pack(_x.effectDuration.secs, _x.effectDuration.nsecs, _x.priority))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","title":""},{"docid":"e7c91e59e2861fb2c15993f73e326eee","score":"0.501091","text":"def serialize_numpy(self, buff, numpy):\n try:\n length = len(self.all_car)\n buff.write(_struct_I.pack(length))\n for val1 in self.all_car:\n _v9 = val1.header\n _x = _v9.seq\n buff.write(_get_struct_I().pack(_x))\n _v10 = _v9.stamp\n _x = _v10\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\n _x = _v9.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n _x = val1.id\n buff.write(_get_struct_b().pack(_x))\n length = len(val1.mask)\n buff.write(_struct_I.pack(length))\n pattern = '<%sB'%length\n buff.write(val1.mask.tostring())\n _x = val1.valid_sequence_num\n buff.write(_get_struct_b().pack(_x))\n length = len(val1.trajectory)\n buff.write(_struct_I.pack(length))\n for val2 in val1.trajectory:\n _v11 = val2.header\n _x = _v11.seq\n buff.write(_get_struct_I().pack(_x))\n _v12 = _v11.stamp\n _x = _v12\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\n _x = _v11.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n _x = val2\n buff.write(_get_struct_IB9f().pack(_x.id, _x.classification, _x.x, _x.y, _x.yaw, _x.v, _x.yawrate, _x.a, _x.delta, _x.L, _x.W))\n _x = self.host_car_id\n buff.write(_get_struct_b().pack(_x))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","title":""},{"docid":"32ced6b8c522a0f0ff98d0228e78b17c","score":"0.50039107","text":"def irsensors_callback(self, data):\n self.ir_data_lock.acquire()\n self.ir_data[data.header.frame_id] = data.signal\n self.ir_data_lock.release()","title":""},{"docid":"32ced6b8c522a0f0ff98d0228e78b17c","score":"0.50039107","text":"def irsensors_callback(self, data):\n self.ir_data_lock.acquire()\n self.ir_data[data.header.frame_id] = data.signal\n self.ir_data_lock.release()","title":""},{"docid":"c5a39f3d612653cfaf54990f82dd568e","score":"0.4998454","text":"def run(self):\n is_running = True\n while is_running:\n try:\n # Wait the next byte frame from device\n data_frame = ord(self.serial.read())\n\n # Parse the byte as 8 digital samples\n data_array = np.zeros(Oscilloscope.BYTE_LENGTH)\n for i in range(Oscilloscope.BYTE_LENGTH):\n data_array[Oscilloscope.BYTE_LENGTH - 1 - i] = 1 if data_frame & 0x01 > 0 else 0\n data_frame = data_frame >> 1\n \n\n # Update oscilloscope print\n self.monitor.add_data(data_array)\n except KeyboardInterrupt:\n is_running = False","title":""},{"docid":"57f7a28accb42fce4cd29c4e4ecf4677","score":"0.4990443","text":"def dataReceived(self):\n \n # Alternates the communication LED\n self.CommunicationLED.write(0)\n time.sleep(1)\n self.CommunicationLED.write(1)\n time.sleep(1)\n self.CommunicationLED.write(0)\n \n self.Helpers.logger.info(\"Communication LED ready!\")","title":""},{"docid":"87dd1e3801b3bd037a6eed3740e814a8","score":"0.49889302","text":"def get_data(self):\n while True:\n message = self._subscriber.queue.get()\n parsed = ArrayMessage.deserialize(message)\n self._subscriber.queue.task_done()\n yield DataContainer.from_array(\n parsed.data,\n int(parsed.data.shape[0] / ArrayMessage.duration)\n )","title":""},{"docid":"8c08d39ac9fbf45b92c5158a43f56ae9","score":"0.49814352","text":"def rx(self, buf):\n for c in buf:\n self.rx_state(c)","title":""},{"docid":"8e20f10a36fb6295035b037e02366c18","score":"0.49778795","text":"def callback_SSS_S(n_bytes, x):\n # print('callback_SSS_S')\n # print(\"+++ Python SSS_S {} bytes in x={}\".format(n_bytes, x))\n shp = (n_bytes // BYTES_PER_SAMPLE_INCOME_STREAM // MAX_ODAS_SOURCES, MAX_ODAS_SOURCES) # shape\n n_frames = shp[0] // CHUNK_SIZE_INCOME_STREAM # I assume shp[0] is always a multiple integer, which in my understanding seems to be the case with odas\n buf = np.ctypeslib.as_array(x, shape=shp)\n # if MULTI_THREAD:\n assert SSS_queue is not None\n if isinstance(SSS_queue, list): # It means we want to demux the signal\n for i in range(0, MAX_ODAS_SOURCES):\n try:\n for _fr in range(n_frames): # there could be more than one frame so put in queue with the expected length\n _idl = _fr * CHUNK_SIZE_INCOME_STREAM\n _idh = _idl + CHUNK_SIZE_INCOME_STREAM\n _ch_buf = buf[_idl:_idh, i]\n # print(\"extract buffer source {} - frame {}. 3 samples {} ...\".format(i, _fr, _ch_buf[0:3]))\n SSS_queue[i].put_nowait(_ch_buf) # eventually_ch_buf this has to be transformed in bytes or store as a byte IO?\n if DUMP_CALLBACK:\n # write_wave(dump_callback_file[i], buf)\n if USE_SOUNDFILE:\n #dump_callback_file[i].write(_ch_buf)#, np.int16)\t# removing header! #\n _bb = _ch_buf.copy() # need to create a copy, to make the array contiguos, otherwise i get an exception\n dump_callback_file[i].write(_bb)\n else:\n\t\t\t\t\t\t\t# Solution wave, this write always the latest buffer and doesnt happend it!\n dump_callback_file[i].writeframesraw(_ch_buf) # removing header!\t\n except Full:\n _LOGGER.warning(\"SSS_S receiving Queue_\" + str(i) + \" is Full, skipping frame (TODO: lost for now, change in deque!)\")\n # do nothing for now, perhaps extract the first to make space? Kind of circular queue behavior.....\n pass\n else:\n try:\n SSS_queue.put_nowait(buf)\n if DUMP_CALLBACK:\n write_wave(dump_callback_file, buf)\n except Full:\n _LOGGER.warning(\"SSS_S receiving Queue is Full, skipping frame (TODO: lost for now, change in deque!)\")\n # do nothing for now, perhaps extract the first to make space? Kind of circular queue behavior.....\n pass","title":""},{"docid":"4b6b7919b2883800ca56e6e468eee7a0","score":"0.49703982","text":"def onAdaptorData(self, message):\n #logging.debug(\"%s onadaptorData, message: %s\", ModuleName, message)\n if message[\"characteristic\"] == \"acceleration\":\n for a in self.accel:\n if a.id == self.idToName[message[\"id\"]]: \n a.processAccel(message)\n break\n elif message[\"characteristic\"] == \"temperature\":\n for t in self.temp:\n if t.id == self.idToName[message[\"id\"]]:\n t.processTemp(message)\n break\n elif message[\"characteristic\"] == \"ir_temperature\":\n for t in self.irTemp:\n if t.id == self.idToName[message[\"id\"]]:\n t.processIrTemp(message)\n break\n elif message[\"characteristic\"] == \"gyro\":\n for g in self.gyro:\n if g.id == self.idToName[message[\"id\"]]:\n g.processGyro(message)\n break\n elif message[\"characteristic\"] == \"magnetometer\":\n for g in self.magnet:\n if g.id == self.idToName[message[\"id\"]]:\n g.processMagnet(message)\n break\n elif message[\"characteristic\"] == \"buttons\":\n for b in self.buttons:\n if b.id == self.idToName[message[\"id\"]]:\n b.processButtons(message)\n break\n elif message[\"characteristic\"] == \"humidity\":\n for b in self.humidity:\n if b.id == self.idToName[message[\"id\"]]:\n b.processHumidity(message)\n break\n elif message[\"characteristic\"] == \"binary_sensor\":\n for b in self.binary:\n if b.id == self.idToName[message[\"id\"]]:\n b.processBinary(message)\n break\n elif message[\"characteristic\"] == \"power\":\n for b in self.power:\n if b.id == self.idToName[message[\"id\"]]:\n b.processPower(message)\n break\n elif message[\"characteristic\"] == \"luminance\":\n for b in self.luminance:\n if b.id == self.idToName[message[\"id\"]]:\n b.processLuminance(message)\n break","title":""},{"docid":"06ae330bf7ee6a56cb9ea7b31eb6a291","score":"0.49631587","text":"def _sendDataToGPU(self,returnData):\n\n newIncData=returnData[0].to('cuda:{}'.format(self.device))\n newCovData=[]\n for i,arr in enumerate(returnData[1]):\n newCovData+=[arr.to('cuda:{}'.format(self.device))]\n\n if returnData[2] is not None:\n newTargetData=returnData[2].to('cuda:{}'.format(self.device))\n else:\n newTargetData=None\n if returnData[3] is not None:\n newScoreData=returnData[3].to('cuda:{}'.format(self.device))\n else:\n newScoreData=None\n\n return (newIncData,newCovData,newTargetData,newScoreData)","title":""},{"docid":"de6c9be68245a0679c212351cf031dbe","score":"0.49566287","text":"def raw2vpvol_nc_batch():","title":""},{"docid":"fbd2a6488b70e42f1537e51278e9ee16","score":"0.49507663","text":"def dataReceived(self, data):\n self.bridge.push_input(data)","title":""},{"docid":"c8c90637c65e5e318a8dd3280eb698fb","score":"0.4930326","text":"def receive_dataset(self):\n\n X = read_data(self.root_connection)\n\n return X","title":""},{"docid":"acedb9e17bab557541fc7b7a4bc8c026","score":"0.49091378","text":"def read(self):\n \n data = self.bus.read_i2c_block_data(self.address, 0x1D, 6)\n\n # Convert the data\n xGyro = data[0] * 256 + data[1]\n if xGyro > 32767 :\n xGyro -= 65536\n xGyro = xGyro / self.scalar # To get degrees per second\n yGyro = data[2] * 256 + data[3]\n if yGyro > 32767 :\n yGyro -= 65536\n yGyro = yGyro / self.scalar\n zGyro = data[4] * 256 + data[5]\n if zGyro > 32767 :\n zGyro -= 65536\n zGyro = zGyro / self.scalar\n #return (xGyro, yGyro, zGyro)\n return np.array([xGyro, yGyro, zGyro])","title":""},{"docid":"b17f5fd67bca4c4595213a88a57839e5","score":"0.49080834","text":"def send_mb(self, arrays):\n # The buffer protocol only works on contiguous arrays\n arrays = [numpy.ascontiguousarray(array) for array in arrays]\n headers = [numpy.lib.format.header_data_from_array_1_0(array)\n for array in arrays]\n self.asocket.send_json(headers, zmq.SNDMORE)\n for array in arrays[:-1]:\n self.asocket.send(array, zmq.SNDMORE)\n self.asocket.send(arrays[-1])","title":""},{"docid":"d1d5bae0b9e197cbc16851dfffee1511","score":"0.48967984","text":"def read_data(self, buf):\n\n # SECOND CRITICAL POINT\n textdf = self.text\n names = self.channel_names\n\n relevant = textdf[[\"GTI$SAMPLEID\", \"$BEGINDATA\", \"$DATASIZE\"]]\n relevant[\"$PAR\"] = textdf[\"$PAR\"].astype(int)\n relevant[\"$TOT\"] = textdf[\"$TOT\"].astype(int)\n segment_storage = []\n for i in range(len(relevant)):\n buf.seek(relevant[\"$BEGINDATA\"][i])\n segment = buf.read(relevant[\"$DATASIZE\"][i]+1)\n # THIS IS THE MOST IMPORTANT PART\n segment_value = np.frombuffer(segment, dtype=np.float32)\n # reshaping the array into proper form\n segment_value = segment_value.reshape(\n (relevant[\"$TOT\"][i], relevant[\"$PAR\"][i]))\n segment_frame = pd.DataFrame(data=segment_value, columns=names)\n segment_frame.insert(0, \"Sample\", relevant[\"GTI$SAMPLEID\"][i])\n segment_storage.append(segment_frame)\n data = pd.concat(segment_storage)\n ####DODATI RESET INDEX#####\n self.data = data\n self.samples = list(relevant[\"GTI$SAMPLEID\"])","title":""},{"docid":"5ffc730629847ee67272eebd34355898","score":"0.48873717","text":"def _arduino_cmd_callback(self, msg):\n data: dict = msg['data']\n registers = data.keys()\n a = []\n for reg_name in registers:\n reg_num = self.register_name_map[reg_name]\n value = data[reg_name]\n a.append((reg_num, value))\n self.protocol_executor.set_data(a)","title":""},{"docid":"b8a1b26c7402af87f9f489f33a958625","score":"0.48857716","text":"def get_data_ready(self): \n self._logger.debug(\"In Get Data Ready\") \n self._i2c.msg_write([0x02, 0x02])\n msgs = self._i2c.msg_read(3) \n #for msg in msgs:\n # self._logger.detail(\"Msg: \" + str(msg))\n # for d in msg.data:\n # self._logger.detail(\"Data: \" + str(hex(d)))\n #self._logger.detail(\"Data: \" + str(msgs[0].data[1]))\n return msgs[0].data[1]","title":""},{"docid":"ad91694dd9be28c52f57621c9b5d4b58","score":"0.48786342","text":"def read_data():\n \"\"\"Esto esta hecho para leer 64 bits en los brams... hay que modificar el modelo\"\"\"\n global fpga\n #A2 = struct.unpack('>16384Q', fpga.read('A2', 16384*8,0)) #me parece que es struct.unpack('>16384', fpga.read('A2', 16384*8,0))\n #B2 = struct.unpack('>16384Q', fpga.read('B2', 16384*8,0))\n #AB_re = struct.unpack('>16384Q', fpga.read('AB_re', 16384*8, 0))\n #AB_im = struct.unpack('>16384Q', fpga.read('AB_im', 16384*8, 0))\n A2 = np.random.rand(1, 16384)\n B2 = np.random.rand(1, 16384)\n AB_re = np.random.rand(1, 16384)\n AB_im = np.random.rand(1, 16384)\n log_a = 10*np.log10(A2)\n log_b = 10*np.log10(B2)\n ang = np.arctan2(AB_im, AB_re)\n return [ang, log_a, log_b]","title":""},{"docid":"3f8d3176329072bc60fa6da9eeae445a","score":"0.48783943","text":"def serialize_numpy(self, buff, numpy):\n try:\n _x = self\n buff.write(_get_struct_3I().pack(_x.trajectory.header.seq, _x.trajectory.header.stamp.secs, _x.trajectory.header.stamp.nsecs))\n _x = self.trajectory.header.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.pack('<I%ss'%length, length, _x))\n length = len(self.trajectory.joint_names)\n buff.write(_struct_I.pack(length))\n for val1 in self.trajectory.joint_names:\n length = len(val1)\n if python3 or type(val1) == unicode:\n val1 = val1.encode('utf-8')\n length = len(val1)\n buff.write(struct.pack('<I%ss'%length, length, val1))\n length = len(self.trajectory.points)\n buff.write(_struct_I.pack(length))\n for val1 in self.trajectory.points:\n length = len(val1.positions)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(val1.positions.tostring())\n length = len(val1.velocities)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(val1.velocities.tostring())\n length = len(val1.accelerations)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(val1.accelerations.tostring())\n length = len(val1.effort)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(val1.effort.tostring())\n _v3 = val1.time_from_start\n _x = _v3\n buff.write(_get_struct_2i().pack(_x.secs, _x.nsecs))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","title":""},{"docid":"ed2bf9eba5d02a1edb7e0766434d23c3","score":"0.48738414","text":"def myStreamCB(cHandle, data): \n \n \n unpack3Dformat(data)\n #unpackDirectControFormat(data)\n #unpack2Dformat(data)","title":""},{"docid":"2fba4c9366be5b2564bb77e811e0c6b4","score":"0.48727003","text":"def _get_array(self):\n if (not self.attendance_array):\n expanded = bz2.decompress(self.attendance)\n attendance_str = str(expanded)\n attendance_array = array.array(\"B\")\n attendance_array.fromstring(attendance_str)\n self.attendance_array = attendance_array\n return self.attendance_array","title":""},{"docid":"fa3f292fa6db8984db005864e511d1bc","score":"0.48697826","text":"def portData(arg):\n global dataarray\n\n dataarray = []\n # 이차 배열의 첫 배열 dataarray.\n recVal = str(arg)\n # 받은 arg 스트링을 recVal에 넣음.\n strArray = recVal.split(\",\")\n # recVal 스트링을 , 경계로 쪼갬.\n for i in range(0, 51):\n # 51가지의 데이터가 들어옴.\n dataarray.append(strArray[i])\n # print(strArray[i])\n # dataarray에 strArray[i] 첨부.\n # print(strArray[i])\n # createTimer(0.03, deformface)\n deformface()","title":""},{"docid":"c3afb0a21b7cab7fc9edcd2f1c991c6c","score":"0.48610887","text":"def generate_compressed_data(self):\n \n # Check if the output data batch is in right format and size (ttl added, shape etc)\n if type(self.output_data_batch) != np.ndarray or np.shape(self.output_data_batch)[0] != (self.num_channels):\n raise Exception('Output Data Batch is not specified properly!')\n else: \n self.output_data_batch = self.output_data_batch.astype(np.int16)\n\n # Copy ttl data (triggers):\n sample_ttl = np.zeros((2,self.output_data_batch.shape[1]),dtype = 'uint8') \n sample_ttl[0,:] = np.right_shift(np.bitwise_and(self.output_data_batch[0:1,:],0xFF00),8)\n sample_ttl[1,:] = np.bitwise_and(self.output_data_batch[0:1,:],0x00FF)\n\n # Compress recorded data:\n sample = np.zeros((self.output_data_batch.shape[0]-1,self.output_data_batch.shape[1]),dtype = 'uint16')\n sample = (self.output_data_batch[1:,:] + 2048).astype('uint16')\n self.sample_combined=np.zeros((int((self.output_data_batch.shape[0]-1)*1.5),self.output_data_batch.shape[1]),\n dtype='uint8')\n\n for first_of_two_channel in range(0,self.num_channels-1,2):\n self.sample_combined[int(first_of_two_channel*1.5),:] = np.right_shift(sample[first_of_two_channel,:],4)\n self.sample_combined[int(first_of_two_channel*1.5)+1,:] = np.left_shift(np.bitwise_and(\n sample[first_of_two_channel,:],0x000F),4) + np.right_shift(sample[first_of_two_channel+1,:],8)\n self.sample_combined[int(first_of_two_channel*1.5)+2,:] = np.bitwise_and(sample[first_of_two_channel+1,:],0x00FF)\n\n # Combine ttl and recorded data to be written into bin file:\n self.sample_combined = np.concatenate((sample_ttl, self.sample_combined), axis=0)","title":""},{"docid":"e19e23e1574f729b4ecd4b6d6b9e8923","score":"0.48574728","text":"def decode(self, array):\n\t\tif len(array) != 4:\n\t\t\traise ValueError(\"Array of wrong size\")\n\t\tbytestr = struct.pack('>'+'H'*len(array), *array)\n\t\tbytearray = struct.unpack('>'+'B'*(2*len(array)), bytestr)\n\t\tprint(bytearray)\n\t\tself.mbaddr = bytearray[0]\n\t\tself.mbcls = bytearray[1]\n\t\tself.adcinpsel = bytearray[2]\n\t\tself.adcrepeat = bytearray[3]\n\t\tself.adcsampleint = bytearray[4]\n\t\tself.adcwait = bytearray[5]\n\t\tself.i2cchip = bytearray[6]\n\t\t# last element is padding","title":""},{"docid":"45865d6940bfc88dc90af80b05cdfe62","score":"0.48548877","text":"def __array__(self, dtype=None):\n return np.asarray(self.data, dtype)","title":""},{"docid":"e85f74f8e66c3982779141861678fa23","score":"0.4851704","text":"def sensor_data(self):\r\n pass","title":""},{"docid":"e1b629cf4c6376be5715a10dfad39871","score":"0.48516345","text":"def onPortDataChanged(self, inputPort):","title":""},{"docid":"03a32a7075a336c277ac9d54de78e7c2","score":"0.4849438","text":"def sensor_readings():\n global ser\n data = b'5' # data sent\n ser.write(data) # Serial port write data\n data = ser.read(size=14)\n print(\"receive data is:\", list(data))\n return data","title":""},{"docid":"3667b9585f54a4fb41cdfdd407f7ba08","score":"0.48482788","text":"def _decomp_to_array(self, decomp_list):\n raise NotImplementedError('Not implemented in base (BaseEnsembleClassifier) class')","title":""},{"docid":"7bca58cb1ff8663a926989f82e55a2de","score":"0.48469204","text":"def _recv_arrays(self):\n headers = self.socket.recv_json()\n if 'stop' in headers:\n raise StopIteration\n arrays = []\n\n for header in headers:\n data = self.socket.recv()\n buf = buffer_(data)\n array = np.frombuffer(buf, dtype=np.dtype(header['descr']))\n array.shape = make_tuple(header['shape'])\n\n if header['fortran_order']:\n array.shape = header['shape'][::-1]\n array = array.transpose()\n arrays.append(array)\n\n return arrays","title":""},{"docid":"34dd72cf8314e24e47f90cee9d8cb363","score":"0.48467925","text":"def get_data(self):\n\n # Read data/mask specified by first contour\n data_array = self._single_targets_list[0].get_data().astype(np.uint8)\n\n # Add data/masks specified by subsequent contours\n for i in range(1, len(self._single_targets_list)):\n data_array += self._single_targets_list[\n i].get_data().astype(np.uint8)\n\n return data_array","title":""},{"docid":"9142a0fc47f8e28997a4c6ec29fe0627","score":"0.48377708","text":"def get_central_event_teltrg_list(self):\n num_teltrig= self.lib.get_num_tel_trig()\n if num_teltrig >= 0:\n array = np.zeros(num_teltrig,dtype=np.int32)\n self.lib.get_central_event_teltrg_list(array)\n return array\n else:\n raise(HessioGeneralError(\"hsdata is not available\"))","title":""},{"docid":"7b457c6ef4e90178e4b948f8d11287ab","score":"0.4837254","text":"def read_data(self, subdatablock):\n beams = self.header['Beams']\n data = np.frombuffer(subdatablock, dtype = Data7018.data_dtype)\n self.mag = data['Amp'].astype('f')\n self.mag.shape = (-1,beams)\n self.phase = data['Phs'].astype('f')\n self.phase.shape = (-1,beams)","title":""},{"docid":"565e7e952cc474a2a0bb4a79e6705d7a","score":"0.4832906","text":"def receive_data(self, data):\n\t\tself.data.update(data)\n\t\tself.update()","title":""},{"docid":"8de0931db5690963a246549d0a54de56","score":"0.48321775","text":"def from_ndarray(self, array, sampler):","title":""},{"docid":"459c944d37d543bf2ba78c29ddf6eedb","score":"0.48306078","text":"def callback(self, in_data, frame_count, time_info, status):\n data0 = np.frombuffer(in_data, dtype='int16')\n if np.abs(data0).mean() < self.silence_threshold:\n print('-', sep='', end='', flush=True)\n else:\n print('.', sep='', end='', flush=True)\n self.data = np.append(self.data,data0)\n if len(self.data) > self.window_samples:\n self.data = self.data[-self.window_samples:]\n self.q.put(self.data)\n return (in_data, pyaudio.paContinue)","title":""},{"docid":"9bab1d2cd3288ca23adbc0ddcfee7371","score":"0.48281434","text":"def write(self, intarray):\n\t\tself._spi.writebytes(intarray)","title":""},{"docid":"7c49735337788fe77bee6539403125bf","score":"0.48279488","text":"def array(self) -> NDArray:","title":""},{"docid":"e9969edfba81aaf431baf1edd1cc9e91","score":"0.4824319","text":"def data_read(self):\n\n dc_meas = None\n if self.dc_measurement_device is not None:\n try:\n dc_meas = self.dc_measurement_device.measurements_get()\n # if self.ts is not None:\n # self.ts.log_debug('The DC measurements are %s' % dc_meas)\n # else:\n # print('The DC measurements are %s' % dc_meas)\n except Exception as e:\n self.ts.log_debug('Could not get data from DC Measurement Object. %s' % e)\n\n try:\n data = []\n for chan in self.data_points_device:\n # self.ts.log_debug('self.data_points = %s' % self.data_points)\n signal = self.data_point_map[chan] # get signal name associated with data name\n # self.ts.log_debug('Chan = %s, signal = %s' % (chan, signal))\n if signal is None: # skip the signals that have no mapping to the simulink model\n\n # search the dc measurement object for the data that isn't in the opal_points_map\n if self.dc_measurement_device is not None:\n dc_value = dc_meas.get(chan) # signal = 'DC_V', 'DC_I', or 'DC_P'\n # if self.ts is not None:\n # self.ts.log_debug('Setting Chan = %s to dc_value = %s' % (chan, dc_value))\n # else:\n # print('Setting Chan = %s to dc_value = %s' % (chan, dc_value))\n if dc_value is not None:\n data.append(dc_value)\n else: # Channel data missing\n # self.ts.log_debug('Appending None for data point: %s' % chan)\n data.append(None)\n else: # DC Measurement Object missing\n # self.ts.log_debug('Appending None for data point: %s' % chan)\n data.append(None)\n continue\n\n # verify the model is runing before getting the signal data.\n status, _ = RtlabApi.GetModelState()\n if status == RtlabApi.MODEL_RUNNING:\n signal_value = RtlabApi.GetSignalsByName(signal)\n # self.ts.log_warning('signal_value is %s from Opal' % signal_value)\n else:\n signal_value = None\n # self.ts.log_warning('Signal_value set to None because Opal isn\\'t running')\n\n # self.ts.log_debug('Signal %s = %s' % (signal, signal_value))\n # self.ts.log_warning('type(sig) %s' % type(signal_value))\n if signal_value is not None and signal_value is not 'None':\n data.append(signal_value)\n else:\n data.append(None)\n\n except Exception as e:\n self.ts.log_debug('Could not get data. Simulation likely completed. Error: %s' % e)\n # self.ts.log_warning('self.data_points = %s. Writing all Nones.' % self.data_points)\n # self.ts.log_warning('self.data_points = %s. Writing all Nones.' % self.data_points_device)\n data = [None]*len(self.data_points_device) # Return list of Nones when simulations stops.\n # todo: this should be fixed in das.py sometime where a None can be returned and not added to the database\n\n # self.ts.log_debug('Data list %s' % data)\n return data","title":""},{"docid":"867cab66e72cd08372c609cf61d3e775","score":"0.48237202","text":"def read_raw_data(self, result):\n\n self._l1_barray[0] = self._mode\n self.i2c.writeto_mem(self.bme_address, BME280_REGISTER_CONTROL_HUM,\n self._l1_barray)\n self._l1_barray[0] = self._mode << 5 | self._mode << 2 | 1\n self.i2c.writeto_mem(self.bme_address, BME280_REGISTER_CONTROL,\n self._l1_barray)\n\n sleep_time = 1250 + 2300 * (1 << self._mode)\n sleep_time = sleep_time + 2300 * (1 << self._mode) + 575\n sleep_time = sleep_time + 2300 * (1 << self._mode) + 575\n time.sleep_us(sleep_time) # Wait the required time\n\n # burst readout from 0xF7 to 0xFE, recommended by datasheet\n self.i2c.readfrom_mem_into(self.bme_address, 0xF7, self._l8_barray)\n readout = self._l8_barray\n # pressure(0xF7): ((msb << 16) | (lsb << 8) | xlsb) >> 4\n raw_press = ((readout[0] << 16) | (readout[1] << 8) | readout[2]) >> 4\n # temperature(0xFA): ((msb << 16) | (lsb << 8) | xlsb) >> 4\n raw_temp = ((readout[3] << 16) | (readout[4] << 8) | readout[5]) >> 4\n # humidity(0xFD): (msb << 8) | lsb\n raw_hum = (readout[6] << 8) | readout[7]\n\n result[0] = raw_temp\n result[1] = raw_press\n result[2] = raw_hum","title":""},{"docid":"b260a5c596df2f2110ec2456aa1d1925","score":"0.48224577","text":"def get_bulk(grideye, i2caddr, addr, num_bytes):\n self = grideye.i2c # Psuedo I2C device function\n self.send_start_bit()\n stat = self.bulk_trans(2, [i2caddr << 1, addr])\n self.send_start_bit()\n stat += self.bulk_trans(1, [i2caddr << 1 | 1])\n ret = []\n for i in range(num_bytes-1):\n ret.append(self.read_byte())\n self.send_ack()\n ret.append(self.read_byte())\n self.send_nack()\n self.send_stop_bit()\n return ret","title":""},{"docid":"2f641f553cde73dcad0a0e28276f3387","score":"0.48203704","text":"def _transform_buffer(self, buffer):\n if (self.sampling_duration\n and self.sampling_duration < time.time() - self.start_time):\n return None\n\n for index in range(len(buffer)):\n time_before_read = time.time()\n try:\n data = self._monsoon.Protocol.DEVICE.read(\n # Magic value for USB bulk reads.\n 0x81,\n Packet.MAX_PACKET_SIZE,\n # In milliseconds.\n timeout=1000)\n except Exception as e:\n logging.warning(e)\n continue\n time_after_read = time.time()\n time_data = struct.pack('dd', time_after_read,\n time_after_read - time_before_read)\n buffer[index] = time_data + data.tobytes()\n\n return buffer","title":""},{"docid":"7160bd08103650cdb96d22c0136354fa","score":"0.48158497","text":"def to_array(self):\n return _np.copy(self.data)","title":""},{"docid":"09121afbdf4195c222121d5ce9e8a99d","score":"0.48153296","text":"def _updateToArray(self):\n\n for i, p in enumerate(self._source):\n\n p[...] = self._smooth_growth_data[i]","title":""},{"docid":"5f906693d9c4ac7f2f9d62ea2803670f","score":"0.48118","text":"def numpy_array(self, data):\n self._data = data.view()\n self._data_needs_writeback = True\n data.setflags(write=False)","title":""}],"string":"[\n {\n \"docid\": \"5e8c673c8e23761577125b46a7aa4813\",\n \"score\": \"0.5778601\",\n \"text\": \"def __raw_data_to_array(sequence, sampling_rate):\\n return sequence['wave']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cdd4c901322160893c3d8e9de33a45d\",\n \"score\": \"0.576799\",\n \"text\": \"def to_array(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ced1af086ee4f1a72cdd12120246b2\",\n \"score\": \"0.5705713\",\n \"text\": \"def bytes_to_value(self, byte_array): \\n self._logger.debug(\\\"In Bytes To Value\\\") \\n \\n # Array for value bytes (exclude crc check byte)\\n bytes_buf = [0]*12 # 2 words for each co2, temperature, humidity\\n l = len(bytes_buf)\\n ld = len(byte_array)\\n # array for word conversion - two words per value\\n word_buf = [0]*int(l/2)\\n # final data structure - one place per value\\n data = [0]*int(len(word_buf)/2)\\n\\n # Load bytes_buffer, strip crc bytes\\n y = 0\\n for x in range(0, ld, 6):\\n# print(\\\"x: \\\" + str(x) + \\\" y: \\\" + str(y))\\n bytes_buf[y] = byte_array[x]\\n bytes_buf[y+1] = byte_array[x+1]\\n bytes_buf[y+2] = byte_array[x+3]\\n bytes_buf[y+3] = byte_array[x+4] \\n y += 4\\n self._logger.detail(\\\"bytes_buf: \\\" + str(bytes_buf)) \\n \\n # Convert sensor data reads to physical value per Sensirion specification\\n # Load buffer with values\\n # Cast 4 bytes to one unsigned 32 bit integer\\n # Cast unsigned 32 bit integer to 32 bit float\\n\\n # Convert bytes to words\\n for i in range(len(word_buf)):\\n word_buf[i] = (bytes_buf[i*2] << 8) | bytes_buf[i*2+1]\\n self._logger.detail(str(hex(bytes_buf[i])) + \\\" \\\" + str(hex(bytes_buf[i+1])))\\n self._logger.detail(hex(word_buf[i]))\\n\\n #convert words to int32\\n data[self.CO2_DATA_INDEX] = (word_buf[0] << 16) | word_buf[1]\\n data[self.TEMP_DATA_INDEX] = (word_buf[2] << 16) | word_buf[3]\\n data[self.HUMIDITY_DATA_INDEX] = (word_buf[4] << 16) | word_buf[5]\\n\\n #Convert int32 data to float32\\n floatData = numpy.array(data, dtype=numpy.int32)\\n data = floatData.view('float32')\\n\\n self._logger.detail(\\\"CO2: \\\" + str(data[self.CO2_DATA_INDEX]))\\n self._logger.detail(\\\"Temp: \\\" + str(data[self.TEMP_DATA_INDEX]))\\n self._logger.detail(\\\"RH: \\\" + str(data[self.HUMIDITY_DATA_INDEX]))\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c874375ec7252367b2475addc13e61e1\",\n \"score\": \"0.55983055\",\n \"text\": \"def transmission_data(self, thi: float) -> Tuple[NDArray, NDArray]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3104745bed1b3efbfa3318ec20f65305\",\n \"score\": \"0.5570566\",\n \"text\": \"def to_array(self):\\n\\n values = []\\n values.append(int(self.sec))\\n values.append(int(self.nano))\\n\\n if self.type == 'T': # timestamp\\n values.append(0)\\n elif self.type == 'S': # system\\n values.append(1)\\n elif self.type in ('A', 'F'): # adds\\n values.append(2)\\n elif self.type == 'X': # cancel\\n values.append(3)\\n elif self.type == 'D': # delete\\n values.append(4)\\n elif self.type == 'E': # execute\\n values.append(5)\\n elif self.type == 'C': # execute w/ price\\n values.append(6)\\n elif self.type == 'U': # replace\\n values.append(7)\\n else:\\n values.append(-1) # other (ignored)\\n\\n if self.event == 'O': # start messages\\n values.append(0)\\n elif self.event == 'S': # start system hours\\n values.append(1)\\n elif self.event == 'Q': # start market hours\\n values.append(2)\\n elif self.event == 'M': # end market hours\\n values.append(3)\\n elif self.event == 'E': # end system hours\\n values.append(4)\\n elif self.event == 'C': # end messages\\n values.append(5)\\n elif self.event == 'A': # halt trading\\n values.append(6)\\n elif self.event == 'R': # quotes only\\n values.append(7)\\n elif self.event == 'B': # resume trading\\n values.append(8)\\n else:\\n values.append(-1) # no event\\n\\n if self.buysell == 'B': # bid\\n values.append(1)\\n elif self.buysell == 'S': # ask\\n values.append(-1)\\n else:\\n values.append(0)\\n\\n values.append(int(self.price))\\n values.append(int(self.shares))\\n values.append(int(self.refno))\\n values.append(int(self.newrefno))\\n\\n return np.array(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0ef17087d0509f9b06982f27ba37ec\",\n \"score\": \"0.5542718\",\n \"text\": \"def receive_data(self, data: np.ndarray, data_changed: bool):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c489f8077716efdcb08090c9a7089aa\",\n \"score\": \"0.5527125\",\n \"text\": \"def get_np_array(self, connection_socket):\\n message = self._receiving_subroutine(connection_socket)\\n received_weights = pickle.loads(message)\\n return received_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3690d71231b39e5a24b1a4def621d368\",\n \"score\": \"0.5498337\",\n \"text\": \"def get_data(self):\\n data = self.board.read(self.sample_no)\\n try:\\n data = np.array(struct.unpack('b'*self.sample_no, data))\\n except struct.error as e:\\n print(data)\\n raise e\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbc5e9cf1a530cf2b8b2b39d3da7551\",\n \"score\": \"0.5458467\",\n \"text\": \"def recv_array(socket, flags=0, copy=True, track=False):\\n md = socket.recv_json(flags=flags)\\n msg = socket.recv(flags=flags, copy=copy, track=track)\\n buf = buffer(msg)\\n A = np.frombuffer(buf, dtype=md['dtype'])\\n return A.reshape(md['shape'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f0353f92c1db96e3af050d7c30078\",\n \"score\": \"0.5386292\",\n \"text\": \"def send_array(self, arr):\\n flat = np.array(arr).flatten()\\n self.send_raw(flat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f278bd3356724cb1ef2cf526a81cef4\",\n \"score\": \"0.53614944\",\n \"text\": \"def get_data(sequence, sampling_rate):\\n data_type = sequence['type']\\n switch = {DataTypes.RAW_DATA: Sequencer.__raw_data_to_array,\\n DataTypes.QU_PULSE: Sequencer.__qupulse_template_to_array}\\n to_array_function = switch[data_type]\\n return to_array_function(sequence, sampling_rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b06a7c045eb00a1cc8933ea8d8a1531\",\n \"score\": \"0.53585905\",\n \"text\": \"def read_data(self):\\r\\n\\r\\n data = self.serial.read(22)\\r\\n data_hex = data.hex()\\r\\n if self.debug: self.vertify_data(data_hex)\\r\\n pm1_cf = int(data_hex[4]+data_hex[5]+data_hex[6]+data_hex[7],16)\\r\\n pm25_cf = int(data_hex[8]+data_hex[9]+data_hex[10]+data_hex[11],16)\\r\\n pm10_cf = int(data_hex[12]+data_hex[13]+data_hex[14]+data_hex[15],16)\\r\\n pm1 = int(data_hex[16]+data_hex[17]+data_hex[18]+data_hex[19],16)\\r\\n pm25 = int(data_hex[20]+data_hex[21]+data_hex[22]+data_hex[23],16)\\r\\n pm10 = int(data_hex[24]+data_hex[25]+data_hex[26]+data_hex[27],16)\\r\\n if self.debug: print(\\\"pm1_cf: \\\"+str(pm1_cf))\\r\\n if self.debug: print(\\\"pm25_cf: \\\"+str(pm25_cf))\\r\\n if self.debug: print(\\\"pm10_cf: \\\"+str(pm10_cf))\\r\\n if self.debug: print(\\\"pm1: \\\"+str(pm1))\\r\\n if self.debug: print(\\\"pm25: \\\"+str(pm25))\\r\\n if self.debug: print(\\\"pm10: \\\"+str(pm10))\\r\\n data = [pm1_cf, pm10_cf, pm25_cf, pm1, pm10, pm25]\\r\\n self.serial.close()\\r\\n\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca680230fe6bf4476d019580ebebd45\",\n \"score\": \"0.5346419\",\n \"text\": \"def recv_array(socket: zmq.Socket, flags: int=0, copy: int=True, track: bool=False):\\n dictionary = socket.recv_json(flags=flags)\\n message = socket.recv(flags=flags, copy=copy, track=track)\\n buffer = memoryview(message)\\n array = np.frombuffer(buffer, dtype=dictionary['dtype'])\\n return array.reshape(dictionary['shape'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ff98d094e10125d03f304c1185b6ac\",\n \"score\": \"0.53384554\",\n \"text\": \"def _transformDataToArray(self) -> None:\\n result = []\\n for sequence in self.data:\\n arr = np.empty((len(sequence), 2))\\n for i, (itemOne, itemTwo) in enumerate(sequence):\\n arr[i][0] = itemOne\\n arr[i][1] = itemTwo\\n result.append(arr)\\n self._data = result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9a1aadcdfa79c746d1bb37bd1f62155\",\n \"score\": \"0.53358775\",\n \"text\": \"def data_b(self) -> np.ndarray:\\n return self.ch_b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d520ce82eef85664f1406d6c96a135dc\",\n \"score\": \"0.53045624\",\n \"text\": \"def prtSensorValuesInBuffer(self):\\n\\n for i in self.data:\\n print i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2769ee83b6ab0e2a8a3d7ff6b691968e\",\n \"score\": \"0.52978307\",\n \"text\": \"def to_ndarray(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d39defa9871fdffb62b6e48db7d72b9\",\n \"score\": \"0.5293051\",\n \"text\": \"def data(self, data):\\n data = data if self._bitmode == 8 else \\\\\\n bytes_to_nibbles(data)\\n super(parallel_device, self).data(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abaeb9fb016354a2f06e33ca09267398\",\n \"score\": \"0.52670586\",\n \"text\": \"def get_ary(self, dbuf):\\n hbuf_out = []\\n # Initiate the transfer\\n for idx, ctx, dbuf, strm in zip(self.device_ids, self.ctxs, dbuf, self.streams):\\n ctx.push()\\n hbuf_out.append(dbuf.get_async(strm))\\n ctx.pop()\\n return np.hstack(hbuf_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a1e59c5ef5811b247bd253db1cd168\",\n \"score\": \"0.5233872\",\n \"text\": \"def read_input_buffer(self):\\r\\n\\r\\n if not self.connected:\\r\\n return False\\r\\n\\r\\n data_changed = False\\r\\n\\r\\n # Read all the data...\\r\\n while self.serial_port.inWaiting() > (self.channels + 1 * 4 + 3):\\r\\n\\r\\n # New data is raw, as returned from the microprocessor, but scaled -1 -> +1.\\r\\n # Here, we scale it -> uV.\\r\\n new_data = self._read_serial_binary()\\r\\n\\r\\n ########## Uncomment this next line for debugging purposes. #######\\r\\n # new_data[1:] = self._read_mock_data() # overwrites real data with mock data.\\r\\n\\r\\n # Now, we scale from -1 -> +1, to uV:\\r\\n new_data[1:] = new_data[1:] * creare_volts_per_FS * 1.0e6 # now uV.\\r\\n self.history.append(new_data)\\r\\n data_changed = True\\r\\n\\r\\n if data_changed:\\r\\n # If the history gets too long, cull it:\\r\\n if len(self.history) > MAX_HISTORY_LENGTH:\\r\\n self.history = self.history[-MIN_HISTORY_LENGTH:]\\r\\n\\r\\n # Update the numpy timeseries array.\\r\\n self.timeseries = np.array(self.history[-TIMESERIES_LENGTH:])\\r\\n\\r\\n # Infer which channels are on/off... we don't keep track of this\\r\\n # internally b/c it's safer to infer it from the Arduino output.\\r\\n [self.channel_1_enabled,\\r\\n self.channel_2_enabled,\\r\\n self.channel_3_enabled,\\r\\n self.channel_4_enabled,\\r\\n self.channel_5_enabled,\\r\\n self.channel_6_enabled,\\r\\n self.channel_7_enabled,\\r\\n self.channel_8_enabled] = np.logical_not(np.isnan(self.history[-1][1:])).tolist()\\r\\n self.data_changed = True # fire data_changed event.\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0053c169e400f30c7530094fd225af43\",\n \"score\": \"0.52330863\",\n \"text\": \"def read_from_buffer(self):\\n for pa in self.arrays:\\n pa.read_from_buffer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce7d1ce4d9a7ce9d1dc25058fb07bc83\",\n \"score\": \"0.52072465\",\n \"text\": \"def to_array(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc2a207a1c67dd4cc9a1da1e2f81f2e6\",\n \"score\": \"0.5193455\",\n \"text\": \"def data_a(self) -> np.ndarray:\\n if self.marker_a is None and self.marker_b is None:\\n return self.ch_a\\n\\n if self.ch_a is None:\\n raise NotImplementedError('What data should be used in a?')\\n\\n # copy channel information\\n data = np.array(self.ch_a)\\n\\n if self.marker_a is not None:\\n data.reshape(-1, 8)[1::2, :].flat |= (1 << 14) * self.marker_a.astype(np.uint16)\\n\\n if self.marker_b is not None:\\n data.reshape(-1, 8)[1::2, :].flat |= (1 << 15) * self.marker_b.astype(np.uint16)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252496336671ef06fa8db59605d219a6\",\n \"score\": \"0.5170924\",\n \"text\": \"def to_numpy(t):\\n return t.data.cpu().numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bc6bc755ac2add92cea897b7f6a161\",\n \"score\": \"0.51457644\",\n \"text\": \"def read_buffer(self, channel, start=0, length=None, format='A'):\\n\\n cmd = 'TRCA'\\n if not length:\\n length = self.buffer_length\\n self.send('{}? {},{},{}'.format(cmd, channel, start, length))\\n if cmd == 'TRCA':\\n data = self.recv()\\n return np.fromstring(data, sep=',') * ureg.volt\\n else:\\n raise ValueError('{} transfer format is not implemented'.format(format))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777a10cdf2c096d719946f7c2f5043a8\",\n \"score\": \"0.5137951\",\n \"text\": \"def _read_data(self):\\n\\n # Start the integration\\n self._device.ctrl_transfer(StellarNet._OUT_DEVICE, 0xB2, 0, 0)\\n\\n # Set the timeout to be 500ms longer than the expected integration time\\n int_time = self._config['int_time']\\n timeout = (int_time + 500)/1000.0\\n\\n # Sleep for 90% of the integration time to avoid busy waiting\\n # for the result and causing unnecessary usb traffic\\n start = time.time()\\n time.sleep(int_time*.0009)\\n \\n while True:\\n # Check if data is available\\n reponse = self._device.ctrl_transfer(StellarNet._IN_DEVICE, 0xB3, 0, 0, 2)\\n\\n # If device is busy wait for next check else if device is ready wait\\n # before reading data. Note: reading data without waiting occasionally\\n # results in a right-shifted spectrum which becomes more frequent with\\n # shorter integration times..\\n time.sleep(.003)\\n if reponse[1]:\\n break\\n \\n if time.time() - start > timeout:\\n raise TimeoutError('Read spectrum')\\n\\n # Read the CCD data and convert it to an array of \\n # 16-bit integers from the little-endian data buffer\\n pixels = StellarNet._PIXEL_MAP[self._config['det_type']]\\n return struct.unpack_from('<{}H'.format(pixels),\\n self._device.read(usb.util.ENDPOINT_IN | 8, pixels*2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c6c18c9a3b270ad555be4dc010f776\",\n \"score\": \"0.5131457\",\n \"text\": \"def get_datas(callback, macs=[], run_flag=RunFlag(), bt_device=''):\\n\\n log.info('Get latest data for sensors. Stop with Ctrl+C.')\\n log.info('MACs: %s', macs)\\n\\n for new_data in RuuviTagSensor._get_ruuvitag_datas(macs, None, run_flag, bt_device):\\n callback(new_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc51cef5f4c8afb2fbdd0a679664decc\",\n \"score\": \"0.51259863\",\n \"text\": \"def accrue_sensors_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b7b4f72a1e94123408b6b58f915aaf\",\n \"score\": \"0.5108878\",\n \"text\": \"def run(self):\\n\\n try:\\n while not self.__stop_event.isSet():\\n samples = self.__serial.get_samples()\\n\\n if not self.__stop_event.isSet():\\n self.__buffer.extend(samples)\\n except Exception as e:\\n self.__serial.close()\\n messagebox.showerror(\\\"Communication Error\\\",\\n \\\"Data read timeout occurred!\\\")\\n self.__btn_txt.set(\\\"START\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1be313c830064e3f23f31ca00ea082\",\n \"score\": \"0.5105884\",\n \"text\": \"def convertData():\\n\\tglobal inputData, npData\\n\\tLOGGER.info(\\\"CONV_DATA: Starting to convert data format\\\")\\n\\tLOGGER.debug(\\\"Optimal format is NumPy\\\")\\n\\tdata_transformer = NpDataFromRaw(inputData.data)\\n\\tnpData = data_transformer()\\n\\tLOGGER.info(\\\"CONV_DATA: Finished data transformation\\\")\\n\\tdel inputData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f41e603bbc930a09b0b268e05ba373\",\n \"score\": \"0.50931275\",\n \"text\": \"def _pack_get_event(evt):\\n t = evt.traces\\n packed = np.zeros((len(t), 517), dtype='int16')\\n packed[:, 0] = t['cobo']\\n packed[:, 1] = t['asad']\\n packed[:, 2] = t['aget']\\n packed[:, 3] = t['channel']\\n packed[:, 4] = t['pad']\\n packed[:, 5:] = t['data']\\n return packed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e414747bba685d9f2a62d2cb04a2ca7\",\n \"score\": \"0.5089171\",\n \"text\": \"def onRecv(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a1cd65020872c63a44173452e025bd\",\n \"score\": \"0.5077538\",\n \"text\": \"def array(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5abb8a543bed9ee589783e67435f73f9\",\n \"score\": \"0.5077118\",\n \"text\": \"def push_data(x_diode, y_diode, x_diode_t, y_diode_t, buffer):\\n \\n x_diode_data = pd.Series(x_diode, index=x_diode_t)\\n y_diode_data = pd.Series(y_diode, index=y_diode_t)\\n zipped = basic_event_builder(x_diode=x_diode_data, y_diode=y_diode_data)\\n buffer.send(zipped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ea041c7d0885530531a5e308fb769e\",\n \"score\": \"0.50664616\",\n \"text\": \"def on_tx_event(self, data):\\n # data = array.array('B', [1, 255, 133, 128, 8, 0, 128, 0])\\n page_to_send = self._get_next_page()\\n data_payload = page_to_send.to_payload() # get new data payload to sent at this TX event.\\n # call channel's send_broadcast_data to set the TX buffer to new data.\\n self.channel.send_broadcast_data(data_payload)\\n print(\\\"send TX\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd1deb7f19ad713d7bc9af74feb8f9c\",\n \"score\": \"0.5063892\",\n \"text\": \"def _put_array(self):\\n self.attendance = \\\\\\n bz2.compress(self.attendance_array.tostring())\\n self.put()\\n return self.attendance_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4714f3b7a5e7af44a82a490a0c1e961\",\n \"score\": \"0.5062884\",\n \"text\": \"def _read(self):\\n\\n # Check buffer size and limits\\n buffer_size = self.device.in_waiting\\n if buffer_size == 1020:\\n # The OS serial buffer can hold up to 1020 bytes\\n self.logger.warn('Internal buffer saturated. Increase graph rate or decrease device rate.')\\n\\n # Compute the maximum number of samples we can get\\n sample_count = int(buffer_size / self.sample_size)\\n\\n # Read raw samples from device\\n now = clock.now()\\n raw = self.device.read(sample_count * self.sample_size)\\n\\n # Initialize the output matrix\\n data = np.full((sample_count, 3), np.nan, np.uint32)\\n\\n # Parse the raw data\\n for sample_number in range(sample_count):\\n\\n # Extract sample\\n start = sample_number * self.sample_size\\n stop = start + self.sample_size\\n sample = raw[start:stop]\\n\\n # Check (dummy) CRC\\n crc = sample[8]\\n if crc != 0:\\n self.logger.warn('Corrupted sample.')\\n continue\\n\\n # Parse sample\\n data[sample_number, 0] = int.from_bytes(sample[0:2], byteorder='little', signed=False)\\n data[sample_number, 1] = int.from_bytes(sample[2:6], byteorder='little', signed=False)\\n data[sample_number, 2] = int.from_bytes(sample[6:8], byteorder='little', signed=False)\\n\\n # Did we miss any sample?\\n # Check for discontinuity in the internal sample counter (2 bytes).\\n sample_counter = data[sample_number, 0]\\n if sample_counter == self._sample_counter + 1:\\n pass\\n elif sample_counter == 0 and self._sample_counter == 65535:\\n pass\\n else:\\n self.logger.warn('Missed sample.')\\n self._sample_counter = sample_counter\\n\\n # Compute indices\\n indices = clock.time_range(self._timestamp, now, len(data))\\n self._timestamp = now\\n\\n return data, indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3f98594b05870f5a06daa1fc749162\",\n \"score\": \"0.50610167\",\n \"text\": \"def data(self):\\n return self.array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3f98594b05870f5a06daa1fc749162\",\n \"score\": \"0.50610167\",\n \"text\": \"def data(self):\\n return self.array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7193bdc1e1d7026f25af1697c6f9caed\",\n \"score\": \"0.5057166\",\n \"text\": \"def getData(self) -> \\\"void *\\\":\\n return _coin.SoSensor_getData(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe35628b4a487495c5cbc1e02022937e\",\n \"score\": \"0.5038984\",\n \"text\": \"def get_data(fb_arr) -> np.ndarray:\\n raw_data = fb_arr.DataAsNumpy()\\n type_map = {\\n DType.uint8: np.uint8,\\n DType.int8: np.int8,\\n DType.uint16: np.uint16,\\n DType.int16: np.int16,\\n DType.uint32: np.uint32,\\n DType.int32: np.int32,\\n DType.uint64: np.uint64,\\n DType.int64: np.int64,\\n DType.float32: np.float32,\\n DType.float64: np.float64,\\n }\\n return raw_data.view(type_map[fb_arr.DataType()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ab2ab6a398a3ad13c21ee2d6259f43\",\n \"score\": \"0.50361073\",\n \"text\": \"def to_np(src_tens: torch.Tensor) -> np.ndarray:\\n return src_tens.cpu().detach().numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7600a74b4389b7d6e17b4855297f4b5e\",\n \"score\": \"0.503004\",\n \"text\": \"def flush(self, data):\\n\\n if not self._sacn:\\n raise Exception(\\\"sACN sender not started.\\\")\\n if data.size != self._config[\\\"channel_count\\\"]:\\n raise Exception(\\n f\\\"Invalid buffer size. {data.size} != {self._config['channel_count']}\\\"\\n )\\n\\n data = data.flatten()\\n current_index = 0\\n for universe in range(\\n self._config[\\\"universe\\\"], self._config[\\\"universe_end\\\"] + 1\\n ):\\n # Calculate offset into the provide input buffer for the channel. There are some\\n # cleaner ways this can be done... This is just the quick and dirty\\n universe_start = (\\n universe - self._config[\\\"universe\\\"]\\n ) * self._config[\\\"universe_size\\\"]\\n universe_end = (\\n universe - self._config[\\\"universe\\\"] + 1\\n ) * self._config[\\\"universe_size\\\"]\\n\\n dmx_start = (\\n max(universe_start, self._config[\\\"channel_offset\\\"])\\n % self._config[\\\"universe_size\\\"]\\n )\\n dmx_end = (\\n min(\\n universe_end,\\n self._config[\\\"channel_offset\\\"]\\n + self._config[\\\"channel_count\\\"],\\n )\\n % self._config[\\\"universe_size\\\"]\\n )\\n if dmx_end == 0:\\n dmx_end = self._config[\\\"universe_size\\\"]\\n\\n input_start = current_index\\n input_end = current_index + dmx_end - dmx_start\\n current_index = input_end\\n\\n dmx_data = np.array(self._sacn[universe].dmx_data)\\n dmx_data[dmx_start:dmx_end] = data[input_start:input_end]\\n\\n # Because the sACN library checks for data to be of int type, we have to\\n # convert the numpy array into a python list of ints using tolist()\\n self._sacn[universe].dmx_data = dmx_data.clip(0, 255).tolist()\\n # output = dmx_data.clip(0, 255)\\n\\n # This is ugly - weird race condition where loading on startup from a device with a short ID results in the sACN thread trying to send data to NoneType.\\n # No idea how to properly handle it - but this stops it breaking and seems to be reasonably resilient. Sorry to whoever stumbles onto it. -Shaun\\n try:\\n self._sacn.flush()\\n except AttributeError:\\n _LOGGER.info(\\n \\\"Attempted to start sACN thread prior to sACN activating. Restarting sACN thread.\\\"\\n )\\n self.activate\\n\\n # # Hack up a manual flush of the E1.31 data vs having a background thread\\n # if self._sacn._output_thread._socket:\\n # for output in list(self._sacn._output_thread._outputs.values()):\\n # self._sacn._output_thread.send_out(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915475475e2adb5986929ad5310ac2c2\",\n \"score\": \"0.5025835\",\n \"text\": \"def ConvertDataToArrays(the_features):\\n\\n return np.asarray(the_features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9320ee0c79c40beb966dfe6b2b12db22\",\n \"score\": \"0.5023859\",\n \"text\": \"def Rx(t):\\n return np.array(((1,0,0),(0,np.cos(t),-np.sin(t)),(0,np.sin(t),np.cos(t))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a983405ac46867ac1dd863c25f5183a\",\n \"score\": \"0.501484\",\n \"text\": \"def set_to_array(self, points):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa0ddaf3f0146200a25f1fbf75b3a8ee\",\n \"score\": \"0.50135607\",\n \"text\": \"def serialize_numpy(self, buff, numpy):\\n try:\\n length = len(self.devices)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sI'%length\\n buff.write(self.devices.tostring())\\n _x = self\\n buff.write(_get_struct_B4f2i8f2iB8f4i20fB().pack(_x.params.effectType, _x.params.fixed_color.color.r, _x.params.fixed_color.color.g, _x.params.fixed_color.color.b, _x.params.fixed_color.color.a, _x.params.rainbow.transition_duration.secs, _x.params.rainbow.transition_duration.nsecs, _x.params.fade.first_color.r, _x.params.fade.first_color.g, _x.params.fade.first_color.b, _x.params.fade.first_color.a, _x.params.fade.second_color.r, _x.params.fade.second_color.g, _x.params.fade.second_color.b, _x.params.fade.second_color.a, _x.params.fade.transition_duration.secs, _x.params.fade.transition_duration.nsecs, _x.params.fade.reverse_fade, _x.params.blink.first_color.r, _x.params.blink.first_color.g, _x.params.blink.first_color.b, _x.params.blink.first_color.a, _x.params.blink.second_color.r, _x.params.blink.second_color.g, _x.params.blink.second_color.b, _x.params.blink.second_color.a, _x.params.blink.first_color_duration.secs, _x.params.blink.first_color_duration.nsecs, _x.params.blink.second_color_duration.secs, _x.params.blink.second_color_duration.nsecs, _x.params.progress.first_color.r, _x.params.progress.first_color.g, _x.params.progress.first_color.b, _x.params.progress.first_color.a, _x.params.progress.second_color.r, _x.params.progress.second_color.g, _x.params.progress.second_color.b, _x.params.progress.second_color.a, _x.params.progress.percentage, _x.params.progress.led_offset, _x.params.flow.first_color.r, _x.params.flow.first_color.g, _x.params.flow.first_color.b, _x.params.flow.first_color.a, _x.params.flow.second_color.r, _x.params.flow.second_color.g, _x.params.flow.second_color.b, _x.params.flow.second_color.a, _x.params.flow.percentage, _x.params.flow.velocity, _x.params.preprogrammed.preprogrammed_id))\\n _x = self.params.effect_via_topic.topic_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n length = len(self.params.data_array.data)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.params.data_array.data:\\n _x = val1\\n buff.write(_get_struct_4f().pack(_x.r, _x.g, _x.b, _x.a))\\n _x = self\\n buff.write(_get_struct_2iB().pack(_x.effectDuration.secs, _x.effectDuration.nsecs, _x.priority))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c91e59e2861fb2c15993f73e326eee\",\n \"score\": \"0.501091\",\n \"text\": \"def serialize_numpy(self, buff, numpy):\\n try:\\n length = len(self.all_car)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.all_car:\\n _v9 = val1.header\\n _x = _v9.seq\\n buff.write(_get_struct_I().pack(_x))\\n _v10 = _v9.stamp\\n _x = _v10\\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\\n _x = _v9.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n _x = val1.id\\n buff.write(_get_struct_b().pack(_x))\\n length = len(val1.mask)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sB'%length\\n buff.write(val1.mask.tostring())\\n _x = val1.valid_sequence_num\\n buff.write(_get_struct_b().pack(_x))\\n length = len(val1.trajectory)\\n buff.write(_struct_I.pack(length))\\n for val2 in val1.trajectory:\\n _v11 = val2.header\\n _x = _v11.seq\\n buff.write(_get_struct_I().pack(_x))\\n _v12 = _v11.stamp\\n _x = _v12\\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\\n _x = _v11.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n _x = val2\\n buff.write(_get_struct_IB9f().pack(_x.id, _x.classification, _x.x, _x.y, _x.yaw, _x.v, _x.yawrate, _x.a, _x.delta, _x.L, _x.W))\\n _x = self.host_car_id\\n buff.write(_get_struct_b().pack(_x))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ced6b8c522a0f0ff98d0228e78b17c\",\n \"score\": \"0.50039107\",\n \"text\": \"def irsensors_callback(self, data):\\n self.ir_data_lock.acquire()\\n self.ir_data[data.header.frame_id] = data.signal\\n self.ir_data_lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ced6b8c522a0f0ff98d0228e78b17c\",\n \"score\": \"0.50039107\",\n \"text\": \"def irsensors_callback(self, data):\\n self.ir_data_lock.acquire()\\n self.ir_data[data.header.frame_id] = data.signal\\n self.ir_data_lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5a39f3d612653cfaf54990f82dd568e\",\n \"score\": \"0.4998454\",\n \"text\": \"def run(self):\\n is_running = True\\n while is_running:\\n try:\\n # Wait the next byte frame from device\\n data_frame = ord(self.serial.read())\\n\\n # Parse the byte as 8 digital samples\\n data_array = np.zeros(Oscilloscope.BYTE_LENGTH)\\n for i in range(Oscilloscope.BYTE_LENGTH):\\n data_array[Oscilloscope.BYTE_LENGTH - 1 - i] = 1 if data_frame & 0x01 > 0 else 0\\n data_frame = data_frame >> 1\\n \\n\\n # Update oscilloscope print\\n self.monitor.add_data(data_array)\\n except KeyboardInterrupt:\\n is_running = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f7a28accb42fce4cd29c4e4ecf4677\",\n \"score\": \"0.4990443\",\n \"text\": \"def dataReceived(self):\\n \\n # Alternates the communication LED\\n self.CommunicationLED.write(0)\\n time.sleep(1)\\n self.CommunicationLED.write(1)\\n time.sleep(1)\\n self.CommunicationLED.write(0)\\n \\n self.Helpers.logger.info(\\\"Communication LED ready!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87dd1e3801b3bd037a6eed3740e814a8\",\n \"score\": \"0.49889302\",\n \"text\": \"def get_data(self):\\n while True:\\n message = self._subscriber.queue.get()\\n parsed = ArrayMessage.deserialize(message)\\n self._subscriber.queue.task_done()\\n yield DataContainer.from_array(\\n parsed.data,\\n int(parsed.data.shape[0] / ArrayMessage.duration)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c08d39ac9fbf45b92c5158a43f56ae9\",\n \"score\": \"0.49814352\",\n \"text\": \"def rx(self, buf):\\n for c in buf:\\n self.rx_state(c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e20f10a36fb6295035b037e02366c18\",\n \"score\": \"0.49778795\",\n \"text\": \"def callback_SSS_S(n_bytes, x):\\n # print('callback_SSS_S')\\n # print(\\\"+++ Python SSS_S {} bytes in x={}\\\".format(n_bytes, x))\\n shp = (n_bytes // BYTES_PER_SAMPLE_INCOME_STREAM // MAX_ODAS_SOURCES, MAX_ODAS_SOURCES) # shape\\n n_frames = shp[0] // CHUNK_SIZE_INCOME_STREAM # I assume shp[0] is always a multiple integer, which in my understanding seems to be the case with odas\\n buf = np.ctypeslib.as_array(x, shape=shp)\\n # if MULTI_THREAD:\\n assert SSS_queue is not None\\n if isinstance(SSS_queue, list): # It means we want to demux the signal\\n for i in range(0, MAX_ODAS_SOURCES):\\n try:\\n for _fr in range(n_frames): # there could be more than one frame so put in queue with the expected length\\n _idl = _fr * CHUNK_SIZE_INCOME_STREAM\\n _idh = _idl + CHUNK_SIZE_INCOME_STREAM\\n _ch_buf = buf[_idl:_idh, i]\\n # print(\\\"extract buffer source {} - frame {}. 3 samples {} ...\\\".format(i, _fr, _ch_buf[0:3]))\\n SSS_queue[i].put_nowait(_ch_buf) # eventually_ch_buf this has to be transformed in bytes or store as a byte IO?\\n if DUMP_CALLBACK:\\n # write_wave(dump_callback_file[i], buf)\\n if USE_SOUNDFILE:\\n #dump_callback_file[i].write(_ch_buf)#, np.int16)\\t# removing header! #\\n _bb = _ch_buf.copy() # need to create a copy, to make the array contiguos, otherwise i get an exception\\n dump_callback_file[i].write(_bb)\\n else:\\n\\t\\t\\t\\t\\t\\t\\t# Solution wave, this write always the latest buffer and doesnt happend it!\\n dump_callback_file[i].writeframesraw(_ch_buf) # removing header!\\t\\n except Full:\\n _LOGGER.warning(\\\"SSS_S receiving Queue_\\\" + str(i) + \\\" is Full, skipping frame (TODO: lost for now, change in deque!)\\\")\\n # do nothing for now, perhaps extract the first to make space? Kind of circular queue behavior.....\\n pass\\n else:\\n try:\\n SSS_queue.put_nowait(buf)\\n if DUMP_CALLBACK:\\n write_wave(dump_callback_file, buf)\\n except Full:\\n _LOGGER.warning(\\\"SSS_S receiving Queue is Full, skipping frame (TODO: lost for now, change in deque!)\\\")\\n # do nothing for now, perhaps extract the first to make space? Kind of circular queue behavior.....\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6b7919b2883800ca56e6e468eee7a0\",\n \"score\": \"0.49703982\",\n \"text\": \"def onAdaptorData(self, message):\\n #logging.debug(\\\"%s onadaptorData, message: %s\\\", ModuleName, message)\\n if message[\\\"characteristic\\\"] == \\\"acceleration\\\":\\n for a in self.accel:\\n if a.id == self.idToName[message[\\\"id\\\"]]: \\n a.processAccel(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"temperature\\\":\\n for t in self.temp:\\n if t.id == self.idToName[message[\\\"id\\\"]]:\\n t.processTemp(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"ir_temperature\\\":\\n for t in self.irTemp:\\n if t.id == self.idToName[message[\\\"id\\\"]]:\\n t.processIrTemp(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"gyro\\\":\\n for g in self.gyro:\\n if g.id == self.idToName[message[\\\"id\\\"]]:\\n g.processGyro(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"magnetometer\\\":\\n for g in self.magnet:\\n if g.id == self.idToName[message[\\\"id\\\"]]:\\n g.processMagnet(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"buttons\\\":\\n for b in self.buttons:\\n if b.id == self.idToName[message[\\\"id\\\"]]:\\n b.processButtons(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"humidity\\\":\\n for b in self.humidity:\\n if b.id == self.idToName[message[\\\"id\\\"]]:\\n b.processHumidity(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"binary_sensor\\\":\\n for b in self.binary:\\n if b.id == self.idToName[message[\\\"id\\\"]]:\\n b.processBinary(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"power\\\":\\n for b in self.power:\\n if b.id == self.idToName[message[\\\"id\\\"]]:\\n b.processPower(message)\\n break\\n elif message[\\\"characteristic\\\"] == \\\"luminance\\\":\\n for b in self.luminance:\\n if b.id == self.idToName[message[\\\"id\\\"]]:\\n b.processLuminance(message)\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ae330bf7ee6a56cb9ea7b31eb6a291\",\n \"score\": \"0.49631587\",\n \"text\": \"def _sendDataToGPU(self,returnData):\\n\\n newIncData=returnData[0].to('cuda:{}'.format(self.device))\\n newCovData=[]\\n for i,arr in enumerate(returnData[1]):\\n newCovData+=[arr.to('cuda:{}'.format(self.device))]\\n\\n if returnData[2] is not None:\\n newTargetData=returnData[2].to('cuda:{}'.format(self.device))\\n else:\\n newTargetData=None\\n if returnData[3] is not None:\\n newScoreData=returnData[3].to('cuda:{}'.format(self.device))\\n else:\\n newScoreData=None\\n\\n return (newIncData,newCovData,newTargetData,newScoreData)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6c9be68245a0679c212351cf031dbe\",\n \"score\": \"0.49566287\",\n \"text\": \"def raw2vpvol_nc_batch():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd2a6488b70e42f1537e51278e9ee16\",\n \"score\": \"0.49507663\",\n \"text\": \"def dataReceived(self, data):\\n self.bridge.push_input(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c90637c65e5e318a8dd3280eb698fb\",\n \"score\": \"0.4930326\",\n \"text\": \"def receive_dataset(self):\\n\\n X = read_data(self.root_connection)\\n\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acedb9e17bab557541fc7b7a4bc8c026\",\n \"score\": \"0.49091378\",\n \"text\": \"def read(self):\\n \\n data = self.bus.read_i2c_block_data(self.address, 0x1D, 6)\\n\\n # Convert the data\\n xGyro = data[0] * 256 + data[1]\\n if xGyro > 32767 :\\n xGyro -= 65536\\n xGyro = xGyro / self.scalar # To get degrees per second\\n yGyro = data[2] * 256 + data[3]\\n if yGyro > 32767 :\\n yGyro -= 65536\\n yGyro = yGyro / self.scalar\\n zGyro = data[4] * 256 + data[5]\\n if zGyro > 32767 :\\n zGyro -= 65536\\n zGyro = zGyro / self.scalar\\n #return (xGyro, yGyro, zGyro)\\n return np.array([xGyro, yGyro, zGyro])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b17f5fd67bca4c4595213a88a57839e5\",\n \"score\": \"0.49080834\",\n \"text\": \"def send_mb(self, arrays):\\n # The buffer protocol only works on contiguous arrays\\n arrays = [numpy.ascontiguousarray(array) for array in arrays]\\n headers = [numpy.lib.format.header_data_from_array_1_0(array)\\n for array in arrays]\\n self.asocket.send_json(headers, zmq.SNDMORE)\\n for array in arrays[:-1]:\\n self.asocket.send(array, zmq.SNDMORE)\\n self.asocket.send(arrays[-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d5bae0b9e197cbc16851dfffee1511\",\n \"score\": \"0.48967984\",\n \"text\": \"def read_data(self, buf):\\n\\n # SECOND CRITICAL POINT\\n textdf = self.text\\n names = self.channel_names\\n\\n relevant = textdf[[\\\"GTI$SAMPLEID\\\", \\\"$BEGINDATA\\\", \\\"$DATASIZE\\\"]]\\n relevant[\\\"$PAR\\\"] = textdf[\\\"$PAR\\\"].astype(int)\\n relevant[\\\"$TOT\\\"] = textdf[\\\"$TOT\\\"].astype(int)\\n segment_storage = []\\n for i in range(len(relevant)):\\n buf.seek(relevant[\\\"$BEGINDATA\\\"][i])\\n segment = buf.read(relevant[\\\"$DATASIZE\\\"][i]+1)\\n # THIS IS THE MOST IMPORTANT PART\\n segment_value = np.frombuffer(segment, dtype=np.float32)\\n # reshaping the array into proper form\\n segment_value = segment_value.reshape(\\n (relevant[\\\"$TOT\\\"][i], relevant[\\\"$PAR\\\"][i]))\\n segment_frame = pd.DataFrame(data=segment_value, columns=names)\\n segment_frame.insert(0, \\\"Sample\\\", relevant[\\\"GTI$SAMPLEID\\\"][i])\\n segment_storage.append(segment_frame)\\n data = pd.concat(segment_storage)\\n ####DODATI RESET INDEX#####\\n self.data = data\\n self.samples = list(relevant[\\\"GTI$SAMPLEID\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffc730629847ee67272eebd34355898\",\n \"score\": \"0.48873717\",\n \"text\": \"def _arduino_cmd_callback(self, msg):\\n data: dict = msg['data']\\n registers = data.keys()\\n a = []\\n for reg_name in registers:\\n reg_num = self.register_name_map[reg_name]\\n value = data[reg_name]\\n a.append((reg_num, value))\\n self.protocol_executor.set_data(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a1b26c7402af87f9f489f33a958625\",\n \"score\": \"0.48857716\",\n \"text\": \"def get_data_ready(self): \\n self._logger.debug(\\\"In Get Data Ready\\\") \\n self._i2c.msg_write([0x02, 0x02])\\n msgs = self._i2c.msg_read(3) \\n #for msg in msgs:\\n # self._logger.detail(\\\"Msg: \\\" + str(msg))\\n # for d in msg.data:\\n # self._logger.detail(\\\"Data: \\\" + str(hex(d)))\\n #self._logger.detail(\\\"Data: \\\" + str(msgs[0].data[1]))\\n return msgs[0].data[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad91694dd9be28c52f57621c9b5d4b58\",\n \"score\": \"0.48786342\",\n \"text\": \"def read_data():\\n \\\"\\\"\\\"Esto esta hecho para leer 64 bits en los brams... hay que modificar el modelo\\\"\\\"\\\"\\n global fpga\\n #A2 = struct.unpack('>16384Q', fpga.read('A2', 16384*8,0)) #me parece que es struct.unpack('>16384', fpga.read('A2', 16384*8,0))\\n #B2 = struct.unpack('>16384Q', fpga.read('B2', 16384*8,0))\\n #AB_re = struct.unpack('>16384Q', fpga.read('AB_re', 16384*8, 0))\\n #AB_im = struct.unpack('>16384Q', fpga.read('AB_im', 16384*8, 0))\\n A2 = np.random.rand(1, 16384)\\n B2 = np.random.rand(1, 16384)\\n AB_re = np.random.rand(1, 16384)\\n AB_im = np.random.rand(1, 16384)\\n log_a = 10*np.log10(A2)\\n log_b = 10*np.log10(B2)\\n ang = np.arctan2(AB_im, AB_re)\\n return [ang, log_a, log_b]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8d3176329072bc60fa6da9eeae445a\",\n \"score\": \"0.48783943\",\n \"text\": \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self\\n buff.write(_get_struct_3I().pack(_x.trajectory.header.seq, _x.trajectory.header.stamp.secs, _x.trajectory.header.stamp.nsecs))\\n _x = self.trajectory.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.trajectory.joint_names)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.trajectory.joint_names:\\n length = len(val1)\\n if python3 or type(val1) == unicode:\\n val1 = val1.encode('utf-8')\\n length = len(val1)\\n buff.write(struct.pack('<I%ss'%length, length, val1))\\n length = len(self.trajectory.points)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.trajectory.points:\\n length = len(val1.positions)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(val1.positions.tostring())\\n length = len(val1.velocities)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(val1.velocities.tostring())\\n length = len(val1.accelerations)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(val1.accelerations.tostring())\\n length = len(val1.effort)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(val1.effort.tostring())\\n _v3 = val1.time_from_start\\n _x = _v3\\n buff.write(_get_struct_2i().pack(_x.secs, _x.nsecs))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed2bf9eba5d02a1edb7e0766434d23c3\",\n \"score\": \"0.48738414\",\n \"text\": \"def myStreamCB(cHandle, data): \\n \\n \\n unpack3Dformat(data)\\n #unpackDirectControFormat(data)\\n #unpack2Dformat(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fba4c9366be5b2564bb77e811e0c6b4\",\n \"score\": \"0.48727003\",\n \"text\": \"def _get_array(self):\\n if (not self.attendance_array):\\n expanded = bz2.decompress(self.attendance)\\n attendance_str = str(expanded)\\n attendance_array = array.array(\\\"B\\\")\\n attendance_array.fromstring(attendance_str)\\n self.attendance_array = attendance_array\\n return self.attendance_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3f292fa6db8984db005864e511d1bc\",\n \"score\": \"0.48697826\",\n \"text\": \"def portData(arg):\\n global dataarray\\n\\n dataarray = []\\n # 이차 배열의 첫 배열 dataarray.\\n recVal = str(arg)\\n # 받은 arg 스트링을 recVal에 넣음.\\n strArray = recVal.split(\\\",\\\")\\n # recVal 스트링을 , 경계로 쪼갬.\\n for i in range(0, 51):\\n # 51가지의 데이터가 들어옴.\\n dataarray.append(strArray[i])\\n # print(strArray[i])\\n # dataarray에 strArray[i] 첨부.\\n # print(strArray[i])\\n # createTimer(0.03, deformface)\\n deformface()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3afb0a21b7cab7fc9edcd2f1c991c6c\",\n \"score\": \"0.48610887\",\n \"text\": \"def generate_compressed_data(self):\\n \\n # Check if the output data batch is in right format and size (ttl added, shape etc)\\n if type(self.output_data_batch) != np.ndarray or np.shape(self.output_data_batch)[0] != (self.num_channels):\\n raise Exception('Output Data Batch is not specified properly!')\\n else: \\n self.output_data_batch = self.output_data_batch.astype(np.int16)\\n\\n # Copy ttl data (triggers):\\n sample_ttl = np.zeros((2,self.output_data_batch.shape[1]),dtype = 'uint8') \\n sample_ttl[0,:] = np.right_shift(np.bitwise_and(self.output_data_batch[0:1,:],0xFF00),8)\\n sample_ttl[1,:] = np.bitwise_and(self.output_data_batch[0:1,:],0x00FF)\\n\\n # Compress recorded data:\\n sample = np.zeros((self.output_data_batch.shape[0]-1,self.output_data_batch.shape[1]),dtype = 'uint16')\\n sample = (self.output_data_batch[1:,:] + 2048).astype('uint16')\\n self.sample_combined=np.zeros((int((self.output_data_batch.shape[0]-1)*1.5),self.output_data_batch.shape[1]),\\n dtype='uint8')\\n\\n for first_of_two_channel in range(0,self.num_channels-1,2):\\n self.sample_combined[int(first_of_two_channel*1.5),:] = np.right_shift(sample[first_of_two_channel,:],4)\\n self.sample_combined[int(first_of_two_channel*1.5)+1,:] = np.left_shift(np.bitwise_and(\\n sample[first_of_two_channel,:],0x000F),4) + np.right_shift(sample[first_of_two_channel+1,:],8)\\n self.sample_combined[int(first_of_two_channel*1.5)+2,:] = np.bitwise_and(sample[first_of_two_channel+1,:],0x00FF)\\n\\n # Combine ttl and recorded data to be written into bin file:\\n self.sample_combined = np.concatenate((sample_ttl, self.sample_combined), axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19e23e1574f729b4ecd4b6d6b9e8923\",\n \"score\": \"0.48574728\",\n \"text\": \"def decode(self, array):\\n\\t\\tif len(array) != 4:\\n\\t\\t\\traise ValueError(\\\"Array of wrong size\\\")\\n\\t\\tbytestr = struct.pack('>'+'H'*len(array), *array)\\n\\t\\tbytearray = struct.unpack('>'+'B'*(2*len(array)), bytestr)\\n\\t\\tprint(bytearray)\\n\\t\\tself.mbaddr = bytearray[0]\\n\\t\\tself.mbcls = bytearray[1]\\n\\t\\tself.adcinpsel = bytearray[2]\\n\\t\\tself.adcrepeat = bytearray[3]\\n\\t\\tself.adcsampleint = bytearray[4]\\n\\t\\tself.adcwait = bytearray[5]\\n\\t\\tself.i2cchip = bytearray[6]\\n\\t\\t# last element is padding\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45865d6940bfc88dc90af80b05cdfe62\",\n \"score\": \"0.48548877\",\n \"text\": \"def __array__(self, dtype=None):\\n return np.asarray(self.data, dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85f74f8e66c3982779141861678fa23\",\n \"score\": \"0.4851704\",\n \"text\": \"def sensor_data(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b629cf4c6376be5715a10dfad39871\",\n \"score\": \"0.48516345\",\n \"text\": \"def onPortDataChanged(self, inputPort):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a32a7075a336c277ac9d54de78e7c2\",\n \"score\": \"0.4849438\",\n \"text\": \"def sensor_readings():\\n global ser\\n data = b'5' # data sent\\n ser.write(data) # Serial port write data\\n data = ser.read(size=14)\\n print(\\\"receive data is:\\\", list(data))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3667b9585f54a4fb41cdfdd407f7ba08\",\n \"score\": \"0.48482788\",\n \"text\": \"def _decomp_to_array(self, decomp_list):\\n raise NotImplementedError('Not implemented in base (BaseEnsembleClassifier) class')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bca58cb1ff8663a926989f82e55a2de\",\n \"score\": \"0.48469204\",\n \"text\": \"def _recv_arrays(self):\\n headers = self.socket.recv_json()\\n if 'stop' in headers:\\n raise StopIteration\\n arrays = []\\n\\n for header in headers:\\n data = self.socket.recv()\\n buf = buffer_(data)\\n array = np.frombuffer(buf, dtype=np.dtype(header['descr']))\\n array.shape = make_tuple(header['shape'])\\n\\n if header['fortran_order']:\\n array.shape = header['shape'][::-1]\\n array = array.transpose()\\n arrays.append(array)\\n\\n return arrays\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34dd72cf8314e24e47f90cee9d8cb363\",\n \"score\": \"0.48467925\",\n \"text\": \"def get_data(self):\\n\\n # Read data/mask specified by first contour\\n data_array = self._single_targets_list[0].get_data().astype(np.uint8)\\n\\n # Add data/masks specified by subsequent contours\\n for i in range(1, len(self._single_targets_list)):\\n data_array += self._single_targets_list[\\n i].get_data().astype(np.uint8)\\n\\n return data_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9142a0fc47f8e28997a4c6ec29fe0627\",\n \"score\": \"0.48377708\",\n \"text\": \"def get_central_event_teltrg_list(self):\\n num_teltrig= self.lib.get_num_tel_trig()\\n if num_teltrig >= 0:\\n array = np.zeros(num_teltrig,dtype=np.int32)\\n self.lib.get_central_event_teltrg_list(array)\\n return array\\n else:\\n raise(HessioGeneralError(\\\"hsdata is not available\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b457c6ef4e90178e4b948f8d11287ab\",\n \"score\": \"0.4837254\",\n \"text\": \"def read_data(self, subdatablock):\\n beams = self.header['Beams']\\n data = np.frombuffer(subdatablock, dtype = Data7018.data_dtype)\\n self.mag = data['Amp'].astype('f')\\n self.mag.shape = (-1,beams)\\n self.phase = data['Phs'].astype('f')\\n self.phase.shape = (-1,beams)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565e7e952cc474a2a0bb4a79e6705d7a\",\n \"score\": \"0.4832906\",\n \"text\": \"def receive_data(self, data):\\n\\t\\tself.data.update(data)\\n\\t\\tself.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de0931db5690963a246549d0a54de56\",\n \"score\": \"0.48321775\",\n \"text\": \"def from_ndarray(self, array, sampler):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459c944d37d543bf2ba78c29ddf6eedb\",\n \"score\": \"0.48306078\",\n \"text\": \"def callback(self, in_data, frame_count, time_info, status):\\n data0 = np.frombuffer(in_data, dtype='int16')\\n if np.abs(data0).mean() < self.silence_threshold:\\n print('-', sep='', end='', flush=True)\\n else:\\n print('.', sep='', end='', flush=True)\\n self.data = np.append(self.data,data0)\\n if len(self.data) > self.window_samples:\\n self.data = self.data[-self.window_samples:]\\n self.q.put(self.data)\\n return (in_data, pyaudio.paContinue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bab1d2cd3288ca23adbc0ddcfee7371\",\n \"score\": \"0.48281434\",\n \"text\": \"def write(self, intarray):\\n\\t\\tself._spi.writebytes(intarray)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c49735337788fe77bee6539403125bf\",\n \"score\": \"0.48279488\",\n \"text\": \"def array(self) -> NDArray:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9969edfba81aaf431baf1edd1cc9e91\",\n \"score\": \"0.4824319\",\n \"text\": \"def data_read(self):\\n\\n dc_meas = None\\n if self.dc_measurement_device is not None:\\n try:\\n dc_meas = self.dc_measurement_device.measurements_get()\\n # if self.ts is not None:\\n # self.ts.log_debug('The DC measurements are %s' % dc_meas)\\n # else:\\n # print('The DC measurements are %s' % dc_meas)\\n except Exception as e:\\n self.ts.log_debug('Could not get data from DC Measurement Object. %s' % e)\\n\\n try:\\n data = []\\n for chan in self.data_points_device:\\n # self.ts.log_debug('self.data_points = %s' % self.data_points)\\n signal = self.data_point_map[chan] # get signal name associated with data name\\n # self.ts.log_debug('Chan = %s, signal = %s' % (chan, signal))\\n if signal is None: # skip the signals that have no mapping to the simulink model\\n\\n # search the dc measurement object for the data that isn't in the opal_points_map\\n if self.dc_measurement_device is not None:\\n dc_value = dc_meas.get(chan) # signal = 'DC_V', 'DC_I', or 'DC_P'\\n # if self.ts is not None:\\n # self.ts.log_debug('Setting Chan = %s to dc_value = %s' % (chan, dc_value))\\n # else:\\n # print('Setting Chan = %s to dc_value = %s' % (chan, dc_value))\\n if dc_value is not None:\\n data.append(dc_value)\\n else: # Channel data missing\\n # self.ts.log_debug('Appending None for data point: %s' % chan)\\n data.append(None)\\n else: # DC Measurement Object missing\\n # self.ts.log_debug('Appending None for data point: %s' % chan)\\n data.append(None)\\n continue\\n\\n # verify the model is runing before getting the signal data.\\n status, _ = RtlabApi.GetModelState()\\n if status == RtlabApi.MODEL_RUNNING:\\n signal_value = RtlabApi.GetSignalsByName(signal)\\n # self.ts.log_warning('signal_value is %s from Opal' % signal_value)\\n else:\\n signal_value = None\\n # self.ts.log_warning('Signal_value set to None because Opal isn\\\\'t running')\\n\\n # self.ts.log_debug('Signal %s = %s' % (signal, signal_value))\\n # self.ts.log_warning('type(sig) %s' % type(signal_value))\\n if signal_value is not None and signal_value is not 'None':\\n data.append(signal_value)\\n else:\\n data.append(None)\\n\\n except Exception as e:\\n self.ts.log_debug('Could not get data. Simulation likely completed. Error: %s' % e)\\n # self.ts.log_warning('self.data_points = %s. Writing all Nones.' % self.data_points)\\n # self.ts.log_warning('self.data_points = %s. Writing all Nones.' % self.data_points_device)\\n data = [None]*len(self.data_points_device) # Return list of Nones when simulations stops.\\n # todo: this should be fixed in das.py sometime where a None can be returned and not added to the database\\n\\n # self.ts.log_debug('Data list %s' % data)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867cab66e72cd08372c609cf61d3e775\",\n \"score\": \"0.48237202\",\n \"text\": \"def read_raw_data(self, result):\\n\\n self._l1_barray[0] = self._mode\\n self.i2c.writeto_mem(self.bme_address, BME280_REGISTER_CONTROL_HUM,\\n self._l1_barray)\\n self._l1_barray[0] = self._mode << 5 | self._mode << 2 | 1\\n self.i2c.writeto_mem(self.bme_address, BME280_REGISTER_CONTROL,\\n self._l1_barray)\\n\\n sleep_time = 1250 + 2300 * (1 << self._mode)\\n sleep_time = sleep_time + 2300 * (1 << self._mode) + 575\\n sleep_time = sleep_time + 2300 * (1 << self._mode) + 575\\n time.sleep_us(sleep_time) # Wait the required time\\n\\n # burst readout from 0xF7 to 0xFE, recommended by datasheet\\n self.i2c.readfrom_mem_into(self.bme_address, 0xF7, self._l8_barray)\\n readout = self._l8_barray\\n # pressure(0xF7): ((msb << 16) | (lsb << 8) | xlsb) >> 4\\n raw_press = ((readout[0] << 16) | (readout[1] << 8) | readout[2]) >> 4\\n # temperature(0xFA): ((msb << 16) | (lsb << 8) | xlsb) >> 4\\n raw_temp = ((readout[3] << 16) | (readout[4] << 8) | readout[5]) >> 4\\n # humidity(0xFD): (msb << 8) | lsb\\n raw_hum = (readout[6] << 8) | readout[7]\\n\\n result[0] = raw_temp\\n result[1] = raw_press\\n result[2] = raw_hum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b260a5c596df2f2110ec2456aa1d1925\",\n \"score\": \"0.48224577\",\n \"text\": \"def get_bulk(grideye, i2caddr, addr, num_bytes):\\n self = grideye.i2c # Psuedo I2C device function\\n self.send_start_bit()\\n stat = self.bulk_trans(2, [i2caddr << 1, addr])\\n self.send_start_bit()\\n stat += self.bulk_trans(1, [i2caddr << 1 | 1])\\n ret = []\\n for i in range(num_bytes-1):\\n ret.append(self.read_byte())\\n self.send_ack()\\n ret.append(self.read_byte())\\n self.send_nack()\\n self.send_stop_bit()\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f641f553cde73dcad0a0e28276f3387\",\n \"score\": \"0.48203704\",\n \"text\": \"def _transform_buffer(self, buffer):\\n if (self.sampling_duration\\n and self.sampling_duration < time.time() - self.start_time):\\n return None\\n\\n for index in range(len(buffer)):\\n time_before_read = time.time()\\n try:\\n data = self._monsoon.Protocol.DEVICE.read(\\n # Magic value for USB bulk reads.\\n 0x81,\\n Packet.MAX_PACKET_SIZE,\\n # In milliseconds.\\n timeout=1000)\\n except Exception as e:\\n logging.warning(e)\\n continue\\n time_after_read = time.time()\\n time_data = struct.pack('dd', time_after_read,\\n time_after_read - time_before_read)\\n buffer[index] = time_data + data.tobytes()\\n\\n return buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7160bd08103650cdb96d22c0136354fa\",\n \"score\": \"0.48158497\",\n \"text\": \"def to_array(self):\\n return _np.copy(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09121afbdf4195c222121d5ce9e8a99d\",\n \"score\": \"0.48153296\",\n \"text\": \"def _updateToArray(self):\\n\\n for i, p in enumerate(self._source):\\n\\n p[...] = self._smooth_growth_data[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f906693d9c4ac7f2f9d62ea2803670f\",\n \"score\": \"0.48118\",\n \"text\": \"def numpy_array(self, data):\\n self._data = data.view()\\n self._data_needs_writeback = True\\n data.setflags(write=False)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993690,"cells":{"query_id":{"kind":"string","value":"fb22f1b9401be2b7df1b042d77eca87f"},"query":{"kind":"string","value":"Handles receiving a line"},"positive_passages":{"kind":"list like","value":[{"docid":"368c708b261ca98ca1f3b807dfd56602","score":"0.5919577","text":"def recv_line(data):\n global board_elements\n global canvas\n if len(data) == 4:\n top_left = (int(data[1].split(\" \")[0]), int(data[1].split(\" \")[1]))\n bottom_right = (int(data[2].split(\" \")[0]), int(data[2].split(\" \")[1]))\n colour = (int(data[3].split(\" \")[0]), int(\n data[3].split(\" \")[1]), int(data[3].split(\" \")[2]))\n line = shapes.Line(top_left, bottom_right, colour)\n board_elements.append(line)\n canvas = line.mark(canvas)","title":""}],"string":"[\n {\n \"docid\": \"368c708b261ca98ca1f3b807dfd56602\",\n \"score\": \"0.5919577\",\n \"text\": \"def recv_line(data):\\n global board_elements\\n global canvas\\n if len(data) == 4:\\n top_left = (int(data[1].split(\\\" \\\")[0]), int(data[1].split(\\\" \\\")[1]))\\n bottom_right = (int(data[2].split(\\\" \\\")[0]), int(data[2].split(\\\" \\\")[1]))\\n colour = (int(data[3].split(\\\" \\\")[0]), int(\\n data[3].split(\\\" \\\")[1]), int(data[3].split(\\\" \\\")[2]))\\n line = shapes.Line(top_left, bottom_right, colour)\\n board_elements.append(line)\\n canvas = line.mark(canvas)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"86af36f9f2c5f587adb43d6c498dd5a3","score":"0.84185743","text":"def line_received(self, line):\n return","title":""},{"docid":"438969ca43b81399f41b78da3ee8d66f","score":"0.82127386","text":"def lineReceived(self, line):\n\n self.on_line(line)","title":""},{"docid":"5a3333566a2c218ddc406070085ff2a6","score":"0.7986448","text":"def _OnLineReceived(line):\n if line:\n print(line)","title":""},{"docid":"5b401a214bc0e6a52285e74e956aadf6","score":"0.77242255","text":"def lineReceived(self, line):\n if self.timeout > 0:\n self.resetTimeout()\n\n state = self.state\n self.state = None\n state = getattr(self, 'state_' + state)(line) or state\n if self.state is None:\n self.state = state","title":""},{"docid":"2f16b213a71dc8640b79f20a07514084","score":"0.7708595","text":"def handle_line(self, line):\n rawlogger.debug(line)\n self.messagebus.trigger_event(SERIAL_RAW_LINE, SERIAL_ENDPOINT, self.entity, line)","title":""},{"docid":"dcb31acf8390b67ec726b4724cae19d0","score":"0.7433005","text":"def handle_line(self, line):\n raise NotImplementedError('please implement functionality in handle_line')","title":""},{"docid":"06e99bcb7786925945f0e8ee74038d21","score":"0.7288259","text":"def lineReceived(self, line):\r\n if line == \"ping\":\r\n self.sendLine(\"pong\")\r\n else:\r\n log(\"lineReceived\", line)\r\n message = Message.create_message(line)\r\n self.factory.network.peers.add_message(message)","title":""},{"docid":"ab63a5f368f4a11e5ed144eac92e4c0a","score":"0.7272639","text":"def _handle_line(self, line):\n\n (tags, prefix, command, args) = protocol.parse(line)\n\n # translate numerics\n command = events.numeric.get(command, command).lower()\n if not command:\n return\n\n log.debug('<- p: {0} c: {1} a: {2}'.format(prefix, command, args))\n\n if command == 'welcome':\n # record the nickname in case the server changed it\n self.nickname = args[0]\n\n # reset the reconnect count\n self.reconnect_count = 1\n\n\n # handle privmsg/notice special to split out the CTCP stuff\n if command in ('privmsg', 'notice'):\n self._on_message(prefix, command, args, tags)\n\n # handle server pings internally\n elif command == 'ping':\n self.pong(args[0])\n\n # otherwise, find a handler, and call it\n else:\n\n response = None\n handler = getattr(self, 'on_%s' % command, None)\n if handler:\n event = events.Event(prefix, command, args, tags)\n\n def delegate(event):\n response = handler(event)\n if response:\n self._send(response)\n\n self.loop.call_soon(delegate, event)","title":""},{"docid":"facdcf88ceb4f395e0ba42e34325c3d3","score":"0.71677774","text":"def _process_line(self, line):\n config = self.config[\"watcher\"]\n data = d.Parser(line, config[\"nick\"])\n data._load()\n if data.msg_type == \"PRIVMSG\":\n # If the channel is not in our list of channels,\n # do nothing with it, just in case somebody tries\n #to PM us with false data.\n if not data.chan in config[\"channels\"]:\n return\n\n # Process the line.\n rc_data = RC(data)\n\n # Handle a line from the recent changes server\n self.handle_rc_event(rc_data, data.chan)\n\n elif data.msg_type == \"376\":\n for chan in config[\"channels\"]:\n self.join(chan)\n for chan in config[\"report_chans\"]:\n self._connection.join(chan)","title":""},{"docid":"47942e0fcd5d044ba09606221de1b60c","score":"0.71497595","text":"def lineReceived(self, line): \n self.transport.write(line[::-1]+ '\\r\\n')","title":""},{"docid":"7c11413eac5cf449e4bc43623dd68cbb","score":"0.7133134","text":"def subscribe_handle(self, sender, **kwargs):\n line = kwargs[\"line\"]\n if line == self.expect:\n self.received = True","title":""},{"docid":"6e02df5e4a1ec00370cbbf3b1c48bdb3","score":"0.70772827","text":"def accept_line(event: E) -> None:\n event.current_buffer.validate_and_handle()","title":""},{"docid":"15c6de4e019f2a40f18c9ef8d2fc8176","score":"0.6969847","text":"def lineReceived(self, line):\n self.nBytes += len(line)\n logging.debug(\"> %s\", line.rstrip(\"\\r\\n\"))\n if self.nBytes > self.ctx.maximumSize:\n self.ignoring = True\n else:\n self.lines.append(line)","title":""},{"docid":"431246405938881603f52c2e337808af","score":"0.6834815","text":"async def handle(self, line):\n \n # lines recieved must be converted from bytes to strings\n decoded = line.decode(self.encoding).rstrip(constants.CRLF)\n \n # see utils.ircsplit\n prefix, command, params = utils.ircsplit(decoded)\n \n # command is here twice so that it is passed to the appropriate listeners\n # while maintaining the generality of `self.trigger`'s function signature\n triggered = await self.trigger(command, prefix, command, *params)\n \n return triggered","title":""},{"docid":"dcf4083509415cc7999239ea18bbc99b","score":"0.6708258","text":"def line_handler(self,line):\n if self.logenabled:\n log_line(line,self.log.serial_log_file, self.log.serial_info_file)\n else:\n pass\n return","title":""},{"docid":"f91ccefbd3a5d35cda525e9619403b31","score":"0.67066795","text":"def _recv(self, line):\n if line == '':\n return\n\n # it seems caseta hub returns strings starting with GNET> so strip it off\n if line[0] == 'G':\n line = line[6:]\n # Only handle query response messages, which are also sent on remote status\n # updates (e.g. user manually pressed a keypad button)\n if line[0] != Lutron.OP_RESPONSE:\n _LOGGER.debug(\"ignoring %s\" % line)\n return\n parts = line[1:].split(',')\n cmd_type = parts[0]\n integration_id = int(parts[1])\n args = parts[2:]\n if cmd_type not in self._ids:\n _LOGGER.info(\"Unknown cmd %s (%s)\" % (cmd_type, line))\n return\n ids = self._ids[cmd_type]\n if integration_id not in ids:\n _LOGGER.warning(\"Unknown id %d (%s)\" % (integration_id, line))\n return\n obj = ids[integration_id]\n # First let the device update itself\n handled = obj.handle_update(args)\n # Now notify anyone who cares that device may have changed\n if handled and obj in self._subscribers:\n self._subscribers[obj](obj)","title":""},{"docid":"e7de9f63aba149aafa316ce8faac8f54","score":"0.6615805","text":"def data_received(self, data):\n self.buffer.push(data.decode())\n for line in self.buffer:\n if line:\n self._handle_line(line)","title":""},{"docid":"4c5f944c56ffab812bce8a710e52f01f","score":"0.6602743","text":"def on_line(self, line):\n\n line = self._unquote(line).strip()\n\n is_player = self.re_response_type.match(line)\n line_type = \"player\" if is_player else \"server\"\n\n self.logger.debug(\"%s: [%s]\" % (line_type, line))\n\n parts = line.split()\n player = parts[0] if is_player else None\n parts = parts[1:] if is_player else parts\n params = list()\n\n if parts[-1] == \"?\": return\n\n while parts:\n method_name = \"_\".join(parts)\n try:\n method_key = \"%s_%s\" % (line_type, method_name)\n method = getattr(self, \"on_%s\" % (method_key))\n method_params = params[::-1]\n if player is not None: method_params.insert(0, player)\n method(*method_params)\n if method_key in self.service.callbacks:\n self.service.callbacks.get(method_key)(*method_params)\n break\n except AttributeError:\n params.append(parts[-1])\n parts = parts[:-1]\n except KeyboardInterrupt:\n sys.exit(0)\n except Exception, e:\n print e","title":""},{"docid":"e9d2679624b8745b5e70c8d5be558a4b","score":"0.65742546","text":"def command_event_handler(self, event):\n curr_line = self.platform.get_current_line()\n curr_comm = curr_line[len(self.config['prompt']):].strip()\n\n # rewind recorder\n if self.recorder.status():\n self.recorder.rewind()\n # if command not empty, it should be added to recorder\n if curr_comm:\n self.recorder.commit(curr_comm)\n # start processing\n self.platform.writer('\\n')\n self.parser.parse(self, self.platform, curr_comm)\n #self.platform.writer('\\n')\n self.platform.reader(self.config['prompt'])\n # self.platform.writer(self.config['prompt'])","title":""},{"docid":"06a68ba72ea392c8ff863687df71748b","score":"0.65234387","text":"def line_handler (self, line, number):\n\n return","title":""},{"docid":"715b40da3dfda9dee60486ac1bd2e811","score":"0.6503571","text":"def recieveLine(socket):\n data = \"\"\n line = \"\"\n while(data != '\\n'):\n line += data\n data = socket.recv(1)\n if not data:\n print \"Connection is closed from the server\\n\"\n exit(0)\n return line","title":""},{"docid":"0dcb089bdf3cf03d626745ca0d553538","score":"0.64738107","text":"def receive(self):\n buf = []\n while buf[-2:] != [b'\\r', b'\\n']:\n c = self._sock.recv(1)\n if not c:\n raise ConnectionClosed\n buf.append(c)\n\n try:\n line = b''.join(buf).decode()\n except (UnicodeError):\n self.handle_encoding_error()\n return ''\n print('received:', line)\n return line","title":""},{"docid":"8ca8218026f36da761618e6582e49f98","score":"0.6460493","text":"def handle(self, ch ):\n pass","title":""},{"docid":"059c6e552073fb913e32d722c03eade1","score":"0.6409392","text":"def text_input(self, line):\n pass","title":""},{"docid":"636b60ed2d8f6de600c58ca83bd01841","score":"0.64006376","text":"def lineReceived(self, line):\n rbuffer = StringIO()\n po = sys.stdout\n sys.stdout = rbuffer\n err = False\n try:\n exec line in globals(),locals()\n except Exception as e:\n err = e\n sys.stdout = po\n print '>u> ' + line\n if err:\n out = self.pre_e + str(e) + self.post_e\n else:\n out = rbuffer.getvalue()\n if out != \"\":\n print '>s> ' + out","title":""},{"docid":"ee8b93e4fdf4199aa0bd569a4cab3d03","score":"0.63878894","text":"def readline(self, *args, **kw):","title":""},{"docid":"9e877f91b3287da9aee3411f522dcf09","score":"0.6271138","text":"def send(self, line):\n assert isinstance(line, GCodeStreamer.Line)\n self.pending_lines.append(line)\n self.poll_transmission()","title":""},{"docid":"c8c0ea048e7b71d51eea12d2b08ed5b4","score":"0.62662375","text":"def lineReceived(self, line):\n if line in self.factory.messages:\n #print(\"LargeMessageProducer got line: [%s]\" % line)\n msg = self.factory.messages.pop(line) # Get the value and remove from dict\n producer = Producer(self, msg)\n self.transport.registerProducer(producer, True)\n producer.resumeProducing()\n else:\n # Someone used an invalid key!\n log.err(\"Error: LargeMessageProducer got invalid key! [%s]\" % line)\n self.sendLine(\"Invalid key\")","title":""},{"docid":"b15f9d5744232fdca3a22b055d7c2436","score":"0.6259526","text":"def _on_read_line(self, line):\n try:\n response = json.loads(line)\n except ValueError:\n raise _errors.MalformedJSONReceived(line)\n\n if response[\"id\"] not in self._request_futures:\n raise _errors.UnexpectedResponseID(\n \"Received response with unexpected ID {}\".format(\n response[\"id\"]))\n\n self._request_futures[response[\"id\"]].set_result(response)","title":""},{"docid":"efe316ba6daf501d1ad3ac6855050485","score":"0.62491894","text":"def data_received(self, data):\n\n data = self.decode(data)\n if not data.endswith(\"\\r\\n\"):\n self.linebuffer += data\n return\n\n for line in (self.linebuffer + data).rstrip().split(\"\\r\\n\"):\n\n print(\"[N to B]\\t%s\" % line)\n\n l = line.split(\" \", 2)\n if l[0] == \"PING\": self.send(\"PONG\", l[1])\n elif l[1] == \"PONG\": self.forward(\"PONG\", l[2])\n elif l[0] == \"PRIVMSG\" or l[1] == \"PRIVMSG\":\n\n self.chat_history.append(line)\n\n else:\n\n self.chat_history.append(line)\n self.server_log.append(line)\n\n self.linebuffer = \"\"\n\n if self.bouncer.clients:\n while self.chat_history:\n line = self.chat_history.popleft()\n self.forward(line)","title":""},{"docid":"aa5ffe3a9e734c01295cfa4d6cf6e770","score":"0.62188923","text":"def GotLine(self, line):\n if line.strip():\n # a new line in this block\n if _HasQuotes(line):\n parts = bup.shquote.quotesplit(line)\n self.lines.append([word for unused_offset, word in parts])\n else:\n self.lines.append(line.split())\n else:\n # blank line means end of block\n lines = self.lines\n self.lines = []\n result = self.handle_lines_func(lines)\n if result is None:\n return None\n else:\n return self.RenderBlock(result)","title":""},{"docid":"e0da6fd379a5e8e88c7dbd3667f4cd14","score":"0.62120855","text":"def parse_raw_line(self, line):\n line = line.replace('\\0', '')\n\n if len(self.stack) > 0:\n line = '{old}{new}'.format(old=self.stack.pop(), new=line)\n\n if self.filter_pattern:\n remaining, filtered, reached_end_of_line = _filter_line(line, self.filter_pattern)\n if filtered:\n logger.debug(filtered)\n self.messagebus.trigger_event(\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, filtered)\n if reached_end_of_line:\n self.stack.append(remaining)\n else:\n self.parse_raw_line(remaining)\n else:\n logger.debug(remaining)\n self.messagebus.trigger_event(\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, remaining)\n else:\n logger.debug(line)\n self.messagebus.trigger_event(\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, line)","title":""},{"docid":"8377d00d5ffe288d441c72c61aefd02a","score":"0.62081015","text":"def receive_command():\n pass","title":""},{"docid":"19d7aa7ce3345f74f1f6d2c7f5d54462","score":"0.6178368","text":"def send(self, line):\r\n\t\t_client.send(line)","title":""},{"docid":"4b9b1a5ec8ff64c1b1abe9901396db98","score":"0.6163223","text":"def data_received(self, data):\n self.buf += data\n if b'\\n' in self.buf:\n lines = self.buf.split(b'\\n')\n self.buf = lines[-1] # whatever was left over\n for line in lines[:-1]:\n print(f'Reader received: {line.decode()}')\n self.msgs_recvd += 1\n if self.msgs_recvd == 4:\n self.transport.close()","title":""},{"docid":"81de080b8827e257426181a0edaf5391","score":"0.61627465","text":"def send_line(self, output, line):\n try:\n output.send(line)\n except StopIteration:\n self.logger.debug(\"End of file reached\")\n pass\n return","title":""},{"docid":"24515c48fcee471b4395d29413285663","score":"0.6158032","text":"def readline(self, size=0):\n\n # Get line\n line = self._channel.recv(True)\n\n # Echo\n if line and self._echo is not None:\n self._echo.send(line)\n\n # Make sure it ends with newline\n if not line.endswith(\"\\n\"):\n line += \"\\n\"\n\n # Decrease size?\n if size:\n line = line[:size]\n\n # Done\n if PY2:\n return line.encode(\"utf-8\")\n else:\n return line","title":""},{"docid":"0c29bf971fa609109c276c4d69525e41","score":"0.6151506","text":"def readline(self):\n raise NotImplementedError","title":""},{"docid":"03841abecf630fb04bb69e03feb30a2c","score":"0.6131059","text":"def onREAD(self, sock, data):\n\n\t\tlines, buffer = splitLines(data, self.__buffers[sock])\n\t\tself.__buffers[sock] = buffer\n\t\tfor line in lines:\n\t\t\tself.push(Raw(sock, line), \"raw\", self.channel)","title":""},{"docid":"da2723790d40a6d65ea83ca23ca7691f","score":"0.6116464","text":"def read_line(self):\n while True:\n newline_idx = self.buf.find(b\"\\n\")\n if newline_idx >= 0:\n res = self.buf[:newline_idx]\n self.buf = self.buf[newline_idx + 1:]\n return res\n chunk = self.f.recv(4096)\n if not chunk:\n raise EndOfStreamError()\n self.buf += chunk","title":""},{"docid":"901b921bb1414a9cf2b954c19ee39f4e","score":"0.6101103","text":"def __process_header_line(self, line):\n self.__header_lines += 1\n\n if self.__header_lines == 1:\n self.first_line_received(line)\n elif len(line) == 0:\n self.__header_end_received()\n else:\n self.__header_line_received(line)","title":""},{"docid":"1b71c041dde1b591b1dc48b2144f7237","score":"0.6084031","text":"def unread_line(line):\n global linebuffered\n global linebuf\n u.verbose(3, \"unread_line on %s\" % line.rstrip())\n if linebuffered:\n u.error(\"internal error: multiple line unread\")\n linebuffered = True\n linebuf = line","title":""},{"docid":"2896a13b2f15a874f14ed3e3e6f9515c","score":"0.6081038","text":"def readline(self):\n pass # implemented in Ada","title":""},{"docid":"61f59f765d0d53fec75ed1e87df3e726","score":"0.60719717","text":"def on_new_line(self, line, is_full_line):\n if is_full_line:\n try:\n self._parse_trans_recv_loss_time_plus_errors(line)\n self._parse_trans_recv_loss_time(line)\n self._parse_min_avg_max_mdev_unit_time(line)\n except ParsingDone:\n pass # line has been fully parsed by one of above parse-methods\n return super(Ping, self).on_new_line(line, is_full_line)","title":""},{"docid":"78f8cf461f08f0b20bf8d14d3d1d87fd","score":"0.60252434","text":"def nextLine():","title":""},{"docid":"a8d8c46352c5affcf33f5e9f680a58bb","score":"0.5989319","text":"def onReceived(self, payload, timestamp=None):\n\n # assert payload contains 3 digit opcode\n assert len(payload) >= 3, \"Invalid request length\"\n\n # parse the incoming message into the format [opcode|args] with opcode as a 3 digit integer\n opcode, args = payload[:3], payload[3:]\n\n # assert the opcode is available\n assert opcode in self.commands, f'Invalid request OPCODE = \"{opcode}\"'\n\n # assert waiting for a file before receiving a segment\n assert (not self.fileToReceive) or (\n self.fileToReceive and opcode == b'212'), \"Cannot make another request while receiving a file\"\n\n #super().onReceived(payload)\n\n\n # executing the command requested by the client\n try:\n back = self.commands[opcode](args, timestamp)\n\n except AssertionError as e:\n print(f'[{self.name}]:', e)\n self.sendMessage(f'400 ' + str(e))","title":""},{"docid":"9b62d97ceff9f99f81c84215a2d0891c","score":"0.5978791","text":"def handle_message(self, data, user, msg):\n msg = tools.strip_name(msg)\n args = msg.split()\n\n if args[0] == \"!commands\" or args[0] == \"!comm\":\n self.setup_commands(user, args)\n else:\n self.run_commands(user, args)\n\n self.lines_received += 1","title":""},{"docid":"d51b293900d0d8cd6a34942f7447f31c","score":"0.59698224","text":"def on_receive(self, message):\n pass","title":""},{"docid":"88d36849c4fc30963fef41223cbe28fc","score":"0.59584075","text":"def receive(self):","title":""},{"docid":"22054425d918fa83f76d6b8216850afc","score":"0.5957546","text":"def _read_line(self):\n print('My brand!')\n return","title":""},{"docid":"f3c71ae90cb8006f9e79602db6787966","score":"0.5952475","text":"def get_line(self):\r\n line = self.pgn_file.readline()\r\n if isinstance(line,bytes):\r\n line = line.decode(\"utf-8\")\r\n if line == \"\":\r\n self.current_path_index += 1\r\n if self.current_path_index >= len(self.pgn_paths):\r\n self.current_path_index = 0\r\n self.get_new_file(self.current_path_index)\r\n line = self.pgn_file.readline()\r\n self.current_line = line[:-1].split()","title":""},{"docid":"403bc89a0d60d1a285aea73266f294ef","score":"0.5949775","text":"def read_line():\n # try reading a line, removing any extra whitespace\n try:\n line = sub.sys.stdin.readline().strip()\n # i3status sends EOF, or an empty line\n if not line:\n sub.sys.exit(3)\n return line\n # exit on ctrl-c\n except KeyboardInterrupt:\n sub.sys.exit()","title":""},{"docid":"4575ff26c1399669d9d0f3adfd8d7964","score":"0.5944485","text":"async def printLine(self, line):\n sender = self.fromParty.protocol.formatSender(self.fromParty.alias)\n text = sender + self.toParty.protocol.format(line)\n \n userColor = self.fromParty.user.profile.color\n if userColor:\n text = self.toParty.protocol.format(text, color=userColor)\n \n await self.messageReceiver(text)","title":""},{"docid":"69a7cb8991b880c9990c7cf00ea471cd","score":"0.5940643","text":"def do_chat(self, line):\n if len(line) == 0:\n return self.do_help('chat')\n\n self.chat(line)","title":""},{"docid":"b6629ed0554b2e7a4d930ba775949837","score":"0.59335047","text":"def on_new_line(self, line, is_full_line):\n try:\n self._parse_failure_indication(line)\n self._send_commands_before_establish_connection_if_requested(line, is_full_line)\n self._send_login_if_requested(line)\n self._send_password_if_requested(line)\n self._just_connected(line)\n self._send_commands_after_establish_connection_if_requested(line, is_full_line)\n self._settings_after_login(line, is_full_line)\n self._detect_prompt_after_exception(line)\n except ParsingDone:\n pass","title":""},{"docid":"53a93215b74fa57b3dbd922de78d60f9","score":"0.5921312","text":"def data_received(self, data):\n self.buffer += data\n pts = self.buffer.split(b\"\\n\")\n self.buffer = pts.pop()\n for el in pts:\n asyncio.ensure_future(self.client.data_received(el))","title":""},{"docid":"ea2d9e4cf68c394d3fac603adc442e55","score":"0.5918216","text":"def write_line(self, line):\n\t\tif not self.is_alive:\n\t\t\treturn False\n\t\ttry:\n\t\t\tself.command_process.stdin.write(\"SEND %s\\n\" % (line,))\n\t\t\tself.command_process.stdin.flush()\n\t\texcept (OSError, IOError):\n\t\t\tself.kill()","title":""},{"docid":"cfa7d602ca463d2eeeebb310944510c3","score":"0.59146094","text":"def handle(line, mc):\n match = linere.match(line)\n if match:\n fields = match.groupdict()\n mc.call_hooks('all', fields=fields)\n print(\"<\", fields['message'], file=sys.stderr)\n\n match = chatre.match(fields['message'])\n if match:\n chat = match.groupdict()\n if chat['message'][0] == \"!\":\n command = chat['message'][1:].split()\n mc.call_hooks(\n 'command',\n command=command[0],\n args=command[1:],\n player=chat['player'],\n )\n else:\n mc.call_hooks(\n 'chat',\n message=chat['message'],\n player=chat['player'],\n )\n\n match = sayre.match(fields['message'])\n if match:\n say = match.groupdict()\n mc.call_hooks('say', message=say['message'])\n\n match = joinleftre.match(fields['message'])\n if match:\n joinleft = match.groupdict()\n if joinleft['change'] == \"joined\":\n mc.call_hooks('join', player=joinleft['player'])\n mc.players.add(joinleft['player'])\n else:\n mc.call_hooks('leave', player=joinleft['player'])\n mc.players.remove(joinleft['player'])","title":""},{"docid":"5c53d94d2c6183101ab9dc571d6e9b30","score":"0.59130716","text":"def process_received_message(self, msg):\n if msg[2:3] == ':':\n cmd = msg[0:2]\n payload = msg[3:].replace('\\r', '')\n else:\n self.log.warning(\"Received malformed message: %s\", msg)\n return\n\n # Can't use try since it swallows too many errors for now\n if cmd in self.fast_commands:\n self.fast_commands[cmd](payload)\n else:\n self.log.warning(\"Received unknown serial command? %s. (This is ok\"\n \" to ignore for now while the FAST platform is in \"\n \"development)\", msg)","title":""},{"docid":"d7885955291760723d25b2a06a706477","score":"0.5889952","text":"def end_of_line(event: E) -> None:\n buff = event.current_buffer\n buff.cursor_position += buff.document.get_end_of_line_position()","title":""},{"docid":"d32b950eec7b5f91310e1d0af563dc98","score":"0.58889556","text":"def get_buffer_line(self, line):\r\n return self.screen.get_buffer_line(line)","title":""},{"docid":"6bb9ae1d6e645d7aeacef760a72763e0","score":"0.58860815","text":"def complete(self, line):\n pass","title":""},{"docid":"6ec9456633a566b030660a2da7cc692c","score":"0.5879773","text":"def get_line(self):\n line_str = None\n while supervisor.runtime.serial_bytes_available:\n ch = sys.stdin.read(1)\n if self.escape:\n # continue the escape sequence\n self._continue_escape(ch)\n \n # handle escape\n elif ch[0] == '\\x1b':\n # start the escape sequence\n self.escape = Console.escape_tree\n \n # handle backspace\n elif ch[0] == '\\x7f' or ch[0] == '\\x08':\n if self.cursor > 0:\n current_cursor = self.cursor\n self.cursor -= 1\n self._delete_and_show(current_cursor)\n \n # handle newline\n elif ch[0] == '\\n':\n print('')\n if self.inbuffer:\n self._add_to_history(self.inbuffer)\n line_str = self.inbuffer\n self._reset_inbuffer()\n self.history_pos = 1\n break\n \n # handle normal characters\n else:\n self._insert_and_show(ch)\n \n return line_str","title":""},{"docid":"ae7d581c4e3426dc8f7f254aae492b91","score":"0.58662874","text":"def onReceived(self, payload, timestamp=None):\n\n # assert payload contains 3 digit opcode\n assert len(payload) >= 3, \"Invalid request length\"\n\n # parse the incoming message into the format [opcode|args] with opcode as a 3 digit integer\n opcode, args = payload[:3], payload[3:]\n\n # assert the opcode is available\n assert opcode in self.commands, f'Invalid request OPCODE = \"{opcode}\"'\n\n # assert waiting for a file before receiving a segment\n assert (not self.fileToReceive) or (\n self.fileToReceive and opcode == b'212'), \"Cannot make another request while receiving a file\"\n\n # super().onReceived(payload)\n\n # executing the command requested by the client\n try:\n self.commands[opcode](args, timestamp)\n\n except AssertionError as e:\n print(f'[{self.name}]:', e)\n self.sendMessage(f'400 ' + str(e))","title":""},{"docid":"31ec07f18e36c21b8a86d274b269b007","score":"0.58596385","text":"def dataReceived(self, data):\r\n\t\tself.dataBuffer.append(data)","title":""},{"docid":"ab98c7ab104c3b372f19369cffe45a8e","score":"0.58592516","text":"def next_line(self, line):\n pass","title":""},{"docid":"ff8a93cd2e68cad757767cb0d0a2a31c","score":"0.5853252","text":"def onRAW(self, sock, line):\n\n\t\tif self.__states[sock] == self.COMMAND:\n\t\t\tif not line:\n\t\t\t\tself.write(sock, \"500 Syntax error, command unrecognized\\r\\n\")\n\t\t\t\treturn\n\n\t\t\tmethod = None\n\n\t\t\ti = line.find(\" \")\n\t\t\tif i < 0:\n\t\t\t\tcommand = line.upper()\n\t\t\t\targ = None\n\t\t\telse:\n\t\t\t\tcommand = line[:i].upper()\n\t\t\t\targ = line[i+1:].strip()\n\n\t\t\tmethod = getattr(self, \"smtp\" + command, None)\n\n\t\t\tif not method:\n\t\t\t\tself.write(sock, \"502 Command not implemented\\r\\n\")\n\t\t\telse:\n\t\t\t\tmethod(sock, arg)\n\t\telse:\n\t\t\tif self.__states[sock] != self.DATA:\n\t\t\t\tself.write(sock, \"451 Internal confusion\\r\\n\")\n\t\t\t\treturn\n\n\t\t\tif line and re.match(\"^\\.$\", line):\n\t\t\t\t\tself.__data.flush()\n\t\t\t\t\tself.__data.seek(0)\n\t\t\t\t\tstatus = self.processMessage(sock,\n\t\t\t\t\t\t\tself.__mailfrom, self.__rcpttos, self.__data)\n\n\t\t\t\t\tself.reset()\n\n\t\t\t\t\tif not status:\n\t\t\t\t\t\tself.write(sock, \"250 Ok\\r\\n\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.write(sock, status + \"\\r\\n\")\n\t\t\telse:\n\t\t\t\tif line and line[0] == \".\":\n\t\t\t\t\tself.__data.write(line[1:] + \"\\n\")\n\t\t\t\telse:\n\t\t\t\t\tself.__data.write(line + \"\\n\")","title":""},{"docid":"66b2edbad1f6d95ebdefcdc498a4e2f8","score":"0.5851008","text":"def first_line_received(self, line):\n\n line = line.decode('utf-8', 'replace')\n m = self.first_line_re.match(line)\n if m:\n for name, value in m.groupdict().items():\n if name == 'status_code':\n value = int(value)\n setattr(self, name, value)\n else:\n self.parse_error('invalid first line')","title":""},{"docid":"78baff1df0db041469c030766207a665","score":"0.58450633","text":"def sendCommandLine(self, line):\n\t\tself.hostRobot.writeline(line)","title":""},{"docid":"f8219a73ab38cfb8eb916708ed9aabda","score":"0.58448005","text":"def process_response(self, response):\n match = self.RESPONSE_REGEX.search(response)\n if match: # received text is a valid response to a line\n # Pop oldest line\n line = self.sent_lines.pop(0)\n line.set_status(response)\n\n # Send next line (if possible)\n self.poll_transmission()\n\n # Raiser exception on error\n # IMPORTANT: must be the last thing this function does\n if match.group('keyword').lower() == 'error':\n raise GCodeStreamException(\"error on gcode: '{gcode}' {msg}\".format(\n gcode=line.gcode,\n msg=response\n ))\n\n else:\n raise GCodeStreamException(\"unidentified message: %s\" % response)","title":""},{"docid":"c04e39e5df1735f0ea73ea80bb874d2b","score":"0.5839755","text":"def recv_line(self, terminator=\"\\n\", strip=True, timeout=DEFAULT_TIMEOUT):\n while terminator not in self.stream:\n yield self._fetch()\n index = self.stream.index(terminator)\n line = self.stream[:index]\n self.stream = self.stream[index+len(terminator):]\n if strip: line = line.strip()\n yield Return(line)","title":""},{"docid":"ed22d936b32aa7635766df4769e2fe76","score":"0.58389306","text":"def read_line():\n b = next_byte()\n while b != b'\\x0a':\n msg = msg + b.decode('ASCII')\n return msg + '\\n'","title":""},{"docid":"0bb8bd5bf1609c2d94ed5937f521e28c","score":"0.58369267","text":"def enable_receiving(self, type):\n if type == 'a':\n if os.linesep == '\\r\\n':\n self.data_wrapper = lambda x: x\n else:\n self.data_wrapper = lambda x: x.replace('\\r\\n', os.linesep)\n elif type == 'i':\n self.data_wrapper = lambda x: x\n else:\n raise TypeError, \"Unsupported type\"\n self.receive = True","title":""},{"docid":"4f915c8697e81b68656ee9646a62f6a7","score":"0.5835847","text":"def parse_line(self, line: str):\n pass","title":""},{"docid":"d89382eaaa7c14f8e393111aed0ef570","score":"0.58292437","text":"def process_input_line(self, line):\r\n #print \"input='%s'\"%self.input\r\n stdout = sys.stdout\r\n try:\r\n sys.stdout = self.cout\r\n self.IP.push_line(line)\r\n finally:\r\n sys.stdout = stdout","title":""},{"docid":"903dcb87655b183eae3af41935615047","score":"0.582443","text":"def _get_line_data(self):\n\n # check that client is connected\n self._check_connection()\n\n data = b\"\"\n while True:\n data += self.socket.recv(self.__class__.CHIRP_LINE_MAX)\n if data.decode()[-1] == \"\\n\":\n break\n return data.decode()","title":""},{"docid":"ba50468d521590d1ed7e2a1fb059adcc","score":"0.5822928","text":"def read_line(self):\n return self._read_until( '\\r', self.timeout )","title":""},{"docid":"360c320a6d7a10f749cef58a3fecbd52","score":"0.5818135","text":"def _lines_callback(self, msg):\n if 'line' not in self._evaluated_classes:\n return\n # Add lock\n self._lock += 1\n # Get the measurement env for the image and class\n measurement = self._get_image_measurement(int(msg.header.frame_id)).evaluations['line']\n # Mark as received\n measurement.received_message = True\n # Measure duration of processing\n measurement.duration = self._measure_timing(msg.header)\n # Generating and matching masks\n measurement.pixel_mask_rates = self._match_masks(\n self._generate_line_mask_from_vectors(\n Evaluator._extract_vectors_from_annotations(\n self._images[int(msg.header.frame_id)]['annotations'],\n typename='line'\n ),\n np.zeros(self._label_shape, dtype=np.uint8)),\n self.bridge.imgmsg_to_cv2(msg, '8UC1'))\n # Release lock\n self._lock -= 1","title":""},{"docid":"cb3dd204637b6e03de50bb1ddd948bc4","score":"0.5806855","text":"def onecmd(self, line):\n cmd, arg, line = self.parseline(line)\n if not line:\n return self.emptyline()\n if cmd is None:\n return self.default(line)\n self.lastcmd = line\n if line == 'EOF' :\n self.lastcmd = ''\n if cmd == '':\n return self.default(line)\n else:\n try:\n func = getattr(self, 'do_' + cmd)\n except AttributeError:\n return self.default(line)\n return func(arg)","title":""},{"docid":"b8e5039c1935c8fb98ce6ba10abc046a","score":"0.5801057","text":"def receive(self, reply):\n data = reply.readAll()\n self.warning(\"!\", \"Received! %s\" % data)\n self.hide()\n self.finished.emit()\n self.deleteLater()","title":""},{"docid":"c5f3bbc04c0ba318d393a718117ec215","score":"0.5796391","text":"def get_line():\n line = instream.readline()\n # 'U' mode works only with text files\n if line.endswith(b\"\\r\\n\"):\n lineends[b\"\\r\\n\"] += 1\n elif line.endswith(b\"\\n\"):\n lineends[b\"\\n\"] += 1\n elif line.endswith(b\"\\r\"):\n lineends[b\"\\r\"] += 1\n return line","title":""},{"docid":"1c12fa92a88f6be62380af032ded27d1","score":"0.5796175","text":"def signalClientOutput(self, line):\n self.clientOutput.emit(line)","title":""},{"docid":"1c52b419f546de485bb98480127d2304","score":"0.57920325","text":"def receiveSegment(self, args, timestamp):\n # example: 212001\\x1cDATASEGMENT\n # args: mytext\\x1ctxt -> split with \\x1c -> [mytext, txt, 200]\n\n assert self.fileToReceive is not None, \"Server not expecting file\"\n assert self.fileToReceive['received'] != self.fileToReceive['total'], \"Received all segments\"\n\n data = args\n # save the segment data\n self.fileToReceive['segments'].append(data)\n self.fileToReceive['timestamps'].append(timestamp)\n self.fileToReceive['received']+=1\n\n self.fileToReceive['rate'] = getAverageRate(\n sampleNumber=self.fileToReceive['received'],\n oldAverage=self.fileToReceive['rate'],\n newSampleSize=len(data),\n duration= timestamp - self.fileToReceive['timestamps'][-2]\n )\n\n print(f'[{self.name}]:',\n f'Receiving file [{self.fileToReceive[\"name\"]}.{self.fileToReceive[\"type\"]}] - ({self.fileToReceive[\"received\"]}/{self.fileToReceive[\"total\"]}) - {round(self.fileToReceive[\"rate\"])} bps ')\n\n self.sendMessage('100 received')\n if self.fileToReceive['received'] == self.fileToReceive['total']:\n data = compileData(self.fileToReceive['segments'])\n writeFile(self.fileToReceive['name'], self.fileToReceive['type'], FILE_PATH, data)\n\n print(f'[{self.name}]:',\n f'file [{self.fileToReceive[\"name\"]}.{self.fileToReceive[\"type\"]}] Received successfully')\n\n self.fileToReceive = None","title":""},{"docid":"4d35952679dc2eb7ed5a03dcbbcad047","score":"0.5788396","text":"def _onInputActivity(self, fd, ev):\n self._read()\n if self.inBuf:\n active, ret = self.onReceiveCB(self)\n if not active:\n self.inBuf = \"\"","title":""},{"docid":"b1ae66f63a6ff33174d14f4806f47c29","score":"0.5780222","text":"def read(self):\n p=self.packet()\n p.read_line()\n ans=yield p.send()\n returnValue(ans.read_line)","title":""},{"docid":"03687bef4c9b02efdb9334bcbc099c77","score":"0.57705706","text":"def read_line(line_amount, data_socket):\n line = ''\n for i in range(0, line_amount):\n\n char = next_byte(data_socket).decode()\n while char != '\\n':\n line = line + char\n char = next_byte(data_socket).decode()\n\n line += '\\r\\n'\n\n return line","title":""},{"docid":"7cd2359131a9920ebc09714ba42ecd80","score":"0.5769172","text":"def receiveSegment(self, args, timestamp):\n # example: 212001\\x1cDATASEGMENT\n # args: mytext\\x1ctxt -> split with \\x1c -> [mytext, txt, 200]\n\n assert self.fileToReceive is not None, \"Server not expecting file\"\n assert self.fileToReceive['received'] != self.fileToReceive['total'], \"Received all segments\"\n\n # find index of first separator\n separateAt = args.find(self.SEPERATOR)\n assert separateAt != -1, \"Expected 2 arguments: sequence number, data\"\n # split args\n seqNum, data = [args[:separateAt], args[separateAt+1:]]\n seqNum = int(seqNum.decode('utf-8'))\n\n assert seqNum < self.fileToReceive['total'], \"Sequence number not in range\"\n assert self.fileToReceive['segments'][seqNum] is None, \"Segment already received\"\n if self.fileToReceive['segments'][seqNum] is not None:\n self.sendMessage(f'100{seqNum}')\n return\n\n # increment the number of segments received\n self.fileToReceive[\"received\"] += 1\n # save the segment data\n self.fileToReceive['segments'][seqNum] = data\n # add the timestamp of arrival of segment\n self.fileToReceive['timestamps'].append(timestamp)\n\n self.fileToReceive['rate'] = getAverageRate(\n sampleNumber=self.fileToReceive['received'],\n oldAverage=self.fileToReceive['rate'],\n newSampleSize=len(data),\n duration=timestamp - self.fileToReceive['timestamps'][-2]\n )\n\n print(f'[{self.name}]:',\n f'Receiving file [{self.fileToReceive[\"name\"]}.{self.fileToReceive[\"type\"]}] - ({self.fileToReceive[\"received\"]}/{self.fileToReceive[\"total\"]}) - {round(self.fileToReceive[\"rate\"])} bps ')\n\n self.sendMessage(f'100{seqNum}')\n\n if self.fileToReceive['received'] == self.fileToReceive['total']:\n data = compileData(self.fileToReceive['segments'])\n writeFile(self.fileToReceive['name'], self.fileToReceive['type'], FILE_PATH, data)\n\n # self.sendMessage(f'100')\n print(f'[{self.name}]:',\n f'file [{self.fileToReceive[\"name\"]}.{self.fileToReceive[\"type\"]}] Received successfully')\n\n self.fileToReceive = None","title":""},{"docid":"1039393b7209602ece90a9986e2d3e9f","score":"0.5768869","text":"def read_one_line():\r\n newline_received = False\r\n message = \"\"\r\n while not newline_received:\r\n character = client_socket.recv(1).decode()\r\n if character == '\\n':\r\n newline_received = True\r\n elif character == '\\r':\r\n pass\r\n else:\r\n message += character\r\n return message","title":""},{"docid":"926db70b376b7be06f70dd1eca9599eb","score":"0.5756967","text":"def read_line(self):\n buffer = bytes()\n max_counter = 100\n current_counter = 0\n line = bytes()\n while True:\n rc, data = win32file.ReadFile(self.http_usb_handle, 1)\n #log.debug(\"read_line rc = {}\".format(rc))\n if len(data) == 0:\n current_counter += 1\n if current_counter >= max_counter:\n break\n line += data\n if data == b'\\n':\n break\n return line","title":""},{"docid":"9cc92d984a64467f855a93b24b2d9652","score":"0.5756349","text":"def dataReceived(self, data):\r\n\t\tself.stompBuffer.appendData(data.replace('\\0', '\\0\\n'))\r\n\t\t\r\n\t\twhile True:\r\n\t\t\tmsg = self.stompBuffer.getOneMessage()\r\n\t\t\t\r\n\t\t\tif self.stompBuffer.buffer.startswith('\\n'):\r\n\t\t\t\tself.stompBuffer.buffer = self.stompBuffer.buffer[1:]\r\n\t\t\t\r\n\t\t\tif msg is None:\r\n\t\t\t\tbreak\r\n\t\t\t\r\n\t\t\treturned = self.mp.react(msg)\r\n\t\t\tif returned:\r\n\t\t\t\tself.transport.write(returned)","title":""},{"docid":"07bc10dc76fda5f7cfb6fda03992cc7e","score":"0.5750866","text":"def __process_trailer(self, line):\n if len(line) > 0:\n return\n self.message_end_received()\n self.reset()","title":""},{"docid":"b96fa84a3e3810d35b4231c85f537995","score":"0.5738555","text":"def __header_line_received(self, line):\n if line[0] in b\" \\t\":\n if self.__last_header_field:\n self.__last_header_field.value += line.strip()\n else:\n self.parse_error('first header field cannot be a continuation')\n else:\n self.__header_field_received(line)","title":""},{"docid":"040628e94b4d8e9d2f88393fc65ccd54","score":"0.5736089","text":"def enter(self,e):\n string = self.ttyText.get(1.0, tk.END)[self.line_start:]\n self.line_start+=len(string)\n self.p.stdin.write(string.encode())\n self.p.stdin.flush()","title":""},{"docid":"3d9ef4679ddf5af50c071c0a91ecf457","score":"0.57179374","text":"def _write_line(self, line_for_send):\n self.telnet.write(line_for_send.encode('ascii') + b'\\n')\n return","title":""},{"docid":"51e717cdaecfffd96eb5e40e86770e0e","score":"0.5717294","text":"def output_callback(self, line):\n self.interrupted = False\n self.cfg.match(line, print, **{'end': ''})\n sys.stdout.flush()","title":""},{"docid":"65397c0d1401b45a632ccfc0ed7e0d9e","score":"0.57154626","text":"def get_line(self):\n pass # implemented in Ada","title":""},{"docid":"48cb90d25afe8cee3c990da3cc6025c5","score":"0.57089055","text":"def readline(self, timeout=0.5):\n poll_result = self.poller.poll(timeout)\n if poll_result:\n line = self.io.readline().strip()\n if self.io == sys.stdin and line == '[':\n # skip first event line wrt issue #19\n line = self.io.readline().strip()\n try:\n # python3 compatibility code\n line = line.decode()\n except (AttributeError, UnicodeDecodeError):\n pass\n return line\n else:\n return None","title":""},{"docid":"f9cc15df76f4d5079837d3cc22a796a7","score":"0.5708118","text":"def get_current_line(self):","title":""},{"docid":"9c5df7dd601ddc76626305b5e0c4f12a","score":"0.57072186","text":"def read_line():\n # try reading a line, removing any extra whitespace\n try:\n line = sys.stdin.readline().strip()\n # i3status sends EOF, or an empty line\n if not line:\n sys.exit(3)\n return line\n # exit on ctrl-c\n except KeyboardInterrupt:\n sys.exit()","title":""},{"docid":"9c5df7dd601ddc76626305b5e0c4f12a","score":"0.57072186","text":"def read_line():\n # try reading a line, removing any extra whitespace\n try:\n line = sys.stdin.readline().strip()\n # i3status sends EOF, or an empty line\n if not line:\n sys.exit(3)\n return line\n # exit on ctrl-c\n except KeyboardInterrupt:\n sys.exit()","title":""}],"string":"[\n {\n \"docid\": \"86af36f9f2c5f587adb43d6c498dd5a3\",\n \"score\": \"0.84185743\",\n \"text\": \"def line_received(self, line):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438969ca43b81399f41b78da3ee8d66f\",\n \"score\": \"0.82127386\",\n \"text\": \"def lineReceived(self, line):\\n\\n self.on_line(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3333566a2c218ddc406070085ff2a6\",\n \"score\": \"0.7986448\",\n \"text\": \"def _OnLineReceived(line):\\n if line:\\n print(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b401a214bc0e6a52285e74e956aadf6\",\n \"score\": \"0.77242255\",\n \"text\": \"def lineReceived(self, line):\\n if self.timeout > 0:\\n self.resetTimeout()\\n\\n state = self.state\\n self.state = None\\n state = getattr(self, 'state_' + state)(line) or state\\n if self.state is None:\\n self.state = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f16b213a71dc8640b79f20a07514084\",\n \"score\": \"0.7708595\",\n \"text\": \"def handle_line(self, line):\\n rawlogger.debug(line)\\n self.messagebus.trigger_event(SERIAL_RAW_LINE, SERIAL_ENDPOINT, self.entity, line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb31acf8390b67ec726b4724cae19d0\",\n \"score\": \"0.7433005\",\n \"text\": \"def handle_line(self, line):\\n raise NotImplementedError('please implement functionality in handle_line')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e99bcb7786925945f0e8ee74038d21\",\n \"score\": \"0.7288259\",\n \"text\": \"def lineReceived(self, line):\\r\\n if line == \\\"ping\\\":\\r\\n self.sendLine(\\\"pong\\\")\\r\\n else:\\r\\n log(\\\"lineReceived\\\", line)\\r\\n message = Message.create_message(line)\\r\\n self.factory.network.peers.add_message(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab63a5f368f4a11e5ed144eac92e4c0a\",\n \"score\": \"0.7272639\",\n \"text\": \"def _handle_line(self, line):\\n\\n (tags, prefix, command, args) = protocol.parse(line)\\n\\n # translate numerics\\n command = events.numeric.get(command, command).lower()\\n if not command:\\n return\\n\\n log.debug('<- p: {0} c: {1} a: {2}'.format(prefix, command, args))\\n\\n if command == 'welcome':\\n # record the nickname in case the server changed it\\n self.nickname = args[0]\\n\\n # reset the reconnect count\\n self.reconnect_count = 1\\n\\n\\n # handle privmsg/notice special to split out the CTCP stuff\\n if command in ('privmsg', 'notice'):\\n self._on_message(prefix, command, args, tags)\\n\\n # handle server pings internally\\n elif command == 'ping':\\n self.pong(args[0])\\n\\n # otherwise, find a handler, and call it\\n else:\\n\\n response = None\\n handler = getattr(self, 'on_%s' % command, None)\\n if handler:\\n event = events.Event(prefix, command, args, tags)\\n\\n def delegate(event):\\n response = handler(event)\\n if response:\\n self._send(response)\\n\\n self.loop.call_soon(delegate, event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"facdcf88ceb4f395e0ba42e34325c3d3\",\n \"score\": \"0.71677774\",\n \"text\": \"def _process_line(self, line):\\n config = self.config[\\\"watcher\\\"]\\n data = d.Parser(line, config[\\\"nick\\\"])\\n data._load()\\n if data.msg_type == \\\"PRIVMSG\\\":\\n # If the channel is not in our list of channels,\\n # do nothing with it, just in case somebody tries\\n #to PM us with false data.\\n if not data.chan in config[\\\"channels\\\"]:\\n return\\n\\n # Process the line.\\n rc_data = RC(data)\\n\\n # Handle a line from the recent changes server\\n self.handle_rc_event(rc_data, data.chan)\\n\\n elif data.msg_type == \\\"376\\\":\\n for chan in config[\\\"channels\\\"]:\\n self.join(chan)\\n for chan in config[\\\"report_chans\\\"]:\\n self._connection.join(chan)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47942e0fcd5d044ba09606221de1b60c\",\n \"score\": \"0.71497595\",\n \"text\": \"def lineReceived(self, line): \\n self.transport.write(line[::-1]+ '\\\\r\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c11413eac5cf449e4bc43623dd68cbb\",\n \"score\": \"0.7133134\",\n \"text\": \"def subscribe_handle(self, sender, **kwargs):\\n line = kwargs[\\\"line\\\"]\\n if line == self.expect:\\n self.received = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e02df5e4a1ec00370cbbf3b1c48bdb3\",\n \"score\": \"0.70772827\",\n \"text\": \"def accept_line(event: E) -> None:\\n event.current_buffer.validate_and_handle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c6de4e019f2a40f18c9ef8d2fc8176\",\n \"score\": \"0.6969847\",\n \"text\": \"def lineReceived(self, line):\\n self.nBytes += len(line)\\n logging.debug(\\\"> %s\\\", line.rstrip(\\\"\\\\r\\\\n\\\"))\\n if self.nBytes > self.ctx.maximumSize:\\n self.ignoring = True\\n else:\\n self.lines.append(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"431246405938881603f52c2e337808af\",\n \"score\": \"0.6834815\",\n \"text\": \"async def handle(self, line):\\n \\n # lines recieved must be converted from bytes to strings\\n decoded = line.decode(self.encoding).rstrip(constants.CRLF)\\n \\n # see utils.ircsplit\\n prefix, command, params = utils.ircsplit(decoded)\\n \\n # command is here twice so that it is passed to the appropriate listeners\\n # while maintaining the generality of `self.trigger`'s function signature\\n triggered = await self.trigger(command, prefix, command, *params)\\n \\n return triggered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf4083509415cc7999239ea18bbc99b\",\n \"score\": \"0.6708258\",\n \"text\": \"def line_handler(self,line):\\n if self.logenabled:\\n log_line(line,self.log.serial_log_file, self.log.serial_info_file)\\n else:\\n pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91ccefbd3a5d35cda525e9619403b31\",\n \"score\": \"0.67066795\",\n \"text\": \"def _recv(self, line):\\n if line == '':\\n return\\n\\n # it seems caseta hub returns strings starting with GNET> so strip it off\\n if line[0] == 'G':\\n line = line[6:]\\n # Only handle query response messages, which are also sent on remote status\\n # updates (e.g. user manually pressed a keypad button)\\n if line[0] != Lutron.OP_RESPONSE:\\n _LOGGER.debug(\\\"ignoring %s\\\" % line)\\n return\\n parts = line[1:].split(',')\\n cmd_type = parts[0]\\n integration_id = int(parts[1])\\n args = parts[2:]\\n if cmd_type not in self._ids:\\n _LOGGER.info(\\\"Unknown cmd %s (%s)\\\" % (cmd_type, line))\\n return\\n ids = self._ids[cmd_type]\\n if integration_id not in ids:\\n _LOGGER.warning(\\\"Unknown id %d (%s)\\\" % (integration_id, line))\\n return\\n obj = ids[integration_id]\\n # First let the device update itself\\n handled = obj.handle_update(args)\\n # Now notify anyone who cares that device may have changed\\n if handled and obj in self._subscribers:\\n self._subscribers[obj](obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7de9f63aba149aafa316ce8faac8f54\",\n \"score\": \"0.6615805\",\n \"text\": \"def data_received(self, data):\\n self.buffer.push(data.decode())\\n for line in self.buffer:\\n if line:\\n self._handle_line(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5f944c56ffab812bce8a710e52f01f\",\n \"score\": \"0.6602743\",\n \"text\": \"def on_line(self, line):\\n\\n line = self._unquote(line).strip()\\n\\n is_player = self.re_response_type.match(line)\\n line_type = \\\"player\\\" if is_player else \\\"server\\\"\\n\\n self.logger.debug(\\\"%s: [%s]\\\" % (line_type, line))\\n\\n parts = line.split()\\n player = parts[0] if is_player else None\\n parts = parts[1:] if is_player else parts\\n params = list()\\n\\n if parts[-1] == \\\"?\\\": return\\n\\n while parts:\\n method_name = \\\"_\\\".join(parts)\\n try:\\n method_key = \\\"%s_%s\\\" % (line_type, method_name)\\n method = getattr(self, \\\"on_%s\\\" % (method_key))\\n method_params = params[::-1]\\n if player is not None: method_params.insert(0, player)\\n method(*method_params)\\n if method_key in self.service.callbacks:\\n self.service.callbacks.get(method_key)(*method_params)\\n break\\n except AttributeError:\\n params.append(parts[-1])\\n parts = parts[:-1]\\n except KeyboardInterrupt:\\n sys.exit(0)\\n except Exception, e:\\n print e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d2679624b8745b5e70c8d5be558a4b\",\n \"score\": \"0.65742546\",\n \"text\": \"def command_event_handler(self, event):\\n curr_line = self.platform.get_current_line()\\n curr_comm = curr_line[len(self.config['prompt']):].strip()\\n\\n # rewind recorder\\n if self.recorder.status():\\n self.recorder.rewind()\\n # if command not empty, it should be added to recorder\\n if curr_comm:\\n self.recorder.commit(curr_comm)\\n # start processing\\n self.platform.writer('\\\\n')\\n self.parser.parse(self, self.platform, curr_comm)\\n #self.platform.writer('\\\\n')\\n self.platform.reader(self.config['prompt'])\\n # self.platform.writer(self.config['prompt'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a68ba72ea392c8ff863687df71748b\",\n \"score\": \"0.65234387\",\n \"text\": \"def line_handler (self, line, number):\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715b40da3dfda9dee60486ac1bd2e811\",\n \"score\": \"0.6503571\",\n \"text\": \"def recieveLine(socket):\\n data = \\\"\\\"\\n line = \\\"\\\"\\n while(data != '\\\\n'):\\n line += data\\n data = socket.recv(1)\\n if not data:\\n print \\\"Connection is closed from the server\\\\n\\\"\\n exit(0)\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcb089bdf3cf03d626745ca0d553538\",\n \"score\": \"0.64738107\",\n \"text\": \"def receive(self):\\n buf = []\\n while buf[-2:] != [b'\\\\r', b'\\\\n']:\\n c = self._sock.recv(1)\\n if not c:\\n raise ConnectionClosed\\n buf.append(c)\\n\\n try:\\n line = b''.join(buf).decode()\\n except (UnicodeError):\\n self.handle_encoding_error()\\n return ''\\n print('received:', line)\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca8218026f36da761618e6582e49f98\",\n \"score\": \"0.6460493\",\n \"text\": \"def handle(self, ch ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059c6e552073fb913e32d722c03eade1\",\n \"score\": \"0.6409392\",\n \"text\": \"def text_input(self, line):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636b60ed2d8f6de600c58ca83bd01841\",\n \"score\": \"0.64006376\",\n \"text\": \"def lineReceived(self, line):\\n rbuffer = StringIO()\\n po = sys.stdout\\n sys.stdout = rbuffer\\n err = False\\n try:\\n exec line in globals(),locals()\\n except Exception as e:\\n err = e\\n sys.stdout = po\\n print '>u> ' + line\\n if err:\\n out = self.pre_e + str(e) + self.post_e\\n else:\\n out = rbuffer.getvalue()\\n if out != \\\"\\\":\\n print '>s> ' + out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee8b93e4fdf4199aa0bd569a4cab3d03\",\n \"score\": \"0.63878894\",\n \"text\": \"def readline(self, *args, **kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e877f91b3287da9aee3411f522dcf09\",\n \"score\": \"0.6271138\",\n \"text\": \"def send(self, line):\\n assert isinstance(line, GCodeStreamer.Line)\\n self.pending_lines.append(line)\\n self.poll_transmission()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c0ea048e7b71d51eea12d2b08ed5b4\",\n \"score\": \"0.62662375\",\n \"text\": \"def lineReceived(self, line):\\n if line in self.factory.messages:\\n #print(\\\"LargeMessageProducer got line: [%s]\\\" % line)\\n msg = self.factory.messages.pop(line) # Get the value and remove from dict\\n producer = Producer(self, msg)\\n self.transport.registerProducer(producer, True)\\n producer.resumeProducing()\\n else:\\n # Someone used an invalid key!\\n log.err(\\\"Error: LargeMessageProducer got invalid key! [%s]\\\" % line)\\n self.sendLine(\\\"Invalid key\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15f9d5744232fdca3a22b055d7c2436\",\n \"score\": \"0.6259526\",\n \"text\": \"def _on_read_line(self, line):\\n try:\\n response = json.loads(line)\\n except ValueError:\\n raise _errors.MalformedJSONReceived(line)\\n\\n if response[\\\"id\\\"] not in self._request_futures:\\n raise _errors.UnexpectedResponseID(\\n \\\"Received response with unexpected ID {}\\\".format(\\n response[\\\"id\\\"]))\\n\\n self._request_futures[response[\\\"id\\\"]].set_result(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe316ba6daf501d1ad3ac6855050485\",\n \"score\": \"0.62491894\",\n \"text\": \"def data_received(self, data):\\n\\n data = self.decode(data)\\n if not data.endswith(\\\"\\\\r\\\\n\\\"):\\n self.linebuffer += data\\n return\\n\\n for line in (self.linebuffer + data).rstrip().split(\\\"\\\\r\\\\n\\\"):\\n\\n print(\\\"[N to B]\\\\t%s\\\" % line)\\n\\n l = line.split(\\\" \\\", 2)\\n if l[0] == \\\"PING\\\": self.send(\\\"PONG\\\", l[1])\\n elif l[1] == \\\"PONG\\\": self.forward(\\\"PONG\\\", l[2])\\n elif l[0] == \\\"PRIVMSG\\\" or l[1] == \\\"PRIVMSG\\\":\\n\\n self.chat_history.append(line)\\n\\n else:\\n\\n self.chat_history.append(line)\\n self.server_log.append(line)\\n\\n self.linebuffer = \\\"\\\"\\n\\n if self.bouncer.clients:\\n while self.chat_history:\\n line = self.chat_history.popleft()\\n self.forward(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5ffe3a9e734c01295cfa4d6cf6e770\",\n \"score\": \"0.62188923\",\n \"text\": \"def GotLine(self, line):\\n if line.strip():\\n # a new line in this block\\n if _HasQuotes(line):\\n parts = bup.shquote.quotesplit(line)\\n self.lines.append([word for unused_offset, word in parts])\\n else:\\n self.lines.append(line.split())\\n else:\\n # blank line means end of block\\n lines = self.lines\\n self.lines = []\\n result = self.handle_lines_func(lines)\\n if result is None:\\n return None\\n else:\\n return self.RenderBlock(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0da6fd379a5e8e88c7dbd3667f4cd14\",\n \"score\": \"0.62120855\",\n \"text\": \"def parse_raw_line(self, line):\\n line = line.replace('\\\\0', '')\\n\\n if len(self.stack) > 0:\\n line = '{old}{new}'.format(old=self.stack.pop(), new=line)\\n\\n if self.filter_pattern:\\n remaining, filtered, reached_end_of_line = _filter_line(line, self.filter_pattern)\\n if filtered:\\n logger.debug(filtered)\\n self.messagebus.trigger_event(\\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, filtered)\\n if reached_end_of_line:\\n self.stack.append(remaining)\\n else:\\n self.parse_raw_line(remaining)\\n else:\\n logger.debug(remaining)\\n self.messagebus.trigger_event(\\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, remaining)\\n else:\\n logger.debug(line)\\n self.messagebus.trigger_event(\\n LOG_LINE_RECEIVED, SERIAL_ENDPOINT, self.log_entity, line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8377d00d5ffe288d441c72c61aefd02a\",\n \"score\": \"0.62081015\",\n \"text\": \"def receive_command():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19d7aa7ce3345f74f1f6d2c7f5d54462\",\n \"score\": \"0.6178368\",\n \"text\": \"def send(self, line):\\r\\n\\t\\t_client.send(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b9b1a5ec8ff64c1b1abe9901396db98\",\n \"score\": \"0.6163223\",\n \"text\": \"def data_received(self, data):\\n self.buf += data\\n if b'\\\\n' in self.buf:\\n lines = self.buf.split(b'\\\\n')\\n self.buf = lines[-1] # whatever was left over\\n for line in lines[:-1]:\\n print(f'Reader received: {line.decode()}')\\n self.msgs_recvd += 1\\n if self.msgs_recvd == 4:\\n self.transport.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81de080b8827e257426181a0edaf5391\",\n \"score\": \"0.61627465\",\n \"text\": \"def send_line(self, output, line):\\n try:\\n output.send(line)\\n except StopIteration:\\n self.logger.debug(\\\"End of file reached\\\")\\n pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24515c48fcee471b4395d29413285663\",\n \"score\": \"0.6158032\",\n \"text\": \"def readline(self, size=0):\\n\\n # Get line\\n line = self._channel.recv(True)\\n\\n # Echo\\n if line and self._echo is not None:\\n self._echo.send(line)\\n\\n # Make sure it ends with newline\\n if not line.endswith(\\\"\\\\n\\\"):\\n line += \\\"\\\\n\\\"\\n\\n # Decrease size?\\n if size:\\n line = line[:size]\\n\\n # Done\\n if PY2:\\n return line.encode(\\\"utf-8\\\")\\n else:\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c29bf971fa609109c276c4d69525e41\",\n \"score\": \"0.6151506\",\n \"text\": \"def readline(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03841abecf630fb04bb69e03feb30a2c\",\n \"score\": \"0.6131059\",\n \"text\": \"def onREAD(self, sock, data):\\n\\n\\t\\tlines, buffer = splitLines(data, self.__buffers[sock])\\n\\t\\tself.__buffers[sock] = buffer\\n\\t\\tfor line in lines:\\n\\t\\t\\tself.push(Raw(sock, line), \\\"raw\\\", self.channel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2723790d40a6d65ea83ca23ca7691f\",\n \"score\": \"0.6116464\",\n \"text\": \"def read_line(self):\\n while True:\\n newline_idx = self.buf.find(b\\\"\\\\n\\\")\\n if newline_idx >= 0:\\n res = self.buf[:newline_idx]\\n self.buf = self.buf[newline_idx + 1:]\\n return res\\n chunk = self.f.recv(4096)\\n if not chunk:\\n raise EndOfStreamError()\\n self.buf += chunk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901b921bb1414a9cf2b954c19ee39f4e\",\n \"score\": \"0.6101103\",\n \"text\": \"def __process_header_line(self, line):\\n self.__header_lines += 1\\n\\n if self.__header_lines == 1:\\n self.first_line_received(line)\\n elif len(line) == 0:\\n self.__header_end_received()\\n else:\\n self.__header_line_received(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b71c041dde1b591b1dc48b2144f7237\",\n \"score\": \"0.6084031\",\n \"text\": \"def unread_line(line):\\n global linebuffered\\n global linebuf\\n u.verbose(3, \\\"unread_line on %s\\\" % line.rstrip())\\n if linebuffered:\\n u.error(\\\"internal error: multiple line unread\\\")\\n linebuffered = True\\n linebuf = line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2896a13b2f15a874f14ed3e3e6f9515c\",\n \"score\": \"0.6081038\",\n \"text\": \"def readline(self):\\n pass # implemented in Ada\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f59f765d0d53fec75ed1e87df3e726\",\n \"score\": \"0.60719717\",\n \"text\": \"def on_new_line(self, line, is_full_line):\\n if is_full_line:\\n try:\\n self._parse_trans_recv_loss_time_plus_errors(line)\\n self._parse_trans_recv_loss_time(line)\\n self._parse_min_avg_max_mdev_unit_time(line)\\n except ParsingDone:\\n pass # line has been fully parsed by one of above parse-methods\\n return super(Ping, self).on_new_line(line, is_full_line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78f8cf461f08f0b20bf8d14d3d1d87fd\",\n \"score\": \"0.60252434\",\n \"text\": \"def nextLine():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d8c46352c5affcf33f5e9f680a58bb\",\n \"score\": \"0.5989319\",\n \"text\": \"def onReceived(self, payload, timestamp=None):\\n\\n # assert payload contains 3 digit opcode\\n assert len(payload) >= 3, \\\"Invalid request length\\\"\\n\\n # parse the incoming message into the format [opcode|args] with opcode as a 3 digit integer\\n opcode, args = payload[:3], payload[3:]\\n\\n # assert the opcode is available\\n assert opcode in self.commands, f'Invalid request OPCODE = \\\"{opcode}\\\"'\\n\\n # assert waiting for a file before receiving a segment\\n assert (not self.fileToReceive) or (\\n self.fileToReceive and opcode == b'212'), \\\"Cannot make another request while receiving a file\\\"\\n\\n #super().onReceived(payload)\\n\\n\\n # executing the command requested by the client\\n try:\\n back = self.commands[opcode](args, timestamp)\\n\\n except AssertionError as e:\\n print(f'[{self.name}]:', e)\\n self.sendMessage(f'400 ' + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b62d97ceff9f99f81c84215a2d0891c\",\n \"score\": \"0.5978791\",\n \"text\": \"def handle_message(self, data, user, msg):\\n msg = tools.strip_name(msg)\\n args = msg.split()\\n\\n if args[0] == \\\"!commands\\\" or args[0] == \\\"!comm\\\":\\n self.setup_commands(user, args)\\n else:\\n self.run_commands(user, args)\\n\\n self.lines_received += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d51b293900d0d8cd6a34942f7447f31c\",\n \"score\": \"0.59698224\",\n \"text\": \"def on_receive(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d36849c4fc30963fef41223cbe28fc\",\n \"score\": \"0.59584075\",\n \"text\": \"def receive(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22054425d918fa83f76d6b8216850afc\",\n \"score\": \"0.5957546\",\n \"text\": \"def _read_line(self):\\n print('My brand!')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c71ae90cb8006f9e79602db6787966\",\n \"score\": \"0.5952475\",\n \"text\": \"def get_line(self):\\r\\n line = self.pgn_file.readline()\\r\\n if isinstance(line,bytes):\\r\\n line = line.decode(\\\"utf-8\\\")\\r\\n if line == \\\"\\\":\\r\\n self.current_path_index += 1\\r\\n if self.current_path_index >= len(self.pgn_paths):\\r\\n self.current_path_index = 0\\r\\n self.get_new_file(self.current_path_index)\\r\\n line = self.pgn_file.readline()\\r\\n self.current_line = line[:-1].split()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403bc89a0d60d1a285aea73266f294ef\",\n \"score\": \"0.5949775\",\n \"text\": \"def read_line():\\n # try reading a line, removing any extra whitespace\\n try:\\n line = sub.sys.stdin.readline().strip()\\n # i3status sends EOF, or an empty line\\n if not line:\\n sub.sys.exit(3)\\n return line\\n # exit on ctrl-c\\n except KeyboardInterrupt:\\n sub.sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4575ff26c1399669d9d0f3adfd8d7964\",\n \"score\": \"0.5944485\",\n \"text\": \"async def printLine(self, line):\\n sender = self.fromParty.protocol.formatSender(self.fromParty.alias)\\n text = sender + self.toParty.protocol.format(line)\\n \\n userColor = self.fromParty.user.profile.color\\n if userColor:\\n text = self.toParty.protocol.format(text, color=userColor)\\n \\n await self.messageReceiver(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a7cb8991b880c9990c7cf00ea471cd\",\n \"score\": \"0.5940643\",\n \"text\": \"def do_chat(self, line):\\n if len(line) == 0:\\n return self.do_help('chat')\\n\\n self.chat(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6629ed0554b2e7a4d930ba775949837\",\n \"score\": \"0.59335047\",\n \"text\": \"def on_new_line(self, line, is_full_line):\\n try:\\n self._parse_failure_indication(line)\\n self._send_commands_before_establish_connection_if_requested(line, is_full_line)\\n self._send_login_if_requested(line)\\n self._send_password_if_requested(line)\\n self._just_connected(line)\\n self._send_commands_after_establish_connection_if_requested(line, is_full_line)\\n self._settings_after_login(line, is_full_line)\\n self._detect_prompt_after_exception(line)\\n except ParsingDone:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a93215b74fa57b3dbd922de78d60f9\",\n \"score\": \"0.5921312\",\n \"text\": \"def data_received(self, data):\\n self.buffer += data\\n pts = self.buffer.split(b\\\"\\\\n\\\")\\n self.buffer = pts.pop()\\n for el in pts:\\n asyncio.ensure_future(self.client.data_received(el))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2d9e4cf68c394d3fac603adc442e55\",\n \"score\": \"0.5918216\",\n \"text\": \"def write_line(self, line):\\n\\t\\tif not self.is_alive:\\n\\t\\t\\treturn False\\n\\t\\ttry:\\n\\t\\t\\tself.command_process.stdin.write(\\\"SEND %s\\\\n\\\" % (line,))\\n\\t\\t\\tself.command_process.stdin.flush()\\n\\t\\texcept (OSError, IOError):\\n\\t\\t\\tself.kill()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa7d602ca463d2eeeebb310944510c3\",\n \"score\": \"0.59146094\",\n \"text\": \"def handle(line, mc):\\n match = linere.match(line)\\n if match:\\n fields = match.groupdict()\\n mc.call_hooks('all', fields=fields)\\n print(\\\"<\\\", fields['message'], file=sys.stderr)\\n\\n match = chatre.match(fields['message'])\\n if match:\\n chat = match.groupdict()\\n if chat['message'][0] == \\\"!\\\":\\n command = chat['message'][1:].split()\\n mc.call_hooks(\\n 'command',\\n command=command[0],\\n args=command[1:],\\n player=chat['player'],\\n )\\n else:\\n mc.call_hooks(\\n 'chat',\\n message=chat['message'],\\n player=chat['player'],\\n )\\n\\n match = sayre.match(fields['message'])\\n if match:\\n say = match.groupdict()\\n mc.call_hooks('say', message=say['message'])\\n\\n match = joinleftre.match(fields['message'])\\n if match:\\n joinleft = match.groupdict()\\n if joinleft['change'] == \\\"joined\\\":\\n mc.call_hooks('join', player=joinleft['player'])\\n mc.players.add(joinleft['player'])\\n else:\\n mc.call_hooks('leave', player=joinleft['player'])\\n mc.players.remove(joinleft['player'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c53d94d2c6183101ab9dc571d6e9b30\",\n \"score\": \"0.59130716\",\n \"text\": \"def process_received_message(self, msg):\\n if msg[2:3] == ':':\\n cmd = msg[0:2]\\n payload = msg[3:].replace('\\\\r', '')\\n else:\\n self.log.warning(\\\"Received malformed message: %s\\\", msg)\\n return\\n\\n # Can't use try since it swallows too many errors for now\\n if cmd in self.fast_commands:\\n self.fast_commands[cmd](payload)\\n else:\\n self.log.warning(\\\"Received unknown serial command? %s. (This is ok\\\"\\n \\\" to ignore for now while the FAST platform is in \\\"\\n \\\"development)\\\", msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7885955291760723d25b2a06a706477\",\n \"score\": \"0.5889952\",\n \"text\": \"def end_of_line(event: E) -> None:\\n buff = event.current_buffer\\n buff.cursor_position += buff.document.get_end_of_line_position()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32b950eec7b5f91310e1d0af563dc98\",\n \"score\": \"0.58889556\",\n \"text\": \"def get_buffer_line(self, line):\\r\\n return self.screen.get_buffer_line(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb9ae1d6e645d7aeacef760a72763e0\",\n \"score\": \"0.58860815\",\n \"text\": \"def complete(self, line):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec9456633a566b030660a2da7cc692c\",\n \"score\": \"0.5879773\",\n \"text\": \"def get_line(self):\\n line_str = None\\n while supervisor.runtime.serial_bytes_available:\\n ch = sys.stdin.read(1)\\n if self.escape:\\n # continue the escape sequence\\n self._continue_escape(ch)\\n \\n # handle escape\\n elif ch[0] == '\\\\x1b':\\n # start the escape sequence\\n self.escape = Console.escape_tree\\n \\n # handle backspace\\n elif ch[0] == '\\\\x7f' or ch[0] == '\\\\x08':\\n if self.cursor > 0:\\n current_cursor = self.cursor\\n self.cursor -= 1\\n self._delete_and_show(current_cursor)\\n \\n # handle newline\\n elif ch[0] == '\\\\n':\\n print('')\\n if self.inbuffer:\\n self._add_to_history(self.inbuffer)\\n line_str = self.inbuffer\\n self._reset_inbuffer()\\n self.history_pos = 1\\n break\\n \\n # handle normal characters\\n else:\\n self._insert_and_show(ch)\\n \\n return line_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7d581c4e3426dc8f7f254aae492b91\",\n \"score\": \"0.58662874\",\n \"text\": \"def onReceived(self, payload, timestamp=None):\\n\\n # assert payload contains 3 digit opcode\\n assert len(payload) >= 3, \\\"Invalid request length\\\"\\n\\n # parse the incoming message into the format [opcode|args] with opcode as a 3 digit integer\\n opcode, args = payload[:3], payload[3:]\\n\\n # assert the opcode is available\\n assert opcode in self.commands, f'Invalid request OPCODE = \\\"{opcode}\\\"'\\n\\n # assert waiting for a file before receiving a segment\\n assert (not self.fileToReceive) or (\\n self.fileToReceive and opcode == b'212'), \\\"Cannot make another request while receiving a file\\\"\\n\\n # super().onReceived(payload)\\n\\n # executing the command requested by the client\\n try:\\n self.commands[opcode](args, timestamp)\\n\\n except AssertionError as e:\\n print(f'[{self.name}]:', e)\\n self.sendMessage(f'400 ' + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31ec07f18e36c21b8a86d274b269b007\",\n \"score\": \"0.58596385\",\n \"text\": \"def dataReceived(self, data):\\r\\n\\t\\tself.dataBuffer.append(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab98c7ab104c3b372f19369cffe45a8e\",\n \"score\": \"0.58592516\",\n \"text\": \"def next_line(self, line):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8a93cd2e68cad757767cb0d0a2a31c\",\n \"score\": \"0.5853252\",\n \"text\": \"def onRAW(self, sock, line):\\n\\n\\t\\tif self.__states[sock] == self.COMMAND:\\n\\t\\t\\tif not line:\\n\\t\\t\\t\\tself.write(sock, \\\"500 Syntax error, command unrecognized\\\\r\\\\n\\\")\\n\\t\\t\\t\\treturn\\n\\n\\t\\t\\tmethod = None\\n\\n\\t\\t\\ti = line.find(\\\" \\\")\\n\\t\\t\\tif i < 0:\\n\\t\\t\\t\\tcommand = line.upper()\\n\\t\\t\\t\\targ = None\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcommand = line[:i].upper()\\n\\t\\t\\t\\targ = line[i+1:].strip()\\n\\n\\t\\t\\tmethod = getattr(self, \\\"smtp\\\" + command, None)\\n\\n\\t\\t\\tif not method:\\n\\t\\t\\t\\tself.write(sock, \\\"502 Command not implemented\\\\r\\\\n\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tmethod(sock, arg)\\n\\t\\telse:\\n\\t\\t\\tif self.__states[sock] != self.DATA:\\n\\t\\t\\t\\tself.write(sock, \\\"451 Internal confusion\\\\r\\\\n\\\")\\n\\t\\t\\t\\treturn\\n\\n\\t\\t\\tif line and re.match(\\\"^\\\\.$\\\", line):\\n\\t\\t\\t\\t\\tself.__data.flush()\\n\\t\\t\\t\\t\\tself.__data.seek(0)\\n\\t\\t\\t\\t\\tstatus = self.processMessage(sock,\\n\\t\\t\\t\\t\\t\\t\\tself.__mailfrom, self.__rcpttos, self.__data)\\n\\n\\t\\t\\t\\t\\tself.reset()\\n\\n\\t\\t\\t\\t\\tif not status:\\n\\t\\t\\t\\t\\t\\tself.write(sock, \\\"250 Ok\\\\r\\\\n\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tself.write(sock, status + \\\"\\\\r\\\\n\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif line and line[0] == \\\".\\\":\\n\\t\\t\\t\\t\\tself.__data.write(line[1:] + \\\"\\\\n\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.__data.write(line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b2edbad1f6d95ebdefcdc498a4e2f8\",\n \"score\": \"0.5851008\",\n \"text\": \"def first_line_received(self, line):\\n\\n line = line.decode('utf-8', 'replace')\\n m = self.first_line_re.match(line)\\n if m:\\n for name, value in m.groupdict().items():\\n if name == 'status_code':\\n value = int(value)\\n setattr(self, name, value)\\n else:\\n self.parse_error('invalid first line')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78baff1df0db041469c030766207a665\",\n \"score\": \"0.58450633\",\n \"text\": \"def sendCommandLine(self, line):\\n\\t\\tself.hostRobot.writeline(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8219a73ab38cfb8eb916708ed9aabda\",\n \"score\": \"0.58448005\",\n \"text\": \"def process_response(self, response):\\n match = self.RESPONSE_REGEX.search(response)\\n if match: # received text is a valid response to a line\\n # Pop oldest line\\n line = self.sent_lines.pop(0)\\n line.set_status(response)\\n\\n # Send next line (if possible)\\n self.poll_transmission()\\n\\n # Raiser exception on error\\n # IMPORTANT: must be the last thing this function does\\n if match.group('keyword').lower() == 'error':\\n raise GCodeStreamException(\\\"error on gcode: '{gcode}' {msg}\\\".format(\\n gcode=line.gcode,\\n msg=response\\n ))\\n\\n else:\\n raise GCodeStreamException(\\\"unidentified message: %s\\\" % response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04e39e5df1735f0ea73ea80bb874d2b\",\n \"score\": \"0.5839755\",\n \"text\": \"def recv_line(self, terminator=\\\"\\\\n\\\", strip=True, timeout=DEFAULT_TIMEOUT):\\n while terminator not in self.stream:\\n yield self._fetch()\\n index = self.stream.index(terminator)\\n line = self.stream[:index]\\n self.stream = self.stream[index+len(terminator):]\\n if strip: line = line.strip()\\n yield Return(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed22d936b32aa7635766df4769e2fe76\",\n \"score\": \"0.58389306\",\n \"text\": \"def read_line():\\n b = next_byte()\\n while b != b'\\\\x0a':\\n msg = msg + b.decode('ASCII')\\n return msg + '\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb8bd5bf1609c2d94ed5937f521e28c\",\n \"score\": \"0.58369267\",\n \"text\": \"def enable_receiving(self, type):\\n if type == 'a':\\n if os.linesep == '\\\\r\\\\n':\\n self.data_wrapper = lambda x: x\\n else:\\n self.data_wrapper = lambda x: x.replace('\\\\r\\\\n', os.linesep)\\n elif type == 'i':\\n self.data_wrapper = lambda x: x\\n else:\\n raise TypeError, \\\"Unsupported type\\\"\\n self.receive = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f915c8697e81b68656ee9646a62f6a7\",\n \"score\": \"0.5835847\",\n \"text\": \"def parse_line(self, line: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d89382eaaa7c14f8e393111aed0ef570\",\n \"score\": \"0.58292437\",\n \"text\": \"def process_input_line(self, line):\\r\\n #print \\\"input='%s'\\\"%self.input\\r\\n stdout = sys.stdout\\r\\n try:\\r\\n sys.stdout = self.cout\\r\\n self.IP.push_line(line)\\r\\n finally:\\r\\n sys.stdout = stdout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903dcb87655b183eae3af41935615047\",\n \"score\": \"0.582443\",\n \"text\": \"def _get_line_data(self):\\n\\n # check that client is connected\\n self._check_connection()\\n\\n data = b\\\"\\\"\\n while True:\\n data += self.socket.recv(self.__class__.CHIRP_LINE_MAX)\\n if data.decode()[-1] == \\\"\\\\n\\\":\\n break\\n return data.decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba50468d521590d1ed7e2a1fb059adcc\",\n \"score\": \"0.5822928\",\n \"text\": \"def read_line(self):\\n return self._read_until( '\\\\r', self.timeout )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360c320a6d7a10f749cef58a3fecbd52\",\n \"score\": \"0.5818135\",\n \"text\": \"def _lines_callback(self, msg):\\n if 'line' not in self._evaluated_classes:\\n return\\n # Add lock\\n self._lock += 1\\n # Get the measurement env for the image and class\\n measurement = self._get_image_measurement(int(msg.header.frame_id)).evaluations['line']\\n # Mark as received\\n measurement.received_message = True\\n # Measure duration of processing\\n measurement.duration = self._measure_timing(msg.header)\\n # Generating and matching masks\\n measurement.pixel_mask_rates = self._match_masks(\\n self._generate_line_mask_from_vectors(\\n Evaluator._extract_vectors_from_annotations(\\n self._images[int(msg.header.frame_id)]['annotations'],\\n typename='line'\\n ),\\n np.zeros(self._label_shape, dtype=np.uint8)),\\n self.bridge.imgmsg_to_cv2(msg, '8UC1'))\\n # Release lock\\n self._lock -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3dd204637b6e03de50bb1ddd948bc4\",\n \"score\": \"0.5806855\",\n \"text\": \"def onecmd(self, line):\\n cmd, arg, line = self.parseline(line)\\n if not line:\\n return self.emptyline()\\n if cmd is None:\\n return self.default(line)\\n self.lastcmd = line\\n if line == 'EOF' :\\n self.lastcmd = ''\\n if cmd == '':\\n return self.default(line)\\n else:\\n try:\\n func = getattr(self, 'do_' + cmd)\\n except AttributeError:\\n return self.default(line)\\n return func(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e5039c1935c8fb98ce6ba10abc046a\",\n \"score\": \"0.5801057\",\n \"text\": \"def receive(self, reply):\\n data = reply.readAll()\\n self.warning(\\\"!\\\", \\\"Received! %s\\\" % data)\\n self.hide()\\n self.finished.emit()\\n self.deleteLater()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f3bbc04c0ba318d393a718117ec215\",\n \"score\": \"0.5796391\",\n \"text\": \"def get_line():\\n line = instream.readline()\\n # 'U' mode works only with text files\\n if line.endswith(b\\\"\\\\r\\\\n\\\"):\\n lineends[b\\\"\\\\r\\\\n\\\"] += 1\\n elif line.endswith(b\\\"\\\\n\\\"):\\n lineends[b\\\"\\\\n\\\"] += 1\\n elif line.endswith(b\\\"\\\\r\\\"):\\n lineends[b\\\"\\\\r\\\"] += 1\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c12fa92a88f6be62380af032ded27d1\",\n \"score\": \"0.5796175\",\n \"text\": \"def signalClientOutput(self, line):\\n self.clientOutput.emit(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c52b419f546de485bb98480127d2304\",\n \"score\": \"0.57920325\",\n \"text\": \"def receiveSegment(self, args, timestamp):\\n # example: 212001\\\\x1cDATASEGMENT\\n # args: mytext\\\\x1ctxt -> split with \\\\x1c -> [mytext, txt, 200]\\n\\n assert self.fileToReceive is not None, \\\"Server not expecting file\\\"\\n assert self.fileToReceive['received'] != self.fileToReceive['total'], \\\"Received all segments\\\"\\n\\n data = args\\n # save the segment data\\n self.fileToReceive['segments'].append(data)\\n self.fileToReceive['timestamps'].append(timestamp)\\n self.fileToReceive['received']+=1\\n\\n self.fileToReceive['rate'] = getAverageRate(\\n sampleNumber=self.fileToReceive['received'],\\n oldAverage=self.fileToReceive['rate'],\\n newSampleSize=len(data),\\n duration= timestamp - self.fileToReceive['timestamps'][-2]\\n )\\n\\n print(f'[{self.name}]:',\\n f'Receiving file [{self.fileToReceive[\\\"name\\\"]}.{self.fileToReceive[\\\"type\\\"]}] - ({self.fileToReceive[\\\"received\\\"]}/{self.fileToReceive[\\\"total\\\"]}) - {round(self.fileToReceive[\\\"rate\\\"])} bps ')\\n\\n self.sendMessage('100 received')\\n if self.fileToReceive['received'] == self.fileToReceive['total']:\\n data = compileData(self.fileToReceive['segments'])\\n writeFile(self.fileToReceive['name'], self.fileToReceive['type'], FILE_PATH, data)\\n\\n print(f'[{self.name}]:',\\n f'file [{self.fileToReceive[\\\"name\\\"]}.{self.fileToReceive[\\\"type\\\"]}] Received successfully')\\n\\n self.fileToReceive = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d35952679dc2eb7ed5a03dcbbcad047\",\n \"score\": \"0.5788396\",\n \"text\": \"def _onInputActivity(self, fd, ev):\\n self._read()\\n if self.inBuf:\\n active, ret = self.onReceiveCB(self)\\n if not active:\\n self.inBuf = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ae66f63a6ff33174d14f4806f47c29\",\n \"score\": \"0.5780222\",\n \"text\": \"def read(self):\\n p=self.packet()\\n p.read_line()\\n ans=yield p.send()\\n returnValue(ans.read_line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03687bef4c9b02efdb9334bcbc099c77\",\n \"score\": \"0.57705706\",\n \"text\": \"def read_line(line_amount, data_socket):\\n line = ''\\n for i in range(0, line_amount):\\n\\n char = next_byte(data_socket).decode()\\n while char != '\\\\n':\\n line = line + char\\n char = next_byte(data_socket).decode()\\n\\n line += '\\\\r\\\\n'\\n\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd2359131a9920ebc09714ba42ecd80\",\n \"score\": \"0.5769172\",\n \"text\": \"def receiveSegment(self, args, timestamp):\\n # example: 212001\\\\x1cDATASEGMENT\\n # args: mytext\\\\x1ctxt -> split with \\\\x1c -> [mytext, txt, 200]\\n\\n assert self.fileToReceive is not None, \\\"Server not expecting file\\\"\\n assert self.fileToReceive['received'] != self.fileToReceive['total'], \\\"Received all segments\\\"\\n\\n # find index of first separator\\n separateAt = args.find(self.SEPERATOR)\\n assert separateAt != -1, \\\"Expected 2 arguments: sequence number, data\\\"\\n # split args\\n seqNum, data = [args[:separateAt], args[separateAt+1:]]\\n seqNum = int(seqNum.decode('utf-8'))\\n\\n assert seqNum < self.fileToReceive['total'], \\\"Sequence number not in range\\\"\\n assert self.fileToReceive['segments'][seqNum] is None, \\\"Segment already received\\\"\\n if self.fileToReceive['segments'][seqNum] is not None:\\n self.sendMessage(f'100{seqNum}')\\n return\\n\\n # increment the number of segments received\\n self.fileToReceive[\\\"received\\\"] += 1\\n # save the segment data\\n self.fileToReceive['segments'][seqNum] = data\\n # add the timestamp of arrival of segment\\n self.fileToReceive['timestamps'].append(timestamp)\\n\\n self.fileToReceive['rate'] = getAverageRate(\\n sampleNumber=self.fileToReceive['received'],\\n oldAverage=self.fileToReceive['rate'],\\n newSampleSize=len(data),\\n duration=timestamp - self.fileToReceive['timestamps'][-2]\\n )\\n\\n print(f'[{self.name}]:',\\n f'Receiving file [{self.fileToReceive[\\\"name\\\"]}.{self.fileToReceive[\\\"type\\\"]}] - ({self.fileToReceive[\\\"received\\\"]}/{self.fileToReceive[\\\"total\\\"]}) - {round(self.fileToReceive[\\\"rate\\\"])} bps ')\\n\\n self.sendMessage(f'100{seqNum}')\\n\\n if self.fileToReceive['received'] == self.fileToReceive['total']:\\n data = compileData(self.fileToReceive['segments'])\\n writeFile(self.fileToReceive['name'], self.fileToReceive['type'], FILE_PATH, data)\\n\\n # self.sendMessage(f'100')\\n print(f'[{self.name}]:',\\n f'file [{self.fileToReceive[\\\"name\\\"]}.{self.fileToReceive[\\\"type\\\"]}] Received successfully')\\n\\n self.fileToReceive = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1039393b7209602ece90a9986e2d3e9f\",\n \"score\": \"0.5768869\",\n \"text\": \"def read_one_line():\\r\\n newline_received = False\\r\\n message = \\\"\\\"\\r\\n while not newline_received:\\r\\n character = client_socket.recv(1).decode()\\r\\n if character == '\\\\n':\\r\\n newline_received = True\\r\\n elif character == '\\\\r':\\r\\n pass\\r\\n else:\\r\\n message += character\\r\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926db70b376b7be06f70dd1eca9599eb\",\n \"score\": \"0.5756967\",\n \"text\": \"def read_line(self):\\n buffer = bytes()\\n max_counter = 100\\n current_counter = 0\\n line = bytes()\\n while True:\\n rc, data = win32file.ReadFile(self.http_usb_handle, 1)\\n #log.debug(\\\"read_line rc = {}\\\".format(rc))\\n if len(data) == 0:\\n current_counter += 1\\n if current_counter >= max_counter:\\n break\\n line += data\\n if data == b'\\\\n':\\n break\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc92d984a64467f855a93b24b2d9652\",\n \"score\": \"0.5756349\",\n \"text\": \"def dataReceived(self, data):\\r\\n\\t\\tself.stompBuffer.appendData(data.replace('\\\\0', '\\\\0\\\\n'))\\r\\n\\t\\t\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tmsg = self.stompBuffer.getOneMessage()\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif self.stompBuffer.buffer.startswith('\\\\n'):\\r\\n\\t\\t\\t\\tself.stompBuffer.buffer = self.stompBuffer.buffer[1:]\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif msg is None:\\r\\n\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\r\\n\\t\\t\\treturned = self.mp.react(msg)\\r\\n\\t\\t\\tif returned:\\r\\n\\t\\t\\t\\tself.transport.write(returned)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07bc10dc76fda5f7cfb6fda03992cc7e\",\n \"score\": \"0.5750866\",\n \"text\": \"def __process_trailer(self, line):\\n if len(line) > 0:\\n return\\n self.message_end_received()\\n self.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96fa84a3e3810d35b4231c85f537995\",\n \"score\": \"0.5738555\",\n \"text\": \"def __header_line_received(self, line):\\n if line[0] in b\\\" \\\\t\\\":\\n if self.__last_header_field:\\n self.__last_header_field.value += line.strip()\\n else:\\n self.parse_error('first header field cannot be a continuation')\\n else:\\n self.__header_field_received(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"040628e94b4d8e9d2f88393fc65ccd54\",\n \"score\": \"0.5736089\",\n \"text\": \"def enter(self,e):\\n string = self.ttyText.get(1.0, tk.END)[self.line_start:]\\n self.line_start+=len(string)\\n self.p.stdin.write(string.encode())\\n self.p.stdin.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9ef4679ddf5af50c071c0a91ecf457\",\n \"score\": \"0.57179374\",\n \"text\": \"def _write_line(self, line_for_send):\\n self.telnet.write(line_for_send.encode('ascii') + b'\\\\n')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e717cdaecfffd96eb5e40e86770e0e\",\n \"score\": \"0.5717294\",\n \"text\": \"def output_callback(self, line):\\n self.interrupted = False\\n self.cfg.match(line, print, **{'end': ''})\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65397c0d1401b45a632ccfc0ed7e0d9e\",\n \"score\": \"0.57154626\",\n \"text\": \"def get_line(self):\\n pass # implemented in Ada\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48cb90d25afe8cee3c990da3cc6025c5\",\n \"score\": \"0.57089055\",\n \"text\": \"def readline(self, timeout=0.5):\\n poll_result = self.poller.poll(timeout)\\n if poll_result:\\n line = self.io.readline().strip()\\n if self.io == sys.stdin and line == '[':\\n # skip first event line wrt issue #19\\n line = self.io.readline().strip()\\n try:\\n # python3 compatibility code\\n line = line.decode()\\n except (AttributeError, UnicodeDecodeError):\\n pass\\n return line\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9cc15df76f4d5079837d3cc22a796a7\",\n \"score\": \"0.5708118\",\n \"text\": \"def get_current_line(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5df7dd601ddc76626305b5e0c4f12a\",\n \"score\": \"0.57072186\",\n \"text\": \"def read_line():\\n # try reading a line, removing any extra whitespace\\n try:\\n line = sys.stdin.readline().strip()\\n # i3status sends EOF, or an empty line\\n if not line:\\n sys.exit(3)\\n return line\\n # exit on ctrl-c\\n except KeyboardInterrupt:\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5df7dd601ddc76626305b5e0c4f12a\",\n \"score\": \"0.57072186\",\n \"text\": \"def read_line():\\n # try reading a line, removing any extra whitespace\\n try:\\n line = sys.stdin.readline().strip()\\n # i3status sends EOF, or an empty line\\n if not line:\\n sys.exit(3)\\n return line\\n # exit on ctrl-c\\n except KeyboardInterrupt:\\n sys.exit()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993691,"cells":{"query_id":{"kind":"string","value":"147bd0f59ffe20aebac804e6ad054b7d"},"query":{"kind":"string","value":"Get for all complete groups an array of shrinkages"},"positive_passages":{"kind":"list like","value":[{"docid":"2375913ef054d497d82a1003805966db","score":"0.5447506","text":"def __get_shrinkage_factor(self, X_group):\n group_colnames = X_group.columns.to_list()\n counts = X_group.groupby(group_colnames).size()\n\n # Groups that are split on all\n most_granular_groups = [\n grp for grp in self.groups_ if len(as_list(grp)) == len(group_colnames)\n ]\n\n # For each hierarchy level in each most granular group, get the number of observations\n hierarchical_counts = {\n granular_group: [\n counts[tuple(subgroup)].sum()\n for subgroup in expanding_list(granular_group, tuple)\n ]\n for granular_group in most_granular_groups\n }\n\n # For each hierarchy level in each most granular group, get the shrinkage factor\n shrinkage_factors = {\n group: self.shrinkage_function_(counts, **self.shrinkage_kwargs)\n for group, counts in hierarchical_counts.items()\n }\n\n # Make sure that the factors sum to one\n shrinkage_factors = {\n group: value / value.sum() for group, value in shrinkage_factors.items()\n }\n\n return shrinkage_factors","title":""}],"string":"[\n {\n \"docid\": \"2375913ef054d497d82a1003805966db\",\n \"score\": \"0.5447506\",\n \"text\": \"def __get_shrinkage_factor(self, X_group):\\n group_colnames = X_group.columns.to_list()\\n counts = X_group.groupby(group_colnames).size()\\n\\n # Groups that are split on all\\n most_granular_groups = [\\n grp for grp in self.groups_ if len(as_list(grp)) == len(group_colnames)\\n ]\\n\\n # For each hierarchy level in each most granular group, get the number of observations\\n hierarchical_counts = {\\n granular_group: [\\n counts[tuple(subgroup)].sum()\\n for subgroup in expanding_list(granular_group, tuple)\\n ]\\n for granular_group in most_granular_groups\\n }\\n\\n # For each hierarchy level in each most granular group, get the shrinkage factor\\n shrinkage_factors = {\\n group: self.shrinkage_function_(counts, **self.shrinkage_kwargs)\\n for group, counts in hierarchical_counts.items()\\n }\\n\\n # Make sure that the factors sum to one\\n shrinkage_factors = {\\n group: value / value.sum() for group, value in shrinkage_factors.items()\\n }\\n\\n return shrinkage_factors\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"71185b4db892fbe483f9ea0dd369d699","score":"0.6408654","text":"def optimal_solution(self, groupSizes: List[int]) -> List[List[int]]:\n buckets = defaultdict(list)\n for i, size in enumerate(groupSizes):\n buckets[size].append(i)\n result = []\n for size, bucket in buckets.items():\n if len(bucket) > size:\n placeholder = []\n while len(bucket) > size:\n placeholder.append(bucket.pop())\n if len(placeholder) == size:\n result.append(placeholder)\n placeholder = []\n result.append(bucket)\n return result","title":""},{"docid":"54aefebea77541718d0bd6eef8024472","score":"0.6142382","text":"def finalize(self) -> List[Tuple[Set[int], float]]:\n allocation, total_bids = self._maximize_welfare(self.all_bids)\n return [\n (\n items,\n # p_i; if/else not necessary but improves performance\n self._maximize_welfare(self.all_bids[:i] + self.all_bids[i+1:])[1] \\\n - (total_bids - b_i) if items else 0\n ) for i, (items, b_i) in enumerate(allocation)\n ]","title":""},{"docid":"f35591f60ff1d24dcb29ca814c40f1bd","score":"0.6078587","text":"def groupThePeople(self, groupSizes: List[int]) -> List[List[int]]:\n return self.optimal_solution(groupSizes)","title":""},{"docid":"f349147348bc957f4d867d3124c62ef0","score":"0.5844362","text":"def initial_solution(self, groupSizes: List[int]) -> List[List[int]]:\n prev, so_far, result = 0, 0, []\n for p, size in sorted(zip(range(len(groupSizes)), groupSizes),\n key=lambda a: a[1]):\n if prev != size or so_far >= size:\n so_far = 0\n result.append([])\n result[-1].append(p)\n prev, so_far = size, so_far + 1\n return result","title":""},{"docid":"2d1fd0590f952d6cdc308fdc954ef647","score":"0.5642378","text":"def GetShrinkFactors(self):\n ...","title":""},{"docid":"5e9c16dd8c9451e5a664f568f5a153af","score":"0.5618619","text":"def divisor_subgroups(self):\n v = self.__H\n ans = []\n for M in self.level().divisors():\n w = [a % M for a in v if a%M]\n ans.append(GammaH_constructor(M, w))\n return ans","title":""},{"docid":"7d836615596a3ca95e461d6fcb4446ab","score":"0.54804164","text":"def worker_placement_groups(args: Args) -> List[ray.PlacementGroupID]:\n pgs = [ray.util.placement_group([{\"worker\": 1}]) for _ in range(args.num_reducers)]\n ray.get([pg.ready() for pg in pgs])\n try:\n yield pgs\n finally:\n for pg in pgs:\n ray.util.remove_placement_group(pg)","title":""},{"docid":"05a1c107ca2a1cb4c6ac20afefa68e6a","score":"0.5404229","text":"def summarize_gaps(values):\n\n complete_set = set(range(1, max(values) + 1))\n frame_set = set(values)\n full_set = list(complete_set.difference(frame_set))\n return summarize_list(full_set)","title":""},{"docid":"81c275858a9ec55c183668217369367d","score":"0.53388566","text":"def sub_scale(self, indices):\n sub_scale = []\n for item in indices:\n if item >= len(self.chromatic):\n pass\n else:\n sub_scale.append(self.chromatic[item])\n return sub_scale","title":""},{"docid":"8c0aa689ddcafc8afc075358be376149","score":"0.5262088","text":"def div_group(ls, g, t):\n\n if not isinstance(ls, list) or not isinstance(g, int) or not isinstance(t, int):\n return []\n\n ls_return = []\n\n for i in range(0, g * t, t):\n ls_return.append((ls[i:i + t]))\n\n if g * t < len(ls):\n for i in ls_return:\n for k in i:\n ls.remove(k)\n\n for i in ls:\n print('Team ' + str(i) + ' has been removed')\n logger.debug('Team ' + str(i) + ' has been removed')\n\n return ls_return","title":""},{"docid":"6d32222f949eeb0cd9b0612a22893249","score":"0.52050626","text":"def allowed_reductions(self):\n return (\n (i for i in range(1, self.size_x + 1) if self.size_x % i == 0),\n (i for i in range(1, self.size_y + 1) if self.size_y % i == 0),\n (i for i in range(1, self.size_z + 1) if self.size_z % i == 0)\n )","title":""},{"docid":"b1adef06b928542415c8f808d9f6744c","score":"0.5172568","text":"def scale_shape_group(shapes, x_amp, y_amp, x_offset, y_offset):\n for i in range(len(shapes)):\n shapes[i] = scale_shape(shapes[i], x_amp, y_amp, x_offset, y_offset)\n return shapes","title":""},{"docid":"a72d63bc97239fffbf5bdb426bab0f16","score":"0.5145254","text":"def get_group(r, group, ranges, r_factor):\n if not group:\n return\n out = []\n max_height = 0\n ranges.append(GrpRanges(r, []))\n curr_ranges = []\n for subgroup in group:\n elements, height = get_subgroup(r, subgroup, ranges, curr_ranges,\n r_factor)\n out.extend(elements)\n if height > max_height:\n max_height = height\n return out, max_height","title":""},{"docid":"ec5a9ccbe14ec836ca8420a4128c3957","score":"0.5119607","text":"def list_transformations(grouped_configs, max_energy, N, basis):\n \n transformation_list = []\n degen_list = []\n if max_energy == 0:\n max_energy = len(grouped_configs)\n for entry in grouped_configs[:max_energy]:\n single_list = []\n \n if max_energy == 1:\n if len(entry)>1:\n #index = 0\n #for num in entry[:-1]:\n #index = index + 1\n \n for index, num1 in enumerate(entry[:-1]):\n \n bas = basis.state(num1)\n \n for num2 in entry[index+1 : ]:\n bas2 = basis.state(num2)\n diff = bas - bas2\n \n nonzero = np.nonzero(diff)[0]\n if nonzero.size > N/2: #This is the piece that will throw out hamming distances greater than N/2\n continue\n \n else:\n substate1 = basis.index(bas[nonzero])\n substate2 = basis.index(bas2[nonzero])\n\n transformation = [nonzero, substate1, substate2]\n degen_list.append([num1, num2, transformation])\n single_list.append(transformation)\n\n transformation_list.append(single_list)\n else:\n if len(entry)>1:\n #index = 0\n #for num in entry[:-1]:\n #index = index + 1\n \n for index, num in enumerate(entry[:-1]):\n \n num = basis.state(num)\n \n for num2 in entry[index+1 : ]:\n num2 = basis.state(num2)\n diff = num - num2\n \n nonzero = np.nonzero(diff)[0]\n if nonzero.size > N/2: #This is the piece that will throw out hamming distances greater than N/2\n continue\n \n else:\n substate1 = basis.index(num[nonzero])\n substate2 = basis.index(num2[nonzero])\n\n transformation = [nonzero, substate1, substate2]\n single_list.append(transformation)\n\n transformation_list.append(single_list)\n \n return transformation_list, degen_list","title":""},{"docid":"69b428275c4e9ba52756b30aec6ffc05","score":"0.5099304","text":"def shrink(self):\n new_arr = [None] * (self.capacity // 2)\n for i in range(self.num_items):\n new_arr[i] = self.arr[i]\n self.arr = new_arr\n self.capacity = self.capacity // 2","title":""},{"docid":"4a3342edd15f241391f97a351dbcee1e","score":"0.5093482","text":"def reduce_halfspaces(halfspaces):\n reduced_halfspaces = set()\n for halfspace in halfspaces:\n reduced_halfspaces.add(tuple(halfspace))\n return np.array([list(i) for i in reduced_halfspaces])","title":""},{"docid":"91992521191b866b521143c088ae3e04","score":"0.50841415","text":"def _assemble_all_gages(self):\n gages_ld = []\n if self.gages:\n for gage in self.gages:\n gages_ld.append(self._assemble_gage(gage))\n return gages_ld","title":""},{"docid":"c0c7a9ebab0a65997cfbebd3095b8b18","score":"0.5081407","text":"def groupWorkouts(workouts):\n n_workouts = len(workouts)\n if n_workouts == 1:\n return [workouts]\n\n groups = []\n avail = [True]*n_workouts\n for i in xrange(n_workouts):\n if avail[i]:\n group = [workouts[i]]\n avail[i] = False\n else:\n continue\n for j in xrange(i+1, n_workouts):\n if avail[j]:\n dis = computeWorkoutsDis(workouts[i], workouts[j])\n if dis < 0.1:\n group.append(workouts[j])\n avail[j] = False\n groups.append(group)\n return groups","title":""},{"docid":"4183ec56689a197dd3f0873c58d2587f","score":"0.50549597","text":"def get_part_svg(elements, r_factor):\n if not elements:\n return []\n out = []\n radii = get_radii(elements, r_factor)\n ranges = []\n max_height = 0\n for r, element in zip(reversed(radii), reversed(elements)):\n group, height = get_group(r, element[1:], ranges, r_factor)\n out.extend(group)\n if height > max_height:\n max_height = height\n out.reverse()\n return out, max_height","title":""},{"docid":"ac69afc835eae5b5f73ddfcac2df5756","score":"0.50334626","text":"def apply_grouping(items, groupxs, axis=0):\n # Should there be a contiguous check here?\n # items_ = np.ascontiguousarray(items)\n return [items.take(xs, axis=axis) for xs in groupxs]","title":""},{"docid":"85a8925b55ca829496e2dd359de11a3a","score":"0.50334203","text":"def __predict_shrinkage_groups(self, X_group, X_value, method=\"predict\"):\n # DataFrame with predictions for each hierarchy level, per row. Missing groups errors are thrown here.\n hierarchical_predictions = pd.concat(\n [\n pd.Series(\n self.__predict_groups(\n X_group, X_value, level_columns, method=method\n )\n )\n for level_columns in self.group_colnames_hierarchical_\n ],\n axis=1,\n )\n\n # This is a Series with values the tuples of hierarchical grouping\n prediction_groups = pd.Series(\n [tuple(_) for _ in X_group.itertuples(index=False)]\n )\n\n # This is a Series of arrays\n shrinkage_factors = prediction_groups.map(self.shrinkage_factors_)\n\n # Convert the Series of arrays it to a DataFrame\n shrinkage_factors = pd.DataFrame.from_dict(shrinkage_factors.to_dict()).T\n return (hierarchical_predictions * shrinkage_factors).sum(axis=1)","title":""},{"docid":"362e99ec7c97d20b84daf05c97a92c75","score":"0.50243205","text":"def slice_wfd_area(nslice, target_map, scale_down_factor=0.2):\n # Make it so things still sum to one.\n scale_up_factor = nslice - scale_down_factor*(nslice-1)\n\n wfd = target_map['r'] * 0\n wfd_indices = np.where(target_map['r'] == 1)[0]\n wfd[wfd_indices] = 1\n wfd_accum = np.cumsum(wfd)\n split_wfd_indices = np.floor(np.max(wfd_accum)/nslice*(np.arange(nslice)+1)).astype(int)\n split_wfd_indices = split_wfd_indices.tolist()\n split_wfd_indices = [0] + split_wfd_indices\n\n all_scaled_down = {}\n for filtername in target_map:\n all_scaled_down[filtername] = target_map[filtername]+0\n all_scaled_down[filtername][wfd_indices] *= scale_down_factor\n\n scaled_maps = []\n for i in range(len(split_wfd_indices)-1):\n new_map = {}\n indices = wfd_indices[split_wfd_indices[i]:split_wfd_indices[i+1]]\n for filtername in all_scaled_down:\n new_map[filtername] = all_scaled_down[filtername] + 0\n new_map[filtername][indices] = target_map[filtername][indices]*scale_up_factor\n scaled_maps.append(new_map)\n\n return scaled_maps","title":""},{"docid":"a660ca103a8fc41de651ac42ee0bfb4f","score":"0.5007573","text":"def _partition_array_weight(a, k, w):\n\n ret = [Partition() for _ in range(k)]\n curr = 0\n for i in range(len(a)):\n if ret[curr].total + a[i] <= w:\n ret[curr].end = i\n ret[curr].total += a[i]\n elif curr >= k - 1:\n return None\n else:\n # Start new partition\n curr += 1\n ret[curr].end = i\n ret[curr].total = a[i]\n \n return ret","title":""},{"docid":"1b14fd41bb5a4f4dc3d0a1f4f3e1b164","score":"0.49978846","text":"def __check_shrinkage_func(self):\n group_lengths = [10, 5, 2]\n expected_shape = np.array(group_lengths).shape\n try:\n result = self.shrinkage(group_lengths)\n except Exception as e:\n raise ValueError(\n f\"Caught an exception while checking the shrinkage function: {str(e)}\"\n ) from e\n else:\n if not isinstance(result, np.ndarray):\n raise ValueError(\n f\"shrinkage_function({group_lengths}) should return an np.ndarray\"\n )\n if result.shape != expected_shape:\n raise ValueError(\n f\"shrinkage_function({group_lengths}).shape should be {expected_shape}\"\n )","title":""},{"docid":"38d0dd30fbfe91413018d999800cf4f0","score":"0.4989977","text":"def chunks(self, array, granular, arr_len):\n\t\tfor i in xrange(0, arr_len, granular):\n\t\t\tyield array[i:i+granular]","title":""},{"docid":"db26f14609808bce8459a5a49914acb6","score":"0.4978328","text":"def reduce_exemplar_set(self, m):\n for i, exemplar_set in enumerate(self.exemplar_sets):\n self.exemplar_sets[i] = exemplar_set[:m]\n return self.exemplar_sets","title":""},{"docid":"b4c6ce43e7f7a6071770c8207681b0df","score":"0.49737713","text":"def test_find_mssm_decay_groups(self):\n\n mssm = import_ufo.import_model('mssm')\n decay_mssm = decay_objects.DecayModel(mssm, True)\n decay_mssm.find_decay_groups()\n goal_groups = [[25, 35, 36, 37],\n [1000001, 1000002, 1000003, 1000004, 1000005, 1000006, 1000011, 1000012, 1000013, 1000014, 1000015, 1000016, 1000021, 1000022, 1000023, 1000024, 1000025, 1000035, 1000037, 2000001, 2000002, 2000003, 2000004, 2000005, 2000006, 2000011, 2000013, 2000015]]\n\n # find_decay_groups_general should be run automatically\n for i, group in enumerate(decay_mssm['decay_groups']):\n self.assertEqual(sorted([p.get('pdg_code') for p in group]),\n goal_groups[i])","title":""},{"docid":"0338b49c6dbc6bb7c4fbfc28c7e60398","score":"0.49721316","text":"def extract_structure(self):\n bursts = defaultdict(list)\n for i in range(1, self.k):\n burst_times = np.argwhere(self.q >= i).flatten()\n bid = 0\n start = burst_times[0]\n last_bt = start\n weight = self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\n for j in range(1, len(burst_times)):\n # print(burst_times[j], last_bt+1)\n if burst_times[j] == last_bt + 1:\n last_bt = burst_times[j]\n weight += self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\n else:\n bursts[i].append([bid, start, last_bt, weight])\n bid += 1\n start = burst_times[j]\n last_bt = start\n weight = self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\n bursts[i].append([bid, start, last_bt, weight])\n return bursts","title":""},{"docid":"2aaa5c1e66a18d9a9f924fe73467fc0e","score":"0.4957383","text":"def interval_partition(set_of_jobs):\n released = [] # holds index of resources in r that are free\n occupied = [] # holds index of resources in r that are occupied\n resources = [] # holds all resources as lists of jobs that they have\n\n quick_sort(set_of_jobs, 0, len(set_of_jobs) -1) # sort jobs by start time\n for j in set_of_jobs:\n # move all free resources in occupied to released\n released, occupied = adjust(j[0], released, occupied, resources)\n if released:\n r = released[0] # choose a resource that is free\n released.remove(r) # resource is no longer free\n else:\n r = len(resources)\n resources.append([]) # create a new resource to assign jobs to\n occupied.append(r) # move the resource into occupied\n resources[r].append(j) # assign this job to the resource\n return resources","title":""},{"docid":"54de3f210cfef862abd79475938288b5","score":"0.49391165","text":"def gramians(self):\n raise NotImplementedError","title":""},{"docid":"393c459578ad742b86674a45cd541ef4","score":"0.49327806","text":"def kListGenerator(groups, s=1):\n\n klist = []\n for i in groups:\n klist.append(s*len(i))\n return klist","title":""},{"docid":"3f164d2169c7b333d5f52a1fbccc1ba6","score":"0.49275732","text":"def shrink_mod_scale(x, scale):\n return [_x - _x % _s for _x, _s in zip(x, to_list(scale, 2))]","title":""},{"docid":"c124322e2f485766bd817cf1b04cdc47","score":"0.49249062","text":"def constituent_groups(self):\n return []","title":""},{"docid":"249ea79979813b222e95d638230f9919","score":"0.4922036","text":"def group_and_assign(df, groupby='condition', key='frac_mass'):\n dfs = []\n for g, d in df.groupby(groupby):\n _df = prot.viz.assign_rect_bounds(d, key)\n dfs.append(_df)\n return pd.concat(dfs)","title":""},{"docid":"1de2cb1643840e6a8ce38f44a8c3929b","score":"0.49206603","text":"def group_fn(self, sizes):\n raise NotImplementedError()","title":""},{"docid":"21cb4ab296b47c74568196183028693f","score":"0.49135706","text":"def create_groups(self):\n # compute histogram\n self.create_histogram()\n cl = dict()\n list_rows = self.band_matrix.index.values\n remaining = len(list_rows)\n\n list_sensitive_added = list()\n group_dict = dict()\n\n value = 1\n old_value = 0\n\n while value > 0:\n for j in self.csi.index.values:\n\n if self.check_if_already_in(j, list_sensitive_added):\n continue\n\n t = self.band_matrix.loc[j, :]\n\n cl[j] = list()\n\n index = numpy.where(list_rows == j)[0][0]\n\n pos_idx = index + 1\n neg_idx = index - 1\n counter = 0\n stop = 2 * self.alpha * self.p\n\n not_groupable = False\n\n while counter < stop:\n\n if neg_idx >= 0:\n counter += self.populate_cl(cl, j, list_rows[neg_idx])\n neg_idx -= 1\n\n if pos_idx < remaining:\n counter += self.populate_cl(cl, j, list_rows[pos_idx])\n pos_idx += 1\n\n if pos_idx == remaining and neg_idx == -1:\n\n if len(cl[j]) < self.p - 1:\n not_groupable = True\n\n break\n\n if not_groupable:\n continue\n\n list_similar = self.qid_similarity(cl[j], t)\n\n for idx in list_similar:\n if idx in self.sensitive_row:\n list_sensitive_added.append(idx)\n for si in self.sensitive_row[idx]:\n self.sensitive_histogram[si] -= 1\n\n if self.privacy_requisite_chek(remaining):\n\n for elem in list_similar:\n list_rows = list_rows[list_rows != elem]\n\n remaining = len(list_rows)\n # inserisco il gruppo nella struttura a dict\n group_dict[t.name] = list_similar\n pass\n\n else:\n for idx in list_similar:\n # print(list_sensitive_added, idx)\n if idx in self.sensitive_row:\n list_sensitive_added.remove(idx)\n for si in self.sensitive_row[idx]:\n self.sensitive_histogram[si] += 1\n continue\n\n value = 0\n\n for histogram_value in self.sensitive_histogram.items():\n value += histogram_value[1]\n\n if value > 0 and old_value == value:\n # l'istogramma non è cambiato quindi non posso migliorare nulla\n print(f\"Grado di privacy {self.p} non soddisfacibile\")\n return -1\n\n elif value > 0:\n old_value = value\n\n print(\"Grado di privacy soddisfacibile.\")\n\n counter = 0\n\n result = dict()\n\n for group in group_dict:\n counter += 1\n final_anonymized = pandas.DataFrame()\n # print(\"GRUPPO\", counter, \"con Sensitive Item\", self.sensitive_row[group])\n\n for row in group_dict[group]:\n new_row = self.band_matrix.iloc[lambda x: x.index == row]\n final_anonymized = final_anonymized.append(other=new_row)\n\n result[group] = final_anonymized\n # print(final_anonymized)\n\n final_anonymized = pandas.DataFrame()\n for non_sensitive_row_index in list_rows:\n new_row = self.band_matrix.iloc[lambda x: x.index == non_sensitive_row_index]\n final_anonymized = final_anonymized.append(other=new_row)\n\n # print(\"GRUPPO\", (counter + 1), \"non sensitive\")\n # print(final_anonymized)\n result[-1] = final_anonymized\n\n return result","title":""},{"docid":"60ad9af90fe5cc7d9ae4068624166c65","score":"0.49049625","text":"def getGroups():","title":""},{"docid":"f5386667e493b019611d7079e79f5e93","score":"0.4890527","text":"def capacity_values(self, qs=None, tasks=None, breakdown=False, graph=None):\n if qs is None: # Optionally BYOQS - bring your own queryset\n qs = self.all().prefetch_related('instances')\n instance_ig_mapping, ig_ig_mapping = self.capacity_mapping(qs=qs)\n\n if tasks is None:\n tasks = self.model.unifiedjob_set.related.related_model.objects.filter(\n status__in=('running', 'waiting'))\n\n if graph is None:\n graph = {group.name: {} for group in qs}\n for group_name in graph:\n self.zero_out_group(graph, group_name, breakdown)\n for t in tasks:\n # TODO: dock capacity for isolated job management tasks running in queue\n impact = t.task_impact\n if t.status == 'waiting' or not t.execution_node:\n # Subtract capacity from any peer groups that share instances\n if not t.instance_group:\n impacted_groups = []\n elif t.instance_group.name not in ig_ig_mapping:\n # Waiting job in group with 0 capacity has no collateral impact\n impacted_groups = [t.instance_group.name]\n else:\n impacted_groups = ig_ig_mapping[t.instance_group.name]\n for group_name in impacted_groups:\n if group_name not in graph:\n self.zero_out_group(graph, group_name, breakdown)\n graph[group_name]['consumed_capacity'] += impact\n if breakdown:\n graph[group_name]['committed_capacity'] += impact\n elif t.status == 'running':\n # Subtract capacity from all groups that contain the instance\n if t.execution_node not in instance_ig_mapping:\n if not t.is_containerized:\n logger.warning('Detected %s running inside lost instance, '\n 'may still be waiting for reaper.', t.log_format)\n if t.instance_group:\n impacted_groups = [t.instance_group.name]\n else:\n impacted_groups = []\n else:\n impacted_groups = instance_ig_mapping[t.execution_node]\n for group_name in impacted_groups:\n if group_name not in graph:\n self.zero_out_group(graph, group_name, breakdown)\n graph[group_name]['consumed_capacity'] += impact\n if breakdown:\n graph[group_name]['running_capacity'] += impact\n else:\n logger.error('Programming error, %s not in [\"running\", \"waiting\"]', t.log_format)\n return graph","title":""},{"docid":"cf4b834164834a82bc541494755c4148","score":"0.48843947","text":"def group_inds(self, values):\n results = []\n\n for i, v in enumerate(values):\n if i == 0:\n mini = maxi = v\n temp = [v]\n else:\n # SETS 4 CADENCE LIMIT\n if (np.abs(v-maxi) <= 3):\n temp.append(v)\n if v > maxi:\n maxi = v\n if v < mini:\n mini = v\n else:\n results.append(temp)\n mini = maxi = v\n temp = [v]\n \n # GETS THE LAST GROUP\n if i == len(values)-1:\n results.append(temp)\n\n return np.array(results)","title":""},{"docid":"e8e387caf976c38b620ee8ca28f38c66","score":"0.4881927","text":"def scaffold_to_smiles(all_smiles: List[str], use_indices: bool = False) -> Dict[str, Union[Set[str], Set[int]]]:\n scaffolds = defaultdict(set)\n for i, smiles in tqdm(enumerate(all_smiles), total=len(all_smiles)):\n scaffold = generate_scaffold(smiles)\n if use_indices:\n scaffolds[scaffold].add(i)\n else:\n scaffolds[scaffold].add(smiles)\n\n return scaffolds","title":""},{"docid":"54e22a73fb73768d7b99e76ab297d6f4","score":"0.4874075","text":"def get_weight(groups=None):\n ncat = len(groups)\n nitems = 0\n for group in groups:\n for items in group:\n temp = np.max(items)\n if temp > nitems:\n nitems = temp\n weight = np.zeros((ncat, nitems+1, nitems+1))\n for i in range(ncat):\n for group in groups[i]:\n for id1, id2 in itertools.product(group, group):\n if id1 != id2:\n weight[i, id1, id2] = 1\n norm = weight[i].sum(0)[:, np.newaxis]\n weight[i] /= norm\n\n return weight","title":""},{"docid":"d8678e1237ebb7d0c86f7c5df3b1e771","score":"0.48491552","text":"def _update_individual(X, W, R, gamma):\n subjs = len(X)\n S = []\n for i in range(subjs):\n S.append(RSRM._shrink(X[i] - W[i].dot(R), gamma))\n return S","title":""},{"docid":"e505b7e115bcda69934bdd7bfa54b845","score":"0.4835165","text":"def _make_chunks(self, chunk_size, holeNumbers):\n flag = np.concatenate(([True], holeNumbers[1:] != holeNumbers[:-1]))\n self._zmw_idx = np.flatnonzero(flag)\n self._chunks = []\n self._chunk_idx = []\n last_start_idx = 0\n k = 1\n while last_start_idx < self.nReads:\n last_start = self._zmw_idx[last_start_idx]\n next_start_idx = np.argmax(self._zmw_idx > k * chunk_size)\n if next_start_idx == 0:\n self._chunks.append((last_start, self.nReads - last_start))\n self._chunk_idx.append(\n self._zmw_idx[last_start_idx:] - last_start)\n break\n next_start = self._zmw_idx[next_start_idx]\n self._chunks.append((last_start, next_start - last_start))\n idx_sel = self._zmw_idx[last_start_idx:next_start_idx]\n self._chunk_idx.append(idx_sel - last_start)\n last_start_idx = next_start_idx\n k += 1","title":""},{"docid":"7ac71360ec8e462d4d45d280094900f8","score":"0.48279884","text":"def renumber_plateaus(array) -> list:\n group = 1\n seq = []\n for a, b in zip(array, array[1:]):\n seq.append((lambda x: group if x > 0 else 0)(a))\n if a == 1 and b == 0:\n group += 1\n return seq + [group * array[-1]]","title":""},{"docid":"1874523fc1157afad55402f9b5366091","score":"0.4820153","text":"def shard_list_uniform(input: Iterable[T], num_per_group: int) -> Sequence[Sequence[T]]:\n assert num_per_group >= 1, \"need a positive number of list elements per group\"\n input_list = list(input)\n length = len(input_list)\n if length == 0:\n return []\n\n num_groups = ceil(length / num_per_group)\n\n # this will be a floating point number, not an integer\n num_per_group_revised = length / num_groups\n\n output = []\n\n # leftover error, akin to Floyd-Steinberg dithering\n # (https://en.wikipedia.org/wiki/Floyd%E2%80%93Steinberg_dithering)\n residual = 0.0\n\n while input_list:\n current_num_per_group_float = num_per_group_revised + residual\n current_num_per_group_int = floor(num_per_group_revised + residual)\n residual = current_num_per_group_float - current_num_per_group_int\n\n # With floating point approximations, we might get really close but not quite, so we'll compensate\n # for this by saying if we're within epsilon of adding one more, we'll treat it as good enough.\n if residual > 0.99:\n current_num_per_group_int += 1\n residual = current_num_per_group_float - current_num_per_group_int\n\n output.append(input_list[:current_num_per_group_int])\n input_list = input_list[current_num_per_group_int:]\n\n return output","title":""},{"docid":"4ae8be22581cc36cbcfb60a823672df0","score":"0.48130313","text":"def getAllGroupFairness(_data,_sensi_att,_target_att,_reverse_atts,_cut_point,_sensi_bound):\n\n # get the protected group\n pro_index=[idx for idx,row in _data.iterrows() if row[_sensi_att] == _sensi_bound]\n # remove sensitive attribute\n input_scores=_data[_target_att]\n if _target_att in _reverse_atts:\n input_ranking=sorted(range(len(input_scores)), key=lambda k: input_scores[k])\n else:\n input_ranking=sorted(range(len(input_scores)), key=lambda k: input_scores[k], reverse=True)\n # get the maximum value first to run faster\n user_N=len(input_scores)\n pro_N=len(pro_index)\n\n max_rKL=measures.getNormalizer(user_N,pro_N,KL_DIVERGENCE) \n max_rND=measures.getNormalizer(user_N,pro_N,ND_DIFFERENCE)\n max_rRD=measures.getNormalizer(user_N,pro_N,RD_DIFFERENCE)\n \n gf_rKL=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,KL_DIVERGENCE,max_rKL)\n gf_rND=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,ND_DIFFERENCE,max_rND)\n gf_rRD=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,RD_DIFFERENCE,max_rRD)\n \n return pro_N, gf_rKL, gf_rND, gf_rRD","title":""},{"docid":"2613b905a4bb1bc9a90342a853d5d98c","score":"0.47948572","text":"def clean_group_volatility(groups):\n MOVEMENT_VOL = 3\n DEVIATION_VOL = 4\n group_ids = set([groups[k] for k in groups.keys()])\n for i in range(3, len(group_volatility)-1):\n r = group_volatility[i-3:i+1]\n for vol_type in [MOVEMENT_VOL, DEVIATION_VOL]:\n for k in group_ids:\n vol_range = [v[vol_type][k] for v in r]\n max_range = max(vol_range) - min(vol_range)\n diffs = map(lambda i: abs(i[0][vol_type][k] - i[1][vol_type][k]) / max_range, zip(r[1:], r)) if max_range else tuple([1.0] * 3)\n sq = (diffs[1] * diffs[2]) if (diffs[1] * diffs[2]) else 1.0\n\n # Check if squared normalized difference is out of proportion\n if diffs[0] / sq and diffs[0] / sq < .1 and sq > .85:\n group_volatility[i-1][vol_type][k] = r[-3][vol_type][k] + (r[-1][vol_type][k] - r[-3][vol_type][k]) / 2","title":""},{"docid":"fc56db6c028388357069b8f595ec9544","score":"0.4794661","text":"def all_metric(self,basegroup=4,upbeat=0):\n if self._all_metric:\n return self._all_metric\n L=self.all_permutations()\n S=[]\n for i in L:\n if i.beatscore==1.0 and i.basegroup==basegroup \\\n and i.upbeat==upbeat and i.extra_exclude()==False:\n S.append(i)\n self._all_metric = S\n return S","title":""},{"docid":"ed37f02b184e0bf7cc2b10ed6b5fa1e8","score":"0.47852597","text":"def divideSets(lst):\n ret = []\n for _ in xrange(numWorker):\n ret.append(set())\n index = 0\n while index < len(lst):\n ret[index%numWorker].add(lst[index])\n index += 1\n return ret","title":""},{"docid":"cf5a5f94ade0bc46bd0d0b7678160a6a","score":"0.47809753","text":"def gather(self):\r\n\t\t\r\n\t\t# begin list\r\n\t\tl = []\r\n\t\t\r\n\t\t# gather shelves\r\n\t\tl.append('shelves:')\r\n\t\tfor n,i in enumerate(self):\r\n\t\t\tl.append('[shelf ' + str(n) + ']')\r\n\t\t\tg = i.gather()\r\n\t\t\tfor j in g:\r\n\t\t\t\tl.append(j)\r\n\t\t\r\n\t\treturn l","title":""},{"docid":"cd68c8c3b72305984c851119bdc57ddf","score":"0.47806868","text":"def group(*groups):","title":""},{"docid":"23685f2c723786056a48e38b6c2ff084","score":"0.47771925","text":"def test_find_mssm_decay_groups_modified_mssm(self):\n\n mssm = import_ufo.import_model('mssm')\n particles = mssm.get('particles')\n no_want_particle_codes = [1000022, 1000023, 1000024, -1000024, \n 1000025, 1000035, 1000037, -1000037]\n no_want_particles = [p for p in particles if p.get('pdg_code') in \\\n no_want_particle_codes]\n\n for particle in no_want_particles:\n particles.remove(particle)\n\n interactions = mssm.get('interactions')\n inter_list = copy.copy(interactions)\n\n for interaction in inter_list:\n if any([p.get('pdg_code') in no_want_particle_codes for p in \\\n interaction.get('particles')]):\n interactions.remove(interaction)\n \n mssm.set('particles', particles)\n mssm.set('interactions', interactions)\n decay_mssm = decay_objects.DecayModel(mssm, True)\n\n decay_mssm.find_decay_groups()\n goal_groups = set([(25, 35, 36, 37),\n (1000001, 1000002, 1000003, 1000004, 1000005, \n 1000006, 1000021, 2000001, 2000002, 2000003, \n 2000004, 2000005, 2000006), \n (1000011, 1000012), \n (1000013, 1000014), \n (1000015, 1000016, 2000015), \n (2000011,), \n (2000013,)])\n\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\n group])) \\\n for group in decay_mssm['decay_groups']]),\n goal_groups)","title":""},{"docid":"b0d890d4a4de8b11a86542eafb823875","score":"0.47742474","text":"def n_groups():\n return 2","title":""},{"docid":"07da2f3b76e8354abeaabefed81886df","score":"0.4772879","text":"def block_groups(num_rows, num_groups):\n possible_seats = [[] for i in range(num_groups)]\n max_rows_per_group = num_rows / num_groups + 1\n for i in range(1, num_rows + 1):\n for j in range(0, 6):\n possible_seats[i / max_rows_per_group].append(6 * (i - 1) + j)\n\n for group in possible_seats:\n shuffle(group)\n return possible_seats","title":""},{"docid":"68dfee875c1fc8d84893bff225016368","score":"0.4760658","text":"def create_groupings():","title":""},{"docid":"8aa9e570d7941b4d490c2f9fd23996ea","score":"0.4750715","text":"def get_groups(self):\n groups = [k * np.ones(len(d)) for k, d in enumerate(self.datasets)]\n return np.concatenate(groups)","title":""},{"docid":"34c933826bf954ad0db83398a5b658d8","score":"0.4748252","text":"def split(self, **kwargs):\n groups = [] # tuples of (list, set) -- list of reactions, set of substance keys\n for i, r in enumerate(self.rxns):\n for gr, gs in groups: # check if reaction is part of group, break out\n rks = r.keys()\n group_found = False\n for k in rks:\n if k in gs:\n gr.append(i)\n gs.update(rks)\n group_found = True\n break\n if group_found:\n break\n else: # reaction did not fit any group\n groups.append(([i], set(r.keys())))\n # We might have too many groups as this point, we will now recursively fuse groups\n i = 0\n while True:\n for j in range(i + 1, len(groups)):\n if groups[i][1] & groups[j][1]: # do groups share a substance?\n groups[i][0].extend(groups[j][0])\n groups[i][1].update(groups[j][1])\n groups.pop(j)\n break\n else:\n i += 1\n if i >= len(groups):\n break\n return [\n self.__class__(\n [self.rxns[ri] for ri in gr],\n OrderedDict([(k, v) for k, v in self.substances.items() if k in gs]),\n **kwargs\n )\n for gr, gs in groups\n ]","title":""},{"docid":"cab78db2da6217dd3f36c29f7b086cf5","score":"0.47479752","text":"def balanced_repartition_per_step(size):\n array = [1/size for i in range(size)]\n return array","title":""},{"docid":"f1ff694b21c83a6fb3b807e7416aba86","score":"0.4746939","text":"def bufferFillPerGroup(entries, groups=None):\n def label(entry):\n return int(entry.strip().split()[1])\n\n # 'Enough' of a group means as many entries of that group as of the biggest group.\n labels = [label(e) for e in entries]\n if groups is None:\n groups = [[g] for g in set(labels)]\n group_sizes = [len([e for e in entries if label(e) in group]) for group in groups]\n m = max(group_sizes)\n\n # The strategy here is to iterate over each group until we have enough of that group.\n def group_iterator(entries, group):\n \"\"\"Iterates forever over the entries that are in group\"\"\"\n labels = [int(e.split()[1]) for e in entries]\n relevant = [e for e in entries if label(e) in group]\n i = 0\n while(True):\n yield(relevant[i])\n i += 1\n if i == len(relevant):\n i = 0\n\n new_entries = []\n for group in groups:\n print 'Generating group', group\n time.sleep(0.5)\n count = 0\n for entry in group_iterator(entries, group):\n new_entries.append(entry)\n count += 1\n if count >= m:\n break\n return new_entries","title":""},{"docid":"0f4798121d7f9ac219532e8fb73f0048","score":"0.4746029","text":"def _get_scales(w):\n if size_set == 'full':\n scales = (1, np.arange(1, 3), np.arange(1, 4), np.arange(1, 5))\n else:\n scales = (1, np.arange(1, 3))\n return scales","title":""},{"docid":"07ee02aba9379ff8f7dca67e068b884b","score":"0.47425914","text":"def summarize_for_greater_scale(data: np.ndarray, divide: int) -> np.ndarray:\n shape = data.shape\n out_shape = tuple(v // divide for v in shape)\n output = np.empty(out_shape)\n for y in range(0, shape[0], divide):\n for x in range(0, shape[1], divide):\n output[y // divide, x // divide] = data[y:y + divide, x:x + divide].sum()\n return output","title":""},{"docid":"ec24bd0237c8432dc9002cba52158e71","score":"0.4742162","text":"def test_find_mssm_decay_groups_general(self):\n\n mssm = import_ufo.import_model('mssm')\n decay_mssm = decay_objects.DecayModel(mssm, True)\n # Read data to find massless SM-like particle\n param_path = os.path.join(_file_path,\n '../input_files/param_card_mssm.dat')\n decay_mssm.read_param_card(param_path)\n\n # In first group, 15 and from 23 are calculated,\n # others are massless default.\n goal_groups = [[1,2,3,4,11,12,13,14, 15, 16,21,22, 23, 24, 25, 35, 36, 37], \n [1000001, 1000002, 1000003, 1000004, 1000011, 1000012, 1000013, 1000014, 1000015, 1000016, 1000021, 1000022, 1000023, 1000024, 1000025, 1000035, 1000037, 2000001, 2000002, 2000003, 2000004, 2000011, 2000013, 2000015],\n [1000005, 1000006, 2000005, 2000006], \n [5, 6]]\n goal_stable_particle_ids = set([(1,2,3,4,11,12,13,14,16,21,22),\n (5,),\n (1000022,)])\n for i, group in enumerate(decay_mssm.get('decay_groups')):\n pdg = sorted([p.get('pdg_code') for p in group])\n self.assertTrue( pdg in goal_groups)\n self.assertEqual(len(goal_groups), i+1)\n \n # Test if all useless interactions are deleted.\n for inter in decay_mssm['reduced_interactions']:\n self.assertTrue(len(inter['particles']))\n\n # Reset decay_groups, test the auto run from find_stable_particles\n decay_mssm['decay_groups'] = []\n \n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in plist])) for plist in decay_mssm.get('stable_particles')]), goal_stable_particle_ids)","title":""},{"docid":"4e483361bbf01020799cfa9aed064475","score":"0.47401917","text":"def GC_chunk(size, step):","title":""},{"docid":"b933fd4ea1bcaeccac940035d3a59a89","score":"0.47342616","text":"def reduce(self):\n self.stats['initial_complexity'] = self.complexity()\n\n moves = []\n\n while True:\n moves.append([])\n\n for cell in self.open(1):\n cell.value = next(iter(cell.values))\n self.stats['singles'] += 1\n moves[-1].append((tuple(cell.coord), cell.value))\n\n for group in chain(self.blocks, self.columns, self.rows):\n group.reduce()\n\n if not next(self.open(1), None):\n break\n\n self.block_stats = reduce(operator.add, map(lambda x: x.stats, self.blocks), Counter())\n self.row_stats = reduce(operator.add, map(lambda x: x.stats, self.rows), Counter())\n self.column_stats = reduce(operator.add, map(lambda x: x.stats, self.columns), Counter())\n\n self.stats['final_complexity'] = self.complexity()\n self.stats['reduce'] += len(moves)\n\n return moves","title":""},{"docid":"ce96b8803285e305913a7b190bb3a196","score":"0.4732599","text":"def _group_gens(self):\n if (self.conductor() % 2) == 0:\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(0), ZZ(-1), ZZ(1), ZZ(0)] ]\n else:\n p = self.prime()\n if p == 2:\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(1), ZZ(0), ZZ(p), ZZ(1)] ]\n else:\n a = Zmod(p**(self.u() + 1))(ZZ(Zmod(p).unit_gens()[0]))\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(1), ZZ(0), ZZ(p), ZZ(1)],\n [ZZ(a), 0, 0, ZZ(~a)] ]","title":""},{"docid":"373594eefe1c00f3d82872cf124439e2","score":"0.47316873","text":"def _get_cut_data(ZZ, leaf_data, groups, bounds_min):\n ZZ = ZZ.sort_index()\n\n groups = [group for group in groups if group != ZZ.index.max()]\n\n levels = groups.copy()\n height_bounds = []\n for level in levels:\n h = ZZ.loc[level, \"height\"] if level in ZZ.index else 0.\n parent = (ZZ[\"children\"].apply(lambda children: level in children)\n .idxmax())\n parent_h = ZZ.loc[parent, \"height\"]\n height_bounds.append([h, parent_h])\n groups = [[group] for group in groups]\n\n for cluster, children in ZZ[\"children\"].items():\n if not all(child in levels for child in children):\n continue\n ks = [levels.index(child) for child in children]\n bounds = [height_bounds[k] for k in ks]\n h_min = max(bds[0] for bds in bounds)\n h_max = min(bds[1] for bds in bounds)\n dh = h_max - h_min\n if dh <= 0.:\n continue\n if dh < bounds_min and all(dh < bds[1] - bds[0] for bds in bounds):\n continue\n ks = sorted(ks, reverse=True)\n for k in ks:\n levels.pop(k)\n height_bounds.pop(k)\n levels.append(cluster)\n height_bounds.append([h_min, h_max])\n groups.append(\n list(itertools.chain.from_iterable(groups.pop(k) for k in ks)))\n heights = [sum(bds) / len(bds) for bds in height_bounds]\n\n heights = itertools.chain.from_iterable(\n [h] * len(gs) for gs, h in zip(groups, heights))\n heights = [[h, h] for h in heights]\n groups = list(itertools.chain.from_iterable(groups))\n\n breadths = []\n for group in groups:\n leaves = ZZ.loc[group, \"leaves\"] if group in ZZ.index else [group]\n b_min = leaf_data.loc[leaves[0], \"breadth\"] - 0.5\n b_max = leaf_data.loc[leaves[-1], \"breadth\"] + 0.5\n breadths.append([b_min, b_max])\n\n df = pandas.DataFrame({\"breadths\": breadths, \"heights\": heights},\n index=pandas.Index(groups, name=\"group\"))\n df.sort_index(inplace=True)\n\n return df","title":""},{"docid":"f99a11af3f9828476d5b054c5c4c138c","score":"0.4728363","text":"def reduce_for_guide_based_blending(multipanel, data):\r\n if isinstance(data, list):\r\n return [data[multipanel.reduced.ind_for_reduc[ind]] \\\r\n for ind in range(multipanel.reduced.n_panels)]\r\n\r\n return np.array([data[multipanel.reduced.ind_for_reduc[ind]] \\\r\n for ind in range(multipanel.reduced.n_panels)])","title":""},{"docid":"2e06735981e6512aec9cc63a60d26191","score":"0.47277448","text":"def chunks_arr(self, array, granular_array):\n\t\tindex = 0\n\t\tfor i in xrange(0, len(granular_array)):\n\t\t\tyield array[index : index + granular_array[i]]\n\t\t\tindex = index + granular_array[i]","title":""},{"docid":"03e253dd0a2966a9ca1e1f6553738df7","score":"0.4721469","text":"def get_expanded_params(self):\n\n QUANT = 8\n STRIDE = 2\n BOT_MUL = 1.0\n\n # Compute the block widths. Each stage has one unique block width\n widths_cont = np.arange(self.depth) * self.w_a + self.w_0\n block_capacity = np.round(np.log(widths_cont / self.w_0) / np.log(self.w_m))\n block_widths = (\n np.round(np.divide(self.w_0 * np.power(self.w_m, block_capacity), QUANT))\n * QUANT\n )\n num_stages = len(np.unique(block_widths))\n block_widths = block_widths.astype(int).tolist()\n\n # Convert to per stage parameters\n split_helper = zip(\n block_widths + [0],\n [0] + block_widths,\n block_widths + [0],\n [0] + block_widths,\n )\n splits = [w != wp or r != rp for w, wp, r, rp in split_helper]\n\n stage_widths = [w for w, t in zip(block_widths, splits[:-1]) if t]\n stage_depths = np.diff([d for d, t in enumerate(splits) if t]).tolist()\n\n strides = [STRIDE] * num_stages\n bot_muls = [BOT_MUL] * num_stages\n group_widths = [self.group_w] * num_stages\n\n # Adjust the compatibility of stage widths and group widths\n stage_widths, group_widths = _adjust_widths_groups_compatibilty(\n stage_widths, bot_muls, group_widths\n )\n\n return zip(stage_widths, strides, stage_depths, bot_muls, group_widths)","title":""},{"docid":"869f7a63ffdc89bc50d82085ba87e937","score":"0.47176874","text":"def heuristic_resize(self, percent=1.0):\n trimed = []\n t_cpu = 0\n t_mem = 0\n t_ubd = 0\n t_dbd = 0\n c_cpu = 0\n c_mem = 0\n c_ubd = 0\n c_dbd = 0\n\n # Calcute SRouters' total needs\n for sr in self.srouters:\n t_cpu += sr['cpu']\n t_mem += sr['mem']\n t_ubd += sr['ubandwidth']\n t_dbd += sr['dbandwidth']\n\n # Get minimun node set satisfying the needs\n s_states = sorted(self.original_states, key=lambda k: k['pref'], reverse=True)\n for n in s_states:\n trimed.append(n)\n c_cpu += n['cpu']\n c_mem += n['mem']\n c_ubd += n['ubandwidth']\n c_dbd += n['dbandwidth']\n if ( c_cpu >= t_cpu and c_mem >= t_mem and\n c_ubd >= t_ubd and c_dbd >= t_dbd ):\n break\n\n # back-off strategy, give extra node than needed.\n extra = int(math.ceil(len(trimed) * percent))\n trimed = s_states[:extra]\n self.states = trimed\n print \"Trimed node set:\", len(trimed)\n pass","title":""},{"docid":"1845c5a2c146fa1e8ab4525091cc6044","score":"0.47082856","text":"def _process_memory_breakdowns(self):\n self.breakdowns = [[] for _ in range(len(self._lifetime))]\n for index, breakdown in enumerate(self._lifetime):\n for t_id in breakdown:\n tensor = self.tensors.get(t_id)\n source_node = tensor.source_node\n if not source_node:\n continue\n node = self.nodes.get(source_node)\n tensor_dict = self._get_tensor_dict(node, tensor, t_id)\n self.breakdowns[index].append(tensor_dict)","title":""},{"docid":"3ac76cf8fb725acaf40117322448ac84","score":"0.47044617","text":"def sort_cells_into_lineages_chunked(props, scan_eps = 12, min_samples =10, \n init_group_size = 300, trailing_group_size = 120):\n\n def get_group_idx(t_frames):\n uniq_t_frames = np.unique(t_frames)\n t_frames_trailing = uniq_t_frames[uniq_t_frames > init_group_size]\n t_splits = np.array_split(t_frames_trailing, (len(t_frames_trailing)//trailing_group_size)+1)\n t_splits.insert(0,uniq_t_frames[uniq_t_frames <= init_group_size])\n group_idx = [t_frames.isin(ts).values.astype(np.uint16)*i for i,ts in enumerate(t_splits)]\n group_idx = np.sum(group_idx,axis=0)\n return group_idx\n \n def cluster_it(prop):\n\n group_idx = get_group_idx(prop.t_frame)\n gb = prop.groupby(group_idx)\n print(np.unique(prop.pos_num))\n print(\"mem used: %2.3f\" % (int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)/(10**6)))\n init_clusters = []\n cluster_cents = []\n cluster_cent_idcs = []\n \n for group in gb:\n chunk = np.transpose(np.vstack((group[1].centx,group[1].centy)))\n predicted_clusters = predict_clusters(chunk, scan_eps = scan_eps, min_samples=min_samples)\n max_cluster_idx = np.max([0]+init_clusters)\n predicted_clusters[predicted_clusters > -1] = predicted_clusters[predicted_clusters > -1] + max_cluster_idx\n init_clusters.extend(predicted_clusters.tolist())\n for pred_cluster in np.unique(predicted_clusters):\n cluster_cents.append(np.mean(chunk[predicted_clusters == pred_cluster],axis=0))\n cluster_cent_idcs.append(pred_cluster)\n\n cluster_cent_idcs_arr = np.array(cluster_cent_idcs)\n cluster_cents_arr = np.array(cluster_cents)[cluster_cent_idcs_arr > -1]\n cluster_cent_idcs_arr = cluster_cent_idcs_arr[cluster_cent_idcs_arr > -1]\n\n clustered_clusters = predict_clusters(cluster_cents_arr[:,0].reshape(-1,1),min_samples=7,scan_eps=4)\n init_clusters = np.array(init_clusters)\n for i,clust in enumerate(cluster_cent_idcs_arr):\n init_clusters[init_clusters == clust] = clustered_clusters[i] \n\n prop['lineage_idx'] = init_clusters\n return prop\n \n \n props = props.groupby(['lane_num','pos_num']).apply(lambda x: cluster_it(x))\n\n # remove cells that aren't in a lineage\n props = props[props.lineage_idx > -1]\n props['centy_flipped'] = (props['centy']*props['trench_inversion_mult'])\n props_sort = props.sort_values(['lane_num','pos_num','lineage_idx','t_frame','centy_flipped'])\n\n return props_sort","title":""},{"docid":"448ea5ac82f8eee90955d8da49a18cd7","score":"0.47004184","text":"def reduction(self):\n return DependenceGroup(i for i in self if i.is_reduction)","title":""},{"docid":"f1475d3eb838801b56bd1f97b253405c","score":"0.4700053","text":"def hyper_permute(self, all_intervals):\n pool = defaultdict(list)\n for key in all_intervals:\n base = key.split('_')[0]\n pool[base].append(key)\n\n hyperspaces = [{key: all_intervals[key] for key in keys} for keys in product(*pool.values())]\n print('Number of hyperparameter spaces to be distributed: {}'.format(len(hyperspaces)))\n return hyperspaces","title":""},{"docid":"7009882e4bab1ddf2e27d0fc05243432","score":"0.46947148","text":"def energy_groups(structure='wims69'):\n\n if structure == 'wims69':\n \"\"\" WIMS 69-group structure.\n\n Stammler, R. J., and M. J. Abbate. \"Methods of Steady-State Reactor\n Analysis in Nuclear Design.\" (1984).\n \"\"\"\n eb = np.array([1.00000000e+07, 6.06550000e+06, 3.67900000e+06,\n 2.23100000e+06, 1.35300000e+06, 8.21000000e+05,\n 5.00000000e+05, 3.02500000e+05, 1.83000000e+05,\n 1.11000000e+05, 6.73400000e+04, 4.08500000e+04,\n 2.47800000e+04, 1.50300000e+04, 9.11800000e+03,\n 5.53000000e+03, 3.51910000e+03, 2.23945000e+03,\n 1.42510000e+03, 9.06899000e+02, 3.67263000e+02,\n 1.48729000e+02, 7.55014000e+01, 4.80520000e+01,\n 2.77000000e+01, 1.59680000e+01, 9.87700000e+00,\n 4.00000000e+00, 3.30000000e+00, 2.60000000e+00,\n 2.10000000e+00, 1.50000000e+00, 1.30000000e+00,\n 1.15000000e+00, 1.12300000e+00, 1.09700000e+00,\n 1.07100000e+00, 1.04500000e+00, 1.02000000e+00,\n 9.96000000e-01, 9.72000000e-01, 9.50000000e-01,\n 9.10000000e-01, 8.50000000e-01, 7.80000000e-01,\n 6.25000000e-01, 5.00000000e-01, 4.00000000e-01,\n 3.50000000e-01, 3.20000000e-01, 3.00000000e-01,\n 2.80000000e-01, 2.50000000e-01, 2.20000000e-01,\n 1.80000000e-01, 1.40000000e-01, 1.00000000e-01,\n 8.00000000e-02, 6.70000000e-02, 5.80000000e-02,\n 5.00000000e-02, 4.20000000e-02, 3.50000000e-02,\n 3.00000000e-02, 2.50000000e-02, 2.00000000e-02,\n 1.50000000e-02, 1.00000000e-02, 5.00000000e-03,\n 1.00000000e-05])\n\n elif structure == 'tg0_625':\n \"\"\" Two-group stucture divided at 0.625. \"\"\"\n eb = np.array([1e7, 0.625, 1e-5])\n\n elif structure == 'wims56':\n \"\"\" WIMS 56-group structure.\n\n Stammler, R. J., and M. J. Abbate. \"Methods of Steady-State Reactor\n Analysis in Nuclear Design.\" (1984).\n \"\"\"\n eb = np.array([1.00000000e+07, 6.06550000e+06, 3.67900000e+06,\n 2.23100000e+06, 1.35300000e+06, 8.21000000e+05,\n 5.00000000e+05, 3.02500000e+05, 1.83000000e+05,\n 1.11000000e+05, 6.73400000e+04, 4.08500000e+04,\n 2.47800000e+04, 1.50300000e+04, 9.11800000e+03,\n 5.53000000e+03, 3.51910000e+03, 2.23945000e+03,\n 1.42510000e+03, 9.06899000e+02, 3.67263000e+02,\n 1.48729000e+02, 7.55014000e+01, 4.80520000e+01,\n 2.77000000e+01, 1.59680000e+01, 9.87700000e+00,\n 4.00000000e+00, 3.30000000e+00, 2.10000000e+00,\n 1.30000000e+00, 8.50000000e-01, 6.25000000e-01,\n 5.00000000e-01, 4.00000000e-01, 3.50000000e-01,\n 3.20000000e-01, 3.00000000e-01, 2.80000000e-01,\n 2.50000000e-01, 2.20000000e-01, 1.80000000e-01,\n 1.40000000e-01, 1.00000000e-01, 8.00000000e-02,\n 6.70000000e-02, 5.80000000e-02, 5.00000000e-02,\n 4.20000000e-02, 3.50000000e-02, 3.00000000e-02,\n 2.50000000e-02, 2.00000000e-02, 1.50000000e-02,\n 1.00000000e-02, 5.00000000e-03, 1.00000000e-05])\n\n elif structure == 'wims172':\n \"\"\" WIMS 172-group structure.\n\n https://www-nds.iaea.org/wimsd/energy.htm\n \"\"\"\n eb = np.array([1.96000000e+07, 1.73000000e+07, 1.49000000e+07,\n 1.38000000e+07, 1.16000000e+07, 1.00000000e+07,\n 8.19000000e+06, 6.70000000e+06, 6.07000000e+06,\n 5.49000000e+06, 4.49000000e+06, 3.68000000e+06,\n 3.01000000e+06, 2.47000000e+06, 2.23000000e+06,\n 2.02000000e+06, 1.65000000e+06, 1.35000000e+06,\n 1.22000000e+06, 1.11000000e+06, 1.00000000e+06,\n 9.07000000e+05, 8.21000000e+05, 6.08000000e+05,\n 5.50000000e+05, 4.98000000e+05, 4.50000000e+05,\n 4.08000000e+05, 3.02000000e+05, 2.73000000e+05,\n 2.47000000e+05, 1.83000000e+05, 1.23000000e+05,\n 1.11000000e+05, 8.23000000e+04, 6.74000000e+04,\n 5.52000000e+04, 4.09000000e+04, 3.70000000e+04,\n 2.93000000e+04, 2.74000000e+04, 2.48000000e+04,\n 1.66000000e+04, 1.50000000e+04, 1.11000000e+04,\n 9.12000000e+03, 7.47000000e+03, 5.53000000e+03,\n 5.00000000e+03, 3.53000000e+03, 3.35000000e+03,\n 2.25000000e+03, 2.03000000e+03, 1.51000000e+03,\n 1.43000000e+03, 1.23000000e+03, 1.01000000e+03,\n 9.14000000e+02, 7.49000000e+02, 6.77000000e+02,\n 4.54000000e+02, 3.72000000e+02, 3.04000000e+02,\n 2.04000000e+02, 1.49000000e+02, 1.37000000e+02,\n 9.17000000e+01, 7.57000000e+01, 6.79000000e+01,\n 5.56000000e+01, 5.16000000e+01, 4.83000000e+01,\n 4.55000000e+01, 4.02000000e+01, 3.73000000e+01,\n 3.37000000e+01, 3.05000000e+01, 2.76000000e+01,\n 2.50000000e+01, 2.26000000e+01, 1.95000000e+01,\n 1.59000000e+01, 1.37000000e+01, 1.12000000e+01,\n 9.91000000e+00, 9.19000000e+00, 8.32000000e+00,\n 7.52000000e+00, 6.16000000e+00, 5.35000000e+00,\n 5.04000000e+00, 4.13000000e+00, 4.00000000e+00,\n 3.38000000e+00, 3.30000000e+00, 2.77000000e+00,\n 2.72000000e+00, 2.60000000e+00, 2.55000000e+00,\n 2.36000000e+00, 2.13000000e+00, 2.10000000e+00,\n 2.02000000e+00, 1.93000000e+00, 1.84000000e+00,\n 1.76000000e+00, 1.67000000e+00, 1.59000000e+00,\n 1.50000000e+00, 1.48000000e+00, 1.44000000e+00,\n 1.37000000e+00, 1.34000000e+00, 1.30000000e+00,\n 1.24000000e+00, 1.17000000e+00, 1.15000000e+00,\n 1.13000000e+00, 1.11000000e+00, 1.10000000e+00,\n 1.07000000e+00, 1.05000000e+00, 1.04000000e+00,\n 1.02000000e+00, 9.96000000e-01, 9.86000000e-01,\n 9.72000000e-01, 9.50000000e-01, 9.30000000e-01,\n 9.10000000e-01, 8.60000000e-01, 8.50000000e-01,\n 7.90000000e-01, 7.80000000e-01, 7.05000000e-01,\n 6.25000000e-01, 5.40000000e-01, 5.00000000e-01,\n 4.85000000e-01, 4.33000000e-01, 4.00000000e-01,\n 3.91000000e-01, 3.50000000e-01, 3.20000000e-01,\n 3.15000000e-01, 3.00000000e-01, 2.80000000e-01,\n 2.48000000e-01, 2.20000000e-01, 1.89000000e-01,\n 1.80000000e-01, 1.60000000e-01, 1.40000000e-01,\n 1.34000000e-01, 1.15000000e-01, 1.00000000e-01,\n 9.50000000e-02, 8.00000000e-02, 7.70000000e-02,\n 6.70000000e-02, 5.80000000e-02, 5.00000000e-02,\n 4.20000000e-02, 3.50000000e-02, 3.00000000e-02,\n 2.50000000e-02, 2.00000000e-02, 1.50000000e-02,\n 1.00000000e-02, 6.90000000e-03, 5.00000000e-03,\n 3.00000000e-03, 1.00000000e-05])\n\n elif structure == 'lwr32':\n \"\"\" 32-group structure for LWR analysis\n\n Stammler, R. J., and M. J. Abbate. \"Methods of Steady-State Reactor\n Analysis in Nuclear Design.\" (1984).\n \"\"\"\n eb = np.array([1.00000000e+07, 3.67900000e+06, 1.35300000e+06,\n 5.00000000e+05, 4.08500000e+04, 9.11800000e+03,\n 1.48729000e+02, 4.80520000e+01, 1.59680000e+01,\n 9.87700000e+00, 4.00000000e+00, 3.30000000e+00,\n 2.60000000e+00, 2.10000000e+00, 1.30000000e+00,\n 1.15000000e+00, 1.09700000e+00, 1.02000000e+00,\n 9.72000000e-01, 9.50000000e-01, 8.50000000e-01,\n 6.25000000e-01, 4.00000000e-01, 3.20000000e-01,\n 2.50000000e-01, 1.80000000e-01, 1.40000000e-01,\n 1.00000000e-01, 8.00000000e-02, 5.00000000e-02,\n 3.00000000e-02, 1.50000000e-02, 1.00000000e-05])\n\n elif structure == 'lwr28':\n \"\"\" 28-group LWR-WIMS library\n\n Stammler, R. J., and M. J. Abbate. \"Methods of Steady-State Reactor\n Analysis in Nuclear Design.\" (1984).\n \"\"\"\n eb = np.array([1.00000000e+07, 2.23100000e+06, 8.21000000e+05,\n 1.11000000e+05, 9.11800000e+03, 1.48729000e+02,\n 1.59680000e+01, 9.87700000e+00, 4.00000000e+00,\n 3.30000000e+00, 2.60000000e+00, 2.10000000e+00,\n 1.30000000e+00, 1.15000000e+00, 1.09700000e+00,\n 1.02000000e+00, 9.72000000e-01, 9.50000000e-01,\n 8.50000000e-01, 6.25000000e-01, 4.00000000e-01,\n 3.20000000e-01, 2.50000000e-01, 1.40000000e-01,\n 6.70000000e-02, 5.00000000e-02, 3.00000000e-02,\n 1.50000000e-02, 1.00000000e-05])\n\n elif structure == 'phoenix25':\n \"\"\" 25-group PHOENIX structure for LWR analysis\n\n Stammler, R. J., and M. J. Abbate. \"Methods of Steady-State Reactor\n Analysis in Nuclear Design.\" (1984).\n \"\"\"\n eb = np.array([1.00000000e+07, 3.67900000e+06, 1.35300000e+06,\n 5.00000000e+05, 9.11800000e+03, 1.48729000e+02,\n 4.80520000e+01, 1.59680000e+01, 9.87700000e+00,\n 4.00000000e+00, 1.30000000e+00, 1.09700000e+00,\n 1.02000000e+00, 9.50000000e-01, 6.25000000e-01,\n 3.50000000e-01, 2.80000000e-01, 1.80000000e-01,\n 1.40000000e-01, 1.00000000e-01, 8.00000000e-02,\n 5.80000000e-02, 4.20000000e-02, 3.00000000e-02,\n 1.50000000e-02, 1.00000000e-05])\n\n elif structure == 'scale44':\n \"\"\" 44-group SCALE format\n\n @book{ivanov2013benchmark,\n title={Benchmark for uncertainty analysis in modeling (UAM) for design, operation and safety analysis of LWRs},\n author={Ivanov, Kalcko and Avramova, M and Kamerow, S and Kodeli, Ivan Aleksander and Sartori, E and Ivanov, E and Cabellos, O},\n volume={1},\n year={2013},\n publisher={Citeseer}\n }\n \"\"\"\n\n eb = np.array([2.00000000e+07, 8.18730000e+06, 6.43400000e+06,\n 4.80000000e+06, 3.00000000e+06, 2.47900000e+06,\n 2.35400000e+06, 1.85000000e+06, 1.40000000e+06,\n 9.00000000e+05, 4.00000000e+05, 1.00000000e+05,\n 2.50000000e+04, 1.70000000e+04, 3.00000000e+03,\n 5.50000000e+02, 1.00000000e+02, 3.00000000e+01,\n 1.00000000e+01, 8.10000000e+00, 6.00000000e+00,\n 4.75000000e+00, 3.00000000e+00, 1.77000000e+00,\n 1.00000000e+00, 6.25000000e-01, 4.00000000e-01,\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\n 2.75000000e-01, 2.50000000e-01, 2.25000000e-01,\n 2.00000000e-01, 1.50000000e-01, 1.00000000e-01,\n 7.00000000e-02, 5.00000000e-02, 4.00000000e-02,\n 3.00000000e-02, 2.53000000e-02, 1.00000000e-02,\n 7.50000000e-03, 3.00000000e-03, 1.00000000e-05])\n\n elif structure == 'scale56':\n \"\"\" 56-group SCALE format\n\n @book{ivanov2013benchmark,\n title={Benchmark for uncertainty analysis in modeling (UAM) for design, operation and safety analysis of LWRs},\n author={Ivanov, Kalcko and Avramova, M and Kamerow, S and Kodeli, Ivan Aleksander and Sartori, E and Ivanov, E and Cabellos, O},\n volume={1},\n year={2013},\n publisher={Citeseer}\n }\n \"\"\"\n\n eb = np.array([2.00000000e+07, 6.43000000e+06, 4.30000000e+06,\n 3.00000000e+06, 1.85000000e+06, 1.50000000e+06,\n 1.20000000e+06, 8.61000000e+05, 7.50000000e+05,\n 6.00000000e+05, 4.70000000e+05, 3.30000000e+05,\n 2.70000000e+05, 2.00000000e+05, 5.00000000e+04,\n 2.00000000e+04, 1.70000000e+04, 3.74000000e+03,\n 2.25000000e+03, 1.92000000e+02, 1.88000000e+02,\n 1.18000000e+02, 1.16000000e+02, 1.05000000e+02,\n 1.01000000e+02, 6.75000000e+01, 6.50000000e+01,\n 3.71000000e+01, 3.60000000e+01, 2.18000000e+01,\n 2.12000000e+01, 2.05000000e+01, 7.00000000e+00,\n 6.88000000e+00, 6.50000000e+00, 6.25000000e+00,\n 5.00000000e+00, 1.13000000e+00, 1.08000000e+00,\n 1.01000000e+00, 6.25000000e-01, 4.50000000e-01,\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\n 2.50000000e-01, 2.00000000e-01, 1.50000000e-01,\n 1.00000000e-01, 8.00000000e-02, 6.00000000e-02,\n 5.00000000e-02, 4.00000000e-02, 2.53000000e-02,\n 1.00000000e-02, 4.00000000e-03, 1.00000000e-05])\n\n elif structure == 'scale238':\n \"\"\" 238-group SCALE format\n\n ORNL, SCALE Manual Scale. \"A Comprehensive Modeling and Simulation\n Suite for Nuclear Safety Analysis and Design.\" ORNL/TM-2005/39,\n Version 6.\n \"\"\"\n eb = np.array([2.00000000e+07, 1.73330000e+07, 1.56830000e+07,\n 1.45500000e+07, 1.38400000e+07, 1.28400000e+07,\n 1.00000000e+07, 8.18730000e+06, 6.43400000e+06,\n 4.80000000e+06, 4.30400000e+06, 3.00000000e+06,\n 2.47900000e+06, 2.35400000e+06, 1.85000000e+06,\n 1.50000000e+06, 1.40000000e+06, 1.35600000e+06,\n 1.31700000e+06, 1.25000000e+06, 1.20000000e+06,\n 1.10000000e+06, 1.01000000e+06, 9.20000000e+05,\n 9.00000000e+05, 8.75000000e+05, 8.61100000e+05,\n 8.20000000e+05, 7.50000000e+05, 6.79000000e+05,\n 6.70000000e+05, 6.00000000e+05, 5.73000000e+05,\n 5.50000000e+05, 4.99520000e+05, 4.70000000e+05,\n 4.40000000e+05, 4.20000000e+05, 4.00000000e+05,\n 3.30000000e+05, 2.70000000e+05, 2.00000000e+05,\n 1.50000000e+05, 1.28300000e+05, 1.00000000e+05,\n 8.50000000e+04, 8.20000000e+04, 7.50000000e+04,\n 7.30000000e+04, 6.00000000e+04, 5.20000000e+04,\n 5.00000000e+04, 4.50000000e+04, 3.00000000e+04,\n 2.50000000e+04, 1.70000000e+04, 1.30000000e+04,\n 9.50000000e+03, 8.03000000e+03, 6.00000000e+03,\n 3.90000000e+03, 3.74000000e+03, 3.00000000e+03,\n 2.58000000e+03, 2.29000000e+03, 2.20000000e+03,\n 1.80000000e+03, 1.55000000e+03, 1.50000000e+03,\n 1.15000000e+03, 9.50000000e+02, 6.83000000e+02,\n 6.70000000e+02, 5.50000000e+02, 3.05000000e+02,\n 2.85000000e+02, 2.40000000e+02, 2.10000000e+02,\n 2.07500000e+02, 1.92500000e+02, 1.86000000e+02,\n 1.22000000e+02, 1.19000000e+02, 1.15000000e+02,\n 1.08000000e+02, 1.00000000e+02, 9.00000000e+01,\n 8.20000000e+01, 8.00000000e+01, 7.60000000e+01,\n 7.20000000e+01, 6.75000000e+01, 6.50000000e+01,\n 6.10000000e+01, 5.90000000e+01, 5.34000000e+01,\n 5.20000000e+01, 5.06000000e+01, 4.92000000e+01,\n 4.83000000e+01, 4.70000000e+01, 4.52000000e+01,\n 4.40000000e+01, 4.24000000e+01, 4.10000000e+01,\n 3.96000000e+01, 3.91000000e+01, 3.80000000e+01,\n 3.70000000e+01, 3.55000000e+01, 3.46000000e+01,\n 3.37500000e+01, 3.32500000e+01, 3.17500000e+01,\n 3.12500000e+01, 3.00000000e+01, 2.75000000e+01,\n 2.50000000e+01, 2.25000000e+01, 2.10000000e+01,\n 2.00000000e+01, 1.90000000e+01, 1.85000000e+01,\n 1.70000000e+01, 1.60000000e+01, 1.51000000e+01,\n 1.44000000e+01, 1.37500000e+01, 1.29000000e+01,\n 1.19000000e+01, 1.15000000e+01, 1.00000000e+01,\n 9.10000000e+00, 8.10000000e+00, 7.15000000e+00,\n 7.00000000e+00, 6.75000000e+00, 6.50000000e+00,\n 6.25000000e+00, 6.00000000e+00, 5.40000000e+00,\n 5.00000000e+00, 4.75000000e+00, 4.00000000e+00,\n 3.73000000e+00, 3.50000000e+00, 3.15000000e+00,\n 3.05000000e+00, 3.00000000e+00, 2.97000000e+00,\n 2.87000000e+00, 2.77000000e+00, 2.67000000e+00,\n 2.57000000e+00, 2.47000000e+00, 2.38000000e+00,\n 2.30000000e+00, 2.21000000e+00, 2.12000000e+00,\n 2.00000000e+00, 1.94000000e+00, 1.86000000e+00,\n 1.77000000e+00, 1.68000000e+00, 1.59000000e+00,\n 1.50000000e+00, 1.45000000e+00, 1.40000000e+00,\n 1.35000000e+00, 1.30000000e+00, 1.25000000e+00,\n 1.22500000e+00, 1.20000000e+00, 1.17500000e+00,\n 1.15000000e+00, 1.14000000e+00, 1.13000000e+00,\n 1.12000000e+00, 1.11000000e+00, 1.10000000e+00,\n 1.09000000e+00, 1.08000000e+00, 1.07000000e+00,\n 1.06000000e+00, 1.05000000e+00, 1.04000000e+00,\n 1.03000000e+00, 1.02000000e+00, 1.01000000e+00,\n 1.00000000e+00, 9.75000000e-01, 9.50000000e-01,\n 9.25000000e-01, 9.00000000e-01, 8.50000000e-01,\n 8.00000000e-01, 7.50000000e-01, 7.00000000e-01,\n 6.50000000e-01, 6.25000000e-01, 6.00000000e-01,\n 5.50000000e-01, 5.00000000e-01, 4.50000000e-01,\n 4.00000000e-01, 3.75000000e-01, 3.50000000e-01,\n 3.25000000e-01, 3.00000000e-01, 2.75000000e-01,\n 2.50000000e-01, 2.25000000e-01, 2.00000000e-01,\n 1.75000000e-01, 1.50000000e-01, 1.25000000e-01,\n 1.00000000e-01, 9.00000000e-02, 8.00000000e-02,\n 7.00000000e-02, 6.00000000e-02, 5.00000000e-02,\n 4.00000000e-02, 3.00000000e-02, 2.53000000e-02,\n 1.00000000e-02, 7.50000000e-03, 5.00000000e-03,\n 4.00000000e-03, 3.00000000e-03, 2.50000000e-03,\n 2.00000000e-03, 1.50000000e-03, 1.20000000e-03,\n 1.00000000e-03, 7.50000000e-04, 5.00000000e-04,\n 1.00000000e-04, 1.00000000e-05])\n\n elif structure == 'scale252':\n \"\"\" 252-group SCALE format\n\n ORNL, SCALE Manual Scale. \"A Comprehensive Modeling and Simulation\n Suite for Nuclear Safety Analysis and Design.\" ORNL/TM-2005/39,\n Version 6.\n \"\"\"\n\n eb = np.array([2.00000000e+07, 1.73000000e+07, 1.57000000e+07,\n 1.46000000e+07, 1.38000000e+07, 1.28000000e+07,\n 1.00000000e+07, 8.19000000e+06, 6.43000000e+06,\n 4.80000000e+06, 4.30000000e+06, 3.00000000e+06,\n 2.48000000e+06, 2.35000000e+06, 1.85000000e+06,\n 1.50000000e+06, 1.40000000e+06, 1.36000000e+06,\n 1.32000000e+06, 1.25000000e+06, 1.20000000e+06,\n 1.10000000e+06, 1.01000000e+06, 9.20000000e+05,\n 9.00000000e+05, 8.75000000e+05, 8.61000000e+05,\n 8.20000000e+05, 7.50000000e+05, 6.79000000e+05,\n 6.70000000e+05, 6.00000000e+05, 5.73000000e+05,\n 5.50000000e+05, 4.92000000e+05, 4.70000000e+05,\n 4.40000000e+05, 4.20000000e+05, 4.00000000e+05,\n 3.30000000e+05, 2.70000000e+05, 2.00000000e+05,\n 1.49000000e+05, 1.28000000e+05, 1.00000000e+05,\n 8.50000000e+04, 8.20000000e+04, 7.50000000e+04,\n 7.30000000e+04, 6.00000000e+04, 5.20000000e+04,\n 5.00000000e+04, 4.50000000e+04, 3.00000000e+04,\n 2.00000000e+04, 1.70000000e+04, 1.30000000e+04,\n 9.50000000e+03, 8.03000000e+03, 5.70000000e+03,\n 3.90000000e+03, 3.74000000e+03, 3.00000000e+03,\n 2.50000000e+03, 2.25000000e+03, 2.20000000e+03,\n 1.80000000e+03, 1.55000000e+03, 1.50000000e+03,\n 1.15000000e+03, 9.50000000e+02, 6.83000000e+02,\n 6.70000000e+02, 5.50000000e+02, 3.05000000e+02,\n 2.85000000e+02, 2.40000000e+02, 2.20000000e+02,\n 2.10000000e+02, 2.07000000e+02, 2.02000000e+02,\n 1.93000000e+02, 1.92000000e+02, 1.89000000e+02,\n 1.88000000e+02, 1.80000000e+02, 1.70000000e+02,\n 1.43000000e+02, 1.22000000e+02, 1.19000000e+02,\n 1.18000000e+02, 1.16000000e+02, 1.13000000e+02,\n 1.08000000e+02, 1.05000000e+02, 1.01000000e+02,\n 9.70000000e+01, 9.00000000e+01, 8.17000000e+01,\n 8.00000000e+01, 7.60000000e+01, 7.20000000e+01,\n 6.75000000e+01, 6.50000000e+01, 6.30000000e+01,\n 6.10000000e+01, 5.80000000e+01, 5.34000000e+01,\n 5.06000000e+01, 4.83000000e+01, 4.52000000e+01,\n 4.40000000e+01, 4.24000000e+01, 4.10000000e+01,\n 3.96000000e+01, 3.91000000e+01, 3.80000000e+01,\n 3.76000000e+01, 3.73000000e+01, 3.71000000e+01,\n 3.70000000e+01, 3.60000000e+01, 3.55000000e+01,\n 3.50000000e+01, 3.38000000e+01, 3.33000000e+01,\n 3.18000000e+01, 3.13000000e+01, 3.00000000e+01,\n 2.75000000e+01, 2.50000000e+01, 2.25000000e+01,\n 2.18000000e+01, 2.12000000e+01, 2.05000000e+01,\n 2.00000000e+01, 1.94000000e+01, 1.85000000e+01,\n 1.70000000e+01, 1.60000000e+01, 1.44000000e+01,\n 1.29000000e+01, 1.19000000e+01, 1.15000000e+01,\n 1.00000000e+01, 9.10000000e+00, 8.10000000e+00,\n 7.15000000e+00, 7.00000000e+00, 6.88000000e+00,\n 6.75000000e+00, 6.50000000e+00, 6.25000000e+00,\n 6.00000000e+00, 5.40000000e+00, 5.00000000e+00,\n 4.70000000e+00, 4.10000000e+00, 3.73000000e+00,\n 3.50000000e+00, 3.20000000e+00, 3.10000000e+00,\n 3.00000000e+00, 2.97000000e+00, 2.87000000e+00,\n 2.77000000e+00, 2.67000000e+00, 2.57000000e+00,\n 2.47000000e+00, 2.38000000e+00, 2.30000000e+00,\n 2.21000000e+00, 2.12000000e+00, 2.00000000e+00,\n 1.94000000e+00, 1.86000000e+00, 1.77000000e+00,\n 1.68000000e+00, 1.59000000e+00, 1.50000000e+00,\n 1.45000000e+00, 1.40000000e+00, 1.35000000e+00,\n 1.30000000e+00, 1.25000000e+00, 1.23000000e+00,\n 1.20000000e+00, 1.18000000e+00, 1.15000000e+00,\n 1.14000000e+00, 1.13000000e+00, 1.12000000e+00,\n 1.11000000e+00, 1.10000000e+00, 1.09000000e+00,\n 1.08000000e+00, 1.07000000e+00, 1.06000000e+00,\n 1.05000000e+00, 1.04000000e+00, 1.03000000e+00,\n 1.02000000e+00, 1.01000000e+00, 1.00000000e+00,\n 9.75000000e-01, 9.50000000e-01, 9.25000000e-01,\n 9.00000000e-01, 8.50000000e-01, 8.00000000e-01,\n 7.50000000e-01, 7.00000000e-01, 6.50000000e-01,\n 6.25000000e-01, 6.00000000e-01, 5.50000000e-01,\n 5.00000000e-01, 4.50000000e-01, 4.00000000e-01,\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\n 3.00000000e-01, 2.75000000e-01, 2.50000000e-01,\n 2.25000000e-01, 2.00000000e-01, 1.75000000e-01,\n 1.50000000e-01, 1.25000000e-01, 1.00000000e-01,\n 9.00000000e-02, 8.00000000e-02, 7.00000000e-02,\n 6.00000000e-02, 5.00000000e-02, 4.00000000e-02,\n 3.00000000e-02, 2.53000000e-02, 1.00000000e-02,\n 7.50000000e-03, 5.00000000e-03, 4.00000000e-03,\n 3.00000000e-03, 2.50000000e-03, 2.00000000e-03,\n 1.50000000e-03, 1.20000000e-03, 1.00000000e-03,\n 7.50000000e-04, 5.00000000e-04, 1.00000000e-04,\n 1.00000000e-05])\n\n elif structure == 'shem281':\n \"\"\" SHEM 281-group Structure\n\n\n These bounds were derived from the lethargy values given in the\n reference.\n\n Hfaiedh, Noureddine. Nouvelle methodologie de calcul de l'absorption\n resonnante. Diss. Strasbourg 1, 2006.\n \"\"\"\n eb = np.array([1.96403298e+07, 1.49182470e+07, 1.38403065e+07,\n 1.16183424e+07, 1.00000000e+07, 9.04837418e+06,\n 8.18730753e+06, 7.40818221e+06, 6.06530660e+06,\n 6.06530660e+06, 4.96585304e+06, 4.06569660e+06,\n 3.32871084e+06, 2.72531793e+06, 2.23130160e+06,\n 1.90138980e+06, 1.63654137e+06, 1.40576986e+06,\n 1.33694224e+06, 1.28696289e+06, 1.16204931e+06,\n 1.05115030e+06, 9.51120128e+05, 8.60006865e+05,\n 7.06512131e+05, 5.78443209e+05, 4.94002398e+05,\n 4.56021689e+05, 4.12501719e+05, 3.83883980e+05,\n 3.20646853e+05, 9.85279606e+04, 2.30014045e+05,\n 1.95007959e+05, 1.64999158e+05, 1.39999713e+05,\n 1.22773399e+05, 1.15623633e+05, 9.46646240e+04,\n 8.22974705e+04, 6.73794700e+04, 5.51656442e+04,\n 4.99159391e+04, 4.08677144e+04, 3.69786372e+04,\n 3.34596546e+04, 2.92810448e+04, 2.73944482e+04,\n 2.51873119e+04, 2.49991137e+04, 2.26994427e+04,\n 1.85847318e+04, 1.62004719e+04, 1.48996895e+04,\n 1.36036804e+04, 1.11377515e+04, 9.11881966e+03,\n 7.46585808e+03, 6.11252761e+03, 5.00451433e+03,\n 4.09734979e+03, 3.48107228e+03, 2.99618668e+03,\n 2.57884177e+03, 2.21962968e+03, 1.91045297e+03,\n 1.61404003e+03, 1.34506235e+03, 1.13500876e+03,\n 1.06496334e+03, 9.07501892e+02, 7.48518299e+02,\n 6.12834951e+02, 5.01746821e+02, 4.10795552e+02,\n 3.53575009e+02, 3.19927898e+02, 2.83750591e+02,\n 2.41796304e+02, 1.97966070e+02, 1.62080909e+02,\n 1.32700625e+02, 1.08646083e+02, 8.89518890e+01,\n 7.50455792e+01, 6.14421235e+01, 5.26726134e+01,\n 4.57913703e+01, 4.39958649e+01, 4.01690039e+01,\n 3.37201523e+01, 2.76077257e+01, 2.45889130e+01,\n 2.25243208e+01, 2.23783875e+01, 2.21557189e+01,\n 2.20011704e+01, 2.17018096e+01, 2.14858730e+01,\n 2.13359971e+01, 2.12295834e+01, 2.11448346e+01,\n 2.10604242e+01, 2.09763508e+01, 2.07676326e+01,\n 2.06847280e+01, 2.06021544e+01, 2.05199103e+01,\n 2.04175669e+01, 2.02751430e+01, 2.00734019e+01,\n 1.95973754e+01, 1.93926795e+01, 1.91997191e+01,\n 1.90848657e+01, 1.79590767e+01, 1.77590572e+01,\n 1.76210754e+01, 1.74457428e+01, 1.68305494e+01,\n 1.65501580e+01, 1.60497882e+01, 1.57792479e+01,\n 1.48662813e+01, 1.47301388e+01, 1.45952430e+01,\n 1.44702621e+01, 1.42505521e+01, 1.40496242e+01,\n 1.35460589e+01, 1.33297136e+01, 1.25999853e+01,\n 1.24721187e+01, 1.23085686e+01, 1.21301689e+01,\n 1.19794856e+01, 1.18153041e+01, 1.17094435e+01,\n 1.15894552e+01, 1.12694514e+01, 1.10529311e+01,\n 1.08037754e+01, 1.05792618e+01, 9.50003660e+00,\n 9.14032233e+00, 8.97996200e+00, 8.80038659e+00,\n 8.67370167e+00, 8.52408470e+00, 8.30033314e+00,\n 8.13028234e+00, 7.97008893e+00, 7.83966145e+00,\n 7.73995328e+00, 7.60036028e+00, 7.38016245e+00,\n 7.13987773e+00, 6.99430083e+00, 6.91778513e+00,\n 6.87021672e+00, 6.83526781e+00, 6.81070508e+00,\n 6.79166178e+00, 6.77605891e+00, 6.75981587e+00,\n 6.74226317e+00, 6.71669119e+00, 6.63126543e+00,\n 6.60611444e+00, 6.58830199e+00, 6.57185181e+00,\n 6.55609828e+00, 6.53907456e+00, 6.51492469e+00,\n 6.48178316e+00, 6.43206509e+00, 6.35979186e+00,\n 6.28016121e+00, 6.16011626e+00, 6.05991426e+00,\n 5.96015001e+00, 5.80021886e+00, 5.72015356e+00,\n 5.61979761e+00, 5.53004333e+00, 5.48817430e+00,\n 5.41025189e+00, 5.38003915e+00, 5.32011889e+00,\n 5.21008229e+00, 5.10998082e+00, 4.93323841e+00,\n 4.78791795e+00, 4.41980532e+00, 4.30981789e+00,\n 4.21983341e+00, 4.00000516e+00, 3.88217534e+00,\n 3.71209219e+00, 3.54307733e+00, 3.14211346e+00,\n 2.88405027e+00, 2.77512461e+00, 2.74092554e+00,\n 2.71990146e+00, 2.70011847e+00, 2.64004418e+00,\n 2.62005589e+00, 2.59009784e+00, 2.55000584e+00,\n 2.46994443e+00, 2.33006380e+00, 2.27298888e+00,\n 2.21709028e+00, 2.15695100e+00, 2.07009738e+00,\n 1.98992242e+00, 1.90007935e+00, 1.77996847e+00,\n 1.66895161e+00, 1.58803222e+00, 1.51997768e+00,\n 1.44396911e+00, 1.41000838e+00, 1.38098312e+00,\n 1.33095356e+00, 1.29303950e+00, 1.25094048e+00,\n 1.21396960e+00, 1.16999053e+00, 1.14797056e+00,\n 1.12997516e+00, 1.11604998e+00, 1.10395110e+00,\n 1.09198337e+00, 1.07798726e+00, 1.03499402e+00,\n 1.02101339e+00, 1.00903623e+00, 9.96501784e-01,\n 9.81960349e-01, 9.63961089e-01, 9.44023377e-01,\n 9.19979079e-01, 8.80025540e-01, 8.20038204e-01,\n 7.19999797e-01, 6.24999550e-01, 5.94993767e-01,\n 5.54990463e-01, 5.20011502e-01, 4.75017160e-01,\n 4.31579153e-01, 3.90001634e-01, 3.52993954e-01,\n 3.25008368e-01, 3.05011942e-01, 2.79989172e-01,\n 2.54996783e-01, 2.31192554e-01, 2.09610476e-01,\n 1.90005103e-01, 1.61895478e-01, 1.37999620e-01,\n 1.19995103e-01, 1.04297869e-01, 8.97969428e-02,\n 7.64969545e-02, 6.51994433e-02, 5.54982190e-02,\n 4.73019220e-02, 4.02999783e-02, 3.43998062e-02,\n 2.92989280e-02, 2.49394511e-02, 2.00103758e-02,\n 1.48299818e-02, 1.04505116e-02, 7.14527236e-03,\n 4.55602681e-03, 2.49990050e-03, 1.00000000e-05])\n\n elif structure == 'shem361':\n \"\"\" SHEM 361-group Structure\n\n\n @book{ngeleka2012examination,\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\n author={Ngeleka, Tholakele Prisca},\n year={2012}\n }\n \"\"\"\n\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\n 1.16000000e+07, 1.00000000e+07, 9.05000000e+06,\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\n 6.07000000e+06, 4.97000000e+06, 4.07000000e+06,\n 3.33000000e+06, 2.73000000e+06, 2.23000000e+06,\n 1.90000000e+06, 1.64000000e+06, 1.41000000e+06,\n 1.34000000e+06, 1.29000000e+06, 1.16000000e+06,\n 1.05000000e+06, 9.51000000e+05, 8.60000000e+05,\n 7.07000000e+05, 5.78000000e+05, 4.94000000e+05,\n 4.56000000e+05, 4.13000000e+05, 3.84000000e+05,\n 3.21000000e+05, 2.68000000e+05, 2.30000000e+05,\n 1.95000000e+05, 1.65000000e+05, 1.40000000e+05,\n 1.23000000e+05, 1.16000000e+05, 9.47000000e+04,\n 8.23000000e+04, 6.74000000e+04, 5.52000000e+04,\n 4.99000000e+04, 4.09000000e+04, 3.70000000e+04,\n 3.35000000e+04, 2.93000000e+04, 2.74000000e+04,\n 2.61000000e+04, 2.50000000e+04, 2.27000000e+04,\n 1.86000000e+04, 1.62000000e+04, 1.49000000e+04,\n 1.36000000e+04, 1.11000000e+04, 9.12000000e+03,\n 7.47000000e+03, 6.11000000e+03, 5.00000000e+03,\n 4.10000000e+03, 3.48000000e+03, 3.00000000e+03,\n 2.70000000e+03, 2.40000000e+03, 2.08000000e+03,\n 1.81000000e+03, 1.59000000e+03, 1.34000000e+03,\n 1.13000000e+03, 1.06000000e+03, 9.82000000e+02,\n 9.10000000e+02, 8.32000000e+02, 7.49000000e+02,\n 6.77000000e+02, 6.47000000e+02, 6.13000000e+02,\n 6.00000000e+02, 5.93000000e+02, 5.77000000e+02,\n 5.39000000e+02, 5.02000000e+02, 4.54000000e+02,\n 4.19000000e+02, 3.91000000e+02, 3.72000000e+02,\n 3.54000000e+02, 3.35000000e+02, 3.20000000e+02,\n 2.96000000e+02, 2.88000000e+02, 2.85000000e+02,\n 2.76000000e+02, 2.68000000e+02, 2.57000000e+02,\n 2.42000000e+02, 2.36000000e+02, 2.24000000e+02,\n 2.12000000e+02, 2.01000000e+02, 1.96000000e+02,\n 1.93000000e+02, 1.90000000e+02, 1.89000000e+02,\n 1.88000000e+02, 1.86000000e+02, 1.85000000e+02,\n 1.83000000e+02, 1.75000000e+02, 1.68000000e+02,\n 1.63000000e+02, 1.54000000e+02, 1.47000000e+02,\n 1.40000000e+02, 1.33000000e+02, 1.26000000e+02,\n 1.21000000e+02, 1.18000000e+02, 1.17000000e+02,\n 1.15000000e+02, 1.13000000e+02, 1.10000000e+02,\n 1.06000000e+02, 1.03000000e+02, 1.02000000e+02,\n 1.02000000e+02, 1.01000000e+02, 1.01000000e+02,\n 9.73000000e+01, 9.33000000e+01, 8.88000000e+01,\n 8.39000000e+01, 7.94000000e+01, 7.63000000e+01,\n 7.36000000e+01, 7.19000000e+01, 6.91000000e+01,\n 6.68000000e+01, 6.65000000e+01, 6.62000000e+01,\n 6.58000000e+01, 6.55000000e+01, 6.50000000e+01,\n 6.46000000e+01, 6.36000000e+01, 6.23000000e+01,\n 5.99000000e+01, 5.71000000e+01, 5.41000000e+01,\n 5.30000000e+01, 5.18000000e+01, 4.93000000e+01,\n 4.75000000e+01, 4.62000000e+01, 4.53000000e+01,\n 4.42000000e+01, 4.31000000e+01, 4.21000000e+01,\n 4.12000000e+01, 3.97000000e+01, 3.88000000e+01,\n 3.78000000e+01, 3.73000000e+01, 3.69000000e+01,\n 3.64000000e+01, 3.61000000e+01, 3.57000000e+01,\n 3.45000000e+01, 3.31000000e+01, 3.17000000e+01,\n 2.79000000e+01, 2.47000000e+01, 2.25000000e+01,\n 2.24000000e+01, 2.22000000e+01, 2.20000000e+01,\n 2.17000000e+01, 2.15000000e+01, 2.13000000e+01,\n 2.12000000e+01, 2.11000000e+01, 2.11000000e+01,\n 2.10000000e+01, 2.08000000e+01, 2.07000000e+01,\n 2.06000000e+01, 2.05000000e+01, 2.04000000e+01,\n 2.03000000e+01, 2.01000000e+01, 1.96000000e+01,\n 1.94000000e+01, 1.92000000e+01, 1.91000000e+01,\n 1.80000000e+01, 1.78000000e+01, 1.76000000e+01,\n 1.74000000e+01, 1.68000000e+01, 1.66000000e+01,\n 1.60000000e+01, 1.58000000e+01, 1.49000000e+01,\n 1.47000000e+01, 1.46000000e+01, 1.45000000e+01,\n 1.43000000e+01, 1.40000000e+01, 1.35000000e+01,\n 1.33000000e+01, 1.26000000e+01, 1.25000000e+01,\n 1.23000000e+01, 1.21000000e+01, 1.20000000e+01,\n 1.18000000e+01, 1.17000000e+01, 1.16000000e+01,\n 1.13000000e+01, 1.11000000e+01, 1.08000000e+01,\n 1.06000000e+01, 9.50000000e+00, 9.14000000e+00,\n 8.98000000e+00, 8.80000000e+00, 8.67000000e+00,\n 8.52000000e+00, 8.30000000e+00, 8.13000000e+00,\n 7.97000000e+00, 7.84000000e+00, 7.74000000e+00,\n 7.60000000e+00, 7.38000000e+00, 7.14000000e+00,\n 6.99000000e+00, 6.92000000e+00, 6.87000000e+00,\n 6.84000000e+00, 6.81000000e+00, 6.79000000e+00,\n 6.78000000e+00, 6.76000000e+00, 6.74000000e+00,\n 6.72000000e+00, 6.63000000e+00, 6.61000000e+00,\n 6.59000000e+00, 6.57000000e+00, 6.56000000e+00,\n 6.54000000e+00, 6.51000000e+00, 6.48000000e+00,\n 6.43000000e+00, 6.36000000e+00, 6.28000000e+00,\n 6.16000000e+00, 6.06000000e+00, 5.96000000e+00,\n 5.80000000e+00, 5.72000000e+00, 5.62000000e+00,\n 5.53000000e+00, 5.49000000e+00, 5.41000000e+00,\n 5.38000000e+00, 5.32000000e+00, 5.21000000e+00,\n 5.11000000e+00, 4.93000000e+00, 4.77000000e+00,\n 4.42000000e+00, 4.31000000e+00, 4.22000000e+00,\n 4.00000000e+00, 3.88000000e+00, 3.71000000e+00,\n 3.54000000e+00, 3.14000000e+00, 2.88000000e+00,\n 2.78000000e+00, 2.74000000e+00, 2.72000000e+00,\n 2.70000000e+00, 2.64000000e+00, 2.62000000e+00,\n 2.59000000e+00, 2.55000000e+00, 2.47000000e+00,\n 2.33000000e+00, 2.27000000e+00, 2.22000000e+00,\n 2.16000000e+00, 2.07000000e+00, 1.99000000e+00,\n 1.90000000e+00, 1.78000000e+00, 1.67000000e+00,\n 1.59000000e+00, 1.52000000e+00, 1.44000000e+00,\n 1.41000000e+00, 1.38000000e+00, 1.33000000e+00,\n 1.29000000e+00, 1.25000000e+00, 1.21000000e+00,\n 1.17000000e+00, 1.15000000e+00, 1.13000000e+00,\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\n 1.08000000e+00, 1.03000000e+00, 1.02000000e+00,\n 1.01000000e+00, 9.97000000e-01, 9.82000000e-01,\n 9.64000000e-01, 9.44000000e-01, 9.20000000e-01,\n 8.80000000e-01, 8.00000000e-01, 7.20000000e-01,\n 6.25000000e-01, 5.95000000e-01, 5.55000000e-01,\n 5.20000000e-01, 4.75000000e-01, 4.32000000e-01,\n 3.90000000e-01, 3.53000000e-01, 3.25000000e-01,\n 3.05000000e-01, 2.80000000e-01, 2.55000000e-01,\n 2.31000000e-01, 2.10000000e-01, 1.90000000e-01,\n 1.62000000e-01, 1.38000000e-01, 1.20000000e-01,\n 1.04000000e-01, 8.98000000e-02, 7.65000000e-02,\n 6.52000000e-02, 5.55000000e-02, 4.73000000e-02,\n 4.03000000e-02, 3.44000000e-02, 2.93000000e-02,\n 2.49000000e-02, 2.00000000e-02, 1.48000000e-02,\n 1.05000000e-02, 7.15000000e-03, 4.56000000e-03,\n 2.50000000e-03, 1.00000000e-05])\n\n elif structure == 'shem407':\n \"\"\" SHEM 407-group Structure\n\n\n @book{ngeleka2012examination,\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\n author={Ngeleka, Tholakele Prisca},\n year={2012}\n }\n \"\"\"\n\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\n 1.27000000e+07, 1.16000000e+07, 1.00000000e+07,\n 9.05000000e+06, 8.19000000e+06, 7.41000000e+06,\n 6.70000000e+06, 6.07000000e+06, 5.52000000e+06,\n 4.97000000e+06, 4.52000000e+06, 4.29000000e+06,\n 4.07000000e+06, 3.70000000e+06, 3.33000000e+06,\n 3.03000000e+06, 2.73000000e+06, 2.66000000e+06,\n 2.60000000e+06, 2.48000000e+06, 2.35000000e+06,\n 2.23000000e+06, 2.07000000e+06, 1.98000000e+06,\n 1.90000000e+06, 1.84000000e+06, 1.77000000e+06,\n 1.70000000e+06, 1.64000000e+06, 1.58000000e+06,\n 1.52000000e+06, 1.46000000e+06, 1.41000000e+06,\n 1.34000000e+06, 1.29000000e+06, 1.22000000e+06,\n 1.16000000e+06, 1.11000000e+06, 1.05000000e+06,\n 1.00000000e+06, 9.51000000e+05, 9.06000000e+05,\n 8.60000000e+05, 8.22000000e+05, 7.83000000e+05,\n 7.45000000e+05, 7.07000000e+05, 6.74000000e+05,\n 6.42000000e+05, 6.10000000e+05, 5.78000000e+05,\n 5.57000000e+05, 5.36000000e+05, 5.15000000e+05,\n 4.85000000e+05, 4.56000000e+05, 4.34000000e+05,\n 4.05000000e+05, 3.84000000e+05, 3.52000000e+05,\n 3.21000000e+05, 2.94000000e+05, 2.81000000e+05,\n 2.68000000e+05, 2.49000000e+05, 2.30000000e+05,\n 2.13000000e+05, 1.95000000e+05, 1.80000000e+05,\n 1.65000000e+05, 1.52000000e+05, 1.46000000e+05,\n 1.40000000e+05, 1.31000000e+05, 1.27000000e+05,\n 1.23000000e+05, 1.16000000e+05, 1.05000000e+05,\n 9.99000000e+04, 9.47000000e+04, 8.85000000e+04,\n 8.23000000e+04, 7.48000000e+04, 6.74000000e+04,\n 6.13000000e+04, 5.52000000e+04, 5.25000000e+04,\n 4.99000000e+04, 4.54000000e+04, 4.09000000e+04,\n 3.70000000e+04, 3.35000000e+04, 3.14000000e+04,\n 2.93000000e+04, 2.74000000e+04, 2.61000000e+04,\n 2.50000000e+04, 2.38000000e+04, 2.27000000e+04,\n 2.06000000e+04, 1.86000000e+04, 1.74000000e+04,\n 1.62000000e+04, 1.49000000e+04, 1.36000000e+04,\n 1.24000000e+04, 1.11000000e+04, 1.01000000e+04,\n 9.12000000e+03, 8.29000000e+03, 7.47000000e+03,\n 6.79000000e+03, 6.11000000e+03, 5.56000000e+03,\n 5.00000000e+03, 4.55000000e+03, 4.10000000e+03,\n 3.79000000e+03, 3.48000000e+03, 3.24000000e+03,\n 3.00000000e+03, 2.79000000e+03, 2.58000000e+03,\n 2.40000000e+03, 2.22000000e+03, 2.07000000e+03,\n 1.91000000e+03, 1.76000000e+03, 1.61000000e+03,\n 1.48000000e+03, 1.35000000e+03, 1.24000000e+03,\n 1.14000000e+03, 1.06000000e+03, 9.86000000e+02,\n 9.08000000e+02, 8.28000000e+02, 7.49000000e+02,\n 6.81000000e+02, 6.13000000e+02, 5.57000000e+02,\n 5.02000000e+02, 4.56000000e+02, 4.11000000e+02,\n 3.82000000e+02, 3.54000000e+02, 3.20000000e+02,\n 3.02000000e+02, 2.84000000e+02, 2.63000000e+02,\n 2.42000000e+02, 2.20000000e+02, 1.98000000e+02,\n 1.80000000e+02, 1.62000000e+02, 1.47000000e+02,\n 1.33000000e+02, 1.21000000e+02, 1.09000000e+02,\n 9.88000000e+01, 8.90000000e+01, 8.20000000e+01,\n 7.50000000e+01, 6.82000000e+01, 6.14000000e+01,\n 5.71000000e+01, 5.27000000e+01, 4.92000000e+01,\n 4.58000000e+01, 4.40000000e+01, 4.21000000e+01,\n 4.02000000e+01, 3.69000000e+01, 3.37000000e+01,\n 3.22000000e+01, 3.07000000e+01, 2.91000000e+01,\n 2.76000000e+01, 2.61000000e+01, 2.46000000e+01,\n 2.36000000e+01, 2.25000000e+01, 2.24000000e+01,\n 2.22000000e+01, 2.20000000e+01, 2.17000000e+01,\n 2.15000000e+01, 2.13000000e+01, 2.12000000e+01,\n 2.11000000e+01, 2.11000000e+01, 2.10000000e+01,\n 2.08000000e+01, 2.07000000e+01, 2.06000000e+01,\n 2.05000000e+01, 2.04000000e+01, 2.03000000e+01,\n 2.01000000e+01, 1.96000000e+01, 1.94000000e+01,\n 1.92000000e+01, 1.91000000e+01, 1.80000000e+01,\n 1.78000000e+01, 1.76000000e+01, 1.74000000e+01,\n 1.68000000e+01, 1.66000000e+01, 1.60000000e+01,\n 1.58000000e+01, 1.49000000e+01, 1.47000000e+01,\n 1.46000000e+01, 1.45000000e+01, 1.43000000e+01,\n 1.40000000e+01, 1.35000000e+01, 1.33000000e+01,\n 1.26000000e+01, 1.25000000e+01, 1.23000000e+01,\n 1.21000000e+01, 1.20000000e+01, 1.18000000e+01,\n 1.17000000e+01, 1.16000000e+01, 1.13000000e+01,\n 1.11000000e+01, 1.08000000e+01, 1.06000000e+01,\n 1.00000000e+01, 9.50000000e+00, 9.14000000e+00,\n 8.98000000e+00, 8.80000000e+00, 8.67000000e+00,\n 8.52000000e+00, 8.30000000e+00, 8.13000000e+00,\n 7.97000000e+00, 7.84000000e+00, 7.74000000e+00,\n 7.60000000e+00, 7.38000000e+00, 7.14000000e+00,\n 6.99000000e+00, 6.92000000e+00, 6.87000000e+00,\n 6.84000000e+00, 6.81000000e+00, 6.79000000e+00,\n 6.78000000e+00, 6.76000000e+00, 6.74000000e+00,\n 6.72000000e+00, 6.63000000e+00, 6.61000000e+00,\n 6.59000000e+00, 6.57000000e+00, 6.56000000e+00,\n 6.54000000e+00, 6.51000000e+00, 6.48000000e+00,\n 6.43000000e+00, 6.36000000e+00, 6.28000000e+00,\n 6.16000000e+00, 6.06000000e+00, 5.96000000e+00,\n 5.80000000e+00, 5.72000000e+00, 5.62000000e+00,\n 5.53000000e+00, 5.49000000e+00, 5.41000000e+00,\n 5.38000000e+00, 5.32000000e+00, 5.21000000e+00,\n 5.11000000e+00, 4.93000000e+00, 4.77000000e+00,\n 4.59000000e+00, 4.42000000e+00, 4.31000000e+00,\n 4.22000000e+00, 4.00000000e+00, 3.88000000e+00,\n 3.71000000e+00, 3.54000000e+00, 3.34000000e+00,\n 3.14000000e+00, 3.08000000e+00, 3.01000000e+00,\n 2.95000000e+00, 2.88000000e+00, 2.83000000e+00,\n 2.78000000e+00, 2.74000000e+00, 2.72000000e+00,\n 2.70000000e+00, 2.64000000e+00, 2.62000000e+00,\n 2.59000000e+00, 2.55000000e+00, 2.47000000e+00,\n 2.40000000e+00, 2.33000000e+00, 2.27000000e+00,\n 2.22000000e+00, 2.16000000e+00, 2.07000000e+00,\n 1.99000000e+00, 1.90000000e+00, 1.84000000e+00,\n 1.78000000e+00, 1.72000000e+00, 1.67000000e+00,\n 1.63000000e+00, 1.59000000e+00, 1.52000000e+00,\n 1.44000000e+00, 1.41000000e+00, 1.38000000e+00,\n 1.33000000e+00, 1.29000000e+00, 1.25000000e+00,\n 1.21000000e+00, 1.17000000e+00, 1.15000000e+00,\n 1.13000000e+00, 1.12000000e+00, 1.10000000e+00,\n 1.09000000e+00, 1.08000000e+00, 1.06000000e+00,\n 1.03000000e+00, 1.02000000e+00, 1.01000000e+00,\n 9.97000000e-01, 9.82000000e-01, 9.64000000e-01,\n 9.44000000e-01, 9.20000000e-01, 8.80000000e-01,\n 8.50000000e-01, 8.20000000e-01, 7.70000000e-01,\n 7.45000000e-01, 7.20000000e-01, 6.96000000e-01,\n 6.72000000e-01, 6.49000000e-01, 6.25000000e-01,\n 6.10000000e-01, 5.95000000e-01, 5.75000000e-01,\n 5.55000000e-01, 5.38000000e-01, 5.20000000e-01,\n 4.98000000e-01, 4.75000000e-01, 4.53000000e-01,\n 4.32000000e-01, 4.11000000e-01, 3.90000000e-01,\n 3.71000000e-01, 3.53000000e-01, 3.39000000e-01,\n 3.32000000e-01, 3.25000000e-01, 3.15000000e-01,\n 3.05000000e-01, 2.93000000e-01, 2.80000000e-01,\n 2.67000000e-01, 2.55000000e-01, 2.43000000e-01,\n 2.31000000e-01, 2.20000000e-01, 2.10000000e-01,\n 2.00000000e-01, 1.90000000e-01, 1.76000000e-01,\n 1.62000000e-01, 1.50000000e-01, 1.38000000e-01,\n 1.20000000e-01, 1.04000000e-01, 8.98000000e-02,\n 7.65000000e-02, 6.52000000e-02, 5.55000000e-02,\n 4.73000000e-02, 4.03000000e-02, 3.44000000e-02,\n 2.95000000e-02, 2.49000000e-02, 2.00000000e-02,\n 1.48000000e-02, 1.05000000e-02, 7.15000000e-03,\n 4.56000000e-03, 2.50000000e-03, 1.00000000e-05])\n\n elif structure == 'shem531':\n \"\"\" SHEM 531-group Structure\n\n\n @book{ngeleka2012examination,\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\n author={Ngeleka, Tholakele Prisca},\n year={2012}\n }\n \"\"\"\n\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\n 1.27000000e+07, 1.16000000e+07, 1.00000000e+07,\n 9.05000000e+06, 8.19000000e+06, 7.41000000e+06,\n 6.70000000e+06, 6.07000000e+06, 5.52000000e+06,\n 4.97000000e+06, 4.52000000e+06, 4.29000000e+06,\n 4.07000000e+06, 3.70000000e+06, 3.33000000e+06,\n 3.03000000e+06, 2.73000000e+06, 2.66000000e+06,\n 2.60000000e+06, 2.48000000e+06, 2.35000000e+06,\n 2.23000000e+06, 2.07000000e+06, 1.98000000e+06,\n 1.90000000e+06, 1.84000000e+06, 1.77000000e+06,\n 1.70000000e+06, 1.64000000e+06, 1.58000000e+06,\n 1.52000000e+06, 1.46000000e+06, 1.41000000e+06,\n 1.34000000e+06, 1.29000000e+06, 1.22000000e+06,\n 1.16000000e+06, 1.11000000e+06, 1.05000000e+06,\n 1.00000000e+06, 9.51000000e+05, 9.06000000e+05,\n 8.60000000e+05, 8.22000000e+05, 7.83000000e+05,\n 7.45000000e+05, 7.07000000e+05, 6.74000000e+05,\n 6.42000000e+05, 6.10000000e+05, 5.78000000e+05,\n 5.57000000e+05, 5.36000000e+05, 5.15000000e+05,\n 4.85000000e+05, 4.56000000e+05, 4.34000000e+05,\n 4.05000000e+05, 3.84000000e+05, 3.52000000e+05,\n 3.21000000e+05, 2.94000000e+05, 2.81000000e+05,\n 2.68000000e+05, 2.49000000e+05, 2.30000000e+05,\n 2.13000000e+05, 1.95000000e+05, 1.80000000e+05,\n 1.65000000e+05, 1.52000000e+05, 1.46000000e+05,\n 1.40000000e+05, 1.31000000e+05, 1.27000000e+05,\n 1.23000000e+05, 1.16000000e+05, 1.10000000e+05,\n 1.05000000e+05, 9.99000000e+04, 9.47000000e+04,\n 9.16000000e+04, 8.85000000e+04, 8.54000000e+04,\n 8.23000000e+04, 7.86000000e+04, 7.48000000e+04,\n 7.11000000e+04, 6.74000000e+04, 6.43000000e+04,\n 6.13000000e+04, 5.82000000e+04, 5.52000000e+04,\n 5.25000000e+04, 4.99000000e+04, 4.77000000e+04,\n 4.54000000e+04, 4.31000000e+04, 4.09000000e+04,\n 3.89000000e+04, 3.70000000e+04, 3.52000000e+04,\n 3.35000000e+04, 3.14000000e+04, 3.03000000e+04,\n 2.93000000e+04, 2.83000000e+04, 2.74000000e+04,\n 2.67000000e+04, 2.61000000e+04, 2.55000000e+04,\n 2.50000000e+04, 2.38000000e+04, 2.27000000e+04,\n 2.06000000e+04, 1.96000000e+04, 1.86000000e+04,\n 1.74000000e+04, 1.62000000e+04, 1.56000000e+04,\n 1.49000000e+04, 1.43000000e+04, 1.36000000e+04,\n 1.30000000e+04, 1.24000000e+04, 1.18000000e+04,\n 1.11000000e+04, 1.06000000e+04, 1.01000000e+04,\n 9.62000000e+03, 9.12000000e+03, 8.71000000e+03,\n 8.29000000e+03, 7.88000000e+03, 7.47000000e+03,\n 7.13000000e+03, 6.79000000e+03, 6.45000000e+03,\n 6.11000000e+03, 5.84000000e+03, 5.56000000e+03,\n 5.28000000e+03, 5.00000000e+03, 4.55000000e+03,\n 4.32000000e+03, 4.10000000e+03, 3.79000000e+03,\n 6.74000000e+05, 6.42000000e+05, 6.10000000e+05,\n 5.78000000e+05, 5.57000000e+05, 5.36000000e+05,\n 5.15000000e+05, 4.85000000e+05, 4.56000000e+05,\n 4.34000000e+05, 4.05000000e+05, 3.84000000e+05,\n 3.52000000e+05, 3.21000000e+05, 2.94000000e+05,\n 2.81000000e+05, 2.68000000e+05, 2.49000000e+05,\n 2.30000000e+05, 2.13000000e+05, 1.95000000e+05,\n 1.80000000e+05, 1.65000000e+05, 1.52000000e+05,\n 1.46000000e+05, 1.40000000e+05, 1.31000000e+05,\n 1.27000000e+05, 1.23000000e+05, 1.16000000e+05,\n 1.10000000e+05, 1.05000000e+05, 9.99000000e+04,\n 9.47000000e+04, 9.16000000e+04, 8.85000000e+04,\n 8.54000000e+04, 8.23000000e+04, 7.86000000e+04,\n 7.48000000e+04, 7.11000000e+04, 6.74000000e+04,\n 6.43000000e+04, 6.13000000e+04, 5.82000000e+04,\n 5.52000000e+04, 5.25000000e+04, 4.99000000e+04,\n 4.77000000e+04, 4.54000000e+04, 3.54000000e+02,\n 3.44000000e+02, 3.35000000e+02, 3.28000000e+02,\n 3.20000000e+02, 3.08000000e+02, 2.96000000e+02,\n 2.88000000e+02, 2.85000000e+02, 2.76000000e+02,\n 2.68000000e+02, 2.57000000e+02, 2.49000000e+02,\n 2.42000000e+02, 2.36000000e+02, 2.30000000e+02,\n 2.24000000e+02, 2.18000000e+02, 2.12000000e+02,\n 2.07000000e+02, 2.01000000e+02, 1.96000000e+02,\n 1.93000000e+02, 1.90000000e+02, 1.89000000e+02,\n 1.88000000e+02, 1.86000000e+02, 1.85000000e+02,\n 1.83000000e+02, 1.75000000e+02, 1.68000000e+02,\n 1.63000000e+02, 1.54000000e+02, 1.47000000e+02,\n 1.43000000e+02, 1.40000000e+02, 1.33000000e+02,\n 1.26000000e+02, 1.21000000e+02, 1.18000000e+02,\n 1.17000000e+02, 1.15000000e+02, 1.13000000e+02,\n 1.10000000e+02, 1.06000000e+02, 1.03000000e+02,\n 1.02000000e+02, 1.02000000e+02, 1.01000000e+02,\n 1.01000000e+02, 9.73000000e+01, 9.33000000e+01,\n 9.10000000e+01, 8.88000000e+01, 8.64000000e+01,\n 8.39000000e+01, 8.17000000e+01, 7.94000000e+01,\n 7.63000000e+01, 7.36000000e+01, 7.19000000e+01,\n 6.91000000e+01, 6.68000000e+01, 6.65000000e+01,\n 6.62000000e+01, 6.58000000e+01, 6.55000000e+01,\n 6.50000000e+01, 6.46000000e+01, 6.36000000e+01,\n 6.23000000e+01, 5.99000000e+01, 5.85000000e+01,\n 5.71000000e+01, 5.56000000e+01, 5.41000000e+01,\n 5.30000000e+01, 5.18000000e+01, 5.05000000e+01,\n 4.93000000e+01, 4.75000000e+01, 4.62000000e+01,\n 4.53000000e+01, 4.42000000e+01, 4.31000000e+01,\n 4.21000000e+01, 4.12000000e+01, 3.97000000e+01,\n 3.88000000e+01, 3.78000000e+01, 3.73000000e+01,\n 3.69000000e+01, 3.64000000e+01, 3.61000000e+01,\n 3.57000000e+01, 3.45000000e+01, 3.31000000e+01,\n 3.17000000e+01, 2.98000000e+01, 2.79000000e+01,\n 2.63000000e+01, 2.47000000e+01, 2.36000000e+01,\n 2.25000000e+01, 2.24000000e+01, 2.22000000e+01,\n 2.20000000e+01, 2.17000000e+01, 2.15000000e+01,\n 2.13000000e+01, 2.12000000e+01, 2.11000000e+01,\n 2.11000000e+01, 2.10000000e+01, 2.08000000e+01,\n 2.07000000e+01, 2.06000000e+01, 2.05000000e+01,\n 2.04000000e+01, 2.03000000e+01, 2.01000000e+01,\n 1.96000000e+01, 1.94000000e+01, 1.92000000e+01,\n 1.91000000e+01, 1.85000000e+01, 1.80000000e+01,\n 1.78000000e+01, 1.76000000e+01, 1.74000000e+01,\n 1.68000000e+01, 1.66000000e+01, 1.60000000e+01,\n 1.58000000e+01, 1.53000000e+01, 1.49000000e+01,\n 1.47000000e+01, 1.46000000e+01, 1.45000000e+01,\n 1.43000000e+01, 1.40000000e+01, 1.35000000e+01,\n 1.33000000e+01, 1.30000000e+01, 1.26000000e+01,\n 1.25000000e+01, 1.23000000e+01, 1.21000000e+01,\n 1.20000000e+01, 1.18000000e+01, 1.17000000e+01,\n 1.16000000e+01, 1.13000000e+01, 1.11000000e+01,\n 1.08000000e+01, 1.06000000e+01, 1.00000000e+01,\n 9.50000000e+00, 9.14000000e+00, 8.98000000e+00,\n 8.80000000e+00, 8.67000000e+00, 8.52000000e+00,\n 8.30000000e+00, 8.13000000e+00, 7.97000000e+00,\n 7.84000000e+00, 7.74000000e+00, 7.60000000e+00,\n 7.38000000e+00, 7.14000000e+00, 6.99000000e+00,\n 6.92000000e+00, 6.87000000e+00, 6.84000000e+00,\n 6.81000000e+00, 6.79000000e+00, 6.78000000e+00,\n 6.76000000e+00, 6.74000000e+00, 6.72000000e+00,\n 6.63000000e+00, 6.61000000e+00, 6.59000000e+00,\n 6.57000000e+00, 6.56000000e+00, 6.54000000e+00,\n 6.51000000e+00, 6.48000000e+00, 6.43000000e+00,\n 6.36000000e+00, 6.28000000e+00, 6.16000000e+00,\n 6.06000000e+00, 5.96000000e+00, 5.80000000e+00,\n 5.72000000e+00, 5.62000000e+00, 5.53000000e+00,\n 5.49000000e+00, 5.41000000e+00, 5.38000000e+00,\n 5.32000000e+00, 5.21000000e+00, 5.11000000e+00,\n 4.93000000e+00, 4.77000000e+00, 4.59000000e+00,\n 4.42000000e+00, 4.31000000e+00, 4.22000000e+00,\n 4.00000000e+00, 3.88000000e+00, 3.71000000e+00,\n 3.54000000e+00, 3.34000000e+00, 3.14000000e+00,\n 3.08000000e+00, 3.01000000e+00, 2.95000000e+00,\n 2.88000000e+00, 2.83000000e+00, 2.78000000e+00,\n 2.74000000e+00, 2.72000000e+00, 2.70000000e+00,\n 2.64000000e+00, 2.62000000e+00, 2.59000000e+00,\n 2.55000000e+00, 2.47000000e+00, 2.40000000e+00,\n 2.33000000e+00, 2.27000000e+00, 2.22000000e+00,\n 2.16000000e+00, 2.07000000e+00, 1.99000000e+00,\n 1.90000000e+00, 1.84000000e+00, 1.78000000e+00,\n 1.72000000e+00, 1.67000000e+00, 1.63000000e+00,\n 1.59000000e+00, 1.52000000e+00, 1.44000000e+00,\n 1.41000000e+00, 1.38000000e+00, 1.33000000e+00,\n 1.29000000e+00, 1.25000000e+00, 1.21000000e+00,\n 1.17000000e+00, 1.15000000e+00, 1.13000000e+00,\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\n 1.08000000e+00, 1.06000000e+00, 1.03000000e+00,\n 1.02000000e+00, 1.01000000e+00, 9.97000000e-01,\n 9.82000000e-01, 9.64000000e-01, 9.44000000e-01,\n 9.20000000e-01, 9.00000000e-01, 8.80000000e-01,\n 8.50000000e-01, 8.35000000e-01, 8.20000000e-01,\n 7.95000000e-01, 7.70000000e-01, 7.45000000e-01,\n 7.20000000e-01, 6.96000000e-01, 6.72000000e-01,\n 6.49000000e-01, 6.25000000e-01, 6.10000000e-01,\n 5.95000000e-01, 5.75000000e-01, 5.55000000e-01,\n 5.38000000e-01, 5.20000000e-01, 4.98000000e-01,\n 4.75000000e-01, 4.53000000e-01, 4.32000000e-01,\n 4.11000000e-01, 3.90000000e-01, 3.71000000e-01,\n 3.53000000e-01, 3.39000000e-01, 3.32000000e-01,\n 3.20000000e-01, 3.05000000e-01, 2.93000000e-01,\n 2.80000000e-01, 2.67000000e-01, 2.55000000e-01,\n 2.43000000e-01, 2.31000000e-01, 2.20000000e-01,\n 2.10000000e-01, 2.00000000e-01, 1.83000000e-01,\n 1.62000000e-01, 1.50000000e-01, 1.38000000e-01,\n 1.29000000e-01, 1.20000000e-01, 1.04000000e-01,\n 8.98000000e-02, 7.65000000e-02, 6.52000000e-02,\n 5.55000000e-02, 4.73000000e-02, 4.03000000e-02,\n 3.44000000e-02, 2.95000000e-02, 2.49000000e-02,\n 2.00000000e-02, 1.48000000e-02, 1.05000000e-02,\n 7.15000000e-03, 4.56000000e-03, 2.50000000e-03,\n 1.00000000e-05])\n\n elif structure == 'ga193':\n \"\"\" GA-TPN 193-group Structure\n\n\n @book{ngeleka2012examination,\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\n author={Ngeleka, Tholakele Prisca},\n year={2012}\n }\n \"\"\"\n\n eb = np.array([1.49000000e+07, 1.35000000e+07, 1.22000000e+07,\n 1.11000000e+07, 1.00000000e+07, 9.05000000e+06,\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\n 6.07000000e+06, 5.49000000e+06, 4.97000000e+06,\n 4.49000000e+06, 4.07000000e+06, 3.68000000e+06,\n 3.33000000e+06, 3.01000000e+06, 2.73000000e+06,\n 2.47000000e+06, 2.23000000e+06, 2.02000000e+06,\n 1.83000000e+06, 1.65000000e+06, 1.50000000e+06,\n 1.35000000e+06, 1.22000000e+06, 1.11000000e+06,\n 1.00000000e+06, 9.07000000e+05, 8.21000000e+05,\n 7.43000000e+05, 6.72000000e+05, 6.08000000e+05,\n 5.50000000e+05, 4.98000000e+05, 4.50000000e+05,\n 4.08000000e+05, 3.69000000e+05, 3.34000000e+05,\n 3.02000000e+05, 2.73000000e+05, 2.47000000e+05,\n 2.24000000e+05, 2.02000000e+05, 1.83000000e+05,\n 1.66000000e+05, 1.50000000e+05, 1.36000000e+05,\n 1.23000000e+05, 1.11000000e+05, 8.65000000e+04,\n 6.74000000e+04, 5.25000000e+04, 4.09000000e+04,\n 3.18000000e+04, 2.48000000e+04, 1.93000000e+04,\n 1.50000000e+04, 1.17000000e+04, 9.12000000e+03,\n 7.10000000e+03, 5.53000000e+03, 4.31000000e+03,\n 3.35000000e+03, 2.61000000e+03, 2.03000000e+03,\n 1.58000000e+03, 1.23000000e+03, 9.61000000e+02,\n 7.49000000e+02, 5.83000000e+02, 4.54000000e+02,\n 3.54000000e+02, 2.75000000e+02, 2.14000000e+02,\n 1.67000000e+02, 1.30000000e+02, 1.01000000e+02,\n 7.89000000e+01, 6.14000000e+01, 4.79000000e+01,\n 3.73000000e+01, 2.90000000e+01, 2.26000000e+01,\n 1.76000000e+01, 1.37000000e+01, 1.07000000e+01,\n 8.32000000e+00, 6.48000000e+00, 5.04000000e+00,\n 3.93000000e+00, 3.06000000e+00, 2.38000000e+00,\n 2.36000000e+00, 2.31000000e+00, 2.25000000e+00,\n 2.15000000e+00, 2.05000000e+00, 1.95000000e+00,\n 1.88000000e+00, 1.82000000e+00, 1.74000000e+00,\n 1.65000000e+00, 1.55000000e+00, 1.47000000e+00,\n 1.40000000e+00, 1.33000000e+00, 1.28000000e+00,\n 1.23000000e+00, 1.18000000e+00, 1.14000000e+00,\n 1.13000000e+00, 1.12000000e+00, 1.10000000e+00,\n 1.09000000e+00, 1.08000000e+00, 1.07000000e+00,\n 1.06000000e+00, 1.04000000e+00, 1.01000000e+00,\n 9.95000000e-01, 9.85000000e-01, 9.75000000e-01,\n 9.60000000e-01, 9.40000000e-01, 9.20000000e-01,\n 9.00000000e-01, 8.83000000e-01, 8.63000000e-01,\n 8.25000000e-01, 7.75000000e-01, 7.25000000e-01,\n 6.92000000e-01, 6.67000000e-01, 6.38000000e-01,\n 6.13000000e-01, 5.95000000e-01, 5.83000000e-01,\n 5.63000000e-01, 5.41000000e-01, 5.16000000e-01,\n 4.95000000e-01, 4.85000000e-01, 4.78000000e-01,\n 4.73000000e-01, 4.65000000e-01, 4.55000000e-01,\n 4.40000000e-01, 4.25000000e-01, 4.17000000e-01,\n 3.97000000e-01, 3.70000000e-01, 3.55000000e-01,\n 3.45000000e-01, 3.35000000e-01, 3.25000000e-01,\n 3.15000000e-01, 3.05000000e-01, 2.95000000e-01,\n 2.85000000e-01, 2.75000000e-01, 2.65000000e-01,\n 2.55000000e-01, 2.45000000e-01, 2.35000000e-01,\n 2.25000000e-01, 2.10000000e-01, 1.90000000e-01,\n 1.70000000e-01, 1.50000000e-01, 1.30000000e-01,\n 1.10000000e-01, 9.75000000e-02, 9.25000000e-02,\n 8.75000000e-02, 8.25000000e-02, 7.75000000e-02,\n 7.25000000e-02, 6.75000000e-02, 6.25000000e-02,\n 5.50000000e-02, 4.50000000e-02, 3.50000000e-02,\n 2.77000000e-02, 2.27000000e-02, 1.75000000e-02,\n 1.25000000e-02, 9.00000000e-03, 7.50000000e-03,\n 6.00000000e-03, 4.50000000e-03, 3.00000000e-03,\n 1.50000000e-03, 1.00000000e-05])\n\n elif structure == 'ga537':\n \"\"\" GA-TPN 537-group Structure\n\n\n @book{ngeleka2012examination,\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\n author={Ngeleka, Tholakele Prisca},\n year={2012}\n }\n \"\"\"\n\n eb = np.array([1.49000000e+07, 1.35000000e+07, 1.22000000e+07,\n 1.11000000e+07, 1.00000000e+07, 9.05000000e+06,\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\n 6.07000000e+06, 5.49000000e+06, 4.97000000e+06,\n 4.49000000e+06, 4.07000000e+06, 3.68000000e+06,\n 3.33000000e+06, 3.17000000e+06, 3.01000000e+06,\n 2.87000000e+06, 2.73000000e+06, 2.60000000e+06,\n 2.47000000e+06, 2.35000000e+06, 2.23000000e+06,\n 2.13000000e+06, 2.02000000e+06, 1.92000000e+06,\n 1.83000000e+06, 1.74000000e+06, 1.65000000e+06,\n 1.57000000e+06, 1.50000000e+06, 1.42000000e+06,\n 1.35000000e+06, 1.29000000e+06, 1.22000000e+06,\n 1.17000000e+06, 1.11000000e+06, 1.06000000e+06,\n 1.00000000e+06, 9.55000000e+05, 9.07000000e+05,\n 8.64000000e+05, 8.21000000e+05, 7.82000000e+05,\n 7.43000000e+05, 7.07000000e+05, 6.72000000e+05,\n 6.40000000e+05, 6.08000000e+05, 5.79000000e+05,\n 5.50000000e+05, 5.24000000e+05, 4.98000000e+05,\n 4.74000000e+05, 4.50000000e+05, 4.29000000e+05,\n 4.08000000e+05, 3.88000000e+05, 3.69000000e+05,\n 3.51000000e+05, 3.34000000e+05, 3.18000000e+05,\n 3.02000000e+05, 2.88000000e+05, 2.73000000e+05,\n 2.60000000e+05, 2.47000000e+05, 2.35000000e+05,\n 2.24000000e+05, 2.13000000e+05, 2.02000000e+05,\n 1.93000000e+05, 1.83000000e+05, 1.74000000e+05,\n 1.66000000e+05, 1.58000000e+05, 1.50000000e+05,\n 1.43000000e+05, 1.36000000e+05, 1.29000000e+05,\n 1.23000000e+05, 1.17000000e+05, 1.11000000e+05,\n 1.08000000e+05, 1.05000000e+05, 1.02000000e+05,\n 9.88000000e+04, 9.57000000e+04, 9.27000000e+04,\n 8.96000000e+04, 8.65000000e+04, 8.41000000e+04,\n 8.17000000e+04, 7.93000000e+04, 7.69000000e+04,\n 7.46000000e+04, 7.22000000e+04, 6.98000000e+04,\n 6.74000000e+04, 6.55000000e+04, 6.37000000e+04,\n 6.18000000e+04, 5.99000000e+04, 5.81000000e+04,\n 5.62000000e+04, 5.43000000e+04, 5.25000000e+04,\n 5.10000000e+04, 4.96000000e+04, 4.81000000e+04,\n 4.67000000e+04, 4.52000000e+04, 4.38000000e+04,\n 4.23000000e+04, 4.09000000e+04, 3.97000000e+04,\n 3.86000000e+04, 3.75000000e+04, 3.63000000e+04,\n 3.52000000e+04, 3.41000000e+04, 3.30000000e+04,\n 3.18000000e+04, 3.09000000e+04, 3.01000000e+04,\n 2.92000000e+04, 2.83000000e+04, 2.74000000e+04,\n 2.65000000e+04, 2.57000000e+04, 2.48000000e+04,\n 2.41000000e+04, 2.34000000e+04, 2.27000000e+04,\n 2.20000000e+04, 2.14000000e+04, 2.07000000e+04,\n 2.00000000e+04, 1.93000000e+04, 1.88000000e+04,\n 1.82000000e+04, 1.77000000e+04, 1.72000000e+04,\n 1.66000000e+04, 1.61000000e+04, 1.56000000e+04,\n 1.50000000e+04, 1.46000000e+04, 1.42000000e+04,\n 1.38000000e+04, 1.34000000e+04, 1.30000000e+04,\n 1.25000000e+04, 1.21000000e+04, 1.17000000e+04,\n 1.14000000e+04, 1.11000000e+04, 1.07000000e+04,\n 1.04000000e+04, 1.01000000e+04, 9.77000000e+03,\n 9.44000000e+03, 9.12000000e+03, 8.87000000e+03,\n 8.61000000e+03, 8.36000000e+03, 8.11000000e+03,\n 7.86000000e+03, 7.61000000e+03, 7.35000000e+03,\n 7.10000000e+03, 6.91000000e+03, 6.71000000e+03,\n 6.51000000e+03, 6.32000000e+03, 6.12000000e+03,\n 5.92000000e+03, 5.73000000e+03, 5.53000000e+03,\n 5.38000000e+03, 5.22000000e+03, 5.07000000e+03,\n 4.92000000e+03, 4.77000000e+03, 4.61000000e+03,\n 4.46000000e+03, 4.31000000e+03, 4.19000000e+03,\n 4.07000000e+03, 3.95000000e+03, 3.83000000e+03,\n 3.71000000e+03, 3.59000000e+03, 3.47000000e+03,\n 3.35000000e+03, 3.26000000e+03, 3.17000000e+03,\n 3.08000000e+03, 2.98000000e+03, 2.89000000e+03,\n 2.80000000e+03, 2.71000000e+03, 2.61000000e+03,\n 2.54000000e+03, 2.47000000e+03, 2.40000000e+03,\n 2.32000000e+03, 2.25000000e+03, 2.18000000e+03,\n 2.11000000e+03, 2.03000000e+03, 1.98000000e+03,\n 1.92000000e+03, 1.87000000e+03, 1.81000000e+03,\n 1.75000000e+03, 1.70000000e+03, 1.64000000e+03,\n 1.58000000e+03, 1.54000000e+03, 1.50000000e+03,\n 1.45000000e+03, 1.41000000e+03, 1.37000000e+03,\n 1.32000000e+03, 1.28000000e+03, 1.23000000e+03,\n 1.20000000e+03, 1.17000000e+03, 1.13000000e+03,\n 1.10000000e+03, 1.06000000e+03, 1.03000000e+03,\n 9.95000000e+02, 9.61000000e+02, 9.35000000e+02,\n 9.08000000e+02, 8.81000000e+02, 8.55000000e+02,\n 8.28000000e+02, 8.02000000e+02, 7.75000000e+02,\n 7.49000000e+02, 7.28000000e+02, 7.07000000e+02,\n 6.86000000e+02, 6.66000000e+02, 6.45000000e+02,\n 6.24000000e+02, 6.04000000e+02, 5.83000000e+02,\n 5.67000000e+02, 5.51000000e+02, 5.35000000e+02,\n 5.18000000e+02, 5.02000000e+02, 4.86000000e+02,\n 4.70000000e+02, 4.54000000e+02, 4.41000000e+02,\n 4.29000000e+02, 4.16000000e+02, 4.04000000e+02,\n 3.91000000e+02, 3.79000000e+02, 3.66000000e+02,\n 3.54000000e+02, 3.44000000e+02, 3.34000000e+02,\n 3.24000000e+02, 3.14000000e+02, 3.05000000e+02,\n 2.95000000e+02, 2.85000000e+02, 2.75000000e+02,\n 2.68000000e+02, 2.60000000e+02, 2.53000000e+02,\n 2.45000000e+02, 2.37000000e+02, 2.30000000e+02,\n 2.22000000e+02, 2.14000000e+02, 2.09000000e+02,\n 2.03000000e+02, 1.97000000e+02, 1.91000000e+02,\n 1.85000000e+02, 1.79000000e+02, 1.73000000e+02,\n 1.67000000e+02, 1.62000000e+02, 1.58000000e+02,\n 1.53000000e+02, 1.49000000e+02, 1.44000000e+02,\n 1.39000000e+02, 1.35000000e+02, 1.30000000e+02,\n 1.26000000e+02, 1.23000000e+02, 1.19000000e+02,\n 1.16000000e+02, 1.12000000e+02, 1.08000000e+02,\n 1.05000000e+02, 1.01000000e+02, 9.85000000e+01,\n 9.57000000e+01, 9.29000000e+01, 9.01000000e+01,\n 8.73000000e+01, 8.45000000e+01, 8.17000000e+01,\n 7.89000000e+01, 7.67000000e+01, 7.45000000e+01,\n 7.23000000e+01, 7.02000000e+01, 6.80000000e+01,\n 6.58000000e+01, 6.36000000e+01, 6.14000000e+01,\n 5.97000000e+01, 5.80000000e+01, 5.63000000e+01,\n 5.46000000e+01, 5.29000000e+01, 5.12000000e+01,\n 4.95000000e+01, 4.79000000e+01, 4.65000000e+01,\n 4.52000000e+01, 4.39000000e+01, 4.26000000e+01,\n 4.12000000e+01, 3.99000000e+01, 3.83000000e+01,\n 3.67000000e+01, 3.49000000e+01, 3.40000000e+01,\n 3.31000000e+01, 3.21000000e+01, 3.11000000e+01,\n 3.01000000e+01, 2.90000000e+01, 2.82000000e+01,\n 2.74000000e+01, 2.66000000e+01, 2.58000000e+01,\n 2.50000000e+01, 2.42000000e+01, 2.33000000e+01,\n 2.25000000e+01, 2.19000000e+01, 2.13000000e+01,\n 2.01000000e+01, 1.95000000e+01, 1.89000000e+01,\n 1.82000000e+01, 1.76000000e+01, 1.71000000e+01,\n 1.66000000e+01, 1.61000000e+01, 1.57000000e+01,\n 1.52000000e+01, 1.47000000e+01, 1.42000000e+01,\n 1.37000000e+01, 1.33000000e+01, 1.30000000e+01,\n 1.26000000e+01, 1.22000000e+01, 1.18000000e+01,\n 1.14000000e+01, 1.11000000e+01, 1.07000000e+01,\n 1.04000000e+01, 1.01000000e+01, 9.79000000e+00,\n 9.50000000e+00, 9.20000000e+00, 8.91000000e+00,\n 8.61000000e+00, 8.32000000e+00, 8.09000000e+00,\n 7.86000000e+00, 7.65000000e+00, 7.45000000e+00,\n 7.35000000e+00, 7.25000000e+00, 7.05000000e+00,\n 6.48000000e+00, 6.30000000e+00, 6.12000000e+00,\n 5.94000000e+00, 5.76000000e+00, 5.58000000e+00,\n 5.40000000e+00, 5.22000000e+00, 5.04000000e+00,\n 4.90000000e+00, 4.76000000e+00, 4.63000000e+00,\n 4.49000000e+00, 4.35000000e+00, 4.21000000e+00,\n 4.07000000e+00, 3.93000000e+00, 3.82000000e+00,\n 3.71000000e+00, 3.60000000e+00, 3.49000000e+00,\n 3.38000000e+00, 3.28000000e+00, 3.17000000e+00,\n 3.06000000e+00, 2.89000000e+00, 2.72000000e+00,\n 2.55000000e+00, 2.38000000e+00, 2.36000000e+00,\n 2.31000000e+00, 2.25000000e+00, 2.15000000e+00,\n 2.05000000e+00, 1.95000000e+00, 1.88000000e+00,\n 1.82000000e+00, 1.74000000e+00, 1.65000000e+00,\n 1.55000000e+00, 1.47000000e+00, 1.40000000e+00,\n 1.33000000e+00, 1.28000000e+00, 1.23000000e+00,\n 1.18000000e+00, 1.14000000e+00, 1.13000000e+00,\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\n 1.08000000e+00, 1.07000000e+00, 1.06000000e+00,\n 1.04000000e+00, 1.01000000e+00, 9.95000000e-01,\n 9.85000000e-01, 9.75000000e-01, 9.60000000e-01,\n 9.40000000e-01, 9.20000000e-01, 9.00000000e-01,\n 8.83000000e-01, 8.63000000e-01, 8.25000000e-01,\n 7.75000000e-01, 7.50000000e-01, 7.25000000e-01,\n 6.92000000e-01, 6.67000000e-01, 6.38000000e-01,\n 6.13000000e-01, 5.95000000e-01, 5.83000000e-01,\n 5.63000000e-01, 5.41000000e-01, 5.16000000e-01,\n 4.95000000e-01, 4.85000000e-01, 4.78000000e-01,\n 4.73000000e-01, 4.65000000e-01, 4.55000000e-01,\n 4.40000000e-01, 4.25000000e-01, 4.17000000e-01,\n 3.97000000e-01, 3.84000000e-01, 3.70000000e-01,\n 3.55000000e-01, 3.45000000e-01, 3.35000000e-01,\n 3.25000000e-01, 3.15000000e-01, 3.05000000e-01,\n 2.95000000e-01, 2.85000000e-01, 2.75000000e-01,\n 2.65000000e-01, 2.55000000e-01, 2.45000000e-01,\n 2.35000000e-01, 2.25000000e-01, 2.18000000e-01,\n 2.10000000e-01, 2.00000000e-01, 1.90000000e-01,\n 1.80000000e-01, 1.70000000e-01, 1.60000000e-01,\n 1.55000000e-01, 1.50000000e-01, 1.45000000e-01,\n 1.40000000e-01, 1.35000000e-01, 1.30000000e-01,\n 1.25000000e-01, 1.20000000e-01, 1.15000000e-01,\n 1.10000000e-01, 1.06000000e-01, 1.02000000e-01,\n 9.50000000e-02, 9.25000000e-02, 8.75000000e-02,\n 8.25000000e-02, 7.75000000e-02, 7.25000000e-02,\n 6.75000000e-02, 6.25000000e-02, 5.50000000e-02,\n 4.50000000e-02, 3.50000000e-02, 2.77000000e-02,\n 2.27000000e-02, 1.75000000e-02, 1.25000000e-02,\n 9.00000000e-03, 7.50000000e-03, 6.00000000e-03,\n 4.50000000e-03, 3.00000000e-03, 1.50000000e-03,\n 1.00000000e-05])\n\n elif structure == 'hr6':\n \"\"\" Hansen-Roach 6-group Format\n\n Hansen, G.E. and Roach, W.H. Six and sixteen group cross sections\n for fast and intermediate critical assemblies. No. LAMS-2543. Los\n Alamos Scientific Lab., N. Mex., 1961.\n \"\"\"\n eb = np.array([2.00000000e+07, 3.00000000e+06, 1.40000000e+06,\n 9.00000000e+05, 4.00000000e+05, 1.00000000e+05,\n 1.00000000e-05])\n\n elif structure == 'hr16':\n \"\"\" Hansen-Roach 16-group Format\n\n Hansen, G.E. and Roach, W.H. Six and sixteen group cross sections\n for fast and intermediate critical assemblies. No. LAMS-2543. Los\n Alamos Scientific Lab., N. Mex., 1961.\n \"\"\"\n eb = np.array([2.00000000e+07, 3.00000000e+06, 1.40000000e+06,\n 9.00000000e+05, 4.00000000e+03, 1.00000000e+05,\n 1.70000000e+04, 3.00000000e+03, 5.50000000e+02,\n 1.00000000e+02, 3.00000000e+01, 1.00000000e+01,\n 3.00000000e+00, 1.00000000e+00, 4.00000000e-01,\n 1.00000000e-01, 1.00000000e-05])\n\n eb /= 1e6\n return eb[::-1]","title":""},{"docid":"d1ae4f1282542b1395c79d4d0c02565e","score":"0.46946234","text":"def shrink(self):\n\n if 0 < self.size <= self.capacity//4 and self.capacity//2 >= 4:\n\n # self.capacity //= 2\n\n old = self.data\n old_cap = self.capacity\n\n self.capacity //= 2\n\n self.data = [None] * self.capacity\n walk = self.head\n for i in range(self.size):\n self.data[i] = old[walk]\n walk = (1 + walk) % old_cap\n\n self.head = 0\n self.tail = (self.head + self.size) % self.capacity","title":""},{"docid":"a4113b033421afc7f863f775faf976a6","score":"0.46941668","text":"def _groups(self):\n return self._angel_data[1]","title":""},{"docid":"3ecde4868c96c9630267afd1632933ef","score":"0.4689384","text":"def willump_dfs_find_efficient_features(partitioned_features: List[List[FeatureBase]], partition_costs: List[float],\n partition_importances: List[float], cost_cutoff: float) \\\n -> Tuple[List[FeatureBase], List[FeatureBase], float, float]:\n\n def knapsack_dp(values, weights, capacity):\n # Credit: https://gist.github.com/KaiyangZhou/71a473b1561e0ea64f97d0132fe07736\n n_items = len(values)\n table = np.zeros((n_items + 1, capacity + 1), dtype=np.float32)\n keep = np.zeros((n_items + 1, capacity + 1), dtype=np.float32)\n\n for i in range(1, n_items + 1):\n for w in range(0, capacity + 1):\n wi = weights[i - 1] # weight of current item\n vi = values[i - 1] # value of current item\n if (wi <= w) and (vi + table[i - 1, w - wi] > table[i - 1, w]):\n table[i, w] = vi + table[i - 1, w - wi]\n keep[i, w] = 1\n else:\n table[i, w] = table[i - 1, w]\n picks = []\n K = capacity\n for i in range(n_items, 0, -1):\n if keep[i, K] == 1:\n picks.append(i)\n K -= weights[i - 1]\n picks.sort()\n picks = [x - 1 for x in picks] # change to 0-index\n return picks\n\n scaled_total_cost = 1000\n orig_total_cost = sum(partition_costs)\n scale_factor = scaled_total_cost / orig_total_cost\n scaled_costs = [round(cost * scale_factor) for cost in partition_costs]\n partition_ids = range(len(partitioned_features))\n more_important_partitions = knapsack_dp(partition_importances, scaled_costs, int(scaled_total_cost * cost_cutoff))\n mi_cost = sum([scaled_costs[i] for i in more_important_partitions])\n less_important_partitions = [p_id for p_id in partition_ids if p_id not in more_important_partitions]\n more_important_features, less_important_features = [], []\n for p_id in more_important_partitions:\n more_important_features += partitioned_features[p_id]\n for p_id in less_important_partitions:\n less_important_features += partitioned_features[p_id]\n return more_important_features, less_important_features, mi_cost, scaled_total_cost","title":""},{"docid":"1f8f9bac36b63fd912cc3a53ddff61be","score":"0.46887454","text":"def expand_group(group, freqs, freq_tol, max_harmonics=0):\n if len(group) == 0:\n return group\n if max_harmonics <= 0:\n max_harmonics = 10000\n if max_harmonics <= len(group):\n return group\n fzero = group[0,0]\n ## indices = np.where(np.abs(freqs[:,0] - np.round(freqs[:,0]/fzero)*fzero) < 2.0*freq_tol)[0]\n ## \n indices = []\n group_freqs = list(group[:,0])\n prev_h = len(group_freqs)\n prev_fe = 0.0\n for i, f in enumerate(freqs[:,0]):\n if f <= group[-1,0] + 0.5*fzero:\n continue\n h = m.floor(f/fzero + 0.5) # round\n if h > max_harmonics:\n break\n if m.fabs(f/h - fzero) > freq_tol:\n continue\n df = f - group_freqs[-1]\n if df <= 0.5*fzero:\n if len(group_freqs)>1:\n df = f - group_freqs[-2]\n else:\n df = h*fzero\n dh = m.floor(df/fzero + 0.5)\n fe = m.fabs(df/dh - fzero)\n if fe > 2.0*freq_tol:\n continue\n if h > prev_h or fe < prev_fe:\n if h == prev_h and len(group_freqs) > 0:\n group_freqs.pop()\n indices.pop()\n ## do we realy need fill ins? It is not working anyways!\n ## else:\n ## while prev_h < h-2:\n ## indices.append(-1)\n ## prev_h += 1\n group_freqs.append(f)\n indices.append(i)\n prev_h = h\n prev_fe = fe\n # assemble group:\n new_group = freqs[indices,:group.shape[1]]\n ## indices = np.array(indices)\n ## new_group = np.zeros((len(indices),group.shape[1]))\n ## new_group[indices>=0,:] = freqs[indices[indices>=0],:group.shape[1]]\n ## fill_in = np.where(indices<0)[0]\n ## new_group[indices<0,0] = fzero*(fill_in+1)\n #group[:,0] = np.arange(1,len(group)+1)*fzero\n return np.vstack((group, new_group))","title":""},{"docid":"05805fe12ef2b6ef8648c2ac4f6b215a","score":"0.4688406","text":"def tightest_fit(items, boxes):\r\n for item in items:\r\n boxes = sort_boxes(boxes)\r\n for box in boxes:\r\n if item.weight <= box.capacity:\r\n box.items.append(item)\r\n box.capacity -= item.weight\r\n break\r\n print(\"Box 1 of weight 12 capacity contains\")\r\n for i in boxes[0].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n print(\"Box 2 of weight 12 capacity contains\")\r\n for i in boxes[1].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n print(\"Box 3 of weight 12 capacity contains\")\r\n for i in boxes[2].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n if boxes[0].capacity + boxes[1].capacity + boxes[2].capacity == 0:\r\n print(\"Successfully able to pack all items\")\r\n else:\r\n print(\"Unable to pack all items\")","title":""},{"docid":"be01f99f9e5de26c04122286300322ba","score":"0.4685241","text":"def get_group_cv(gr_mon, start, stop):\n cv = []\n for i in range(gr_mon.source.N):\n sm = gr_mon[i]\n times = sm[sm >= start]\n times = times[times <= stop]\n isi = numpy.diff(times)\n cv.append(isi.std() / isi.mean())\n return cv","title":""},{"docid":"66c6a5b7bc6f01aec177b4cb90d80645","score":"0.46804777","text":"def generate(self):\n\n self._removal = np.zeros((self._num_energy_groups,))\n\n for i in xrange(self._num_energy_groups):\n self._removal[i] = self._total[i] - self._scattering[i, i]","title":""},{"docid":"0c532432533362db1483557e7f5130f7","score":"0.46748284","text":"def _reduce_cages(kenken: KenKenPuzzle) -> None:\n for cage in kenken.cages:\n all_combos = _get_possible_combos(cage, kenken.size)\n legal_combos = _remove_illegal(all_combos, kenken.candidates)\n kenken._replace(_merge_combos(legal_combos))","title":""},{"docid":"3064e5bfa036d618a8e327164c8c3924","score":"0.46735695","text":"def groups(data,length):\n\tfor i in xrange(0,len(data),length):\n\t\tyield data[i:i+length]","title":""},{"docid":"e86e81fddda7bb8193de279f7a39b581","score":"0.46735185","text":"def shrink(self):\n prior = self.data\n change = self.head\n recent_size = self.size\n empty = [None]\n floor_by_four = self.capacity // 4\n floor_by_two = self.capacity // 2\n capacity = self.capacity\n\n if (floor_by_two < 4) != self.size: # Need to be tested\n if self.size <= floor_by_four:\n self.data = empty * floor_by_two\n i = 0\n while i < recent_size:\n self.data[i] = prior[change]\n change += 1\n change = change % capacity\n i += 1\n\n self.head = 0\n self.tail = recent_size\n self.capacity = floor_by_two","title":""},{"docid":"ff1a1e780c42b2c5ade076f2754cad21","score":"0.46710825","text":"def roomiest(items, boxes):\r\n for item in items:\r\n largest_box = boxes[0]\r\n for box in boxes:\r\n if box.capacity > largest_box.capacity:\r\n largest_box = box\r\n if item.weight <= largest_box.capacity:\r\n largest_box.items.append(item)\r\n largest_box.capacity -= item.weight\r\n print(\"Box 1 of weight 12 capacity contains\")\r\n for i in boxes[0].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n print(\"Box 2 of weight 12 capacity contains\")\r\n for i in boxes[1].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n print(\"Box 3 of weight 12 capacity contains\")\r\n for i in boxes[2].items:\r\n print(str(i.name) + \" of weight \" + str(i.weight))\r\n if boxes[0].capacity + boxes[1].capacity + boxes[2].capacity == 0:\r\n print(\"Successfully able to pack all items\")\r\n else:\r\n print(\"Unable to pack all items\")","title":""},{"docid":"77f1b49ab72a3a58f1c2ff0bdfd4cb35","score":"0.46672967","text":"def read_pump_groups(wds):\n pump_groups = []\n group = ['gx']\n for pump in wds.pumps:\n uid = pump.uid\n if uid[uid.rfind('g'):] == group[0][group[0].rfind('g'):]:\n group.append(uid)\n else:\n pump_groups.append(group)\n group = [uid]\n pump_groups.append(group)\n return pump_groups[1:]","title":""},{"docid":"ebc0dce6a6b0b8324015e87099751991","score":"0.46630502","text":"def getReducedLoads(self):\r\n scale_factors = []\r\n loads = []\r\n load_scale = self.scale # global\r\n for (loadsPack, i_scale) in izip(self.loadIDs, self.scaleFactors):\r\n scale = i_scale * load_scale # actual scale = global * local\r\n for load in loadsPack:\r\n if (isinstance(load, Force) or isinstance(load, Moment) or\r\n isinstance(load, PLOAD4) or isinstance(load, GRAV)):\r\n loads.append(load)\r\n scale_factors.append(scale) # local\r\n elif isinstance(load, LOAD):\r\n load_data = load.getReducedLoads()\r\n (reduced_scale_factors, reduced_loads) = load_data\r\n\r\n loads += reduced_loads\r\n scale_factors += [scale * j_scale for j_scale\r\n in reduced_scale_factors]\r\n else:\r\n msg = ('%s isnt supported in getReducedLoads method'\r\n % load.__class__.__name__)\r\n raise NotImplementedError(msg)\r\n\r\n return (scale_factors, loads)","title":""},{"docid":"4c766c65b7fb4df238e1bc5ddfc9230e","score":"0.4659431","text":"def breedingControl(self, species, percentKept):\n nextGenerationUnsorted = []\n\n # Iterate over each species\n breedingList = self.getBreedingPairs(species, percentKept)\n\n for breedingPair in breedingList: # breeding our actual input pairs\n nextGenerationUnsorted.append(self.breedGenome(breedingPair[0], breedingPair[1]))\n return nextGenerationUnsorted # An unsorted list of our next","title":""},{"docid":"a1055fd23e4b064380952a08bdddb4aa","score":"0.46576226","text":"def create_groups(groups, k):\n group_samples = defaultdict(list)\n for sample_idx, group_idx in enumerate(groups):\n group_samples[group_idx].append(sample_idx)\n\n keys_to_remove = []\n for key in group_samples:\n if len(group_samples[key]) < k:\n keys_to_remove.append(key)\n continue\n\n for key in keys_to_remove:\n group_samples.pop(key)\n\n return group_samples","title":""},{"docid":"458adea54bad6e674caf8f6fadf121ff","score":"0.46449077","text":"def random_repartition_per_step(size):\n array = [random.randrange(0,100) for i in range(size)]\n s = sum(array)\n for i in range(size):\n array[i] = array[i]/s\n return array","title":""},{"docid":"142d4f855baa97d07836034d50129947","score":"0.464298","text":"def gc_blocks(seq, block_size):\n block_list = []\n step = 0\n for i in range(len(seq)//block_size):\n block_list.append(seq[step:step+block_size])\n step += block_size\n# print(block_list)\n\n #calculate GC Content\n gc_list = []\n for block in block_list:\n gc_list.append(gc_content(block))\n return tuple(gc_list), block_list","title":""},{"docid":"7247a2287f0dc3e5dc0c67a5dd7bc4ea","score":"0.4639938","text":"def shrink(M,levels):\n while levels>2:\n \n listofclust = []\n smallest = np.min(M[np.nonzero(M)])\n xs,ys = np.where(M==smallest)\n \n # x and y cordinates.\n xcord = xs[0]\n ycord = ys[0]\n # split matrix into 3 and gather smallest row.\n ''' this part retuns a matrix with the two rows replaced by the minimum of the two rows\n this matrix is still n*n'''\n #also we split colums into 3\n firstrow = M[:,xcord:xcord+1]\n secondrow = M[:,xcord+1:xcord+2]\n firstcolum = M[:ycord,:]\n secondcolum= M[ycord:ycord+1,:]\n restofRows = M[:,xcord+2:M.shape[0]]\n restofcolums = M[xcord+2:,:]\n # find the minimum of each row and colum\n minrow = np.minimum(firstrow,secondrow)\n mincolum = np.minimum(firstcolum,secondcolum)\n \n newminrow = np.delete(minrow,ycord,0) #This is the new shortened row\n rem = np.delete(M,ycord,0)\n rem2 = np.delete(rem,ycord,1)# rem2 is our final matrix.\n \n print(rem2)\n smallMatrix = rem2\n print(smallMatrix)\n clusterdlist = [xcord,ycord]\n print(clusterdlist)\n print (\"clustered\",xcord,ycord)\n listofclust.append(clusterdlist)\n \n #return smallMatrix,listofclust\n return shrink(smallMatrix,levels-1)","title":""},{"docid":"805353d0da2bd05f708b3d1d3b963c27","score":"0.46339577","text":"def get_group_sizes(groups):\n group_sizes = []\n for group in groups:\n group_sizes.append(group[\"num_players\"])\n return group_sizes","title":""},{"docid":"5652f32dc12f5705b4f8ba0fea259b2e","score":"0.46317145","text":"def make_chunks_qso(wa, redshift, divmult=1, forest_divmult=1, debug=False):\n\n cond = np.isnan(wa)\n if np.any(cond):\n warnings.warn('Some wavelengths are NaN, ignoring these pixels.')\n wa = wa[~cond]\n assert len(wa) > 0\n\n zp1 = 1 + redshift\n #reflines = np.array([1025.72, 1215.6701, 1240.14, 1398.0,\n # 1549.06, 1908, 2800 ])\n\n # generate the edges of wavelength chunks to send to fitting routine\n\n # these edges and divisions are generated by trial and error\n\n # for S/N = 15ish and resolution = 2000ish\n\n div = np.rec.fromrecords([(200. , 500. , 25),\n (500. , 800. , 25),\n (800. , 1190., 25),\n (1190., 1213., 4),\n (1213., 1230., 6),\n (1230., 1263., 6),\n (1263., 1290., 5),\n (1290., 1340., 5),\n (1340., 1370., 2),\n (1370., 1410., 5),\n (1410., 1515., 5),\n (1515., 1600., 15),\n (1600., 1800., 8),\n (1800., 1900., 5),\n (1900., 1940., 5),\n (1940., 2240., 15),\n (2240., 3000., 25),\n (3000., 6000., 80),\n (6000., 20000., 100),\n ], names=str('left,right,num'))\n\n div.num[2:] = np.ceil(div.num[2:] * divmult)\n div.num[:2] = np.ceil(div.num[:2] * forest_divmult)\n div.left *= zp1\n div.right *= zp1\n if debug:\n print(div.tolist())\n temp = [np.linspace(left, right, n+1)[:-1] for left,right,n in div]\n edges = np.concatenate(temp)\n\n i0,i1,i2 = edges.searchsorted([wa[0], 1210*zp1, wa[-1]])\n if debug:\n print(i0,i1,i2)\n return edges[i0:i2]","title":""},{"docid":"af4600210841ee1afc72a7cbc571a9e2","score":"0.46290246","text":"def _find_complete_sets(self):\n sorted_by_rank = sorted(self._cards, key=lambda card: card.rank.value)\n grouped_by_rank = groupby(sorted_by_rank, key=lambda card: card.rank)\n\n for _, cards_of_rank_x in grouped_by_rank:\n cards = list(cards_of_rank_x)\n if len(cards) == 4:\n # 4 distinct 3-long sets, 1 4-long set\n for combo in combinations(cards, 3):\n self.create_meld(*combo)\n self.create_meld(*cards)\n elif len(cards) == 3:\n self.create_meld(*cards)","title":""},{"docid":"45b047d0dcb16fc4cbf8a1530b1108ed","score":"0.4622879","text":"def test_find_full_sm_decay_groups(self):\n\n # Import the full sm with param_card\n sm_path = import_ufo.find_ufo_path('sm')\n full_sm_base = import_ufo.import_full_model(sm_path)\n full_sm = decay_objects.DecayModel(full_sm_base, True)\n param_path = os.path.join(_file_path,\n '../input_files/param_card_full_sm.dat')\n full_sm.read_param_card(param_path)\n \n # Stage 1: Find stable particles with \n # nonzero neutrino masses, quark masses\n # Turned on light quark and neutrino masses\n neutrinos = [12, 14, 16]\n lightquarks1 = [1, 3]\n for npid in neutrinos:\n full_sm.get_particle(npid)['mass'] = '1E-6'\n full_sm.get_particle(-npid)['mass'] = '1E-6'\n for qpid in lightquarks1:\n full_sm.get_particle(qpid)['mass'] = '5'\n full_sm.get_particle(2)['mass'] = '1'\n\n # the stable particles are the ones with lightest mass in\n # their group\n goal_groups_1 = set([(21,22, 23,25), # 23 and 25 are calculated\n # others are massless default\n (1,), (2,), (3,), (4,), (5,), (6,),\n (11,), (12,), (13,), (14,), (15,), (16,),\n (24,)])\n goal_stable_particles_1 = set([(21,22),\n (2,),\n (11,), (12,), (14,), (16,)])\n\n # Test decay groups\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\n group])) \\\n for group in full_sm.get('decay_groups')]),\n goal_groups_1)\n # Test stable particles\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\n group])) \\\n for group in full_sm.get('stable_particles')]),\n goal_stable_particles_1)\n\n\n # Stage 2: turn off the neutrino mass\n full_sm.get_particle(12)['mass'] = 'ZERO'\n full_sm.get_particle(14)['mass'] = 'ZERO'\n full_sm.get_particle(16)['mass'] = 'ZERO'\n full_sm.get_particle(-12)['mass'] = 'ZERO'\n full_sm.get_particle(-14)['mass'] = 'ZERO'\n full_sm.get_particle(-16)['mass'] = 'ZERO'\n\n full_sm['decay_groups'] = []\n full_sm['stable_particles'] = []\n for p in full_sm['particles']:\n p['is_stable'] = False\n\n goal_groups_2 = set([(12,14,16,21,22, 23,25), # 23,25 are\n # calculated, others are massless\n (1,), (2,), (3,), (4,), (5,), (6,),\n (11,13,15,24)])\n goal_stable_particles_2 = set([(12,14,16,21,22),\n (2,),\n (11,)])\n goal_stable_pid_2 = [2, 11, 12,14,16,21,22]\n\n # Test decay groups\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\n group])) \\\n for group in full_sm.get('decay_groups')]),\n goal_groups_2)\n\n # Test stable particles\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\n group])) \\\n for group in full_sm.get('stable_particles')]),\n goal_stable_particles_2)\n\n # Test the assignment of is_stable\n self.assertEqual(sorted([p.get_pdg_code() \\\n for p in full_sm.get('particles') \\\n if p.get('is_stable')]), goal_stable_pid_2)","title":""},{"docid":"d97e4fbdf96ddfb972d0dbc945225e9b","score":"0.4618307","text":"def matrizes_rigidez_elementos(self) -> List[np.ndarray]:\n logger.debug('Calculando as matrizes de rigidez dos elementos...')\n\n nel = len(self.elementos)\n kels = []\n c = 5\n for i, e in enumerate(self.elementos):\n if c <= (perc := (int(100 * (i + 1) / nel))):\n c += 5\n logger.debug(f'{perc}%')\n kels.append(e.matriz_rigidez())\n return kels","title":""},{"docid":"1e91ac1bbe134075af3e1c52cd310bb3","score":"0.46178484","text":"def _extract_gs(dataset):\n\n matrix_group_prefix = \"bns_matrix\"\n new_dataset = list()\n for data_row in dataset:\n answer_id = data_row[\"_uuid\"]\n filtered_row = {k: v for (k, v) in data_row.items() if matrix_group_prefix in k}\n gs = set([k.split('/')[0][len(matrix_group_prefix)+1:] for k in filtered_row.keys()])\n\n for item in gs:\n row = dict()\n row[\"answer_id\"] = answer_id\n row[\"gs\"] = item\n row[\"have\"] = True if filtered_row[\n \"{0}_{1}/{0}_{1}_{2}\".format(matrix_group_prefix, item, \"possess\")] == 'yes' else False\n row[\"necessary\"] = True if filtered_row[\"{0}_{1}/{0}_{1}_{2}\".format(matrix_group_prefix,item,\n \"necessary\")] == 'yes' else False\n if \"{0}_{1}/{0}_{1}_{2}\".format(matrix_group_prefix,item, \"number\") not in filtered_row or not row[\"have\"]:\n row[\"quantity\"] = None\n else:\n row[\"quantity\"] = filtered_row[\"{0}_{1}/{0}_{1}_{2}\".format(matrix_group_prefix, item, \"number\")]\n\n new_dataset.append(row)\n\n return tablib.Dataset().load(json.dumps(new_dataset, sort_keys=True))","title":""},{"docid":"cb55f196b2ea7ec43a089a643b7dff85","score":"0.46172634","text":"def fixPartition(G, partition, num_buses, bus_capacity, rowdy_groups): \n \n og_partition_len = len(partition)\n \n if len(partition) > num_buses:\n while len(partition) > num_buses:\n min_comm_key = minCommKey(partition)\n min_comm = partition[min_comm_key]\n del partition[min_comm_key]\n for comm_key in partition.keys():\n if len(partition[comm_key]) < bus_capacity:\n while len(min_comm) > 0 and len(partition[comm_key]) < bus_capacity:\n partition[comm_key].append(min_comm.pop())\n if len(min_comm) == 0:\n break;\n \n \n if len(partition) < num_buses:\n original_keys = list(partition.keys())\n for bus in range(og_partition_len, num_buses):\n partition[bus] = []\n \n while [] in partition.values():\n students_to_move = []\n while len(students_to_move) < num_buses - og_partition_len:\n for bus in range(og_partition_len):\n if len(students_to_move) == num_buses - og_partition_len:\n break;\n if len(partition[bus]) > 1:\n students_to_move.append(partition[bus].pop())\n \n for bus in range(og_partition_len, num_buses):\n partition[bus].append(students_to_move.pop()) \n \n fixBusOverflow(partition, num_buses, bus_capacity, rowdy_groups)\n return partition","title":""}],"string":"[\n {\n \"docid\": \"71185b4db892fbe483f9ea0dd369d699\",\n \"score\": \"0.6408654\",\n \"text\": \"def optimal_solution(self, groupSizes: List[int]) -> List[List[int]]:\\n buckets = defaultdict(list)\\n for i, size in enumerate(groupSizes):\\n buckets[size].append(i)\\n result = []\\n for size, bucket in buckets.items():\\n if len(bucket) > size:\\n placeholder = []\\n while len(bucket) > size:\\n placeholder.append(bucket.pop())\\n if len(placeholder) == size:\\n result.append(placeholder)\\n placeholder = []\\n result.append(bucket)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54aefebea77541718d0bd6eef8024472\",\n \"score\": \"0.6142382\",\n \"text\": \"def finalize(self) -> List[Tuple[Set[int], float]]:\\n allocation, total_bids = self._maximize_welfare(self.all_bids)\\n return [\\n (\\n items,\\n # p_i; if/else not necessary but improves performance\\n self._maximize_welfare(self.all_bids[:i] + self.all_bids[i+1:])[1] \\\\\\n - (total_bids - b_i) if items else 0\\n ) for i, (items, b_i) in enumerate(allocation)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35591f60ff1d24dcb29ca814c40f1bd\",\n \"score\": \"0.6078587\",\n \"text\": \"def groupThePeople(self, groupSizes: List[int]) -> List[List[int]]:\\n return self.optimal_solution(groupSizes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f349147348bc957f4d867d3124c62ef0\",\n \"score\": \"0.5844362\",\n \"text\": \"def initial_solution(self, groupSizes: List[int]) -> List[List[int]]:\\n prev, so_far, result = 0, 0, []\\n for p, size in sorted(zip(range(len(groupSizes)), groupSizes),\\n key=lambda a: a[1]):\\n if prev != size or so_far >= size:\\n so_far = 0\\n result.append([])\\n result[-1].append(p)\\n prev, so_far = size, so_far + 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1fd0590f952d6cdc308fdc954ef647\",\n \"score\": \"0.5642378\",\n \"text\": \"def GetShrinkFactors(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e9c16dd8c9451e5a664f568f5a153af\",\n \"score\": \"0.5618619\",\n \"text\": \"def divisor_subgroups(self):\\n v = self.__H\\n ans = []\\n for M in self.level().divisors():\\n w = [a % M for a in v if a%M]\\n ans.append(GammaH_constructor(M, w))\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d836615596a3ca95e461d6fcb4446ab\",\n \"score\": \"0.54804164\",\n \"text\": \"def worker_placement_groups(args: Args) -> List[ray.PlacementGroupID]:\\n pgs = [ray.util.placement_group([{\\\"worker\\\": 1}]) for _ in range(args.num_reducers)]\\n ray.get([pg.ready() for pg in pgs])\\n try:\\n yield pgs\\n finally:\\n for pg in pgs:\\n ray.util.remove_placement_group(pg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a1c107ca2a1cb4c6ac20afefa68e6a\",\n \"score\": \"0.5404229\",\n \"text\": \"def summarize_gaps(values):\\n\\n complete_set = set(range(1, max(values) + 1))\\n frame_set = set(values)\\n full_set = list(complete_set.difference(frame_set))\\n return summarize_list(full_set)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c275858a9ec55c183668217369367d\",\n \"score\": \"0.53388566\",\n \"text\": \"def sub_scale(self, indices):\\n sub_scale = []\\n for item in indices:\\n if item >= len(self.chromatic):\\n pass\\n else:\\n sub_scale.append(self.chromatic[item])\\n return sub_scale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0aa689ddcafc8afc075358be376149\",\n \"score\": \"0.5262088\",\n \"text\": \"def div_group(ls, g, t):\\n\\n if not isinstance(ls, list) or not isinstance(g, int) or not isinstance(t, int):\\n return []\\n\\n ls_return = []\\n\\n for i in range(0, g * t, t):\\n ls_return.append((ls[i:i + t]))\\n\\n if g * t < len(ls):\\n for i in ls_return:\\n for k in i:\\n ls.remove(k)\\n\\n for i in ls:\\n print('Team ' + str(i) + ' has been removed')\\n logger.debug('Team ' + str(i) + ' has been removed')\\n\\n return ls_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d32222f949eeb0cd9b0612a22893249\",\n \"score\": \"0.52050626\",\n \"text\": \"def allowed_reductions(self):\\n return (\\n (i for i in range(1, self.size_x + 1) if self.size_x % i == 0),\\n (i for i in range(1, self.size_y + 1) if self.size_y % i == 0),\\n (i for i in range(1, self.size_z + 1) if self.size_z % i == 0)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1adef06b928542415c8f808d9f6744c\",\n \"score\": \"0.5172568\",\n \"text\": \"def scale_shape_group(shapes, x_amp, y_amp, x_offset, y_offset):\\n for i in range(len(shapes)):\\n shapes[i] = scale_shape(shapes[i], x_amp, y_amp, x_offset, y_offset)\\n return shapes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72d63bc97239fffbf5bdb426bab0f16\",\n \"score\": \"0.5145254\",\n \"text\": \"def get_group(r, group, ranges, r_factor):\\n if not group:\\n return\\n out = []\\n max_height = 0\\n ranges.append(GrpRanges(r, []))\\n curr_ranges = []\\n for subgroup in group:\\n elements, height = get_subgroup(r, subgroup, ranges, curr_ranges,\\n r_factor)\\n out.extend(elements)\\n if height > max_height:\\n max_height = height\\n return out, max_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5a9ccbe14ec836ca8420a4128c3957\",\n \"score\": \"0.5119607\",\n \"text\": \"def list_transformations(grouped_configs, max_energy, N, basis):\\n \\n transformation_list = []\\n degen_list = []\\n if max_energy == 0:\\n max_energy = len(grouped_configs)\\n for entry in grouped_configs[:max_energy]:\\n single_list = []\\n \\n if max_energy == 1:\\n if len(entry)>1:\\n #index = 0\\n #for num in entry[:-1]:\\n #index = index + 1\\n \\n for index, num1 in enumerate(entry[:-1]):\\n \\n bas = basis.state(num1)\\n \\n for num2 in entry[index+1 : ]:\\n bas2 = basis.state(num2)\\n diff = bas - bas2\\n \\n nonzero = np.nonzero(diff)[0]\\n if nonzero.size > N/2: #This is the piece that will throw out hamming distances greater than N/2\\n continue\\n \\n else:\\n substate1 = basis.index(bas[nonzero])\\n substate2 = basis.index(bas2[nonzero])\\n\\n transformation = [nonzero, substate1, substate2]\\n degen_list.append([num1, num2, transformation])\\n single_list.append(transformation)\\n\\n transformation_list.append(single_list)\\n else:\\n if len(entry)>1:\\n #index = 0\\n #for num in entry[:-1]:\\n #index = index + 1\\n \\n for index, num in enumerate(entry[:-1]):\\n \\n num = basis.state(num)\\n \\n for num2 in entry[index+1 : ]:\\n num2 = basis.state(num2)\\n diff = num - num2\\n \\n nonzero = np.nonzero(diff)[0]\\n if nonzero.size > N/2: #This is the piece that will throw out hamming distances greater than N/2\\n continue\\n \\n else:\\n substate1 = basis.index(num[nonzero])\\n substate2 = basis.index(num2[nonzero])\\n\\n transformation = [nonzero, substate1, substate2]\\n single_list.append(transformation)\\n\\n transformation_list.append(single_list)\\n \\n return transformation_list, degen_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b428275c4e9ba52756b30aec6ffc05\",\n \"score\": \"0.5099304\",\n \"text\": \"def shrink(self):\\n new_arr = [None] * (self.capacity // 2)\\n for i in range(self.num_items):\\n new_arr[i] = self.arr[i]\\n self.arr = new_arr\\n self.capacity = self.capacity // 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3342edd15f241391f97a351dbcee1e\",\n \"score\": \"0.5093482\",\n \"text\": \"def reduce_halfspaces(halfspaces):\\n reduced_halfspaces = set()\\n for halfspace in halfspaces:\\n reduced_halfspaces.add(tuple(halfspace))\\n return np.array([list(i) for i in reduced_halfspaces])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91992521191b866b521143c088ae3e04\",\n \"score\": \"0.50841415\",\n \"text\": \"def _assemble_all_gages(self):\\n gages_ld = []\\n if self.gages:\\n for gage in self.gages:\\n gages_ld.append(self._assemble_gage(gage))\\n return gages_ld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c7a9ebab0a65997cfbebd3095b8b18\",\n \"score\": \"0.5081407\",\n \"text\": \"def groupWorkouts(workouts):\\n n_workouts = len(workouts)\\n if n_workouts == 1:\\n return [workouts]\\n\\n groups = []\\n avail = [True]*n_workouts\\n for i in xrange(n_workouts):\\n if avail[i]:\\n group = [workouts[i]]\\n avail[i] = False\\n else:\\n continue\\n for j in xrange(i+1, n_workouts):\\n if avail[j]:\\n dis = computeWorkoutsDis(workouts[i], workouts[j])\\n if dis < 0.1:\\n group.append(workouts[j])\\n avail[j] = False\\n groups.append(group)\\n return groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4183ec56689a197dd3f0873c58d2587f\",\n \"score\": \"0.50549597\",\n \"text\": \"def get_part_svg(elements, r_factor):\\n if not elements:\\n return []\\n out = []\\n radii = get_radii(elements, r_factor)\\n ranges = []\\n max_height = 0\\n for r, element in zip(reversed(radii), reversed(elements)):\\n group, height = get_group(r, element[1:], ranges, r_factor)\\n out.extend(group)\\n if height > max_height:\\n max_height = height\\n out.reverse()\\n return out, max_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac69afc835eae5b5f73ddfcac2df5756\",\n \"score\": \"0.50334626\",\n \"text\": \"def apply_grouping(items, groupxs, axis=0):\\n # Should there be a contiguous check here?\\n # items_ = np.ascontiguousarray(items)\\n return [items.take(xs, axis=axis) for xs in groupxs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85a8925b55ca829496e2dd359de11a3a\",\n \"score\": \"0.50334203\",\n \"text\": \"def __predict_shrinkage_groups(self, X_group, X_value, method=\\\"predict\\\"):\\n # DataFrame with predictions for each hierarchy level, per row. Missing groups errors are thrown here.\\n hierarchical_predictions = pd.concat(\\n [\\n pd.Series(\\n self.__predict_groups(\\n X_group, X_value, level_columns, method=method\\n )\\n )\\n for level_columns in self.group_colnames_hierarchical_\\n ],\\n axis=1,\\n )\\n\\n # This is a Series with values the tuples of hierarchical grouping\\n prediction_groups = pd.Series(\\n [tuple(_) for _ in X_group.itertuples(index=False)]\\n )\\n\\n # This is a Series of arrays\\n shrinkage_factors = prediction_groups.map(self.shrinkage_factors_)\\n\\n # Convert the Series of arrays it to a DataFrame\\n shrinkage_factors = pd.DataFrame.from_dict(shrinkage_factors.to_dict()).T\\n return (hierarchical_predictions * shrinkage_factors).sum(axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362e99ec7c97d20b84daf05c97a92c75\",\n \"score\": \"0.50243205\",\n \"text\": \"def slice_wfd_area(nslice, target_map, scale_down_factor=0.2):\\n # Make it so things still sum to one.\\n scale_up_factor = nslice - scale_down_factor*(nslice-1)\\n\\n wfd = target_map['r'] * 0\\n wfd_indices = np.where(target_map['r'] == 1)[0]\\n wfd[wfd_indices] = 1\\n wfd_accum = np.cumsum(wfd)\\n split_wfd_indices = np.floor(np.max(wfd_accum)/nslice*(np.arange(nslice)+1)).astype(int)\\n split_wfd_indices = split_wfd_indices.tolist()\\n split_wfd_indices = [0] + split_wfd_indices\\n\\n all_scaled_down = {}\\n for filtername in target_map:\\n all_scaled_down[filtername] = target_map[filtername]+0\\n all_scaled_down[filtername][wfd_indices] *= scale_down_factor\\n\\n scaled_maps = []\\n for i in range(len(split_wfd_indices)-1):\\n new_map = {}\\n indices = wfd_indices[split_wfd_indices[i]:split_wfd_indices[i+1]]\\n for filtername in all_scaled_down:\\n new_map[filtername] = all_scaled_down[filtername] + 0\\n new_map[filtername][indices] = target_map[filtername][indices]*scale_up_factor\\n scaled_maps.append(new_map)\\n\\n return scaled_maps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a660ca103a8fc41de651ac42ee0bfb4f\",\n \"score\": \"0.5007573\",\n \"text\": \"def _partition_array_weight(a, k, w):\\n\\n ret = [Partition() for _ in range(k)]\\n curr = 0\\n for i in range(len(a)):\\n if ret[curr].total + a[i] <= w:\\n ret[curr].end = i\\n ret[curr].total += a[i]\\n elif curr >= k - 1:\\n return None\\n else:\\n # Start new partition\\n curr += 1\\n ret[curr].end = i\\n ret[curr].total = a[i]\\n \\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b14fd41bb5a4f4dc3d0a1f4f3e1b164\",\n \"score\": \"0.49978846\",\n \"text\": \"def __check_shrinkage_func(self):\\n group_lengths = [10, 5, 2]\\n expected_shape = np.array(group_lengths).shape\\n try:\\n result = self.shrinkage(group_lengths)\\n except Exception as e:\\n raise ValueError(\\n f\\\"Caught an exception while checking the shrinkage function: {str(e)}\\\"\\n ) from e\\n else:\\n if not isinstance(result, np.ndarray):\\n raise ValueError(\\n f\\\"shrinkage_function({group_lengths}) should return an np.ndarray\\\"\\n )\\n if result.shape != expected_shape:\\n raise ValueError(\\n f\\\"shrinkage_function({group_lengths}).shape should be {expected_shape}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d0dd30fbfe91413018d999800cf4f0\",\n \"score\": \"0.4989977\",\n \"text\": \"def chunks(self, array, granular, arr_len):\\n\\t\\tfor i in xrange(0, arr_len, granular):\\n\\t\\t\\tyield array[i:i+granular]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db26f14609808bce8459a5a49914acb6\",\n \"score\": \"0.4978328\",\n \"text\": \"def reduce_exemplar_set(self, m):\\n for i, exemplar_set in enumerate(self.exemplar_sets):\\n self.exemplar_sets[i] = exemplar_set[:m]\\n return self.exemplar_sets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c6ce43e7f7a6071770c8207681b0df\",\n \"score\": \"0.49737713\",\n \"text\": \"def test_find_mssm_decay_groups(self):\\n\\n mssm = import_ufo.import_model('mssm')\\n decay_mssm = decay_objects.DecayModel(mssm, True)\\n decay_mssm.find_decay_groups()\\n goal_groups = [[25, 35, 36, 37],\\n [1000001, 1000002, 1000003, 1000004, 1000005, 1000006, 1000011, 1000012, 1000013, 1000014, 1000015, 1000016, 1000021, 1000022, 1000023, 1000024, 1000025, 1000035, 1000037, 2000001, 2000002, 2000003, 2000004, 2000005, 2000006, 2000011, 2000013, 2000015]]\\n\\n # find_decay_groups_general should be run automatically\\n for i, group in enumerate(decay_mssm['decay_groups']):\\n self.assertEqual(sorted([p.get('pdg_code') for p in group]),\\n goal_groups[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0338b49c6dbc6bb7c4fbfc28c7e60398\",\n \"score\": \"0.49721316\",\n \"text\": \"def extract_structure(self):\\n bursts = defaultdict(list)\\n for i in range(1, self.k):\\n burst_times = np.argwhere(self.q >= i).flatten()\\n bid = 0\\n start = burst_times[0]\\n last_bt = start\\n weight = self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\\n for j in range(1, len(burst_times)):\\n # print(burst_times[j], last_bt+1)\\n if burst_times[j] == last_bt + 1:\\n last_bt = burst_times[j]\\n weight += self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\\n else:\\n bursts[i].append([bid, start, last_bt, weight])\\n bid += 1\\n start = burst_times[j]\\n last_bt = start\\n weight = self.burst_weight(self.p[0], self.p[i], self.d[last_bt], self.r[last_bt])\\n bursts[i].append([bid, start, last_bt, weight])\\n return bursts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aaa5c1e66a18d9a9f924fe73467fc0e\",\n \"score\": \"0.4957383\",\n \"text\": \"def interval_partition(set_of_jobs):\\n released = [] # holds index of resources in r that are free\\n occupied = [] # holds index of resources in r that are occupied\\n resources = [] # holds all resources as lists of jobs that they have\\n\\n quick_sort(set_of_jobs, 0, len(set_of_jobs) -1) # sort jobs by start time\\n for j in set_of_jobs:\\n # move all free resources in occupied to released\\n released, occupied = adjust(j[0], released, occupied, resources)\\n if released:\\n r = released[0] # choose a resource that is free\\n released.remove(r) # resource is no longer free\\n else:\\n r = len(resources)\\n resources.append([]) # create a new resource to assign jobs to\\n occupied.append(r) # move the resource into occupied\\n resources[r].append(j) # assign this job to the resource\\n return resources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54de3f210cfef862abd79475938288b5\",\n \"score\": \"0.49391165\",\n \"text\": \"def gramians(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393c459578ad742b86674a45cd541ef4\",\n \"score\": \"0.49327806\",\n \"text\": \"def kListGenerator(groups, s=1):\\n\\n klist = []\\n for i in groups:\\n klist.append(s*len(i))\\n return klist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f164d2169c7b333d5f52a1fbccc1ba6\",\n \"score\": \"0.49275732\",\n \"text\": \"def shrink_mod_scale(x, scale):\\n return [_x - _x % _s for _x, _s in zip(x, to_list(scale, 2))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c124322e2f485766bd817cf1b04cdc47\",\n \"score\": \"0.49249062\",\n \"text\": \"def constituent_groups(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"249ea79979813b222e95d638230f9919\",\n \"score\": \"0.4922036\",\n \"text\": \"def group_and_assign(df, groupby='condition', key='frac_mass'):\\n dfs = []\\n for g, d in df.groupby(groupby):\\n _df = prot.viz.assign_rect_bounds(d, key)\\n dfs.append(_df)\\n return pd.concat(dfs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1de2cb1643840e6a8ce38f44a8c3929b\",\n \"score\": \"0.49206603\",\n \"text\": \"def group_fn(self, sizes):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21cb4ab296b47c74568196183028693f\",\n \"score\": \"0.49135706\",\n \"text\": \"def create_groups(self):\\n # compute histogram\\n self.create_histogram()\\n cl = dict()\\n list_rows = self.band_matrix.index.values\\n remaining = len(list_rows)\\n\\n list_sensitive_added = list()\\n group_dict = dict()\\n\\n value = 1\\n old_value = 0\\n\\n while value > 0:\\n for j in self.csi.index.values:\\n\\n if self.check_if_already_in(j, list_sensitive_added):\\n continue\\n\\n t = self.band_matrix.loc[j, :]\\n\\n cl[j] = list()\\n\\n index = numpy.where(list_rows == j)[0][0]\\n\\n pos_idx = index + 1\\n neg_idx = index - 1\\n counter = 0\\n stop = 2 * self.alpha * self.p\\n\\n not_groupable = False\\n\\n while counter < stop:\\n\\n if neg_idx >= 0:\\n counter += self.populate_cl(cl, j, list_rows[neg_idx])\\n neg_idx -= 1\\n\\n if pos_idx < remaining:\\n counter += self.populate_cl(cl, j, list_rows[pos_idx])\\n pos_idx += 1\\n\\n if pos_idx == remaining and neg_idx == -1:\\n\\n if len(cl[j]) < self.p - 1:\\n not_groupable = True\\n\\n break\\n\\n if not_groupable:\\n continue\\n\\n list_similar = self.qid_similarity(cl[j], t)\\n\\n for idx in list_similar:\\n if idx in self.sensitive_row:\\n list_sensitive_added.append(idx)\\n for si in self.sensitive_row[idx]:\\n self.sensitive_histogram[si] -= 1\\n\\n if self.privacy_requisite_chek(remaining):\\n\\n for elem in list_similar:\\n list_rows = list_rows[list_rows != elem]\\n\\n remaining = len(list_rows)\\n # inserisco il gruppo nella struttura a dict\\n group_dict[t.name] = list_similar\\n pass\\n\\n else:\\n for idx in list_similar:\\n # print(list_sensitive_added, idx)\\n if idx in self.sensitive_row:\\n list_sensitive_added.remove(idx)\\n for si in self.sensitive_row[idx]:\\n self.sensitive_histogram[si] += 1\\n continue\\n\\n value = 0\\n\\n for histogram_value in self.sensitive_histogram.items():\\n value += histogram_value[1]\\n\\n if value > 0 and old_value == value:\\n # l'istogramma non è cambiato quindi non posso migliorare nulla\\n print(f\\\"Grado di privacy {self.p} non soddisfacibile\\\")\\n return -1\\n\\n elif value > 0:\\n old_value = value\\n\\n print(\\\"Grado di privacy soddisfacibile.\\\")\\n\\n counter = 0\\n\\n result = dict()\\n\\n for group in group_dict:\\n counter += 1\\n final_anonymized = pandas.DataFrame()\\n # print(\\\"GRUPPO\\\", counter, \\\"con Sensitive Item\\\", self.sensitive_row[group])\\n\\n for row in group_dict[group]:\\n new_row = self.band_matrix.iloc[lambda x: x.index == row]\\n final_anonymized = final_anonymized.append(other=new_row)\\n\\n result[group] = final_anonymized\\n # print(final_anonymized)\\n\\n final_anonymized = pandas.DataFrame()\\n for non_sensitive_row_index in list_rows:\\n new_row = self.band_matrix.iloc[lambda x: x.index == non_sensitive_row_index]\\n final_anonymized = final_anonymized.append(other=new_row)\\n\\n # print(\\\"GRUPPO\\\", (counter + 1), \\\"non sensitive\\\")\\n # print(final_anonymized)\\n result[-1] = final_anonymized\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ad9af90fe5cc7d9ae4068624166c65\",\n \"score\": \"0.49049625\",\n \"text\": \"def getGroups():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5386667e493b019611d7079e79f5e93\",\n \"score\": \"0.4890527\",\n \"text\": \"def capacity_values(self, qs=None, tasks=None, breakdown=False, graph=None):\\n if qs is None: # Optionally BYOQS - bring your own queryset\\n qs = self.all().prefetch_related('instances')\\n instance_ig_mapping, ig_ig_mapping = self.capacity_mapping(qs=qs)\\n\\n if tasks is None:\\n tasks = self.model.unifiedjob_set.related.related_model.objects.filter(\\n status__in=('running', 'waiting'))\\n\\n if graph is None:\\n graph = {group.name: {} for group in qs}\\n for group_name in graph:\\n self.zero_out_group(graph, group_name, breakdown)\\n for t in tasks:\\n # TODO: dock capacity for isolated job management tasks running in queue\\n impact = t.task_impact\\n if t.status == 'waiting' or not t.execution_node:\\n # Subtract capacity from any peer groups that share instances\\n if not t.instance_group:\\n impacted_groups = []\\n elif t.instance_group.name not in ig_ig_mapping:\\n # Waiting job in group with 0 capacity has no collateral impact\\n impacted_groups = [t.instance_group.name]\\n else:\\n impacted_groups = ig_ig_mapping[t.instance_group.name]\\n for group_name in impacted_groups:\\n if group_name not in graph:\\n self.zero_out_group(graph, group_name, breakdown)\\n graph[group_name]['consumed_capacity'] += impact\\n if breakdown:\\n graph[group_name]['committed_capacity'] += impact\\n elif t.status == 'running':\\n # Subtract capacity from all groups that contain the instance\\n if t.execution_node not in instance_ig_mapping:\\n if not t.is_containerized:\\n logger.warning('Detected %s running inside lost instance, '\\n 'may still be waiting for reaper.', t.log_format)\\n if t.instance_group:\\n impacted_groups = [t.instance_group.name]\\n else:\\n impacted_groups = []\\n else:\\n impacted_groups = instance_ig_mapping[t.execution_node]\\n for group_name in impacted_groups:\\n if group_name not in graph:\\n self.zero_out_group(graph, group_name, breakdown)\\n graph[group_name]['consumed_capacity'] += impact\\n if breakdown:\\n graph[group_name]['running_capacity'] += impact\\n else:\\n logger.error('Programming error, %s not in [\\\"running\\\", \\\"waiting\\\"]', t.log_format)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4b834164834a82bc541494755c4148\",\n \"score\": \"0.48843947\",\n \"text\": \"def group_inds(self, values):\\n results = []\\n\\n for i, v in enumerate(values):\\n if i == 0:\\n mini = maxi = v\\n temp = [v]\\n else:\\n # SETS 4 CADENCE LIMIT\\n if (np.abs(v-maxi) <= 3):\\n temp.append(v)\\n if v > maxi:\\n maxi = v\\n if v < mini:\\n mini = v\\n else:\\n results.append(temp)\\n mini = maxi = v\\n temp = [v]\\n \\n # GETS THE LAST GROUP\\n if i == len(values)-1:\\n results.append(temp)\\n\\n return np.array(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e387caf976c38b620ee8ca28f38c66\",\n \"score\": \"0.4881927\",\n \"text\": \"def scaffold_to_smiles(all_smiles: List[str], use_indices: bool = False) -> Dict[str, Union[Set[str], Set[int]]]:\\n scaffolds = defaultdict(set)\\n for i, smiles in tqdm(enumerate(all_smiles), total=len(all_smiles)):\\n scaffold = generate_scaffold(smiles)\\n if use_indices:\\n scaffolds[scaffold].add(i)\\n else:\\n scaffolds[scaffold].add(smiles)\\n\\n return scaffolds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e22a73fb73768d7b99e76ab297d6f4\",\n \"score\": \"0.4874075\",\n \"text\": \"def get_weight(groups=None):\\n ncat = len(groups)\\n nitems = 0\\n for group in groups:\\n for items in group:\\n temp = np.max(items)\\n if temp > nitems:\\n nitems = temp\\n weight = np.zeros((ncat, nitems+1, nitems+1))\\n for i in range(ncat):\\n for group in groups[i]:\\n for id1, id2 in itertools.product(group, group):\\n if id1 != id2:\\n weight[i, id1, id2] = 1\\n norm = weight[i].sum(0)[:, np.newaxis]\\n weight[i] /= norm\\n\\n return weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8678e1237ebb7d0c86f7c5df3b1e771\",\n \"score\": \"0.48491552\",\n \"text\": \"def _update_individual(X, W, R, gamma):\\n subjs = len(X)\\n S = []\\n for i in range(subjs):\\n S.append(RSRM._shrink(X[i] - W[i].dot(R), gamma))\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e505b7e115bcda69934bdd7bfa54b845\",\n \"score\": \"0.4835165\",\n \"text\": \"def _make_chunks(self, chunk_size, holeNumbers):\\n flag = np.concatenate(([True], holeNumbers[1:] != holeNumbers[:-1]))\\n self._zmw_idx = np.flatnonzero(flag)\\n self._chunks = []\\n self._chunk_idx = []\\n last_start_idx = 0\\n k = 1\\n while last_start_idx < self.nReads:\\n last_start = self._zmw_idx[last_start_idx]\\n next_start_idx = np.argmax(self._zmw_idx > k * chunk_size)\\n if next_start_idx == 0:\\n self._chunks.append((last_start, self.nReads - last_start))\\n self._chunk_idx.append(\\n self._zmw_idx[last_start_idx:] - last_start)\\n break\\n next_start = self._zmw_idx[next_start_idx]\\n self._chunks.append((last_start, next_start - last_start))\\n idx_sel = self._zmw_idx[last_start_idx:next_start_idx]\\n self._chunk_idx.append(idx_sel - last_start)\\n last_start_idx = next_start_idx\\n k += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac71360ec8e462d4d45d280094900f8\",\n \"score\": \"0.48279884\",\n \"text\": \"def renumber_plateaus(array) -> list:\\n group = 1\\n seq = []\\n for a, b in zip(array, array[1:]):\\n seq.append((lambda x: group if x > 0 else 0)(a))\\n if a == 1 and b == 0:\\n group += 1\\n return seq + [group * array[-1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1874523fc1157afad55402f9b5366091\",\n \"score\": \"0.4820153\",\n \"text\": \"def shard_list_uniform(input: Iterable[T], num_per_group: int) -> Sequence[Sequence[T]]:\\n assert num_per_group >= 1, \\\"need a positive number of list elements per group\\\"\\n input_list = list(input)\\n length = len(input_list)\\n if length == 0:\\n return []\\n\\n num_groups = ceil(length / num_per_group)\\n\\n # this will be a floating point number, not an integer\\n num_per_group_revised = length / num_groups\\n\\n output = []\\n\\n # leftover error, akin to Floyd-Steinberg dithering\\n # (https://en.wikipedia.org/wiki/Floyd%E2%80%93Steinberg_dithering)\\n residual = 0.0\\n\\n while input_list:\\n current_num_per_group_float = num_per_group_revised + residual\\n current_num_per_group_int = floor(num_per_group_revised + residual)\\n residual = current_num_per_group_float - current_num_per_group_int\\n\\n # With floating point approximations, we might get really close but not quite, so we'll compensate\\n # for this by saying if we're within epsilon of adding one more, we'll treat it as good enough.\\n if residual > 0.99:\\n current_num_per_group_int += 1\\n residual = current_num_per_group_float - current_num_per_group_int\\n\\n output.append(input_list[:current_num_per_group_int])\\n input_list = input_list[current_num_per_group_int:]\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae8be22581cc36cbcfb60a823672df0\",\n \"score\": \"0.48130313\",\n \"text\": \"def getAllGroupFairness(_data,_sensi_att,_target_att,_reverse_atts,_cut_point,_sensi_bound):\\n\\n # get the protected group\\n pro_index=[idx for idx,row in _data.iterrows() if row[_sensi_att] == _sensi_bound]\\n # remove sensitive attribute\\n input_scores=_data[_target_att]\\n if _target_att in _reverse_atts:\\n input_ranking=sorted(range(len(input_scores)), key=lambda k: input_scores[k])\\n else:\\n input_ranking=sorted(range(len(input_scores)), key=lambda k: input_scores[k], reverse=True)\\n # get the maximum value first to run faster\\n user_N=len(input_scores)\\n pro_N=len(pro_index)\\n\\n max_rKL=measures.getNormalizer(user_N,pro_N,KL_DIVERGENCE) \\n max_rND=measures.getNormalizer(user_N,pro_N,ND_DIFFERENCE)\\n max_rRD=measures.getNormalizer(user_N,pro_N,RD_DIFFERENCE)\\n \\n gf_rKL=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,KL_DIVERGENCE,max_rKL)\\n gf_rND=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,ND_DIFFERENCE,max_rND)\\n gf_rRD=measures.calculateNDFairness(input_ranking, pro_index, _cut_point,RD_DIFFERENCE,max_rRD)\\n \\n return pro_N, gf_rKL, gf_rND, gf_rRD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2613b905a4bb1bc9a90342a853d5d98c\",\n \"score\": \"0.47948572\",\n \"text\": \"def clean_group_volatility(groups):\\n MOVEMENT_VOL = 3\\n DEVIATION_VOL = 4\\n group_ids = set([groups[k] for k in groups.keys()])\\n for i in range(3, len(group_volatility)-1):\\n r = group_volatility[i-3:i+1]\\n for vol_type in [MOVEMENT_VOL, DEVIATION_VOL]:\\n for k in group_ids:\\n vol_range = [v[vol_type][k] for v in r]\\n max_range = max(vol_range) - min(vol_range)\\n diffs = map(lambda i: abs(i[0][vol_type][k] - i[1][vol_type][k]) / max_range, zip(r[1:], r)) if max_range else tuple([1.0] * 3)\\n sq = (diffs[1] * diffs[2]) if (diffs[1] * diffs[2]) else 1.0\\n\\n # Check if squared normalized difference is out of proportion\\n if diffs[0] / sq and diffs[0] / sq < .1 and sq > .85:\\n group_volatility[i-1][vol_type][k] = r[-3][vol_type][k] + (r[-1][vol_type][k] - r[-3][vol_type][k]) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc56db6c028388357069b8f595ec9544\",\n \"score\": \"0.4794661\",\n \"text\": \"def all_metric(self,basegroup=4,upbeat=0):\\n if self._all_metric:\\n return self._all_metric\\n L=self.all_permutations()\\n S=[]\\n for i in L:\\n if i.beatscore==1.0 and i.basegroup==basegroup \\\\\\n and i.upbeat==upbeat and i.extra_exclude()==False:\\n S.append(i)\\n self._all_metric = S\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed37f02b184e0bf7cc2b10ed6b5fa1e8\",\n \"score\": \"0.47852597\",\n \"text\": \"def divideSets(lst):\\n ret = []\\n for _ in xrange(numWorker):\\n ret.append(set())\\n index = 0\\n while index < len(lst):\\n ret[index%numWorker].add(lst[index])\\n index += 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5a5f94ade0bc46bd0d0b7678160a6a\",\n \"score\": \"0.47809753\",\n \"text\": \"def gather(self):\\r\\n\\t\\t\\r\\n\\t\\t# begin list\\r\\n\\t\\tl = []\\r\\n\\t\\t\\r\\n\\t\\t# gather shelves\\r\\n\\t\\tl.append('shelves:')\\r\\n\\t\\tfor n,i in enumerate(self):\\r\\n\\t\\t\\tl.append('[shelf ' + str(n) + ']')\\r\\n\\t\\t\\tg = i.gather()\\r\\n\\t\\t\\tfor j in g:\\r\\n\\t\\t\\t\\tl.append(j)\\r\\n\\t\\t\\r\\n\\t\\treturn l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd68c8c3b72305984c851119bdc57ddf\",\n \"score\": \"0.47806868\",\n \"text\": \"def group(*groups):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23685f2c723786056a48e38b6c2ff084\",\n \"score\": \"0.47771925\",\n \"text\": \"def test_find_mssm_decay_groups_modified_mssm(self):\\n\\n mssm = import_ufo.import_model('mssm')\\n particles = mssm.get('particles')\\n no_want_particle_codes = [1000022, 1000023, 1000024, -1000024, \\n 1000025, 1000035, 1000037, -1000037]\\n no_want_particles = [p for p in particles if p.get('pdg_code') in \\\\\\n no_want_particle_codes]\\n\\n for particle in no_want_particles:\\n particles.remove(particle)\\n\\n interactions = mssm.get('interactions')\\n inter_list = copy.copy(interactions)\\n\\n for interaction in inter_list:\\n if any([p.get('pdg_code') in no_want_particle_codes for p in \\\\\\n interaction.get('particles')]):\\n interactions.remove(interaction)\\n \\n mssm.set('particles', particles)\\n mssm.set('interactions', interactions)\\n decay_mssm = decay_objects.DecayModel(mssm, True)\\n\\n decay_mssm.find_decay_groups()\\n goal_groups = set([(25, 35, 36, 37),\\n (1000001, 1000002, 1000003, 1000004, 1000005, \\n 1000006, 1000021, 2000001, 2000002, 2000003, \\n 2000004, 2000005, 2000006), \\n (1000011, 1000012), \\n (1000013, 1000014), \\n (1000015, 1000016, 2000015), \\n (2000011,), \\n (2000013,)])\\n\\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\\\\n group])) \\\\\\n for group in decay_mssm['decay_groups']]),\\n goal_groups)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d890d4a4de8b11a86542eafb823875\",\n \"score\": \"0.47742474\",\n \"text\": \"def n_groups():\\n return 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07da2f3b76e8354abeaabefed81886df\",\n \"score\": \"0.4772879\",\n \"text\": \"def block_groups(num_rows, num_groups):\\n possible_seats = [[] for i in range(num_groups)]\\n max_rows_per_group = num_rows / num_groups + 1\\n for i in range(1, num_rows + 1):\\n for j in range(0, 6):\\n possible_seats[i / max_rows_per_group].append(6 * (i - 1) + j)\\n\\n for group in possible_seats:\\n shuffle(group)\\n return possible_seats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68dfee875c1fc8d84893bff225016368\",\n \"score\": \"0.4760658\",\n \"text\": \"def create_groupings():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa9e570d7941b4d490c2f9fd23996ea\",\n \"score\": \"0.4750715\",\n \"text\": \"def get_groups(self):\\n groups = [k * np.ones(len(d)) for k, d in enumerate(self.datasets)]\\n return np.concatenate(groups)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c933826bf954ad0db83398a5b658d8\",\n \"score\": \"0.4748252\",\n \"text\": \"def split(self, **kwargs):\\n groups = [] # tuples of (list, set) -- list of reactions, set of substance keys\\n for i, r in enumerate(self.rxns):\\n for gr, gs in groups: # check if reaction is part of group, break out\\n rks = r.keys()\\n group_found = False\\n for k in rks:\\n if k in gs:\\n gr.append(i)\\n gs.update(rks)\\n group_found = True\\n break\\n if group_found:\\n break\\n else: # reaction did not fit any group\\n groups.append(([i], set(r.keys())))\\n # We might have too many groups as this point, we will now recursively fuse groups\\n i = 0\\n while True:\\n for j in range(i + 1, len(groups)):\\n if groups[i][1] & groups[j][1]: # do groups share a substance?\\n groups[i][0].extend(groups[j][0])\\n groups[i][1].update(groups[j][1])\\n groups.pop(j)\\n break\\n else:\\n i += 1\\n if i >= len(groups):\\n break\\n return [\\n self.__class__(\\n [self.rxns[ri] for ri in gr],\\n OrderedDict([(k, v) for k, v in self.substances.items() if k in gs]),\\n **kwargs\\n )\\n for gr, gs in groups\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab78db2da6217dd3f36c29f7b086cf5\",\n \"score\": \"0.47479752\",\n \"text\": \"def balanced_repartition_per_step(size):\\n array = [1/size for i in range(size)]\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ff694b21c83a6fb3b807e7416aba86\",\n \"score\": \"0.4746939\",\n \"text\": \"def bufferFillPerGroup(entries, groups=None):\\n def label(entry):\\n return int(entry.strip().split()[1])\\n\\n # 'Enough' of a group means as many entries of that group as of the biggest group.\\n labels = [label(e) for e in entries]\\n if groups is None:\\n groups = [[g] for g in set(labels)]\\n group_sizes = [len([e for e in entries if label(e) in group]) for group in groups]\\n m = max(group_sizes)\\n\\n # The strategy here is to iterate over each group until we have enough of that group.\\n def group_iterator(entries, group):\\n \\\"\\\"\\\"Iterates forever over the entries that are in group\\\"\\\"\\\"\\n labels = [int(e.split()[1]) for e in entries]\\n relevant = [e for e in entries if label(e) in group]\\n i = 0\\n while(True):\\n yield(relevant[i])\\n i += 1\\n if i == len(relevant):\\n i = 0\\n\\n new_entries = []\\n for group in groups:\\n print 'Generating group', group\\n time.sleep(0.5)\\n count = 0\\n for entry in group_iterator(entries, group):\\n new_entries.append(entry)\\n count += 1\\n if count >= m:\\n break\\n return new_entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4798121d7f9ac219532e8fb73f0048\",\n \"score\": \"0.4746029\",\n \"text\": \"def _get_scales(w):\\n if size_set == 'full':\\n scales = (1, np.arange(1, 3), np.arange(1, 4), np.arange(1, 5))\\n else:\\n scales = (1, np.arange(1, 3))\\n return scales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ee02aba9379ff8f7dca67e068b884b\",\n \"score\": \"0.47425914\",\n \"text\": \"def summarize_for_greater_scale(data: np.ndarray, divide: int) -> np.ndarray:\\n shape = data.shape\\n out_shape = tuple(v // divide for v in shape)\\n output = np.empty(out_shape)\\n for y in range(0, shape[0], divide):\\n for x in range(0, shape[1], divide):\\n output[y // divide, x // divide] = data[y:y + divide, x:x + divide].sum()\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec24bd0237c8432dc9002cba52158e71\",\n \"score\": \"0.4742162\",\n \"text\": \"def test_find_mssm_decay_groups_general(self):\\n\\n mssm = import_ufo.import_model('mssm')\\n decay_mssm = decay_objects.DecayModel(mssm, True)\\n # Read data to find massless SM-like particle\\n param_path = os.path.join(_file_path,\\n '../input_files/param_card_mssm.dat')\\n decay_mssm.read_param_card(param_path)\\n\\n # In first group, 15 and from 23 are calculated,\\n # others are massless default.\\n goal_groups = [[1,2,3,4,11,12,13,14, 15, 16,21,22, 23, 24, 25, 35, 36, 37], \\n [1000001, 1000002, 1000003, 1000004, 1000011, 1000012, 1000013, 1000014, 1000015, 1000016, 1000021, 1000022, 1000023, 1000024, 1000025, 1000035, 1000037, 2000001, 2000002, 2000003, 2000004, 2000011, 2000013, 2000015],\\n [1000005, 1000006, 2000005, 2000006], \\n [5, 6]]\\n goal_stable_particle_ids = set([(1,2,3,4,11,12,13,14,16,21,22),\\n (5,),\\n (1000022,)])\\n for i, group in enumerate(decay_mssm.get('decay_groups')):\\n pdg = sorted([p.get('pdg_code') for p in group])\\n self.assertTrue( pdg in goal_groups)\\n self.assertEqual(len(goal_groups), i+1)\\n \\n # Test if all useless interactions are deleted.\\n for inter in decay_mssm['reduced_interactions']:\\n self.assertTrue(len(inter['particles']))\\n\\n # Reset decay_groups, test the auto run from find_stable_particles\\n decay_mssm['decay_groups'] = []\\n \\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in plist])) for plist in decay_mssm.get('stable_particles')]), goal_stable_particle_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e483361bbf01020799cfa9aed064475\",\n \"score\": \"0.47401917\",\n \"text\": \"def GC_chunk(size, step):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b933fd4ea1bcaeccac940035d3a59a89\",\n \"score\": \"0.47342616\",\n \"text\": \"def reduce(self):\\n self.stats['initial_complexity'] = self.complexity()\\n\\n moves = []\\n\\n while True:\\n moves.append([])\\n\\n for cell in self.open(1):\\n cell.value = next(iter(cell.values))\\n self.stats['singles'] += 1\\n moves[-1].append((tuple(cell.coord), cell.value))\\n\\n for group in chain(self.blocks, self.columns, self.rows):\\n group.reduce()\\n\\n if not next(self.open(1), None):\\n break\\n\\n self.block_stats = reduce(operator.add, map(lambda x: x.stats, self.blocks), Counter())\\n self.row_stats = reduce(operator.add, map(lambda x: x.stats, self.rows), Counter())\\n self.column_stats = reduce(operator.add, map(lambda x: x.stats, self.columns), Counter())\\n\\n self.stats['final_complexity'] = self.complexity()\\n self.stats['reduce'] += len(moves)\\n\\n return moves\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce96b8803285e305913a7b190bb3a196\",\n \"score\": \"0.4732599\",\n \"text\": \"def _group_gens(self):\\n if (self.conductor() % 2) == 0:\\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(0), ZZ(-1), ZZ(1), ZZ(0)] ]\\n else:\\n p = self.prime()\\n if p == 2:\\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(1), ZZ(0), ZZ(p), ZZ(1)] ]\\n else:\\n a = Zmod(p**(self.u() + 1))(ZZ(Zmod(p).unit_gens()[0]))\\n return [ [ZZ(1), ZZ(1), ZZ(0), ZZ(1)], [ZZ(1), ZZ(0), ZZ(p), ZZ(1)],\\n [ZZ(a), 0, 0, ZZ(~a)] ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373594eefe1c00f3d82872cf124439e2\",\n \"score\": \"0.47316873\",\n \"text\": \"def _get_cut_data(ZZ, leaf_data, groups, bounds_min):\\n ZZ = ZZ.sort_index()\\n\\n groups = [group for group in groups if group != ZZ.index.max()]\\n\\n levels = groups.copy()\\n height_bounds = []\\n for level in levels:\\n h = ZZ.loc[level, \\\"height\\\"] if level in ZZ.index else 0.\\n parent = (ZZ[\\\"children\\\"].apply(lambda children: level in children)\\n .idxmax())\\n parent_h = ZZ.loc[parent, \\\"height\\\"]\\n height_bounds.append([h, parent_h])\\n groups = [[group] for group in groups]\\n\\n for cluster, children in ZZ[\\\"children\\\"].items():\\n if not all(child in levels for child in children):\\n continue\\n ks = [levels.index(child) for child in children]\\n bounds = [height_bounds[k] for k in ks]\\n h_min = max(bds[0] for bds in bounds)\\n h_max = min(bds[1] for bds in bounds)\\n dh = h_max - h_min\\n if dh <= 0.:\\n continue\\n if dh < bounds_min and all(dh < bds[1] - bds[0] for bds in bounds):\\n continue\\n ks = sorted(ks, reverse=True)\\n for k in ks:\\n levels.pop(k)\\n height_bounds.pop(k)\\n levels.append(cluster)\\n height_bounds.append([h_min, h_max])\\n groups.append(\\n list(itertools.chain.from_iterable(groups.pop(k) for k in ks)))\\n heights = [sum(bds) / len(bds) for bds in height_bounds]\\n\\n heights = itertools.chain.from_iterable(\\n [h] * len(gs) for gs, h in zip(groups, heights))\\n heights = [[h, h] for h in heights]\\n groups = list(itertools.chain.from_iterable(groups))\\n\\n breadths = []\\n for group in groups:\\n leaves = ZZ.loc[group, \\\"leaves\\\"] if group in ZZ.index else [group]\\n b_min = leaf_data.loc[leaves[0], \\\"breadth\\\"] - 0.5\\n b_max = leaf_data.loc[leaves[-1], \\\"breadth\\\"] + 0.5\\n breadths.append([b_min, b_max])\\n\\n df = pandas.DataFrame({\\\"breadths\\\": breadths, \\\"heights\\\": heights},\\n index=pandas.Index(groups, name=\\\"group\\\"))\\n df.sort_index(inplace=True)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99a11af3f9828476d5b054c5c4c138c\",\n \"score\": \"0.4728363\",\n \"text\": \"def reduce_for_guide_based_blending(multipanel, data):\\r\\n if isinstance(data, list):\\r\\n return [data[multipanel.reduced.ind_for_reduc[ind]] \\\\\\r\\n for ind in range(multipanel.reduced.n_panels)]\\r\\n\\r\\n return np.array([data[multipanel.reduced.ind_for_reduc[ind]] \\\\\\r\\n for ind in range(multipanel.reduced.n_panels)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e06735981e6512aec9cc63a60d26191\",\n \"score\": \"0.47277448\",\n \"text\": \"def chunks_arr(self, array, granular_array):\\n\\t\\tindex = 0\\n\\t\\tfor i in xrange(0, len(granular_array)):\\n\\t\\t\\tyield array[index : index + granular_array[i]]\\n\\t\\t\\tindex = index + granular_array[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e253dd0a2966a9ca1e1f6553738df7\",\n \"score\": \"0.4721469\",\n \"text\": \"def get_expanded_params(self):\\n\\n QUANT = 8\\n STRIDE = 2\\n BOT_MUL = 1.0\\n\\n # Compute the block widths. Each stage has one unique block width\\n widths_cont = np.arange(self.depth) * self.w_a + self.w_0\\n block_capacity = np.round(np.log(widths_cont / self.w_0) / np.log(self.w_m))\\n block_widths = (\\n np.round(np.divide(self.w_0 * np.power(self.w_m, block_capacity), QUANT))\\n * QUANT\\n )\\n num_stages = len(np.unique(block_widths))\\n block_widths = block_widths.astype(int).tolist()\\n\\n # Convert to per stage parameters\\n split_helper = zip(\\n block_widths + [0],\\n [0] + block_widths,\\n block_widths + [0],\\n [0] + block_widths,\\n )\\n splits = [w != wp or r != rp for w, wp, r, rp in split_helper]\\n\\n stage_widths = [w for w, t in zip(block_widths, splits[:-1]) if t]\\n stage_depths = np.diff([d for d, t in enumerate(splits) if t]).tolist()\\n\\n strides = [STRIDE] * num_stages\\n bot_muls = [BOT_MUL] * num_stages\\n group_widths = [self.group_w] * num_stages\\n\\n # Adjust the compatibility of stage widths and group widths\\n stage_widths, group_widths = _adjust_widths_groups_compatibilty(\\n stage_widths, bot_muls, group_widths\\n )\\n\\n return zip(stage_widths, strides, stage_depths, bot_muls, group_widths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869f7a63ffdc89bc50d82085ba87e937\",\n \"score\": \"0.47176874\",\n \"text\": \"def heuristic_resize(self, percent=1.0):\\n trimed = []\\n t_cpu = 0\\n t_mem = 0\\n t_ubd = 0\\n t_dbd = 0\\n c_cpu = 0\\n c_mem = 0\\n c_ubd = 0\\n c_dbd = 0\\n\\n # Calcute SRouters' total needs\\n for sr in self.srouters:\\n t_cpu += sr['cpu']\\n t_mem += sr['mem']\\n t_ubd += sr['ubandwidth']\\n t_dbd += sr['dbandwidth']\\n\\n # Get minimun node set satisfying the needs\\n s_states = sorted(self.original_states, key=lambda k: k['pref'], reverse=True)\\n for n in s_states:\\n trimed.append(n)\\n c_cpu += n['cpu']\\n c_mem += n['mem']\\n c_ubd += n['ubandwidth']\\n c_dbd += n['dbandwidth']\\n if ( c_cpu >= t_cpu and c_mem >= t_mem and\\n c_ubd >= t_ubd and c_dbd >= t_dbd ):\\n break\\n\\n # back-off strategy, give extra node than needed.\\n extra = int(math.ceil(len(trimed) * percent))\\n trimed = s_states[:extra]\\n self.states = trimed\\n print \\\"Trimed node set:\\\", len(trimed)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1845c5a2c146fa1e8ab4525091cc6044\",\n \"score\": \"0.47082856\",\n \"text\": \"def _process_memory_breakdowns(self):\\n self.breakdowns = [[] for _ in range(len(self._lifetime))]\\n for index, breakdown in enumerate(self._lifetime):\\n for t_id in breakdown:\\n tensor = self.tensors.get(t_id)\\n source_node = tensor.source_node\\n if not source_node:\\n continue\\n node = self.nodes.get(source_node)\\n tensor_dict = self._get_tensor_dict(node, tensor, t_id)\\n self.breakdowns[index].append(tensor_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ac76cf8fb725acaf40117322448ac84\",\n \"score\": \"0.47044617\",\n \"text\": \"def sort_cells_into_lineages_chunked(props, scan_eps = 12, min_samples =10, \\n init_group_size = 300, trailing_group_size = 120):\\n\\n def get_group_idx(t_frames):\\n uniq_t_frames = np.unique(t_frames)\\n t_frames_trailing = uniq_t_frames[uniq_t_frames > init_group_size]\\n t_splits = np.array_split(t_frames_trailing, (len(t_frames_trailing)//trailing_group_size)+1)\\n t_splits.insert(0,uniq_t_frames[uniq_t_frames <= init_group_size])\\n group_idx = [t_frames.isin(ts).values.astype(np.uint16)*i for i,ts in enumerate(t_splits)]\\n group_idx = np.sum(group_idx,axis=0)\\n return group_idx\\n \\n def cluster_it(prop):\\n\\n group_idx = get_group_idx(prop.t_frame)\\n gb = prop.groupby(group_idx)\\n print(np.unique(prop.pos_num))\\n print(\\\"mem used: %2.3f\\\" % (int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)/(10**6)))\\n init_clusters = []\\n cluster_cents = []\\n cluster_cent_idcs = []\\n \\n for group in gb:\\n chunk = np.transpose(np.vstack((group[1].centx,group[1].centy)))\\n predicted_clusters = predict_clusters(chunk, scan_eps = scan_eps, min_samples=min_samples)\\n max_cluster_idx = np.max([0]+init_clusters)\\n predicted_clusters[predicted_clusters > -1] = predicted_clusters[predicted_clusters > -1] + max_cluster_idx\\n init_clusters.extend(predicted_clusters.tolist())\\n for pred_cluster in np.unique(predicted_clusters):\\n cluster_cents.append(np.mean(chunk[predicted_clusters == pred_cluster],axis=0))\\n cluster_cent_idcs.append(pred_cluster)\\n\\n cluster_cent_idcs_arr = np.array(cluster_cent_idcs)\\n cluster_cents_arr = np.array(cluster_cents)[cluster_cent_idcs_arr > -1]\\n cluster_cent_idcs_arr = cluster_cent_idcs_arr[cluster_cent_idcs_arr > -1]\\n\\n clustered_clusters = predict_clusters(cluster_cents_arr[:,0].reshape(-1,1),min_samples=7,scan_eps=4)\\n init_clusters = np.array(init_clusters)\\n for i,clust in enumerate(cluster_cent_idcs_arr):\\n init_clusters[init_clusters == clust] = clustered_clusters[i] \\n\\n prop['lineage_idx'] = init_clusters\\n return prop\\n \\n \\n props = props.groupby(['lane_num','pos_num']).apply(lambda x: cluster_it(x))\\n\\n # remove cells that aren't in a lineage\\n props = props[props.lineage_idx > -1]\\n props['centy_flipped'] = (props['centy']*props['trench_inversion_mult'])\\n props_sort = props.sort_values(['lane_num','pos_num','lineage_idx','t_frame','centy_flipped'])\\n\\n return props_sort\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448ea5ac82f8eee90955d8da49a18cd7\",\n \"score\": \"0.47004184\",\n \"text\": \"def reduction(self):\\n return DependenceGroup(i for i in self if i.is_reduction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1475d3eb838801b56bd1f97b253405c\",\n \"score\": \"0.4700053\",\n \"text\": \"def hyper_permute(self, all_intervals):\\n pool = defaultdict(list)\\n for key in all_intervals:\\n base = key.split('_')[0]\\n pool[base].append(key)\\n\\n hyperspaces = [{key: all_intervals[key] for key in keys} for keys in product(*pool.values())]\\n print('Number of hyperparameter spaces to be distributed: {}'.format(len(hyperspaces)))\\n return hyperspaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7009882e4bab1ddf2e27d0fc05243432\",\n \"score\": \"0.46947148\",\n \"text\": \"def energy_groups(structure='wims69'):\\n\\n if structure == 'wims69':\\n \\\"\\\"\\\" WIMS 69-group structure.\\n\\n Stammler, R. J., and M. J. Abbate. \\\"Methods of Steady-State Reactor\\n Analysis in Nuclear Design.\\\" (1984).\\n \\\"\\\"\\\"\\n eb = np.array([1.00000000e+07, 6.06550000e+06, 3.67900000e+06,\\n 2.23100000e+06, 1.35300000e+06, 8.21000000e+05,\\n 5.00000000e+05, 3.02500000e+05, 1.83000000e+05,\\n 1.11000000e+05, 6.73400000e+04, 4.08500000e+04,\\n 2.47800000e+04, 1.50300000e+04, 9.11800000e+03,\\n 5.53000000e+03, 3.51910000e+03, 2.23945000e+03,\\n 1.42510000e+03, 9.06899000e+02, 3.67263000e+02,\\n 1.48729000e+02, 7.55014000e+01, 4.80520000e+01,\\n 2.77000000e+01, 1.59680000e+01, 9.87700000e+00,\\n 4.00000000e+00, 3.30000000e+00, 2.60000000e+00,\\n 2.10000000e+00, 1.50000000e+00, 1.30000000e+00,\\n 1.15000000e+00, 1.12300000e+00, 1.09700000e+00,\\n 1.07100000e+00, 1.04500000e+00, 1.02000000e+00,\\n 9.96000000e-01, 9.72000000e-01, 9.50000000e-01,\\n 9.10000000e-01, 8.50000000e-01, 7.80000000e-01,\\n 6.25000000e-01, 5.00000000e-01, 4.00000000e-01,\\n 3.50000000e-01, 3.20000000e-01, 3.00000000e-01,\\n 2.80000000e-01, 2.50000000e-01, 2.20000000e-01,\\n 1.80000000e-01, 1.40000000e-01, 1.00000000e-01,\\n 8.00000000e-02, 6.70000000e-02, 5.80000000e-02,\\n 5.00000000e-02, 4.20000000e-02, 3.50000000e-02,\\n 3.00000000e-02, 2.50000000e-02, 2.00000000e-02,\\n 1.50000000e-02, 1.00000000e-02, 5.00000000e-03,\\n 1.00000000e-05])\\n\\n elif structure == 'tg0_625':\\n \\\"\\\"\\\" Two-group stucture divided at 0.625. \\\"\\\"\\\"\\n eb = np.array([1e7, 0.625, 1e-5])\\n\\n elif structure == 'wims56':\\n \\\"\\\"\\\" WIMS 56-group structure.\\n\\n Stammler, R. J., and M. J. Abbate. \\\"Methods of Steady-State Reactor\\n Analysis in Nuclear Design.\\\" (1984).\\n \\\"\\\"\\\"\\n eb = np.array([1.00000000e+07, 6.06550000e+06, 3.67900000e+06,\\n 2.23100000e+06, 1.35300000e+06, 8.21000000e+05,\\n 5.00000000e+05, 3.02500000e+05, 1.83000000e+05,\\n 1.11000000e+05, 6.73400000e+04, 4.08500000e+04,\\n 2.47800000e+04, 1.50300000e+04, 9.11800000e+03,\\n 5.53000000e+03, 3.51910000e+03, 2.23945000e+03,\\n 1.42510000e+03, 9.06899000e+02, 3.67263000e+02,\\n 1.48729000e+02, 7.55014000e+01, 4.80520000e+01,\\n 2.77000000e+01, 1.59680000e+01, 9.87700000e+00,\\n 4.00000000e+00, 3.30000000e+00, 2.10000000e+00,\\n 1.30000000e+00, 8.50000000e-01, 6.25000000e-01,\\n 5.00000000e-01, 4.00000000e-01, 3.50000000e-01,\\n 3.20000000e-01, 3.00000000e-01, 2.80000000e-01,\\n 2.50000000e-01, 2.20000000e-01, 1.80000000e-01,\\n 1.40000000e-01, 1.00000000e-01, 8.00000000e-02,\\n 6.70000000e-02, 5.80000000e-02, 5.00000000e-02,\\n 4.20000000e-02, 3.50000000e-02, 3.00000000e-02,\\n 2.50000000e-02, 2.00000000e-02, 1.50000000e-02,\\n 1.00000000e-02, 5.00000000e-03, 1.00000000e-05])\\n\\n elif structure == 'wims172':\\n \\\"\\\"\\\" WIMS 172-group structure.\\n\\n https://www-nds.iaea.org/wimsd/energy.htm\\n \\\"\\\"\\\"\\n eb = np.array([1.96000000e+07, 1.73000000e+07, 1.49000000e+07,\\n 1.38000000e+07, 1.16000000e+07, 1.00000000e+07,\\n 8.19000000e+06, 6.70000000e+06, 6.07000000e+06,\\n 5.49000000e+06, 4.49000000e+06, 3.68000000e+06,\\n 3.01000000e+06, 2.47000000e+06, 2.23000000e+06,\\n 2.02000000e+06, 1.65000000e+06, 1.35000000e+06,\\n 1.22000000e+06, 1.11000000e+06, 1.00000000e+06,\\n 9.07000000e+05, 8.21000000e+05, 6.08000000e+05,\\n 5.50000000e+05, 4.98000000e+05, 4.50000000e+05,\\n 4.08000000e+05, 3.02000000e+05, 2.73000000e+05,\\n 2.47000000e+05, 1.83000000e+05, 1.23000000e+05,\\n 1.11000000e+05, 8.23000000e+04, 6.74000000e+04,\\n 5.52000000e+04, 4.09000000e+04, 3.70000000e+04,\\n 2.93000000e+04, 2.74000000e+04, 2.48000000e+04,\\n 1.66000000e+04, 1.50000000e+04, 1.11000000e+04,\\n 9.12000000e+03, 7.47000000e+03, 5.53000000e+03,\\n 5.00000000e+03, 3.53000000e+03, 3.35000000e+03,\\n 2.25000000e+03, 2.03000000e+03, 1.51000000e+03,\\n 1.43000000e+03, 1.23000000e+03, 1.01000000e+03,\\n 9.14000000e+02, 7.49000000e+02, 6.77000000e+02,\\n 4.54000000e+02, 3.72000000e+02, 3.04000000e+02,\\n 2.04000000e+02, 1.49000000e+02, 1.37000000e+02,\\n 9.17000000e+01, 7.57000000e+01, 6.79000000e+01,\\n 5.56000000e+01, 5.16000000e+01, 4.83000000e+01,\\n 4.55000000e+01, 4.02000000e+01, 3.73000000e+01,\\n 3.37000000e+01, 3.05000000e+01, 2.76000000e+01,\\n 2.50000000e+01, 2.26000000e+01, 1.95000000e+01,\\n 1.59000000e+01, 1.37000000e+01, 1.12000000e+01,\\n 9.91000000e+00, 9.19000000e+00, 8.32000000e+00,\\n 7.52000000e+00, 6.16000000e+00, 5.35000000e+00,\\n 5.04000000e+00, 4.13000000e+00, 4.00000000e+00,\\n 3.38000000e+00, 3.30000000e+00, 2.77000000e+00,\\n 2.72000000e+00, 2.60000000e+00, 2.55000000e+00,\\n 2.36000000e+00, 2.13000000e+00, 2.10000000e+00,\\n 2.02000000e+00, 1.93000000e+00, 1.84000000e+00,\\n 1.76000000e+00, 1.67000000e+00, 1.59000000e+00,\\n 1.50000000e+00, 1.48000000e+00, 1.44000000e+00,\\n 1.37000000e+00, 1.34000000e+00, 1.30000000e+00,\\n 1.24000000e+00, 1.17000000e+00, 1.15000000e+00,\\n 1.13000000e+00, 1.11000000e+00, 1.10000000e+00,\\n 1.07000000e+00, 1.05000000e+00, 1.04000000e+00,\\n 1.02000000e+00, 9.96000000e-01, 9.86000000e-01,\\n 9.72000000e-01, 9.50000000e-01, 9.30000000e-01,\\n 9.10000000e-01, 8.60000000e-01, 8.50000000e-01,\\n 7.90000000e-01, 7.80000000e-01, 7.05000000e-01,\\n 6.25000000e-01, 5.40000000e-01, 5.00000000e-01,\\n 4.85000000e-01, 4.33000000e-01, 4.00000000e-01,\\n 3.91000000e-01, 3.50000000e-01, 3.20000000e-01,\\n 3.15000000e-01, 3.00000000e-01, 2.80000000e-01,\\n 2.48000000e-01, 2.20000000e-01, 1.89000000e-01,\\n 1.80000000e-01, 1.60000000e-01, 1.40000000e-01,\\n 1.34000000e-01, 1.15000000e-01, 1.00000000e-01,\\n 9.50000000e-02, 8.00000000e-02, 7.70000000e-02,\\n 6.70000000e-02, 5.80000000e-02, 5.00000000e-02,\\n 4.20000000e-02, 3.50000000e-02, 3.00000000e-02,\\n 2.50000000e-02, 2.00000000e-02, 1.50000000e-02,\\n 1.00000000e-02, 6.90000000e-03, 5.00000000e-03,\\n 3.00000000e-03, 1.00000000e-05])\\n\\n elif structure == 'lwr32':\\n \\\"\\\"\\\" 32-group structure for LWR analysis\\n\\n Stammler, R. J., and M. J. Abbate. \\\"Methods of Steady-State Reactor\\n Analysis in Nuclear Design.\\\" (1984).\\n \\\"\\\"\\\"\\n eb = np.array([1.00000000e+07, 3.67900000e+06, 1.35300000e+06,\\n 5.00000000e+05, 4.08500000e+04, 9.11800000e+03,\\n 1.48729000e+02, 4.80520000e+01, 1.59680000e+01,\\n 9.87700000e+00, 4.00000000e+00, 3.30000000e+00,\\n 2.60000000e+00, 2.10000000e+00, 1.30000000e+00,\\n 1.15000000e+00, 1.09700000e+00, 1.02000000e+00,\\n 9.72000000e-01, 9.50000000e-01, 8.50000000e-01,\\n 6.25000000e-01, 4.00000000e-01, 3.20000000e-01,\\n 2.50000000e-01, 1.80000000e-01, 1.40000000e-01,\\n 1.00000000e-01, 8.00000000e-02, 5.00000000e-02,\\n 3.00000000e-02, 1.50000000e-02, 1.00000000e-05])\\n\\n elif structure == 'lwr28':\\n \\\"\\\"\\\" 28-group LWR-WIMS library\\n\\n Stammler, R. J., and M. J. Abbate. \\\"Methods of Steady-State Reactor\\n Analysis in Nuclear Design.\\\" (1984).\\n \\\"\\\"\\\"\\n eb = np.array([1.00000000e+07, 2.23100000e+06, 8.21000000e+05,\\n 1.11000000e+05, 9.11800000e+03, 1.48729000e+02,\\n 1.59680000e+01, 9.87700000e+00, 4.00000000e+00,\\n 3.30000000e+00, 2.60000000e+00, 2.10000000e+00,\\n 1.30000000e+00, 1.15000000e+00, 1.09700000e+00,\\n 1.02000000e+00, 9.72000000e-01, 9.50000000e-01,\\n 8.50000000e-01, 6.25000000e-01, 4.00000000e-01,\\n 3.20000000e-01, 2.50000000e-01, 1.40000000e-01,\\n 6.70000000e-02, 5.00000000e-02, 3.00000000e-02,\\n 1.50000000e-02, 1.00000000e-05])\\n\\n elif structure == 'phoenix25':\\n \\\"\\\"\\\" 25-group PHOENIX structure for LWR analysis\\n\\n Stammler, R. J., and M. J. Abbate. \\\"Methods of Steady-State Reactor\\n Analysis in Nuclear Design.\\\" (1984).\\n \\\"\\\"\\\"\\n eb = np.array([1.00000000e+07, 3.67900000e+06, 1.35300000e+06,\\n 5.00000000e+05, 9.11800000e+03, 1.48729000e+02,\\n 4.80520000e+01, 1.59680000e+01, 9.87700000e+00,\\n 4.00000000e+00, 1.30000000e+00, 1.09700000e+00,\\n 1.02000000e+00, 9.50000000e-01, 6.25000000e-01,\\n 3.50000000e-01, 2.80000000e-01, 1.80000000e-01,\\n 1.40000000e-01, 1.00000000e-01, 8.00000000e-02,\\n 5.80000000e-02, 4.20000000e-02, 3.00000000e-02,\\n 1.50000000e-02, 1.00000000e-05])\\n\\n elif structure == 'scale44':\\n \\\"\\\"\\\" 44-group SCALE format\\n\\n @book{ivanov2013benchmark,\\n title={Benchmark for uncertainty analysis in modeling (UAM) for design, operation and safety analysis of LWRs},\\n author={Ivanov, Kalcko and Avramova, M and Kamerow, S and Kodeli, Ivan Aleksander and Sartori, E and Ivanov, E and Cabellos, O},\\n volume={1},\\n year={2013},\\n publisher={Citeseer}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([2.00000000e+07, 8.18730000e+06, 6.43400000e+06,\\n 4.80000000e+06, 3.00000000e+06, 2.47900000e+06,\\n 2.35400000e+06, 1.85000000e+06, 1.40000000e+06,\\n 9.00000000e+05, 4.00000000e+05, 1.00000000e+05,\\n 2.50000000e+04, 1.70000000e+04, 3.00000000e+03,\\n 5.50000000e+02, 1.00000000e+02, 3.00000000e+01,\\n 1.00000000e+01, 8.10000000e+00, 6.00000000e+00,\\n 4.75000000e+00, 3.00000000e+00, 1.77000000e+00,\\n 1.00000000e+00, 6.25000000e-01, 4.00000000e-01,\\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\\n 2.75000000e-01, 2.50000000e-01, 2.25000000e-01,\\n 2.00000000e-01, 1.50000000e-01, 1.00000000e-01,\\n 7.00000000e-02, 5.00000000e-02, 4.00000000e-02,\\n 3.00000000e-02, 2.53000000e-02, 1.00000000e-02,\\n 7.50000000e-03, 3.00000000e-03, 1.00000000e-05])\\n\\n elif structure == 'scale56':\\n \\\"\\\"\\\" 56-group SCALE format\\n\\n @book{ivanov2013benchmark,\\n title={Benchmark for uncertainty analysis in modeling (UAM) for design, operation and safety analysis of LWRs},\\n author={Ivanov, Kalcko and Avramova, M and Kamerow, S and Kodeli, Ivan Aleksander and Sartori, E and Ivanov, E and Cabellos, O},\\n volume={1},\\n year={2013},\\n publisher={Citeseer}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([2.00000000e+07, 6.43000000e+06, 4.30000000e+06,\\n 3.00000000e+06, 1.85000000e+06, 1.50000000e+06,\\n 1.20000000e+06, 8.61000000e+05, 7.50000000e+05,\\n 6.00000000e+05, 4.70000000e+05, 3.30000000e+05,\\n 2.70000000e+05, 2.00000000e+05, 5.00000000e+04,\\n 2.00000000e+04, 1.70000000e+04, 3.74000000e+03,\\n 2.25000000e+03, 1.92000000e+02, 1.88000000e+02,\\n 1.18000000e+02, 1.16000000e+02, 1.05000000e+02,\\n 1.01000000e+02, 6.75000000e+01, 6.50000000e+01,\\n 3.71000000e+01, 3.60000000e+01, 2.18000000e+01,\\n 2.12000000e+01, 2.05000000e+01, 7.00000000e+00,\\n 6.88000000e+00, 6.50000000e+00, 6.25000000e+00,\\n 5.00000000e+00, 1.13000000e+00, 1.08000000e+00,\\n 1.01000000e+00, 6.25000000e-01, 4.50000000e-01,\\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\\n 2.50000000e-01, 2.00000000e-01, 1.50000000e-01,\\n 1.00000000e-01, 8.00000000e-02, 6.00000000e-02,\\n 5.00000000e-02, 4.00000000e-02, 2.53000000e-02,\\n 1.00000000e-02, 4.00000000e-03, 1.00000000e-05])\\n\\n elif structure == 'scale238':\\n \\\"\\\"\\\" 238-group SCALE format\\n\\n ORNL, SCALE Manual Scale. \\\"A Comprehensive Modeling and Simulation\\n Suite for Nuclear Safety Analysis and Design.\\\" ORNL/TM-2005/39,\\n Version 6.\\n \\\"\\\"\\\"\\n eb = np.array([2.00000000e+07, 1.73330000e+07, 1.56830000e+07,\\n 1.45500000e+07, 1.38400000e+07, 1.28400000e+07,\\n 1.00000000e+07, 8.18730000e+06, 6.43400000e+06,\\n 4.80000000e+06, 4.30400000e+06, 3.00000000e+06,\\n 2.47900000e+06, 2.35400000e+06, 1.85000000e+06,\\n 1.50000000e+06, 1.40000000e+06, 1.35600000e+06,\\n 1.31700000e+06, 1.25000000e+06, 1.20000000e+06,\\n 1.10000000e+06, 1.01000000e+06, 9.20000000e+05,\\n 9.00000000e+05, 8.75000000e+05, 8.61100000e+05,\\n 8.20000000e+05, 7.50000000e+05, 6.79000000e+05,\\n 6.70000000e+05, 6.00000000e+05, 5.73000000e+05,\\n 5.50000000e+05, 4.99520000e+05, 4.70000000e+05,\\n 4.40000000e+05, 4.20000000e+05, 4.00000000e+05,\\n 3.30000000e+05, 2.70000000e+05, 2.00000000e+05,\\n 1.50000000e+05, 1.28300000e+05, 1.00000000e+05,\\n 8.50000000e+04, 8.20000000e+04, 7.50000000e+04,\\n 7.30000000e+04, 6.00000000e+04, 5.20000000e+04,\\n 5.00000000e+04, 4.50000000e+04, 3.00000000e+04,\\n 2.50000000e+04, 1.70000000e+04, 1.30000000e+04,\\n 9.50000000e+03, 8.03000000e+03, 6.00000000e+03,\\n 3.90000000e+03, 3.74000000e+03, 3.00000000e+03,\\n 2.58000000e+03, 2.29000000e+03, 2.20000000e+03,\\n 1.80000000e+03, 1.55000000e+03, 1.50000000e+03,\\n 1.15000000e+03, 9.50000000e+02, 6.83000000e+02,\\n 6.70000000e+02, 5.50000000e+02, 3.05000000e+02,\\n 2.85000000e+02, 2.40000000e+02, 2.10000000e+02,\\n 2.07500000e+02, 1.92500000e+02, 1.86000000e+02,\\n 1.22000000e+02, 1.19000000e+02, 1.15000000e+02,\\n 1.08000000e+02, 1.00000000e+02, 9.00000000e+01,\\n 8.20000000e+01, 8.00000000e+01, 7.60000000e+01,\\n 7.20000000e+01, 6.75000000e+01, 6.50000000e+01,\\n 6.10000000e+01, 5.90000000e+01, 5.34000000e+01,\\n 5.20000000e+01, 5.06000000e+01, 4.92000000e+01,\\n 4.83000000e+01, 4.70000000e+01, 4.52000000e+01,\\n 4.40000000e+01, 4.24000000e+01, 4.10000000e+01,\\n 3.96000000e+01, 3.91000000e+01, 3.80000000e+01,\\n 3.70000000e+01, 3.55000000e+01, 3.46000000e+01,\\n 3.37500000e+01, 3.32500000e+01, 3.17500000e+01,\\n 3.12500000e+01, 3.00000000e+01, 2.75000000e+01,\\n 2.50000000e+01, 2.25000000e+01, 2.10000000e+01,\\n 2.00000000e+01, 1.90000000e+01, 1.85000000e+01,\\n 1.70000000e+01, 1.60000000e+01, 1.51000000e+01,\\n 1.44000000e+01, 1.37500000e+01, 1.29000000e+01,\\n 1.19000000e+01, 1.15000000e+01, 1.00000000e+01,\\n 9.10000000e+00, 8.10000000e+00, 7.15000000e+00,\\n 7.00000000e+00, 6.75000000e+00, 6.50000000e+00,\\n 6.25000000e+00, 6.00000000e+00, 5.40000000e+00,\\n 5.00000000e+00, 4.75000000e+00, 4.00000000e+00,\\n 3.73000000e+00, 3.50000000e+00, 3.15000000e+00,\\n 3.05000000e+00, 3.00000000e+00, 2.97000000e+00,\\n 2.87000000e+00, 2.77000000e+00, 2.67000000e+00,\\n 2.57000000e+00, 2.47000000e+00, 2.38000000e+00,\\n 2.30000000e+00, 2.21000000e+00, 2.12000000e+00,\\n 2.00000000e+00, 1.94000000e+00, 1.86000000e+00,\\n 1.77000000e+00, 1.68000000e+00, 1.59000000e+00,\\n 1.50000000e+00, 1.45000000e+00, 1.40000000e+00,\\n 1.35000000e+00, 1.30000000e+00, 1.25000000e+00,\\n 1.22500000e+00, 1.20000000e+00, 1.17500000e+00,\\n 1.15000000e+00, 1.14000000e+00, 1.13000000e+00,\\n 1.12000000e+00, 1.11000000e+00, 1.10000000e+00,\\n 1.09000000e+00, 1.08000000e+00, 1.07000000e+00,\\n 1.06000000e+00, 1.05000000e+00, 1.04000000e+00,\\n 1.03000000e+00, 1.02000000e+00, 1.01000000e+00,\\n 1.00000000e+00, 9.75000000e-01, 9.50000000e-01,\\n 9.25000000e-01, 9.00000000e-01, 8.50000000e-01,\\n 8.00000000e-01, 7.50000000e-01, 7.00000000e-01,\\n 6.50000000e-01, 6.25000000e-01, 6.00000000e-01,\\n 5.50000000e-01, 5.00000000e-01, 4.50000000e-01,\\n 4.00000000e-01, 3.75000000e-01, 3.50000000e-01,\\n 3.25000000e-01, 3.00000000e-01, 2.75000000e-01,\\n 2.50000000e-01, 2.25000000e-01, 2.00000000e-01,\\n 1.75000000e-01, 1.50000000e-01, 1.25000000e-01,\\n 1.00000000e-01, 9.00000000e-02, 8.00000000e-02,\\n 7.00000000e-02, 6.00000000e-02, 5.00000000e-02,\\n 4.00000000e-02, 3.00000000e-02, 2.53000000e-02,\\n 1.00000000e-02, 7.50000000e-03, 5.00000000e-03,\\n 4.00000000e-03, 3.00000000e-03, 2.50000000e-03,\\n 2.00000000e-03, 1.50000000e-03, 1.20000000e-03,\\n 1.00000000e-03, 7.50000000e-04, 5.00000000e-04,\\n 1.00000000e-04, 1.00000000e-05])\\n\\n elif structure == 'scale252':\\n \\\"\\\"\\\" 252-group SCALE format\\n\\n ORNL, SCALE Manual Scale. \\\"A Comprehensive Modeling and Simulation\\n Suite for Nuclear Safety Analysis and Design.\\\" ORNL/TM-2005/39,\\n Version 6.\\n \\\"\\\"\\\"\\n\\n eb = np.array([2.00000000e+07, 1.73000000e+07, 1.57000000e+07,\\n 1.46000000e+07, 1.38000000e+07, 1.28000000e+07,\\n 1.00000000e+07, 8.19000000e+06, 6.43000000e+06,\\n 4.80000000e+06, 4.30000000e+06, 3.00000000e+06,\\n 2.48000000e+06, 2.35000000e+06, 1.85000000e+06,\\n 1.50000000e+06, 1.40000000e+06, 1.36000000e+06,\\n 1.32000000e+06, 1.25000000e+06, 1.20000000e+06,\\n 1.10000000e+06, 1.01000000e+06, 9.20000000e+05,\\n 9.00000000e+05, 8.75000000e+05, 8.61000000e+05,\\n 8.20000000e+05, 7.50000000e+05, 6.79000000e+05,\\n 6.70000000e+05, 6.00000000e+05, 5.73000000e+05,\\n 5.50000000e+05, 4.92000000e+05, 4.70000000e+05,\\n 4.40000000e+05, 4.20000000e+05, 4.00000000e+05,\\n 3.30000000e+05, 2.70000000e+05, 2.00000000e+05,\\n 1.49000000e+05, 1.28000000e+05, 1.00000000e+05,\\n 8.50000000e+04, 8.20000000e+04, 7.50000000e+04,\\n 7.30000000e+04, 6.00000000e+04, 5.20000000e+04,\\n 5.00000000e+04, 4.50000000e+04, 3.00000000e+04,\\n 2.00000000e+04, 1.70000000e+04, 1.30000000e+04,\\n 9.50000000e+03, 8.03000000e+03, 5.70000000e+03,\\n 3.90000000e+03, 3.74000000e+03, 3.00000000e+03,\\n 2.50000000e+03, 2.25000000e+03, 2.20000000e+03,\\n 1.80000000e+03, 1.55000000e+03, 1.50000000e+03,\\n 1.15000000e+03, 9.50000000e+02, 6.83000000e+02,\\n 6.70000000e+02, 5.50000000e+02, 3.05000000e+02,\\n 2.85000000e+02, 2.40000000e+02, 2.20000000e+02,\\n 2.10000000e+02, 2.07000000e+02, 2.02000000e+02,\\n 1.93000000e+02, 1.92000000e+02, 1.89000000e+02,\\n 1.88000000e+02, 1.80000000e+02, 1.70000000e+02,\\n 1.43000000e+02, 1.22000000e+02, 1.19000000e+02,\\n 1.18000000e+02, 1.16000000e+02, 1.13000000e+02,\\n 1.08000000e+02, 1.05000000e+02, 1.01000000e+02,\\n 9.70000000e+01, 9.00000000e+01, 8.17000000e+01,\\n 8.00000000e+01, 7.60000000e+01, 7.20000000e+01,\\n 6.75000000e+01, 6.50000000e+01, 6.30000000e+01,\\n 6.10000000e+01, 5.80000000e+01, 5.34000000e+01,\\n 5.06000000e+01, 4.83000000e+01, 4.52000000e+01,\\n 4.40000000e+01, 4.24000000e+01, 4.10000000e+01,\\n 3.96000000e+01, 3.91000000e+01, 3.80000000e+01,\\n 3.76000000e+01, 3.73000000e+01, 3.71000000e+01,\\n 3.70000000e+01, 3.60000000e+01, 3.55000000e+01,\\n 3.50000000e+01, 3.38000000e+01, 3.33000000e+01,\\n 3.18000000e+01, 3.13000000e+01, 3.00000000e+01,\\n 2.75000000e+01, 2.50000000e+01, 2.25000000e+01,\\n 2.18000000e+01, 2.12000000e+01, 2.05000000e+01,\\n 2.00000000e+01, 1.94000000e+01, 1.85000000e+01,\\n 1.70000000e+01, 1.60000000e+01, 1.44000000e+01,\\n 1.29000000e+01, 1.19000000e+01, 1.15000000e+01,\\n 1.00000000e+01, 9.10000000e+00, 8.10000000e+00,\\n 7.15000000e+00, 7.00000000e+00, 6.88000000e+00,\\n 6.75000000e+00, 6.50000000e+00, 6.25000000e+00,\\n 6.00000000e+00, 5.40000000e+00, 5.00000000e+00,\\n 4.70000000e+00, 4.10000000e+00, 3.73000000e+00,\\n 3.50000000e+00, 3.20000000e+00, 3.10000000e+00,\\n 3.00000000e+00, 2.97000000e+00, 2.87000000e+00,\\n 2.77000000e+00, 2.67000000e+00, 2.57000000e+00,\\n 2.47000000e+00, 2.38000000e+00, 2.30000000e+00,\\n 2.21000000e+00, 2.12000000e+00, 2.00000000e+00,\\n 1.94000000e+00, 1.86000000e+00, 1.77000000e+00,\\n 1.68000000e+00, 1.59000000e+00, 1.50000000e+00,\\n 1.45000000e+00, 1.40000000e+00, 1.35000000e+00,\\n 1.30000000e+00, 1.25000000e+00, 1.23000000e+00,\\n 1.20000000e+00, 1.18000000e+00, 1.15000000e+00,\\n 1.14000000e+00, 1.13000000e+00, 1.12000000e+00,\\n 1.11000000e+00, 1.10000000e+00, 1.09000000e+00,\\n 1.08000000e+00, 1.07000000e+00, 1.06000000e+00,\\n 1.05000000e+00, 1.04000000e+00, 1.03000000e+00,\\n 1.02000000e+00, 1.01000000e+00, 1.00000000e+00,\\n 9.75000000e-01, 9.50000000e-01, 9.25000000e-01,\\n 9.00000000e-01, 8.50000000e-01, 8.00000000e-01,\\n 7.50000000e-01, 7.00000000e-01, 6.50000000e-01,\\n 6.25000000e-01, 6.00000000e-01, 5.50000000e-01,\\n 5.00000000e-01, 4.50000000e-01, 4.00000000e-01,\\n 3.75000000e-01, 3.50000000e-01, 3.25000000e-01,\\n 3.00000000e-01, 2.75000000e-01, 2.50000000e-01,\\n 2.25000000e-01, 2.00000000e-01, 1.75000000e-01,\\n 1.50000000e-01, 1.25000000e-01, 1.00000000e-01,\\n 9.00000000e-02, 8.00000000e-02, 7.00000000e-02,\\n 6.00000000e-02, 5.00000000e-02, 4.00000000e-02,\\n 3.00000000e-02, 2.53000000e-02, 1.00000000e-02,\\n 7.50000000e-03, 5.00000000e-03, 4.00000000e-03,\\n 3.00000000e-03, 2.50000000e-03, 2.00000000e-03,\\n 1.50000000e-03, 1.20000000e-03, 1.00000000e-03,\\n 7.50000000e-04, 5.00000000e-04, 1.00000000e-04,\\n 1.00000000e-05])\\n\\n elif structure == 'shem281':\\n \\\"\\\"\\\" SHEM 281-group Structure\\n\\n\\n These bounds were derived from the lethargy values given in the\\n reference.\\n\\n Hfaiedh, Noureddine. Nouvelle methodologie de calcul de l'absorption\\n resonnante. Diss. Strasbourg 1, 2006.\\n \\\"\\\"\\\"\\n eb = np.array([1.96403298e+07, 1.49182470e+07, 1.38403065e+07,\\n 1.16183424e+07, 1.00000000e+07, 9.04837418e+06,\\n 8.18730753e+06, 7.40818221e+06, 6.06530660e+06,\\n 6.06530660e+06, 4.96585304e+06, 4.06569660e+06,\\n 3.32871084e+06, 2.72531793e+06, 2.23130160e+06,\\n 1.90138980e+06, 1.63654137e+06, 1.40576986e+06,\\n 1.33694224e+06, 1.28696289e+06, 1.16204931e+06,\\n 1.05115030e+06, 9.51120128e+05, 8.60006865e+05,\\n 7.06512131e+05, 5.78443209e+05, 4.94002398e+05,\\n 4.56021689e+05, 4.12501719e+05, 3.83883980e+05,\\n 3.20646853e+05, 9.85279606e+04, 2.30014045e+05,\\n 1.95007959e+05, 1.64999158e+05, 1.39999713e+05,\\n 1.22773399e+05, 1.15623633e+05, 9.46646240e+04,\\n 8.22974705e+04, 6.73794700e+04, 5.51656442e+04,\\n 4.99159391e+04, 4.08677144e+04, 3.69786372e+04,\\n 3.34596546e+04, 2.92810448e+04, 2.73944482e+04,\\n 2.51873119e+04, 2.49991137e+04, 2.26994427e+04,\\n 1.85847318e+04, 1.62004719e+04, 1.48996895e+04,\\n 1.36036804e+04, 1.11377515e+04, 9.11881966e+03,\\n 7.46585808e+03, 6.11252761e+03, 5.00451433e+03,\\n 4.09734979e+03, 3.48107228e+03, 2.99618668e+03,\\n 2.57884177e+03, 2.21962968e+03, 1.91045297e+03,\\n 1.61404003e+03, 1.34506235e+03, 1.13500876e+03,\\n 1.06496334e+03, 9.07501892e+02, 7.48518299e+02,\\n 6.12834951e+02, 5.01746821e+02, 4.10795552e+02,\\n 3.53575009e+02, 3.19927898e+02, 2.83750591e+02,\\n 2.41796304e+02, 1.97966070e+02, 1.62080909e+02,\\n 1.32700625e+02, 1.08646083e+02, 8.89518890e+01,\\n 7.50455792e+01, 6.14421235e+01, 5.26726134e+01,\\n 4.57913703e+01, 4.39958649e+01, 4.01690039e+01,\\n 3.37201523e+01, 2.76077257e+01, 2.45889130e+01,\\n 2.25243208e+01, 2.23783875e+01, 2.21557189e+01,\\n 2.20011704e+01, 2.17018096e+01, 2.14858730e+01,\\n 2.13359971e+01, 2.12295834e+01, 2.11448346e+01,\\n 2.10604242e+01, 2.09763508e+01, 2.07676326e+01,\\n 2.06847280e+01, 2.06021544e+01, 2.05199103e+01,\\n 2.04175669e+01, 2.02751430e+01, 2.00734019e+01,\\n 1.95973754e+01, 1.93926795e+01, 1.91997191e+01,\\n 1.90848657e+01, 1.79590767e+01, 1.77590572e+01,\\n 1.76210754e+01, 1.74457428e+01, 1.68305494e+01,\\n 1.65501580e+01, 1.60497882e+01, 1.57792479e+01,\\n 1.48662813e+01, 1.47301388e+01, 1.45952430e+01,\\n 1.44702621e+01, 1.42505521e+01, 1.40496242e+01,\\n 1.35460589e+01, 1.33297136e+01, 1.25999853e+01,\\n 1.24721187e+01, 1.23085686e+01, 1.21301689e+01,\\n 1.19794856e+01, 1.18153041e+01, 1.17094435e+01,\\n 1.15894552e+01, 1.12694514e+01, 1.10529311e+01,\\n 1.08037754e+01, 1.05792618e+01, 9.50003660e+00,\\n 9.14032233e+00, 8.97996200e+00, 8.80038659e+00,\\n 8.67370167e+00, 8.52408470e+00, 8.30033314e+00,\\n 8.13028234e+00, 7.97008893e+00, 7.83966145e+00,\\n 7.73995328e+00, 7.60036028e+00, 7.38016245e+00,\\n 7.13987773e+00, 6.99430083e+00, 6.91778513e+00,\\n 6.87021672e+00, 6.83526781e+00, 6.81070508e+00,\\n 6.79166178e+00, 6.77605891e+00, 6.75981587e+00,\\n 6.74226317e+00, 6.71669119e+00, 6.63126543e+00,\\n 6.60611444e+00, 6.58830199e+00, 6.57185181e+00,\\n 6.55609828e+00, 6.53907456e+00, 6.51492469e+00,\\n 6.48178316e+00, 6.43206509e+00, 6.35979186e+00,\\n 6.28016121e+00, 6.16011626e+00, 6.05991426e+00,\\n 5.96015001e+00, 5.80021886e+00, 5.72015356e+00,\\n 5.61979761e+00, 5.53004333e+00, 5.48817430e+00,\\n 5.41025189e+00, 5.38003915e+00, 5.32011889e+00,\\n 5.21008229e+00, 5.10998082e+00, 4.93323841e+00,\\n 4.78791795e+00, 4.41980532e+00, 4.30981789e+00,\\n 4.21983341e+00, 4.00000516e+00, 3.88217534e+00,\\n 3.71209219e+00, 3.54307733e+00, 3.14211346e+00,\\n 2.88405027e+00, 2.77512461e+00, 2.74092554e+00,\\n 2.71990146e+00, 2.70011847e+00, 2.64004418e+00,\\n 2.62005589e+00, 2.59009784e+00, 2.55000584e+00,\\n 2.46994443e+00, 2.33006380e+00, 2.27298888e+00,\\n 2.21709028e+00, 2.15695100e+00, 2.07009738e+00,\\n 1.98992242e+00, 1.90007935e+00, 1.77996847e+00,\\n 1.66895161e+00, 1.58803222e+00, 1.51997768e+00,\\n 1.44396911e+00, 1.41000838e+00, 1.38098312e+00,\\n 1.33095356e+00, 1.29303950e+00, 1.25094048e+00,\\n 1.21396960e+00, 1.16999053e+00, 1.14797056e+00,\\n 1.12997516e+00, 1.11604998e+00, 1.10395110e+00,\\n 1.09198337e+00, 1.07798726e+00, 1.03499402e+00,\\n 1.02101339e+00, 1.00903623e+00, 9.96501784e-01,\\n 9.81960349e-01, 9.63961089e-01, 9.44023377e-01,\\n 9.19979079e-01, 8.80025540e-01, 8.20038204e-01,\\n 7.19999797e-01, 6.24999550e-01, 5.94993767e-01,\\n 5.54990463e-01, 5.20011502e-01, 4.75017160e-01,\\n 4.31579153e-01, 3.90001634e-01, 3.52993954e-01,\\n 3.25008368e-01, 3.05011942e-01, 2.79989172e-01,\\n 2.54996783e-01, 2.31192554e-01, 2.09610476e-01,\\n 1.90005103e-01, 1.61895478e-01, 1.37999620e-01,\\n 1.19995103e-01, 1.04297869e-01, 8.97969428e-02,\\n 7.64969545e-02, 6.51994433e-02, 5.54982190e-02,\\n 4.73019220e-02, 4.02999783e-02, 3.43998062e-02,\\n 2.92989280e-02, 2.49394511e-02, 2.00103758e-02,\\n 1.48299818e-02, 1.04505116e-02, 7.14527236e-03,\\n 4.55602681e-03, 2.49990050e-03, 1.00000000e-05])\\n\\n elif structure == 'shem361':\\n \\\"\\\"\\\" SHEM 361-group Structure\\n\\n\\n @book{ngeleka2012examination,\\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\\n author={Ngeleka, Tholakele Prisca},\\n year={2012}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\\n 1.16000000e+07, 1.00000000e+07, 9.05000000e+06,\\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\\n 6.07000000e+06, 4.97000000e+06, 4.07000000e+06,\\n 3.33000000e+06, 2.73000000e+06, 2.23000000e+06,\\n 1.90000000e+06, 1.64000000e+06, 1.41000000e+06,\\n 1.34000000e+06, 1.29000000e+06, 1.16000000e+06,\\n 1.05000000e+06, 9.51000000e+05, 8.60000000e+05,\\n 7.07000000e+05, 5.78000000e+05, 4.94000000e+05,\\n 4.56000000e+05, 4.13000000e+05, 3.84000000e+05,\\n 3.21000000e+05, 2.68000000e+05, 2.30000000e+05,\\n 1.95000000e+05, 1.65000000e+05, 1.40000000e+05,\\n 1.23000000e+05, 1.16000000e+05, 9.47000000e+04,\\n 8.23000000e+04, 6.74000000e+04, 5.52000000e+04,\\n 4.99000000e+04, 4.09000000e+04, 3.70000000e+04,\\n 3.35000000e+04, 2.93000000e+04, 2.74000000e+04,\\n 2.61000000e+04, 2.50000000e+04, 2.27000000e+04,\\n 1.86000000e+04, 1.62000000e+04, 1.49000000e+04,\\n 1.36000000e+04, 1.11000000e+04, 9.12000000e+03,\\n 7.47000000e+03, 6.11000000e+03, 5.00000000e+03,\\n 4.10000000e+03, 3.48000000e+03, 3.00000000e+03,\\n 2.70000000e+03, 2.40000000e+03, 2.08000000e+03,\\n 1.81000000e+03, 1.59000000e+03, 1.34000000e+03,\\n 1.13000000e+03, 1.06000000e+03, 9.82000000e+02,\\n 9.10000000e+02, 8.32000000e+02, 7.49000000e+02,\\n 6.77000000e+02, 6.47000000e+02, 6.13000000e+02,\\n 6.00000000e+02, 5.93000000e+02, 5.77000000e+02,\\n 5.39000000e+02, 5.02000000e+02, 4.54000000e+02,\\n 4.19000000e+02, 3.91000000e+02, 3.72000000e+02,\\n 3.54000000e+02, 3.35000000e+02, 3.20000000e+02,\\n 2.96000000e+02, 2.88000000e+02, 2.85000000e+02,\\n 2.76000000e+02, 2.68000000e+02, 2.57000000e+02,\\n 2.42000000e+02, 2.36000000e+02, 2.24000000e+02,\\n 2.12000000e+02, 2.01000000e+02, 1.96000000e+02,\\n 1.93000000e+02, 1.90000000e+02, 1.89000000e+02,\\n 1.88000000e+02, 1.86000000e+02, 1.85000000e+02,\\n 1.83000000e+02, 1.75000000e+02, 1.68000000e+02,\\n 1.63000000e+02, 1.54000000e+02, 1.47000000e+02,\\n 1.40000000e+02, 1.33000000e+02, 1.26000000e+02,\\n 1.21000000e+02, 1.18000000e+02, 1.17000000e+02,\\n 1.15000000e+02, 1.13000000e+02, 1.10000000e+02,\\n 1.06000000e+02, 1.03000000e+02, 1.02000000e+02,\\n 1.02000000e+02, 1.01000000e+02, 1.01000000e+02,\\n 9.73000000e+01, 9.33000000e+01, 8.88000000e+01,\\n 8.39000000e+01, 7.94000000e+01, 7.63000000e+01,\\n 7.36000000e+01, 7.19000000e+01, 6.91000000e+01,\\n 6.68000000e+01, 6.65000000e+01, 6.62000000e+01,\\n 6.58000000e+01, 6.55000000e+01, 6.50000000e+01,\\n 6.46000000e+01, 6.36000000e+01, 6.23000000e+01,\\n 5.99000000e+01, 5.71000000e+01, 5.41000000e+01,\\n 5.30000000e+01, 5.18000000e+01, 4.93000000e+01,\\n 4.75000000e+01, 4.62000000e+01, 4.53000000e+01,\\n 4.42000000e+01, 4.31000000e+01, 4.21000000e+01,\\n 4.12000000e+01, 3.97000000e+01, 3.88000000e+01,\\n 3.78000000e+01, 3.73000000e+01, 3.69000000e+01,\\n 3.64000000e+01, 3.61000000e+01, 3.57000000e+01,\\n 3.45000000e+01, 3.31000000e+01, 3.17000000e+01,\\n 2.79000000e+01, 2.47000000e+01, 2.25000000e+01,\\n 2.24000000e+01, 2.22000000e+01, 2.20000000e+01,\\n 2.17000000e+01, 2.15000000e+01, 2.13000000e+01,\\n 2.12000000e+01, 2.11000000e+01, 2.11000000e+01,\\n 2.10000000e+01, 2.08000000e+01, 2.07000000e+01,\\n 2.06000000e+01, 2.05000000e+01, 2.04000000e+01,\\n 2.03000000e+01, 2.01000000e+01, 1.96000000e+01,\\n 1.94000000e+01, 1.92000000e+01, 1.91000000e+01,\\n 1.80000000e+01, 1.78000000e+01, 1.76000000e+01,\\n 1.74000000e+01, 1.68000000e+01, 1.66000000e+01,\\n 1.60000000e+01, 1.58000000e+01, 1.49000000e+01,\\n 1.47000000e+01, 1.46000000e+01, 1.45000000e+01,\\n 1.43000000e+01, 1.40000000e+01, 1.35000000e+01,\\n 1.33000000e+01, 1.26000000e+01, 1.25000000e+01,\\n 1.23000000e+01, 1.21000000e+01, 1.20000000e+01,\\n 1.18000000e+01, 1.17000000e+01, 1.16000000e+01,\\n 1.13000000e+01, 1.11000000e+01, 1.08000000e+01,\\n 1.06000000e+01, 9.50000000e+00, 9.14000000e+00,\\n 8.98000000e+00, 8.80000000e+00, 8.67000000e+00,\\n 8.52000000e+00, 8.30000000e+00, 8.13000000e+00,\\n 7.97000000e+00, 7.84000000e+00, 7.74000000e+00,\\n 7.60000000e+00, 7.38000000e+00, 7.14000000e+00,\\n 6.99000000e+00, 6.92000000e+00, 6.87000000e+00,\\n 6.84000000e+00, 6.81000000e+00, 6.79000000e+00,\\n 6.78000000e+00, 6.76000000e+00, 6.74000000e+00,\\n 6.72000000e+00, 6.63000000e+00, 6.61000000e+00,\\n 6.59000000e+00, 6.57000000e+00, 6.56000000e+00,\\n 6.54000000e+00, 6.51000000e+00, 6.48000000e+00,\\n 6.43000000e+00, 6.36000000e+00, 6.28000000e+00,\\n 6.16000000e+00, 6.06000000e+00, 5.96000000e+00,\\n 5.80000000e+00, 5.72000000e+00, 5.62000000e+00,\\n 5.53000000e+00, 5.49000000e+00, 5.41000000e+00,\\n 5.38000000e+00, 5.32000000e+00, 5.21000000e+00,\\n 5.11000000e+00, 4.93000000e+00, 4.77000000e+00,\\n 4.42000000e+00, 4.31000000e+00, 4.22000000e+00,\\n 4.00000000e+00, 3.88000000e+00, 3.71000000e+00,\\n 3.54000000e+00, 3.14000000e+00, 2.88000000e+00,\\n 2.78000000e+00, 2.74000000e+00, 2.72000000e+00,\\n 2.70000000e+00, 2.64000000e+00, 2.62000000e+00,\\n 2.59000000e+00, 2.55000000e+00, 2.47000000e+00,\\n 2.33000000e+00, 2.27000000e+00, 2.22000000e+00,\\n 2.16000000e+00, 2.07000000e+00, 1.99000000e+00,\\n 1.90000000e+00, 1.78000000e+00, 1.67000000e+00,\\n 1.59000000e+00, 1.52000000e+00, 1.44000000e+00,\\n 1.41000000e+00, 1.38000000e+00, 1.33000000e+00,\\n 1.29000000e+00, 1.25000000e+00, 1.21000000e+00,\\n 1.17000000e+00, 1.15000000e+00, 1.13000000e+00,\\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\\n 1.08000000e+00, 1.03000000e+00, 1.02000000e+00,\\n 1.01000000e+00, 9.97000000e-01, 9.82000000e-01,\\n 9.64000000e-01, 9.44000000e-01, 9.20000000e-01,\\n 8.80000000e-01, 8.00000000e-01, 7.20000000e-01,\\n 6.25000000e-01, 5.95000000e-01, 5.55000000e-01,\\n 5.20000000e-01, 4.75000000e-01, 4.32000000e-01,\\n 3.90000000e-01, 3.53000000e-01, 3.25000000e-01,\\n 3.05000000e-01, 2.80000000e-01, 2.55000000e-01,\\n 2.31000000e-01, 2.10000000e-01, 1.90000000e-01,\\n 1.62000000e-01, 1.38000000e-01, 1.20000000e-01,\\n 1.04000000e-01, 8.98000000e-02, 7.65000000e-02,\\n 6.52000000e-02, 5.55000000e-02, 4.73000000e-02,\\n 4.03000000e-02, 3.44000000e-02, 2.93000000e-02,\\n 2.49000000e-02, 2.00000000e-02, 1.48000000e-02,\\n 1.05000000e-02, 7.15000000e-03, 4.56000000e-03,\\n 2.50000000e-03, 1.00000000e-05])\\n\\n elif structure == 'shem407':\\n \\\"\\\"\\\" SHEM 407-group Structure\\n\\n\\n @book{ngeleka2012examination,\\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\\n author={Ngeleka, Tholakele Prisca},\\n year={2012}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\\n 1.27000000e+07, 1.16000000e+07, 1.00000000e+07,\\n 9.05000000e+06, 8.19000000e+06, 7.41000000e+06,\\n 6.70000000e+06, 6.07000000e+06, 5.52000000e+06,\\n 4.97000000e+06, 4.52000000e+06, 4.29000000e+06,\\n 4.07000000e+06, 3.70000000e+06, 3.33000000e+06,\\n 3.03000000e+06, 2.73000000e+06, 2.66000000e+06,\\n 2.60000000e+06, 2.48000000e+06, 2.35000000e+06,\\n 2.23000000e+06, 2.07000000e+06, 1.98000000e+06,\\n 1.90000000e+06, 1.84000000e+06, 1.77000000e+06,\\n 1.70000000e+06, 1.64000000e+06, 1.58000000e+06,\\n 1.52000000e+06, 1.46000000e+06, 1.41000000e+06,\\n 1.34000000e+06, 1.29000000e+06, 1.22000000e+06,\\n 1.16000000e+06, 1.11000000e+06, 1.05000000e+06,\\n 1.00000000e+06, 9.51000000e+05, 9.06000000e+05,\\n 8.60000000e+05, 8.22000000e+05, 7.83000000e+05,\\n 7.45000000e+05, 7.07000000e+05, 6.74000000e+05,\\n 6.42000000e+05, 6.10000000e+05, 5.78000000e+05,\\n 5.57000000e+05, 5.36000000e+05, 5.15000000e+05,\\n 4.85000000e+05, 4.56000000e+05, 4.34000000e+05,\\n 4.05000000e+05, 3.84000000e+05, 3.52000000e+05,\\n 3.21000000e+05, 2.94000000e+05, 2.81000000e+05,\\n 2.68000000e+05, 2.49000000e+05, 2.30000000e+05,\\n 2.13000000e+05, 1.95000000e+05, 1.80000000e+05,\\n 1.65000000e+05, 1.52000000e+05, 1.46000000e+05,\\n 1.40000000e+05, 1.31000000e+05, 1.27000000e+05,\\n 1.23000000e+05, 1.16000000e+05, 1.05000000e+05,\\n 9.99000000e+04, 9.47000000e+04, 8.85000000e+04,\\n 8.23000000e+04, 7.48000000e+04, 6.74000000e+04,\\n 6.13000000e+04, 5.52000000e+04, 5.25000000e+04,\\n 4.99000000e+04, 4.54000000e+04, 4.09000000e+04,\\n 3.70000000e+04, 3.35000000e+04, 3.14000000e+04,\\n 2.93000000e+04, 2.74000000e+04, 2.61000000e+04,\\n 2.50000000e+04, 2.38000000e+04, 2.27000000e+04,\\n 2.06000000e+04, 1.86000000e+04, 1.74000000e+04,\\n 1.62000000e+04, 1.49000000e+04, 1.36000000e+04,\\n 1.24000000e+04, 1.11000000e+04, 1.01000000e+04,\\n 9.12000000e+03, 8.29000000e+03, 7.47000000e+03,\\n 6.79000000e+03, 6.11000000e+03, 5.56000000e+03,\\n 5.00000000e+03, 4.55000000e+03, 4.10000000e+03,\\n 3.79000000e+03, 3.48000000e+03, 3.24000000e+03,\\n 3.00000000e+03, 2.79000000e+03, 2.58000000e+03,\\n 2.40000000e+03, 2.22000000e+03, 2.07000000e+03,\\n 1.91000000e+03, 1.76000000e+03, 1.61000000e+03,\\n 1.48000000e+03, 1.35000000e+03, 1.24000000e+03,\\n 1.14000000e+03, 1.06000000e+03, 9.86000000e+02,\\n 9.08000000e+02, 8.28000000e+02, 7.49000000e+02,\\n 6.81000000e+02, 6.13000000e+02, 5.57000000e+02,\\n 5.02000000e+02, 4.56000000e+02, 4.11000000e+02,\\n 3.82000000e+02, 3.54000000e+02, 3.20000000e+02,\\n 3.02000000e+02, 2.84000000e+02, 2.63000000e+02,\\n 2.42000000e+02, 2.20000000e+02, 1.98000000e+02,\\n 1.80000000e+02, 1.62000000e+02, 1.47000000e+02,\\n 1.33000000e+02, 1.21000000e+02, 1.09000000e+02,\\n 9.88000000e+01, 8.90000000e+01, 8.20000000e+01,\\n 7.50000000e+01, 6.82000000e+01, 6.14000000e+01,\\n 5.71000000e+01, 5.27000000e+01, 4.92000000e+01,\\n 4.58000000e+01, 4.40000000e+01, 4.21000000e+01,\\n 4.02000000e+01, 3.69000000e+01, 3.37000000e+01,\\n 3.22000000e+01, 3.07000000e+01, 2.91000000e+01,\\n 2.76000000e+01, 2.61000000e+01, 2.46000000e+01,\\n 2.36000000e+01, 2.25000000e+01, 2.24000000e+01,\\n 2.22000000e+01, 2.20000000e+01, 2.17000000e+01,\\n 2.15000000e+01, 2.13000000e+01, 2.12000000e+01,\\n 2.11000000e+01, 2.11000000e+01, 2.10000000e+01,\\n 2.08000000e+01, 2.07000000e+01, 2.06000000e+01,\\n 2.05000000e+01, 2.04000000e+01, 2.03000000e+01,\\n 2.01000000e+01, 1.96000000e+01, 1.94000000e+01,\\n 1.92000000e+01, 1.91000000e+01, 1.80000000e+01,\\n 1.78000000e+01, 1.76000000e+01, 1.74000000e+01,\\n 1.68000000e+01, 1.66000000e+01, 1.60000000e+01,\\n 1.58000000e+01, 1.49000000e+01, 1.47000000e+01,\\n 1.46000000e+01, 1.45000000e+01, 1.43000000e+01,\\n 1.40000000e+01, 1.35000000e+01, 1.33000000e+01,\\n 1.26000000e+01, 1.25000000e+01, 1.23000000e+01,\\n 1.21000000e+01, 1.20000000e+01, 1.18000000e+01,\\n 1.17000000e+01, 1.16000000e+01, 1.13000000e+01,\\n 1.11000000e+01, 1.08000000e+01, 1.06000000e+01,\\n 1.00000000e+01, 9.50000000e+00, 9.14000000e+00,\\n 8.98000000e+00, 8.80000000e+00, 8.67000000e+00,\\n 8.52000000e+00, 8.30000000e+00, 8.13000000e+00,\\n 7.97000000e+00, 7.84000000e+00, 7.74000000e+00,\\n 7.60000000e+00, 7.38000000e+00, 7.14000000e+00,\\n 6.99000000e+00, 6.92000000e+00, 6.87000000e+00,\\n 6.84000000e+00, 6.81000000e+00, 6.79000000e+00,\\n 6.78000000e+00, 6.76000000e+00, 6.74000000e+00,\\n 6.72000000e+00, 6.63000000e+00, 6.61000000e+00,\\n 6.59000000e+00, 6.57000000e+00, 6.56000000e+00,\\n 6.54000000e+00, 6.51000000e+00, 6.48000000e+00,\\n 6.43000000e+00, 6.36000000e+00, 6.28000000e+00,\\n 6.16000000e+00, 6.06000000e+00, 5.96000000e+00,\\n 5.80000000e+00, 5.72000000e+00, 5.62000000e+00,\\n 5.53000000e+00, 5.49000000e+00, 5.41000000e+00,\\n 5.38000000e+00, 5.32000000e+00, 5.21000000e+00,\\n 5.11000000e+00, 4.93000000e+00, 4.77000000e+00,\\n 4.59000000e+00, 4.42000000e+00, 4.31000000e+00,\\n 4.22000000e+00, 4.00000000e+00, 3.88000000e+00,\\n 3.71000000e+00, 3.54000000e+00, 3.34000000e+00,\\n 3.14000000e+00, 3.08000000e+00, 3.01000000e+00,\\n 2.95000000e+00, 2.88000000e+00, 2.83000000e+00,\\n 2.78000000e+00, 2.74000000e+00, 2.72000000e+00,\\n 2.70000000e+00, 2.64000000e+00, 2.62000000e+00,\\n 2.59000000e+00, 2.55000000e+00, 2.47000000e+00,\\n 2.40000000e+00, 2.33000000e+00, 2.27000000e+00,\\n 2.22000000e+00, 2.16000000e+00, 2.07000000e+00,\\n 1.99000000e+00, 1.90000000e+00, 1.84000000e+00,\\n 1.78000000e+00, 1.72000000e+00, 1.67000000e+00,\\n 1.63000000e+00, 1.59000000e+00, 1.52000000e+00,\\n 1.44000000e+00, 1.41000000e+00, 1.38000000e+00,\\n 1.33000000e+00, 1.29000000e+00, 1.25000000e+00,\\n 1.21000000e+00, 1.17000000e+00, 1.15000000e+00,\\n 1.13000000e+00, 1.12000000e+00, 1.10000000e+00,\\n 1.09000000e+00, 1.08000000e+00, 1.06000000e+00,\\n 1.03000000e+00, 1.02000000e+00, 1.01000000e+00,\\n 9.97000000e-01, 9.82000000e-01, 9.64000000e-01,\\n 9.44000000e-01, 9.20000000e-01, 8.80000000e-01,\\n 8.50000000e-01, 8.20000000e-01, 7.70000000e-01,\\n 7.45000000e-01, 7.20000000e-01, 6.96000000e-01,\\n 6.72000000e-01, 6.49000000e-01, 6.25000000e-01,\\n 6.10000000e-01, 5.95000000e-01, 5.75000000e-01,\\n 5.55000000e-01, 5.38000000e-01, 5.20000000e-01,\\n 4.98000000e-01, 4.75000000e-01, 4.53000000e-01,\\n 4.32000000e-01, 4.11000000e-01, 3.90000000e-01,\\n 3.71000000e-01, 3.53000000e-01, 3.39000000e-01,\\n 3.32000000e-01, 3.25000000e-01, 3.15000000e-01,\\n 3.05000000e-01, 2.93000000e-01, 2.80000000e-01,\\n 2.67000000e-01, 2.55000000e-01, 2.43000000e-01,\\n 2.31000000e-01, 2.20000000e-01, 2.10000000e-01,\\n 2.00000000e-01, 1.90000000e-01, 1.76000000e-01,\\n 1.62000000e-01, 1.50000000e-01, 1.38000000e-01,\\n 1.20000000e-01, 1.04000000e-01, 8.98000000e-02,\\n 7.65000000e-02, 6.52000000e-02, 5.55000000e-02,\\n 4.73000000e-02, 4.03000000e-02, 3.44000000e-02,\\n 2.95000000e-02, 2.49000000e-02, 2.00000000e-02,\\n 1.48000000e-02, 1.05000000e-02, 7.15000000e-03,\\n 4.56000000e-03, 2.50000000e-03, 1.00000000e-05])\\n\\n elif structure == 'shem531':\\n \\\"\\\"\\\" SHEM 531-group Structure\\n\\n\\n @book{ngeleka2012examination,\\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\\n author={Ngeleka, Tholakele Prisca},\\n year={2012}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([1.96000000e+07, 1.49000000e+07, 1.38000000e+07,\\n 1.27000000e+07, 1.16000000e+07, 1.00000000e+07,\\n 9.05000000e+06, 8.19000000e+06, 7.41000000e+06,\\n 6.70000000e+06, 6.07000000e+06, 5.52000000e+06,\\n 4.97000000e+06, 4.52000000e+06, 4.29000000e+06,\\n 4.07000000e+06, 3.70000000e+06, 3.33000000e+06,\\n 3.03000000e+06, 2.73000000e+06, 2.66000000e+06,\\n 2.60000000e+06, 2.48000000e+06, 2.35000000e+06,\\n 2.23000000e+06, 2.07000000e+06, 1.98000000e+06,\\n 1.90000000e+06, 1.84000000e+06, 1.77000000e+06,\\n 1.70000000e+06, 1.64000000e+06, 1.58000000e+06,\\n 1.52000000e+06, 1.46000000e+06, 1.41000000e+06,\\n 1.34000000e+06, 1.29000000e+06, 1.22000000e+06,\\n 1.16000000e+06, 1.11000000e+06, 1.05000000e+06,\\n 1.00000000e+06, 9.51000000e+05, 9.06000000e+05,\\n 8.60000000e+05, 8.22000000e+05, 7.83000000e+05,\\n 7.45000000e+05, 7.07000000e+05, 6.74000000e+05,\\n 6.42000000e+05, 6.10000000e+05, 5.78000000e+05,\\n 5.57000000e+05, 5.36000000e+05, 5.15000000e+05,\\n 4.85000000e+05, 4.56000000e+05, 4.34000000e+05,\\n 4.05000000e+05, 3.84000000e+05, 3.52000000e+05,\\n 3.21000000e+05, 2.94000000e+05, 2.81000000e+05,\\n 2.68000000e+05, 2.49000000e+05, 2.30000000e+05,\\n 2.13000000e+05, 1.95000000e+05, 1.80000000e+05,\\n 1.65000000e+05, 1.52000000e+05, 1.46000000e+05,\\n 1.40000000e+05, 1.31000000e+05, 1.27000000e+05,\\n 1.23000000e+05, 1.16000000e+05, 1.10000000e+05,\\n 1.05000000e+05, 9.99000000e+04, 9.47000000e+04,\\n 9.16000000e+04, 8.85000000e+04, 8.54000000e+04,\\n 8.23000000e+04, 7.86000000e+04, 7.48000000e+04,\\n 7.11000000e+04, 6.74000000e+04, 6.43000000e+04,\\n 6.13000000e+04, 5.82000000e+04, 5.52000000e+04,\\n 5.25000000e+04, 4.99000000e+04, 4.77000000e+04,\\n 4.54000000e+04, 4.31000000e+04, 4.09000000e+04,\\n 3.89000000e+04, 3.70000000e+04, 3.52000000e+04,\\n 3.35000000e+04, 3.14000000e+04, 3.03000000e+04,\\n 2.93000000e+04, 2.83000000e+04, 2.74000000e+04,\\n 2.67000000e+04, 2.61000000e+04, 2.55000000e+04,\\n 2.50000000e+04, 2.38000000e+04, 2.27000000e+04,\\n 2.06000000e+04, 1.96000000e+04, 1.86000000e+04,\\n 1.74000000e+04, 1.62000000e+04, 1.56000000e+04,\\n 1.49000000e+04, 1.43000000e+04, 1.36000000e+04,\\n 1.30000000e+04, 1.24000000e+04, 1.18000000e+04,\\n 1.11000000e+04, 1.06000000e+04, 1.01000000e+04,\\n 9.62000000e+03, 9.12000000e+03, 8.71000000e+03,\\n 8.29000000e+03, 7.88000000e+03, 7.47000000e+03,\\n 7.13000000e+03, 6.79000000e+03, 6.45000000e+03,\\n 6.11000000e+03, 5.84000000e+03, 5.56000000e+03,\\n 5.28000000e+03, 5.00000000e+03, 4.55000000e+03,\\n 4.32000000e+03, 4.10000000e+03, 3.79000000e+03,\\n 6.74000000e+05, 6.42000000e+05, 6.10000000e+05,\\n 5.78000000e+05, 5.57000000e+05, 5.36000000e+05,\\n 5.15000000e+05, 4.85000000e+05, 4.56000000e+05,\\n 4.34000000e+05, 4.05000000e+05, 3.84000000e+05,\\n 3.52000000e+05, 3.21000000e+05, 2.94000000e+05,\\n 2.81000000e+05, 2.68000000e+05, 2.49000000e+05,\\n 2.30000000e+05, 2.13000000e+05, 1.95000000e+05,\\n 1.80000000e+05, 1.65000000e+05, 1.52000000e+05,\\n 1.46000000e+05, 1.40000000e+05, 1.31000000e+05,\\n 1.27000000e+05, 1.23000000e+05, 1.16000000e+05,\\n 1.10000000e+05, 1.05000000e+05, 9.99000000e+04,\\n 9.47000000e+04, 9.16000000e+04, 8.85000000e+04,\\n 8.54000000e+04, 8.23000000e+04, 7.86000000e+04,\\n 7.48000000e+04, 7.11000000e+04, 6.74000000e+04,\\n 6.43000000e+04, 6.13000000e+04, 5.82000000e+04,\\n 5.52000000e+04, 5.25000000e+04, 4.99000000e+04,\\n 4.77000000e+04, 4.54000000e+04, 3.54000000e+02,\\n 3.44000000e+02, 3.35000000e+02, 3.28000000e+02,\\n 3.20000000e+02, 3.08000000e+02, 2.96000000e+02,\\n 2.88000000e+02, 2.85000000e+02, 2.76000000e+02,\\n 2.68000000e+02, 2.57000000e+02, 2.49000000e+02,\\n 2.42000000e+02, 2.36000000e+02, 2.30000000e+02,\\n 2.24000000e+02, 2.18000000e+02, 2.12000000e+02,\\n 2.07000000e+02, 2.01000000e+02, 1.96000000e+02,\\n 1.93000000e+02, 1.90000000e+02, 1.89000000e+02,\\n 1.88000000e+02, 1.86000000e+02, 1.85000000e+02,\\n 1.83000000e+02, 1.75000000e+02, 1.68000000e+02,\\n 1.63000000e+02, 1.54000000e+02, 1.47000000e+02,\\n 1.43000000e+02, 1.40000000e+02, 1.33000000e+02,\\n 1.26000000e+02, 1.21000000e+02, 1.18000000e+02,\\n 1.17000000e+02, 1.15000000e+02, 1.13000000e+02,\\n 1.10000000e+02, 1.06000000e+02, 1.03000000e+02,\\n 1.02000000e+02, 1.02000000e+02, 1.01000000e+02,\\n 1.01000000e+02, 9.73000000e+01, 9.33000000e+01,\\n 9.10000000e+01, 8.88000000e+01, 8.64000000e+01,\\n 8.39000000e+01, 8.17000000e+01, 7.94000000e+01,\\n 7.63000000e+01, 7.36000000e+01, 7.19000000e+01,\\n 6.91000000e+01, 6.68000000e+01, 6.65000000e+01,\\n 6.62000000e+01, 6.58000000e+01, 6.55000000e+01,\\n 6.50000000e+01, 6.46000000e+01, 6.36000000e+01,\\n 6.23000000e+01, 5.99000000e+01, 5.85000000e+01,\\n 5.71000000e+01, 5.56000000e+01, 5.41000000e+01,\\n 5.30000000e+01, 5.18000000e+01, 5.05000000e+01,\\n 4.93000000e+01, 4.75000000e+01, 4.62000000e+01,\\n 4.53000000e+01, 4.42000000e+01, 4.31000000e+01,\\n 4.21000000e+01, 4.12000000e+01, 3.97000000e+01,\\n 3.88000000e+01, 3.78000000e+01, 3.73000000e+01,\\n 3.69000000e+01, 3.64000000e+01, 3.61000000e+01,\\n 3.57000000e+01, 3.45000000e+01, 3.31000000e+01,\\n 3.17000000e+01, 2.98000000e+01, 2.79000000e+01,\\n 2.63000000e+01, 2.47000000e+01, 2.36000000e+01,\\n 2.25000000e+01, 2.24000000e+01, 2.22000000e+01,\\n 2.20000000e+01, 2.17000000e+01, 2.15000000e+01,\\n 2.13000000e+01, 2.12000000e+01, 2.11000000e+01,\\n 2.11000000e+01, 2.10000000e+01, 2.08000000e+01,\\n 2.07000000e+01, 2.06000000e+01, 2.05000000e+01,\\n 2.04000000e+01, 2.03000000e+01, 2.01000000e+01,\\n 1.96000000e+01, 1.94000000e+01, 1.92000000e+01,\\n 1.91000000e+01, 1.85000000e+01, 1.80000000e+01,\\n 1.78000000e+01, 1.76000000e+01, 1.74000000e+01,\\n 1.68000000e+01, 1.66000000e+01, 1.60000000e+01,\\n 1.58000000e+01, 1.53000000e+01, 1.49000000e+01,\\n 1.47000000e+01, 1.46000000e+01, 1.45000000e+01,\\n 1.43000000e+01, 1.40000000e+01, 1.35000000e+01,\\n 1.33000000e+01, 1.30000000e+01, 1.26000000e+01,\\n 1.25000000e+01, 1.23000000e+01, 1.21000000e+01,\\n 1.20000000e+01, 1.18000000e+01, 1.17000000e+01,\\n 1.16000000e+01, 1.13000000e+01, 1.11000000e+01,\\n 1.08000000e+01, 1.06000000e+01, 1.00000000e+01,\\n 9.50000000e+00, 9.14000000e+00, 8.98000000e+00,\\n 8.80000000e+00, 8.67000000e+00, 8.52000000e+00,\\n 8.30000000e+00, 8.13000000e+00, 7.97000000e+00,\\n 7.84000000e+00, 7.74000000e+00, 7.60000000e+00,\\n 7.38000000e+00, 7.14000000e+00, 6.99000000e+00,\\n 6.92000000e+00, 6.87000000e+00, 6.84000000e+00,\\n 6.81000000e+00, 6.79000000e+00, 6.78000000e+00,\\n 6.76000000e+00, 6.74000000e+00, 6.72000000e+00,\\n 6.63000000e+00, 6.61000000e+00, 6.59000000e+00,\\n 6.57000000e+00, 6.56000000e+00, 6.54000000e+00,\\n 6.51000000e+00, 6.48000000e+00, 6.43000000e+00,\\n 6.36000000e+00, 6.28000000e+00, 6.16000000e+00,\\n 6.06000000e+00, 5.96000000e+00, 5.80000000e+00,\\n 5.72000000e+00, 5.62000000e+00, 5.53000000e+00,\\n 5.49000000e+00, 5.41000000e+00, 5.38000000e+00,\\n 5.32000000e+00, 5.21000000e+00, 5.11000000e+00,\\n 4.93000000e+00, 4.77000000e+00, 4.59000000e+00,\\n 4.42000000e+00, 4.31000000e+00, 4.22000000e+00,\\n 4.00000000e+00, 3.88000000e+00, 3.71000000e+00,\\n 3.54000000e+00, 3.34000000e+00, 3.14000000e+00,\\n 3.08000000e+00, 3.01000000e+00, 2.95000000e+00,\\n 2.88000000e+00, 2.83000000e+00, 2.78000000e+00,\\n 2.74000000e+00, 2.72000000e+00, 2.70000000e+00,\\n 2.64000000e+00, 2.62000000e+00, 2.59000000e+00,\\n 2.55000000e+00, 2.47000000e+00, 2.40000000e+00,\\n 2.33000000e+00, 2.27000000e+00, 2.22000000e+00,\\n 2.16000000e+00, 2.07000000e+00, 1.99000000e+00,\\n 1.90000000e+00, 1.84000000e+00, 1.78000000e+00,\\n 1.72000000e+00, 1.67000000e+00, 1.63000000e+00,\\n 1.59000000e+00, 1.52000000e+00, 1.44000000e+00,\\n 1.41000000e+00, 1.38000000e+00, 1.33000000e+00,\\n 1.29000000e+00, 1.25000000e+00, 1.21000000e+00,\\n 1.17000000e+00, 1.15000000e+00, 1.13000000e+00,\\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\\n 1.08000000e+00, 1.06000000e+00, 1.03000000e+00,\\n 1.02000000e+00, 1.01000000e+00, 9.97000000e-01,\\n 9.82000000e-01, 9.64000000e-01, 9.44000000e-01,\\n 9.20000000e-01, 9.00000000e-01, 8.80000000e-01,\\n 8.50000000e-01, 8.35000000e-01, 8.20000000e-01,\\n 7.95000000e-01, 7.70000000e-01, 7.45000000e-01,\\n 7.20000000e-01, 6.96000000e-01, 6.72000000e-01,\\n 6.49000000e-01, 6.25000000e-01, 6.10000000e-01,\\n 5.95000000e-01, 5.75000000e-01, 5.55000000e-01,\\n 5.38000000e-01, 5.20000000e-01, 4.98000000e-01,\\n 4.75000000e-01, 4.53000000e-01, 4.32000000e-01,\\n 4.11000000e-01, 3.90000000e-01, 3.71000000e-01,\\n 3.53000000e-01, 3.39000000e-01, 3.32000000e-01,\\n 3.20000000e-01, 3.05000000e-01, 2.93000000e-01,\\n 2.80000000e-01, 2.67000000e-01, 2.55000000e-01,\\n 2.43000000e-01, 2.31000000e-01, 2.20000000e-01,\\n 2.10000000e-01, 2.00000000e-01, 1.83000000e-01,\\n 1.62000000e-01, 1.50000000e-01, 1.38000000e-01,\\n 1.29000000e-01, 1.20000000e-01, 1.04000000e-01,\\n 8.98000000e-02, 7.65000000e-02, 6.52000000e-02,\\n 5.55000000e-02, 4.73000000e-02, 4.03000000e-02,\\n 3.44000000e-02, 2.95000000e-02, 2.49000000e-02,\\n 2.00000000e-02, 1.48000000e-02, 1.05000000e-02,\\n 7.15000000e-03, 4.56000000e-03, 2.50000000e-03,\\n 1.00000000e-05])\\n\\n elif structure == 'ga193':\\n \\\"\\\"\\\" GA-TPN 193-group Structure\\n\\n\\n @book{ngeleka2012examination,\\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\\n author={Ngeleka, Tholakele Prisca},\\n year={2012}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([1.49000000e+07, 1.35000000e+07, 1.22000000e+07,\\n 1.11000000e+07, 1.00000000e+07, 9.05000000e+06,\\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\\n 6.07000000e+06, 5.49000000e+06, 4.97000000e+06,\\n 4.49000000e+06, 4.07000000e+06, 3.68000000e+06,\\n 3.33000000e+06, 3.01000000e+06, 2.73000000e+06,\\n 2.47000000e+06, 2.23000000e+06, 2.02000000e+06,\\n 1.83000000e+06, 1.65000000e+06, 1.50000000e+06,\\n 1.35000000e+06, 1.22000000e+06, 1.11000000e+06,\\n 1.00000000e+06, 9.07000000e+05, 8.21000000e+05,\\n 7.43000000e+05, 6.72000000e+05, 6.08000000e+05,\\n 5.50000000e+05, 4.98000000e+05, 4.50000000e+05,\\n 4.08000000e+05, 3.69000000e+05, 3.34000000e+05,\\n 3.02000000e+05, 2.73000000e+05, 2.47000000e+05,\\n 2.24000000e+05, 2.02000000e+05, 1.83000000e+05,\\n 1.66000000e+05, 1.50000000e+05, 1.36000000e+05,\\n 1.23000000e+05, 1.11000000e+05, 8.65000000e+04,\\n 6.74000000e+04, 5.25000000e+04, 4.09000000e+04,\\n 3.18000000e+04, 2.48000000e+04, 1.93000000e+04,\\n 1.50000000e+04, 1.17000000e+04, 9.12000000e+03,\\n 7.10000000e+03, 5.53000000e+03, 4.31000000e+03,\\n 3.35000000e+03, 2.61000000e+03, 2.03000000e+03,\\n 1.58000000e+03, 1.23000000e+03, 9.61000000e+02,\\n 7.49000000e+02, 5.83000000e+02, 4.54000000e+02,\\n 3.54000000e+02, 2.75000000e+02, 2.14000000e+02,\\n 1.67000000e+02, 1.30000000e+02, 1.01000000e+02,\\n 7.89000000e+01, 6.14000000e+01, 4.79000000e+01,\\n 3.73000000e+01, 2.90000000e+01, 2.26000000e+01,\\n 1.76000000e+01, 1.37000000e+01, 1.07000000e+01,\\n 8.32000000e+00, 6.48000000e+00, 5.04000000e+00,\\n 3.93000000e+00, 3.06000000e+00, 2.38000000e+00,\\n 2.36000000e+00, 2.31000000e+00, 2.25000000e+00,\\n 2.15000000e+00, 2.05000000e+00, 1.95000000e+00,\\n 1.88000000e+00, 1.82000000e+00, 1.74000000e+00,\\n 1.65000000e+00, 1.55000000e+00, 1.47000000e+00,\\n 1.40000000e+00, 1.33000000e+00, 1.28000000e+00,\\n 1.23000000e+00, 1.18000000e+00, 1.14000000e+00,\\n 1.13000000e+00, 1.12000000e+00, 1.10000000e+00,\\n 1.09000000e+00, 1.08000000e+00, 1.07000000e+00,\\n 1.06000000e+00, 1.04000000e+00, 1.01000000e+00,\\n 9.95000000e-01, 9.85000000e-01, 9.75000000e-01,\\n 9.60000000e-01, 9.40000000e-01, 9.20000000e-01,\\n 9.00000000e-01, 8.83000000e-01, 8.63000000e-01,\\n 8.25000000e-01, 7.75000000e-01, 7.25000000e-01,\\n 6.92000000e-01, 6.67000000e-01, 6.38000000e-01,\\n 6.13000000e-01, 5.95000000e-01, 5.83000000e-01,\\n 5.63000000e-01, 5.41000000e-01, 5.16000000e-01,\\n 4.95000000e-01, 4.85000000e-01, 4.78000000e-01,\\n 4.73000000e-01, 4.65000000e-01, 4.55000000e-01,\\n 4.40000000e-01, 4.25000000e-01, 4.17000000e-01,\\n 3.97000000e-01, 3.70000000e-01, 3.55000000e-01,\\n 3.45000000e-01, 3.35000000e-01, 3.25000000e-01,\\n 3.15000000e-01, 3.05000000e-01, 2.95000000e-01,\\n 2.85000000e-01, 2.75000000e-01, 2.65000000e-01,\\n 2.55000000e-01, 2.45000000e-01, 2.35000000e-01,\\n 2.25000000e-01, 2.10000000e-01, 1.90000000e-01,\\n 1.70000000e-01, 1.50000000e-01, 1.30000000e-01,\\n 1.10000000e-01, 9.75000000e-02, 9.25000000e-02,\\n 8.75000000e-02, 8.25000000e-02, 7.75000000e-02,\\n 7.25000000e-02, 6.75000000e-02, 6.25000000e-02,\\n 5.50000000e-02, 4.50000000e-02, 3.50000000e-02,\\n 2.77000000e-02, 2.27000000e-02, 1.75000000e-02,\\n 1.25000000e-02, 9.00000000e-03, 7.50000000e-03,\\n 6.00000000e-03, 4.50000000e-03, 3.00000000e-03,\\n 1.50000000e-03, 1.00000000e-05])\\n\\n elif structure == 'ga537':\\n \\\"\\\"\\\" GA-TPN 537-group Structure\\n\\n\\n @book{ngeleka2012examination,\\n title={Examination and improvement of the SHEM energy group structure for HTR and deep burn HTR design and analysis},\\n author={Ngeleka, Tholakele Prisca},\\n year={2012}\\n }\\n \\\"\\\"\\\"\\n\\n eb = np.array([1.49000000e+07, 1.35000000e+07, 1.22000000e+07,\\n 1.11000000e+07, 1.00000000e+07, 9.05000000e+06,\\n 8.19000000e+06, 7.41000000e+06, 6.70000000e+06,\\n 6.07000000e+06, 5.49000000e+06, 4.97000000e+06,\\n 4.49000000e+06, 4.07000000e+06, 3.68000000e+06,\\n 3.33000000e+06, 3.17000000e+06, 3.01000000e+06,\\n 2.87000000e+06, 2.73000000e+06, 2.60000000e+06,\\n 2.47000000e+06, 2.35000000e+06, 2.23000000e+06,\\n 2.13000000e+06, 2.02000000e+06, 1.92000000e+06,\\n 1.83000000e+06, 1.74000000e+06, 1.65000000e+06,\\n 1.57000000e+06, 1.50000000e+06, 1.42000000e+06,\\n 1.35000000e+06, 1.29000000e+06, 1.22000000e+06,\\n 1.17000000e+06, 1.11000000e+06, 1.06000000e+06,\\n 1.00000000e+06, 9.55000000e+05, 9.07000000e+05,\\n 8.64000000e+05, 8.21000000e+05, 7.82000000e+05,\\n 7.43000000e+05, 7.07000000e+05, 6.72000000e+05,\\n 6.40000000e+05, 6.08000000e+05, 5.79000000e+05,\\n 5.50000000e+05, 5.24000000e+05, 4.98000000e+05,\\n 4.74000000e+05, 4.50000000e+05, 4.29000000e+05,\\n 4.08000000e+05, 3.88000000e+05, 3.69000000e+05,\\n 3.51000000e+05, 3.34000000e+05, 3.18000000e+05,\\n 3.02000000e+05, 2.88000000e+05, 2.73000000e+05,\\n 2.60000000e+05, 2.47000000e+05, 2.35000000e+05,\\n 2.24000000e+05, 2.13000000e+05, 2.02000000e+05,\\n 1.93000000e+05, 1.83000000e+05, 1.74000000e+05,\\n 1.66000000e+05, 1.58000000e+05, 1.50000000e+05,\\n 1.43000000e+05, 1.36000000e+05, 1.29000000e+05,\\n 1.23000000e+05, 1.17000000e+05, 1.11000000e+05,\\n 1.08000000e+05, 1.05000000e+05, 1.02000000e+05,\\n 9.88000000e+04, 9.57000000e+04, 9.27000000e+04,\\n 8.96000000e+04, 8.65000000e+04, 8.41000000e+04,\\n 8.17000000e+04, 7.93000000e+04, 7.69000000e+04,\\n 7.46000000e+04, 7.22000000e+04, 6.98000000e+04,\\n 6.74000000e+04, 6.55000000e+04, 6.37000000e+04,\\n 6.18000000e+04, 5.99000000e+04, 5.81000000e+04,\\n 5.62000000e+04, 5.43000000e+04, 5.25000000e+04,\\n 5.10000000e+04, 4.96000000e+04, 4.81000000e+04,\\n 4.67000000e+04, 4.52000000e+04, 4.38000000e+04,\\n 4.23000000e+04, 4.09000000e+04, 3.97000000e+04,\\n 3.86000000e+04, 3.75000000e+04, 3.63000000e+04,\\n 3.52000000e+04, 3.41000000e+04, 3.30000000e+04,\\n 3.18000000e+04, 3.09000000e+04, 3.01000000e+04,\\n 2.92000000e+04, 2.83000000e+04, 2.74000000e+04,\\n 2.65000000e+04, 2.57000000e+04, 2.48000000e+04,\\n 2.41000000e+04, 2.34000000e+04, 2.27000000e+04,\\n 2.20000000e+04, 2.14000000e+04, 2.07000000e+04,\\n 2.00000000e+04, 1.93000000e+04, 1.88000000e+04,\\n 1.82000000e+04, 1.77000000e+04, 1.72000000e+04,\\n 1.66000000e+04, 1.61000000e+04, 1.56000000e+04,\\n 1.50000000e+04, 1.46000000e+04, 1.42000000e+04,\\n 1.38000000e+04, 1.34000000e+04, 1.30000000e+04,\\n 1.25000000e+04, 1.21000000e+04, 1.17000000e+04,\\n 1.14000000e+04, 1.11000000e+04, 1.07000000e+04,\\n 1.04000000e+04, 1.01000000e+04, 9.77000000e+03,\\n 9.44000000e+03, 9.12000000e+03, 8.87000000e+03,\\n 8.61000000e+03, 8.36000000e+03, 8.11000000e+03,\\n 7.86000000e+03, 7.61000000e+03, 7.35000000e+03,\\n 7.10000000e+03, 6.91000000e+03, 6.71000000e+03,\\n 6.51000000e+03, 6.32000000e+03, 6.12000000e+03,\\n 5.92000000e+03, 5.73000000e+03, 5.53000000e+03,\\n 5.38000000e+03, 5.22000000e+03, 5.07000000e+03,\\n 4.92000000e+03, 4.77000000e+03, 4.61000000e+03,\\n 4.46000000e+03, 4.31000000e+03, 4.19000000e+03,\\n 4.07000000e+03, 3.95000000e+03, 3.83000000e+03,\\n 3.71000000e+03, 3.59000000e+03, 3.47000000e+03,\\n 3.35000000e+03, 3.26000000e+03, 3.17000000e+03,\\n 3.08000000e+03, 2.98000000e+03, 2.89000000e+03,\\n 2.80000000e+03, 2.71000000e+03, 2.61000000e+03,\\n 2.54000000e+03, 2.47000000e+03, 2.40000000e+03,\\n 2.32000000e+03, 2.25000000e+03, 2.18000000e+03,\\n 2.11000000e+03, 2.03000000e+03, 1.98000000e+03,\\n 1.92000000e+03, 1.87000000e+03, 1.81000000e+03,\\n 1.75000000e+03, 1.70000000e+03, 1.64000000e+03,\\n 1.58000000e+03, 1.54000000e+03, 1.50000000e+03,\\n 1.45000000e+03, 1.41000000e+03, 1.37000000e+03,\\n 1.32000000e+03, 1.28000000e+03, 1.23000000e+03,\\n 1.20000000e+03, 1.17000000e+03, 1.13000000e+03,\\n 1.10000000e+03, 1.06000000e+03, 1.03000000e+03,\\n 9.95000000e+02, 9.61000000e+02, 9.35000000e+02,\\n 9.08000000e+02, 8.81000000e+02, 8.55000000e+02,\\n 8.28000000e+02, 8.02000000e+02, 7.75000000e+02,\\n 7.49000000e+02, 7.28000000e+02, 7.07000000e+02,\\n 6.86000000e+02, 6.66000000e+02, 6.45000000e+02,\\n 6.24000000e+02, 6.04000000e+02, 5.83000000e+02,\\n 5.67000000e+02, 5.51000000e+02, 5.35000000e+02,\\n 5.18000000e+02, 5.02000000e+02, 4.86000000e+02,\\n 4.70000000e+02, 4.54000000e+02, 4.41000000e+02,\\n 4.29000000e+02, 4.16000000e+02, 4.04000000e+02,\\n 3.91000000e+02, 3.79000000e+02, 3.66000000e+02,\\n 3.54000000e+02, 3.44000000e+02, 3.34000000e+02,\\n 3.24000000e+02, 3.14000000e+02, 3.05000000e+02,\\n 2.95000000e+02, 2.85000000e+02, 2.75000000e+02,\\n 2.68000000e+02, 2.60000000e+02, 2.53000000e+02,\\n 2.45000000e+02, 2.37000000e+02, 2.30000000e+02,\\n 2.22000000e+02, 2.14000000e+02, 2.09000000e+02,\\n 2.03000000e+02, 1.97000000e+02, 1.91000000e+02,\\n 1.85000000e+02, 1.79000000e+02, 1.73000000e+02,\\n 1.67000000e+02, 1.62000000e+02, 1.58000000e+02,\\n 1.53000000e+02, 1.49000000e+02, 1.44000000e+02,\\n 1.39000000e+02, 1.35000000e+02, 1.30000000e+02,\\n 1.26000000e+02, 1.23000000e+02, 1.19000000e+02,\\n 1.16000000e+02, 1.12000000e+02, 1.08000000e+02,\\n 1.05000000e+02, 1.01000000e+02, 9.85000000e+01,\\n 9.57000000e+01, 9.29000000e+01, 9.01000000e+01,\\n 8.73000000e+01, 8.45000000e+01, 8.17000000e+01,\\n 7.89000000e+01, 7.67000000e+01, 7.45000000e+01,\\n 7.23000000e+01, 7.02000000e+01, 6.80000000e+01,\\n 6.58000000e+01, 6.36000000e+01, 6.14000000e+01,\\n 5.97000000e+01, 5.80000000e+01, 5.63000000e+01,\\n 5.46000000e+01, 5.29000000e+01, 5.12000000e+01,\\n 4.95000000e+01, 4.79000000e+01, 4.65000000e+01,\\n 4.52000000e+01, 4.39000000e+01, 4.26000000e+01,\\n 4.12000000e+01, 3.99000000e+01, 3.83000000e+01,\\n 3.67000000e+01, 3.49000000e+01, 3.40000000e+01,\\n 3.31000000e+01, 3.21000000e+01, 3.11000000e+01,\\n 3.01000000e+01, 2.90000000e+01, 2.82000000e+01,\\n 2.74000000e+01, 2.66000000e+01, 2.58000000e+01,\\n 2.50000000e+01, 2.42000000e+01, 2.33000000e+01,\\n 2.25000000e+01, 2.19000000e+01, 2.13000000e+01,\\n 2.01000000e+01, 1.95000000e+01, 1.89000000e+01,\\n 1.82000000e+01, 1.76000000e+01, 1.71000000e+01,\\n 1.66000000e+01, 1.61000000e+01, 1.57000000e+01,\\n 1.52000000e+01, 1.47000000e+01, 1.42000000e+01,\\n 1.37000000e+01, 1.33000000e+01, 1.30000000e+01,\\n 1.26000000e+01, 1.22000000e+01, 1.18000000e+01,\\n 1.14000000e+01, 1.11000000e+01, 1.07000000e+01,\\n 1.04000000e+01, 1.01000000e+01, 9.79000000e+00,\\n 9.50000000e+00, 9.20000000e+00, 8.91000000e+00,\\n 8.61000000e+00, 8.32000000e+00, 8.09000000e+00,\\n 7.86000000e+00, 7.65000000e+00, 7.45000000e+00,\\n 7.35000000e+00, 7.25000000e+00, 7.05000000e+00,\\n 6.48000000e+00, 6.30000000e+00, 6.12000000e+00,\\n 5.94000000e+00, 5.76000000e+00, 5.58000000e+00,\\n 5.40000000e+00, 5.22000000e+00, 5.04000000e+00,\\n 4.90000000e+00, 4.76000000e+00, 4.63000000e+00,\\n 4.49000000e+00, 4.35000000e+00, 4.21000000e+00,\\n 4.07000000e+00, 3.93000000e+00, 3.82000000e+00,\\n 3.71000000e+00, 3.60000000e+00, 3.49000000e+00,\\n 3.38000000e+00, 3.28000000e+00, 3.17000000e+00,\\n 3.06000000e+00, 2.89000000e+00, 2.72000000e+00,\\n 2.55000000e+00, 2.38000000e+00, 2.36000000e+00,\\n 2.31000000e+00, 2.25000000e+00, 2.15000000e+00,\\n 2.05000000e+00, 1.95000000e+00, 1.88000000e+00,\\n 1.82000000e+00, 1.74000000e+00, 1.65000000e+00,\\n 1.55000000e+00, 1.47000000e+00, 1.40000000e+00,\\n 1.33000000e+00, 1.28000000e+00, 1.23000000e+00,\\n 1.18000000e+00, 1.14000000e+00, 1.13000000e+00,\\n 1.12000000e+00, 1.10000000e+00, 1.09000000e+00,\\n 1.08000000e+00, 1.07000000e+00, 1.06000000e+00,\\n 1.04000000e+00, 1.01000000e+00, 9.95000000e-01,\\n 9.85000000e-01, 9.75000000e-01, 9.60000000e-01,\\n 9.40000000e-01, 9.20000000e-01, 9.00000000e-01,\\n 8.83000000e-01, 8.63000000e-01, 8.25000000e-01,\\n 7.75000000e-01, 7.50000000e-01, 7.25000000e-01,\\n 6.92000000e-01, 6.67000000e-01, 6.38000000e-01,\\n 6.13000000e-01, 5.95000000e-01, 5.83000000e-01,\\n 5.63000000e-01, 5.41000000e-01, 5.16000000e-01,\\n 4.95000000e-01, 4.85000000e-01, 4.78000000e-01,\\n 4.73000000e-01, 4.65000000e-01, 4.55000000e-01,\\n 4.40000000e-01, 4.25000000e-01, 4.17000000e-01,\\n 3.97000000e-01, 3.84000000e-01, 3.70000000e-01,\\n 3.55000000e-01, 3.45000000e-01, 3.35000000e-01,\\n 3.25000000e-01, 3.15000000e-01, 3.05000000e-01,\\n 2.95000000e-01, 2.85000000e-01, 2.75000000e-01,\\n 2.65000000e-01, 2.55000000e-01, 2.45000000e-01,\\n 2.35000000e-01, 2.25000000e-01, 2.18000000e-01,\\n 2.10000000e-01, 2.00000000e-01, 1.90000000e-01,\\n 1.80000000e-01, 1.70000000e-01, 1.60000000e-01,\\n 1.55000000e-01, 1.50000000e-01, 1.45000000e-01,\\n 1.40000000e-01, 1.35000000e-01, 1.30000000e-01,\\n 1.25000000e-01, 1.20000000e-01, 1.15000000e-01,\\n 1.10000000e-01, 1.06000000e-01, 1.02000000e-01,\\n 9.50000000e-02, 9.25000000e-02, 8.75000000e-02,\\n 8.25000000e-02, 7.75000000e-02, 7.25000000e-02,\\n 6.75000000e-02, 6.25000000e-02, 5.50000000e-02,\\n 4.50000000e-02, 3.50000000e-02, 2.77000000e-02,\\n 2.27000000e-02, 1.75000000e-02, 1.25000000e-02,\\n 9.00000000e-03, 7.50000000e-03, 6.00000000e-03,\\n 4.50000000e-03, 3.00000000e-03, 1.50000000e-03,\\n 1.00000000e-05])\\n\\n elif structure == 'hr6':\\n \\\"\\\"\\\" Hansen-Roach 6-group Format\\n\\n Hansen, G.E. and Roach, W.H. Six and sixteen group cross sections\\n for fast and intermediate critical assemblies. No. LAMS-2543. Los\\n Alamos Scientific Lab., N. Mex., 1961.\\n \\\"\\\"\\\"\\n eb = np.array([2.00000000e+07, 3.00000000e+06, 1.40000000e+06,\\n 9.00000000e+05, 4.00000000e+05, 1.00000000e+05,\\n 1.00000000e-05])\\n\\n elif structure == 'hr16':\\n \\\"\\\"\\\" Hansen-Roach 16-group Format\\n\\n Hansen, G.E. and Roach, W.H. Six and sixteen group cross sections\\n for fast and intermediate critical assemblies. No. LAMS-2543. Los\\n Alamos Scientific Lab., N. Mex., 1961.\\n \\\"\\\"\\\"\\n eb = np.array([2.00000000e+07, 3.00000000e+06, 1.40000000e+06,\\n 9.00000000e+05, 4.00000000e+03, 1.00000000e+05,\\n 1.70000000e+04, 3.00000000e+03, 5.50000000e+02,\\n 1.00000000e+02, 3.00000000e+01, 1.00000000e+01,\\n 3.00000000e+00, 1.00000000e+00, 4.00000000e-01,\\n 1.00000000e-01, 1.00000000e-05])\\n\\n eb /= 1e6\\n return eb[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ae4f1282542b1395c79d4d0c02565e\",\n \"score\": \"0.46946234\",\n \"text\": \"def shrink(self):\\n\\n if 0 < self.size <= self.capacity//4 and self.capacity//2 >= 4:\\n\\n # self.capacity //= 2\\n\\n old = self.data\\n old_cap = self.capacity\\n\\n self.capacity //= 2\\n\\n self.data = [None] * self.capacity\\n walk = self.head\\n for i in range(self.size):\\n self.data[i] = old[walk]\\n walk = (1 + walk) % old_cap\\n\\n self.head = 0\\n self.tail = (self.head + self.size) % self.capacity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4113b033421afc7f863f775faf976a6\",\n \"score\": \"0.46941668\",\n \"text\": \"def _groups(self):\\n return self._angel_data[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ecde4868c96c9630267afd1632933ef\",\n \"score\": \"0.4689384\",\n \"text\": \"def willump_dfs_find_efficient_features(partitioned_features: List[List[FeatureBase]], partition_costs: List[float],\\n partition_importances: List[float], cost_cutoff: float) \\\\\\n -> Tuple[List[FeatureBase], List[FeatureBase], float, float]:\\n\\n def knapsack_dp(values, weights, capacity):\\n # Credit: https://gist.github.com/KaiyangZhou/71a473b1561e0ea64f97d0132fe07736\\n n_items = len(values)\\n table = np.zeros((n_items + 1, capacity + 1), dtype=np.float32)\\n keep = np.zeros((n_items + 1, capacity + 1), dtype=np.float32)\\n\\n for i in range(1, n_items + 1):\\n for w in range(0, capacity + 1):\\n wi = weights[i - 1] # weight of current item\\n vi = values[i - 1] # value of current item\\n if (wi <= w) and (vi + table[i - 1, w - wi] > table[i - 1, w]):\\n table[i, w] = vi + table[i - 1, w - wi]\\n keep[i, w] = 1\\n else:\\n table[i, w] = table[i - 1, w]\\n picks = []\\n K = capacity\\n for i in range(n_items, 0, -1):\\n if keep[i, K] == 1:\\n picks.append(i)\\n K -= weights[i - 1]\\n picks.sort()\\n picks = [x - 1 for x in picks] # change to 0-index\\n return picks\\n\\n scaled_total_cost = 1000\\n orig_total_cost = sum(partition_costs)\\n scale_factor = scaled_total_cost / orig_total_cost\\n scaled_costs = [round(cost * scale_factor) for cost in partition_costs]\\n partition_ids = range(len(partitioned_features))\\n more_important_partitions = knapsack_dp(partition_importances, scaled_costs, int(scaled_total_cost * cost_cutoff))\\n mi_cost = sum([scaled_costs[i] for i in more_important_partitions])\\n less_important_partitions = [p_id for p_id in partition_ids if p_id not in more_important_partitions]\\n more_important_features, less_important_features = [], []\\n for p_id in more_important_partitions:\\n more_important_features += partitioned_features[p_id]\\n for p_id in less_important_partitions:\\n less_important_features += partitioned_features[p_id]\\n return more_important_features, less_important_features, mi_cost, scaled_total_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8f9bac36b63fd912cc3a53ddff61be\",\n \"score\": \"0.46887454\",\n \"text\": \"def expand_group(group, freqs, freq_tol, max_harmonics=0):\\n if len(group) == 0:\\n return group\\n if max_harmonics <= 0:\\n max_harmonics = 10000\\n if max_harmonics <= len(group):\\n return group\\n fzero = group[0,0]\\n ## indices = np.where(np.abs(freqs[:,0] - np.round(freqs[:,0]/fzero)*fzero) < 2.0*freq_tol)[0]\\n ## \\n indices = []\\n group_freqs = list(group[:,0])\\n prev_h = len(group_freqs)\\n prev_fe = 0.0\\n for i, f in enumerate(freqs[:,0]):\\n if f <= group[-1,0] + 0.5*fzero:\\n continue\\n h = m.floor(f/fzero + 0.5) # round\\n if h > max_harmonics:\\n break\\n if m.fabs(f/h - fzero) > freq_tol:\\n continue\\n df = f - group_freqs[-1]\\n if df <= 0.5*fzero:\\n if len(group_freqs)>1:\\n df = f - group_freqs[-2]\\n else:\\n df = h*fzero\\n dh = m.floor(df/fzero + 0.5)\\n fe = m.fabs(df/dh - fzero)\\n if fe > 2.0*freq_tol:\\n continue\\n if h > prev_h or fe < prev_fe:\\n if h == prev_h and len(group_freqs) > 0:\\n group_freqs.pop()\\n indices.pop()\\n ## do we realy need fill ins? It is not working anyways!\\n ## else:\\n ## while prev_h < h-2:\\n ## indices.append(-1)\\n ## prev_h += 1\\n group_freqs.append(f)\\n indices.append(i)\\n prev_h = h\\n prev_fe = fe\\n # assemble group:\\n new_group = freqs[indices,:group.shape[1]]\\n ## indices = np.array(indices)\\n ## new_group = np.zeros((len(indices),group.shape[1]))\\n ## new_group[indices>=0,:] = freqs[indices[indices>=0],:group.shape[1]]\\n ## fill_in = np.where(indices<0)[0]\\n ## new_group[indices<0,0] = fzero*(fill_in+1)\\n #group[:,0] = np.arange(1,len(group)+1)*fzero\\n return np.vstack((group, new_group))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05805fe12ef2b6ef8648c2ac4f6b215a\",\n \"score\": \"0.4688406\",\n \"text\": \"def tightest_fit(items, boxes):\\r\\n for item in items:\\r\\n boxes = sort_boxes(boxes)\\r\\n for box in boxes:\\r\\n if item.weight <= box.capacity:\\r\\n box.items.append(item)\\r\\n box.capacity -= item.weight\\r\\n break\\r\\n print(\\\"Box 1 of weight 12 capacity contains\\\")\\r\\n for i in boxes[0].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n print(\\\"Box 2 of weight 12 capacity contains\\\")\\r\\n for i in boxes[1].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n print(\\\"Box 3 of weight 12 capacity contains\\\")\\r\\n for i in boxes[2].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n if boxes[0].capacity + boxes[1].capacity + boxes[2].capacity == 0:\\r\\n print(\\\"Successfully able to pack all items\\\")\\r\\n else:\\r\\n print(\\\"Unable to pack all items\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be01f99f9e5de26c04122286300322ba\",\n \"score\": \"0.4685241\",\n \"text\": \"def get_group_cv(gr_mon, start, stop):\\n cv = []\\n for i in range(gr_mon.source.N):\\n sm = gr_mon[i]\\n times = sm[sm >= start]\\n times = times[times <= stop]\\n isi = numpy.diff(times)\\n cv.append(isi.std() / isi.mean())\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c6a5b7bc6f01aec177b4cb90d80645\",\n \"score\": \"0.46804777\",\n \"text\": \"def generate(self):\\n\\n self._removal = np.zeros((self._num_energy_groups,))\\n\\n for i in xrange(self._num_energy_groups):\\n self._removal[i] = self._total[i] - self._scattering[i, i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c532432533362db1483557e7f5130f7\",\n \"score\": \"0.46748284\",\n \"text\": \"def _reduce_cages(kenken: KenKenPuzzle) -> None:\\n for cage in kenken.cages:\\n all_combos = _get_possible_combos(cage, kenken.size)\\n legal_combos = _remove_illegal(all_combos, kenken.candidates)\\n kenken._replace(_merge_combos(legal_combos))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3064e5bfa036d618a8e327164c8c3924\",\n \"score\": \"0.46735695\",\n \"text\": \"def groups(data,length):\\n\\tfor i in xrange(0,len(data),length):\\n\\t\\tyield data[i:i+length]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86e81fddda7bb8193de279f7a39b581\",\n \"score\": \"0.46735185\",\n \"text\": \"def shrink(self):\\n prior = self.data\\n change = self.head\\n recent_size = self.size\\n empty = [None]\\n floor_by_four = self.capacity // 4\\n floor_by_two = self.capacity // 2\\n capacity = self.capacity\\n\\n if (floor_by_two < 4) != self.size: # Need to be tested\\n if self.size <= floor_by_four:\\n self.data = empty * floor_by_two\\n i = 0\\n while i < recent_size:\\n self.data[i] = prior[change]\\n change += 1\\n change = change % capacity\\n i += 1\\n\\n self.head = 0\\n self.tail = recent_size\\n self.capacity = floor_by_two\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1a1e780c42b2c5ade076f2754cad21\",\n \"score\": \"0.46710825\",\n \"text\": \"def roomiest(items, boxes):\\r\\n for item in items:\\r\\n largest_box = boxes[0]\\r\\n for box in boxes:\\r\\n if box.capacity > largest_box.capacity:\\r\\n largest_box = box\\r\\n if item.weight <= largest_box.capacity:\\r\\n largest_box.items.append(item)\\r\\n largest_box.capacity -= item.weight\\r\\n print(\\\"Box 1 of weight 12 capacity contains\\\")\\r\\n for i in boxes[0].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n print(\\\"Box 2 of weight 12 capacity contains\\\")\\r\\n for i in boxes[1].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n print(\\\"Box 3 of weight 12 capacity contains\\\")\\r\\n for i in boxes[2].items:\\r\\n print(str(i.name) + \\\" of weight \\\" + str(i.weight))\\r\\n if boxes[0].capacity + boxes[1].capacity + boxes[2].capacity == 0:\\r\\n print(\\\"Successfully able to pack all items\\\")\\r\\n else:\\r\\n print(\\\"Unable to pack all items\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f1b49ab72a3a58f1c2ff0bdfd4cb35\",\n \"score\": \"0.46672967\",\n \"text\": \"def read_pump_groups(wds):\\n pump_groups = []\\n group = ['gx']\\n for pump in wds.pumps:\\n uid = pump.uid\\n if uid[uid.rfind('g'):] == group[0][group[0].rfind('g'):]:\\n group.append(uid)\\n else:\\n pump_groups.append(group)\\n group = [uid]\\n pump_groups.append(group)\\n return pump_groups[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc0dce6a6b0b8324015e87099751991\",\n \"score\": \"0.46630502\",\n \"text\": \"def getReducedLoads(self):\\r\\n scale_factors = []\\r\\n loads = []\\r\\n load_scale = self.scale # global\\r\\n for (loadsPack, i_scale) in izip(self.loadIDs, self.scaleFactors):\\r\\n scale = i_scale * load_scale # actual scale = global * local\\r\\n for load in loadsPack:\\r\\n if (isinstance(load, Force) or isinstance(load, Moment) or\\r\\n isinstance(load, PLOAD4) or isinstance(load, GRAV)):\\r\\n loads.append(load)\\r\\n scale_factors.append(scale) # local\\r\\n elif isinstance(load, LOAD):\\r\\n load_data = load.getReducedLoads()\\r\\n (reduced_scale_factors, reduced_loads) = load_data\\r\\n\\r\\n loads += reduced_loads\\r\\n scale_factors += [scale * j_scale for j_scale\\r\\n in reduced_scale_factors]\\r\\n else:\\r\\n msg = ('%s isnt supported in getReducedLoads method'\\r\\n % load.__class__.__name__)\\r\\n raise NotImplementedError(msg)\\r\\n\\r\\n return (scale_factors, loads)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c766c65b7fb4df238e1bc5ddfc9230e\",\n \"score\": \"0.4659431\",\n \"text\": \"def breedingControl(self, species, percentKept):\\n nextGenerationUnsorted = []\\n\\n # Iterate over each species\\n breedingList = self.getBreedingPairs(species, percentKept)\\n\\n for breedingPair in breedingList: # breeding our actual input pairs\\n nextGenerationUnsorted.append(self.breedGenome(breedingPair[0], breedingPair[1]))\\n return nextGenerationUnsorted # An unsorted list of our next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1055fd23e4b064380952a08bdddb4aa\",\n \"score\": \"0.46576226\",\n \"text\": \"def create_groups(groups, k):\\n group_samples = defaultdict(list)\\n for sample_idx, group_idx in enumerate(groups):\\n group_samples[group_idx].append(sample_idx)\\n\\n keys_to_remove = []\\n for key in group_samples:\\n if len(group_samples[key]) < k:\\n keys_to_remove.append(key)\\n continue\\n\\n for key in keys_to_remove:\\n group_samples.pop(key)\\n\\n return group_samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458adea54bad6e674caf8f6fadf121ff\",\n \"score\": \"0.46449077\",\n \"text\": \"def random_repartition_per_step(size):\\n array = [random.randrange(0,100) for i in range(size)]\\n s = sum(array)\\n for i in range(size):\\n array[i] = array[i]/s\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142d4f855baa97d07836034d50129947\",\n \"score\": \"0.464298\",\n \"text\": \"def gc_blocks(seq, block_size):\\n block_list = []\\n step = 0\\n for i in range(len(seq)//block_size):\\n block_list.append(seq[step:step+block_size])\\n step += block_size\\n# print(block_list)\\n\\n #calculate GC Content\\n gc_list = []\\n for block in block_list:\\n gc_list.append(gc_content(block))\\n return tuple(gc_list), block_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7247a2287f0dc3e5dc0c67a5dd7bc4ea\",\n \"score\": \"0.4639938\",\n \"text\": \"def shrink(M,levels):\\n while levels>2:\\n \\n listofclust = []\\n smallest = np.min(M[np.nonzero(M)])\\n xs,ys = np.where(M==smallest)\\n \\n # x and y cordinates.\\n xcord = xs[0]\\n ycord = ys[0]\\n # split matrix into 3 and gather smallest row.\\n ''' this part retuns a matrix with the two rows replaced by the minimum of the two rows\\n this matrix is still n*n'''\\n #also we split colums into 3\\n firstrow = M[:,xcord:xcord+1]\\n secondrow = M[:,xcord+1:xcord+2]\\n firstcolum = M[:ycord,:]\\n secondcolum= M[ycord:ycord+1,:]\\n restofRows = M[:,xcord+2:M.shape[0]]\\n restofcolums = M[xcord+2:,:]\\n # find the minimum of each row and colum\\n minrow = np.minimum(firstrow,secondrow)\\n mincolum = np.minimum(firstcolum,secondcolum)\\n \\n newminrow = np.delete(minrow,ycord,0) #This is the new shortened row\\n rem = np.delete(M,ycord,0)\\n rem2 = np.delete(rem,ycord,1)# rem2 is our final matrix.\\n \\n print(rem2)\\n smallMatrix = rem2\\n print(smallMatrix)\\n clusterdlist = [xcord,ycord]\\n print(clusterdlist)\\n print (\\\"clustered\\\",xcord,ycord)\\n listofclust.append(clusterdlist)\\n \\n #return smallMatrix,listofclust\\n return shrink(smallMatrix,levels-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805353d0da2bd05f708b3d1d3b963c27\",\n \"score\": \"0.46339577\",\n \"text\": \"def get_group_sizes(groups):\\n group_sizes = []\\n for group in groups:\\n group_sizes.append(group[\\\"num_players\\\"])\\n return group_sizes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5652f32dc12f5705b4f8ba0fea259b2e\",\n \"score\": \"0.46317145\",\n \"text\": \"def make_chunks_qso(wa, redshift, divmult=1, forest_divmult=1, debug=False):\\n\\n cond = np.isnan(wa)\\n if np.any(cond):\\n warnings.warn('Some wavelengths are NaN, ignoring these pixels.')\\n wa = wa[~cond]\\n assert len(wa) > 0\\n\\n zp1 = 1 + redshift\\n #reflines = np.array([1025.72, 1215.6701, 1240.14, 1398.0,\\n # 1549.06, 1908, 2800 ])\\n\\n # generate the edges of wavelength chunks to send to fitting routine\\n\\n # these edges and divisions are generated by trial and error\\n\\n # for S/N = 15ish and resolution = 2000ish\\n\\n div = np.rec.fromrecords([(200. , 500. , 25),\\n (500. , 800. , 25),\\n (800. , 1190., 25),\\n (1190., 1213., 4),\\n (1213., 1230., 6),\\n (1230., 1263., 6),\\n (1263., 1290., 5),\\n (1290., 1340., 5),\\n (1340., 1370., 2),\\n (1370., 1410., 5),\\n (1410., 1515., 5),\\n (1515., 1600., 15),\\n (1600., 1800., 8),\\n (1800., 1900., 5),\\n (1900., 1940., 5),\\n (1940., 2240., 15),\\n (2240., 3000., 25),\\n (3000., 6000., 80),\\n (6000., 20000., 100),\\n ], names=str('left,right,num'))\\n\\n div.num[2:] = np.ceil(div.num[2:] * divmult)\\n div.num[:2] = np.ceil(div.num[:2] * forest_divmult)\\n div.left *= zp1\\n div.right *= zp1\\n if debug:\\n print(div.tolist())\\n temp = [np.linspace(left, right, n+1)[:-1] for left,right,n in div]\\n edges = np.concatenate(temp)\\n\\n i0,i1,i2 = edges.searchsorted([wa[0], 1210*zp1, wa[-1]])\\n if debug:\\n print(i0,i1,i2)\\n return edges[i0:i2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4600210841ee1afc72a7cbc571a9e2\",\n \"score\": \"0.46290246\",\n \"text\": \"def _find_complete_sets(self):\\n sorted_by_rank = sorted(self._cards, key=lambda card: card.rank.value)\\n grouped_by_rank = groupby(sorted_by_rank, key=lambda card: card.rank)\\n\\n for _, cards_of_rank_x in grouped_by_rank:\\n cards = list(cards_of_rank_x)\\n if len(cards) == 4:\\n # 4 distinct 3-long sets, 1 4-long set\\n for combo in combinations(cards, 3):\\n self.create_meld(*combo)\\n self.create_meld(*cards)\\n elif len(cards) == 3:\\n self.create_meld(*cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b047d0dcb16fc4cbf8a1530b1108ed\",\n \"score\": \"0.4622879\",\n \"text\": \"def test_find_full_sm_decay_groups(self):\\n\\n # Import the full sm with param_card\\n sm_path = import_ufo.find_ufo_path('sm')\\n full_sm_base = import_ufo.import_full_model(sm_path)\\n full_sm = decay_objects.DecayModel(full_sm_base, True)\\n param_path = os.path.join(_file_path,\\n '../input_files/param_card_full_sm.dat')\\n full_sm.read_param_card(param_path)\\n \\n # Stage 1: Find stable particles with \\n # nonzero neutrino masses, quark masses\\n # Turned on light quark and neutrino masses\\n neutrinos = [12, 14, 16]\\n lightquarks1 = [1, 3]\\n for npid in neutrinos:\\n full_sm.get_particle(npid)['mass'] = '1E-6'\\n full_sm.get_particle(-npid)['mass'] = '1E-6'\\n for qpid in lightquarks1:\\n full_sm.get_particle(qpid)['mass'] = '5'\\n full_sm.get_particle(2)['mass'] = '1'\\n\\n # the stable particles are the ones with lightest mass in\\n # their group\\n goal_groups_1 = set([(21,22, 23,25), # 23 and 25 are calculated\\n # others are massless default\\n (1,), (2,), (3,), (4,), (5,), (6,),\\n (11,), (12,), (13,), (14,), (15,), (16,),\\n (24,)])\\n goal_stable_particles_1 = set([(21,22),\\n (2,),\\n (11,), (12,), (14,), (16,)])\\n\\n # Test decay groups\\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\\\\n group])) \\\\\\n for group in full_sm.get('decay_groups')]),\\n goal_groups_1)\\n # Test stable particles\\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\\\\n group])) \\\\\\n for group in full_sm.get('stable_particles')]),\\n goal_stable_particles_1)\\n\\n\\n # Stage 2: turn off the neutrino mass\\n full_sm.get_particle(12)['mass'] = 'ZERO'\\n full_sm.get_particle(14)['mass'] = 'ZERO'\\n full_sm.get_particle(16)['mass'] = 'ZERO'\\n full_sm.get_particle(-12)['mass'] = 'ZERO'\\n full_sm.get_particle(-14)['mass'] = 'ZERO'\\n full_sm.get_particle(-16)['mass'] = 'ZERO'\\n\\n full_sm['decay_groups'] = []\\n full_sm['stable_particles'] = []\\n for p in full_sm['particles']:\\n p['is_stable'] = False\\n\\n goal_groups_2 = set([(12,14,16,21,22, 23,25), # 23,25 are\\n # calculated, others are massless\\n (1,), (2,), (3,), (4,), (5,), (6,),\\n (11,13,15,24)])\\n goal_stable_particles_2 = set([(12,14,16,21,22),\\n (2,),\\n (11,)])\\n goal_stable_pid_2 = [2, 11, 12,14,16,21,22]\\n\\n # Test decay groups\\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\\\\n group])) \\\\\\n for group in full_sm.get('decay_groups')]),\\n goal_groups_2)\\n\\n # Test stable particles\\n self.assertEqual(set([tuple(sorted([p.get('pdg_code') for p in \\\\\\n group])) \\\\\\n for group in full_sm.get('stable_particles')]),\\n goal_stable_particles_2)\\n\\n # Test the assignment of is_stable\\n self.assertEqual(sorted([p.get_pdg_code() \\\\\\n for p in full_sm.get('particles') \\\\\\n if p.get('is_stable')]), goal_stable_pid_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97e4fbdf96ddfb972d0dbc945225e9b\",\n \"score\": \"0.4618307\",\n \"text\": \"def matrizes_rigidez_elementos(self) -> List[np.ndarray]:\\n logger.debug('Calculando as matrizes de rigidez dos elementos...')\\n\\n nel = len(self.elementos)\\n kels = []\\n c = 5\\n for i, e in enumerate(self.elementos):\\n if c <= (perc := (int(100 * (i + 1) / nel))):\\n c += 5\\n logger.debug(f'{perc}%')\\n kels.append(e.matriz_rigidez())\\n return kels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e91ac1bbe134075af3e1c52cd310bb3\",\n \"score\": \"0.46178484\",\n \"text\": \"def _extract_gs(dataset):\\n\\n matrix_group_prefix = \\\"bns_matrix\\\"\\n new_dataset = list()\\n for data_row in dataset:\\n answer_id = data_row[\\\"_uuid\\\"]\\n filtered_row = {k: v for (k, v) in data_row.items() if matrix_group_prefix in k}\\n gs = set([k.split('/')[0][len(matrix_group_prefix)+1:] for k in filtered_row.keys()])\\n\\n for item in gs:\\n row = dict()\\n row[\\\"answer_id\\\"] = answer_id\\n row[\\\"gs\\\"] = item\\n row[\\\"have\\\"] = True if filtered_row[\\n \\\"{0}_{1}/{0}_{1}_{2}\\\".format(matrix_group_prefix, item, \\\"possess\\\")] == 'yes' else False\\n row[\\\"necessary\\\"] = True if filtered_row[\\\"{0}_{1}/{0}_{1}_{2}\\\".format(matrix_group_prefix,item,\\n \\\"necessary\\\")] == 'yes' else False\\n if \\\"{0}_{1}/{0}_{1}_{2}\\\".format(matrix_group_prefix,item, \\\"number\\\") not in filtered_row or not row[\\\"have\\\"]:\\n row[\\\"quantity\\\"] = None\\n else:\\n row[\\\"quantity\\\"] = filtered_row[\\\"{0}_{1}/{0}_{1}_{2}\\\".format(matrix_group_prefix, item, \\\"number\\\")]\\n\\n new_dataset.append(row)\\n\\n return tablib.Dataset().load(json.dumps(new_dataset, sort_keys=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb55f196b2ea7ec43a089a643b7dff85\",\n \"score\": \"0.46172634\",\n \"text\": \"def fixPartition(G, partition, num_buses, bus_capacity, rowdy_groups): \\n \\n og_partition_len = len(partition)\\n \\n if len(partition) > num_buses:\\n while len(partition) > num_buses:\\n min_comm_key = minCommKey(partition)\\n min_comm = partition[min_comm_key]\\n del partition[min_comm_key]\\n for comm_key in partition.keys():\\n if len(partition[comm_key]) < bus_capacity:\\n while len(min_comm) > 0 and len(partition[comm_key]) < bus_capacity:\\n partition[comm_key].append(min_comm.pop())\\n if len(min_comm) == 0:\\n break;\\n \\n \\n if len(partition) < num_buses:\\n original_keys = list(partition.keys())\\n for bus in range(og_partition_len, num_buses):\\n partition[bus] = []\\n \\n while [] in partition.values():\\n students_to_move = []\\n while len(students_to_move) < num_buses - og_partition_len:\\n for bus in range(og_partition_len):\\n if len(students_to_move) == num_buses - og_partition_len:\\n break;\\n if len(partition[bus]) > 1:\\n students_to_move.append(partition[bus].pop())\\n \\n for bus in range(og_partition_len, num_buses):\\n partition[bus].append(students_to_move.pop()) \\n \\n fixBusOverflow(partition, num_buses, bus_capacity, rowdy_groups)\\n return partition\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993692,"cells":{"query_id":{"kind":"string","value":"c223d889be4179ae4643b672a0439505"},"query":{"kind":"string","value":"Return a DataFrame with the new engineered features."},"positive_passages":{"kind":"list like","value":[{"docid":"dfa0852d1e350742153d1b8458dc02d6","score":"0.0","text":"def extract_features(df, mode, update_map):\n # Day is a circular variable, hence we represent it using 2 new features :\n new_df = df.copy()\n new_df['x_day'] = df['day'].apply(lambda x: math.cos(2*math.pi*x/30))\n new_df['y_day'] = df['day'].apply(lambda x: math.sin(2*math.pi*x/30))\n ##### Extract new features from the URL ####\n\n # Extract basic features\n new_df['is_secure'] = df['url'].apply(\n lambda x: x.startswith('https')).astype(int)\n new_df['has_htm_ext'] = df['url'].apply(\n lambda x: bool(re.match('.*\\.html?$', x))).astype(int)\n new_df['has_get_tag'] = df['url'].apply(lambda x: '=' in x).astype(int)\n median_length = df['url'].apply(len).median()\n new_df['has_long_url'] = df['url'].apply(\n lambda x: len(x) > median_length).astype(int)\n new_df['has_digit'] = df['url'].apply(\n lambda x: any(i.isdigit() for i in x)).astype(int)\n\n # Extract and categorize the tld (top-level-domain)\n new_df['tld'] = df['url'].apply(lambda x: urlparse(x).hostname.split(\n '.')[-1]) # We consider only the last part of the tld\n new_df['tld_category'] = new_df['tld'].apply(categorize_tld)\n\n # Extract and categorize the subdomain\n # We suppose that a hostname containing at least 3 parts has a subdomain(we care only about the first subdomain).\n # This is not always correct\n # The only consequence of this is that the (few) uncorrectly identified subdomains will fall into the category \"other\" (later)\n # instead of the category \"no_subdomain\" which shouldn't be a big deal\n new_df['subdomain'] = new_df['url'].apply(lambda x: urlparse(\n x).hostname.split('.')[0] if x.count('.') >= 2 else 'no_subdomain')\n new_df['subdomain_category'] = new_df['subdomain'].apply(\n categorize_subdomain)\n\n # Extract and categorize the hostname\n # We will cluster the 100 most used hostnames according to the labels in the urls they were in.\n new_df['hostname'] = new_df['url'].apply(lambda x: urlparse(x).hostname)\n if mode == \"train\" and update_map != -1:\n nb_hostnames = 2 # We categorize the most frequent 400 hostnames\n hostname2vec = get_labels_vector(new_df, nb_hostnames)\n df_vecs = pd.DataFrame(hostname2vec.values())\n clusterer = hdbscan.HDBSCAN(metric='manhattan')\n clusterer.fit(df_vecs)\n hostname_category_map = dict(zip(list(new_df['hostname'].value_counts(\n )[:nb_hostnames].index.tolist()), list(map(int, list(clusterer.labels_)))))\n json.dump(hostname_category_map, open(\n 'hostname_category_map.json', 'w'))\n\n else:\n hostname_category_map = json.load(\n open('hostname_category_map.json', 'r'))\n\n new_df['hostname_category'] = new_df['hostname'].apply(\n lambda x: hostname_category_map[x] if x in hostname_category_map else -2).astype(str)\n\n # OneHotEncode all the categorical columns\n categorical_cols = ['tld_category',\n 'subdomain_category', 'hostname_category']\n if mode == \"train\":\n # The way we categorize the columns shouldn't result in any unknow category\n encoder = OneHotEncoder(handle_unknown=\"error\", sparse=False)\n encoder.fit(new_df[categorical_cols])\n with open(\"encoder.pkl\", \"wb\") as f:\n pickle.dump(encoder, f)\n else:\n with open('encoder.pkl', 'rb') as f:\n encoder = pickle.load(f)\n ohe_array = encoder.transform(new_df[categorical_cols])\n ohe_df = pd.DataFrame(ohe_array)\n\n columns_to_drop = ['url', 'day', 'tld',\n 'subdomain', 'hostname'] + categorical_cols\n new_df = new_df.drop(columns_to_drop, axis=1)\n new_df.reset_index(drop=True, inplace=True)\n ohe_df.reset_index(drop=True, inplace=True)\n new_df = pd.concat([new_df, ohe_df], axis=1)\n\n return new_df","title":""}],"string":"[\n {\n \"docid\": \"dfa0852d1e350742153d1b8458dc02d6\",\n \"score\": \"0.0\",\n \"text\": \"def extract_features(df, mode, update_map):\\n # Day is a circular variable, hence we represent it using 2 new features :\\n new_df = df.copy()\\n new_df['x_day'] = df['day'].apply(lambda x: math.cos(2*math.pi*x/30))\\n new_df['y_day'] = df['day'].apply(lambda x: math.sin(2*math.pi*x/30))\\n ##### Extract new features from the URL ####\\n\\n # Extract basic features\\n new_df['is_secure'] = df['url'].apply(\\n lambda x: x.startswith('https')).astype(int)\\n new_df['has_htm_ext'] = df['url'].apply(\\n lambda x: bool(re.match('.*\\\\.html?$', x))).astype(int)\\n new_df['has_get_tag'] = df['url'].apply(lambda x: '=' in x).astype(int)\\n median_length = df['url'].apply(len).median()\\n new_df['has_long_url'] = df['url'].apply(\\n lambda x: len(x) > median_length).astype(int)\\n new_df['has_digit'] = df['url'].apply(\\n lambda x: any(i.isdigit() for i in x)).astype(int)\\n\\n # Extract and categorize the tld (top-level-domain)\\n new_df['tld'] = df['url'].apply(lambda x: urlparse(x).hostname.split(\\n '.')[-1]) # We consider only the last part of the tld\\n new_df['tld_category'] = new_df['tld'].apply(categorize_tld)\\n\\n # Extract and categorize the subdomain\\n # We suppose that a hostname containing at least 3 parts has a subdomain(we care only about the first subdomain).\\n # This is not always correct\\n # The only consequence of this is that the (few) uncorrectly identified subdomains will fall into the category \\\"other\\\" (later)\\n # instead of the category \\\"no_subdomain\\\" which shouldn't be a big deal\\n new_df['subdomain'] = new_df['url'].apply(lambda x: urlparse(\\n x).hostname.split('.')[0] if x.count('.') >= 2 else 'no_subdomain')\\n new_df['subdomain_category'] = new_df['subdomain'].apply(\\n categorize_subdomain)\\n\\n # Extract and categorize the hostname\\n # We will cluster the 100 most used hostnames according to the labels in the urls they were in.\\n new_df['hostname'] = new_df['url'].apply(lambda x: urlparse(x).hostname)\\n if mode == \\\"train\\\" and update_map != -1:\\n nb_hostnames = 2 # We categorize the most frequent 400 hostnames\\n hostname2vec = get_labels_vector(new_df, nb_hostnames)\\n df_vecs = pd.DataFrame(hostname2vec.values())\\n clusterer = hdbscan.HDBSCAN(metric='manhattan')\\n clusterer.fit(df_vecs)\\n hostname_category_map = dict(zip(list(new_df['hostname'].value_counts(\\n )[:nb_hostnames].index.tolist()), list(map(int, list(clusterer.labels_)))))\\n json.dump(hostname_category_map, open(\\n 'hostname_category_map.json', 'w'))\\n\\n else:\\n hostname_category_map = json.load(\\n open('hostname_category_map.json', 'r'))\\n\\n new_df['hostname_category'] = new_df['hostname'].apply(\\n lambda x: hostname_category_map[x] if x in hostname_category_map else -2).astype(str)\\n\\n # OneHotEncode all the categorical columns\\n categorical_cols = ['tld_category',\\n 'subdomain_category', 'hostname_category']\\n if mode == \\\"train\\\":\\n # The way we categorize the columns shouldn't result in any unknow category\\n encoder = OneHotEncoder(handle_unknown=\\\"error\\\", sparse=False)\\n encoder.fit(new_df[categorical_cols])\\n with open(\\\"encoder.pkl\\\", \\\"wb\\\") as f:\\n pickle.dump(encoder, f)\\n else:\\n with open('encoder.pkl', 'rb') as f:\\n encoder = pickle.load(f)\\n ohe_array = encoder.transform(new_df[categorical_cols])\\n ohe_df = pd.DataFrame(ohe_array)\\n\\n columns_to_drop = ['url', 'day', 'tld',\\n 'subdomain', 'hostname'] + categorical_cols\\n new_df = new_df.drop(columns_to_drop, axis=1)\\n new_df.reset_index(drop=True, inplace=True)\\n ohe_df.reset_index(drop=True, inplace=True)\\n new_df = pd.concat([new_df, ohe_df], axis=1)\\n\\n return new_df\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fe2b04d95a0dda89cf405385e4663a4d","score":"0.7165676","text":"def feature_engineering(train_df, test_df):\n \n #Not used as is part of the data transformation function\n\n return train_df.copy(), test_df.copy()","title":""},{"docid":"c25f7058fbe27ea494d58cbe678a013b","score":"0.7159671","text":"def feature_engineering2(dataframe):\n #Converting categorical attributes to numerical attributes\n weather_dummy = pd.get_dummies(dataframe['weather'])\n dataframe2 = pd.concat([dataframe,weather_dummy],1)\n dataframe2.head()\n\n day_dummy = pd.get_dummies(dataframe2['weekday_name'])\n dataframe2 = pd.concat([dataframe2,day_dummy],1)\n dataframe2.head()\n\n #Drop unncessary columns\n dataframe2.drop(['date','weekday_name','weather','guest-users','registered-users'], axis = 1, inplace = True) \n\n return dataframe2","title":""},{"docid":"228f0d1b7b8d8180d475d0388e3aca56","score":"0.6994307","text":"def get_features(self):\n return pd.DataFrame([self.features])","title":""},{"docid":"f793836209fc1f67afe9c63676cc77ca","score":"0.6805214","text":"def getFeatures(self, data):\n newDF = pd.DataFrame(index=data.index)\n for feature in self.features:\n if feature.lower() not in self.allFeatures:\n raise Exception(\"{} feature is not available\".format(feature))\n if feature.lower() in self.OHLCV:\n newDF[feature] = data[self.getColumnName(data, feature)]\n # TODO: Implement ways to get other features using ta-lib\n\n return newDF","title":""},{"docid":"3da80a6a8ecddc3f5e72733aefd01e8e","score":"0.6691872","text":"def prepare_features(dataframe):\n processed_features = dataframe.copy()\n return processed_features","title":""},{"docid":"cc63393589669ba43ebaa92c930c11ca","score":"0.6650372","text":"def feature_engineering(dataframe):\n\n\n #Attribute creation for 'total users', 'days' and 'months'\n dataframe['total-users'] = dataframe['guest-users'] + dataframe['registered-users']\n\n dataframe['year'] = pd.DatetimeIndex(dataframe['date']).year\n dataframe['month'] = pd.DatetimeIndex(dataframe['date']).month\n dataframe['weekday_name'] = pd.DatetimeIndex(dataframe['date']).weekday_name\n\n\n return dataframe","title":""},{"docid":"72aa8426ff1c1908e1bd5e33c91cbab8","score":"0.65545595","text":"def feature_engineering(df):\n df = pd.get_dummies(df, drop_first=True, columns=['sex', 'region'])\n\n label_encoder = preprocessing.LabelEncoder()\n label_encoder.fit(df.smoker)\n\n X = df.drop(columns=['smoker'])\n y = label_encoder.transform(df.smoker)\n\n return X, y, df, label_encoder","title":""},{"docid":"e6f493a03ebd2d3ec9618d4822e554cd","score":"0.65186316","text":"def clean_and_engineer(df):\n clean_df = drop_features(df)\n return add_features(clean_df)","title":""},{"docid":"5913d3ed50670bd1cf8f6fba5b559f32","score":"0.6478052","text":"def _update_features(self) -> pd.DataFrame:\n if not hasattr(self, 'curr_dates'):\n self.curr_dates = self.last_dates.copy()\n self.test_dates = []\n self.curr_dates += self.freq\n self.test_dates.append(self.curr_dates)\n\n if self.num_threads == 1 or len(self.transforms) == 1:\n features = self._apply_transforms(updates_only=True)\n else:\n features = self._apply_multithreaded_transforms(updates_only=True)\n\n for feature in self.date_features:\n feat_vals = getattr(self.curr_dates, feature).values\n features[feature] = feat_vals.astype(date_features_dtypes[feature])\n\n features_df = pd.DataFrame(features, columns=self.features, index=self.uids)\n nulls_in_cols = features_df.isnull().any()\n if any(nulls_in_cols):\n warnings.warn(\n f'Found null values in {\", \".join(nulls_in_cols[nulls_in_cols].index)}.'\n )\n results_df = self.static_features.join(features_df)\n results_df['ds'] = self.curr_dates\n return results_df","title":""},{"docid":"c29a7b81889f86e1f58a4f32d64c5912","score":"0.64452827","text":"def _init_build_feature_table(self):\n ids = []\n features = {}\n\n for n in range(self.layer.GetFeatureCount()):\n feature = self.layer.GetFeature(n)\n for field in self.fields:\n if field not in features:\n features[field] = []\n features[field].append(feature.GetField(field))\n ids.append(n)\n\n return pd.DataFrame(index=ids, data=features)","title":""},{"docid":"eaa4d22fdf13474d118242e188c71766","score":"0.641813","text":"def to_dataframe(self):\n # convert available ensembles to DataFrame\n if self.ensemble_list:\n ts = np.array(self.ensemble_list)\n cols = self.label_list\n t_index = self.data_lookup['time']\n t = ts[:,t_index]\n index = pd.DatetimeIndex([datetime.fromtimestamp(v) for v in t])\n new_df = pd.DataFrame(data=ts, index=index, columns=cols)\n\n # concatenate new ensembles with existing DataFrame if possible\n if self.df is None:\n self._df = new_df\n else:\n self._df = pd.concat([self.df, new_df])\n\n # reset the ensemble list once added to the DataFrame\n self._ensemble_list = []\n else:\n print(\" WARNING: No ensembles to add to DataFrame.\")","title":""},{"docid":"53edd47eed0c40250ca13d4d62ea3a26","score":"0.638978","text":"def setup_feature(self):\n TimeInspector.set_time_mark()\n\n if len(self._names) == 0:\n names = [\"F%d\" % i for i in range(len(self._fields))]\n else:\n names = self._names\n\n df_features = D.features(self.instruments, self._fields, self.start_date, self.end_date)\n df_features.columns = names\n\n TimeInspector.log_cost_time(\"Finished loading features.\")\n\n return df_features","title":""},{"docid":"c837e217f78d8dfd67d24f5496baef69","score":"0.63860726","text":"def create_feature_set(df):\n # tfidf genre lists\n tfidf = TfidfVectorizer()\n tfidf_matrix = tfidf.fit_transform(df['consolidate_genre_lists'].apply(lambda x:\" \".join(x)))\n genre_df = pd.DataFrame(tfidf_matrix.toarray())\n genre_df.columns = ['genre' + '|' + i for i in tfidf.get_feature_names()]\n genre_df.reset_index(drop = True, inplace = True)\n\n # explicitly_ohe = ohe_prep(df,'explicit','exp)\n year_ohe = ohe_prep(df,'year','year') * YEAR_SCALER\n popularity_ohe = ohe_prep(df,'pop_red','pop') * POPULARITY_SCALER\n\n # scale float columns \n float_cols = find_float_cols(df)\n floats = df[float_cols].reset_index(drop = True)\n scaler = MinMaxScaler()\n floats_scaled = pd.DataFrame(scaler.fit_transform(floats),columns = floats.columns) * FLOAT_SCALER\n\n # Concatenate all features\n final = pd.concat([genre_df,floats_scaled,popularity_ohe,year_ohe],axis = 1)\n\n # Add song id\n final['id'] = df['id'].values\n\n return final","title":""},{"docid":"087ad47d6213b867e65dea77f17918bb","score":"0.6358246","text":"def feature_computer_df(dic):\n dictionary = feature_computer(dic)\n return pd.DataFrame(dictionary, index=[0])","title":""},{"docid":"f0704f141f9a7bd3f92e1f79d4f3fb17","score":"0.63520926","text":"def transform(df):\n import pandas as pd\n all_features = []\n for i in dir(ft):\n if callable(getattr(ft, i)) and (i not in ['pd', 'np', 'defaultdict']) and '__' not in i:\n all_features.append((i, getattr(ft, i)))\n df2 = pd.DataFrame(index = df.index)\n for feature, call in all_features:\n df2[feature] = df['x_train'] * call(df)\n return df2","title":""},{"docid":"fb2d551d7c662acdb338c9b7437076d6","score":"0.6347641","text":"def feature_engineering(column_names, continuous_names, categorical_values, train_csv, valid_csv, test_csv, train_labels, valid_labels, test_labels):\n ### normalize the continuous columns for three sets ###\n nacc_csf_concat = normalize_continuous_data(continuous_names, train_csv, valid_csv, test_csv)\n\n ### make features for each patient ###\n nacc_csf_features = make_features_for_each_patient(column_names, continuous_names, categorical_values, nacc_csf_concat)\n\n ### change the format and split the features into three sets ###\n train_features, valid_features, test_features = change_feature_format(nacc_csf_features, train_labels, valid_labels, test_labels) \n \n return train_features, valid_features, test_features","title":""},{"docid":"01c9c771b3f087521c234dc2a858ea72","score":"0.63287264","text":"def engineer_features(self):\n temperatures = dict()\n holiday_bool = dict()\n for date, _ in self.load.iterrows():\n temperatures[date] = self._get_temperature(date)\n holiday_bool[date] = self._check_for_holiday(date)\n self.load['weekday'] = self.load.index.dayofweek\n self.load['hour_of_day'] = self.load.index.hour\n self.load['temperature'] = self.load.index.map(temperatures)\n self.load['holiday'] = self.load.index.map(holiday_bool)\n self.load['load (t-24)'] = self.load.load_MW.shift(24)","title":""},{"docid":"ee647b44204b0aa9802fad9562d8d60a","score":"0.63263714","text":"def feature_engineering():\n ml_data_train = pd.read_csv(\"ml_data_zero.csv\", delimiter=\",\")\n x_train = ml_data_train[[\"Start Latitude\", \"Start Longitude\", \"Stop Latitude\",\\\n \"Stop Longitude\", \"Distance\", \"Duration\"]].copy()\n y_train = ml_data_train[\"Min Rate Tags\"].copy()\n y_train, train_count = classifier(y_train)\n print(\"Class Sizes: \", train_count)\n one_hot_encoded_tolls = onehotencodedtolls(ml_data_train)\n x_train = pd.concat([x_train, one_hot_encoded_tolls], axis=1)\n return x_train.to_numpy(), np.array(y_train)","title":""},{"docid":"59fa1b08330f03fe97e8c68eca796094","score":"0.6315498","text":"def augment(df):\n df = add_feature(df, avg_sentence_length, 'text', 'avg_sentence_length')\n df = add_feature(df, avg_word_length, 'text', 'avg_word_length')\n df = add_feature(df, character_count, 'text', 'characer_count')\n df = add_feature(df, is_quoted_retweet, 'text', 'is_quoted_retweet')\n df = add_feature(df, count_all_caps, 'text', 'number_all_caps')\n df = add_feature(df, count_random_caps, 'text', 'random_caps')\n df = add_feature(df, is_mention, 'text', 'is_mention')\n df = add_feature(df, ends_with_link, 'text', 'ends_with_link')\n df = add_feature(df, ends_with_hashtag, 'text', 'ends_with_hashtag')\n df = add_feature(df, count_punctuation, 'text')\n df = add_feature(df, period_of_day, 'created', 'period_of_day')\n df = add_feature(df, day_of_week, 'created', 'weekday')\n df = add_feature(df, starts_with_I, 'text', 'starts_with_I')\n df = add_feature(df, count_keywords, 'text')\n df = add_feature(df, get_vader_scores, 'text')\n df = add_feature(df, text_emotion, 'text')\n return df","title":""},{"docid":"53b70b5f3fa9f53d33b8ee3840f507dd","score":"0.63021624","text":"def to_features_dataframe(self) -> pd.DataFrame:\n return pd.DataFrame(dict(self[Features.AXIS].coords))","title":""},{"docid":"8083334aba64e62797798c5ec5cb0847","score":"0.6287876","text":"def Feature_Engineering(DataFrame,train):\n \n \n DataFrame= pd.read_csv(DataFrame)\n titles=DataFrame['Name'].apply(lambda x: x.split(',')[1].split(' ')[1])\n title_mapping = {\"the\":5, \"Mr.\": 1, \"Miss.\": 2, \"Mrs.\": 3, \"Master.\": 4, \"Dr.\": 5, \"Rev.\": 6, \"Major.\": 7, \"Col.\": 7, \"Mlle.\": 2, \"Mme.\": 3, \"Don.\": 9, \"Lady.\": 10, \"Countess.\": 10, \"Jonkheer.\": 10, \"Sir.\": 9, \"Capt.\": 7, \"Ms.\": 2, \"Dona.\": 10}\n for k,v in title_mapping.items():\n titles[titles == k] = v\n DataFrame[\"Title\"] = titles\n\n DataFrame['NameLen']=DataFrame['Name'].apply(lambda x: len(x))\n DataFrame['FamSize']=DataFrame['SibSp']+DataFrame['Parch']\n DataFrame['Has_Cabin'] = DataFrame[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n cabins=DataFrame['Cabin'].apply(lambda x: str(x)[0])\n cabin_mapping={'A':3,'B':5,'C':5,'D':4,'E':4,'F':3,'G':2,'T':1,'n':10}\n for k,v in cabin_mapping.items():\n cabins[cabins==k]=v\n DataFrame['Cabin']=cabins \n del DataFrame['Parch']\n del DataFrame['SibSp']\n del DataFrame['PassengerId']\n\n pclass = pd.get_dummies( DataFrame.Pclass , prefix='Pclass' )\n sex = pd.get_dummies(DataFrame.Sex)\n embarked = pd.get_dummies(DataFrame.Embarked, prefix='Embarked')\n DataFrame=pd.concat([DataFrame,pclass,sex,embarked],axis=1)\n del DataFrame['Pclass']\n del DataFrame['Name']\n del DataFrame['Ticket']\n del DataFrame['Sex']\n del DataFrame['Embarked']\n \n DataFrame['Fare'].fillna(train['Fare'].median(), inplace = True)\n # Mapping Fare\n DataFrame.loc[ DataFrame['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t = 0\n DataFrame.loc[(DataFrame['Fare'] > 7.91) & (DataFrame['Fare'] <= 14.454), 'Fare'] = 1\n DataFrame.loc[(DataFrame['Fare'] > 14.454) & (DataFrame['Fare'] <= 31), 'Fare'] = 2\n DataFrame.loc[ DataFrame['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t = 3\n DataFrame['Fare'] = DataFrame['Fare'].astype(int)\n DataFrame['Age'].fillna(train['Age'].median(), inplace = True)\n\n return DataFrame","title":""},{"docid":"90c05e76f93857f1e763b8cfa41bf416","score":"0.6267703","text":"def engineer_features(data):\n # add date-time columns\n data = add_datetimestop(data)\n data = add_month_weekday(data)\n data = data.dropna(subset=['pct'])\n # convert ht_feet, ht_inch to height \n data = add_height(data)\n # add pct-sector column\n data = add_pct_sector(data)\n # convert yes-no columns to 1-0\n data = y_n_to_1_0_cols(data)\n # clean categories\n data = clean_categories(data)\n return data","title":""},{"docid":"09c860e93c56bae314d43bc28311d4ed","score":"0.6266057","text":"def features(self) -> pd.DataFrame:\n features: pd.DataFrame = self._observations.loc[:, self._feature_names]\n\n if features.columns.name is None:\n features = features.rename_axis(columns=Sample.IDX_FEATURE)\n\n return features","title":""},{"docid":"cb0aa2a54ea1a95048bfac9351474323","score":"0.6223216","text":"def data_with_selected_features(self):\n data_selected = self.df[self.selected_features]\n data_selected['diagnosis'] = encode(self.df['diagnosis'])\n return data_selected","title":""},{"docid":"51a5a57debcabdee2edc24d82d79ac7d","score":"0.62171274","text":"def transform(cls, df: pd.DataFrame) -> pd.DataFrame:\n return df.assign(\n **{\n feature_name: data[\"transformer\"]\n for feature_name, data in cls.features.items()\n }\n )[cls.features.keys()]","title":""},{"docid":"b63a3548266692b5810f24c9c6f2376d","score":"0.6215211","text":"def create_feature_set(df, float_cols):\n\n # tfidf genre lists\n tfidf = TfidfVectorizer()\n tfidf_matrix = tfidf.fit_transform(df['consolidates_genre_lists'].apply(lambda x: \" \".join(x)))\n genre_df = pd.DataFrame(tfidf_matrix.toarray())\n genre_df.columns = ['genre' + \"|\" + i for i in tfidf.get_feature_names()]\n genre_df.reset_index(drop=True, inplace=True)\n\n # explicity_ohe = ohe_prep(df, 'explicit','exp')\n year_ohe = ohe_prep(df, 'year', 'year') * 0.5\n popularity_ohe = ohe_prep(df, 'popularity_red', 'pop') * 0.15\n\n # scale float columns\n floats = df[float_cols].reset_index(drop=True)\n scaler = MinMaxScaler()\n floats_scaled = pd.DataFrame(scaler.fit_transform(floats), columns=floats.columns) * 0.2\n\n # concanenate all features\n final = pd.concat([genre_df, floats_scaled, popularity_ohe, year_ohe], axis=1)\n\n # add song id\n final['id'] = df['id'].values\n\n return final","title":""},{"docid":"cc62effdc028d964423936ec5d4cb313","score":"0.6213039","text":"def to_feature_matrix(self):\n\t\tgame_dicts = [game.__dict__ for game in self.games.values()]\n\t\t# print(game_dicts)\n\t\tdf = pd.DataFrame(game_dicts)\n\t\tgenre_dummies = df[\"genres\"].str.join(sep='*').str.get_dummies(sep=\"*\")\n\t\tdevs_dummies = df[\"developers\"].str.join(sep='*').str.get_dummies(sep=\"*\")\n\t\tpubs_dummies = df[\"publishers\"].str.join(sep='*').str.get_dummies(sep=\"*\")\n\t\tplats_dummies = df[\"platforms\"].str.join(sep='*').str.get_dummies(sep=\"*\")\n\t\tcats_dummies = df[\"categories\"].str.join(sep='*').str.get_dummies(sep=\"*\")\n\n\t\t## remove values that occur very rarely\n\t\tgenre_dummies = genre_dummies.loc[:, (genre_dummies.sum(axis=0) >= 5)]\n\t\tdevs_dummies = devs_dummies.loc[:, (devs_dummies.sum(axis=0) >= 5)]\n\t\tpubs_dummies = pubs_dummies.loc[:, (pubs_dummies.sum(axis=0) >= 5)]\n\t\tcats_dummies = cats_dummies.loc[:, (cats_dummies.sum(axis=0) >= 5)]\n\n\t\t## combine dummies\n\t\tdf_complete = pd.concat([df[\"id\"], genre_dummies, devs_dummies, pubs_dummies, cats_dummies],\n\t\t\t\t\t\taxis=1)\n\t\t## add index from games_idx and sort by it\n\t\tdf_complete[\"idx\"] = df_complete[\"id\"].apply(lambda x: self.games_idx.index(x))\n\t\tdf_complete.sort_values(by=[\"idx\"], axis=0, ascending=True, inplace=True)\n\t\tdf_complete.drop([\"id\"], inplace=True, axis=1)\n\t\tdf_complete.set_index([\"idx\"], inplace=True)\n\n\t\t## normalize rows/games, so that e.g. games with many developers are not\n\t\t## automatically more likely to be regarded as similar\n\t\tdf_norm = df_complete.div(df_complete.sum(axis=1), axis=0)\n\t\tdf_norm.fillna(0, inplace=True)\n\n\t\treturn df_complete\n\n\t\t# colls = defaultdict(list)\n\t\t# for g in self.games.values():\n\t\t# \tcolls[\"dev\"] += g.developers\n\t\t# \tcolls[\"pubs\"] += g.publishers\n\t\t# \tcolls[\"plats\"] += g.platforms\n\t\t# \tcolls[\"cats\"] += g.categories\n\t\t# \tcolls[\"genres\"] += g.genres\n\t\t# for item, cnt in Counter(devs_all).most_common():\n\t\t# \tprint(f\"\\t{item}\\t{cnt}\") ## 5\n\t\t# for item, cnt in Counter(colls[\"pubs\"]).most_common():\n\t\t# \tprint(f\"\\t{item}\\t{cnt}\") ## 5\n\t\t# for item, cnt in Counter(colls[\"plats\"]).most_common():\n\t\t# \tprint(f\"\\t{item}\\t{cnt}\")\n\t\t# for item, cnt in Counter(colls[\"cats\"]).most_common():\n\t\t# \tprint(f\"\\t{item}\\t{cnt}\")\n\t\t# for item, cnt in Counter(colls[\"genres\"]).most_common():\n\t\t# \tprint(f\"\\t{item}\\t{cnt}\")","title":""},{"docid":"80d97b9371a4c352b3269b2d2af10bb2","score":"0.62095433","text":"def engineer_features(data):\n data['policy'] = (data.year < 2013).mul(1)\n data['nonstop_arrests'] = data.arrests - data.stop_arrests\n data['crimerate'] = data.crimes / data.population * 1000\n data['nonstop_arrestrate'] = data.nonstop_arrests / data.population * 1000\n data['stoprate'] = data.stops / data.population * 1000\n data['stop_arrestrate'] = data.stop_arrests / data.population * 1000\n data['arrestrate'] = data.arrests / data.population * 1000\n data['normal_year'] = data.year - 2005\n # Two of the precincts (22 in particular) have outlier crime rates\n # (because they have low residential population).\n # drop the outlier precincts, Central Park and Midtown South.\n data = data[~data.pct.isin([22, 14])]\n # the data is skewed. Generate log columns\n log_cols = ['crimerate', 'nonstop_arrestrate', 'stoprate', 'stop_arrestrate', 'arrestrate',\n 'nonstop_arrests', 'arrests', 'population', 'stops', 'stop_arrests', 'crimes']\n data[[f'log_{col}' for col in log_cols]] = data[log_cols].apply(np.log).copy()\n return data","title":""},{"docid":"ac73b6292e1710b215e54be6f2c3115a","score":"0.620333","text":"def train_data(self) -> pd.DataFrame:\n return self.ltr.read_train()","title":""},{"docid":"81f5f784e47f2064ac61ee355c2a58d4","score":"0.61816573","text":"def df_to_features(vectorizer, df, split):\n words = [row.text for i, row in df.iterrows()]\n\n if split == \"train\":\n feats = vectorizer.fit_transform(words)\n else:\n feats = vectorizer.transform(words)\n X = feats.todense()\n Y = df[\"label\"].values\n return X, Y","title":""},{"docid":"9adabde4b6e54ff9b560a0530419f4ac","score":"0.61643475","text":"def create_features(frame):\n frame['issue month'] = frame['issue date'].dt.month\n frame['issue month'] = frame['issue month'].astype(str)\n frame['issue year'] = frame['issue date'].dt.year\n frame['credit tenure'] = (frame['issue date'] - frame['earliest credit date']).astype('timedelta64[D]')\n frame['fraction funded'] = frame['loan funded'] / frame['loan request']\n frame['request over income'] = frame['loan request'] / frame['income']\n frame['funded over income'] = frame['loan funded'] / frame['income']\n frame['debt over income'] = frame['revolving balance'] / frame['income']\n frame['debt over request'] = frame['revolving balance'] / frame['loan request']\n frame['debt over funded'] = frame['revolving balance'] / frame['loan funded']\n frame['open line fraction'] = frame['open credit lines'] / frame['credit lines']","title":""},{"docid":"b604f468c580f0eb2e3025fc4e537212","score":"0.61447585","text":"def to_df(self):\n df = None\n for cluster, instances in self.raw.items():\n data = dict(instances) # We want self.raw to remain intact\n data['cluster'] = cluster\n next_df = pd.io.json.json_normalize(\n data, 'cluster_instances', ['cluster']\n )\n try:\n df = df.append(next_df)\n except AttributeError:\n df = next_df\n\n new_column_names = {f: f.replace('features.', '') for f in df}\n new_column_names['name'] = 'instance'\n df.rename(columns=new_column_names, inplace=True)\n df.set_index(['cluster', 'instance'], inplace=True)\n return df","title":""},{"docid":"a5b27422fab27778134b07eb53f2c987","score":"0.61444557","text":"def add_handcrafted_features(self):\n\n def statistical_feature_generator(x):\n \"\"\" Sample code to generate features\"\"\"\n fs = []\n\n total_dwell_time = sum(x['dwell_times']) # total dwell time\n avg_dwell_time = np.mean(x['dwell_times'])\n num_area_trajectory_have = len(x['dwell_times']) # the number of area\n num_unique_area_sensed = len(set(x['areas'])) # the number of unique areas\n\n fs.append(total_dwell_time)\n fs.append(avg_dwell_time)\n fs.append(num_area_trajectory_have)\n fs.append(num_unique_area_sensed)\n\n return fs\n\n def add_statistical_features(train_visits):\n df = train_visits.copy()\n\n features = df.apply(lambda x: statistical_feature_generator(x), axis=1)\n feature_name = ['total_dwell_time', 'avg_dwell_time', 'num_area', 'num_unique_area']\n\n fdf = pd.DataFrame(list(np.asarray(features)), index=features.index, columns=feature_name)\n\n # Combine feature values to the dataframe\n df = pd.concat([df, fdf], axis=1)\n\n # Relative dates\n df['date_rel'] = df['date'] - min(df.date)\n del fdf\n\n return df\n\n def add_visit_counts():\n \"\"\" Code block to track the previous number of visits.\n (Actually, the number of visits including the current one. \"\"\"\n self.train_visits['tmp'] = 1\n self.train_visits['nvisits'] = self.train_visits.groupby(['wifi_id'])['tmp'].cumsum()\n\n wid_nvisit = self.train_visits.iloc[list(self.train_visits['wifi_id'].drop_duplicates(keep='last').index)][\n ['wifi_id', 'nvisits']]\n d_wid_nvisit = {}\n for wid, nvisit in zip(wid_nvisit['wifi_id'], wid_nvisit['nvisits']):\n d_wid_nvisit[wid] = nvisit\n\n self.test_visits['tmp'] = 1\n self.test_visits['prev_vcount'] = self.test_visits['wifi_id'].apply(lambda x: d_wid_nvisit.get(x, 0))\n self.test_visits['nvisits'] = self.test_visits.groupby(['wifi_id'])['tmp'].cumsum() + self.test_visits['prev_vcount']\n\n del self.train_visits['tmp'], self.test_visits['tmp'], self.test_visits['prev_vcount'], wid_nvisit\n\n self.train_visits = add_statistical_features(self.train_visits)\n self.test_visits = add_statistical_features(self.test_visits)\n add_visit_counts()\n self.train_visits['wifi_id']","title":""},{"docid":"510aa6dcb77959023305f4f0bd44b508","score":"0.613878","text":"def add_hashed_features(df):\r\n return df.select('label', tuples_to_hash_features_udf('feature').alias('features'))","title":""},{"docid":"bf92dbac6294eeb60776e074bad44624","score":"0.61253005","text":"def add_hashed_features(df):\n return df.select(df.label, tuples_to_hash_features_udf(df.feature).alias('features'))","title":""},{"docid":"869d91e4243d0d739bfced8f1a55bbb3","score":"0.61151063","text":"def feature_engineering(self):\n # Load the complaints with sentiment metrics file\n complaints = pd.read_csv(self.complaints_with_sentiment_file)\n complaints = complaints[\n [\"Complaint ID\", \"Timely response?\", \"Product\", \"corpus_score_sum\", \"corpus_score_ave\", \"negative_ratio\",\n \"most_negative_score\", \"word_num\", \"sentence_num\", \"num_of_question_mark\", \"num_of_exclaimation_mark\",\n \"company_response\"]]\n print(\"Loaded {} complaints with sentiment metrics.\".format(len(complaints)))\n\n # Load the preprocessed narratives\n narrative_processed = pd.read_csv(self.narrative_preprocessed_file)\n print(\"Loaded {} preprocessed narratives.\".format(len(narrative_processed)))\n\n # One hot coding the column \"company_response\", store the column name of dummy columns\n old_column_num = len(complaints.columns)\n complaints = pd.get_dummies(complaints, columns=[\"company_response\"])\n new_column_names = []\n for i in np.arange(old_column_num - 1, len(complaints.columns)):\n new_column_names.append(complaints.columns[i])\n\n # Save the new column names to a file for prediction\n column_name_file = \"trained_models/company_corresponse_variable_names.csv\"\n with open(column_name_file, \"w\") as fobj:\n fobj.write(\",\".join(new_column_names))\n\n # Merge the sentiment metrics and narratives according to their Complaint ID.\n # Those narratives without label do not have sentiment metrics. These records will be discarded.\n complaints_features = pd.merge(complaints.reset_index(drop=True), narrative_processed.reset_index(drop=True),\n how='inner', on=['Complaint ID', 'Complaint ID'])\n\n print(\"Loaded {} complaints \".format(complaints_features.shape))\n\n # Vectorize narratives\n not_to_vectorized_columns = complaints_features.loc[:, \"corpus_score_sum\":\"company_response_Closed with non-monetary relief\"]\n print(\"Tf-idf vectorizing narratives processed for escalation classifier...\")\n vectorized_columns = self.tf_idf_vectorizer.transform(complaints_features[\"processed_narrative\"])\n print(vectorized_columns.shape)\n\n X = hstack((vectorized_columns, np.array(not_to_vectorized_columns)))\n y = [0 if x == \"No\" else 1 for x in complaints_features[\"Consumer disputed?\"]]\n\n column_names = self.tf_idf_vectorizer.get_feature_names()\n column_names.extend(not_to_vectorized_columns.columns)\n print(len(column_names))\n X.columns = column_names\n\n return X, y","title":""},{"docid":"1c3d9aca6431cd2e39a4ac8480beb4c5","score":"0.6113998","text":"def generate_features(df: pd.DataFrame) -> pd.DataFrame:\n df[\"attributes\"] = df[\"attributes\"].apply(json.loads)\n df[\"device_class\"] = df[\"attributes\"].apply(get_device_class)\n df[\"unit_of_measurement\"] = df[\"attributes\"].apply(get_unit_of_measurement)\n df[\"friendly_name\"] = df[\"attributes\"].apply(get_friendly_name)\n return df","title":""},{"docid":"be089676f2396b6a263bd36e1ee29dda","score":"0.6099017","text":"def get_training_df(self, feature_dict, label_map={'cooperative': 1, 'additive': 0}):\n ldict = self.get_feature_all(feature_dict)\n train = pd.DataFrame(ldict)\n train['label'] = self.get_numeric_label(label_map).values\n return train","title":""},{"docid":"e4fe7acb9c9e0f542e7a1d93500fb343","score":"0.6062521","text":"def _init_raw_df(self):\n df_features = self.setup_feature()\n feature_names = df_features.columns\n\n df_labels = self.setup_label()\n label_names = df_labels.columns\n\n raw_df = df_features.merge(df_labels, left_index=True, right_index=True, how=\"left\")\n\n return raw_df, feature_names, label_names","title":""},{"docid":"8f44bd2826ca13298ccfe747de823120","score":"0.60610723","text":"def create_feature_df(ticker):\n # Get all relevant data\n market_returns = get_market_returns()\n prices_df = get_prices_df(ticker)\n fund_df = get_fundamentals_df(ticker)\n\n # Reindex fundamentals dataframe to match prices dataframe,\n # limit fill to 252 days just in case there are gaps in financial statements\n df = fund_df.reindex(index=prices_df.index, method=\"ffill\", limit=252)\n df = df.join(prices_df)\n\n # Create a range of end-of-month dates\n # Skip if index is size 0\n if df.index.size > 0:\n dates = pd.bdate_range(start=df.index[0], end=df.index[-1], freq=\"M\")\n else:\n return None\n rolling_dfs = []\n\n # For each end of month date, select all data from the past year\n # If there isn't enough data, skip\n for date in dates:\n annual_df = df.loc[(date - pd.Timedelta(days=365)) : date]\n if len(annual_df) > 250:\n rolling_dfs.append(annual_df)\n\n # Make features for each rolling 12-month period\n # If rolling_dfs list is empty, there's not enough data so return None\n if rolling_dfs:\n df = pd.DataFrame([make_features(n, market_returns) for n in rolling_dfs])\n else:\n return None\n # Match each months data with the return at the end of the next month\n # for forecasting\n df[\"forward_ret\"] = df[\"monthly_ret\"].shift(-1)\n df[\"ticker\"] = ticker\n df = df.dropna()\n # If dataframe has any rows, write to csv\n if len(df) > 1:\n df.to_csv(f\"csv/{ticker}.csv\", index=False)\n print(f\"{ticker} done!\")\n return None","title":""},{"docid":"513e7895e9b4a07dda94af3988086619","score":"0.60368776","text":"def feature_engineer(data):\n\n df = pd.DataFrame()\n data['totl_anglr_vel'] = (data['angular_velocity_X']**2 + data['angular_velocity_Y']**2 +\n data['angular_velocity_Z']**2) ** 0.5\n data['totl_linr_acc'] = (data['linear_acceleration_X']**2 + data['linear_acceleration_Y']**2 +\n data['linear_acceleration_Z'])**0.5\n\n data['acc_vs_vel'] = data['totl_linr_acc'] / data['totl_anglr_vel']\n\n for col in data.columns:\n if col in ['row_id', 'series_id', 'measurement_number']:\n continue\n df[col + '_mean'] = data.groupby(['series_id'])[col].mean()\n df[col + '_median'] = data.groupby(['series_id'])[col].median()\n df[col + '_max'] = data.groupby(['series_id'])[col].max()\n df[col + '_min'] = data.groupby(['series_id'])[col].min()\n df[col + '_std'] = data.groupby(['series_id'])[col].std()\n df[col + '_range'] = df[col + '_max'] - df[col + '_min']\n df[col + '_maxtoMin'] = df[col + '_max'] / df[col + '_min']\n df[col + '_mean_abs_chg'] = data.groupby(['series_id'])[col].apply(\n lambda x: np.mean(np.abs(np.diff(x))))\n df[col + '_abs_max'] = data.groupby(['series_id']\n )[col].apply(lambda x: np.max(np.abs(x)))\n df[col + '_abs_min'] = data.groupby(['series_id']\n )[col].apply(lambda x: np.min(np.abs(x)))\n df[col + '_abs_avg'] = (df[col + '_abs_min'] + df[col + '_abs_max'])/2\n return df","title":""},{"docid":"422ce551242c6e232756eaa5097ccf08","score":"0.6029527","text":"def createTrainingDataframe(self):\r\n\r\n self.df = pd.read_csv(self.trainingLoc, sep=self.delimiter, names=['label', 'text'])\r\n self.df['label'] = self.df['label'].astype(bool)\r\n\r\n #Stem the Text Column\r\n te.df['stemText'] = te.df['text'].apply(lambda X: ' '.join([te.stemmer.stem(word) for word in word_tokenize(X)]))","title":""},{"docid":"deb4793e66bbacee78964b4a46a16d90","score":"0.60241324","text":"def gen_featureset(df):\n # Load stock data\n stock_data = pd.read_csv(\"output/historical.csv\", parse_dates=[\"Date\"])\n # Loop through each tweets\n featureset = list()\n print('Generating features... (up to 1min)')\n for i in range(len(df.dropna())):\n # Locating relevant information\n ticker = df.loc[i, \"Symbol\"]\n sentiment = df.loc[i, \"Sentiment\"]\n stock_mouvement = df.loc[i, \"Movement\"]\n tweet_date = df.loc[i, \"Datetime\"]\n tweet_text = df.loc[i, \"Text\"]\n\n # Generating features\n volatility = feature_stockprice_volatility(\n ticker, tweet_date, time_before=120, df=stock_data)\n\n # If no volatility information, skip row\n if volatility is not None:\n\n feature_label = list()\n\n feature = {'ticker': ticker, 'sentiment': sentiment,\n 'volatility': volatility}\n feature_label.append(feature)\n feature_label.append(stock_mouvement)\n\n featureset.append(feature_label)\n return featureset","title":""},{"docid":"480f4d485b38f600e50febbed9e7772c","score":"0.60035175","text":"def df_before_transformation():\n\tdf_dict = csv_into_dict_of_data(training_dataset)\n\tdf_dict = remove_zero_columns(df_dict)\n\tidentifier = \"_before_transformation\"\n\tplot_troffic_graph_df_into_graph_and_save(df_dict, identifier)\n\tget_elbow_by_date_and_time(df_dict, identifier)\n\n\t# print(df_dict)","title":""},{"docid":"e644910a6ffbd0826a8bb0623d7f1e59","score":"0.5995331","text":"def create_features(df, label=None):\n df = df.copy()\n df['date'] = df.index\n df['weekday'] = df['date'].dt.day_name()\n df['weekday'] = pd.Categorical(df['weekday'], categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)\n df['users'] = df.Users\n df['hour'] = df['date'].dt.hour\n df['dayofweek'] = df['date'].dt.dayofweek\n df['quarter'] = df['date'].dt.quarter\n df['month'] = df['date'].dt.month\n df['year'] = df['date'].dt.year\n df['dayofyear'] = df['date'].dt.dayofyear\n df['dayofmonth'] = df['date'].dt.day\n df['weekofyear'] = df['date'].dt.weekofyear\n\n X = df[['weekday','users','hour','dayofweek','quarter','month','year',\n 'dayofyear','dayofmonth','weekofyear']]\n return X","title":""},{"docid":"99262d70c01eac648ac641ea239bd0bb","score":"0.59814614","text":"def pull_features(engine):\n logger.debug('Start of pull features function')\n\n features = pd.read_sql('SELECT * FROM features', engine)\n features['startDate'] = features['startDate'].apply(lambda x: datetime.fromisoformat(x) if type(x) == str else x.to_pydatetime())\n\n logger.debug('%s', features.head())\n\n return features","title":""},{"docid":"9bf01e9a1eea4dd3cdaf36f66a4dc115","score":"0.59758395","text":"def additional_features3(users):\n\n return pd.DataFrame({'User': users})","title":""},{"docid":"074468534fde66ff88b80bc4c420e730","score":"0.59621865","text":"def create_newFeatures(dataframe):\r\n\r\n if 'transactiondate' in dataframe.columns:\r\n dataframe['transaction_year'] = dataframe.transactiondate.dt.year.astype(np.int16)\r\n dataframe['transaction_month'] = dataframe.transactiondate.dt.month.astype(np.int8)\r\n # dataframe['transaction_day'] = dataframe.transactiondate.dt.weekday.astype(np.int8)\r\n dataframe['transaction_quarter'] = dataframe.transactiondate.dt.quarter.astype(np.int8)\r\n del dataframe['transactiondate']\r\n else:\r\n df_date = pd.DataFrame({'transaction_year': [2016, 2016, 2016, 2017, 2017, 2017],\r\n 'transaction_month': [10, 11, 12, 10, 11, 12]})\r\n df_date['tmp'] = 1\r\n dataframe['tmp'] = 1\r\n dataframe = pd.merge(dataframe, df_date, on='tmp')\r\n del dataframe['tmp']\r\n dataframe['transaction_quarter'] = dataframe.transaction_year.astype(str)+'-'+dataframe.transaction_month.astype(str).apply(lambda x: ('00'+x)[-2:]) +'-01'\r\n dataframe['transaction_quarter'] = dataframe.transaction_quarter.astype(object)\r\n dataframe['transaction_quarter'] = pd.to_datetime(dataframe['transaction_quarter'])\r\n dataframe['transaction_quarter'] = dataframe.transaction_quarter.dt.quarter\r\n\r\n \r\n dataframe['rawcensustractandblock_states'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[:1]).astype(np.int8)\r\n dataframe['rawcensustractandblock_countries'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[1:4]).astype(np.int8)\r\n dataframe['rawcensustractandblock_tracts'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[4:11]).astype(np.float64)\r\n dataframe['rawcensustractandblock_blocks'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: 0 if x[11:] == '' else x[11:]).astype(np.int8)\r\n \r\n #--- how old is the house? ---\r\n dataframe['house_age'] = dataframe['transaction_year'].astype(np.int16) - dataframe['yearbuilt'].astype(np.int16)\r\n\r\n #--- how many rooms are there? ---\r\n dataframe['tot_rooms'] = dataframe['bathroomcnt'] + dataframe['bedroomcnt']\r\n\r\n #--- does the house have A/C? ---\r\n dataframe['AC'] = np.where(dataframe['airconditioningtypeid']>0, 1, 0)\r\n\r\n #--- Does the house have a deck? ---\r\n dataframe['deck'] = np.where(dataframe['decktypeid']>0, 1, 0)\r\n dataframe.drop('decktypeid', axis=1, inplace=True)\r\n\r\n #--- does the house have a heating system? ---\r\n dataframe['heating_system'] = np.where(dataframe['heatingorsystemtypeid']>0, 1, 0)\r\n\r\n #--- does the house have a garage? ---\r\n dataframe['garage'] = np.where(dataframe['garagecarcnt']>0, 1, 0)\r\n\r\n #--- does the house come with a patio? ---\r\n dataframe['patio'] = np.where(dataframe['yardbuildingsqft17']>0, 1, 0)\r\n\r\n #--- does the house have a pool?\r\n dataframe['pool'] = dataframe['pooltypeid10'] | dataframe['pooltypeid7'] | dataframe['pooltypeid2']\r\n dataframe['pool'] = dataframe['pool'].map({True: 1, False: 0})\r\n\r\n #--- does the house have all of these? -> spa/hot-tub/pool, A/C, heating system , garage, patio\r\n dataframe['exquisite'] = dataframe['pool'] + dataframe['patio'] + dataframe['garage'] + dataframe['heating_system'] + dataframe['AC']\r\n\r\n #--- Features based on location ---\r\n dataframe['x_loc'] = np.cos(dataframe['latitude']) * np.cos(dataframe['longitude'])\r\n dataframe['y_loc'] = np.cos(dataframe['latitude']) * np.sin(dataframe['longitude'])\r\n dataframe['z_loc'] = np.sin(dataframe['latitude'])\r\n\r\n return dataframe","title":""},{"docid":"bf4948a0ff708458c118c20f2534e3b3","score":"0.594195","text":"def to_df(self):\r\n\r\n feature_dict = {'ID':[],'MASS_MGF':[],'MASS_NEUTRAL':[],'RT':[],\r\n 'DTXCID':[],'MASS':[],'FORMULA':[],\r\n 'SINGLE_SCORES':[],'SUM_SCORE':[],'Q-SCORE':[]} \r\n \r\n for feature in self.feature_list:\r\n feature.reference_scores['Q-SCORE'] = [np.nan if max(feature.reference_scores['SUM_SCORE']) == 0 else \r\n score/max(feature.reference_scores['SUM_SCORE']) for score in feature.reference_scores['SUM_SCORE']]\r\n for key in feature_dict.keys(): feature_dict[key].extend(feature.reference_scores.get(key, None))\r\n \r\n return pd.DataFrame.from_dict(feature_dict)","title":""},{"docid":"b647f4a70f06922110051de14fd1e456","score":"0.5936085","text":"def normalize(df):\r\n\r\n processed_features = pd.DataFrame()\r\n\r\n processed_features[\"age\"] = linear_scale(df[\"age\"])\r\n processed_features[\"education-num\"] = linear_scale(df[\"education-num\"])\r\n processed_features[\"education-num\"] = linear_scale(df[\"hours-per-week\"])\r\n\r\n return processed_features","title":""},{"docid":"091b7c5e53afefc1eaf25a63438c9be3","score":"0.5935219","text":"def get_features(self):\n X = self.df_merge.drop(columns = [self.target_name])\n return X","title":""},{"docid":"1d7f7bba9bcbfd91b93df775e9fbe5c6","score":"0.59348565","text":"def feda(self):\n train_domains = []\n test_domains = []\n\n X_train = self.get('X', 'train')\n X_test = self.get('X', 'test')\n\n for i in self.data.index.levels[0]:\n if i in self.data.index:\n train_domains.append(X_train.loc[i].to_numpy())\n test_domains.append(X_test.loc[i].to_numpy())\n\n\n self.da['train'] = pd.DataFrame(feda(train_domains))\n self.da['test'] = pd.DataFrame(feda(test_domains))","title":""},{"docid":"3eb8fb10d91cdb627514c837bbc963de","score":"0.5934169","text":"def extract_all_features(self):\n\n # Data frame for collecting the features\n series_features = pd.Series()\n series_features['Driver'] = self.driver\n series_features['Trip'] = self.trip\n\n for feature in self.features:\n\n # Determine which feature are to be extracted\n feature_method = getattr(self, feature)\n\n # Calculate value of feature\n series_features[feature] = feature_method()\n\n return series_features","title":""},{"docid":"3f478c28b9b33d6c04820f404f3533b6","score":"0.593339","text":"def engineer_features(df,training=True):\n\n ## extract dates\n dates = df['date'].values.copy()\n dates = dates.astype('datetime64[D]')\n\n ## engineer some features\n eng_features = defaultdict(list)\n previous =[7, 14, 28, 70] #[7, 14, 21, 28, 35, 42, 49, 56, 63, 70]\n y = np.zeros(dates.size)\n for d,day in enumerate(dates):\n\n ## use windows in time back from a specific date\n for num in previous:\n current = np.datetime64(day, 'D') \n prev = current - np.timedelta64(num, 'D')\n mask = np.in1d(dates, np.arange(prev,current,dtype='datetime64[D]'))\n eng_features[\"previous_{}\".format(num)].append(df[mask]['revenue'].sum())\n\n ## get get the target revenue \n plus_30 = current + np.timedelta64(30,'D')\n mask = np.in1d(dates, np.arange(current,plus_30,dtype='datetime64[D]'))\n y[d] = df[mask]['revenue'].sum()\n\n ## attempt to capture monthly trend with previous years data (if present)\n start_date = current - np.timedelta64(365,'D')\n stop_date = plus_30 - np.timedelta64(365,'D')\n mask = np.in1d(dates, np.arange(start_date,stop_date,dtype='datetime64[D]'))\n eng_features['previous_year'].append(df[mask]['revenue'].sum())\n\n ## add some non-revenue features\n minus_30 = current - np.timedelta64(30,'D')\n mask = np.in1d(dates, np.arange(minus_30,current,dtype='datetime64[D]'))\n eng_features['recent_invoices'].append(df[mask]['unique_invoices'].mean())\n eng_features['recent_views'].append(df[mask]['total_views'].mean())\n\n X = pd.DataFrame(eng_features)\n ## combine features in to df and remove rows with all zeros\n X.fillna(0,inplace=True)\n mask = X.sum(axis=1)>0\n X = X[mask]\n y = y[mask]\n dates = dates[mask]\n X.reset_index(drop=True, inplace=True)\n\n if training == True:\n ## remove the last 30 days (because the target is not reliable)\n mask = np.arange(X.shape[0]) < np.arange(X.shape[0])[-30]\n X = X[mask]\n y = y[mask]\n dates = dates[mask]\n X.reset_index(drop=True, inplace=True)\n \n return(X,y,dates)","title":""},{"docid":"4fa24a774018222c58523315f7de3926","score":"0.5921966","text":"def _prepare_features(self, instances_df, col_names):\n # type: (pd.DataFrame, ColumnNames) -> None\n pass","title":""},{"docid":"f3028f122185119854becda96f3e60e0","score":"0.589815","text":"def featureEngineer(data, feature_to_drop):\n\tdt = pd.to_datetime(data[\"datetime\"]) # convert string to datetime type\n\tdata[\"year\"] = dt.map(lambda x: x.year)\n\tdata[\"month\"] = dt.map(lambda x: x.month)\n\tdata[\"day\"] = dt.map(lambda x: x.day)\n\tdata[\"hour\"] = dt.map(lambda x: x.hour)\n\tdata[\"weekday\"] = dt.map(lambda x: x.weekday())\n\t# remove outliers detected from temp vs atemp scatter plot\n\ttrain_all = data[np.array(data[\"temp\"] / data[\"atemp\"]) < 2]\n\ttrain_all = train_all.drop(feature_to_drop,\n\t\taxis=1) # include all features and target\n\treturn train_all","title":""},{"docid":"871725935acd8bfd2f20e71cbad07e90","score":"0.5898055","text":"def prepare_features(dataframe):\n processed_features = dataframe.copy()\n\n # Apply linear scaling to price and highway-mpg here\n processed_features[\"price\"] = linear_scale(processed_features[\"price\"])\n processed_features[\"highway-mpg\"] = linear_scale(processed_features[\"highway-mpg\"])\n \n return processed_features","title":""},{"docid":"ff18a6d63ab81915ed931bceb30fdec1","score":"0.5879053","text":"def to_dataframe(self, features=None):\n if not features:\n features = self.features\n\n data = defaultdict(dict)\n for f1 in features:\n for f2 in features:\n data[f1][f2] = self[f1][f2].distance\n\n df = pd.DataFrame.from_dict(data)\n return df","title":""},{"docid":"095985728b8adcfbccfdeeb7cebd2d2a","score":"0.58758074","text":"def query_data_to_features(query_data: dict)->pd.DataFrame:\n query_masses = query_data['prec_mass_list2']\n query_mz = query_data['mono_mzs2']\n query_rt = query_data['rt_list_ms2']\n\n features = pd.DataFrame(np.array([query_masses, query_mz, query_rt]).T, columns = ['mass_matched', 'mz_matched', 'rt_matched'])\n\n features['feature_idx'] = features.index #Index to query_data\n features['query_idx'] = np.arange(len(query_masses))\n\n features = features.sort_values('mass_matched', ascending=True)\n\n return features","title":""},{"docid":"9c6e4c2c8924630c6adf7c6586f85be1","score":"0.5873379","text":"def toDataFrame(KF):\r\n\r\n def splitunit(s):\r\n iu=s.rfind('_[')\r\n if iu>1:\r\n return s[:iu], s[iu:]\r\n else:\r\n return s, ''\r\n\r\n def cleancol(l):\r\n return ['_clean'.join(splitunit(c)) for c in l]\r\n\r\n cols = []\r\n cols += list(KF.X_hat.columns)\r\n cols += cleancol(KF.X_clean.columns)\r\n cols += list(KF.U_hat.columns)\r\n cols += cleancol(KF.U_clean.columns)\r\n cols += ['OUT_'+c for c in list(KF.Y_hat.columns)]\r\n cols += ['OUT_'+c for c in cleancol(KF.Y_clean.columns)]\r\n cols += list(KF.S_hat.columns)\r\n cols += cleancol(KF.S_clean.columns)\r\n df = pd.concat((KF.X_hat, KF.X_clean, KF.U_hat, KF.U_clean, KF.Y_hat, KF.Y_clean, KF.S_hat, KF.S_clean), axis=1)\r\n df.columns = cols\r\n\r\n # \"Accelerations\" \r\n dfAcc = pd.concat((KF.XD_hat, KF.XD_clean), axis=1)\r\n cols = list(KF.XD_hat.columns)\r\n cols += cleancol(KF.XD_clean.columns)\r\n dfAcc.columns=cols\r\n # We keep only the part that is not already in the state vector\r\n col_new = [c for c in cols if c not in df.columns]\r\n dfAcc=dfAcc[col_new] \r\n\r\n # \r\n df = pd.concat((df,dfAcc), axis=1)\r\n\r\n\r\n df.insert(0, 'Time_[s]', KF.time)\r\n return df","title":""},{"docid":"2852ea287df078b189b1fef0897ca81e","score":"0.5867794","text":"def add_features(df):\n df['full_time_employment_rate'] = (df['Employed_full_time_year_round'] /\n df['Employed'])\n df['pay_IQR_range'] = df['P75th'] - df['P25th']\n df['field_of_study'] = df.apply(field_majorcat_mapper, axis=1)\n df['norm_ur'] = normalize_column(df, 'Unemployment_rate')\n df['norm_ftr'] = normalize_column(df, 'full_time_employment_rate')\n df['norm_med_income'] = normalize_column(df, 'Median')\n df['norm_p25th_income'] = normalize_column(df, 'P25th')\n df['norm_p75th_income'] = normalize_column(df, 'P75th')\n df['risk_rating'] = (\n (df.norm_ur - df.norm_ur.median()) +\n (df.norm_ftr.median() - df.norm_ftr) +\n (df.norm_p25th_income.median() - df.norm_p25th_income)\n )\n df['gain_rating'] = (\n (df.norm_med_income - df.norm_med_income.median()) +\n (df.norm_p75th_income - df.norm_p75th_income.median())\n )\n return df","title":""},{"docid":"4969180ae7e462ace385501d6ea60289","score":"0.5867107","text":"def _make_new_table(self, features):\n\n categorical_features = np.array(features[:, self.categorical_ids])\n self._check_same_categories(categorical_features)\n transformed_categorical = self.encoder.transform(categorical_features).toarray()\n\n # If there are non-categorical features in the data\n if len(self.non_categorical_ids) == 0:\n transformed_features = transformed_categorical\n else:\n # Stack transformed categorical and non-categorical data\n non_categorical_features = np.array(features[:, self.non_categorical_ids])\n frames = (non_categorical_features, transformed_categorical)\n transformed_features = np.hstack(frames)\n\n return transformed_features","title":""},{"docid":"42c08ae0a156ee88db5cbc4710012bfd","score":"0.58653075","text":"def get_dataframe(self) -> pd.DataFrame:","title":""},{"docid":"3ab408eb2482eda06508a39ab34a92c2","score":"0.5854169","text":"def rfc_train(df):\n\n assembler_feats=VectorAssembler(inputCols=feat_cols, outputCol='features')\n label_indexer = StringIndexer(inputCol='Label', outputCol=\"target\")\n pipeline = Pipeline(stages=[assembler_feats, label_indexer])\n\n # Transform the data and do a random split\n all_data = pipeline.fit(df).transform(df)\n all_data.cache()\n data_train, data_test = all_data.randomSplit([0.75, 0.25], seed=123)\n # Get class labels for using IndexToString to convert back from indexes\n Labels = all_data.groupBy('Label').count().orderBy('count', ascending=False).select('Label').collect()\n Labels = [row.Label for row in Labels]\n # Convert label indexes back to actual labels\n converter = IndexToString(inputCol='prediction', outputCol='predicted_label', labels=Labels)\n\n # Initiate a RandomForest model and train on the data\n rfc = RandomForestClassifier(labelCol='target', featuresCol='features', numTrees=100)\n trained_model = rfc.fit(data_train)\n predict = trained_model.transform(data_test)\n results = converter.transform(predict)\n\n converted = converter.transform(predict)\n # Calculate the combined accuracy to evaluate the model performance\n evaluator = multi_metric(labelCol='target', predictionCol='prediction', metricName='accuracy')\n print('Accuracy is: ', evaluator.evaluate(results))\n trained_model.write().overwrite().save('s3n://cyber-insight/rfc_model_multi')","title":""},{"docid":"a992bc164aa02d96a5e412a89ac2e79e","score":"0.58522373","text":"def as_dataframe(self):\n colnames = [self._term_full_names(name) for name in self.terms_info]\n data = pd.DataFrame(self.design_matrix)\n data.columns = list(flatten_list(colnames))\n return data","title":""},{"docid":"4e9dee89cd8dda6efe54ab6f0b217b47","score":"0.58505636","text":"def ohe_prep(df,column,new_name):\n\n tf_df = pd.get_dummies(df[column])\n feature_names = tf_df.columns\n tf_df.columns = [new_name + \"|\" + str(i) for i in feature_names]\n tf_df.reset_index(drop = True, inplace = True)\n return tf_df","title":""},{"docid":"8d4f7985a53149b5848b430c7300e076","score":"0.5844174","text":"def encode_and_bind(self, df, features_to_encode):\n\n dummies = pd.get_dummies(df[features_to_encode])\n df_result = pd.concat([dummies, df], axis=1)\n df_result = df_result.drop(features_to_encode, axis=1)\n return df_result","title":""},{"docid":"cece53ae4ec755ea122d014881823dcd","score":"0.5838455","text":"def separate_data(self,df):\n # Simply retrieve Survived column from DataFrame\n labels = df[\"Survived\"]\n # Get all column names of df, but without Survived column\n newcols = df.columns.drop(\"Survived\")\n # Get these columns\n features = df[newcols]\n return features,labels","title":""},{"docid":"736ed7c559f3c85c88cb3e18db9a3f3c","score":"0.5831829","text":"def generate_model_text_features(raw_df_path, save_path=None):\n df = pd.read_csv(raw_df_path)\n df = format_raw_df(df.copy())\n df = df.loc[df[\"is_question\"]].copy()\n df[\"full_text\"] = df[\"Title\"].str.cat(df[\"body_text\"], sep=\" \", na_rep=\"\")\n\n df = add_v1_features(df.copy())\n df = add_v2_text_features(df.copy())\n\n if save_path:\n df.to_csv(save_path)\n return df","title":""},{"docid":"8965e3ff922f4f6ba5eb1de172b6b71b","score":"0.58242166","text":"def add_features(df_in, rolling_win_size=15):\n\n sensor_cols = []\n index = df_in.columns.get_loc('TTF')\n for i in df_in.columns[2:index]:\n sensor_cols.append(i)\n\n sensor_av_cols = [nm+'_av' for nm in sensor_cols]\n sensor_sd_cols = [nm+'_sd' for nm in sensor_cols]\n\n df_out = pd.DataFrame()\n\n ws = rolling_win_size\n\n #calculate rolling stats for each engine id\n\n for m_id in pd.unique(df_in.Turbine_ID):\n\n # get a subset for each engine sensors\n df_engine = df_in[df_in['Turbine_ID'] == m_id]\n df_sub = df_engine[sensor_cols]\n\n # get rolling mean for the subset\n av = df_sub.rolling(ws, min_periods=1).mean()\n av.columns = sensor_av_cols\n\n # get the rolling standard deviation for the subset\n sd = df_sub.rolling(ws, min_periods=1).std().fillna(0)\n sd.columns = sensor_sd_cols\n\n # combine the two new subset dataframes columns to the engine subset\n new_ftrs = pd.concat([df_engine,av,sd], axis=1)\n\n # add the new features rows to the output dataframe\n df_out = pd.concat([df_out,new_ftrs])\n df_out = df_out.sort_values(by=['Turbine_ID', 'Date'] )\n return df_out","title":""},{"docid":"c02393d4f7d027910d541195b3a1ad74","score":"0.58183634","text":"def vectorize_df(trainFrame, holdoutFrame): \n trainDict = trainFrame.to_dict(orient='records')\n holdoutDict = holdoutFrame.to_dict(orient='records') \n \n vec = DictVectorizer()\n vec.fit(trainDict)\n \n trainArray = vec.transform(trainDict).toarray()\n holdoutArray = vec.transform(holdoutDict).toarray()\n\n return trainArray, holdoutArray, vec.get_feature_names()","title":""},{"docid":"d4f14cf8ebd47a85d80d46885e862690","score":"0.5813522","text":"def prepare_data():\n #all data to one table\n df = pd.concat( \\\n [pd.read_csv('data//train.csv', header=0).drop(['Survived'], axis=1),\n pd.read_csv('data//test.csv', header=0)])\n df = df.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis = 1)\n\n # replace missing fares with median fare\n median_fare = df['Fare'].dropna().median()\n df.loc[(df.Fare.isnull()), 'Fare'] = median_fare\n\n # replace missing embarked with mode embarked\n mode_embarked = df['Embarked'].dropna().mode()\n df.loc[(df.Embarked.isnull()), 'Embarked'] = mode_embarked[0]\n #map categorical features to numbers\n df['Sex'] = df['Sex'].map({'female': 0.0, 'male': 1.0}).astype(float)\n df['Embarked'] = df['Embarked'].map( \\\n {'C': 0.0, 'S': 1.0, 'Q': 2.0}).astype(float)\n\n #There are a lot of items with missing age, so we will predict it\n X_train = df[df['Age'].notnull()].drop('Age', axis=1).values\n #add polynomial features\n X_train = PolynomialFeatures(degree=2).fit_transform(X_train)\n #get targets \n y_train = df[df['Age'].notnull()].Age.values\n return df, X_train, y_train","title":""},{"docid":"38092994673f1d12c872dbbc9863bf66","score":"0.5812889","text":"def get_data_frame():\n\n # Define the rows for the DataFrame\n dataframe_labels = [\"Image_Id\", \"Total_Relations\", \"Number_Of_Positive_Relations\",\n \"Number_Of_Negative_Relations\", \"Relations_Accuracy\", \"Positive_Relations_Accuracy\",\n \"Negative_Relations_Accuracy\", \"Old_Predictions\", \"Train_Entity\"]\n\n # Define DataFrame\n df = pd.DataFrame(columns=dataframe_labels)\n return df","title":""},{"docid":"f363b855cc4f2d6d640be4194937f066","score":"0.5810875","text":"def set_data_frame(self):\n self.__data_frame = pd.DataFrame(data = self.data_array,\n index = self.data_names,\n columns = self.feature_names)","title":""},{"docid":"4665cb777adacb3e5ab33b9970ad85c1","score":"0.58048683","text":"def ohe_prep(df, column, new_name):\n\n tf_df = pd.get_dummies(df[column])\n feature_names = tf_df.columns\n tf_df.columns = [new_name + \"|\" + str(i) for i in feature_names]\n tf_df.reset_index(drop=True, inplace=True)\n return tf_df","title":""},{"docid":"a7dc6b1e649c99468c063500048025c2","score":"0.5804095","text":"def transform_data(data):\n\n # Convert from dictionary to dataframe\n df = pd.DataFrame(data)\n\n # Dummify features\n for dummy_feat in feats['to_dummify']:\n if dummy_feat in df:\n df = pd.concat([df, pd.get_dummies(df[dummy_feat],\n prefix=dummy_feat)], axis=1)\n\n # Drop un-dummified features\n df = df.drop(dummy_feat, axis=1)\n\n # Include dummified features in cols_to_use\n cols_to_use = [col for col in df.columns\n if col in feats['as_is']\n or any([dummy_feat in col for dummy_feat in feats['to_dummify']])]\n\n # Drop NA and subset\n df = df[cols_to_use].dropna()\n\n # If after dummifying we are missing any features wrt the training data feature space, add a column of zeros for that feature\n for training_feat in feats['trained_features']:\n if training_feat not in df:\n df[training_feat] = np.zeros((df.shape[0], 1))\n\n return df","title":""},{"docid":"040be6db4cc82fde13f2b623a6ab4339","score":"0.580084","text":"def _add_new_features(self,list_manager_stop,list_vehi):\n data = []\n\n for i,manager_stop in enumerate(list_manager_stop):\n feature_object = derivingFeaturesCreatedDataset.DerivingFeaturesCreatedDataset(manager_stop)\n dict_features= feature_object.derive_features()\n dict_features['class_label'] = min(6,list_vehi[i])\n dict_features['iteration'] = self.iteration\n data.append(dict_features)\n\n header = True\n if os.path.exists(useful_paths.FILE_TO_TREE_CVRPTW):\n header = False\n\n data_results = pd.DataFrame(data)\n data_results.to_csv(useful_paths.FILE_TO_TREE_CVRPTW, header=header, index=False,mode = \"a\")","title":""},{"docid":"6d4ec63022762cdf4117891453941940","score":"0.58001685","text":"def get_features(df, path=PATH_ENCODE_FEATURE, fit_encoder=False):\n\n # perform a few transformation\n # should that be put in cleaning script?\n df['low_max_alt'] = df['maxflt'] < 300 # 30k-35k feet\n df['high_min_alt'] = df['minflt'] > 0\n df['small_radius'] = df['radius'] < 5.0\n df['long_text'] = df['len_txt'] > 100\n\n # text vector\n X_vector = extract_float_vector(df['vector'])\n\n feature_ordinal_interval_names = [\n 'high_min_alt', 'low_max_alt', \n 'diurnal_duration',\n 'long_text', 'small_radius',\n 'n_locations']\n\n feature_categorical_names = [\n 'scope', 'FIR_12', 'trafficind', \n 'code_23', 'code_45']\n\n # encode categorical features\n n_samples = len(X_vector)\n n_features = len(feature_categorical_names)\n X_categorical = np.zeros((n_samples, n_features))\n\n if fit_encoder:\n le_dict = {}\n\n enc = preprocessing.OneHotEncoder()\n\n X_tmp = []\n for i,feature in enumerate(feature_categorical_names):\n # print('Encoding {}'.format(feature))\n le_dict[feature] = preprocessing.LabelEncoder()\n le_dict[feature].fit(\n df[feature].append(pd.Series(['other'])).astype(str))\n\n X_categorical[:, i] = le_dict[feature].transform(\n df[feature].astype(str))\n\n labels = le_dict[feature].transform(le_dict[feature].classes_)\n enc.fit(labels.reshape(-1,1))\n\n test = enc.transform(X_categorical[:, i].reshape(-1,1))\n\n X_tmp.append(test.toarray())\n\n X_categorical = np.concatenate(X_tmp, axis=1)\n\n with open(path, 'wb') as file_out:\n pickle.dump(le_dict, file_out)\n\n else:\n with open(path, 'rb') as file_in:\n le_dict = pickle.load(file_in)\n\n enc = preprocessing.OneHotEncoder()\n\n X_tmp = []\n for i,feature in enumerate(feature_categorical_names):\n # print('Encoding {}'.format(feature))\n\n # make sure the category was in the training sample\n # ignore it otherwise\n categories = df[feature].astype(str).map(\n lambda s: 'other' if s not in le_dict[feature].classes_ else s)\n\n X_categorical[:, i] = le_dict[feature].transform(\n categories)\n\n labels = le_dict[feature].transform(le_dict[feature].classes_)\n \n enc.fit(labels.reshape(-1,1))\n\n test = enc.transform(X_categorical[:, i].reshape(-1,1))\n X_tmp.append(test.toarray())\n \n X_categorical = np.concatenate(X_tmp, axis=1)\n\n # ordinal and binary variables\n X_ordinal = df[feature_ordinal_interval_names].fillna(0)\n\n # print(X_vector.shape, X_categorical.shape, X_ordinal.shape)\n\n X = np.concatenate(\n (X_vector, X_categorical, X_ordinal), \n axis=1)\n\n return X","title":""},{"docid":"81f07f4dc4b11a9a855124eba2fe8798","score":"0.5798212","text":"def create_augmented_df(self, text_col, features):\n\t\t\n\t\tfeatures_to_be_dropped = []\n\t\tfor col in features.columns:\n\t\t\tif not re.search(r'_emb\\d{3}$', col):\n\t\t\t\tfeatures_to_be_dropped.append(col)\n\n\t\tfeatures = features.drop(features_to_be_dropped, axis=1)\n\t\tfeatures['augmented_text_col'] = text_col\n\t\treturn features","title":""},{"docid":"5648e34bbaa33554c8b5f2a8a721aa94","score":"0.5797754","text":"def preprocess_features(X_all):\n # Initialize new output DataFrame\n processed_features = pd.DataFrame(index=X_all.index)\n\n # Investigate each feature column for the data\n for feature_name, feature_value in X_all.iteritems():\n\n # Investigate each feature column for the data\n if feature_value.dtype == object:\n feature_value = feature_value.replace(['yes', 'no'], [1, 0])\n\n # If data type is categorical, convert to dummy variables\n if feature_value.dtype == object:\n # Example: 'school' => 'school_GP' and 'school_MS'\n feature_value = pd.get_dummies(feature_value, prefix=feature_name)\n processed_features = processed_features.join(feature_value)\n\n _logger.info(\"processd features, count is {}, features are {}\".format(len(processed_features.columns),\n list(processed_features.columns)))\n return processed_features","title":""},{"docid":"d6c4b0d53c38c2eb9a18f82940381dd1","score":"0.57966113","text":"def polynomial_features(df, excluded_columns=None, order=3):\n poly = PolynomialFeatures(order, include_bias=False)\n selected_cols = [x for x in df.columns.values if x not in excluded_columns]\n X = poly.fit_transform(df[selected_cols])\n new_feature_names = poly.get_feature_names(selected_cols)\n new_feature_names.extend(excluded_columns)\n\n # add target columns to dataframe\n if excluded_columns:\n X = np.concatenate((X, df[excluded_columns].values), axis=1)\n return pd.DataFrame(X, columns=new_feature_names)","title":""},{"docid":"757fcf58871a734311a68e0be70ef2ff","score":"0.57960546","text":"def build_real_news_dataframe(include_celeb: bool = False):\n # real news dataset\n realDf = pd.DataFrame()\n\n # build the dataframe with no celebrity news\n realDf = realDf.append(get_dataset_from_file(\"fakeNewsDataset\", False, False), ignore_index=True)\n\n # include data from the celebrity news\n if include_celeb:\n realDf = realDf.append(get_dataset_from_file(\"celebrityDataset\", False, True), ignore_index=True)\n\n return realDf","title":""},{"docid":"a08138a3dccf0b1c16ceea3acead8356","score":"0.5777577","text":"def make_sentiment_features_NLTK(df):\n \n sid = SentimentIntensityAnalyzer()\n \n df_time = df.iloc[:, 0:1]\n df_sen = df.iloc[:, 2:22].applymap(lambda x: text_sentiment_NLTK(x, sid))\n \n # Concat to single data frame\n df_feature = pd.concat([df_time, df_sen], axis=1, sort=False)\n\n return df_feature","title":""},{"docid":"adce83ff33d5252d1c15e9ac5bf682cd","score":"0.5777483","text":"def _make_features(self, data):\n return get_cols_by_name(data,\n ['measure_coursestddev_{window}' ,\n 'measure_speedstddev_{window}',\n 'measure_speedavg_{window}',\n # 'distance_to_shore' # XXX waiting for this feature to be re-added\n ], window=self.window)","title":""},{"docid":"504ceef5aadce8ca356ad19ef266f4dd","score":"0.57735866","text":"def dataToVectorForLinear(clickDF , categoricalColumnsNames , numericColumnNames): \n print (categoricalColumnsNames)\n clickDF = oneHotEncoding(clickDF,categoricalColumnsNames)\n clickDF = impute(clickDF,numericColumnNames)\n all_feature_columns = numericColumnNames + [columnName + ONE_HOT_ENCODER_OUT_SUFFIX for columnName in categoricalColumnsNames];\n feature_assembler = VectorAssembler(inputCols=all_feature_columns,outputCol=\"features\")\n return feature_assembler.transform(clickDF);","title":""},{"docid":"82e3bb36eab0f223c5dcfef9a84dd562","score":"0.5768839","text":"def populate_features():\n\n # Populate the features for instruments from old features #\n for ticker in Ticker.objects.all():\n print('Populating features for ticker %s' % ticker)\n ticker.save() # populate_features should call off post_save","title":""},{"docid":"1103b3e52980036a64264bfff5ef238d","score":"0.5768487","text":"def encode_features(df, LE_fname):\n \n LE_dict = pickle.load(open(LE_fname))\n LE_dict.pop(\"loan_status\", None)\n LE_dict.pop(\"inc_categ\", None)\n \n for col, le in LE_dict.iteritems():\n drop_these = ~df[col].isin(set(LE_dict[col].classes_))\n if drop_these.sum() > 0:\n df = df.drop(df[drop_these].index, axis=0) \n for col, le in LE_dict.iteritems():\n df[col] = le.transform(df[col])\n \n return df","title":""},{"docid":"7c1b7ffaa1dbdbbb0bdd9c99eaf377c5","score":"0.5767351","text":"def extract_features(self, df):\n X_lst = []\n for (name, feature_extractor) in self.steps:\n X_lst.append(feature_extractor.extract(df))\n return pd.concat(X_lst, axis=1, sort=False)","title":""},{"docid":"bb53854c64f5f1fbf9429b6ad75f0fed","score":"0.576498","text":"def get_data_as_df(self):\n data = dict()\n \n # X/y\n data['X'] = pd.DataFrame(self.data['X'], columns=self.feat_name)\n if 'y' in self.data:\n data['y'] = pd.DataFrame(self.data['y'], columns=self.label_name)\n\n # Train/test\n if 'X_train' and 'X_test' in self.data:\n data['X_train'] = pd.DataFrame(\n self.data['X_train'], columns=self.feat_name)\n data['X_test'] = pd.DataFrame(\n self.data['X_test'], columns=self.feat_name)\n if 'y_train' and 'y_test' in self.data:\n data['y_train'] = pd.DataFrame(\n self.data['y_train'], columns=self.label_name)\n data['y_test'] = pd.DataFrame(\n self.data['y_test'], columns=self.label_name)\n \n return data","title":""},{"docid":"dbb82b982e6293b70bf9f714eeef9f3a","score":"0.5764644","text":"def proccess_X(db, features):\n\n db1 = db.copy()\n\n X = pd.DataFrame()\n\n # 'Premium Offered'\n var = 'Premium Offered'\n if var in features:\n X[var] = db1[var].copy()\n\n # 'Price Sensitivity'\n var = 'Price Sensitivity'\n if var in features:\n db1.loc[np.isnan(db1[var]), var] = 7\n X[var] = db1[var].copy()\n\n # 'PhoneType'\n var = 'PhoneType'\n if var in features:\n le = LabelEncoder()\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\n X[var] = db1[var].copy()\n\n # Email\n var = 'Email'\n if var in features:\n X[var] = db1[var].copy()\n\n # Tenure\n var = 'Tenure'\n if var in features:\n X[var] = db1[var].copy()\n\n # NumberofCampaigns\n var = 'NumberofCampaigns'\n if var in features:\n X[var] = db1[var].copy()\n\n # ProdActive\n var = 'ProdActive'\n if var in features:\n X[var] = db1[var].copy()\n\n # ProdBought\n var = 'ProdBought'\n if var in features:\n X[var] = db1[var].copy()\n\n # 'Birthdate'\n var = 'Birthdate'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # Socieconomic Status\n var = 'Socieconomic Status'\n if var in features:\n db1.loc[db1[var] == 'Low', var] = 1\n db1.loc[db1[var] == 'Medium', var] = 2\n db1.loc[db1[var] == 'High', var] = 3\n db1.loc[db1[var] == 'Very High', var] = 4\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # Province\n var = 'Province'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = 'Unknown'\n le = LabelEncoder()\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\n X[var] = db1[var].copy()\n\n # 'Right Address'\n var = 'Right Address'\n if var in features:\n db1.loc[db1[var] == 'Right', var] = 1\n db1.loc[db1[var] == 'Wrong', var] = 0\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\n X[var] = db1[var].copy()\n\n # 'Living Area (m^2)'\n var = 'Living Area (m^2)'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'House Price'\n var = 'House Price'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Income'\n var = 'Income'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'yearBuilt'\n var = 'yearBuilt'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\n X[var] = db1[var].copy()\n\n # 'House Insurance'\n var = 'House Insurance'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Pension Plan'\n var = 'Pension Plan'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Estimated number of cars'\n var = 'Estimated number of cars'\n if var in features:\n db1.loc[db1[var] == 'None', var] = 0\n db1.loc[db1[var] == 'One', var] = 1\n db1.loc[db1[var] == 'two', var] = 2\n db1.loc[db1[var] == 'Three', var] = 3\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Probability of Second Residence'\n var = 'Probability of Second Residence'\n if var in features:\n db1.loc[db1[var] == 'Low', var] = 0.25\n db1.loc[db1[var] == 'Medium', var] = 0.5\n db1.loc[db1[var] == 'High', var] = 0.75\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\n X[var] = db1[var].copy()\n\n # 'Credit'\n var = 'Credit'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Savings'\n var = 'Savings'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Number of Mobile Phones'\n var = 'Number of Mobile Phones'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # 'Number of Fixed Lines'\n var = 'Number of Fixed Lines'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\n X[var] = db1[var].copy()\n\n # ADSL\n var = 'ADSL'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\n X[var] = db1[var].copy()\n\n # '3G Devices'\n var = '3G Devices'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\n X[var] = db1[var].copy()\n\n ## 'Type of House'\n var = 'Type of House'\n if var in features:\n db1.loc[pd.isnull(db1[var]), var] = 'Unknown'\n le = LabelEncoder()\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\n X[var] = db1[var].copy()\n\n # Convert X to matrix\n Xmat = X.as_matrix()\n\n return Xmat","title":""},{"docid":"04a10b976dc946315cf4becb22c42412","score":"0.57626414","text":"def transform(self, X):\n df = X.copy()\n df = dummify(\n df,\n self.cat_features,\n )\n\n # Remove untrained columns\n for col in self.train_columns:\n if col not in df.columns:\n df[col] = 0\n\n # Add trained on columns\n for col in df.columns:\n if col not in self.train_columns:\n df.drop(col, axis=1, inplace=True)\n\n return df[self.train_columns]","title":""},{"docid":"0aefd0fed83294227f1e94205de8fb42","score":"0.57623297","text":"def execute_feature_engineering(self):\n self.prepare_data()\n self.select_features()","title":""},{"docid":"115ed3cb48f4bdf290ee2bf586ff3b67","score":"0.57598","text":"def add_to_ensembled_preds_df_with_ensemble_func(self):","title":""},{"docid":"37b70719d49f3b9d12fd888a9dc4e9c1","score":"0.5756143","text":"def presplit_feature_engineering(rawdata: pd.DataFrame, lags_range: int = 3):\n rawdata, lag_features_organizer = generate_lag_features(rawdata, lags_range=lags_range)\n rawdata, moving_averages_features_organizer = generate_moving_averages(rawdata, lag_features_organizer) \n rawdata, dates_diff_organizer = generate_time_diffrences_features(rawdata)\n \n return FeatureEngineeringResponse(\n enriched_data=rawdata,\n lag_features_organizer=lag_features_organizer,\n moving_averages_features_organizer=moving_averages_features_organizer,\n dates_diff_organizer=moving_averages_features_organizer\n )","title":""},{"docid":"311d4abba5f9c2ac4d40f0c3758e74fc","score":"0.57560354","text":"def generate(self) -> pd.DataFrame:\n # generate a combination of date and features data\n ts: pd.DataFrame = pd.DataFrame(\n product(list(self._date_range), *self._features.values()),\n columns=[\"date\"] + list(self._features.keys()),\n )\n\n # Add base amount\n ts[\"base_amount\"] = self._base_value\n\n # Merge the factors on the base_df\n for f in self._factors:\n if f.apply_to_all:\n f.features = self._features # apply all features to the factor\n df: pd.DataFrame = f.generate(\n start_date=self._date_range[0], end_date=self._date_range[-1]\n )\n if f.date_col_name != \"date\":\n df.rename(\n columns={f.date_col_name: \"date\"}\n ) # rename date column to standard \"date\" name\n\n ts = ts.merge(\n df,\n how=\"left\",\n on=list(f.features.keys()) + [\"date\"], # Add date to merge columns\n ).fillna(\n 1\n ) # Factor 1 means no effect\n\n factor_names = list(map(lambda factor: factor.col_name, self._factors))\n if len(factor_names) != len(set(factor_names)):\n raise DuplicateNameError(\n f'duplicate factor names in factor names: \"{factor_names}\"'\n )\n\n ts[\"total_factor\"] = ts[factor_names].prod(axis=1)\n ts[\"value\"] = ts[\"total_factor\"] * ts[\"base_amount\"]\n self._ts = ts\n\n return ts","title":""},{"docid":"e12a88e40cedbadd60cdff4c4ed1d8d8","score":"0.57521504","text":"def generate_df(cls, n_samples, features, target='y', noise=10, random_state=0):\n X, y, coef = cls.generate(n_samples, len(features), noise, random_state)\n X_df = pd.DataFrame(X, columns=features)\n y_df = pd.DataFrame(y, columns=[target])\n df = X_df.join(y_df)\n return df, coef","title":""},{"docid":"2eb86f9542aa1fd9ecf12620694c8b69","score":"0.57479185","text":"def pandas_data(self, savepath=None):\n # add artists, titles\n raw_df = pd.DataFrame(data=self.raw_features, columns=self.feat_names)\n identity = pd.DataFrame(data={'item_id': self.collection_ids,\n 'url': self.urls,\n 'cluster_id': self.cluster_labels,\n 'title':self.titles,\n 'medium': self.medium})\n raw_df = pd.concat([identity, raw_df], axis=1)\n\n # get the scaled data\n df = pd.DataFrame(data=self.features, columns=self.feat_names)\n df = pd.concat([identity, df], axis=1)\n\n if savepath is not None:\n raw_df.to_csv(savepath + 'drizl_raw.csv')\n df.to_csv(savepath + 'drizl_scaled.csv')\n return df","title":""},{"docid":"7040f8559e5a7ca90fe09eced7689d7f","score":"0.5747388","text":"def test_all_features(X, y, column_names, verbose=False):\n X, X_encoded, X_vectorizer = one_hot_dataframe(X, column_names)\n cm_df = create_and_test_model(X_encoded, y, verbose=verbose)\n return cm_df","title":""},{"docid":"f8e3177048e90d78203dfc60b574078a","score":"0.57372105","text":"def transform(self, features):","title":""},{"docid":"342701fee179b379a7c4405524920447","score":"0.57361203","text":"def preprocess_features(dataframe):\n processed_features = dataframe.copy()\n processed_features = processed_features.drop(labels='High-grade tumor-1', axis=1) # Remove targets\n return processed_features","title":""},{"docid":"93b4b689d2e47403baa0b66c63221c67","score":"0.57275903","text":"def add_custom_features(dataframe):\r\n\r\n print \"Adding custom features\"\r\n print \"Adding defensive team timeouts left pre\"\r\n dataframe['defteam_timeouts_pre'] = dataframe.apply(lambda row: defteam_timeouts_pre(row), axis=1)\r\n print \"Adding Injury Timeout\"\r\n dataframe['Injury_Timeout'] = dataframe.apply(lambda row: InjuryTimeout(row), axis=1)\r\n print \"Adding Home Team Score\"\r\n dataframe['HomeTeamScore'] = dataframe.apply(lambda row: HomeTeamScore(row), axis=1)\r\n print \"Adding Away Team Score\"\r\n dataframe['AwayTeamScore'] = dataframe.apply(lambda row: AwayTeamScore(row), axis=1)\r\n print \"Adding Abs Possession Score Difference\"\r\n dataframe['Abs_Possession_Difference'] = dataframe.apply(lambda row: Abs_Possession_Difference(row), axis=1)\r\n print \"Adding Possession Score Difference\"\r\n dataframe['Possession_Difference'] = dataframe.apply(lambda row: Abs_Possession_Difference(row), axis=1)\r\n print \"Adding post play yrdline100\"\r\n dataframe['yrdline100_post'] = dataframe.apply(lambda row: yrdline100_post(row), axis=1)\r\n print \"Adding post 2 Minute Warning Feature\"\r\n dataframe['after_two_minute_warning'] = dataframe.apply(lambda row: after_two_minute_warning(row), axis=1)\r\n print \"Adding Potential Clock Running Feature\"\r\n dataframe['PotentialClockRunning'] = dataframe.apply(lambda row: PotentialClockRunning(row), axis=1)\r\n\r\n return dataframe","title":""}],"string":"[\n {\n \"docid\": \"fe2b04d95a0dda89cf405385e4663a4d\",\n \"score\": \"0.7165676\",\n \"text\": \"def feature_engineering(train_df, test_df):\\n \\n #Not used as is part of the data transformation function\\n\\n return train_df.copy(), test_df.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25f7058fbe27ea494d58cbe678a013b\",\n \"score\": \"0.7159671\",\n \"text\": \"def feature_engineering2(dataframe):\\n #Converting categorical attributes to numerical attributes\\n weather_dummy = pd.get_dummies(dataframe['weather'])\\n dataframe2 = pd.concat([dataframe,weather_dummy],1)\\n dataframe2.head()\\n\\n day_dummy = pd.get_dummies(dataframe2['weekday_name'])\\n dataframe2 = pd.concat([dataframe2,day_dummy],1)\\n dataframe2.head()\\n\\n #Drop unncessary columns\\n dataframe2.drop(['date','weekday_name','weather','guest-users','registered-users'], axis = 1, inplace = True) \\n\\n return dataframe2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228f0d1b7b8d8180d475d0388e3aca56\",\n \"score\": \"0.6994307\",\n \"text\": \"def get_features(self):\\n return pd.DataFrame([self.features])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f793836209fc1f67afe9c63676cc77ca\",\n \"score\": \"0.6805214\",\n \"text\": \"def getFeatures(self, data):\\n newDF = pd.DataFrame(index=data.index)\\n for feature in self.features:\\n if feature.lower() not in self.allFeatures:\\n raise Exception(\\\"{} feature is not available\\\".format(feature))\\n if feature.lower() in self.OHLCV:\\n newDF[feature] = data[self.getColumnName(data, feature)]\\n # TODO: Implement ways to get other features using ta-lib\\n\\n return newDF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da80a6a8ecddc3f5e72733aefd01e8e\",\n \"score\": \"0.6691872\",\n \"text\": \"def prepare_features(dataframe):\\n processed_features = dataframe.copy()\\n return processed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc63393589669ba43ebaa92c930c11ca\",\n \"score\": \"0.6650372\",\n \"text\": \"def feature_engineering(dataframe):\\n\\n\\n #Attribute creation for 'total users', 'days' and 'months'\\n dataframe['total-users'] = dataframe['guest-users'] + dataframe['registered-users']\\n\\n dataframe['year'] = pd.DatetimeIndex(dataframe['date']).year\\n dataframe['month'] = pd.DatetimeIndex(dataframe['date']).month\\n dataframe['weekday_name'] = pd.DatetimeIndex(dataframe['date']).weekday_name\\n\\n\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72aa8426ff1c1908e1bd5e33c91cbab8\",\n \"score\": \"0.65545595\",\n \"text\": \"def feature_engineering(df):\\n df = pd.get_dummies(df, drop_first=True, columns=['sex', 'region'])\\n\\n label_encoder = preprocessing.LabelEncoder()\\n label_encoder.fit(df.smoker)\\n\\n X = df.drop(columns=['smoker'])\\n y = label_encoder.transform(df.smoker)\\n\\n return X, y, df, label_encoder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6f493a03ebd2d3ec9618d4822e554cd\",\n \"score\": \"0.65186316\",\n \"text\": \"def clean_and_engineer(df):\\n clean_df = drop_features(df)\\n return add_features(clean_df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5913d3ed50670bd1cf8f6fba5b559f32\",\n \"score\": \"0.6478052\",\n \"text\": \"def _update_features(self) -> pd.DataFrame:\\n if not hasattr(self, 'curr_dates'):\\n self.curr_dates = self.last_dates.copy()\\n self.test_dates = []\\n self.curr_dates += self.freq\\n self.test_dates.append(self.curr_dates)\\n\\n if self.num_threads == 1 or len(self.transforms) == 1:\\n features = self._apply_transforms(updates_only=True)\\n else:\\n features = self._apply_multithreaded_transforms(updates_only=True)\\n\\n for feature in self.date_features:\\n feat_vals = getattr(self.curr_dates, feature).values\\n features[feature] = feat_vals.astype(date_features_dtypes[feature])\\n\\n features_df = pd.DataFrame(features, columns=self.features, index=self.uids)\\n nulls_in_cols = features_df.isnull().any()\\n if any(nulls_in_cols):\\n warnings.warn(\\n f'Found null values in {\\\", \\\".join(nulls_in_cols[nulls_in_cols].index)}.'\\n )\\n results_df = self.static_features.join(features_df)\\n results_df['ds'] = self.curr_dates\\n return results_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29a7b81889f86e1f58a4f32d64c5912\",\n \"score\": \"0.64452827\",\n \"text\": \"def _init_build_feature_table(self):\\n ids = []\\n features = {}\\n\\n for n in range(self.layer.GetFeatureCount()):\\n feature = self.layer.GetFeature(n)\\n for field in self.fields:\\n if field not in features:\\n features[field] = []\\n features[field].append(feature.GetField(field))\\n ids.append(n)\\n\\n return pd.DataFrame(index=ids, data=features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa4d22fdf13474d118242e188c71766\",\n \"score\": \"0.641813\",\n \"text\": \"def to_dataframe(self):\\n # convert available ensembles to DataFrame\\n if self.ensemble_list:\\n ts = np.array(self.ensemble_list)\\n cols = self.label_list\\n t_index = self.data_lookup['time']\\n t = ts[:,t_index]\\n index = pd.DatetimeIndex([datetime.fromtimestamp(v) for v in t])\\n new_df = pd.DataFrame(data=ts, index=index, columns=cols)\\n\\n # concatenate new ensembles with existing DataFrame if possible\\n if self.df is None:\\n self._df = new_df\\n else:\\n self._df = pd.concat([self.df, new_df])\\n\\n # reset the ensemble list once added to the DataFrame\\n self._ensemble_list = []\\n else:\\n print(\\\" WARNING: No ensembles to add to DataFrame.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53edd47eed0c40250ca13d4d62ea3a26\",\n \"score\": \"0.638978\",\n \"text\": \"def setup_feature(self):\\n TimeInspector.set_time_mark()\\n\\n if len(self._names) == 0:\\n names = [\\\"F%d\\\" % i for i in range(len(self._fields))]\\n else:\\n names = self._names\\n\\n df_features = D.features(self.instruments, self._fields, self.start_date, self.end_date)\\n df_features.columns = names\\n\\n TimeInspector.log_cost_time(\\\"Finished loading features.\\\")\\n\\n return df_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c837e217f78d8dfd67d24f5496baef69\",\n \"score\": \"0.63860726\",\n \"text\": \"def create_feature_set(df):\\n # tfidf genre lists\\n tfidf = TfidfVectorizer()\\n tfidf_matrix = tfidf.fit_transform(df['consolidate_genre_lists'].apply(lambda x:\\\" \\\".join(x)))\\n genre_df = pd.DataFrame(tfidf_matrix.toarray())\\n genre_df.columns = ['genre' + '|' + i for i in tfidf.get_feature_names()]\\n genre_df.reset_index(drop = True, inplace = True)\\n\\n # explicitly_ohe = ohe_prep(df,'explicit','exp)\\n year_ohe = ohe_prep(df,'year','year') * YEAR_SCALER\\n popularity_ohe = ohe_prep(df,'pop_red','pop') * POPULARITY_SCALER\\n\\n # scale float columns \\n float_cols = find_float_cols(df)\\n floats = df[float_cols].reset_index(drop = True)\\n scaler = MinMaxScaler()\\n floats_scaled = pd.DataFrame(scaler.fit_transform(floats),columns = floats.columns) * FLOAT_SCALER\\n\\n # Concatenate all features\\n final = pd.concat([genre_df,floats_scaled,popularity_ohe,year_ohe],axis = 1)\\n\\n # Add song id\\n final['id'] = df['id'].values\\n\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087ad47d6213b867e65dea77f17918bb\",\n \"score\": \"0.6358246\",\n \"text\": \"def feature_computer_df(dic):\\n dictionary = feature_computer(dic)\\n return pd.DataFrame(dictionary, index=[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0704f141f9a7bd3f92e1f79d4f3fb17\",\n \"score\": \"0.63520926\",\n \"text\": \"def transform(df):\\n import pandas as pd\\n all_features = []\\n for i in dir(ft):\\n if callable(getattr(ft, i)) and (i not in ['pd', 'np', 'defaultdict']) and '__' not in i:\\n all_features.append((i, getattr(ft, i)))\\n df2 = pd.DataFrame(index = df.index)\\n for feature, call in all_features:\\n df2[feature] = df['x_train'] * call(df)\\n return df2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2d551d7c662acdb338c9b7437076d6\",\n \"score\": \"0.6347641\",\n \"text\": \"def feature_engineering(column_names, continuous_names, categorical_values, train_csv, valid_csv, test_csv, train_labels, valid_labels, test_labels):\\n ### normalize the continuous columns for three sets ###\\n nacc_csf_concat = normalize_continuous_data(continuous_names, train_csv, valid_csv, test_csv)\\n\\n ### make features for each patient ###\\n nacc_csf_features = make_features_for_each_patient(column_names, continuous_names, categorical_values, nacc_csf_concat)\\n\\n ### change the format and split the features into three sets ###\\n train_features, valid_features, test_features = change_feature_format(nacc_csf_features, train_labels, valid_labels, test_labels) \\n \\n return train_features, valid_features, test_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c9c771b3f087521c234dc2a858ea72\",\n \"score\": \"0.63287264\",\n \"text\": \"def engineer_features(self):\\n temperatures = dict()\\n holiday_bool = dict()\\n for date, _ in self.load.iterrows():\\n temperatures[date] = self._get_temperature(date)\\n holiday_bool[date] = self._check_for_holiday(date)\\n self.load['weekday'] = self.load.index.dayofweek\\n self.load['hour_of_day'] = self.load.index.hour\\n self.load['temperature'] = self.load.index.map(temperatures)\\n self.load['holiday'] = self.load.index.map(holiday_bool)\\n self.load['load (t-24)'] = self.load.load_MW.shift(24)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee647b44204b0aa9802fad9562d8d60a\",\n \"score\": \"0.63263714\",\n \"text\": \"def feature_engineering():\\n ml_data_train = pd.read_csv(\\\"ml_data_zero.csv\\\", delimiter=\\\",\\\")\\n x_train = ml_data_train[[\\\"Start Latitude\\\", \\\"Start Longitude\\\", \\\"Stop Latitude\\\",\\\\\\n \\\"Stop Longitude\\\", \\\"Distance\\\", \\\"Duration\\\"]].copy()\\n y_train = ml_data_train[\\\"Min Rate Tags\\\"].copy()\\n y_train, train_count = classifier(y_train)\\n print(\\\"Class Sizes: \\\", train_count)\\n one_hot_encoded_tolls = onehotencodedtolls(ml_data_train)\\n x_train = pd.concat([x_train, one_hot_encoded_tolls], axis=1)\\n return x_train.to_numpy(), np.array(y_train)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fa1b08330f03fe97e8c68eca796094\",\n \"score\": \"0.6315498\",\n \"text\": \"def augment(df):\\n df = add_feature(df, avg_sentence_length, 'text', 'avg_sentence_length')\\n df = add_feature(df, avg_word_length, 'text', 'avg_word_length')\\n df = add_feature(df, character_count, 'text', 'characer_count')\\n df = add_feature(df, is_quoted_retweet, 'text', 'is_quoted_retweet')\\n df = add_feature(df, count_all_caps, 'text', 'number_all_caps')\\n df = add_feature(df, count_random_caps, 'text', 'random_caps')\\n df = add_feature(df, is_mention, 'text', 'is_mention')\\n df = add_feature(df, ends_with_link, 'text', 'ends_with_link')\\n df = add_feature(df, ends_with_hashtag, 'text', 'ends_with_hashtag')\\n df = add_feature(df, count_punctuation, 'text')\\n df = add_feature(df, period_of_day, 'created', 'period_of_day')\\n df = add_feature(df, day_of_week, 'created', 'weekday')\\n df = add_feature(df, starts_with_I, 'text', 'starts_with_I')\\n df = add_feature(df, count_keywords, 'text')\\n df = add_feature(df, get_vader_scores, 'text')\\n df = add_feature(df, text_emotion, 'text')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53b70b5f3fa9f53d33b8ee3840f507dd\",\n \"score\": \"0.63021624\",\n \"text\": \"def to_features_dataframe(self) -> pd.DataFrame:\\n return pd.DataFrame(dict(self[Features.AXIS].coords))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8083334aba64e62797798c5ec5cb0847\",\n \"score\": \"0.6287876\",\n \"text\": \"def Feature_Engineering(DataFrame,train):\\n \\n \\n DataFrame= pd.read_csv(DataFrame)\\n titles=DataFrame['Name'].apply(lambda x: x.split(',')[1].split(' ')[1])\\n title_mapping = {\\\"the\\\":5, \\\"Mr.\\\": 1, \\\"Miss.\\\": 2, \\\"Mrs.\\\": 3, \\\"Master.\\\": 4, \\\"Dr.\\\": 5, \\\"Rev.\\\": 6, \\\"Major.\\\": 7, \\\"Col.\\\": 7, \\\"Mlle.\\\": 2, \\\"Mme.\\\": 3, \\\"Don.\\\": 9, \\\"Lady.\\\": 10, \\\"Countess.\\\": 10, \\\"Jonkheer.\\\": 10, \\\"Sir.\\\": 9, \\\"Capt.\\\": 7, \\\"Ms.\\\": 2, \\\"Dona.\\\": 10}\\n for k,v in title_mapping.items():\\n titles[titles == k] = v\\n DataFrame[\\\"Title\\\"] = titles\\n\\n DataFrame['NameLen']=DataFrame['Name'].apply(lambda x: len(x))\\n DataFrame['FamSize']=DataFrame['SibSp']+DataFrame['Parch']\\n DataFrame['Has_Cabin'] = DataFrame[\\\"Cabin\\\"].apply(lambda x: 0 if type(x) == float else 1)\\n cabins=DataFrame['Cabin'].apply(lambda x: str(x)[0])\\n cabin_mapping={'A':3,'B':5,'C':5,'D':4,'E':4,'F':3,'G':2,'T':1,'n':10}\\n for k,v in cabin_mapping.items():\\n cabins[cabins==k]=v\\n DataFrame['Cabin']=cabins \\n del DataFrame['Parch']\\n del DataFrame['SibSp']\\n del DataFrame['PassengerId']\\n\\n pclass = pd.get_dummies( DataFrame.Pclass , prefix='Pclass' )\\n sex = pd.get_dummies(DataFrame.Sex)\\n embarked = pd.get_dummies(DataFrame.Embarked, prefix='Embarked')\\n DataFrame=pd.concat([DataFrame,pclass,sex,embarked],axis=1)\\n del DataFrame['Pclass']\\n del DataFrame['Name']\\n del DataFrame['Ticket']\\n del DataFrame['Sex']\\n del DataFrame['Embarked']\\n \\n DataFrame['Fare'].fillna(train['Fare'].median(), inplace = True)\\n # Mapping Fare\\n DataFrame.loc[ DataFrame['Fare'] <= 7.91, 'Fare'] \\t\\t\\t\\t\\t\\t = 0\\n DataFrame.loc[(DataFrame['Fare'] > 7.91) & (DataFrame['Fare'] <= 14.454), 'Fare'] = 1\\n DataFrame.loc[(DataFrame['Fare'] > 14.454) & (DataFrame['Fare'] <= 31), 'Fare'] = 2\\n DataFrame.loc[ DataFrame['Fare'] > 31, 'Fare'] \\t\\t\\t\\t\\t\\t\\t = 3\\n DataFrame['Fare'] = DataFrame['Fare'].astype(int)\\n DataFrame['Age'].fillna(train['Age'].median(), inplace = True)\\n\\n return DataFrame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c05e76f93857f1e763b8cfa41bf416\",\n \"score\": \"0.6267703\",\n \"text\": \"def engineer_features(data):\\n # add date-time columns\\n data = add_datetimestop(data)\\n data = add_month_weekday(data)\\n data = data.dropna(subset=['pct'])\\n # convert ht_feet, ht_inch to height \\n data = add_height(data)\\n # add pct-sector column\\n data = add_pct_sector(data)\\n # convert yes-no columns to 1-0\\n data = y_n_to_1_0_cols(data)\\n # clean categories\\n data = clean_categories(data)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c860e93c56bae314d43bc28311d4ed\",\n \"score\": \"0.6266057\",\n \"text\": \"def features(self) -> pd.DataFrame:\\n features: pd.DataFrame = self._observations.loc[:, self._feature_names]\\n\\n if features.columns.name is None:\\n features = features.rename_axis(columns=Sample.IDX_FEATURE)\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0aa2a54ea1a95048bfac9351474323\",\n \"score\": \"0.6223216\",\n \"text\": \"def data_with_selected_features(self):\\n data_selected = self.df[self.selected_features]\\n data_selected['diagnosis'] = encode(self.df['diagnosis'])\\n return data_selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a5a57debcabdee2edc24d82d79ac7d\",\n \"score\": \"0.62171274\",\n \"text\": \"def transform(cls, df: pd.DataFrame) -> pd.DataFrame:\\n return df.assign(\\n **{\\n feature_name: data[\\\"transformer\\\"]\\n for feature_name, data in cls.features.items()\\n }\\n )[cls.features.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b63a3548266692b5810f24c9c6f2376d\",\n \"score\": \"0.6215211\",\n \"text\": \"def create_feature_set(df, float_cols):\\n\\n # tfidf genre lists\\n tfidf = TfidfVectorizer()\\n tfidf_matrix = tfidf.fit_transform(df['consolidates_genre_lists'].apply(lambda x: \\\" \\\".join(x)))\\n genre_df = pd.DataFrame(tfidf_matrix.toarray())\\n genre_df.columns = ['genre' + \\\"|\\\" + i for i in tfidf.get_feature_names()]\\n genre_df.reset_index(drop=True, inplace=True)\\n\\n # explicity_ohe = ohe_prep(df, 'explicit','exp')\\n year_ohe = ohe_prep(df, 'year', 'year') * 0.5\\n popularity_ohe = ohe_prep(df, 'popularity_red', 'pop') * 0.15\\n\\n # scale float columns\\n floats = df[float_cols].reset_index(drop=True)\\n scaler = MinMaxScaler()\\n floats_scaled = pd.DataFrame(scaler.fit_transform(floats), columns=floats.columns) * 0.2\\n\\n # concanenate all features\\n final = pd.concat([genre_df, floats_scaled, popularity_ohe, year_ohe], axis=1)\\n\\n # add song id\\n final['id'] = df['id'].values\\n\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc62effdc028d964423936ec5d4cb313\",\n \"score\": \"0.6213039\",\n \"text\": \"def to_feature_matrix(self):\\n\\t\\tgame_dicts = [game.__dict__ for game in self.games.values()]\\n\\t\\t# print(game_dicts)\\n\\t\\tdf = pd.DataFrame(game_dicts)\\n\\t\\tgenre_dummies = df[\\\"genres\\\"].str.join(sep='*').str.get_dummies(sep=\\\"*\\\")\\n\\t\\tdevs_dummies = df[\\\"developers\\\"].str.join(sep='*').str.get_dummies(sep=\\\"*\\\")\\n\\t\\tpubs_dummies = df[\\\"publishers\\\"].str.join(sep='*').str.get_dummies(sep=\\\"*\\\")\\n\\t\\tplats_dummies = df[\\\"platforms\\\"].str.join(sep='*').str.get_dummies(sep=\\\"*\\\")\\n\\t\\tcats_dummies = df[\\\"categories\\\"].str.join(sep='*').str.get_dummies(sep=\\\"*\\\")\\n\\n\\t\\t## remove values that occur very rarely\\n\\t\\tgenre_dummies = genre_dummies.loc[:, (genre_dummies.sum(axis=0) >= 5)]\\n\\t\\tdevs_dummies = devs_dummies.loc[:, (devs_dummies.sum(axis=0) >= 5)]\\n\\t\\tpubs_dummies = pubs_dummies.loc[:, (pubs_dummies.sum(axis=0) >= 5)]\\n\\t\\tcats_dummies = cats_dummies.loc[:, (cats_dummies.sum(axis=0) >= 5)]\\n\\n\\t\\t## combine dummies\\n\\t\\tdf_complete = pd.concat([df[\\\"id\\\"], genre_dummies, devs_dummies, pubs_dummies, cats_dummies],\\n\\t\\t\\t\\t\\t\\taxis=1)\\n\\t\\t## add index from games_idx and sort by it\\n\\t\\tdf_complete[\\\"idx\\\"] = df_complete[\\\"id\\\"].apply(lambda x: self.games_idx.index(x))\\n\\t\\tdf_complete.sort_values(by=[\\\"idx\\\"], axis=0, ascending=True, inplace=True)\\n\\t\\tdf_complete.drop([\\\"id\\\"], inplace=True, axis=1)\\n\\t\\tdf_complete.set_index([\\\"idx\\\"], inplace=True)\\n\\n\\t\\t## normalize rows/games, so that e.g. games with many developers are not\\n\\t\\t## automatically more likely to be regarded as similar\\n\\t\\tdf_norm = df_complete.div(df_complete.sum(axis=1), axis=0)\\n\\t\\tdf_norm.fillna(0, inplace=True)\\n\\n\\t\\treturn df_complete\\n\\n\\t\\t# colls = defaultdict(list)\\n\\t\\t# for g in self.games.values():\\n\\t\\t# \\tcolls[\\\"dev\\\"] += g.developers\\n\\t\\t# \\tcolls[\\\"pubs\\\"] += g.publishers\\n\\t\\t# \\tcolls[\\\"plats\\\"] += g.platforms\\n\\t\\t# \\tcolls[\\\"cats\\\"] += g.categories\\n\\t\\t# \\tcolls[\\\"genres\\\"] += g.genres\\n\\t\\t# for item, cnt in Counter(devs_all).most_common():\\n\\t\\t# \\tprint(f\\\"\\\\t{item}\\\\t{cnt}\\\") ## 5\\n\\t\\t# for item, cnt in Counter(colls[\\\"pubs\\\"]).most_common():\\n\\t\\t# \\tprint(f\\\"\\\\t{item}\\\\t{cnt}\\\") ## 5\\n\\t\\t# for item, cnt in Counter(colls[\\\"plats\\\"]).most_common():\\n\\t\\t# \\tprint(f\\\"\\\\t{item}\\\\t{cnt}\\\")\\n\\t\\t# for item, cnt in Counter(colls[\\\"cats\\\"]).most_common():\\n\\t\\t# \\tprint(f\\\"\\\\t{item}\\\\t{cnt}\\\")\\n\\t\\t# for item, cnt in Counter(colls[\\\"genres\\\"]).most_common():\\n\\t\\t# \\tprint(f\\\"\\\\t{item}\\\\t{cnt}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d97b9371a4c352b3269b2d2af10bb2\",\n \"score\": \"0.62095433\",\n \"text\": \"def engineer_features(data):\\n data['policy'] = (data.year < 2013).mul(1)\\n data['nonstop_arrests'] = data.arrests - data.stop_arrests\\n data['crimerate'] = data.crimes / data.population * 1000\\n data['nonstop_arrestrate'] = data.nonstop_arrests / data.population * 1000\\n data['stoprate'] = data.stops / data.population * 1000\\n data['stop_arrestrate'] = data.stop_arrests / data.population * 1000\\n data['arrestrate'] = data.arrests / data.population * 1000\\n data['normal_year'] = data.year - 2005\\n # Two of the precincts (22 in particular) have outlier crime rates\\n # (because they have low residential population).\\n # drop the outlier precincts, Central Park and Midtown South.\\n data = data[~data.pct.isin([22, 14])]\\n # the data is skewed. Generate log columns\\n log_cols = ['crimerate', 'nonstop_arrestrate', 'stoprate', 'stop_arrestrate', 'arrestrate',\\n 'nonstop_arrests', 'arrests', 'population', 'stops', 'stop_arrests', 'crimes']\\n data[[f'log_{col}' for col in log_cols]] = data[log_cols].apply(np.log).copy()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac73b6292e1710b215e54be6f2c3115a\",\n \"score\": \"0.620333\",\n \"text\": \"def train_data(self) -> pd.DataFrame:\\n return self.ltr.read_train()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f5f784e47f2064ac61ee355c2a58d4\",\n \"score\": \"0.61816573\",\n \"text\": \"def df_to_features(vectorizer, df, split):\\n words = [row.text for i, row in df.iterrows()]\\n\\n if split == \\\"train\\\":\\n feats = vectorizer.fit_transform(words)\\n else:\\n feats = vectorizer.transform(words)\\n X = feats.todense()\\n Y = df[\\\"label\\\"].values\\n return X, Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9adabde4b6e54ff9b560a0530419f4ac\",\n \"score\": \"0.61643475\",\n \"text\": \"def create_features(frame):\\n frame['issue month'] = frame['issue date'].dt.month\\n frame['issue month'] = frame['issue month'].astype(str)\\n frame['issue year'] = frame['issue date'].dt.year\\n frame['credit tenure'] = (frame['issue date'] - frame['earliest credit date']).astype('timedelta64[D]')\\n frame['fraction funded'] = frame['loan funded'] / frame['loan request']\\n frame['request over income'] = frame['loan request'] / frame['income']\\n frame['funded over income'] = frame['loan funded'] / frame['income']\\n frame['debt over income'] = frame['revolving balance'] / frame['income']\\n frame['debt over request'] = frame['revolving balance'] / frame['loan request']\\n frame['debt over funded'] = frame['revolving balance'] / frame['loan funded']\\n frame['open line fraction'] = frame['open credit lines'] / frame['credit lines']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b604f468c580f0eb2e3025fc4e537212\",\n \"score\": \"0.61447585\",\n \"text\": \"def to_df(self):\\n df = None\\n for cluster, instances in self.raw.items():\\n data = dict(instances) # We want self.raw to remain intact\\n data['cluster'] = cluster\\n next_df = pd.io.json.json_normalize(\\n data, 'cluster_instances', ['cluster']\\n )\\n try:\\n df = df.append(next_df)\\n except AttributeError:\\n df = next_df\\n\\n new_column_names = {f: f.replace('features.', '') for f in df}\\n new_column_names['name'] = 'instance'\\n df.rename(columns=new_column_names, inplace=True)\\n df.set_index(['cluster', 'instance'], inplace=True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b27422fab27778134b07eb53f2c987\",\n \"score\": \"0.61444557\",\n \"text\": \"def add_handcrafted_features(self):\\n\\n def statistical_feature_generator(x):\\n \\\"\\\"\\\" Sample code to generate features\\\"\\\"\\\"\\n fs = []\\n\\n total_dwell_time = sum(x['dwell_times']) # total dwell time\\n avg_dwell_time = np.mean(x['dwell_times'])\\n num_area_trajectory_have = len(x['dwell_times']) # the number of area\\n num_unique_area_sensed = len(set(x['areas'])) # the number of unique areas\\n\\n fs.append(total_dwell_time)\\n fs.append(avg_dwell_time)\\n fs.append(num_area_trajectory_have)\\n fs.append(num_unique_area_sensed)\\n\\n return fs\\n\\n def add_statistical_features(train_visits):\\n df = train_visits.copy()\\n\\n features = df.apply(lambda x: statistical_feature_generator(x), axis=1)\\n feature_name = ['total_dwell_time', 'avg_dwell_time', 'num_area', 'num_unique_area']\\n\\n fdf = pd.DataFrame(list(np.asarray(features)), index=features.index, columns=feature_name)\\n\\n # Combine feature values to the dataframe\\n df = pd.concat([df, fdf], axis=1)\\n\\n # Relative dates\\n df['date_rel'] = df['date'] - min(df.date)\\n del fdf\\n\\n return df\\n\\n def add_visit_counts():\\n \\\"\\\"\\\" Code block to track the previous number of visits.\\n (Actually, the number of visits including the current one. \\\"\\\"\\\"\\n self.train_visits['tmp'] = 1\\n self.train_visits['nvisits'] = self.train_visits.groupby(['wifi_id'])['tmp'].cumsum()\\n\\n wid_nvisit = self.train_visits.iloc[list(self.train_visits['wifi_id'].drop_duplicates(keep='last').index)][\\n ['wifi_id', 'nvisits']]\\n d_wid_nvisit = {}\\n for wid, nvisit in zip(wid_nvisit['wifi_id'], wid_nvisit['nvisits']):\\n d_wid_nvisit[wid] = nvisit\\n\\n self.test_visits['tmp'] = 1\\n self.test_visits['prev_vcount'] = self.test_visits['wifi_id'].apply(lambda x: d_wid_nvisit.get(x, 0))\\n self.test_visits['nvisits'] = self.test_visits.groupby(['wifi_id'])['tmp'].cumsum() + self.test_visits['prev_vcount']\\n\\n del self.train_visits['tmp'], self.test_visits['tmp'], self.test_visits['prev_vcount'], wid_nvisit\\n\\n self.train_visits = add_statistical_features(self.train_visits)\\n self.test_visits = add_statistical_features(self.test_visits)\\n add_visit_counts()\\n self.train_visits['wifi_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"510aa6dcb77959023305f4f0bd44b508\",\n \"score\": \"0.613878\",\n \"text\": \"def add_hashed_features(df):\\r\\n return df.select('label', tuples_to_hash_features_udf('feature').alias('features'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf92dbac6294eeb60776e074bad44624\",\n \"score\": \"0.61253005\",\n \"text\": \"def add_hashed_features(df):\\n return df.select(df.label, tuples_to_hash_features_udf(df.feature).alias('features'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869d91e4243d0d739bfced8f1a55bbb3\",\n \"score\": \"0.61151063\",\n \"text\": \"def feature_engineering(self):\\n # Load the complaints with sentiment metrics file\\n complaints = pd.read_csv(self.complaints_with_sentiment_file)\\n complaints = complaints[\\n [\\\"Complaint ID\\\", \\\"Timely response?\\\", \\\"Product\\\", \\\"corpus_score_sum\\\", \\\"corpus_score_ave\\\", \\\"negative_ratio\\\",\\n \\\"most_negative_score\\\", \\\"word_num\\\", \\\"sentence_num\\\", \\\"num_of_question_mark\\\", \\\"num_of_exclaimation_mark\\\",\\n \\\"company_response\\\"]]\\n print(\\\"Loaded {} complaints with sentiment metrics.\\\".format(len(complaints)))\\n\\n # Load the preprocessed narratives\\n narrative_processed = pd.read_csv(self.narrative_preprocessed_file)\\n print(\\\"Loaded {} preprocessed narratives.\\\".format(len(narrative_processed)))\\n\\n # One hot coding the column \\\"company_response\\\", store the column name of dummy columns\\n old_column_num = len(complaints.columns)\\n complaints = pd.get_dummies(complaints, columns=[\\\"company_response\\\"])\\n new_column_names = []\\n for i in np.arange(old_column_num - 1, len(complaints.columns)):\\n new_column_names.append(complaints.columns[i])\\n\\n # Save the new column names to a file for prediction\\n column_name_file = \\\"trained_models/company_corresponse_variable_names.csv\\\"\\n with open(column_name_file, \\\"w\\\") as fobj:\\n fobj.write(\\\",\\\".join(new_column_names))\\n\\n # Merge the sentiment metrics and narratives according to their Complaint ID.\\n # Those narratives without label do not have sentiment metrics. These records will be discarded.\\n complaints_features = pd.merge(complaints.reset_index(drop=True), narrative_processed.reset_index(drop=True),\\n how='inner', on=['Complaint ID', 'Complaint ID'])\\n\\n print(\\\"Loaded {} complaints \\\".format(complaints_features.shape))\\n\\n # Vectorize narratives\\n not_to_vectorized_columns = complaints_features.loc[:, \\\"corpus_score_sum\\\":\\\"company_response_Closed with non-monetary relief\\\"]\\n print(\\\"Tf-idf vectorizing narratives processed for escalation classifier...\\\")\\n vectorized_columns = self.tf_idf_vectorizer.transform(complaints_features[\\\"processed_narrative\\\"])\\n print(vectorized_columns.shape)\\n\\n X = hstack((vectorized_columns, np.array(not_to_vectorized_columns)))\\n y = [0 if x == \\\"No\\\" else 1 for x in complaints_features[\\\"Consumer disputed?\\\"]]\\n\\n column_names = self.tf_idf_vectorizer.get_feature_names()\\n column_names.extend(not_to_vectorized_columns.columns)\\n print(len(column_names))\\n X.columns = column_names\\n\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c3d9aca6431cd2e39a4ac8480beb4c5\",\n \"score\": \"0.6113998\",\n \"text\": \"def generate_features(df: pd.DataFrame) -> pd.DataFrame:\\n df[\\\"attributes\\\"] = df[\\\"attributes\\\"].apply(json.loads)\\n df[\\\"device_class\\\"] = df[\\\"attributes\\\"].apply(get_device_class)\\n df[\\\"unit_of_measurement\\\"] = df[\\\"attributes\\\"].apply(get_unit_of_measurement)\\n df[\\\"friendly_name\\\"] = df[\\\"attributes\\\"].apply(get_friendly_name)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be089676f2396b6a263bd36e1ee29dda\",\n \"score\": \"0.6099017\",\n \"text\": \"def get_training_df(self, feature_dict, label_map={'cooperative': 1, 'additive': 0}):\\n ldict = self.get_feature_all(feature_dict)\\n train = pd.DataFrame(ldict)\\n train['label'] = self.get_numeric_label(label_map).values\\n return train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4fe7acb9c9e0f542e7a1d93500fb343\",\n \"score\": \"0.6062521\",\n \"text\": \"def _init_raw_df(self):\\n df_features = self.setup_feature()\\n feature_names = df_features.columns\\n\\n df_labels = self.setup_label()\\n label_names = df_labels.columns\\n\\n raw_df = df_features.merge(df_labels, left_index=True, right_index=True, how=\\\"left\\\")\\n\\n return raw_df, feature_names, label_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f44bd2826ca13298ccfe747de823120\",\n \"score\": \"0.60610723\",\n \"text\": \"def create_feature_df(ticker):\\n # Get all relevant data\\n market_returns = get_market_returns()\\n prices_df = get_prices_df(ticker)\\n fund_df = get_fundamentals_df(ticker)\\n\\n # Reindex fundamentals dataframe to match prices dataframe,\\n # limit fill to 252 days just in case there are gaps in financial statements\\n df = fund_df.reindex(index=prices_df.index, method=\\\"ffill\\\", limit=252)\\n df = df.join(prices_df)\\n\\n # Create a range of end-of-month dates\\n # Skip if index is size 0\\n if df.index.size > 0:\\n dates = pd.bdate_range(start=df.index[0], end=df.index[-1], freq=\\\"M\\\")\\n else:\\n return None\\n rolling_dfs = []\\n\\n # For each end of month date, select all data from the past year\\n # If there isn't enough data, skip\\n for date in dates:\\n annual_df = df.loc[(date - pd.Timedelta(days=365)) : date]\\n if len(annual_df) > 250:\\n rolling_dfs.append(annual_df)\\n\\n # Make features for each rolling 12-month period\\n # If rolling_dfs list is empty, there's not enough data so return None\\n if rolling_dfs:\\n df = pd.DataFrame([make_features(n, market_returns) for n in rolling_dfs])\\n else:\\n return None\\n # Match each months data with the return at the end of the next month\\n # for forecasting\\n df[\\\"forward_ret\\\"] = df[\\\"monthly_ret\\\"].shift(-1)\\n df[\\\"ticker\\\"] = ticker\\n df = df.dropna()\\n # If dataframe has any rows, write to csv\\n if len(df) > 1:\\n df.to_csv(f\\\"csv/{ticker}.csv\\\", index=False)\\n print(f\\\"{ticker} done!\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513e7895e9b4a07dda94af3988086619\",\n \"score\": \"0.60368776\",\n \"text\": \"def feature_engineer(data):\\n\\n df = pd.DataFrame()\\n data['totl_anglr_vel'] = (data['angular_velocity_X']**2 + data['angular_velocity_Y']**2 +\\n data['angular_velocity_Z']**2) ** 0.5\\n data['totl_linr_acc'] = (data['linear_acceleration_X']**2 + data['linear_acceleration_Y']**2 +\\n data['linear_acceleration_Z'])**0.5\\n\\n data['acc_vs_vel'] = data['totl_linr_acc'] / data['totl_anglr_vel']\\n\\n for col in data.columns:\\n if col in ['row_id', 'series_id', 'measurement_number']:\\n continue\\n df[col + '_mean'] = data.groupby(['series_id'])[col].mean()\\n df[col + '_median'] = data.groupby(['series_id'])[col].median()\\n df[col + '_max'] = data.groupby(['series_id'])[col].max()\\n df[col + '_min'] = data.groupby(['series_id'])[col].min()\\n df[col + '_std'] = data.groupby(['series_id'])[col].std()\\n df[col + '_range'] = df[col + '_max'] - df[col + '_min']\\n df[col + '_maxtoMin'] = df[col + '_max'] / df[col + '_min']\\n df[col + '_mean_abs_chg'] = data.groupby(['series_id'])[col].apply(\\n lambda x: np.mean(np.abs(np.diff(x))))\\n df[col + '_abs_max'] = data.groupby(['series_id']\\n )[col].apply(lambda x: np.max(np.abs(x)))\\n df[col + '_abs_min'] = data.groupby(['series_id']\\n )[col].apply(lambda x: np.min(np.abs(x)))\\n df[col + '_abs_avg'] = (df[col + '_abs_min'] + df[col + '_abs_max'])/2\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422ce551242c6e232756eaa5097ccf08\",\n \"score\": \"0.6029527\",\n \"text\": \"def createTrainingDataframe(self):\\r\\n\\r\\n self.df = pd.read_csv(self.trainingLoc, sep=self.delimiter, names=['label', 'text'])\\r\\n self.df['label'] = self.df['label'].astype(bool)\\r\\n\\r\\n #Stem the Text Column\\r\\n te.df['stemText'] = te.df['text'].apply(lambda X: ' '.join([te.stemmer.stem(word) for word in word_tokenize(X)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb4793e66bbacee78964b4a46a16d90\",\n \"score\": \"0.60241324\",\n \"text\": \"def gen_featureset(df):\\n # Load stock data\\n stock_data = pd.read_csv(\\\"output/historical.csv\\\", parse_dates=[\\\"Date\\\"])\\n # Loop through each tweets\\n featureset = list()\\n print('Generating features... (up to 1min)')\\n for i in range(len(df.dropna())):\\n # Locating relevant information\\n ticker = df.loc[i, \\\"Symbol\\\"]\\n sentiment = df.loc[i, \\\"Sentiment\\\"]\\n stock_mouvement = df.loc[i, \\\"Movement\\\"]\\n tweet_date = df.loc[i, \\\"Datetime\\\"]\\n tweet_text = df.loc[i, \\\"Text\\\"]\\n\\n # Generating features\\n volatility = feature_stockprice_volatility(\\n ticker, tweet_date, time_before=120, df=stock_data)\\n\\n # If no volatility information, skip row\\n if volatility is not None:\\n\\n feature_label = list()\\n\\n feature = {'ticker': ticker, 'sentiment': sentiment,\\n 'volatility': volatility}\\n feature_label.append(feature)\\n feature_label.append(stock_mouvement)\\n\\n featureset.append(feature_label)\\n return featureset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480f4d485b38f600e50febbed9e7772c\",\n \"score\": \"0.60035175\",\n \"text\": \"def df_before_transformation():\\n\\tdf_dict = csv_into_dict_of_data(training_dataset)\\n\\tdf_dict = remove_zero_columns(df_dict)\\n\\tidentifier = \\\"_before_transformation\\\"\\n\\tplot_troffic_graph_df_into_graph_and_save(df_dict, identifier)\\n\\tget_elbow_by_date_and_time(df_dict, identifier)\\n\\n\\t# print(df_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e644910a6ffbd0826a8bb0623d7f1e59\",\n \"score\": \"0.5995331\",\n \"text\": \"def create_features(df, label=None):\\n df = df.copy()\\n df['date'] = df.index\\n df['weekday'] = df['date'].dt.day_name()\\n df['weekday'] = pd.Categorical(df['weekday'], categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)\\n df['users'] = df.Users\\n df['hour'] = df['date'].dt.hour\\n df['dayofweek'] = df['date'].dt.dayofweek\\n df['quarter'] = df['date'].dt.quarter\\n df['month'] = df['date'].dt.month\\n df['year'] = df['date'].dt.year\\n df['dayofyear'] = df['date'].dt.dayofyear\\n df['dayofmonth'] = df['date'].dt.day\\n df['weekofyear'] = df['date'].dt.weekofyear\\n\\n X = df[['weekday','users','hour','dayofweek','quarter','month','year',\\n 'dayofyear','dayofmonth','weekofyear']]\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99262d70c01eac648ac641ea239bd0bb\",\n \"score\": \"0.59814614\",\n \"text\": \"def pull_features(engine):\\n logger.debug('Start of pull features function')\\n\\n features = pd.read_sql('SELECT * FROM features', engine)\\n features['startDate'] = features['startDate'].apply(lambda x: datetime.fromisoformat(x) if type(x) == str else x.to_pydatetime())\\n\\n logger.debug('%s', features.head())\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf01e9a1eea4dd3cdaf36f66a4dc115\",\n \"score\": \"0.59758395\",\n \"text\": \"def additional_features3(users):\\n\\n return pd.DataFrame({'User': users})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074468534fde66ff88b80bc4c420e730\",\n \"score\": \"0.59621865\",\n \"text\": \"def create_newFeatures(dataframe):\\r\\n\\r\\n if 'transactiondate' in dataframe.columns:\\r\\n dataframe['transaction_year'] = dataframe.transactiondate.dt.year.astype(np.int16)\\r\\n dataframe['transaction_month'] = dataframe.transactiondate.dt.month.astype(np.int8)\\r\\n # dataframe['transaction_day'] = dataframe.transactiondate.dt.weekday.astype(np.int8)\\r\\n dataframe['transaction_quarter'] = dataframe.transactiondate.dt.quarter.astype(np.int8)\\r\\n del dataframe['transactiondate']\\r\\n else:\\r\\n df_date = pd.DataFrame({'transaction_year': [2016, 2016, 2016, 2017, 2017, 2017],\\r\\n 'transaction_month': [10, 11, 12, 10, 11, 12]})\\r\\n df_date['tmp'] = 1\\r\\n dataframe['tmp'] = 1\\r\\n dataframe = pd.merge(dataframe, df_date, on='tmp')\\r\\n del dataframe['tmp']\\r\\n dataframe['transaction_quarter'] = dataframe.transaction_year.astype(str)+'-'+dataframe.transaction_month.astype(str).apply(lambda x: ('00'+x)[-2:]) +'-01'\\r\\n dataframe['transaction_quarter'] = dataframe.transaction_quarter.astype(object)\\r\\n dataframe['transaction_quarter'] = pd.to_datetime(dataframe['transaction_quarter'])\\r\\n dataframe['transaction_quarter'] = dataframe.transaction_quarter.dt.quarter\\r\\n\\r\\n \\r\\n dataframe['rawcensustractandblock_states'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[:1]).astype(np.int8)\\r\\n dataframe['rawcensustractandblock_countries'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[1:4]).astype(np.int8)\\r\\n dataframe['rawcensustractandblock_tracts'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: x[4:11]).astype(np.float64)\\r\\n dataframe['rawcensustractandblock_blocks'] = dataframe.rawcensustractandblock.astype(str).apply(lambda x: 0 if x[11:] == '' else x[11:]).astype(np.int8)\\r\\n \\r\\n #--- how old is the house? ---\\r\\n dataframe['house_age'] = dataframe['transaction_year'].astype(np.int16) - dataframe['yearbuilt'].astype(np.int16)\\r\\n\\r\\n #--- how many rooms are there? ---\\r\\n dataframe['tot_rooms'] = dataframe['bathroomcnt'] + dataframe['bedroomcnt']\\r\\n\\r\\n #--- does the house have A/C? ---\\r\\n dataframe['AC'] = np.where(dataframe['airconditioningtypeid']>0, 1, 0)\\r\\n\\r\\n #--- Does the house have a deck? ---\\r\\n dataframe['deck'] = np.where(dataframe['decktypeid']>0, 1, 0)\\r\\n dataframe.drop('decktypeid', axis=1, inplace=True)\\r\\n\\r\\n #--- does the house have a heating system? ---\\r\\n dataframe['heating_system'] = np.where(dataframe['heatingorsystemtypeid']>0, 1, 0)\\r\\n\\r\\n #--- does the house have a garage? ---\\r\\n dataframe['garage'] = np.where(dataframe['garagecarcnt']>0, 1, 0)\\r\\n\\r\\n #--- does the house come with a patio? ---\\r\\n dataframe['patio'] = np.where(dataframe['yardbuildingsqft17']>0, 1, 0)\\r\\n\\r\\n #--- does the house have a pool?\\r\\n dataframe['pool'] = dataframe['pooltypeid10'] | dataframe['pooltypeid7'] | dataframe['pooltypeid2']\\r\\n dataframe['pool'] = dataframe['pool'].map({True: 1, False: 0})\\r\\n\\r\\n #--- does the house have all of these? -> spa/hot-tub/pool, A/C, heating system , garage, patio\\r\\n dataframe['exquisite'] = dataframe['pool'] + dataframe['patio'] + dataframe['garage'] + dataframe['heating_system'] + dataframe['AC']\\r\\n\\r\\n #--- Features based on location ---\\r\\n dataframe['x_loc'] = np.cos(dataframe['latitude']) * np.cos(dataframe['longitude'])\\r\\n dataframe['y_loc'] = np.cos(dataframe['latitude']) * np.sin(dataframe['longitude'])\\r\\n dataframe['z_loc'] = np.sin(dataframe['latitude'])\\r\\n\\r\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf4948a0ff708458c118c20f2534e3b3\",\n \"score\": \"0.594195\",\n \"text\": \"def to_df(self):\\r\\n\\r\\n feature_dict = {'ID':[],'MASS_MGF':[],'MASS_NEUTRAL':[],'RT':[],\\r\\n 'DTXCID':[],'MASS':[],'FORMULA':[],\\r\\n 'SINGLE_SCORES':[],'SUM_SCORE':[],'Q-SCORE':[]} \\r\\n \\r\\n for feature in self.feature_list:\\r\\n feature.reference_scores['Q-SCORE'] = [np.nan if max(feature.reference_scores['SUM_SCORE']) == 0 else \\r\\n score/max(feature.reference_scores['SUM_SCORE']) for score in feature.reference_scores['SUM_SCORE']]\\r\\n for key in feature_dict.keys(): feature_dict[key].extend(feature.reference_scores.get(key, None))\\r\\n \\r\\n return pd.DataFrame.from_dict(feature_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b647f4a70f06922110051de14fd1e456\",\n \"score\": \"0.5936085\",\n \"text\": \"def normalize(df):\\r\\n\\r\\n processed_features = pd.DataFrame()\\r\\n\\r\\n processed_features[\\\"age\\\"] = linear_scale(df[\\\"age\\\"])\\r\\n processed_features[\\\"education-num\\\"] = linear_scale(df[\\\"education-num\\\"])\\r\\n processed_features[\\\"education-num\\\"] = linear_scale(df[\\\"hours-per-week\\\"])\\r\\n\\r\\n return processed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091b7c5e53afefc1eaf25a63438c9be3\",\n \"score\": \"0.5935219\",\n \"text\": \"def get_features(self):\\n X = self.df_merge.drop(columns = [self.target_name])\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7f7bba9bcbfd91b93df775e9fbe5c6\",\n \"score\": \"0.59348565\",\n \"text\": \"def feda(self):\\n train_domains = []\\n test_domains = []\\n\\n X_train = self.get('X', 'train')\\n X_test = self.get('X', 'test')\\n\\n for i in self.data.index.levels[0]:\\n if i in self.data.index:\\n train_domains.append(X_train.loc[i].to_numpy())\\n test_domains.append(X_test.loc[i].to_numpy())\\n\\n\\n self.da['train'] = pd.DataFrame(feda(train_domains))\\n self.da['test'] = pd.DataFrame(feda(test_domains))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb8fb10d91cdb627514c837bbc963de\",\n \"score\": \"0.5934169\",\n \"text\": \"def extract_all_features(self):\\n\\n # Data frame for collecting the features\\n series_features = pd.Series()\\n series_features['Driver'] = self.driver\\n series_features['Trip'] = self.trip\\n\\n for feature in self.features:\\n\\n # Determine which feature are to be extracted\\n feature_method = getattr(self, feature)\\n\\n # Calculate value of feature\\n series_features[feature] = feature_method()\\n\\n return series_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f478c28b9b33d6c04820f404f3533b6\",\n \"score\": \"0.593339\",\n \"text\": \"def engineer_features(df,training=True):\\n\\n ## extract dates\\n dates = df['date'].values.copy()\\n dates = dates.astype('datetime64[D]')\\n\\n ## engineer some features\\n eng_features = defaultdict(list)\\n previous =[7, 14, 28, 70] #[7, 14, 21, 28, 35, 42, 49, 56, 63, 70]\\n y = np.zeros(dates.size)\\n for d,day in enumerate(dates):\\n\\n ## use windows in time back from a specific date\\n for num in previous:\\n current = np.datetime64(day, 'D') \\n prev = current - np.timedelta64(num, 'D')\\n mask = np.in1d(dates, np.arange(prev,current,dtype='datetime64[D]'))\\n eng_features[\\\"previous_{}\\\".format(num)].append(df[mask]['revenue'].sum())\\n\\n ## get get the target revenue \\n plus_30 = current + np.timedelta64(30,'D')\\n mask = np.in1d(dates, np.arange(current,plus_30,dtype='datetime64[D]'))\\n y[d] = df[mask]['revenue'].sum()\\n\\n ## attempt to capture monthly trend with previous years data (if present)\\n start_date = current - np.timedelta64(365,'D')\\n stop_date = plus_30 - np.timedelta64(365,'D')\\n mask = np.in1d(dates, np.arange(start_date,stop_date,dtype='datetime64[D]'))\\n eng_features['previous_year'].append(df[mask]['revenue'].sum())\\n\\n ## add some non-revenue features\\n minus_30 = current - np.timedelta64(30,'D')\\n mask = np.in1d(dates, np.arange(minus_30,current,dtype='datetime64[D]'))\\n eng_features['recent_invoices'].append(df[mask]['unique_invoices'].mean())\\n eng_features['recent_views'].append(df[mask]['total_views'].mean())\\n\\n X = pd.DataFrame(eng_features)\\n ## combine features in to df and remove rows with all zeros\\n X.fillna(0,inplace=True)\\n mask = X.sum(axis=1)>0\\n X = X[mask]\\n y = y[mask]\\n dates = dates[mask]\\n X.reset_index(drop=True, inplace=True)\\n\\n if training == True:\\n ## remove the last 30 days (because the target is not reliable)\\n mask = np.arange(X.shape[0]) < np.arange(X.shape[0])[-30]\\n X = X[mask]\\n y = y[mask]\\n dates = dates[mask]\\n X.reset_index(drop=True, inplace=True)\\n \\n return(X,y,dates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa24a774018222c58523315f7de3926\",\n \"score\": \"0.5921966\",\n \"text\": \"def _prepare_features(self, instances_df, col_names):\\n # type: (pd.DataFrame, ColumnNames) -> None\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3028f122185119854becda96f3e60e0\",\n \"score\": \"0.589815\",\n \"text\": \"def featureEngineer(data, feature_to_drop):\\n\\tdt = pd.to_datetime(data[\\\"datetime\\\"]) # convert string to datetime type\\n\\tdata[\\\"year\\\"] = dt.map(lambda x: x.year)\\n\\tdata[\\\"month\\\"] = dt.map(lambda x: x.month)\\n\\tdata[\\\"day\\\"] = dt.map(lambda x: x.day)\\n\\tdata[\\\"hour\\\"] = dt.map(lambda x: x.hour)\\n\\tdata[\\\"weekday\\\"] = dt.map(lambda x: x.weekday())\\n\\t# remove outliers detected from temp vs atemp scatter plot\\n\\ttrain_all = data[np.array(data[\\\"temp\\\"] / data[\\\"atemp\\\"]) < 2]\\n\\ttrain_all = train_all.drop(feature_to_drop,\\n\\t\\taxis=1) # include all features and target\\n\\treturn train_all\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871725935acd8bfd2f20e71cbad07e90\",\n \"score\": \"0.5898055\",\n \"text\": \"def prepare_features(dataframe):\\n processed_features = dataframe.copy()\\n\\n # Apply linear scaling to price and highway-mpg here\\n processed_features[\\\"price\\\"] = linear_scale(processed_features[\\\"price\\\"])\\n processed_features[\\\"highway-mpg\\\"] = linear_scale(processed_features[\\\"highway-mpg\\\"])\\n \\n return processed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff18a6d63ab81915ed931bceb30fdec1\",\n \"score\": \"0.5879053\",\n \"text\": \"def to_dataframe(self, features=None):\\n if not features:\\n features = self.features\\n\\n data = defaultdict(dict)\\n for f1 in features:\\n for f2 in features:\\n data[f1][f2] = self[f1][f2].distance\\n\\n df = pd.DataFrame.from_dict(data)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095985728b8adcfbccfdeeb7cebd2d2a\",\n \"score\": \"0.58758074\",\n \"text\": \"def query_data_to_features(query_data: dict)->pd.DataFrame:\\n query_masses = query_data['prec_mass_list2']\\n query_mz = query_data['mono_mzs2']\\n query_rt = query_data['rt_list_ms2']\\n\\n features = pd.DataFrame(np.array([query_masses, query_mz, query_rt]).T, columns = ['mass_matched', 'mz_matched', 'rt_matched'])\\n\\n features['feature_idx'] = features.index #Index to query_data\\n features['query_idx'] = np.arange(len(query_masses))\\n\\n features = features.sort_values('mass_matched', ascending=True)\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6e4c2c8924630c6adf7c6586f85be1\",\n \"score\": \"0.5873379\",\n \"text\": \"def toDataFrame(KF):\\r\\n\\r\\n def splitunit(s):\\r\\n iu=s.rfind('_[')\\r\\n if iu>1:\\r\\n return s[:iu], s[iu:]\\r\\n else:\\r\\n return s, ''\\r\\n\\r\\n def cleancol(l):\\r\\n return ['_clean'.join(splitunit(c)) for c in l]\\r\\n\\r\\n cols = []\\r\\n cols += list(KF.X_hat.columns)\\r\\n cols += cleancol(KF.X_clean.columns)\\r\\n cols += list(KF.U_hat.columns)\\r\\n cols += cleancol(KF.U_clean.columns)\\r\\n cols += ['OUT_'+c for c in list(KF.Y_hat.columns)]\\r\\n cols += ['OUT_'+c for c in cleancol(KF.Y_clean.columns)]\\r\\n cols += list(KF.S_hat.columns)\\r\\n cols += cleancol(KF.S_clean.columns)\\r\\n df = pd.concat((KF.X_hat, KF.X_clean, KF.U_hat, KF.U_clean, KF.Y_hat, KF.Y_clean, KF.S_hat, KF.S_clean), axis=1)\\r\\n df.columns = cols\\r\\n\\r\\n # \\\"Accelerations\\\" \\r\\n dfAcc = pd.concat((KF.XD_hat, KF.XD_clean), axis=1)\\r\\n cols = list(KF.XD_hat.columns)\\r\\n cols += cleancol(KF.XD_clean.columns)\\r\\n dfAcc.columns=cols\\r\\n # We keep only the part that is not already in the state vector\\r\\n col_new = [c for c in cols if c not in df.columns]\\r\\n dfAcc=dfAcc[col_new] \\r\\n\\r\\n # \\r\\n df = pd.concat((df,dfAcc), axis=1)\\r\\n\\r\\n\\r\\n df.insert(0, 'Time_[s]', KF.time)\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2852ea287df078b189b1fef0897ca81e\",\n \"score\": \"0.5867794\",\n \"text\": \"def add_features(df):\\n df['full_time_employment_rate'] = (df['Employed_full_time_year_round'] /\\n df['Employed'])\\n df['pay_IQR_range'] = df['P75th'] - df['P25th']\\n df['field_of_study'] = df.apply(field_majorcat_mapper, axis=1)\\n df['norm_ur'] = normalize_column(df, 'Unemployment_rate')\\n df['norm_ftr'] = normalize_column(df, 'full_time_employment_rate')\\n df['norm_med_income'] = normalize_column(df, 'Median')\\n df['norm_p25th_income'] = normalize_column(df, 'P25th')\\n df['norm_p75th_income'] = normalize_column(df, 'P75th')\\n df['risk_rating'] = (\\n (df.norm_ur - df.norm_ur.median()) +\\n (df.norm_ftr.median() - df.norm_ftr) +\\n (df.norm_p25th_income.median() - df.norm_p25th_income)\\n )\\n df['gain_rating'] = (\\n (df.norm_med_income - df.norm_med_income.median()) +\\n (df.norm_p75th_income - df.norm_p75th_income.median())\\n )\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4969180ae7e462ace385501d6ea60289\",\n \"score\": \"0.5867107\",\n \"text\": \"def _make_new_table(self, features):\\n\\n categorical_features = np.array(features[:, self.categorical_ids])\\n self._check_same_categories(categorical_features)\\n transformed_categorical = self.encoder.transform(categorical_features).toarray()\\n\\n # If there are non-categorical features in the data\\n if len(self.non_categorical_ids) == 0:\\n transformed_features = transformed_categorical\\n else:\\n # Stack transformed categorical and non-categorical data\\n non_categorical_features = np.array(features[:, self.non_categorical_ids])\\n frames = (non_categorical_features, transformed_categorical)\\n transformed_features = np.hstack(frames)\\n\\n return transformed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c08ae0a156ee88db5cbc4710012bfd\",\n \"score\": \"0.58653075\",\n \"text\": \"def get_dataframe(self) -> pd.DataFrame:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab408eb2482eda06508a39ab34a92c2\",\n \"score\": \"0.5854169\",\n \"text\": \"def rfc_train(df):\\n\\n assembler_feats=VectorAssembler(inputCols=feat_cols, outputCol='features')\\n label_indexer = StringIndexer(inputCol='Label', outputCol=\\\"target\\\")\\n pipeline = Pipeline(stages=[assembler_feats, label_indexer])\\n\\n # Transform the data and do a random split\\n all_data = pipeline.fit(df).transform(df)\\n all_data.cache()\\n data_train, data_test = all_data.randomSplit([0.75, 0.25], seed=123)\\n # Get class labels for using IndexToString to convert back from indexes\\n Labels = all_data.groupBy('Label').count().orderBy('count', ascending=False).select('Label').collect()\\n Labels = [row.Label for row in Labels]\\n # Convert label indexes back to actual labels\\n converter = IndexToString(inputCol='prediction', outputCol='predicted_label', labels=Labels)\\n\\n # Initiate a RandomForest model and train on the data\\n rfc = RandomForestClassifier(labelCol='target', featuresCol='features', numTrees=100)\\n trained_model = rfc.fit(data_train)\\n predict = trained_model.transform(data_test)\\n results = converter.transform(predict)\\n\\n converted = converter.transform(predict)\\n # Calculate the combined accuracy to evaluate the model performance\\n evaluator = multi_metric(labelCol='target', predictionCol='prediction', metricName='accuracy')\\n print('Accuracy is: ', evaluator.evaluate(results))\\n trained_model.write().overwrite().save('s3n://cyber-insight/rfc_model_multi')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a992bc164aa02d96a5e412a89ac2e79e\",\n \"score\": \"0.58522373\",\n \"text\": \"def as_dataframe(self):\\n colnames = [self._term_full_names(name) for name in self.terms_info]\\n data = pd.DataFrame(self.design_matrix)\\n data.columns = list(flatten_list(colnames))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9dee89cd8dda6efe54ab6f0b217b47\",\n \"score\": \"0.58505636\",\n \"text\": \"def ohe_prep(df,column,new_name):\\n\\n tf_df = pd.get_dummies(df[column])\\n feature_names = tf_df.columns\\n tf_df.columns = [new_name + \\\"|\\\" + str(i) for i in feature_names]\\n tf_df.reset_index(drop = True, inplace = True)\\n return tf_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4f7985a53149b5848b430c7300e076\",\n \"score\": \"0.5844174\",\n \"text\": \"def encode_and_bind(self, df, features_to_encode):\\n\\n dummies = pd.get_dummies(df[features_to_encode])\\n df_result = pd.concat([dummies, df], axis=1)\\n df_result = df_result.drop(features_to_encode, axis=1)\\n return df_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cece53ae4ec755ea122d014881823dcd\",\n \"score\": \"0.5838455\",\n \"text\": \"def separate_data(self,df):\\n # Simply retrieve Survived column from DataFrame\\n labels = df[\\\"Survived\\\"]\\n # Get all column names of df, but without Survived column\\n newcols = df.columns.drop(\\\"Survived\\\")\\n # Get these columns\\n features = df[newcols]\\n return features,labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"736ed7c559f3c85c88cb3e18db9a3f3c\",\n \"score\": \"0.5831829\",\n \"text\": \"def generate_model_text_features(raw_df_path, save_path=None):\\n df = pd.read_csv(raw_df_path)\\n df = format_raw_df(df.copy())\\n df = df.loc[df[\\\"is_question\\\"]].copy()\\n df[\\\"full_text\\\"] = df[\\\"Title\\\"].str.cat(df[\\\"body_text\\\"], sep=\\\" \\\", na_rep=\\\"\\\")\\n\\n df = add_v1_features(df.copy())\\n df = add_v2_text_features(df.copy())\\n\\n if save_path:\\n df.to_csv(save_path)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8965e3ff922f4f6ba5eb1de172b6b71b\",\n \"score\": \"0.58242166\",\n \"text\": \"def add_features(df_in, rolling_win_size=15):\\n\\n sensor_cols = []\\n index = df_in.columns.get_loc('TTF')\\n for i in df_in.columns[2:index]:\\n sensor_cols.append(i)\\n\\n sensor_av_cols = [nm+'_av' for nm in sensor_cols]\\n sensor_sd_cols = [nm+'_sd' for nm in sensor_cols]\\n\\n df_out = pd.DataFrame()\\n\\n ws = rolling_win_size\\n\\n #calculate rolling stats for each engine id\\n\\n for m_id in pd.unique(df_in.Turbine_ID):\\n\\n # get a subset for each engine sensors\\n df_engine = df_in[df_in['Turbine_ID'] == m_id]\\n df_sub = df_engine[sensor_cols]\\n\\n # get rolling mean for the subset\\n av = df_sub.rolling(ws, min_periods=1).mean()\\n av.columns = sensor_av_cols\\n\\n # get the rolling standard deviation for the subset\\n sd = df_sub.rolling(ws, min_periods=1).std().fillna(0)\\n sd.columns = sensor_sd_cols\\n\\n # combine the two new subset dataframes columns to the engine subset\\n new_ftrs = pd.concat([df_engine,av,sd], axis=1)\\n\\n # add the new features rows to the output dataframe\\n df_out = pd.concat([df_out,new_ftrs])\\n df_out = df_out.sort_values(by=['Turbine_ID', 'Date'] )\\n return df_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c02393d4f7d027910d541195b3a1ad74\",\n \"score\": \"0.58183634\",\n \"text\": \"def vectorize_df(trainFrame, holdoutFrame): \\n trainDict = trainFrame.to_dict(orient='records')\\n holdoutDict = holdoutFrame.to_dict(orient='records') \\n \\n vec = DictVectorizer()\\n vec.fit(trainDict)\\n \\n trainArray = vec.transform(trainDict).toarray()\\n holdoutArray = vec.transform(holdoutDict).toarray()\\n\\n return trainArray, holdoutArray, vec.get_feature_names()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f14cf8ebd47a85d80d46885e862690\",\n \"score\": \"0.5813522\",\n \"text\": \"def prepare_data():\\n #all data to one table\\n df = pd.concat( \\\\\\n [pd.read_csv('data//train.csv', header=0).drop(['Survived'], axis=1),\\n pd.read_csv('data//test.csv', header=0)])\\n df = df.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis = 1)\\n\\n # replace missing fares with median fare\\n median_fare = df['Fare'].dropna().median()\\n df.loc[(df.Fare.isnull()), 'Fare'] = median_fare\\n\\n # replace missing embarked with mode embarked\\n mode_embarked = df['Embarked'].dropna().mode()\\n df.loc[(df.Embarked.isnull()), 'Embarked'] = mode_embarked[0]\\n #map categorical features to numbers\\n df['Sex'] = df['Sex'].map({'female': 0.0, 'male': 1.0}).astype(float)\\n df['Embarked'] = df['Embarked'].map( \\\\\\n {'C': 0.0, 'S': 1.0, 'Q': 2.0}).astype(float)\\n\\n #There are a lot of items with missing age, so we will predict it\\n X_train = df[df['Age'].notnull()].drop('Age', axis=1).values\\n #add polynomial features\\n X_train = PolynomialFeatures(degree=2).fit_transform(X_train)\\n #get targets \\n y_train = df[df['Age'].notnull()].Age.values\\n return df, X_train, y_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38092994673f1d12c872dbbc9863bf66\",\n \"score\": \"0.5812889\",\n \"text\": \"def get_data_frame():\\n\\n # Define the rows for the DataFrame\\n dataframe_labels = [\\\"Image_Id\\\", \\\"Total_Relations\\\", \\\"Number_Of_Positive_Relations\\\",\\n \\\"Number_Of_Negative_Relations\\\", \\\"Relations_Accuracy\\\", \\\"Positive_Relations_Accuracy\\\",\\n \\\"Negative_Relations_Accuracy\\\", \\\"Old_Predictions\\\", \\\"Train_Entity\\\"]\\n\\n # Define DataFrame\\n df = pd.DataFrame(columns=dataframe_labels)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f363b855cc4f2d6d640be4194937f066\",\n \"score\": \"0.5810875\",\n \"text\": \"def set_data_frame(self):\\n self.__data_frame = pd.DataFrame(data = self.data_array,\\n index = self.data_names,\\n columns = self.feature_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4665cb777adacb3e5ab33b9970ad85c1\",\n \"score\": \"0.58048683\",\n \"text\": \"def ohe_prep(df, column, new_name):\\n\\n tf_df = pd.get_dummies(df[column])\\n feature_names = tf_df.columns\\n tf_df.columns = [new_name + \\\"|\\\" + str(i) for i in feature_names]\\n tf_df.reset_index(drop=True, inplace=True)\\n return tf_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7dc6b1e649c99468c063500048025c2\",\n \"score\": \"0.5804095\",\n \"text\": \"def transform_data(data):\\n\\n # Convert from dictionary to dataframe\\n df = pd.DataFrame(data)\\n\\n # Dummify features\\n for dummy_feat in feats['to_dummify']:\\n if dummy_feat in df:\\n df = pd.concat([df, pd.get_dummies(df[dummy_feat],\\n prefix=dummy_feat)], axis=1)\\n\\n # Drop un-dummified features\\n df = df.drop(dummy_feat, axis=1)\\n\\n # Include dummified features in cols_to_use\\n cols_to_use = [col for col in df.columns\\n if col in feats['as_is']\\n or any([dummy_feat in col for dummy_feat in feats['to_dummify']])]\\n\\n # Drop NA and subset\\n df = df[cols_to_use].dropna()\\n\\n # If after dummifying we are missing any features wrt the training data feature space, add a column of zeros for that feature\\n for training_feat in feats['trained_features']:\\n if training_feat not in df:\\n df[training_feat] = np.zeros((df.shape[0], 1))\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"040be6db4cc82fde13f2b623a6ab4339\",\n \"score\": \"0.580084\",\n \"text\": \"def _add_new_features(self,list_manager_stop,list_vehi):\\n data = []\\n\\n for i,manager_stop in enumerate(list_manager_stop):\\n feature_object = derivingFeaturesCreatedDataset.DerivingFeaturesCreatedDataset(manager_stop)\\n dict_features= feature_object.derive_features()\\n dict_features['class_label'] = min(6,list_vehi[i])\\n dict_features['iteration'] = self.iteration\\n data.append(dict_features)\\n\\n header = True\\n if os.path.exists(useful_paths.FILE_TO_TREE_CVRPTW):\\n header = False\\n\\n data_results = pd.DataFrame(data)\\n data_results.to_csv(useful_paths.FILE_TO_TREE_CVRPTW, header=header, index=False,mode = \\\"a\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4ec63022762cdf4117891453941940\",\n \"score\": \"0.58001685\",\n \"text\": \"def get_features(df, path=PATH_ENCODE_FEATURE, fit_encoder=False):\\n\\n # perform a few transformation\\n # should that be put in cleaning script?\\n df['low_max_alt'] = df['maxflt'] < 300 # 30k-35k feet\\n df['high_min_alt'] = df['minflt'] > 0\\n df['small_radius'] = df['radius'] < 5.0\\n df['long_text'] = df['len_txt'] > 100\\n\\n # text vector\\n X_vector = extract_float_vector(df['vector'])\\n\\n feature_ordinal_interval_names = [\\n 'high_min_alt', 'low_max_alt', \\n 'diurnal_duration',\\n 'long_text', 'small_radius',\\n 'n_locations']\\n\\n feature_categorical_names = [\\n 'scope', 'FIR_12', 'trafficind', \\n 'code_23', 'code_45']\\n\\n # encode categorical features\\n n_samples = len(X_vector)\\n n_features = len(feature_categorical_names)\\n X_categorical = np.zeros((n_samples, n_features))\\n\\n if fit_encoder:\\n le_dict = {}\\n\\n enc = preprocessing.OneHotEncoder()\\n\\n X_tmp = []\\n for i,feature in enumerate(feature_categorical_names):\\n # print('Encoding {}'.format(feature))\\n le_dict[feature] = preprocessing.LabelEncoder()\\n le_dict[feature].fit(\\n df[feature].append(pd.Series(['other'])).astype(str))\\n\\n X_categorical[:, i] = le_dict[feature].transform(\\n df[feature].astype(str))\\n\\n labels = le_dict[feature].transform(le_dict[feature].classes_)\\n enc.fit(labels.reshape(-1,1))\\n\\n test = enc.transform(X_categorical[:, i].reshape(-1,1))\\n\\n X_tmp.append(test.toarray())\\n\\n X_categorical = np.concatenate(X_tmp, axis=1)\\n\\n with open(path, 'wb') as file_out:\\n pickle.dump(le_dict, file_out)\\n\\n else:\\n with open(path, 'rb') as file_in:\\n le_dict = pickle.load(file_in)\\n\\n enc = preprocessing.OneHotEncoder()\\n\\n X_tmp = []\\n for i,feature in enumerate(feature_categorical_names):\\n # print('Encoding {}'.format(feature))\\n\\n # make sure the category was in the training sample\\n # ignore it otherwise\\n categories = df[feature].astype(str).map(\\n lambda s: 'other' if s not in le_dict[feature].classes_ else s)\\n\\n X_categorical[:, i] = le_dict[feature].transform(\\n categories)\\n\\n labels = le_dict[feature].transform(le_dict[feature].classes_)\\n \\n enc.fit(labels.reshape(-1,1))\\n\\n test = enc.transform(X_categorical[:, i].reshape(-1,1))\\n X_tmp.append(test.toarray())\\n \\n X_categorical = np.concatenate(X_tmp, axis=1)\\n\\n # ordinal and binary variables\\n X_ordinal = df[feature_ordinal_interval_names].fillna(0)\\n\\n # print(X_vector.shape, X_categorical.shape, X_ordinal.shape)\\n\\n X = np.concatenate(\\n (X_vector, X_categorical, X_ordinal), \\n axis=1)\\n\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f07f4dc4b11a9a855124eba2fe8798\",\n \"score\": \"0.5798212\",\n \"text\": \"def create_augmented_df(self, text_col, features):\\n\\t\\t\\n\\t\\tfeatures_to_be_dropped = []\\n\\t\\tfor col in features.columns:\\n\\t\\t\\tif not re.search(r'_emb\\\\d{3}$', col):\\n\\t\\t\\t\\tfeatures_to_be_dropped.append(col)\\n\\n\\t\\tfeatures = features.drop(features_to_be_dropped, axis=1)\\n\\t\\tfeatures['augmented_text_col'] = text_col\\n\\t\\treturn features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5648e34bbaa33554c8b5f2a8a721aa94\",\n \"score\": \"0.5797754\",\n \"text\": \"def preprocess_features(X_all):\\n # Initialize new output DataFrame\\n processed_features = pd.DataFrame(index=X_all.index)\\n\\n # Investigate each feature column for the data\\n for feature_name, feature_value in X_all.iteritems():\\n\\n # Investigate each feature column for the data\\n if feature_value.dtype == object:\\n feature_value = feature_value.replace(['yes', 'no'], [1, 0])\\n\\n # If data type is categorical, convert to dummy variables\\n if feature_value.dtype == object:\\n # Example: 'school' => 'school_GP' and 'school_MS'\\n feature_value = pd.get_dummies(feature_value, prefix=feature_name)\\n processed_features = processed_features.join(feature_value)\\n\\n _logger.info(\\\"processd features, count is {}, features are {}\\\".format(len(processed_features.columns),\\n list(processed_features.columns)))\\n return processed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c4b0d53c38c2eb9a18f82940381dd1\",\n \"score\": \"0.57966113\",\n \"text\": \"def polynomial_features(df, excluded_columns=None, order=3):\\n poly = PolynomialFeatures(order, include_bias=False)\\n selected_cols = [x for x in df.columns.values if x not in excluded_columns]\\n X = poly.fit_transform(df[selected_cols])\\n new_feature_names = poly.get_feature_names(selected_cols)\\n new_feature_names.extend(excluded_columns)\\n\\n # add target columns to dataframe\\n if excluded_columns:\\n X = np.concatenate((X, df[excluded_columns].values), axis=1)\\n return pd.DataFrame(X, columns=new_feature_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757fcf58871a734311a68e0be70ef2ff\",\n \"score\": \"0.57960546\",\n \"text\": \"def build_real_news_dataframe(include_celeb: bool = False):\\n # real news dataset\\n realDf = pd.DataFrame()\\n\\n # build the dataframe with no celebrity news\\n realDf = realDf.append(get_dataset_from_file(\\\"fakeNewsDataset\\\", False, False), ignore_index=True)\\n\\n # include data from the celebrity news\\n if include_celeb:\\n realDf = realDf.append(get_dataset_from_file(\\\"celebrityDataset\\\", False, True), ignore_index=True)\\n\\n return realDf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08138a3dccf0b1c16ceea3acead8356\",\n \"score\": \"0.5777577\",\n \"text\": \"def make_sentiment_features_NLTK(df):\\n \\n sid = SentimentIntensityAnalyzer()\\n \\n df_time = df.iloc[:, 0:1]\\n df_sen = df.iloc[:, 2:22].applymap(lambda x: text_sentiment_NLTK(x, sid))\\n \\n # Concat to single data frame\\n df_feature = pd.concat([df_time, df_sen], axis=1, sort=False)\\n\\n return df_feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adce83ff33d5252d1c15e9ac5bf682cd\",\n \"score\": \"0.5777483\",\n \"text\": \"def _make_features(self, data):\\n return get_cols_by_name(data,\\n ['measure_coursestddev_{window}' ,\\n 'measure_speedstddev_{window}',\\n 'measure_speedavg_{window}',\\n # 'distance_to_shore' # XXX waiting for this feature to be re-added\\n ], window=self.window)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"504ceef5aadce8ca356ad19ef266f4dd\",\n \"score\": \"0.57735866\",\n \"text\": \"def dataToVectorForLinear(clickDF , categoricalColumnsNames , numericColumnNames): \\n print (categoricalColumnsNames)\\n clickDF = oneHotEncoding(clickDF,categoricalColumnsNames)\\n clickDF = impute(clickDF,numericColumnNames)\\n all_feature_columns = numericColumnNames + [columnName + ONE_HOT_ENCODER_OUT_SUFFIX for columnName in categoricalColumnsNames];\\n feature_assembler = VectorAssembler(inputCols=all_feature_columns,outputCol=\\\"features\\\")\\n return feature_assembler.transform(clickDF);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e3bb36eab0f223c5dcfef9a84dd562\",\n \"score\": \"0.5768839\",\n \"text\": \"def populate_features():\\n\\n # Populate the features for instruments from old features #\\n for ticker in Ticker.objects.all():\\n print('Populating features for ticker %s' % ticker)\\n ticker.save() # populate_features should call off post_save\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1103b3e52980036a64264bfff5ef238d\",\n \"score\": \"0.5768487\",\n \"text\": \"def encode_features(df, LE_fname):\\n \\n LE_dict = pickle.load(open(LE_fname))\\n LE_dict.pop(\\\"loan_status\\\", None)\\n LE_dict.pop(\\\"inc_categ\\\", None)\\n \\n for col, le in LE_dict.iteritems():\\n drop_these = ~df[col].isin(set(LE_dict[col].classes_))\\n if drop_these.sum() > 0:\\n df = df.drop(df[drop_these].index, axis=0) \\n for col, le in LE_dict.iteritems():\\n df[col] = le.transform(df[col])\\n \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c1b7ffaa1dbdbbb0bdd9c99eaf377c5\",\n \"score\": \"0.5767351\",\n \"text\": \"def extract_features(self, df):\\n X_lst = []\\n for (name, feature_extractor) in self.steps:\\n X_lst.append(feature_extractor.extract(df))\\n return pd.concat(X_lst, axis=1, sort=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb53854c64f5f1fbf9429b6ad75f0fed\",\n \"score\": \"0.576498\",\n \"text\": \"def get_data_as_df(self):\\n data = dict()\\n \\n # X/y\\n data['X'] = pd.DataFrame(self.data['X'], columns=self.feat_name)\\n if 'y' in self.data:\\n data['y'] = pd.DataFrame(self.data['y'], columns=self.label_name)\\n\\n # Train/test\\n if 'X_train' and 'X_test' in self.data:\\n data['X_train'] = pd.DataFrame(\\n self.data['X_train'], columns=self.feat_name)\\n data['X_test'] = pd.DataFrame(\\n self.data['X_test'], columns=self.feat_name)\\n if 'y_train' and 'y_test' in self.data:\\n data['y_train'] = pd.DataFrame(\\n self.data['y_train'], columns=self.label_name)\\n data['y_test'] = pd.DataFrame(\\n self.data['y_test'], columns=self.label_name)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb82b982e6293b70bf9f714eeef9f3a\",\n \"score\": \"0.5764644\",\n \"text\": \"def proccess_X(db, features):\\n\\n db1 = db.copy()\\n\\n X = pd.DataFrame()\\n\\n # 'Premium Offered'\\n var = 'Premium Offered'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # 'Price Sensitivity'\\n var = 'Price Sensitivity'\\n if var in features:\\n db1.loc[np.isnan(db1[var]), var] = 7\\n X[var] = db1[var].copy()\\n\\n # 'PhoneType'\\n var = 'PhoneType'\\n if var in features:\\n le = LabelEncoder()\\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\\n X[var] = db1[var].copy()\\n\\n # Email\\n var = 'Email'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # Tenure\\n var = 'Tenure'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # NumberofCampaigns\\n var = 'NumberofCampaigns'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # ProdActive\\n var = 'ProdActive'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # ProdBought\\n var = 'ProdBought'\\n if var in features:\\n X[var] = db1[var].copy()\\n\\n # 'Birthdate'\\n var = 'Birthdate'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # Socieconomic Status\\n var = 'Socieconomic Status'\\n if var in features:\\n db1.loc[db1[var] == 'Low', var] = 1\\n db1.loc[db1[var] == 'Medium', var] = 2\\n db1.loc[db1[var] == 'High', var] = 3\\n db1.loc[db1[var] == 'Very High', var] = 4\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # Province\\n var = 'Province'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = 'Unknown'\\n le = LabelEncoder()\\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\\n X[var] = db1[var].copy()\\n\\n # 'Right Address'\\n var = 'Right Address'\\n if var in features:\\n db1.loc[db1[var] == 'Right', var] = 1\\n db1.loc[db1[var] == 'Wrong', var] = 0\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\\n X[var] = db1[var].copy()\\n\\n # 'Living Area (m^2)'\\n var = 'Living Area (m^2)'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'House Price'\\n var = 'House Price'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Income'\\n var = 'Income'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'yearBuilt'\\n var = 'yearBuilt'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\\n X[var] = db1[var].copy()\\n\\n # 'House Insurance'\\n var = 'House Insurance'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Pension Plan'\\n var = 'Pension Plan'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Estimated number of cars'\\n var = 'Estimated number of cars'\\n if var in features:\\n db1.loc[db1[var] == 'None', var] = 0\\n db1.loc[db1[var] == 'One', var] = 1\\n db1.loc[db1[var] == 'two', var] = 2\\n db1.loc[db1[var] == 'Three', var] = 3\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Probability of Second Residence'\\n var = 'Probability of Second Residence'\\n if var in features:\\n db1.loc[db1[var] == 'Low', var] = 0.25\\n db1.loc[db1[var] == 'Medium', var] = 0.5\\n db1.loc[db1[var] == 'High', var] = 0.75\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\\n X[var] = db1[var].copy()\\n\\n # 'Credit'\\n var = 'Credit'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Savings'\\n var = 'Savings'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Number of Mobile Phones'\\n var = 'Number of Mobile Phones'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # 'Number of Fixed Lines'\\n var = 'Number of Fixed Lines'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].median()\\n X[var] = db1[var].copy()\\n\\n # ADSL\\n var = 'ADSL'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\\n X[var] = db1[var].copy()\\n\\n # '3G Devices'\\n var = '3G Devices'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = db1[var].mean()\\n X[var] = db1[var].copy()\\n\\n ## 'Type of House'\\n var = 'Type of House'\\n if var in features:\\n db1.loc[pd.isnull(db1[var]), var] = 'Unknown'\\n le = LabelEncoder()\\n db1[var] = le.fit_transform(db1[var]).reshape(-1, 1)\\n X[var] = db1[var].copy()\\n\\n # Convert X to matrix\\n Xmat = X.as_matrix()\\n\\n return Xmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a10b976dc946315cf4becb22c42412\",\n \"score\": \"0.57626414\",\n \"text\": \"def transform(self, X):\\n df = X.copy()\\n df = dummify(\\n df,\\n self.cat_features,\\n )\\n\\n # Remove untrained columns\\n for col in self.train_columns:\\n if col not in df.columns:\\n df[col] = 0\\n\\n # Add trained on columns\\n for col in df.columns:\\n if col not in self.train_columns:\\n df.drop(col, axis=1, inplace=True)\\n\\n return df[self.train_columns]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aefd0fed83294227f1e94205de8fb42\",\n \"score\": \"0.57623297\",\n \"text\": \"def execute_feature_engineering(self):\\n self.prepare_data()\\n self.select_features()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115ed3cb48f4bdf290ee2bf586ff3b67\",\n \"score\": \"0.57598\",\n \"text\": \"def add_to_ensembled_preds_df_with_ensemble_func(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b70719d49f3b9d12fd888a9dc4e9c1\",\n \"score\": \"0.5756143\",\n \"text\": \"def presplit_feature_engineering(rawdata: pd.DataFrame, lags_range: int = 3):\\n rawdata, lag_features_organizer = generate_lag_features(rawdata, lags_range=lags_range)\\n rawdata, moving_averages_features_organizer = generate_moving_averages(rawdata, lag_features_organizer) \\n rawdata, dates_diff_organizer = generate_time_diffrences_features(rawdata)\\n \\n return FeatureEngineeringResponse(\\n enriched_data=rawdata,\\n lag_features_organizer=lag_features_organizer,\\n moving_averages_features_organizer=moving_averages_features_organizer,\\n dates_diff_organizer=moving_averages_features_organizer\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311d4abba5f9c2ac4d40f0c3758e74fc\",\n \"score\": \"0.57560354\",\n \"text\": \"def generate(self) -> pd.DataFrame:\\n # generate a combination of date and features data\\n ts: pd.DataFrame = pd.DataFrame(\\n product(list(self._date_range), *self._features.values()),\\n columns=[\\\"date\\\"] + list(self._features.keys()),\\n )\\n\\n # Add base amount\\n ts[\\\"base_amount\\\"] = self._base_value\\n\\n # Merge the factors on the base_df\\n for f in self._factors:\\n if f.apply_to_all:\\n f.features = self._features # apply all features to the factor\\n df: pd.DataFrame = f.generate(\\n start_date=self._date_range[0], end_date=self._date_range[-1]\\n )\\n if f.date_col_name != \\\"date\\\":\\n df.rename(\\n columns={f.date_col_name: \\\"date\\\"}\\n ) # rename date column to standard \\\"date\\\" name\\n\\n ts = ts.merge(\\n df,\\n how=\\\"left\\\",\\n on=list(f.features.keys()) + [\\\"date\\\"], # Add date to merge columns\\n ).fillna(\\n 1\\n ) # Factor 1 means no effect\\n\\n factor_names = list(map(lambda factor: factor.col_name, self._factors))\\n if len(factor_names) != len(set(factor_names)):\\n raise DuplicateNameError(\\n f'duplicate factor names in factor names: \\\"{factor_names}\\\"'\\n )\\n\\n ts[\\\"total_factor\\\"] = ts[factor_names].prod(axis=1)\\n ts[\\\"value\\\"] = ts[\\\"total_factor\\\"] * ts[\\\"base_amount\\\"]\\n self._ts = ts\\n\\n return ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e12a88e40cedbadd60cdff4c4ed1d8d8\",\n \"score\": \"0.57521504\",\n \"text\": \"def generate_df(cls, n_samples, features, target='y', noise=10, random_state=0):\\n X, y, coef = cls.generate(n_samples, len(features), noise, random_state)\\n X_df = pd.DataFrame(X, columns=features)\\n y_df = pd.DataFrame(y, columns=[target])\\n df = X_df.join(y_df)\\n return df, coef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb86f9542aa1fd9ecf12620694c8b69\",\n \"score\": \"0.57479185\",\n \"text\": \"def pandas_data(self, savepath=None):\\n # add artists, titles\\n raw_df = pd.DataFrame(data=self.raw_features, columns=self.feat_names)\\n identity = pd.DataFrame(data={'item_id': self.collection_ids,\\n 'url': self.urls,\\n 'cluster_id': self.cluster_labels,\\n 'title':self.titles,\\n 'medium': self.medium})\\n raw_df = pd.concat([identity, raw_df], axis=1)\\n\\n # get the scaled data\\n df = pd.DataFrame(data=self.features, columns=self.feat_names)\\n df = pd.concat([identity, df], axis=1)\\n\\n if savepath is not None:\\n raw_df.to_csv(savepath + 'drizl_raw.csv')\\n df.to_csv(savepath + 'drizl_scaled.csv')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7040f8559e5a7ca90fe09eced7689d7f\",\n \"score\": \"0.5747388\",\n \"text\": \"def test_all_features(X, y, column_names, verbose=False):\\n X, X_encoded, X_vectorizer = one_hot_dataframe(X, column_names)\\n cm_df = create_and_test_model(X_encoded, y, verbose=verbose)\\n return cm_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e3177048e90d78203dfc60b574078a\",\n \"score\": \"0.57372105\",\n \"text\": \"def transform(self, features):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"342701fee179b379a7c4405524920447\",\n \"score\": \"0.57361203\",\n \"text\": \"def preprocess_features(dataframe):\\n processed_features = dataframe.copy()\\n processed_features = processed_features.drop(labels='High-grade tumor-1', axis=1) # Remove targets\\n return processed_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b4b689d2e47403baa0b66c63221c67\",\n \"score\": \"0.57275903\",\n \"text\": \"def add_custom_features(dataframe):\\r\\n\\r\\n print \\\"Adding custom features\\\"\\r\\n print \\\"Adding defensive team timeouts left pre\\\"\\r\\n dataframe['defteam_timeouts_pre'] = dataframe.apply(lambda row: defteam_timeouts_pre(row), axis=1)\\r\\n print \\\"Adding Injury Timeout\\\"\\r\\n dataframe['Injury_Timeout'] = dataframe.apply(lambda row: InjuryTimeout(row), axis=1)\\r\\n print \\\"Adding Home Team Score\\\"\\r\\n dataframe['HomeTeamScore'] = dataframe.apply(lambda row: HomeTeamScore(row), axis=1)\\r\\n print \\\"Adding Away Team Score\\\"\\r\\n dataframe['AwayTeamScore'] = dataframe.apply(lambda row: AwayTeamScore(row), axis=1)\\r\\n print \\\"Adding Abs Possession Score Difference\\\"\\r\\n dataframe['Abs_Possession_Difference'] = dataframe.apply(lambda row: Abs_Possession_Difference(row), axis=1)\\r\\n print \\\"Adding Possession Score Difference\\\"\\r\\n dataframe['Possession_Difference'] = dataframe.apply(lambda row: Abs_Possession_Difference(row), axis=1)\\r\\n print \\\"Adding post play yrdline100\\\"\\r\\n dataframe['yrdline100_post'] = dataframe.apply(lambda row: yrdline100_post(row), axis=1)\\r\\n print \\\"Adding post 2 Minute Warning Feature\\\"\\r\\n dataframe['after_two_minute_warning'] = dataframe.apply(lambda row: after_two_minute_warning(row), axis=1)\\r\\n print \\\"Adding Potential Clock Running Feature\\\"\\r\\n dataframe['PotentialClockRunning'] = dataframe.apply(lambda row: PotentialClockRunning(row), axis=1)\\r\\n\\r\\n return dataframe\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993693,"cells":{"query_id":{"kind":"string","value":"2f4f315d0039d47a4074d3dc266cb091"},"query":{"kind":"string","value":"Create the extension point."},"positive_passages":{"kind":"list like","value":[{"docid":"f82827c97b51b736dbfc3b699976159b","score":"0.0","text":"def __init__(self, interface):\n property.__init__(self, self.extensions)\n self.interface = interface\n self.__doc__ = 'List of components that implement `%s`' % \\\n self.interface.__name__","title":""}],"string":"[\n {\n \"docid\": \"f82827c97b51b736dbfc3b699976159b\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, interface):\\n property.__init__(self, self.extensions)\\n self.interface = interface\\n self.__doc__ = 'List of components that implement `%s`' % \\\\\\n self.interface.__name__\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c9bf0407a546c47d63f27fc8ba660f5b","score":"0.65477675","text":"def __init__(self, **config):\n\t\t\n\t\tsuper(Extension, self).__init__()","title":""},{"docid":"96fb74a85a7c9e79d2047613ec8afa98","score":"0.65290415","text":"def test_make_extension(self):\n makeExtension()","title":""},{"docid":"a5ebb85a7ff84354b99b0232a48927be","score":"0.62442946","text":"def makeExtension(**kwargs):\n return DenDenExtension(**kwargs)","title":""},{"docid":"f824831fc01a2b9da727871b55983535","score":"0.6189438","text":"def extension():","title":""},{"docid":"ca7b3142f9c7d794321dce14c999864e","score":"0.61609125","text":"def __repr__(self):\n return '<ExtensionPoint %s>' % self.interface.__name__","title":""},{"docid":"7a353d1546d196f8876b102c32b11f88","score":"0.6116655","text":"def create_extension(self, extension):\n if not self.get_extension():\n self.name += \".\" + extension","title":""},{"docid":"5085ffe7724ebae1b77f2b853f3c92eb","score":"0.60060245","text":"def create(self, _extension_name, *args, **kwargs):\n extension = self.get(_extension_name)\n\n option_types = dict(self.option_types)\n for option in extension.__options__ or []:\n name = option[\"name\"]\n option_types[name] = option.get(\"type\", \"string\")\n\n kwargs = coalesce_options(dict(kwargs), option_types)\n\n return extension(*args, **kwargs)","title":""},{"docid":"fb5650a2965cbd8461752b53176d23b5","score":"0.5961563","text":"def create(self, prototype):\n raise NotImplementedError()","title":""},{"docid":"78763176b7aa8f6dff452647da86d427","score":"0.5957297","text":"def extension():\r\n pass","title":""},{"docid":"8e41a215954178f7f30d211a39623777","score":"0.59337294","text":"def createExtension(self, name, creator, folder, params):\n item = self.createItem(\n name,\n creator,\n folder,\n params.get('description')\n )\n return self.setMetadata(item, params)","title":""},{"docid":"4c96cbd9b0671b413a5e479bf605956d","score":"0.5742098","text":"def create_in_hdx(self):\n # type: () -> None\n raise NotImplementedError","title":""},{"docid":"8f8005202afdc6bd80da8f4e8a109e9e","score":"0.5665888","text":"def sql_create_extension(name):","title":""},{"docid":"d3e8c203cb44d0afdafe8258a40671e1","score":"0.56026393","text":"def create_polypoint(self, *args, **kw):\n\t\treturn self._create('polypoint', args, kw)","title":""},{"docid":"d3ec987901a0081ef0e59ab9f101cae8","score":"0.556756","text":"def create(self):\n \n pass","title":""},{"docid":"8ef1a96e210f5b064321951dea5bbec0","score":"0.55634975","text":"def add_extension(self, ext_type, plot_id=None, title='', tool_tip='',\n extension_id=None, image_src=None):\n\n if ext_type not in FireflyClient.EXTENSION_TYPE:\n ext_type = 'NONE'\n\n if not extension_id:\n extension_id = FireflyClient._gen_item_id('Extension')\n\n image_url = FireflyClient.create_image_url(image_src) if image_src else None\n\n extension = {'id': extension_id, 'plotId': plot_id, 'imageUrl': image_url,\n 'title': title, 'extType': ext_type, 'toolTip': tool_tip}\n payload = {'extension': extension}\n return self.dispatch_remote_action_by_post(self.channel, FireflyClient.ACTION_DICT['AddExtension'],\n payload)","title":""},{"docid":"d02cbd29c18174bf7665e0517e119975","score":"0.55629134","text":"def createIt(self, *args):\n \n pass","title":""},{"docid":"f3b2211fe6284f48519483095ffc317a","score":"0.55625504","text":"def __init__(self, descriptor):\n super(AbstractAddon, self).__init__(descriptor)","title":""},{"docid":"7f1a67d0d075a3b4e1175dba152081ea","score":"0.5558122","text":"def __create_object(self):\n pass","title":""},{"docid":"8c12fa5fefa170ccbb139c2ce872b944","score":"0.5497949","text":"def create(cls, extension_name=None, extension_tag=None,\n extension_type=None):\n extension_name = ExtensionName(extension_name)\n extension_tag = ExtensionTag(extension_tag)\n extension_type = ExtensionType(extension_type)\n\n return ExtensionInformation(\n extension_name=extension_name,\n extension_tag=extension_tag,\n extension_type=extension_type)","title":""},{"docid":"d065723d2ca325f24ba5469fa02c7037","score":"0.5497711","text":"def _create(self, version=None):\n pass","title":""},{"docid":"a838c7337af77f80a82965833a450127","score":"0.54869545","text":"def create_hook(self):\n pass","title":""},{"docid":"282f2d522d17278deb6f08889898371a","score":"0.5471452","text":"def __init__(self, value=None):\n super(ExtensionType, self).__init__(value, Tags.EXTENSION_TYPE)","title":""},{"docid":"5caa208160ca8de5985b59c7a0c19074","score":"0.5451167","text":"def __init__(self, value=''):\n super(ExtensionName, self).__init__(value, Tags.EXTENSION_NAME)","title":""},{"docid":"5df29a40e596300a42f40e9c533df35f","score":"0.5417507","text":"def GenExt(self, *args):\n return _snap.TFltPrV_GenExt(self, *args)","title":""},{"docid":"b7fb16292c4c979ae1daf154589fdc73","score":"0.54149276","text":"def add_point(self):\n raise NotImplementedError","title":""},{"docid":"c25875d2b292dfdd875e58804ead6b4b","score":"0.5397596","text":"def create_extension(cls, path_setup_py_file, path_customize_py_file, path_config_py_file, output_dir,\n path_built_distribution=None, path_extension_logo=None, path_company_logo=None, custom_display_name=None, keep_build_dir=False):\n\n LOG.info(\"Creating Extension\")\n\n # Ensure the output_dir exists, we have WRITE access and ensure we can READ setup.py and customize.py\n cls.__validate_directory__(os.W_OK, output_dir)\n cls.__validate_file_paths__(os.R_OK, path_setup_py_file, path_customize_py_file)\n\n # Parse the setup.py file\n setup_py_attributes = cls.__parse_setup_py__(path_setup_py_file, cls.supported_setup_py_attribute_names)\n\n # Validate setup.py attributes\n\n # Validate the name attribute. Raise exception if invalid\n if not cls.__is_valid_package_name__(setup_py_attributes.get(\"name\")):\n raise ExtException(\"'{0}' is not a valid Extension name. The name attribute must be defined and can only include 'a-z and _'.\\nUpdate this value in the setup.py file located at: {1}\".format(setup_py_attributes.get(\"name\"), path_setup_py_file))\n\n # Validate the version attribute. Raise exception if invalid\n if not cls.__is_valid_version_syntax__(setup_py_attributes.get(\"version\")):\n raise ExtException(\"'{0}' is not a valid Extension version syntax. The version attribute must be defined. Example: version=\\\"1.0.0\\\".\\nUpdate this value in the setup.py file located at: {1}\".format(setup_py_attributes.get(\"version\"), path_setup_py_file))\n\n # Validate the url supplied in the setup.py file, set to an empty string if not valid\n if not cls.__is_valid_url__(setup_py_attributes.get(\"url\")):\n LOG.warning(\"WARNING: '%s' is not a valid url. Ignoring.\", setup_py_attributes.get(\"url\"))\n setup_py_attributes[\"url\"] = \"\"\n\n # Get ImportDefinition from customize.py\n customize_py_import_definition = cls.__get_import_definition_from_customize_py__(path_customize_py_file)\n\n # Get the tag name\n tag_name = setup_py_attributes.get(\"name\")\n\n # Add the tag to the import defintion\n customize_py_import_definition = cls.__add_tag_to_import_definition__(tag_name, cls.supported_res_obj_names, customize_py_import_definition)\n\n # Parse the app.configs from the config.py file\n app_configs = cls.__get_configs_from_config_py__(path_config_py_file)\n\n # Generate the name for the extension\n extension_name = \"{0}-{1}\".format(setup_py_attributes.get(\"name\"), setup_py_attributes.get(\"version\"))\n\n # Generate paths to the directories and files we will use in the build directory\n path_build = os.path.join(output_dir, BASE_NAME_BUILD)\n path_extension_json = os.path.join(path_build, BASE_NAME_EXTENSION_JSON)\n path_export_res = os.path.join(path_build, BASE_NAME_EXPORT_RES)\n path_executables = os.path.join(path_build, BASE_NAME_EXECUTABLES)\n path_executable_zip = os.path.join(path_executables, \"{0}{1}\".format(PREFIX_EXECUTABLE_ZIP, extension_name))\n path_executable_json = os.path.join(path_executable_zip, BASE_NAME_EXECUTABLE_JSON)\n path_executable_dockerfile = os.path.join(path_executable_zip, BASE_NAME_EXECUTABLE_DOCKERFILE)\n\n try:\n # If there is an old build directory, remove it first\n if os.path.exists(path_build):\n shutil.rmtree(path_build)\n\n # Create the directories for the path \"/build/executables/exe-<package-name>/\"\n os.makedirs(path_executable_zip)\n\n # If no path_built_distribution is given, use the default: \"<output_dir>/<package-name>.tar.gz\"\n if not path_built_distribution:\n path_built_distribution = os.path.join(output_dir, \"{0}.tar.gz\".format(extension_name))\n\n # Validate the built distribution exists and we have READ access\n cls.__validate_file_paths__(os.R_OK, path_built_distribution)\n\n # Copy the built distribution to the executable_zip dir and enforce rename to .tar.gz\n shutil.copy(path_built_distribution, os.path.join(path_executable_zip, \"{0}.tar.gz\".format(extension_name)))\n\n # Generate the contents for the executable.json file\n the_executable_json_file_contents = {\n \"name\": extension_name\n }\n\n # Write the executable.json file\n cls.__write_file__(path_executable_json, json.dumps(the_executable_json_file_contents, sort_keys=True))\n\n # NOTE: Dockerfile creation commented out for this release\n '''\n # Load Dockerfile template\n docker_file_template = cls.jinja_env.get_template(JINJA_TEMPLATE_DOCKERFILE)\n\n # Render Dockerfile template with required variables\n the_dockerfile_contents = docker_file_template.render({\n \"extension_name\": extension_name,\n \"installed_package_name\": setup_py_attributes.get(\"name\").replace(\"_\", \"-\"),\n \"app_configs\": app_configs[1]\n })\n\n # Write the Dockerfile\n cls.__write_file__(path_executable_dockerfile, the_dockerfile_contents)\n '''\n\n # zip the executable_zip dir\n shutil.make_archive(base_name=path_executable_zip, format=\"zip\", root_dir=path_executable_zip)\n\n # Remove the executable_zip dir\n shutil.rmtree(path_executable_zip)\n\n # Get the extension_logo (icon) and company_logo (author.icon) as base64 encoded strings\n extension_logo = cls.__get_icon__(\n icon_name=os.path.basename(PATH_DEFAULT_ICON_EXTENSION_LOGO),\n path_to_icon=path_extension_logo,\n width_accepted=200,\n height_accepted=72,\n default_path_to_icon=PATH_DEFAULT_ICON_EXTENSION_LOGO)\n\n company_logo = cls.__get_icon__(\n icon_name=os.path.basename(PATH_DEFAULT_ICON_COMPANY_LOGO),\n path_to_icon=path_company_logo,\n width_accepted=100,\n height_accepted=100,\n default_path_to_icon=PATH_DEFAULT_ICON_COMPANY_LOGO)\n\n # Generate the contents for the extension.json file\n the_extension_json_file_contents = {\n \"author\": {\n \"name\": setup_py_attributes.get(\"author\"),\n \"website\": setup_py_attributes.get(\"url\"),\n \"icon\": {\n \"data\": company_logo,\n \"media_type\": \"image/png\"\n }\n },\n \"description\": {\n \"content\": setup_py_attributes.get(\"description\"),\n \"format\": \"text\"\n },\n \"display_name\": custom_display_name if custom_display_name is not None else setup_py_attributes.get(\"name\"),\n \"icon\": {\n \"data\": extension_logo,\n \"media_type\": \"image/png\"\n },\n \"long_description\": {\n \"content\": \"<div>{0}</div>\".format(setup_py_attributes.get(\"long_description\")),\n \"format\": \"html\"\n },\n \"minimum_resilient_version\": {\n \"major\": customize_py_import_definition.get(\"server_version\").get(\"major\"),\n \"minor\": customize_py_import_definition.get(\"server_version\").get(\"minor\"),\n \"build_number\": customize_py_import_definition.get(\"server_version\").get(\"build_number\"),\n \"version\": customize_py_import_definition.get(\"server_version\").get(\"version\")\n },\n \"name\": setup_py_attributes.get(\"name\"),\n \"tag\": {\n \"prefix\": tag_name,\n \"name\": tag_name,\n \"display_name\": tag_name,\n \"uuid\": cls.__generate_uuid_from_string__(tag_name)\n },\n \"uuid\": cls.__generate_uuid_from_string__(\"{0}-{1}\".format(setup_py_attributes.get(\"name\"), setup_py_attributes.get(\"version\"))),\n \"version\": setup_py_attributes.get(\"version\"),\n # TODO: discuss with Sasquatch. Can add the app_config_str here, but will not install\n # TODO: get 'Unrecognized field \"app_config_str\"' error\n # \"app_config_str\": app_configs[0]\n }\n\n # Write the executable.json file\n cls.__write_file__(path_extension_json, json.dumps(the_extension_json_file_contents, sort_keys=True))\n\n # Write the customize ImportDefinition to the export.res file\n cls.__write_file__(path_export_res, json.dumps(customize_py_import_definition, sort_keys=True))\n\n # Copy the built distribution to the build dir, enforce rename to .tar.gz\n shutil.copy(path_built_distribution, os.path.join(path_build, \"{0}.tar.gz\".format(extension_name)))\n\n # create The Extension Zip by zipping the build directory\n extension_zip_base_path = os.path.join(output_dir, \"{0}{1}\".format(PREFIX_EXTENSION_ZIP, extension_name))\n extension_zip_name = shutil.make_archive(base_name=extension_zip_base_path, format=\"zip\", root_dir=path_build)\n path_the_extension_zip = os.path.join(extension_zip_base_path, extension_zip_name)\n\n except ExtException as err:\n raise err\n\n except Exception as err:\n raise ExtException(err)\n\n finally:\n # Remove the executable_zip dir. Keep it if user passes --keep-build-dir\n if not keep_build_dir:\n shutil.rmtree(path_build)\n\n LOG.info(\"Extension %s created\", \"{0}{1}\".format(PREFIX_EXTENSION_ZIP, extension_name))\n\n # Return the path to the extension zip\n return path_the_extension_zip","title":""},{"docid":"dee729d24a847b40973a7fd747c30482","score":"0.5378736","text":"def ext(self):","title":""},{"docid":"ea4900a62b24ecec67e04d1689de2f1f","score":"0.5357597","text":"def new_helper(self):\n\n self._new_template_file('Python', '.py', tmpl.helper_content, 'htk_core_helper_created')","title":""},{"docid":"24db4fc45afb2907fd5650e8f306f66b","score":"0.53510654","text":"def __init__(self, value=0):\n super(ExtensionTag, self).__init__(value, Tags.EXTENSION_TAG)","title":""},{"docid":"6e062aee12fa7aef587de26dd208ea2f","score":"0.53226924","text":"def create(self):\n NotImplementedError","title":""},{"docid":"2bc0b1d9fa9ce0c44205b06072db47cc","score":"0.53007394","text":"def create(self):\n raise NotImplementedError","title":""},{"docid":"28437a90f21b531aca44f58a042addf6","score":"0.52985704","text":"def createIt():\n\n pass","title":""},{"docid":"153ef5ee98f14244eb6d1112f411be8e","score":"0.52963537","text":"def __init__(self, jsondict=None):\n \n super(ProfileExtensionDefnDefinition, self).__init__(jsondict)","title":""},{"docid":"5578df7aba94a50a855a7026608a67b3","score":"0.5273432","text":"def __init__(self, extension_name=None, extension_tag=None,\n extension_type=None):\n super(ExtensionInformation, self).__init__(Tags.EXTENSION_INFORMATION)\n\n if extension_name is None:\n self.extension_name = ExtensionName()\n else:\n self.extension_name = extension_name\n\n self.extension_tag = extension_tag\n self.extension_type = extension_type\n\n self.validate()","title":""},{"docid":"1423bcbf7247629f38c7114ea3d0e44f","score":"0.5256766","text":"def GenExt(self, *args):\n return _snap.TSFltV_GenExt(self, *args)","title":""},{"docid":"5ca425a5ad2951db752f732f6c9b0c92","score":"0.52368397","text":"def GenExt(self, *args):\n return _snap.TFltV_GenExt(self, *args)","title":""},{"docid":"c1dc8742dd43350c0e6701eddfc22006","score":"0.5231864","text":"def makeExtensionLink(self, newEntity=None):\n if not newEntity:\n newEntity = self.entity\n return 'apiext:{}'.format(newEntity)","title":""},{"docid":"69f45cb1e74b82fcb8eb02ad4563d628","score":"0.52308166","text":"def set_extensions(manager, args, model, device, test_loader, \n optimizer, loss_func, eval_func_dict={}):\n \n my_extensions = [\n # # observe, report\n ppe_extensions.observe_lr(optimizer=optimizer),\n # ppe_extensions.ParameterStatistics(model, prefix='model'),\n # ppe_extensions.VariableStatisticsPlot(model),\n ppe_extensions.LogReport(),\n # ppe_extensions.PlotReport(['train/loss', 'val/loss'], 'epoch', filename='loss.png'),\n # ppe_extensions.PlotReport(['lr',], 'epoch', filename='lr.png'),\n ppe_extensions.PrintReport([\n 'epoch', 'iteration', 'lr', 'train/loss', 'val/loss', \"elapsed_time\"]),\n # ppe_extensions.ProgressBar(update_interval=100),\n\n # evaluation\n (\n ppe_extensions.Evaluator(\n test_loader, model,\n eval_func=lambda data, target:\n eval_for_batch(args, model, device, data, target, loss_func, eval_func_dict),\n progress_bar=False),\n (1, \"epoch\"),\n ),\n # save model snapshot.\n (\n ppe_extensions.snapshot(\n target=model, filename=\"snapshot_epoch_{.updater.epoch}.pth\"),\n ppe.training.triggers.MinValueTrigger(key=\"val/loss\", trigger=(1, 'epoch'))\n ),\n ]\n \n # set extensions to manager\n for ext in my_extensions:\n if isinstance(ext, tuple):\n manager.extend(ext[0], trigger=ext[1])\n else:\n manager.extend(ext)\n return manager","title":""},{"docid":"ad6650d5b244a90ea6a51edca205766a","score":"0.5228911","text":"def createObject(self):\n pass","title":""},{"docid":"45b391262d9f85f24b1327ae8b21c113","score":"0.5217516","text":"def __init__(self):\n this = _openmoc.new_Point()\n try:\n self.this.append(this)\n except:\n self.this = this","title":""},{"docid":"5f52b757fb2f30f9b11ea22ffcab9b14","score":"0.5213375","text":"def create(self):\n pass","title":""},{"docid":"8d898783e8d2cf1b6d66bf7734cb69d1","score":"0.5201633","text":"def create(cls):\n ret_val = gxapi_cy.WrapVVEXP._create(GXContext._get_tls_geo())\n return GXVVEXP(ret_val)","title":""},{"docid":"ec1a9bfab86b4d555bf9afee41e3a873","score":"0.51954705","text":"def create_component(self, **args):","title":""},{"docid":"c82593d1b7bea79e185a28fcdafab61b","score":"0.51925826","text":"def GenExt(self, *args):\n return _snap.TFltKdV_GenExt(self, *args)","title":""},{"docid":"d08202e897b964316726f62c8953c47d","score":"0.51913023","text":"def build_extension(self, ext: build_ext):\n print('COPYING')\n copy(ext.bin_dir + '/antsRegistration', 'antsbin/')\n copy(ext.bin_dir + '/antsApplyTransforms', 'antsbin/')","title":""},{"docid":"02e6e43f60e6483602255480b4aac2a8","score":"0.51878244","text":"def extend(self):\n raise NotImplementedError","title":""},{"docid":"e6fb192442d1e90757afacd938a9880f","score":"0.51868564","text":"def GenExt(self, *args):\n return _snap.TFltIntPrKdV_GenExt(self, *args)","title":""},{"docid":"200dca7c312f9e6bbb8cf593e8dde9a0","score":"0.51608056","text":"def extensions(self):\n pass","title":""},{"docid":"f34e47590748476b70d4eba5a7be471d","score":"0.51592577","text":"def new_extension_dict(self) -> 'DXFDictionary':\n xdict = self.drawing.objects.add_dictionary(owner=self.dxf.handle)\n self.set_app_data(ACAD_XDICTIONARY, [(360, xdict.dxf.handle)])\n return xdict","title":""},{"docid":"c4174d376ee31abfe529a63af572d8d0","score":"0.51518637","text":"def __call__(self, prototype):\n return self.create(prototype)","title":""},{"docid":"8bf25fc4bfc86c3468d115c5d3fef265","score":"0.515122","text":"def create(self, prototype):\n return self.cls(\n name=prototype.name,\n plugin_directory=prototype.plugin_directory,\n plugin_name=prototype.plugin_name,\n instance_id=prototype.uid,\n config_ready=prototype.config_ready,\n recovery_action=prototype.recovery_action,\n start=prototype.start,\n data_storetype=prototype.data_storetype,\n mode_active=prototype.mode_active,\n )","title":""},{"docid":"bd4b66b827c95741ef5dd18a38f1094d","score":"0.5148227","text":"def create_example(self, data_sample):\n\n \traise NotImplementedError","title":""},{"docid":"0dd628557739bf1387adaa64293017ef","score":"0.51469743","text":"def GenExt(self, *args):\n return _snap.TFltIntPrV_GenExt(self, *args)","title":""},{"docid":"e2a465c10a32717e86f93ea8708beca9","score":"0.5145585","text":"def __init__(self):\r\n self.label = \"Access Point Placement to Signal Strength Field\"\r\n self.description = '''Takes a point feature class representing potential wifi access point locations \r\n and outputs raster features for each point representing wifi signal strength'''\r\n self.canRunInBackground = False","title":""},{"docid":"cc76b7d30f697c0ab46a731536caae53","score":"0.5143688","text":"def _creation_attributes(self):","title":""},{"docid":"388b50c2a46115b7272eea3a61087328","score":"0.51350296","text":"def create(self, context):\n pass","title":""},{"docid":"5d92ead3f0ca512f625aace023eb6b08","score":"0.51239383","text":"def create_instance(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"b92f9be0d331976693c12cbec0188eba","score":"0.51228404","text":"def entrypoint(self):\n return self.__class__._entrypoint_registry.register","title":""},{"docid":"fef5bcb01a582bf0c683037d50efffbd","score":"0.5094478","text":"def _create_element_in_extension_(self, term, old_term_parent=None):\n if old_term_parent is None or term.parent() is old_term_parent:\n parent = self\n else:\n # Insert an 'if' here once terms can have different\n # coefficient rings, as this will be for L-terms.\n parent = self.change_parameter(\n growth_group=term.parent().growth_group,\n coefficient_ring=term.parent().coefficient_ring)\n return parent(term, simplify=False, convert=False)","title":""},{"docid":"503a121cf1caf27eadd494d3b3ed93b1","score":"0.5090707","text":"def test_create_profile_extension():\n assert _heartbeat(\n client.create_profile_extension(fixtures['profile_list_extension']))","title":""},{"docid":"6abee06cfe7dcc0c31d3cbcde859930b","score":"0.5090653","text":"def create_points_files(c):\n \n pkg_dir = str(Path(__file__).parent.resolve())\n pkg = mp.open_package(pkg_dir)\n points_logger.info(f\"Pkg dir: {pkg_dir}\")\n\n convert_pbf(c)\n \n pylib.build_points(pkg)","title":""},{"docid":"83a702edb314032188538f26c1d1adc4","score":"0.507893","text":"def GenExt(self, *args):\n return _snap.TIntFltPrKdV_GenExt(self, *args)","title":""},{"docid":"279affcfab7d68b232dbe192f95112b4","score":"0.50762135","text":"def __init__(self, polyglot, primary, address, name):\n super(TemplateNode, self).__init__(polyglot, primary, address, name)\n self.poly = polyglot\n self.lpfx = '%s:%s' % (address,name)\n self.poly.onStart(address, self.start)\n self.poly.onPoll(self.poll)","title":""},{"docid":"6444571cb5e0739a1855ed70cda8c9ba","score":"0.50719935","text":"def GenExt(self, *args):\n return _snap.TStrFltPrV_GenExt(self, *args)","title":""},{"docid":"6c672add52f9fe13b941dd9cd6d628ec","score":"0.50669384","text":"def add_source_extension(propname, proptype, vcount, valuetype, getter, setter):\n propname = propname.upper()\n if propname.startswith(\"al.AL_\"):\n pname = propname[3:]\n else:\n pname = propname\n propname = \"al.AL_%s\" % propname\n if al.alIsExtensionPresent(pname) == al.AL_FALSE:\n return False\n\n global _SOURCECALLBACKS\n global _SOURCEPROPMAP\n _SOURCECALLBACKS[proptype] = (vcount, valuetype, getter, setter)\n _SOURCEPROPMAP[propname] = proptype\n return True","title":""},{"docid":"85840d7f37266cea5063ac0d109b178d","score":"0.5063235","text":"def create_module(cls, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"85840d7f37266cea5063ac0d109b178d","score":"0.5063235","text":"def create_module(cls, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"8c535e7c760ff2d0b442b87074bd9ee3","score":"0.5057771","text":"def __init__(self, *args, **kwargs):\n self.anchor = kwargs.pop('anchor', '')\n super(CutExtension, self).__init__(*args, **kwargs)","title":""},{"docid":"8fd7a1f355dee9f2ca7dc4374fa99c90","score":"0.50532156","text":"def msvcrxx__creat(jitter):\n ret_ad, args = jitter.func_args_cdecl([\"filename\", \"pmode\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_cdecl(ret_ad, ret_value)","title":""},{"docid":"9dc0e7111d18cb53ba5650e994ccf39b","score":"0.50530726","text":"def __init__(self):\n Token.set_extension(\"as_ptb\", default=None, force=True)","title":""},{"docid":"0a33e54fb6814e527e9e42757b07a80b","score":"0.50529075","text":"def GenExt(self, *args):\n return _snap.TIntPrFltKdV_GenExt(self, *args)","title":""},{"docid":"fcfe710f6cf53fde81f770b7a81239dc","score":"0.5052664","text":"def genExtensionRange(self, x, t=0):\n raise NotImplementedError(\"Extensions not supported\")","title":""},{"docid":"52156b9481a3ad5925ab11f4d9f05d98","score":"0.5049063","text":"def __init__(self, *args, **kwargs):\n newobj = _core.new_Point(*args, **kwargs)\n self.this = newobj.this\n self.thisown = 1\n del newobj.thisown","title":""},{"docid":"7cc588eae512a204fae807ae4bc31332","score":"0.5047302","text":"def create_extensions(url: str):\n with connect(url=url, autocommit=True) as cnx:\n with cnx.cursor() as crs:\n # Make sure the extensions are installed.\n for sql in sql_phrasebook.create_extensions:\n crs.execute(sql)","title":""},{"docid":"6b5cf0e294cea5518ca6cef8acb7c619","score":"0.5040026","text":"def make(self):\n pass","title":""},{"docid":"64f73b0cb1f8eb55bbf5d8ee7db74825","score":"0.50393397","text":"def __init__(self, *args, **kwargs):\n spexxyObject.__init__(self, *args, **kwargs)","title":""},{"docid":"c50a9725e5a11e5985aece9e5cbc55c4","score":"0.50324184","text":"def create(cls):","title":""},{"docid":"d8202d4e3209a5ce8a3efbe844863a36","score":"0.50321066","text":"def add_extension(request_id, length, reason, custom_due_date, tz_name, content, method, letter_template_id):\n request = Requests.query.filter_by(id=request_id).one()\n previous_due_date = {\"due_date\": request.due_date.isoformat()}\n new_due_date = _get_new_due_date(request_id, length, custom_due_date, tz_name)\n days_until_due = calendar.busdaycount(datetime.utcnow(), new_due_date.replace(hour=23, minute=59, second=59))\n if new_due_date < datetime.utcnow():\n new_status = request_status.OVERDUE\n elif days_until_due <= current_app.config['DUE_SOON_DAYS_THRESHOLD']:\n new_status = request_status.DUE_SOON\n else:\n new_status = request_status.IN_PROGRESS\n update_object(\n {\n 'due_date': new_due_date,\n 'status': new_status\n },\n Requests,\n request_id)\n privacy = RELEASE_AND_PUBLIC\n response = Determinations(\n request_id,\n privacy,\n determination_type.EXTENSION,\n reason,\n new_due_date\n )\n if method == response_type.LETTER:\n response.reason = 'A letter will be mailed to the requester.'\n create_object(response)\n create_response_event(event_type.REQ_EXTENDED, response, previous_value=previous_due_date)\n if method == response_type.LETTER:\n letter_template = LetterTemplates.query.filter_by(id=letter_template_id).one()\n letter_id = _add_letter(request_id, letter_template.title, content, event_type.EXTENSION_LETTER_CREATED)\n _create_communication_method(response.id, letter_id, response_type.LETTER)\n letter = generate_pdf(content)\n email_template = os.path.join(current_app.config['EMAIL_TEMPLATE_DIR'],\n EMAIL_TEMPLATE_FOR_EVENT[event_type.EXTENSION_LETTER_CREATED])\n email_content = render_template(email_template,\n request_id=request_id,\n agency_name=request.agency.name,\n user=current_user\n )\n email_id = safely_send_and_add_email(request_id,\n email_content,\n 'Request {} Extended - Letter'.format(request_id),\n to=get_assigned_users_emails(request_id),\n attachment=letter,\n filename=secure_filename('{}_extension_letter.pdf'.format(request_id)),\n mimetype='application/pdf')\n _create_communication_method(response.id, email_id, response_type.EMAIL)\n else:\n email_id = _send_response_email(request_id,\n privacy,\n content,\n 'Request {} Extended'.format(request_id))\n\n _create_communication_method(response.id, email_id, response_type.EMAIL)","title":""},{"docid":"2db333f1fd333841e39749ac93724c1d","score":"0.50291836","text":"def create_point_source():\n background_molecule = Molecule(\n volume_fraction=1.0,\n absorption_spectrum=AbsorptionSpectrumLibrary.CONSTANT_ABSORBER_ARBITRARY(1e-10),\n scattering_spectrum=ScatteringSpectrumLibrary.CONSTANT_SCATTERING_ARBITRARY(1e-10),\n anisotropy_spectrum=AnisotropySpectrumLibrary.CONSTANT_ANISOTROPY_ARBITRARY(1.0),\n speed_of_sound=SPEED_OF_SOUND\n )\n background_dictionary = Settings()\n background_dictionary[Tags.MOLECULE_COMPOSITION] = (MolecularCompositionGenerator().\n append(background_molecule).\n get_molecular_composition(-1))\n background_dictionary[Tags.STRUCTURE_TYPE] = Tags.BACKGROUND\n\n vessel_molecule = Molecule(\n volume_fraction=1.0,\n absorption_spectrum=AbsorptionSpectrumLibrary.CONSTANT_ABSORBER_ARBITRARY(2),\n scattering_spectrum=ScatteringSpectrumLibrary.CONSTANT_SCATTERING_ARBITRARY(100),\n anisotropy_spectrum=AnisotropySpectrumLibrary.CONSTANT_ANISOTROPY_ARBITRARY(0.9),\n speed_of_sound=SPEED_OF_SOUND+100,\n density=1100\n )\n\n vessel_1_dictionary = Settings()\n vessel_1_dictionary[Tags.PRIORITY] = 3\n vessel_1_dictionary[Tags.STRUCTURE_START_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2-10, 0, 35]\n vessel_1_dictionary[Tags.STRUCTURE_END_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2-10, VOLUME_PLANAR_DIM_IN_MM, 35]\n vessel_1_dictionary[Tags.STRUCTURE_RADIUS_MM] = SPACING\n vessel_1_dictionary[Tags.MOLECULE_COMPOSITION] = (MolecularCompositionGenerator().\n append(vessel_molecule).\n get_molecular_composition(-1))\n vessel_1_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True\n vessel_1_dictionary[Tags.STRUCTURE_TYPE] = Tags.CIRCULAR_TUBULAR_STRUCTURE\n\n tissue_dict = Settings()\n tissue_dict[Tags.BACKGROUND] = background_dictionary\n tissue_dict[\"vessel_1\"] = vessel_1_dictionary\n return tissue_dict","title":""},{"docid":"ccd976eb9986abcd22f0e58c9bb7e9a5","score":"0.5027457","text":"def GenExt(self, *args):\n return _snap.TIntFltPrV_GenExt(self, *args)","title":""},{"docid":"3a7a2f73a19de89b2a0de937bb5d4bff","score":"0.50253975","text":"def create_setup(self):\n return Setup(self)","title":""},{"docid":"4af06ad02e622b44df47a08160328762","score":"0.5023678","text":"def __init__(self, jsondict=None):\n \n self.code = None\n \"\"\" Identifies the extension in this profile.\n Type `str`. \"\"\"\n \n self.context = None\n \"\"\" Where the extension can be used in instances.\n List of `str` items. \"\"\"\n \n self.contextType = None\n \"\"\" resource | datatype | mapping | extension.\n Type `str`. \"\"\"\n \n self.definition = None\n \"\"\" Definition of the extension and its content.\n Type `ProfileExtensionDefnDefinition` (represented as `dict` in JSON). \"\"\"\n \n self.display = None\n \"\"\" Use this name when displaying the value.\n Type `str`. \"\"\"\n \n super(ProfileExtensionDefn, self).__init__(jsondict)","title":""},{"docid":"1bd41ce413ac1cc77610ad41cb92cf7b","score":"0.5020994","text":"def create_experiment(self, context, request_specs):\n raise NotImplementedError()","title":""},{"docid":"8dfcf5bc04b429c8aa7ec62f21099b84","score":"0.5015555","text":"def construct(self):\n raise NotImplementedError()","title":""},{"docid":"f614aaeec54c1777087fed7dc7d37570","score":"0.50124156","text":"def creator(cls):\n \n pass","title":""},{"docid":"df53171b3b6bb6986ae94ececc70b2fc","score":"0.50094825","text":"def GenExt(self, *args):\n return _snap.TFltStrPrPrV_GenExt(self, *args)","title":""},{"docid":"2259c35c896ea85ebc72ff2340e42d21","score":"0.50088334","text":"def create_main(mod):\r\n\t\r\n\ttpointer = Type.pointer(Type.pointer(types.int8, 0), 0)\t\r\n\tft = Type.function(types.integer,[ types.integer, tpointer ] )\r\n\treturn mod.add_function(ft, \"main\")","title":""},{"docid":"d95bc1da1b35d8b74c9e38e033e3aabd","score":"0.5002982","text":"def _createModuleObj(self):\n raise NotImplementedError(\n \"Please implement _createModuleObj() in derived class.\")","title":""},{"docid":"18b30385e1dede501f037d81982e7bae","score":"0.5001066","text":"def _create_objectives(self):\n pass","title":""},{"docid":"268c0db02ecce9d71013d948db913f0f","score":"0.49979195","text":"def create_extension_frame(self, ext_name):\n f = ttk.Frame(self.details_frame, padding=10)\n self.config_frame[ext_name] = f\n entry_area = f\n # create an entry for each configuration option\n for row, opt in enumerate(self.extensions[ext_name]):\n # create a row with a label and entry/checkbutton\n label = ttk.Label(entry_area, text=opt['name'])\n label.grid(row=row, column=0, sticky=NW)\n var = opt['var']\n if opt['type'] == 'bool':\n ttk.Checkbutton(entry_area, variable=var,\n onvalue='True', offvalue='False').grid(row=row,\n column=1, sticky=W, padx=7)\n elif opt['type'] == 'int':\n ttk.Entry(entry_area, textvariable=var, validate='key',\n validatecommand=(self.is_int, '%P')\n ).grid(row=row, column=1, sticky=NSEW, padx=7)\n\n else:\n ttk.Entry(entry_area, textvariable=var\n ).grid(row=row, column=1, sticky=NSEW, padx=7)\n return","title":""},{"docid":"df2af81bb811973f83ebc44838d38b7f","score":"0.4997399","text":"def create(self, kind, key, *args, **kwargs):","title":""},{"docid":"9abed72fa254e08eb8228f4ea26c6aae","score":"0.49925646","text":"def GenExt(self, *args):\n return _snap.TAscFltV_GenExt(self, *args)","title":""},{"docid":"0c7cdff999058ec01ca735cfc7ad4b46","score":"0.49912003","text":"def create_instance(self, instance: 'core.RuntimeInstance') -> 'core.RuntimeInstance':","title":""},{"docid":"766513b2529452445a80d2e73fcc6781","score":"0.498717","text":"def __add__(*args, **kwargs):\n return _core.Point___add__(*args, **kwargs)","title":""},{"docid":"4a7454644733ce35a8ea60036c258e53","score":"0.49763882","text":"def GenExt(self, *args):\n return _snap.TFltIntKdV_GenExt(self, *args)","title":""},{"docid":"7b243d8ee9d9ed8a6141a85f967d5bf0","score":"0.49738428","text":"def make(self, **kwargs):\n raise NotImplementedError","title":""},{"docid":"b2be177c8c9112a655361da6d2cd27e2","score":"0.49734607","text":"def generate(env):\n\n # Create the PythonExtension builder\n create_builder(env)\n\n trace ( \"Initialized UnitTest tool\", \"UnitTest\", 2 )","title":""},{"docid":"a93801f95630d629a98114955e1c1759","score":"0.49697852","text":"def _addPoint(self, obj, lon, lat):\n pt = self.ElementTree.SubElement(obj, 'Point')\n ptlon = self.ElementTree.SubElement(pt, 'PointLongitude')\n ptlon.text = str(self.boundary[lon])\n ptlat = self.ElementTree.SubElement(pt, 'PointLatitude')\n ptlat.text = str(self.boundary[lat])","title":""},{"docid":"091f54c492dc64cf9141030118a0b444","score":"0.49653772","text":"def create(self, extend):\n attributes = dumps(extend.attributes)\n return db.execute('INSERT INTO extend (`type`, `label`, `field`, `key`, `attributes`) VALUES (%s, %s, %s, %s, %s)',\n (extend.type, extend.label, extend.field, extend.key, attributes))","title":""},{"docid":"6432a2c43480f55a6c6e8fd618e1dc14","score":"0.4960268","text":"def make_gui_point(self, parent):\n\n # create widgets\n point_obj = LayerControl(parent, 'Points, map relative %s'\n % str(MRPointShowLevels),\n selectable=True)\n\n # tie to event handler(s)\n point_obj.Bind(EVT_ONOFF, self.pointOnOff)\n point_obj.Bind(EVT_SHOWONOFF, self.pointShowOnOff)\n point_obj.Bind(EVT_SELECTONOFF, self.pointSelectOnOff)\n\n return point_obj","title":""},{"docid":"a2429571b3ab1f34b4823c37dad09f7d","score":"0.49543074","text":"def setup(self):\r\n\r\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"c9bf0407a546c47d63f27fc8ba660f5b\",\n \"score\": \"0.65477675\",\n \"text\": \"def __init__(self, **config):\\n\\t\\t\\n\\t\\tsuper(Extension, self).__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96fb74a85a7c9e79d2047613ec8afa98\",\n \"score\": \"0.65290415\",\n \"text\": \"def test_make_extension(self):\\n makeExtension()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ebb85a7ff84354b99b0232a48927be\",\n \"score\": \"0.62442946\",\n \"text\": \"def makeExtension(**kwargs):\\n return DenDenExtension(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f824831fc01a2b9da727871b55983535\",\n \"score\": \"0.6189438\",\n \"text\": \"def extension():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7b3142f9c7d794321dce14c999864e\",\n \"score\": \"0.61609125\",\n \"text\": \"def __repr__(self):\\n return '<ExtensionPoint %s>' % self.interface.__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a353d1546d196f8876b102c32b11f88\",\n \"score\": \"0.6116655\",\n \"text\": \"def create_extension(self, extension):\\n if not self.get_extension():\\n self.name += \\\".\\\" + extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5085ffe7724ebae1b77f2b853f3c92eb\",\n \"score\": \"0.60060245\",\n \"text\": \"def create(self, _extension_name, *args, **kwargs):\\n extension = self.get(_extension_name)\\n\\n option_types = dict(self.option_types)\\n for option in extension.__options__ or []:\\n name = option[\\\"name\\\"]\\n option_types[name] = option.get(\\\"type\\\", \\\"string\\\")\\n\\n kwargs = coalesce_options(dict(kwargs), option_types)\\n\\n return extension(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5650a2965cbd8461752b53176d23b5\",\n \"score\": \"0.5961563\",\n \"text\": \"def create(self, prototype):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78763176b7aa8f6dff452647da86d427\",\n \"score\": \"0.5957297\",\n \"text\": \"def extension():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e41a215954178f7f30d211a39623777\",\n \"score\": \"0.59337294\",\n \"text\": \"def createExtension(self, name, creator, folder, params):\\n item = self.createItem(\\n name,\\n creator,\\n folder,\\n params.get('description')\\n )\\n return self.setMetadata(item, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c96cbd9b0671b413a5e479bf605956d\",\n \"score\": \"0.5742098\",\n \"text\": \"def create_in_hdx(self):\\n # type: () -> None\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8005202afdc6bd80da8f4e8a109e9e\",\n \"score\": \"0.5665888\",\n \"text\": \"def sql_create_extension(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e8c203cb44d0afdafe8258a40671e1\",\n \"score\": \"0.56026393\",\n \"text\": \"def create_polypoint(self, *args, **kw):\\n\\t\\treturn self._create('polypoint', args, kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ec987901a0081ef0e59ab9f101cae8\",\n \"score\": \"0.556756\",\n \"text\": \"def create(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef1a96e210f5b064321951dea5bbec0\",\n \"score\": \"0.55634975\",\n \"text\": \"def add_extension(self, ext_type, plot_id=None, title='', tool_tip='',\\n extension_id=None, image_src=None):\\n\\n if ext_type not in FireflyClient.EXTENSION_TYPE:\\n ext_type = 'NONE'\\n\\n if not extension_id:\\n extension_id = FireflyClient._gen_item_id('Extension')\\n\\n image_url = FireflyClient.create_image_url(image_src) if image_src else None\\n\\n extension = {'id': extension_id, 'plotId': plot_id, 'imageUrl': image_url,\\n 'title': title, 'extType': ext_type, 'toolTip': tool_tip}\\n payload = {'extension': extension}\\n return self.dispatch_remote_action_by_post(self.channel, FireflyClient.ACTION_DICT['AddExtension'],\\n payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02cbd29c18174bf7665e0517e119975\",\n \"score\": \"0.55629134\",\n \"text\": \"def createIt(self, *args):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b2211fe6284f48519483095ffc317a\",\n \"score\": \"0.55625504\",\n \"text\": \"def __init__(self, descriptor):\\n super(AbstractAddon, self).__init__(descriptor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1a67d0d075a3b4e1175dba152081ea\",\n \"score\": \"0.5558122\",\n \"text\": \"def __create_object(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c12fa5fefa170ccbb139c2ce872b944\",\n \"score\": \"0.5497949\",\n \"text\": \"def create(cls, extension_name=None, extension_tag=None,\\n extension_type=None):\\n extension_name = ExtensionName(extension_name)\\n extension_tag = ExtensionTag(extension_tag)\\n extension_type = ExtensionType(extension_type)\\n\\n return ExtensionInformation(\\n extension_name=extension_name,\\n extension_tag=extension_tag,\\n extension_type=extension_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d065723d2ca325f24ba5469fa02c7037\",\n \"score\": \"0.5497711\",\n \"text\": \"def _create(self, version=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a838c7337af77f80a82965833a450127\",\n \"score\": \"0.54869545\",\n \"text\": \"def create_hook(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282f2d522d17278deb6f08889898371a\",\n \"score\": \"0.5471452\",\n \"text\": \"def __init__(self, value=None):\\n super(ExtensionType, self).__init__(value, Tags.EXTENSION_TYPE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5caa208160ca8de5985b59c7a0c19074\",\n \"score\": \"0.5451167\",\n \"text\": \"def __init__(self, value=''):\\n super(ExtensionName, self).__init__(value, Tags.EXTENSION_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df29a40e596300a42f40e9c533df35f\",\n \"score\": \"0.5417507\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltPrV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7fb16292c4c979ae1daf154589fdc73\",\n \"score\": \"0.54149276\",\n \"text\": \"def add_point(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25875d2b292dfdd875e58804ead6b4b\",\n \"score\": \"0.5397596\",\n \"text\": \"def create_extension(cls, path_setup_py_file, path_customize_py_file, path_config_py_file, output_dir,\\n path_built_distribution=None, path_extension_logo=None, path_company_logo=None, custom_display_name=None, keep_build_dir=False):\\n\\n LOG.info(\\\"Creating Extension\\\")\\n\\n # Ensure the output_dir exists, we have WRITE access and ensure we can READ setup.py and customize.py\\n cls.__validate_directory__(os.W_OK, output_dir)\\n cls.__validate_file_paths__(os.R_OK, path_setup_py_file, path_customize_py_file)\\n\\n # Parse the setup.py file\\n setup_py_attributes = cls.__parse_setup_py__(path_setup_py_file, cls.supported_setup_py_attribute_names)\\n\\n # Validate setup.py attributes\\n\\n # Validate the name attribute. Raise exception if invalid\\n if not cls.__is_valid_package_name__(setup_py_attributes.get(\\\"name\\\")):\\n raise ExtException(\\\"'{0}' is not a valid Extension name. The name attribute must be defined and can only include 'a-z and _'.\\\\nUpdate this value in the setup.py file located at: {1}\\\".format(setup_py_attributes.get(\\\"name\\\"), path_setup_py_file))\\n\\n # Validate the version attribute. Raise exception if invalid\\n if not cls.__is_valid_version_syntax__(setup_py_attributes.get(\\\"version\\\")):\\n raise ExtException(\\\"'{0}' is not a valid Extension version syntax. The version attribute must be defined. Example: version=\\\\\\\"1.0.0\\\\\\\".\\\\nUpdate this value in the setup.py file located at: {1}\\\".format(setup_py_attributes.get(\\\"version\\\"), path_setup_py_file))\\n\\n # Validate the url supplied in the setup.py file, set to an empty string if not valid\\n if not cls.__is_valid_url__(setup_py_attributes.get(\\\"url\\\")):\\n LOG.warning(\\\"WARNING: '%s' is not a valid url. Ignoring.\\\", setup_py_attributes.get(\\\"url\\\"))\\n setup_py_attributes[\\\"url\\\"] = \\\"\\\"\\n\\n # Get ImportDefinition from customize.py\\n customize_py_import_definition = cls.__get_import_definition_from_customize_py__(path_customize_py_file)\\n\\n # Get the tag name\\n tag_name = setup_py_attributes.get(\\\"name\\\")\\n\\n # Add the tag to the import defintion\\n customize_py_import_definition = cls.__add_tag_to_import_definition__(tag_name, cls.supported_res_obj_names, customize_py_import_definition)\\n\\n # Parse the app.configs from the config.py file\\n app_configs = cls.__get_configs_from_config_py__(path_config_py_file)\\n\\n # Generate the name for the extension\\n extension_name = \\\"{0}-{1}\\\".format(setup_py_attributes.get(\\\"name\\\"), setup_py_attributes.get(\\\"version\\\"))\\n\\n # Generate paths to the directories and files we will use in the build directory\\n path_build = os.path.join(output_dir, BASE_NAME_BUILD)\\n path_extension_json = os.path.join(path_build, BASE_NAME_EXTENSION_JSON)\\n path_export_res = os.path.join(path_build, BASE_NAME_EXPORT_RES)\\n path_executables = os.path.join(path_build, BASE_NAME_EXECUTABLES)\\n path_executable_zip = os.path.join(path_executables, \\\"{0}{1}\\\".format(PREFIX_EXECUTABLE_ZIP, extension_name))\\n path_executable_json = os.path.join(path_executable_zip, BASE_NAME_EXECUTABLE_JSON)\\n path_executable_dockerfile = os.path.join(path_executable_zip, BASE_NAME_EXECUTABLE_DOCKERFILE)\\n\\n try:\\n # If there is an old build directory, remove it first\\n if os.path.exists(path_build):\\n shutil.rmtree(path_build)\\n\\n # Create the directories for the path \\\"/build/executables/exe-<package-name>/\\\"\\n os.makedirs(path_executable_zip)\\n\\n # If no path_built_distribution is given, use the default: \\\"<output_dir>/<package-name>.tar.gz\\\"\\n if not path_built_distribution:\\n path_built_distribution = os.path.join(output_dir, \\\"{0}.tar.gz\\\".format(extension_name))\\n\\n # Validate the built distribution exists and we have READ access\\n cls.__validate_file_paths__(os.R_OK, path_built_distribution)\\n\\n # Copy the built distribution to the executable_zip dir and enforce rename to .tar.gz\\n shutil.copy(path_built_distribution, os.path.join(path_executable_zip, \\\"{0}.tar.gz\\\".format(extension_name)))\\n\\n # Generate the contents for the executable.json file\\n the_executable_json_file_contents = {\\n \\\"name\\\": extension_name\\n }\\n\\n # Write the executable.json file\\n cls.__write_file__(path_executable_json, json.dumps(the_executable_json_file_contents, sort_keys=True))\\n\\n # NOTE: Dockerfile creation commented out for this release\\n '''\\n # Load Dockerfile template\\n docker_file_template = cls.jinja_env.get_template(JINJA_TEMPLATE_DOCKERFILE)\\n\\n # Render Dockerfile template with required variables\\n the_dockerfile_contents = docker_file_template.render({\\n \\\"extension_name\\\": extension_name,\\n \\\"installed_package_name\\\": setup_py_attributes.get(\\\"name\\\").replace(\\\"_\\\", \\\"-\\\"),\\n \\\"app_configs\\\": app_configs[1]\\n })\\n\\n # Write the Dockerfile\\n cls.__write_file__(path_executable_dockerfile, the_dockerfile_contents)\\n '''\\n\\n # zip the executable_zip dir\\n shutil.make_archive(base_name=path_executable_zip, format=\\\"zip\\\", root_dir=path_executable_zip)\\n\\n # Remove the executable_zip dir\\n shutil.rmtree(path_executable_zip)\\n\\n # Get the extension_logo (icon) and company_logo (author.icon) as base64 encoded strings\\n extension_logo = cls.__get_icon__(\\n icon_name=os.path.basename(PATH_DEFAULT_ICON_EXTENSION_LOGO),\\n path_to_icon=path_extension_logo,\\n width_accepted=200,\\n height_accepted=72,\\n default_path_to_icon=PATH_DEFAULT_ICON_EXTENSION_LOGO)\\n\\n company_logo = cls.__get_icon__(\\n icon_name=os.path.basename(PATH_DEFAULT_ICON_COMPANY_LOGO),\\n path_to_icon=path_company_logo,\\n width_accepted=100,\\n height_accepted=100,\\n default_path_to_icon=PATH_DEFAULT_ICON_COMPANY_LOGO)\\n\\n # Generate the contents for the extension.json file\\n the_extension_json_file_contents = {\\n \\\"author\\\": {\\n \\\"name\\\": setup_py_attributes.get(\\\"author\\\"),\\n \\\"website\\\": setup_py_attributes.get(\\\"url\\\"),\\n \\\"icon\\\": {\\n \\\"data\\\": company_logo,\\n \\\"media_type\\\": \\\"image/png\\\"\\n }\\n },\\n \\\"description\\\": {\\n \\\"content\\\": setup_py_attributes.get(\\\"description\\\"),\\n \\\"format\\\": \\\"text\\\"\\n },\\n \\\"display_name\\\": custom_display_name if custom_display_name is not None else setup_py_attributes.get(\\\"name\\\"),\\n \\\"icon\\\": {\\n \\\"data\\\": extension_logo,\\n \\\"media_type\\\": \\\"image/png\\\"\\n },\\n \\\"long_description\\\": {\\n \\\"content\\\": \\\"<div>{0}</div>\\\".format(setup_py_attributes.get(\\\"long_description\\\")),\\n \\\"format\\\": \\\"html\\\"\\n },\\n \\\"minimum_resilient_version\\\": {\\n \\\"major\\\": customize_py_import_definition.get(\\\"server_version\\\").get(\\\"major\\\"),\\n \\\"minor\\\": customize_py_import_definition.get(\\\"server_version\\\").get(\\\"minor\\\"),\\n \\\"build_number\\\": customize_py_import_definition.get(\\\"server_version\\\").get(\\\"build_number\\\"),\\n \\\"version\\\": customize_py_import_definition.get(\\\"server_version\\\").get(\\\"version\\\")\\n },\\n \\\"name\\\": setup_py_attributes.get(\\\"name\\\"),\\n \\\"tag\\\": {\\n \\\"prefix\\\": tag_name,\\n \\\"name\\\": tag_name,\\n \\\"display_name\\\": tag_name,\\n \\\"uuid\\\": cls.__generate_uuid_from_string__(tag_name)\\n },\\n \\\"uuid\\\": cls.__generate_uuid_from_string__(\\\"{0}-{1}\\\".format(setup_py_attributes.get(\\\"name\\\"), setup_py_attributes.get(\\\"version\\\"))),\\n \\\"version\\\": setup_py_attributes.get(\\\"version\\\"),\\n # TODO: discuss with Sasquatch. Can add the app_config_str here, but will not install\\n # TODO: get 'Unrecognized field \\\"app_config_str\\\"' error\\n # \\\"app_config_str\\\": app_configs[0]\\n }\\n\\n # Write the executable.json file\\n cls.__write_file__(path_extension_json, json.dumps(the_extension_json_file_contents, sort_keys=True))\\n\\n # Write the customize ImportDefinition to the export.res file\\n cls.__write_file__(path_export_res, json.dumps(customize_py_import_definition, sort_keys=True))\\n\\n # Copy the built distribution to the build dir, enforce rename to .tar.gz\\n shutil.copy(path_built_distribution, os.path.join(path_build, \\\"{0}.tar.gz\\\".format(extension_name)))\\n\\n # create The Extension Zip by zipping the build directory\\n extension_zip_base_path = os.path.join(output_dir, \\\"{0}{1}\\\".format(PREFIX_EXTENSION_ZIP, extension_name))\\n extension_zip_name = shutil.make_archive(base_name=extension_zip_base_path, format=\\\"zip\\\", root_dir=path_build)\\n path_the_extension_zip = os.path.join(extension_zip_base_path, extension_zip_name)\\n\\n except ExtException as err:\\n raise err\\n\\n except Exception as err:\\n raise ExtException(err)\\n\\n finally:\\n # Remove the executable_zip dir. Keep it if user passes --keep-build-dir\\n if not keep_build_dir:\\n shutil.rmtree(path_build)\\n\\n LOG.info(\\\"Extension %s created\\\", \\\"{0}{1}\\\".format(PREFIX_EXTENSION_ZIP, extension_name))\\n\\n # Return the path to the extension zip\\n return path_the_extension_zip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee729d24a847b40973a7fd747c30482\",\n \"score\": \"0.5378736\",\n \"text\": \"def ext(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4900a62b24ecec67e04d1689de2f1f\",\n \"score\": \"0.5357597\",\n \"text\": \"def new_helper(self):\\n\\n self._new_template_file('Python', '.py', tmpl.helper_content, 'htk_core_helper_created')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24db4fc45afb2907fd5650e8f306f66b\",\n \"score\": \"0.53510654\",\n \"text\": \"def __init__(self, value=0):\\n super(ExtensionTag, self).__init__(value, Tags.EXTENSION_TAG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e062aee12fa7aef587de26dd208ea2f\",\n \"score\": \"0.53226924\",\n \"text\": \"def create(self):\\n NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bc0b1d9fa9ce0c44205b06072db47cc\",\n \"score\": \"0.53007394\",\n \"text\": \"def create(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28437a90f21b531aca44f58a042addf6\",\n \"score\": \"0.52985704\",\n \"text\": \"def createIt():\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153ef5ee98f14244eb6d1112f411be8e\",\n \"score\": \"0.52963537\",\n \"text\": \"def __init__(self, jsondict=None):\\n \\n super(ProfileExtensionDefnDefinition, self).__init__(jsondict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5578df7aba94a50a855a7026608a67b3\",\n \"score\": \"0.5273432\",\n \"text\": \"def __init__(self, extension_name=None, extension_tag=None,\\n extension_type=None):\\n super(ExtensionInformation, self).__init__(Tags.EXTENSION_INFORMATION)\\n\\n if extension_name is None:\\n self.extension_name = ExtensionName()\\n else:\\n self.extension_name = extension_name\\n\\n self.extension_tag = extension_tag\\n self.extension_type = extension_type\\n\\n self.validate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1423bcbf7247629f38c7114ea3d0e44f\",\n \"score\": \"0.5256766\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TSFltV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca425a5ad2951db752f732f6c9b0c92\",\n \"score\": \"0.52368397\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dc8742dd43350c0e6701eddfc22006\",\n \"score\": \"0.5231864\",\n \"text\": \"def makeExtensionLink(self, newEntity=None):\\n if not newEntity:\\n newEntity = self.entity\\n return 'apiext:{}'.format(newEntity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f45cb1e74b82fcb8eb02ad4563d628\",\n \"score\": \"0.52308166\",\n \"text\": \"def set_extensions(manager, args, model, device, test_loader, \\n optimizer, loss_func, eval_func_dict={}):\\n \\n my_extensions = [\\n # # observe, report\\n ppe_extensions.observe_lr(optimizer=optimizer),\\n # ppe_extensions.ParameterStatistics(model, prefix='model'),\\n # ppe_extensions.VariableStatisticsPlot(model),\\n ppe_extensions.LogReport(),\\n # ppe_extensions.PlotReport(['train/loss', 'val/loss'], 'epoch', filename='loss.png'),\\n # ppe_extensions.PlotReport(['lr',], 'epoch', filename='lr.png'),\\n ppe_extensions.PrintReport([\\n 'epoch', 'iteration', 'lr', 'train/loss', 'val/loss', \\\"elapsed_time\\\"]),\\n # ppe_extensions.ProgressBar(update_interval=100),\\n\\n # evaluation\\n (\\n ppe_extensions.Evaluator(\\n test_loader, model,\\n eval_func=lambda data, target:\\n eval_for_batch(args, model, device, data, target, loss_func, eval_func_dict),\\n progress_bar=False),\\n (1, \\\"epoch\\\"),\\n ),\\n # save model snapshot.\\n (\\n ppe_extensions.snapshot(\\n target=model, filename=\\\"snapshot_epoch_{.updater.epoch}.pth\\\"),\\n ppe.training.triggers.MinValueTrigger(key=\\\"val/loss\\\", trigger=(1, 'epoch'))\\n ),\\n ]\\n \\n # set extensions to manager\\n for ext in my_extensions:\\n if isinstance(ext, tuple):\\n manager.extend(ext[0], trigger=ext[1])\\n else:\\n manager.extend(ext)\\n return manager\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6650d5b244a90ea6a51edca205766a\",\n \"score\": \"0.5228911\",\n \"text\": \"def createObject(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b391262d9f85f24b1327ae8b21c113\",\n \"score\": \"0.5217516\",\n \"text\": \"def __init__(self):\\n this = _openmoc.new_Point()\\n try:\\n self.this.append(this)\\n except:\\n self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52b757fb2f30f9b11ea22ffcab9b14\",\n \"score\": \"0.5213375\",\n \"text\": \"def create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d898783e8d2cf1b6d66bf7734cb69d1\",\n \"score\": \"0.5201633\",\n \"text\": \"def create(cls):\\n ret_val = gxapi_cy.WrapVVEXP._create(GXContext._get_tls_geo())\\n return GXVVEXP(ret_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1a9bfab86b4d555bf9afee41e3a873\",\n \"score\": \"0.51954705\",\n \"text\": \"def create_component(self, **args):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c82593d1b7bea79e185a28fcdafab61b\",\n \"score\": \"0.51925826\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltKdV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08202e897b964316726f62c8953c47d\",\n \"score\": \"0.51913023\",\n \"text\": \"def build_extension(self, ext: build_ext):\\n print('COPYING')\\n copy(ext.bin_dir + '/antsRegistration', 'antsbin/')\\n copy(ext.bin_dir + '/antsApplyTransforms', 'antsbin/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e6e43f60e6483602255480b4aac2a8\",\n \"score\": \"0.51878244\",\n \"text\": \"def extend(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6fb192442d1e90757afacd938a9880f\",\n \"score\": \"0.51868564\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltIntPrKdV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"200dca7c312f9e6bbb8cf593e8dde9a0\",\n \"score\": \"0.51608056\",\n \"text\": \"def extensions(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f34e47590748476b70d4eba5a7be471d\",\n \"score\": \"0.51592577\",\n \"text\": \"def new_extension_dict(self) -> 'DXFDictionary':\\n xdict = self.drawing.objects.add_dictionary(owner=self.dxf.handle)\\n self.set_app_data(ACAD_XDICTIONARY, [(360, xdict.dxf.handle)])\\n return xdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4174d376ee31abfe529a63af572d8d0\",\n \"score\": \"0.51518637\",\n \"text\": \"def __call__(self, prototype):\\n return self.create(prototype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf25fc4bfc86c3468d115c5d3fef265\",\n \"score\": \"0.515122\",\n \"text\": \"def create(self, prototype):\\n return self.cls(\\n name=prototype.name,\\n plugin_directory=prototype.plugin_directory,\\n plugin_name=prototype.plugin_name,\\n instance_id=prototype.uid,\\n config_ready=prototype.config_ready,\\n recovery_action=prototype.recovery_action,\\n start=prototype.start,\\n data_storetype=prototype.data_storetype,\\n mode_active=prototype.mode_active,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd4b66b827c95741ef5dd18a38f1094d\",\n \"score\": \"0.5148227\",\n \"text\": \"def create_example(self, data_sample):\\n\\n \\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd628557739bf1387adaa64293017ef\",\n \"score\": \"0.51469743\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltIntPrV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a465c10a32717e86f93ea8708beca9\",\n \"score\": \"0.5145585\",\n \"text\": \"def __init__(self):\\r\\n self.label = \\\"Access Point Placement to Signal Strength Field\\\"\\r\\n self.description = '''Takes a point feature class representing potential wifi access point locations \\r\\n and outputs raster features for each point representing wifi signal strength'''\\r\\n self.canRunInBackground = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc76b7d30f697c0ab46a731536caae53\",\n \"score\": \"0.5143688\",\n \"text\": \"def _creation_attributes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388b50c2a46115b7272eea3a61087328\",\n \"score\": \"0.51350296\",\n \"text\": \"def create(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d92ead3f0ca512f625aace023eb6b08\",\n \"score\": \"0.51239383\",\n \"text\": \"def create_instance(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92f9be0d331976693c12cbec0188eba\",\n \"score\": \"0.51228404\",\n \"text\": \"def entrypoint(self):\\n return self.__class__._entrypoint_registry.register\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef5bcb01a582bf0c683037d50efffbd\",\n \"score\": \"0.5094478\",\n \"text\": \"def _create_element_in_extension_(self, term, old_term_parent=None):\\n if old_term_parent is None or term.parent() is old_term_parent:\\n parent = self\\n else:\\n # Insert an 'if' here once terms can have different\\n # coefficient rings, as this will be for L-terms.\\n parent = self.change_parameter(\\n growth_group=term.parent().growth_group,\\n coefficient_ring=term.parent().coefficient_ring)\\n return parent(term, simplify=False, convert=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"503a121cf1caf27eadd494d3b3ed93b1\",\n \"score\": \"0.5090707\",\n \"text\": \"def test_create_profile_extension():\\n assert _heartbeat(\\n client.create_profile_extension(fixtures['profile_list_extension']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abee06cfe7dcc0c31d3cbcde859930b\",\n \"score\": \"0.5090653\",\n \"text\": \"def create_points_files(c):\\n \\n pkg_dir = str(Path(__file__).parent.resolve())\\n pkg = mp.open_package(pkg_dir)\\n points_logger.info(f\\\"Pkg dir: {pkg_dir}\\\")\\n\\n convert_pbf(c)\\n \\n pylib.build_points(pkg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a702edb314032188538f26c1d1adc4\",\n \"score\": \"0.507893\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TIntFltPrKdV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279affcfab7d68b232dbe192f95112b4\",\n \"score\": \"0.50762135\",\n \"text\": \"def __init__(self, polyglot, primary, address, name):\\n super(TemplateNode, self).__init__(polyglot, primary, address, name)\\n self.poly = polyglot\\n self.lpfx = '%s:%s' % (address,name)\\n self.poly.onStart(address, self.start)\\n self.poly.onPoll(self.poll)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6444571cb5e0739a1855ed70cda8c9ba\",\n \"score\": \"0.50719935\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TStrFltPrV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c672add52f9fe13b941dd9cd6d628ec\",\n \"score\": \"0.50669384\",\n \"text\": \"def add_source_extension(propname, proptype, vcount, valuetype, getter, setter):\\n propname = propname.upper()\\n if propname.startswith(\\\"al.AL_\\\"):\\n pname = propname[3:]\\n else:\\n pname = propname\\n propname = \\\"al.AL_%s\\\" % propname\\n if al.alIsExtensionPresent(pname) == al.AL_FALSE:\\n return False\\n\\n global _SOURCECALLBACKS\\n global _SOURCEPROPMAP\\n _SOURCECALLBACKS[proptype] = (vcount, valuetype, getter, setter)\\n _SOURCEPROPMAP[propname] = proptype\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85840d7f37266cea5063ac0d109b178d\",\n \"score\": \"0.5063235\",\n \"text\": \"def create_module(cls, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85840d7f37266cea5063ac0d109b178d\",\n \"score\": \"0.5063235\",\n \"text\": \"def create_module(cls, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c535e7c760ff2d0b442b87074bd9ee3\",\n \"score\": \"0.5057771\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n self.anchor = kwargs.pop('anchor', '')\\n super(CutExtension, self).__init__(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd7a1f355dee9f2ca7dc4374fa99c90\",\n \"score\": \"0.50532156\",\n \"text\": \"def msvcrxx__creat(jitter):\\n ret_ad, args = jitter.func_args_cdecl([\\\"filename\\\", \\\"pmode\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_cdecl(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dc0e7111d18cb53ba5650e994ccf39b\",\n \"score\": \"0.50530726\",\n \"text\": \"def __init__(self):\\n Token.set_extension(\\\"as_ptb\\\", default=None, force=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a33e54fb6814e527e9e42757b07a80b\",\n \"score\": \"0.50529075\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TIntPrFltKdV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfe710f6cf53fde81f770b7a81239dc\",\n \"score\": \"0.5052664\",\n \"text\": \"def genExtensionRange(self, x, t=0):\\n raise NotImplementedError(\\\"Extensions not supported\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52156b9481a3ad5925ab11f4d9f05d98\",\n \"score\": \"0.5049063\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n newobj = _core.new_Point(*args, **kwargs)\\n self.this = newobj.this\\n self.thisown = 1\\n del newobj.thisown\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc588eae512a204fae807ae4bc31332\",\n \"score\": \"0.5047302\",\n \"text\": \"def create_extensions(url: str):\\n with connect(url=url, autocommit=True) as cnx:\\n with cnx.cursor() as crs:\\n # Make sure the extensions are installed.\\n for sql in sql_phrasebook.create_extensions:\\n crs.execute(sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5cf0e294cea5518ca6cef8acb7c619\",\n \"score\": \"0.5040026\",\n \"text\": \"def make(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f73b0cb1f8eb55bbf5d8ee7db74825\",\n \"score\": \"0.50393397\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n spexxyObject.__init__(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50a9725e5a11e5985aece9e5cbc55c4\",\n \"score\": \"0.50324184\",\n \"text\": \"def create(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8202d4e3209a5ce8a3efbe844863a36\",\n \"score\": \"0.50321066\",\n \"text\": \"def add_extension(request_id, length, reason, custom_due_date, tz_name, content, method, letter_template_id):\\n request = Requests.query.filter_by(id=request_id).one()\\n previous_due_date = {\\\"due_date\\\": request.due_date.isoformat()}\\n new_due_date = _get_new_due_date(request_id, length, custom_due_date, tz_name)\\n days_until_due = calendar.busdaycount(datetime.utcnow(), new_due_date.replace(hour=23, minute=59, second=59))\\n if new_due_date < datetime.utcnow():\\n new_status = request_status.OVERDUE\\n elif days_until_due <= current_app.config['DUE_SOON_DAYS_THRESHOLD']:\\n new_status = request_status.DUE_SOON\\n else:\\n new_status = request_status.IN_PROGRESS\\n update_object(\\n {\\n 'due_date': new_due_date,\\n 'status': new_status\\n },\\n Requests,\\n request_id)\\n privacy = RELEASE_AND_PUBLIC\\n response = Determinations(\\n request_id,\\n privacy,\\n determination_type.EXTENSION,\\n reason,\\n new_due_date\\n )\\n if method == response_type.LETTER:\\n response.reason = 'A letter will be mailed to the requester.'\\n create_object(response)\\n create_response_event(event_type.REQ_EXTENDED, response, previous_value=previous_due_date)\\n if method == response_type.LETTER:\\n letter_template = LetterTemplates.query.filter_by(id=letter_template_id).one()\\n letter_id = _add_letter(request_id, letter_template.title, content, event_type.EXTENSION_LETTER_CREATED)\\n _create_communication_method(response.id, letter_id, response_type.LETTER)\\n letter = generate_pdf(content)\\n email_template = os.path.join(current_app.config['EMAIL_TEMPLATE_DIR'],\\n EMAIL_TEMPLATE_FOR_EVENT[event_type.EXTENSION_LETTER_CREATED])\\n email_content = render_template(email_template,\\n request_id=request_id,\\n agency_name=request.agency.name,\\n user=current_user\\n )\\n email_id = safely_send_and_add_email(request_id,\\n email_content,\\n 'Request {} Extended - Letter'.format(request_id),\\n to=get_assigned_users_emails(request_id),\\n attachment=letter,\\n filename=secure_filename('{}_extension_letter.pdf'.format(request_id)),\\n mimetype='application/pdf')\\n _create_communication_method(response.id, email_id, response_type.EMAIL)\\n else:\\n email_id = _send_response_email(request_id,\\n privacy,\\n content,\\n 'Request {} Extended'.format(request_id))\\n\\n _create_communication_method(response.id, email_id, response_type.EMAIL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db333f1fd333841e39749ac93724c1d\",\n \"score\": \"0.50291836\",\n \"text\": \"def create_point_source():\\n background_molecule = Molecule(\\n volume_fraction=1.0,\\n absorption_spectrum=AbsorptionSpectrumLibrary.CONSTANT_ABSORBER_ARBITRARY(1e-10),\\n scattering_spectrum=ScatteringSpectrumLibrary.CONSTANT_SCATTERING_ARBITRARY(1e-10),\\n anisotropy_spectrum=AnisotropySpectrumLibrary.CONSTANT_ANISOTROPY_ARBITRARY(1.0),\\n speed_of_sound=SPEED_OF_SOUND\\n )\\n background_dictionary = Settings()\\n background_dictionary[Tags.MOLECULE_COMPOSITION] = (MolecularCompositionGenerator().\\n append(background_molecule).\\n get_molecular_composition(-1))\\n background_dictionary[Tags.STRUCTURE_TYPE] = Tags.BACKGROUND\\n\\n vessel_molecule = Molecule(\\n volume_fraction=1.0,\\n absorption_spectrum=AbsorptionSpectrumLibrary.CONSTANT_ABSORBER_ARBITRARY(2),\\n scattering_spectrum=ScatteringSpectrumLibrary.CONSTANT_SCATTERING_ARBITRARY(100),\\n anisotropy_spectrum=AnisotropySpectrumLibrary.CONSTANT_ANISOTROPY_ARBITRARY(0.9),\\n speed_of_sound=SPEED_OF_SOUND+100,\\n density=1100\\n )\\n\\n vessel_1_dictionary = Settings()\\n vessel_1_dictionary[Tags.PRIORITY] = 3\\n vessel_1_dictionary[Tags.STRUCTURE_START_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2-10, 0, 35]\\n vessel_1_dictionary[Tags.STRUCTURE_END_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2-10, VOLUME_PLANAR_DIM_IN_MM, 35]\\n vessel_1_dictionary[Tags.STRUCTURE_RADIUS_MM] = SPACING\\n vessel_1_dictionary[Tags.MOLECULE_COMPOSITION] = (MolecularCompositionGenerator().\\n append(vessel_molecule).\\n get_molecular_composition(-1))\\n vessel_1_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True\\n vessel_1_dictionary[Tags.STRUCTURE_TYPE] = Tags.CIRCULAR_TUBULAR_STRUCTURE\\n\\n tissue_dict = Settings()\\n tissue_dict[Tags.BACKGROUND] = background_dictionary\\n tissue_dict[\\\"vessel_1\\\"] = vessel_1_dictionary\\n return tissue_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd976eb9986abcd22f0e58c9bb7e9a5\",\n \"score\": \"0.5027457\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TIntFltPrV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a7a2f73a19de89b2a0de937bb5d4bff\",\n \"score\": \"0.50253975\",\n \"text\": \"def create_setup(self):\\n return Setup(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4af06ad02e622b44df47a08160328762\",\n \"score\": \"0.5023678\",\n \"text\": \"def __init__(self, jsondict=None):\\n \\n self.code = None\\n \\\"\\\"\\\" Identifies the extension in this profile.\\n Type `str`. \\\"\\\"\\\"\\n \\n self.context = None\\n \\\"\\\"\\\" Where the extension can be used in instances.\\n List of `str` items. \\\"\\\"\\\"\\n \\n self.contextType = None\\n \\\"\\\"\\\" resource | datatype | mapping | extension.\\n Type `str`. \\\"\\\"\\\"\\n \\n self.definition = None\\n \\\"\\\"\\\" Definition of the extension and its content.\\n Type `ProfileExtensionDefnDefinition` (represented as `dict` in JSON). \\\"\\\"\\\"\\n \\n self.display = None\\n \\\"\\\"\\\" Use this name when displaying the value.\\n Type `str`. \\\"\\\"\\\"\\n \\n super(ProfileExtensionDefn, self).__init__(jsondict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd41ce413ac1cc77610ad41cb92cf7b\",\n \"score\": \"0.5020994\",\n \"text\": \"def create_experiment(self, context, request_specs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfcf5bc04b429c8aa7ec62f21099b84\",\n \"score\": \"0.5015555\",\n \"text\": \"def construct(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f614aaeec54c1777087fed7dc7d37570\",\n \"score\": \"0.50124156\",\n \"text\": \"def creator(cls):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df53171b3b6bb6986ae94ececc70b2fc\",\n \"score\": \"0.50094825\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltStrPrPrV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2259c35c896ea85ebc72ff2340e42d21\",\n \"score\": \"0.50088334\",\n \"text\": \"def create_main(mod):\\r\\n\\t\\r\\n\\ttpointer = Type.pointer(Type.pointer(types.int8, 0), 0)\\t\\r\\n\\tft = Type.function(types.integer,[ types.integer, tpointer ] )\\r\\n\\treturn mod.add_function(ft, \\\"main\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95bc1da1b35d8b74c9e38e033e3aabd\",\n \"score\": \"0.5002982\",\n \"text\": \"def _createModuleObj(self):\\n raise NotImplementedError(\\n \\\"Please implement _createModuleObj() in derived class.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b30385e1dede501f037d81982e7bae\",\n \"score\": \"0.5001066\",\n \"text\": \"def _create_objectives(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268c0db02ecce9d71013d948db913f0f\",\n \"score\": \"0.49979195\",\n \"text\": \"def create_extension_frame(self, ext_name):\\n f = ttk.Frame(self.details_frame, padding=10)\\n self.config_frame[ext_name] = f\\n entry_area = f\\n # create an entry for each configuration option\\n for row, opt in enumerate(self.extensions[ext_name]):\\n # create a row with a label and entry/checkbutton\\n label = ttk.Label(entry_area, text=opt['name'])\\n label.grid(row=row, column=0, sticky=NW)\\n var = opt['var']\\n if opt['type'] == 'bool':\\n ttk.Checkbutton(entry_area, variable=var,\\n onvalue='True', offvalue='False').grid(row=row,\\n column=1, sticky=W, padx=7)\\n elif opt['type'] == 'int':\\n ttk.Entry(entry_area, textvariable=var, validate='key',\\n validatecommand=(self.is_int, '%P')\\n ).grid(row=row, column=1, sticky=NSEW, padx=7)\\n\\n else:\\n ttk.Entry(entry_area, textvariable=var\\n ).grid(row=row, column=1, sticky=NSEW, padx=7)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2af81bb811973f83ebc44838d38b7f\",\n \"score\": \"0.4997399\",\n \"text\": \"def create(self, kind, key, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abed72fa254e08eb8228f4ea26c6aae\",\n \"score\": \"0.49925646\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TAscFltV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7cdff999058ec01ca735cfc7ad4b46\",\n \"score\": \"0.49912003\",\n \"text\": \"def create_instance(self, instance: 'core.RuntimeInstance') -> 'core.RuntimeInstance':\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766513b2529452445a80d2e73fcc6781\",\n \"score\": \"0.498717\",\n \"text\": \"def __add__(*args, **kwargs):\\n return _core.Point___add__(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7454644733ce35a8ea60036c258e53\",\n \"score\": \"0.49763882\",\n \"text\": \"def GenExt(self, *args):\\n return _snap.TFltIntKdV_GenExt(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b243d8ee9d9ed8a6141a85f967d5bf0\",\n \"score\": \"0.49738428\",\n \"text\": \"def make(self, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2be177c8c9112a655361da6d2cd27e2\",\n \"score\": \"0.49734607\",\n \"text\": \"def generate(env):\\n\\n # Create the PythonExtension builder\\n create_builder(env)\\n\\n trace ( \\\"Initialized UnitTest tool\\\", \\\"UnitTest\\\", 2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a93801f95630d629a98114955e1c1759\",\n \"score\": \"0.49697852\",\n \"text\": \"def _addPoint(self, obj, lon, lat):\\n pt = self.ElementTree.SubElement(obj, 'Point')\\n ptlon = self.ElementTree.SubElement(pt, 'PointLongitude')\\n ptlon.text = str(self.boundary[lon])\\n ptlat = self.ElementTree.SubElement(pt, 'PointLatitude')\\n ptlat.text = str(self.boundary[lat])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091f54c492dc64cf9141030118a0b444\",\n \"score\": \"0.49653772\",\n \"text\": \"def create(self, extend):\\n attributes = dumps(extend.attributes)\\n return db.execute('INSERT INTO extend (`type`, `label`, `field`, `key`, `attributes`) VALUES (%s, %s, %s, %s, %s)',\\n (extend.type, extend.label, extend.field, extend.key, attributes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6432a2c43480f55a6c6e8fd618e1dc14\",\n \"score\": \"0.4960268\",\n \"text\": \"def make_gui_point(self, parent):\\n\\n # create widgets\\n point_obj = LayerControl(parent, 'Points, map relative %s'\\n % str(MRPointShowLevels),\\n selectable=True)\\n\\n # tie to event handler(s)\\n point_obj.Bind(EVT_ONOFF, self.pointOnOff)\\n point_obj.Bind(EVT_SHOWONOFF, self.pointShowOnOff)\\n point_obj.Bind(EVT_SELECTONOFF, self.pointSelectOnOff)\\n\\n return point_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2429571b3ab1f34b4823c37dad09f7d\",\n \"score\": \"0.49543074\",\n \"text\": \"def setup(self):\\r\\n\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993694,"cells":{"query_id":{"kind":"string","value":"cba97e2777e69a0e8feafccd1b304c2b"},"query":{"kind":"string","value":"This method exports conf files to other members."},"positive_passages":{"kind":"list like","value":[{"docid":"ca49f261cf2744bbfc49d6263aa2dec2","score":"0.7163174","text":"def _export_conf_to_members(self, members):\n try:\n for member in members:\n COPY_CMD = (system.SEND_CONF_TO_SERVER % (system.VERTICA_CONF,\n member,\n system.VERTICA_CONF))\n system.shell_execute(COPY_CMD)\n except exception.ProcessExecutionError:\n LOG.exception(\"Cannot export configuration.\")\n raise","title":""}],"string":"[\n {\n \"docid\": \"ca49f261cf2744bbfc49d6263aa2dec2\",\n \"score\": \"0.7163174\",\n \"text\": \"def _export_conf_to_members(self, members):\\n try:\\n for member in members:\\n COPY_CMD = (system.SEND_CONF_TO_SERVER % (system.VERTICA_CONF,\\n member,\\n system.VERTICA_CONF))\\n system.shell_execute(COPY_CMD)\\n except exception.ProcessExecutionError:\\n LOG.exception(\\\"Cannot export configuration.\\\")\\n raise\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"53e11f1817e4cc0e5a994a7e9f2d97f4","score":"0.64293945","text":"def export_config(self, f_name=None):\n self.update_history()\n if f_name is not None:\n self.config.config_export(f_name)\n else:\n self.config.config_export(self.config.confname)","title":""},{"docid":"b67f9998ff554fd0528fcc74b7975252","score":"0.61478794","text":"def save_to_conf(self):\r\n raise NotImplementedError","title":""},{"docid":"c7b61954dc3e1abe030e785fc3ede90c","score":"0.5948918","text":"def assign_conf(self, conf):\n\n if isinstance(conf, str):\n conf = read_conf(conf)\n\n self.conf = conf\n\n try:\n self.archive_id = conf.getint('Archive', 'archive_id')\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Archive', attr))\n except configparser.Error:\n pass\n\n for attr in ['write_wcs_dir']:\n try:\n setattr(self, attr, conf.get('Files', attr))\n except configparser.Error:\n pass","title":""},{"docid":"b19aca0ff25a199bcaea80dc1823b510","score":"0.5835789","text":"def assign_conf(self, conf):\n\n if isinstance(conf, str):\n conf = read_conf(conf)\n\n self.conf = conf\n\n try:\n self.archive_id = conf.getint('Archive', 'archive_id')\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Archive', attr))\n except configparser.Error:\n pass\n\n for attr in ['sextractor_path', 'scamp_path', 'psfex_path',\n 'solve_field_path', 'wcs_to_tan_path']:\n try:\n setattr(self, attr, conf.get('Programs', attr))\n except configparser.Error:\n pass\n\n for attr in ['fits_dir', 'index_dir', 'gaia_dir', 'tycho2_dir',\n 'work_dir', 'write_log_dir', 'write_phot_dir',\n 'write_source_dir', 'write_wcs_dir',\n 'write_db_source_dir', 'write_db_source_calib_dir']:\n try:\n setattr(self, attr, conf.get('Files', attr))\n except configparser.Error:\n pass\n\n if self.write_log_dir:\n self.enable_log = True\n\n for attr in ['use_gaia_fits', 'use_tycho2_fits',\n 'use_ucac4_db', 'use_apass_db',\n 'enable_db_log', 'write_sources_csv']:\n try:\n setattr(self, attr, conf.getboolean('Database', attr))\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Database', attr))\n except configparser.Error:\n pass\n\n for attr in ['ucac4_db_host', 'ucac4_db_user', 'ucac4_db_name',\n 'ucac4_db_passwd', 'ucac4_db_table',\n 'apass_db_host', 'apass_db_user', 'apass_db_name',\n 'apass_db_passwd', 'apass_db_table',\n 'output_db_host', 'output_db_user',\n 'output_db_name', 'output_db_passwd']:\n try:\n setattr(self, attr, conf.get('Database', attr))\n except configparser.Error:\n pass\n\n for attr in ['use_filter', 'use_psf', 'circular_film',\n 'allow_force', 'repeat_find']:\n try:\n setattr(self, attr, conf.getboolean('Solve', attr))\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Solve', attr))\n except configparser.Error:\n pass\n\n for attr in ['plate_epoch', 'threshold_sigma',\n 'psf_threshold_sigma', 'psf_model_sigma',\n 'crossmatch_radius', 'crossmatch_nsigma',\n 'crossmatch_nlogarea', 'crossmatch_maxradius']:\n try:\n setattr(self, attr, conf.getfloat('Solve', attr))\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Solve', attr))\n except configparser.Error:\n pass\n\n for attr in ['sip', 'skip_bright', 'distort', 'subfield_distort',\n 'max_recursion_depth', 'force_recursion_depth',\n 'min_model_sources', 'max_model_sources']:\n try:\n setattr(self, attr, conf.getint('Solve', attr))\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Solve', attr))\n except configparser.Error:\n pass\n\n for attr in ['filter_path', 'astref_catalog', 'photref_catalog']:\n try:\n setattr(self, attr, conf.get('Solve', attr))\n except configparser.Error:\n pass\n\n # Read UCAC4 and APASS table column names from the dedicated sections,\n # named after the tables\n if conf.has_section(self.ucac4_db_table):\n for attr in self.ucac4_columns.keys():\n try:\n colstr = conf.get(self.ucac4_db_table, attr)\n _,typ = self.ucac4_columns[attr]\n self.ucac4_columns[attr] = (colstr, typ)\n except configparser.Error:\n pass\n\n if conf.has_section(self.apass_db_table):\n for attr in self.apass_columns.keys():\n try:\n colstr = conf.get(self.apass_db_table, attr)\n _,typ = self.apass_columns[attr]\n self.apass_columns[attr] = (colstr, typ)\n except configparser.Error:\n pass","title":""},{"docid":"38c049ac21e5a6eb9faf8dbba57e9a59","score":"0.5824831","text":"def __read_conf_file(self):\n\n conf_file_path = os.path.abspath(os.path.dirname(__file__)).split(\"modules\")[0] + \"/agent.conf\"\n self.log.debug(\"Config file path : %r\" % conf_file_path)\n self.__properties = ConfigParser.SafeConfigParser()\n self.__properties.read(conf_file_path)\n\n # set calculated values\n param_file = os.path.abspath(os.path.dirname(__file__)).split(\"modules\")[0] + \"/payload/launch-params\"\n self.__properties.set(\"agent\", constants.PARAM_FILE_PATH, param_file)\n\n plugins_dir = os.path.abspath(os.path.dirname(__file__)).split(\"modules\")[0] + \"/plugins\"\n self.__properties.set(\"agent\", constants.PLUGINS_DIR, plugins_dir)\n\n plugins_dir = os.path.abspath(os.path.dirname(__file__)).split(\"modules\")[0] + \"/extensions/py\"\n self.__properties.set(\"agent\", constants.EXTENSIONS_DIR, plugins_dir)","title":""},{"docid":"845ed9df169b569369f5213eb108be7f","score":"0.57778037","text":"def _config_files(self):\n files = [\"project_nginx.conf\", \"project_supervisor.conf\"]\n folder = os.path.join(self.projectfolder, \"config\")\n\n for conffile in files:\n templatefile = os.path.join(folder, conffile)\n\n self.write(templatefile, os.path.join(\"config\", conffile),\n self.context)","title":""},{"docid":"1a10b0bd49ab83fd7e08d53dda1e5bd1","score":"0.5775637","text":"def _export_config(self):\n exporter = ModelProtoExporter(self.target_dir, self.model_info, self.get_platform)\n exporter.export()","title":""},{"docid":"c6010afffb9e8947d0c258c61ccd8021","score":"0.577253","text":"def __init__(self):\n super().__init__()\n self._filename = \"config\"","title":""},{"docid":"642605f2cc226269761f93b73a6a6abe","score":"0.57679194","text":"def _dump(self):\n filename = os.path.expanduser('~/.mezzanine.cfg')\n parser = Parser()\n parser.add_section('general')\n parser.read(filename)\n parser.set('general', 'refresh_token', self.session.token['refresh_token'])\n with open(filename, 'w') as config_file:\n parser.write(config_file)","title":""},{"docid":"2d1c12e4335a1007c04984af9ef7fb82","score":"0.5765398","text":"def save_conf(self, dev, conf):\n pass","title":""},{"docid":"dbebd73ce7765142fdc3d3361f38d1ab","score":"0.57478935","text":"def write_conf(self):\n\n self.io.row_list = []\n try:\n os.makedirs(os.path.dirname(self.config_file))\n except IOError:\n logger.info(\"Error while trying to create config \"\n \"path as {}\".format(self.config_file))\n except OSError:\n logger.info(\"The config directory already exists.\")\n config = ConfigParser.RawConfigParser()\n for section in CONFIG_SECTIONS:\n config.add_section(section)\n\n for key in self.config.keys():\n if key in RIG_URI_CONFIG:\n config.set(\"Rig URI\", key, self.config[key])\n if key in MONITOR_CONFIG:\n config.set(\"Monitor\", key, self.config[key])\n if key in MAIN_CONFIG:\n config.set(\"Main\", key, self.config[key])\n if key in SCANNING_CONFIG:\n config.set(\"Scanning\", key, self.config[key])\n\n with open(self.config_file, \"wb\") as cf:\n config.write(cf)","title":""},{"docid":"791b39d29f156ad0055810688b0f06e7","score":"0.574321","text":"def export():","title":""},{"docid":"791b39d29f156ad0055810688b0f06e7","score":"0.574321","text":"def export():","title":""},{"docid":"791b39d29f156ad0055810688b0f06e7","score":"0.574321","text":"def export():","title":""},{"docid":"18d7c7352cd2b753eb3e161746be2310","score":"0.5726024","text":"def configure(self, conf):\n return","title":""},{"docid":"18d7c7352cd2b753eb3e161746be2310","score":"0.5726024","text":"def configure(self, conf):\n return","title":""},{"docid":"760f4e4d3cd6bc07213e7d440369ea5d","score":"0.5714147","text":"def _extend_config(self):\n # Split made between project_name, module_name, app_id as these are seperate entries. However for now set as project_name\n # This allows more finegrained control of these config items in future releases if required. \n module_name = self.config.get_entry( ConfigConstants.CONFIG_KEY_MODULE_NAME )\n \n # Specific folder locations for the different category of artifacts.\n output_path_hana = os.path.join(self.config.get_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH ), ConfigConstants.HANA_BASE_PATH )\n output_path_module = os.path.join(output_path_hana, module_name)\n output_path_module_src = os.path.join(output_path_module, ConfigConstants.MODULE_SOURCE_PATH)\n module_template_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), ConfigConstants.PROJECT_TEMPLATEDR)\n grants_path = os.path.join(output_path_module_src, ConfigConstants.GRANTS_SOURCE_PATH)\n synonyms_path = os.path.join(output_path_module_src, ConfigConstants.SYNONYMS_SOURCE_PATH)\n procedures_path = os.path.join(output_path_module_src, ConfigConstants.PROCEDURES_SOURCE_PATH)\n roles_path = os.path.join(output_path_module_src, ConfigConstants.ROLES_SOURCE_PATH)\n cds_path = os.path.join(output_path_module_src, ConfigConstants.CDS_SOURCE_PATH)\n\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_HANA, output_path_hana)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_MODULE, output_path_module)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_MODULE_SRC, output_path_module_src)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_MODULE_TEMPLATE_PATH, module_template_path)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_GRANTS_PATH, grants_path) \n self.config.add_entry( ConfigConstants.CONFIG_KEY_SYNONYMS_PATH, synonyms_path)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_PROCEDURES_PATH, procedures_path)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_ROLES_PATH, roles_path)\n self.config.add_entry( ConfigConstants.CONFIG_KEY_CDS_PATH, cds_path)","title":""},{"docid":"1fcf4697e4e8bb1027a2846d8c35eccd","score":"0.5712404","text":"def write(self):\n\t\twith open(self.path) as configFile:\n\t\t\tself.config.write(configFile)","title":""},{"docid":"7ffe75a9b57de50d035ea275566d5820","score":"0.5706289","text":"def export(self):\n pass","title":""},{"docid":"f2b65498eff9a17070436927e1cf01b9","score":"0.568244","text":"def _writeConfig(self, filename):\n f = open(filename, \"wb\")\n f.write(self._getConfig())\n f.close()\n\n directory = os.path.dirname(filename)\n for d in self.datasets:\n d._writeGeneratedFiles(directory)\n\n print \"Wrote multicrab configuration to %s\" % filename","title":""},{"docid":"ff6f0cdf46bcbc876d1480e08fd11004","score":"0.5678342","text":"def _write(self):\n with open(self.path, 'w') as configfile:\n self.cfg.write(configfile)","title":""},{"docid":"9d871944e0f24cf7eca38ed8dc296361","score":"0.5660896","text":"def _copy_config(self):\n\n\t\tout_config_filename = os.path.join(self.experiment_dir, 'config.json')\n\t\ttempOrderedDict = collections.OrderedDict()\n\t\tfor key in self.cfg.keys():\n\t\t\ttempOrderedDict[key] = self.cfg[key]\n\t\twith open(out_config_filename, 'w') as outfile:\n\t\t\tjson.dump(tempOrderedDict, outfile)\n\t\tthis_filename = sys.argv[0]\n\t\tout_train_filename = os.path.join(self.experiment_dir, 'training_script.py')\n\t\tshutil.copyfile(this_filename, out_train_filename)\n\t\tout_architecture_filename = os.path.join(self.experiment_dir, 'architecture.json')\n\t\tjson.dump(self.cfg['gqcnn_config']['architecture'], open(out_architecture_filename, 'w'))","title":""},{"docid":"bbf29013e33e9947a3b7760c6f0e80eb","score":"0.56565434","text":"def setting_by_cfg_file(self, conf):\n cfg = conf.get(\"default\")\n check_dict = ConfigChecker.check_env(cfg, env_name=\"default\", update_default=True)\n self.__dict__.update(check_dict)\n \n self.project_path = Path(self.project_path)\n self.data_path = Path(self.data_path)\n\n if conf.get(\"experiment\") is not None:\n cfg = conf.get(\"experiment\")\n check_dict = ConfigChecker.check_env(cfg, env_name=\"experiment\", update_default=True)\n self.__dict__.update(check_dict)\n self.exp_path = self.project_path / \"checkpoints\" / self.eval_type\n ConfigChecker.check_dir_exist(self.exp_path, file=False)\n else:\n print(\"No Configure for `experiment`\")\n\n if conf.get(\"postprocessor\") is not None:\n cfg = conf.get(\"postprocessor\")\n check_dict = ConfigChecker.check_env(cfg, env_name=\"postprocessor\", update_default=True)\n else:\n check_dict = ConfigChecker.checkdefault_generator(\"postprocessor\")\n self.__dict__.update(check_dict)","title":""},{"docid":"3517d4ce0dd0f8ea1bbec4ce1fa227af","score":"0.56503063","text":"def write(self):\n for section in self.sections.itervalues():\n section.write(self.config_parser)\n\n with open(self.file_path, 'wb') as config_file:\n self.config_parser.write(config_file)","title":""},{"docid":"c3fbe270e18077982e7c58c291af453b","score":"0.56486994","text":"def init_conf(argv):\n conf = ConfigParser()\n conf.read(argv[1])\n CONF['logfile'] = conf.get('export', 'logfile')\n CONF['magic_number'] = unhexlify(conf.get('export', 'magic_number'))\n CONF['db'] = conf.getint('export', 'db')\n CONF['debug'] = conf.getboolean('export', 'debug')\n CONF['export_dir'] = conf.get('export', 'export_dir')\n CONF['exclude_uagent_string'] = conf.get('export', 'exclude_uagent_string').strip().split(\"\\n\")\n if not os.path.exists(CONF['export_dir']):\n os.makedirs(CONF['export_dir'])","title":""},{"docid":"852648a3bd469f9c3242d4c63854ced0","score":"0.56245977","text":"def __save_config(self) -> None:\n #TODO\n pass","title":""},{"docid":"8935be3016765f0b523295938d3d351c","score":"0.56119084","text":"def setup_allowed_and_configs(cls, allowed_fname, configs_fname):\n with open(allowed_fname, 'w') as out:\n out.write('CONFIG_OLD_ONE')\n with open(configs_fname, 'w') as out:\n out.write('\\n'.join(['CONFIG_OLD_ONE', 'CONFIG_NEW_ONE',\n 'CONFIG_MY_KCONFIG']))","title":""},{"docid":"ab4913c679fda8bb45e65fe7242a88bf","score":"0.5602432","text":"def get_postprocess_config_files(self):\n raise NotImplementedError()","title":""},{"docid":"75f77aae1b71a57050ab266e931af402","score":"0.5593669","text":"def write(self):\n self.name = self[\"name\"]\n PyConfig.write(self)\n self.convirt_generated = True","title":""},{"docid":"de2fd750df16c7e29fe2385e7140d8e9","score":"0.5585339","text":"def _prepare(self):\n logging.info('-> copy configuration...')\n\n self._copy_config_to_expt('path_config')","title":""},{"docid":"16ed173af5ebb7f68aac3d49cb681304","score":"0.55760974","text":"def __init__(self):\n\n configpath = self.get_configpath()\n self.read(configpath)\n if not os.path.exists(configpath):\n self.write(configpath)","title":""},{"docid":"8f4a041ac993c728677ee719e8dcdd67","score":"0.55397725","text":"def __configure(self):\n home_dir = os.path.expanduser('~')\n stripped_root = os.path.basename(os.path.normpath(self.__root))\n conf_path = os.path.join(home_dir, '.conf')\n conf_path = os.path.join(conf_path, 'pysync')\n self.__conf_path = os.path.join(conf_path, stripped_root)\n os.makedirs(self.__conf_path, exist_ok=True)\n self.__conf['MAC'] = ':'.join(re.findall('..', '%012x' % uuid.getnode())),\n if self.__conf['backup']:\n if self.__conf['backup_path'] == 'DEFAULT':\n self.__conf['backup_path'] = os.path.join(self.__conf_path, 'backups')\n os.makedirs(self.__conf['backup_path'], exist_ok=True)","title":""},{"docid":"434b62d444eafa298b47cbdbcf7b53d7","score":"0.55269283","text":"def saveconf(self):\n try:\n self._config['SERIALS'] = {serial:0 for serial in self.config['serials']}\n print (configuration_file)\n self._config.write(open(configuration_file,'wt'))\n # os.sys.exit(-1)\n except Exception,e:\n print e","title":""},{"docid":"6cb649a4dbf8261b32853e97b961a015","score":"0.5522753","text":"def ExportSetup(self):\n pass","title":""},{"docid":"470050f715da8b0ce73bacfefa4b957b","score":"0.55209905","text":"def write(self, path):\n cp = ConfigParser.SafeConfigParser()\n # XXX the use of the disabled_auth file can be removed when\n # compatibility with the older code is no longer needed\n da = ConfigParser.SafeConfigParser()\n cp.optionxform = str # preserve option case\n\n # For compatibility, the preferred-publisher is written out\n # as the preferred-authority. Modify a copy so that we don't\n # change the in-memory copy.\n props = self.properties.copy()\n try:\n del props[\"preferred-publisher\"]\n except KeyError:\n pass\n props[\"preferred-authority\"] = str(self.__publisher_search_order[0])\n props[\"publisher-search-order\"] = str(self.__publisher_search_order)\n\n cp.add_section(\"property\")\n for p in props:\n cp.set(\"property\", p, props[p].encode(\"utf-8\"))\n\n cp.add_section(\"variant\")\n for f in self.variants:\n cp.set(\"variant\", f, str(self.variants[f]))\n\n cp.add_section(\"facet\")\n\n for f in self.facets:\n\n cp.set(\"facet\", f, str(self.facets[f]))\n\n for prefix in self.__publishers:\n pub = self.__publishers[prefix]\n section = \"authority_%s\" % pub.prefix\n\n c = cp\n if pub.disabled:\n c = da\n\n c.add_section(section)\n c.set(section, \"alias\", str(pub.alias))\n c.set(section, \"prefix\", str(pub.prefix))\n c.set(section, \"disabled\", str(pub.disabled))\n c.set(section, \"sticky\", str(pub.sticky))\n\n repo = pub.selected_repository\n\n # For now, write out \"origin\" for compatibility with\n # older clients in addition to \"origins\". Older\n # clients may drop the \"origins\" when rewriting the\n # configuration, but that doesn't really break\n # anything.\n c.set(section, \"origin\", repo.origins[0].uri)\n\n c.set(section, \"origins\",\n str([u.uri for u in repo.origins]))\n c.set(section, \"mirrors\",\n str([u.uri for u in repo.mirrors]))\n\n if repo.system_repo:\n c.set(section, \"sysrepo.uri\",\n str(repo.system_repo))\n c.set(section, \"sysrepo.sock_path\",\n repo.system_repo.socket_path)\n else:\n c.set(section, \"sysrepo.uri\", \"None\")\n c.set(section, \"sysrepo.sock_path\", \"None\")\n\n #\n # For zones, where the reachability of an absolute path\n # changes depending on whether you're in the zone or\n # not. So we have a different policy: ssl_key and\n # ssl_cert are treated as zone root relative.\n #\n ngz = self.variants.get(\"variant.opensolaris.zone\",\n \"global\") == \"nonglobal\"\n p = str(pub[\"ssl_key\"])\n if ngz and self.__imgroot != os.sep and p != \"None\":\n # Trim the imageroot from the path.\n if p.startswith(self.__imgroot):\n p = p[len(self.__imgroot):]\n # XXX this should be per origin or mirror\n c.set(section, \"ssl_key\", p)\n p = str(pub[\"ssl_cert\"])\n if ngz and self.__imgroot != os.sep and p != \"None\":\n if p.startswith(self.__imgroot):\n p = p[len(self.__imgroot):]\n # XXX this should be per origin or mirror\n c.set(section, \"ssl_cert\", p)\n\n # XXX this should really be client_uuid, but is being\n # left with this name for compatibility with older\n # clients.\n c.set(section, \"uuid\", str(pub.client_uuid))\n\n # Write selected repository data.\n # XXX this is temporary until a switch to a more\n # expressive configuration format is made.\n repo = pub.selected_repository\n repo_data = {\n \"collection_type\": repo.collection_type,\n \"description\": repo.description,\n \"legal_uris\": [u.uri for u in repo.legal_uris],\n \"name\": repo.name,\n \"refresh_seconds\": repo.refresh_seconds,\n \"registered\": repo.registered,\n \"registration_uri\": repo.registration_uri,\n \"related_uris\": [u.uri for u in repo.related_uris],\n \"sort_policy\": repo.sort_policy,\n }\n\n for key, val in repo_data.iteritems():\n c.set(section, \"repo.%s\" % key, str(val))\n\n # XXX Child images\n\n for afile, acp in [(CFG_FILE, cp), (DA_FILE, da)]:\n thefile = os.path.join(path, afile)\n if len(acp.sections()) == 0:\n if os.path.exists(thefile):\n portable.remove(thefile)\n continue\n try:\n f = open(thefile, \"w\")\n except EnvironmentError, e:\n if e.errno == errno.EACCES:\n raise api_errors.PermissionsException(\n e.filename)\n if e.errno == errno.EROFS:\n raise api_errors.ReadOnlyFileSystemException(\n e.filename)\n raise\n acp.write(f)","title":""},{"docid":"808e91790dd50e465812c84fb9060a44","score":"0.5520405","text":"def dump(self, conf: \"ONDict\", schema: Optional[\"ONDict\"] = None):\n with open(self, \"w\") as f:\n self.module.dump(conf, f, schema)","title":""},{"docid":"d5531e9862a3c34e7deac9676fe0c727","score":"0.55201316","text":"def _save_conf_file(self):\n path = os.path.join(self._result_log_base_path, \"config.json\")\n with open(path, \"w\") as f:\n f.write(json.dumps(self._conf, indent=4))","title":""},{"docid":"3b2a59d2d3841a3aa23e206bce81afac","score":"0.54912245","text":"def save_config(self) -> None:\n with open(self.SETUP_CONF, \"w\") as configfile:\n self.config.write(configfile)","title":""},{"docid":"465b135b22f7a92c60dd6f2c3ffd13bf","score":"0.54902333","text":"def write(self, config: Config):","title":""},{"docid":"c420ab6d5693d174d45797da38c4b058","score":"0.5472056","text":"def _prepare(self):\n logging.info('-> copy configuration...')\n\n self._copy_config_to_expt('path_config_bUnwarpJ')\n if 'path_config_IJ_SIFT' in self.params:\n self._copy_config_to_expt('path_config_IJ_SIFT')\n if 'path_config_IJ_MOPS' in self.params:\n self._copy_config_to_expt('path_config_IJ_MOPS')","title":""},{"docid":"9079180c2af6f1d241e28ad5de1418a5","score":"0.54701686","text":"def genconf(self, _conf: dict):\n result = self.__gen_conf_file()\n\n if result:\n print(f\"Made {', '.join(result)} successfully\")\n else:\n print(f\"There were configuration files already.\")","title":""},{"docid":"58c22e80c0686a4fa96db8594c8b8c28","score":"0.5469064","text":"def writeConfigToStore(self):\n with self.writeSharedFileStream(\"config.pickle\", isProtected=False) as fileHandle:\n cPickle.dump(self.__config, fileHandle, cPickle.HIGHEST_PROTOCOL)","title":""},{"docid":"c45262bd712e231176fb3b65cce8b22d","score":"0.54503626","text":"def config_dump():\n # TODO - This seems like a candidate to be part of Config class","title":""},{"docid":"c21366e002c98428dd963858e1a24c77","score":"0.5435241","text":"def configs_to_export(context):\n config = {}\n hcpdiff_config = {\"config\": config}\n for key in [\"RegName\", \"Subject\", \"DWIName\"]:\n if key in context.config.keys():\n config[key] = context.config[key]\n\n hcpdiff_config_filename = op.join(\n context.work_dir,\n context.config[\"Subject\"],\n \"{}_{}_hcpfunc_config.json\".format(\n context.config[\"Subject\"], context.config[\"DWIName\"]\n ),\n )\n\n return hcpdiff_config, hcpdiff_config_filename","title":""},{"docid":"3efa1a6ea33dbf2e1abc194440b2b71c","score":"0.54306775","text":"def write_config(self, filename):\n # import json\n # with open(filename, 'w') as fp:\n # json.dump(self.config.dict(), fp)\n self.config.filename = filename\n self.config.write()","title":""},{"docid":"1218d74bfbc5e9f499ceee23eff096d5","score":"0.5391969","text":"def write(self):\n # Not needed for gconf\n pass","title":""},{"docid":"8fccce06c1e500cb114e84b103dec368","score":"0.5389813","text":"def __init__(self):\n self.config = RawConfigParser()\n self.config.read('./configfiles/serialize_conf.cfg')","title":""},{"docid":"c51febfa66a99711ad312f7750fd824f","score":"0.536987","text":"def save(self):# WIP\n logging.debug('Saving current configuration to self.config_path = {0!r}'.format(self.config_path))\n # Collect data together.\n config_data_out = {\n 'csv_filepath': self.csv_filepath,\n 'images_dir': self.images_dir,\n 'zip_path': self.zip_path,\n 'board_name': self.board_name,\n }\n logging.debug('Saving config_data_out = {0!r}'.format(config_data_out))\n # Write data to file.\n with open(self.config_path, 'wb') as save_f:\n yaml.dump(\n data=config_data_out,\n stream=save_f,\n explicit_start=True,# Begin with '---'\n explicit_end=True,# End with '...'\n default_flow_style=False# Output as multiple lines\n )\n return","title":""},{"docid":"e2030559d3eb108e495464bfad687562","score":"0.5368343","text":"def serialize(self):\n\t\tassert ltrace(globals()['TRACE_' + self.name.upper()], '| serialize()')\n\t\twith self.lock:\n\t\t\tif self.changed:\n\n\t\t\t\ttry:\n\t\t\t\t\t#raise the hint level, so as the MOVED_TO event will make it\n\t\t\t\t\t# just back to the state where only ONE event will suffice to\n\t\t\t\t\t# trigger it again.\n\t\t\t\t\tself.conf_hint += 1\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\n\t\t\t\t#print '>> serialize', self.conf_hint\n\n\t\t\t\tftemp, fpath = tempfile.mkstemp(dir=settings.config_dir)\n\n\t\t\t\tos.write(ftemp, '%s\\n' % '\\n'.join(sorted(self.iterkeys())))\n\t\t\t\tos.fchmod(ftemp, 0644)\n\t\t\t\tos.fchown(ftemp, 0, 0)\n\t\t\t\tos.close(ftemp)\n\n\t\t\t\tos.rename(fpath, self.conf_file)\n\t\t\t\tself.changed = False","title":""},{"docid":"098c7289d4cfb0922d29179e5a726bc0","score":"0.53549963","text":"def config():","title":""},{"docid":"3f23d51135e008df8bcbe1c48851137b","score":"0.535434","text":"def config(self):\n pass","title":""},{"docid":"272855bacbee19ab6a037f5cbc8ab885","score":"0.53530526","text":"def write(self):\n with open(self.configfile, 'w') as cfgfile:\n self.config.write(cfgfile)","title":""},{"docid":"d3ff1e890e82ba732b72cd8bd185910b","score":"0.53521955","text":"def __save_file(self, filename):\n\t\ttry:\n\t\t\t# open temporary file for writing\n\t\t\treg_file = open(filename, 'w')\n\t\t\t# write data to file\n\t\t\treg_file.write('# univention_ base.conf\\n\\n')\n\t\t\treg_file.write(self.__str__())\n\t\t\t# flush (meta)data\n\t\t\treg_file.flush()\n\t\t\tos.fsync(reg_file.fileno())\n\t\t\t# close fd\n\t\t\treg_file.close()\n\t\texcept EnvironmentError, ex:\n\t\t\t# suppress certain errors\n\t\t\tif ex.errno != errno.EACCES:\n\t\t\t\traise","title":""},{"docid":"fc10c0bdaa787641a2bf9ed40605cf43","score":"0.5345368","text":"def __init__(self):\n # get directory name\n basedir = os.path.dirname(os.path.abspath(__file__))\n self.__filename = basedir + \"/etc/alarmforwarder.conf\"\n self.__config = configparser.ConfigParser()\n self.__config.read(self.__filename)","title":""},{"docid":"7f1a0be37effff52bc2a209c90b687c0","score":"0.53442496","text":"def SaveConfig(self):\n LOG.info('Saving Config - Version:{}'.format(__version__))\n for l_module in self.m_parts_apis.values():\n l_module.SaveConfig()\n for l_module in self.m_module_apis.values():\n l_module.SaveConfig()\n LOG.info(\"Saved Config.\")","title":""},{"docid":"4da93943f4af4196fa82a91c1f5c6377","score":"0.53321433","text":"def write(self):\n\n self.configobj.write()","title":""},{"docid":"a5bae1fc0afcb5cb2d99cf6787a98d37","score":"0.5331288","text":"def write(self):\n return self.conf.write(open(self.path, 'w'))","title":""},{"docid":"0e713e083fa7ae6cc70c0e52980336a6","score":"0.5325092","text":"def load_from_conf(self):\r\n raise NotImplementedError","title":""},{"docid":"59889e159f5e1c64639db9b23ed15209","score":"0.53057945","text":"def save_config(self):\r\n r = self.resources\r\n self.cfg_listeners.write(r.listeners.get_data())\r\n self.cfg_registration.write(r.registration.get_data())","title":""},{"docid":"eb0b1f03f523df6dc0986bc7d631af5e","score":"0.52941704","text":"def configure(self):","title":""},{"docid":"6e4b994141c4364fb2695a7d3ad9eba8","score":"0.52872485","text":"def apply_config(self):\n pass","title":""},{"docid":"a4143eaf752228a3e37a955ed1e14f45","score":"0.52825373","text":"def save(self):\n logging.debug('Saving current configuration to self.config_path = {0!r}'.format(self.config_path))\n # Collect data together.\n config_data_out = {\n 'csv_filepath': self.csv_filepath,\n 'images_dir': self.images_dir,\n 'zip_path': self.zip_path,\n 'board_name': self.board_name,\n }\n logging.debug('Saving config_data_out = {0!r}'.format(config_data_out))\n # Write data to file.\n with open(self.config_path, 'wb') as save_f:\n yaml.dump(\n data=config_data_out,\n stream=save_f,\n explicit_start=True,# Begin with '---'\n explicit_end=True,# End with '...'\n default_flow_style=False# Output as multiple lines\n )\n return","title":""},{"docid":"00e77b7b2430052b32a911d854a16caf","score":"0.52820677","text":"def save(self):\n t = self._file\n with open(t, \"w\", encoding=\"utf-8\") as f:\n json.dump(self._config, f, ensure_ascii=True, indent=4)\n\n if not os.path.exists(join(self._config[\"PROJECT\"][\"path\"], 'project.ini')):\n for x in self.project_ini.sections():\n for y in self.project_ini.items(x):\n v = str(self._config[x][y[0]])\n self.project_ini[x][y[0]] = v\n with open(join(self._config[\"PROJECT\"][\"path\"], 'project.ini'), 'w', encoding=\"utf-8\") as configfile:\n self._ini_project.write(configfile)\n\n if not os.path.exists(join(self._config[\"PROJECT\"][\"path\"], 'secret.ini')):\n for x in self.project_secret_ini.sections():\n for y in self.project_secret_ini.items(x):\n v = str(self._config[x][y[0]])\n self.project_secret_ini[x][y[0]] = v\n with open(join(self._config[\"PROJECT\"][\"path\"], 'secret.ini'), 'w', encoding=\"utf-8\") as configfile:\n self._ini_secret.write(configfile)\n\n for a in self.backend_ini:\n if not os.path.exists(join(self._config[\"PROJECT\"][\"path\"], \"backapps\", a, 'app.ini')):\n for x in self.backend_ini[a].sections():\n if x == \"BACKEND\":\n for y in self.backend_ini[a].items(x):\n v = str(self._config['BACKEND'][a][y[0]])\n self.backend_ini[a][x][y[0]] = v\n with open(join(self._config[\"PROJECT\"][\"path\"], \"backapps\", a, 'app.ini'), 'w', encoding=\"utf-8\") as configfile:\n self.backend_ini[a].write(configfile)\n\n for a in self.frontend_ini:\n if not os.path.exists(join(self._config[\"PROJECT\"][\"path\"], \"frontapps\", a, 'app.ini')):\n for x in self.frontend_ini[a].sections():\n if x == \"FRONTEND\":\n for y in self.frontend_ini[a].items(x):\n v = str(self._config['FRONTEND'][a][y[0]])\n self.frontend_ini[a][x][y[0]] = v\n with open(join(self._config[\"PROJECT\"][\"path\"], \"frontapps\", a, 'app.ini'), 'w', encoding=\"utf-8\") as configfile:\n self.frontend_ini[a].write(configfile)","title":""},{"docid":"2664c62237247e9723b27ec71c6e8e2e","score":"0.52759707","text":"def save(self):\n self.config.write(self.file)","title":""},{"docid":"6ad6beb440bd362835423396cfa41fc0","score":"0.5269988","text":"def write_conf(run_dir, param):\n cname = param[\"sbatch\"].get(\"conf_name\", \"overwritable_conf\")\n if not cname.endswith(\".yaml\"):\n cname += \".yaml\"\n\n with open(run_dir / cname, \"w\") as f:\n yaml.dump(param[\"config\"], f, default_flow_style=False)\n return run_dir / cname","title":""},{"docid":"cad00c974959668574cc9fe63b21a1cc","score":"0.5261115","text":"def pass_configuration(config):\n global modules\n\n print 'Passing configuration'\n for section in config:\n module = config[section]['module']\n modules[module].pass_config(section, config[section])\n\n print 'Running setup'\n for key in modules:\n modules[key].setup()","title":""},{"docid":"087623e1119f721bd3327faa7dde57ea","score":"0.52575636","text":"def write_config(self):\n assert self.config_file_path is not None, 'config_file_path not set'\n with open(self.config_file_path, 'wb') as config_file:\n self.config.write(config_file)","title":""},{"docid":"fb6b3642d38cb4c4ef4685538d60857e","score":"0.5255247","text":"def __gen_config_file(self):\n \n config_file = '# Platform: %s\\n' % platform.platform(aliased=True)\n config_file += '# Processor: %s\\n' % platform.processor()\n \n try:\n config_file += '# Number of CPUs: %s\\n' % os.sysconf('SC_NPROCESSORS_ONLN')\n #config_file += '# Size of word: %s bits\\n' % os.sysconf('SC_WORD_BIT')\n except:\n pass\n \n config_file += '\\n\\n'\n \n # original file\n first_column = ['* Original File *', '']\n \n # size of original file\n second_column = ['* Size (KB) *', '']\n \n # include or not\n third_column = ['* Include? *', '']\n \n # configuration file\n #fourth_column = ['* Config File? *', '']\n \n # file in the reproducible folder\n #fifth_column = ['* Package File *', '']\n \n ########################################################################\n \n # function used to include information inside the configuration file\n def include_in_config(title, file_dict):\n \"\"\"\n 'title' -> class of file (main program, ...)\n 'file_dict' -> dictionary with files\n \n Returns lists for first, second, third and fourth columns of\n configuration file.\n \"\"\"\n first_column = []\n second_column = []\n third_column = []\n #fourth_column = []\n #fifth_column = []\n \n first_column.append('[%s]' %title)\n second_column.append('')\n third_column.append('')\n #fourth_column.append('')\n #fifth_column.append('')\n \n for file_ in file_dict:\n size = ''\n try:\n size = '%0.2f' %(os.path.getsize(file_)/1024.0)\n except:\n size = 'N/A'\n \n first_column.append(file_)\n second_column.append(size)\n third_column.append('Y')\n #fourth_column.append('N')\n #fifth_column.append(file_dict[file_])\n \n if self.__symlink_to_target.has_key(file_):\n size = ''\n try:\n size = '%0.2f' %(os.path.getsize(self.__symlink_to_target[file_])/1024.0)\n except:\n size = 'N/A'\n \n first_column.append(self.__symlink_to_target[file_])\n second_column.append(size)\n third_column.append('Y')\n #fourth_column.append('N')\n #fifth_column.append(self.__targets[self.__symlink_to_target[file_]])\n \n first_column.append('')\n second_column.append('')\n third_column.append('')\n #fourth_column.append('')\n #fifth_column.append('')\n \n return (first_column, second_column, third_column)\n #return (first_column, second_column, third_column, fourth_column)\n \n ########################################################################\n \n # main program\n if len(self.__main_program) == 1:\n (first, second, third) = include_in_config('main program',\n self.__main_program)\n first_column += first\n second_column += second\n third_column += third\n #fourth_column += fourth\n \n # child programs\n if len(self.__child_programs) > 0:\n (first, second, third) = include_in_config('other programs',\n self.__child_programs)\n first_column += first\n second_column += second\n third_column += third\n #fourth_column += fourth\n \n # main input files\n if len(self.__input_files) > 0:\n (first, second, third) = include_in_config('main input files',\n self.__input_files)\n first_column += first\n second_column += second\n third_column += third\n #fourth_column += fourth\n \n # child input files\n if len(self.__child_input_files) > 0:\n (first, second, third) = include_in_config('other input files',\n self.__child_input_files)\n first_column += first\n second_column += second\n third_column += third\n #fourth_column += fourth\n \n # dependencies\n if len(self.__dependencies) > 0:\n (first, second, third) = include_in_config('dependencies',\n self.__dependencies)\n first_column += first\n second_column += second\n third_column += third\n #fourth_column += fourth\n \n # exclude patterns\n # these patterns should be written using Unix shell-style wildcards,\n # otherwise it will not work\n first_column.append('[exclude]')\n second_column.append('')\n third_column.append('')\n #fourth_column.append('')\n \n # maximum lengths, for the purpose of formatting\n max_first = str(len(max(first_column, key=len)))\n max_second = str(len(max(second_column, key=len)))\n max_third = str(len(max(third_column, key=len)))\n #max_fourth = str(len(max(fourth_column, key=len)))\n \n format = '{:<%s}\\t{:^%s}\\t{:^%s}\\n' % (max_first, max_second, max_third)\n for i in range(len(first_column)):\n config_file += format.format(first_column[i],\n second_column[i],\n third_column[i])\n \n try:\n f = open(reprozip.utils.config_path, 'w')\n f.write(config_file)\n f.close()\n except:\n reprozip.debug.error('Could not save configuration file: %s' %sys.exc_info()[1])\n sys.exit(1)\n \n self.__config_mtime = os.path.getmtime(reprozip.utils.config_path)\n \n print '** Configuration file created in \"%s\" **' % reprozip.utils.config_path","title":""},{"docid":"1c949dc26c4351443e529a7af0dfe6af","score":"0.5254118","text":"def writeConf(self):\n\t\tconfStr = \"#Start\\n[defaults]\\n\"\n\n\t\tfor key in self.defaults:\n\t\t\tconfStr += \"%s = %s\\n\" % (key, str(self.defaults[key]))\n\t\t\n\t\tconfStr += \"#End\\n\"\n\t\t\n\t\tpathName = os.path.expandvars(\"${HOME}\") + \"/.config/tint2/\"\n\t\t\n\t\tf = open(pathName+\"tintwizard.conf\", \"w\")\n\t\t\n\t\tf.write(confStr)\n\t\t\n\t\tf.close()","title":""},{"docid":"eb3e87f6f45c613c415731a37db55201","score":"0.5252355","text":"def _write_config(self):\n if not 'component_config.py' in bpy.data.texts.keys():\n bpy.ops.text.new()\n bpy.data.texts[-1].name = 'component_config.py'\n cfg = bpy.data.texts['component_config.py']\n cfg.clear()\n cfg.write('component_mw = ' + json.dumps(cfg_middleware, indent=1) )\n cfg.write('\\n')\n cfg.write('component_modifier = ' + json.dumps(cfg_modifier, indent=1) )\n cfg.write('\\n')\n cfg.write('component_service = ' + json.dumps(cfg_service, indent=1) )\n cfg.write('\\n')\n cfg.write('overlays = ' + json.dumps(cfg_overlay, indent=1) )\n cfg.write('\\n')","title":""},{"docid":"aaf7c0a298a2b72f7fb854f7987938e1","score":"0.5250742","text":"def __load_config_files(self):\n # Parsed configuration files\n # ==========================\n\n # Main Fields\n self.__main_fields = ('U', 'p')\n p_dict = ParsedParameterFile(self.config_path('0/p'))\n U_dict = ParsedParameterFile(self.config_path('0/U'))\n\n turbulence_props = ParsedParameterFile(\n self.config_path('constant/turbulenceProperties')\n )\n\n # Material values\n transport_props = ParsedParameterFile(\n self.config_path('constant/transportProperties')\n )\n\n # Controls\n fv_schemes = ParsedParameterFile(self.config_path('system/fvSchemes'))\n self.control_dict = ParsedParameterFile(self.config_path('system/controlDict'))\n self.fv_solutions = ParsedParameterFile(self.config_path('system/fvSolution'))\n self._decompose_par_dict = ParsedParameterFile(\n self.config_path('system/decomposeParDict')\n )\n\n # cellZone options\n self.mrf_props = ParsedParameterFile(\n self.config_path('constant/MRFProperties')\n )\n\n # Post-Processing\n self.function_objects_dict = ParsedParameterFile(\n self.config_path('system/functionObjects'),\n noHeader=True, preserveComments=False\n )\n\n # Registered files\n # ================\n self.foam_file('0/p', p_dict)\n self.foam_file('0/U', U_dict)\n\n self.foam_file('system/fvSolution', self.fv_solutions)\n self.foam_file('system/fvSchemes', fv_schemes)\n self.foam_file('system/controlDict', self.control_dict)\n self.foam_file('system/decomposeParDict', self._decompose_par_dict)\n\n self.foam_file('constant/transportProperties', transport_props)\n self.foam_file('constant/turbulenceProperties', turbulence_props)\n\n self.foam_file('constant/MRFProperties', self.mrf_props)\n self.foam_file('system/functionObjects', self.function_objects_dict)","title":""},{"docid":"4b51e89eab7a58de3e0ee1b2c8cc89ae","score":"0.5249466","text":"def export_command(_args):\n config = load_all_config()\n ensure_clean_state(config)\n for lst in augmented_listing(config):\n export(config, lst)","title":""},{"docid":"5cfb8b8b9aad9b541acda760ea33ec1e","score":"0.5236535","text":"def write(self) -> None:\n if not self.new_actions:\n return\n\n file_data = utils.load_yaml(path=self.path)\n file_data.setdefault(self.module, {})\n\n for action_type, action_options in self.new_actions.items():\n file_data[self.module].setdefault(\n action_type,\n [],\n ).extend(action_options)\n\n utils.dump_yaml(\n path=self.path,\n data=file_data,\n )","title":""},{"docid":"b38aad65b6267b16d5762b112e95a38f","score":"0.52214885","text":"def assign_conf(self, conf):\n\n if isinstance(conf, str):\n conf = read_conf(conf)\n\n self.conf = conf\n\n try:\n self.archive_id = conf.getint('Archive', 'archive_id')\n except ValueError:\n print('Error in configuration file '\n '([{}], {})'.format('Archive', attr))\n except configparser.Error:\n pass","title":""},{"docid":"3a80712f0ec344f3e18538c6df3cca05","score":"0.52145886","text":"def Convert_Conf_to_Multiple_Servers(self):\n\n # check if old settings exist\n if self.__dict__.has_key(\"nagios_url\") and \\\n self.__dict__.has_key(\"nagios_cgi_url\") and \\\n self.__dict__.has_key(\"username\") and \\\n self.__dict__.has_key(\"password\") and \\\n self.__dict__.has_key(\"use_proxy_yes\") and \\\n self.__dict__.has_key(\"use_proxy_no\"):\n # create Server and fill it with old settings\n server_name = \"Default\"\n self.servers[server_name] = Server()\n self.servers[server_name].name = server_name\n self.servers[server_name].monitor_url = self.nagios_url\n self.servers[server_name].monitor_cgi_url = self.nagios_cgi_url\n self.servers[server_name].username = self.username\n self.servers[server_name].password = self.password\n # convert VERY old config files\n try:\n self.servers[server_name].use_proxy = self.use_proxy_yes\n except:\n self.servers[server_name].use_proxy = False\n try:\n self.servers[server_name].use_proxy_from_os = self.use_proxy_from_os_yes\n except:\n self.servers[server_name].use_proxy_from_os = False\n # delete old settings from config\n self.__dict__.pop(\"nagios_url\")\n self.__dict__.pop(\"nagios_cgi_url\")\n self.__dict__.pop(\"username\")\n self.__dict__.pop(\"password\")\n self.__dict__.pop(\"use_proxy_yes\")\n self.__dict__.pop(\"use_proxy_no\")","title":""},{"docid":"735022bba88fc7a31f7c936e4d14cc96","score":"0.5203702","text":"async def _export(self, ctx):","title":""},{"docid":"d64a2756c95ee03b323b60f1c05d73ae","score":"0.51988924","text":"def setupMasternodeConfFile(server, label, masternodeConfFile, masternodePort, masternodeOutput, masternodeKey): \r\n print(\"Setup masternode conf file..\")\r\n \r\n # Append to the existing file\r\n with open(masternodeConfFile, 'a') as f:\r\n line = \"\\n{0} {1}:{2} {3} {4} {5}\".format(label, server, masternodePort, masternodeKey, masternodeOutput[\"txhash\"], masternodeOutput[\"outputidx\"])\r\n f.write(line)","title":""},{"docid":"f103611e081a157027980ef701cb7c1c","score":"0.5196658","text":"def write_conf(self):\n f = open(self.output_filename, 'w')\n print(self.t.render(prefixes=self.prefixes), file=f)\n f.close()","title":""},{"docid":"640a1aad4f95bf7188a9c27db156aaa8","score":"0.5195018","text":"def _write_files(self):\n to_save = []\n for file in self.files.values():\n to_save.append(file.meta)\n with open(os.path.join(self.output_directory, \"files.yaml\"), \"w\", encoding=\"utf8\") as f:\n yaml.safe_dump(to_save, f)","title":""},{"docid":"f5c036ce47bfb037c32267a675ec5b74","score":"0.51645446","text":"def config_save(self):\n \n # Create the output directory if needed\n if not os.path.exists(self.output_dir): os.mkdir(self.output_dir)\n\n # Save\n with open(self.output_dir+'/config.pkl', 'wb') as pfile:\n pickle.dump(self.__dict__, pfile, pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"7054b20e252b419382fc4486b2d3e850","score":"0.5157857","text":"def configure(self, filename):\n self.__filename = filename","title":""},{"docid":"fe82d81ade3eed55613745185762cd1c","score":"0.51577884","text":"def file(config):\n pass","title":""},{"docid":"e631a260a80478c63107b8cc521297d5","score":"0.51501733","text":"def saveConfig ():\n global config\n filename = os.path.join (configdir, \"{0}.config\".format (__name__))\n outfile = open (filename, \"w\")\n pickle.dump (config, outfile)\n outfile.close ()","title":""},{"docid":"976c4c26419cd5f425db354e0c484df2","score":"0.5148247","text":"def genConfFile():\n conf_file_template = pkg_resources.resource_string(__name__,'rose.batch.conf')\n f = open(\"rose.batch.conf\", \"w\")\n f.write(conf_file_template)\n f.close()","title":""},{"docid":"b3cbd18bdbd22f860d3d15bb837943bd","score":"0.51464355","text":"def to_file(self):\r\n with open('config.json', 'w') as f:\r\n f.write(self.to_json_string())","title":""},{"docid":"2b59facd977d595b93656619b188e9df","score":"0.51458484","text":"def _writeHostConfig(self, host, out):\n confFName = f\"{self.workDir}/{host}.yaml\"\n with open(confFName, 'w', encoding='utf-8') as fd:\n fd.write(yaml.dump(out))","title":""},{"docid":"6b09cf7cecaa3e3f56dc7faf17734c28","score":"0.51382","text":"def write_config(self, filename, data):\n conf = os.path.join(self.steam_config, filename)\n text = vdf.dumps(data, pretty=True)\n write_file(conf, text)","title":""},{"docid":"819c81fcd36a8e7923a19087b56cea92","score":"0.5136384","text":"def loadConf ( self ):\n\t\tcp2 = ConfigParser.ConfigParser()\n\n\t\tself.workingpath = self.validateDir( os.getcwd() )\n\t\tself.userpath = self.validateDir( os.path.expanduser(\"~\") )\n\n\t\tself.currentFile = None\n\t\tnew = False\n\n\t\tif os.path.isfile('conf.dat') == False:\n\t\t\t#First time using PSPMan or just new conf\n\t\t\tnew = True\n\t\t\tcp2.add_section(_('Options'))\n\t\t\tcp2.set(_('Options'),'PSP_Path','/media/disk/')\n\t\t\tcp2.set(_('Options'),'ISO_Path',self.userpath + 'ISO/')\n\t\t\tcp2.set(_('Options'),'Save_Path',self.userpath + 'SAVEDATA/')\n\t\t\tcp2.set(_('Options'),'OSD', 0)\n\t\t\tcp2.set(_('Options'),'Bell', 0)\n\t\t\tcp2.set(_('Options'),'Expl', 'nautilus')\n\n\t\t\twith open('conf.dat','w') as file:\n\t\t\t cp2.write(file)\n\t\t\t \n\n\t\tcp2.read('conf.dat')\n\n\t\tself.mspath = cp2.get(_('Options'), 'PSP_Path')\n\t\tself.localiso = cp2.get(_('Options'), 'ISO_Path')\n\t\tself.tSave = cp2.get(_('Options'),'Save_Path')\n\t\tself.oOSD = cp2.get(_('Options'),'OSD')\n\t\tself.oBell = cp2.get(_('Options'),'Bell')\n\t\tself.Expl = cp2.get(_('Options'), 'Expl')\n\t\t\n\t\tself.oOSD = bool(int(self.oOSD))\n\t\tself.oBell = bool(int(self.oBell))\n\n\t\tself.mspsp = self.mspath + 'ISO'\n\t\tself.mssave = self.mspath + 'PSP/SAVEDATA'\n\t\tself.busy = False\n\n\t\tif new:\n\t\t\tself.notify(_(\"This is your first run. Please, go to configuration first.\"))","title":""},{"docid":"550cc411450da75926cf306d6f18236e","score":"0.51336646","text":"def _update_conf(conf:Config)->None:\n\n conf_common = get_conf_common(conf)\n conf_dataset = get_conf_dataset(conf)\n experiment_name = conf_common['experiment_name']\n\n # make sure dataroot exists\n dataroot = conf_dataset['dataroot']\n dataroot = utils.full_path(dataroot)\n\n # make sure logdir and expdir exists\n logdir = conf_common['logdir']\n if logdir:\n logdir = utils.full_path(logdir)\n expdir = os.path.join(logdir, experiment_name)\n\n # directory for non-master replica logs\n distdir = os.path.join(expdir, 'dist')\n else:\n expdir = distdir = logdir\n\n # update conf so everyone gets expanded full paths from here on\n # set environment variable so it can be referenced in paths used in config\n conf_common['logdir'] = logdir\n conf_dataset['dataroot'] = dataroot\n conf_common['expdir'] = expdir\n conf_common['distdir'] = distdir","title":""},{"docid":"988f1fc1521444d216741f0aadb3b5cb","score":"0.5130486","text":"def _write_yaml(self):\n # add environment variables\n to_yaml = {}\n to_yaml['ENVIRONMENT_VARIABLES'] = self.environment_variables\n # add blacklist\n plugin_packages = os.listdir(self.local_paths.plugins)\n blacklist_as_dict = {}\n for pkg in plugin_packages:\n if pkg in self.blacklist:\n blacklist_as_dict[pkg] = True\n else:\n blacklist_as_dict[pkg] = False\n to_yaml['BLACKLIST'] = blacklist_as_dict\n # dump all to yaml file\n with open(self.yaml_path, 'w') as stream:\n yaml.dump(to_yaml, stream)","title":""},{"docid":"648b88636bec6087ba4cb30b07466dcb","score":"0.51302344","text":"def __init__(self):\n # check for directory path\n if not os.path.exists(conf_dir):\n os.mkdir(conf_dir)\n if not os.path.exists(tor_data):\n os.mkdir(tor_data)\n\n if not os.path.exists(config_file):\n with open(config_file, 'w') as cfg:\n # XXX. add keys with their default values.\n cfg.write('dio: cane\\n')\n\n # load configuration\n with open(config_file, 'r') as cfg:\n for key, value in (yaml.load(cfg) or dict()).iteritems():\n setattr(self, key, value)","title":""},{"docid":"a2fec3e98f45796cfffef4de89557709","score":"0.5128848","text":"def _cfg_import_export(cfg_interactor, cfg_, prefix='', mode='fill_parser'):\r\n for k in dir(cfg_):\r\n if k[0] == '_': continue # hidden\r\n v = getattr(cfg_, k)\r\n if type(v) in [float, str, int, bool]:\r\n if mode == 'fill_parser':\r\n cfg_interactor.add_argument('--{}{}'.format(prefix, k), type=type(v), help='default: {}'.format(v))\r\n elif mode == 'fill_dict':\r\n cfg_interactor['{}{}'.format(prefix, k)] = v\r\n elif mode == 'override':\r\n prek = '{}{}'.format(prefix, k)\r\n if prek in cfg_interactor:\r\n setattr(cfg_, k, getattr(cfg_interactor, prek))\r\n elif type(v) == Bunch: # recurse; descend into Bunch\r\n _cfg_import_export(cfg_interactor, v, prefix=prefix + k + '.', mode=mode)","title":""},{"docid":"a2b351f4bd4594e909ddcdc4fec7edea","score":"0.51235884","text":"def configure():","title":""},{"docid":"c3624c6dfdec0e7e0894874ee0e1603b","score":"0.5122384","text":"def configure(self):\n \n # working directory of main program\n # this directory will be used to find a common directory structure\n # between programs and files\n wdir = self.__prov_tree.root.execve_pwd\n \n ########################################################################\n \n # function that takes care of including the necessary files in the\n # reproducible package\n def add_rep_file(file, file_dict):\n \"\"\"\n file -> file to be added\n file_dict -> dictionary containing the file\n \"\"\"\n \n file_ = file\n if file_.startswith(os.sep):\n file_ = file_[1:]\n file_dict[file] = os.path.join(reprozip.utils.exp_dir, file_)\n \n # symbolic links\n if self.__symlink_to_target.has_key(file):\n target = self.__symlink_to_target[file]\n self.__targets[target] = None\n add_rep_file(target, self.__targets)\n \n ########################################################################\n \n # child programs\n reprozip.debug.verbose(self.verbose, 'Configuring child programs...')\n for program in self.__child_programs:\n add_rep_file(program, self.__child_programs)\n \n # main program\n reprozip.debug.verbose(self.verbose, 'Configuring main program...')\n add_rep_file(self.__main_program.keys()[0], self.__main_program)\n \n # main input files\n reprozip.debug.verbose(self.verbose, 'Configuring main input files...')\n for input_file in self.__input_files:\n add_rep_file(input_file, self.__input_files)\n \n # child input files\n reprozip.debug.verbose(self.verbose, 'Configuring child input files...')\n for input_file in self.__child_input_files:\n add_rep_file(input_file, self.__child_input_files)\n \n # directories\n reprozip.debug.verbose(self.verbose, 'Configuring directories...')\n for dir in self.__dirs:\n add_rep_file(dir, self.__dirs)\n \n # trying to identify implicit input files among dependencies\n # a dependency is considered an implicit input file if it is\n # located together with other input files (considering the working\n # directory of the main program as the common structure) \n # implicit input files are put together with child input files\n reprozip.debug.verbose(self.verbose, 'Configuring dependencies...')\n \n rm_dependencies = []\n for dependency in self.__dependencies:\n l = [dependency, wdir]\n common_prefix = os.path.normpath(os.path.commonprefix(l))\n if not os.path.exists(common_prefix):\n common_prefix = os.path.dirname(common_prefix)\n \n # if there is no common_prefix\n if (common_prefix == '') or (common_prefix == os.sep):\n continue\n \n # if there is a common prefix\n else:\n self.__child_input_files[dependency] = None\n rm_dependencies.append(dependency)\n \n add_rep_file(dependency, self.__child_input_files)\n \n for rm_dependency in rm_dependencies:\n self.__dependencies.pop(rm_dependency)\n\n # dependencies\n # dependencies are libraries and possible include files\n for dependency in self.__dependencies:\n add_rep_file(dependency, self.__dependencies)\n \n# self.verbose_(['input files: %s' %str(self.__input_files),\n# 'output files: %s' %str(self.__output_files),\n# 'main program: %s' %str(self.__main_program),\n# 'child programs: %s' %str(self.__child_programs),\n# 'child input files: %s' %str(self.__child_input_files),\n# 'dependencies: %s' %str(self.__dependencies)])\n\n # generate configuration file\n reprozip.debug.verbose(self.verbose, 'Writing configuration file...')\n self.__gen_config_file()","title":""},{"docid":"93a3d624364d4ccf35ed926cd1da33f0","score":"0.5121893","text":"def export(self, context):\n context.writeDataFile()","title":""},{"docid":"009cb950ea482d0a40062eece34c54a6","score":"0.5114487","text":"def downloadConfAndMeta(api, project, destDir, api_user, api_password):\n if not os.path.isdir(destDir):\n os.makedirs(destDir)\n for myFile in [\"_meta\", \"_config\"]:\n fileUrl = urllib.quote(\"source/%s/%s\" % (project, myFile))\n fileUrl = \"%s/%s\" % (api, fileUrl)\n retCode = downloadFile(fileUrl, destDir, user=api_user, password=api_password)","title":""},{"docid":"ab6db13bc962ff97c037dc77380348dd","score":"0.510468","text":"async def write_config_to_yml():\n from hummingbot.client.config.in_memory_config_map import in_memory_config_map\n current_strategy = in_memory_config_map.get(\"strategy\").value\n strategy_file_path = in_memory_config_map.get(\"strategy_file_path\").value\n\n if current_strategy is not None and strategy_file_path is not None:\n strategy_config_map = get_strategy_config_map(current_strategy)\n strategy_file_path = join(CONF_FILE_PATH, strategy_file_path)\n await save_to_yml(strategy_file_path, strategy_config_map)\n\n await save_to_yml(GLOBAL_CONFIG_PATH, global_config_map)","title":""},{"docid":"f77ecd2860b6db1f1c31e69ad334ee1c","score":"0.51043546","text":"def _create_new_config_file(self):\n os.makedirs(os.path.dirname(self.path), exist_ok=True)\n self.cfg = configparser.ConfigParser()\n self._set_up_default_config()\n self._write()","title":""},{"docid":"db07be9a7ebc87a60da2be3336c54b08","score":"0.51019675","text":"def writeFile(self): \n\t\tpass","title":""},{"docid":"4fc784122edba566772e2d21f70ec630","score":"0.51015824","text":"def load_conf(self):\n self.net = self.load_net(self.cfgfilepath,\n self.weightsfilepath,\n 0)\n self.meta = self.load_meta(self.datafilepath)","title":""}],"string":"[\n {\n \"docid\": \"53e11f1817e4cc0e5a994a7e9f2d97f4\",\n \"score\": \"0.64293945\",\n \"text\": \"def export_config(self, f_name=None):\\n self.update_history()\\n if f_name is not None:\\n self.config.config_export(f_name)\\n else:\\n self.config.config_export(self.config.confname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67f9998ff554fd0528fcc74b7975252\",\n \"score\": \"0.61478794\",\n \"text\": \"def save_to_conf(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b61954dc3e1abe030e785fc3ede90c\",\n \"score\": \"0.5948918\",\n \"text\": \"def assign_conf(self, conf):\\n\\n if isinstance(conf, str):\\n conf = read_conf(conf)\\n\\n self.conf = conf\\n\\n try:\\n self.archive_id = conf.getint('Archive', 'archive_id')\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Archive', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['write_wcs_dir']:\\n try:\\n setattr(self, attr, conf.get('Files', attr))\\n except configparser.Error:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19aca0ff25a199bcaea80dc1823b510\",\n \"score\": \"0.5835789\",\n \"text\": \"def assign_conf(self, conf):\\n\\n if isinstance(conf, str):\\n conf = read_conf(conf)\\n\\n self.conf = conf\\n\\n try:\\n self.archive_id = conf.getint('Archive', 'archive_id')\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Archive', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['sextractor_path', 'scamp_path', 'psfex_path',\\n 'solve_field_path', 'wcs_to_tan_path']:\\n try:\\n setattr(self, attr, conf.get('Programs', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['fits_dir', 'index_dir', 'gaia_dir', 'tycho2_dir',\\n 'work_dir', 'write_log_dir', 'write_phot_dir',\\n 'write_source_dir', 'write_wcs_dir',\\n 'write_db_source_dir', 'write_db_source_calib_dir']:\\n try:\\n setattr(self, attr, conf.get('Files', attr))\\n except configparser.Error:\\n pass\\n\\n if self.write_log_dir:\\n self.enable_log = True\\n\\n for attr in ['use_gaia_fits', 'use_tycho2_fits',\\n 'use_ucac4_db', 'use_apass_db',\\n 'enable_db_log', 'write_sources_csv']:\\n try:\\n setattr(self, attr, conf.getboolean('Database', attr))\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Database', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['ucac4_db_host', 'ucac4_db_user', 'ucac4_db_name',\\n 'ucac4_db_passwd', 'ucac4_db_table',\\n 'apass_db_host', 'apass_db_user', 'apass_db_name',\\n 'apass_db_passwd', 'apass_db_table',\\n 'output_db_host', 'output_db_user',\\n 'output_db_name', 'output_db_passwd']:\\n try:\\n setattr(self, attr, conf.get('Database', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['use_filter', 'use_psf', 'circular_film',\\n 'allow_force', 'repeat_find']:\\n try:\\n setattr(self, attr, conf.getboolean('Solve', attr))\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Solve', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['plate_epoch', 'threshold_sigma',\\n 'psf_threshold_sigma', 'psf_model_sigma',\\n 'crossmatch_radius', 'crossmatch_nsigma',\\n 'crossmatch_nlogarea', 'crossmatch_maxradius']:\\n try:\\n setattr(self, attr, conf.getfloat('Solve', attr))\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Solve', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['sip', 'skip_bright', 'distort', 'subfield_distort',\\n 'max_recursion_depth', 'force_recursion_depth',\\n 'min_model_sources', 'max_model_sources']:\\n try:\\n setattr(self, attr, conf.getint('Solve', attr))\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Solve', attr))\\n except configparser.Error:\\n pass\\n\\n for attr in ['filter_path', 'astref_catalog', 'photref_catalog']:\\n try:\\n setattr(self, attr, conf.get('Solve', attr))\\n except configparser.Error:\\n pass\\n\\n # Read UCAC4 and APASS table column names from the dedicated sections,\\n # named after the tables\\n if conf.has_section(self.ucac4_db_table):\\n for attr in self.ucac4_columns.keys():\\n try:\\n colstr = conf.get(self.ucac4_db_table, attr)\\n _,typ = self.ucac4_columns[attr]\\n self.ucac4_columns[attr] = (colstr, typ)\\n except configparser.Error:\\n pass\\n\\n if conf.has_section(self.apass_db_table):\\n for attr in self.apass_columns.keys():\\n try:\\n colstr = conf.get(self.apass_db_table, attr)\\n _,typ = self.apass_columns[attr]\\n self.apass_columns[attr] = (colstr, typ)\\n except configparser.Error:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c049ac21e5a6eb9faf8dbba57e9a59\",\n \"score\": \"0.5824831\",\n \"text\": \"def __read_conf_file(self):\\n\\n conf_file_path = os.path.abspath(os.path.dirname(__file__)).split(\\\"modules\\\")[0] + \\\"/agent.conf\\\"\\n self.log.debug(\\\"Config file path : %r\\\" % conf_file_path)\\n self.__properties = ConfigParser.SafeConfigParser()\\n self.__properties.read(conf_file_path)\\n\\n # set calculated values\\n param_file = os.path.abspath(os.path.dirname(__file__)).split(\\\"modules\\\")[0] + \\\"/payload/launch-params\\\"\\n self.__properties.set(\\\"agent\\\", constants.PARAM_FILE_PATH, param_file)\\n\\n plugins_dir = os.path.abspath(os.path.dirname(__file__)).split(\\\"modules\\\")[0] + \\\"/plugins\\\"\\n self.__properties.set(\\\"agent\\\", constants.PLUGINS_DIR, plugins_dir)\\n\\n plugins_dir = os.path.abspath(os.path.dirname(__file__)).split(\\\"modules\\\")[0] + \\\"/extensions/py\\\"\\n self.__properties.set(\\\"agent\\\", constants.EXTENSIONS_DIR, plugins_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"845ed9df169b569369f5213eb108be7f\",\n \"score\": \"0.57778037\",\n \"text\": \"def _config_files(self):\\n files = [\\\"project_nginx.conf\\\", \\\"project_supervisor.conf\\\"]\\n folder = os.path.join(self.projectfolder, \\\"config\\\")\\n\\n for conffile in files:\\n templatefile = os.path.join(folder, conffile)\\n\\n self.write(templatefile, os.path.join(\\\"config\\\", conffile),\\n self.context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a10b0bd49ab83fd7e08d53dda1e5bd1\",\n \"score\": \"0.5775637\",\n \"text\": \"def _export_config(self):\\n exporter = ModelProtoExporter(self.target_dir, self.model_info, self.get_platform)\\n exporter.export()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6010afffb9e8947d0c258c61ccd8021\",\n \"score\": \"0.577253\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n self._filename = \\\"config\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642605f2cc226269761f93b73a6a6abe\",\n \"score\": \"0.57679194\",\n \"text\": \"def _dump(self):\\n filename = os.path.expanduser('~/.mezzanine.cfg')\\n parser = Parser()\\n parser.add_section('general')\\n parser.read(filename)\\n parser.set('general', 'refresh_token', self.session.token['refresh_token'])\\n with open(filename, 'w') as config_file:\\n parser.write(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1c12e4335a1007c04984af9ef7fb82\",\n \"score\": \"0.5765398\",\n \"text\": \"def save_conf(self, dev, conf):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbebd73ce7765142fdc3d3361f38d1ab\",\n \"score\": \"0.57478935\",\n \"text\": \"def write_conf(self):\\n\\n self.io.row_list = []\\n try:\\n os.makedirs(os.path.dirname(self.config_file))\\n except IOError:\\n logger.info(\\\"Error while trying to create config \\\"\\n \\\"path as {}\\\".format(self.config_file))\\n except OSError:\\n logger.info(\\\"The config directory already exists.\\\")\\n config = ConfigParser.RawConfigParser()\\n for section in CONFIG_SECTIONS:\\n config.add_section(section)\\n\\n for key in self.config.keys():\\n if key in RIG_URI_CONFIG:\\n config.set(\\\"Rig URI\\\", key, self.config[key])\\n if key in MONITOR_CONFIG:\\n config.set(\\\"Monitor\\\", key, self.config[key])\\n if key in MAIN_CONFIG:\\n config.set(\\\"Main\\\", key, self.config[key])\\n if key in SCANNING_CONFIG:\\n config.set(\\\"Scanning\\\", key, self.config[key])\\n\\n with open(self.config_file, \\\"wb\\\") as cf:\\n config.write(cf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791b39d29f156ad0055810688b0f06e7\",\n \"score\": \"0.574321\",\n \"text\": \"def export():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791b39d29f156ad0055810688b0f06e7\",\n \"score\": \"0.574321\",\n \"text\": \"def export():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791b39d29f156ad0055810688b0f06e7\",\n \"score\": \"0.574321\",\n \"text\": \"def export():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7c7352cd2b753eb3e161746be2310\",\n \"score\": \"0.5726024\",\n \"text\": \"def configure(self, conf):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7c7352cd2b753eb3e161746be2310\",\n \"score\": \"0.5726024\",\n \"text\": \"def configure(self, conf):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760f4e4d3cd6bc07213e7d440369ea5d\",\n \"score\": \"0.5714147\",\n \"text\": \"def _extend_config(self):\\n # Split made between project_name, module_name, app_id as these are seperate entries. However for now set as project_name\\n # This allows more finegrained control of these config items in future releases if required. \\n module_name = self.config.get_entry( ConfigConstants.CONFIG_KEY_MODULE_NAME )\\n \\n # Specific folder locations for the different category of artifacts.\\n output_path_hana = os.path.join(self.config.get_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH ), ConfigConstants.HANA_BASE_PATH )\\n output_path_module = os.path.join(output_path_hana, module_name)\\n output_path_module_src = os.path.join(output_path_module, ConfigConstants.MODULE_SOURCE_PATH)\\n module_template_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), ConfigConstants.PROJECT_TEMPLATEDR)\\n grants_path = os.path.join(output_path_module_src, ConfigConstants.GRANTS_SOURCE_PATH)\\n synonyms_path = os.path.join(output_path_module_src, ConfigConstants.SYNONYMS_SOURCE_PATH)\\n procedures_path = os.path.join(output_path_module_src, ConfigConstants.PROCEDURES_SOURCE_PATH)\\n roles_path = os.path.join(output_path_module_src, ConfigConstants.ROLES_SOURCE_PATH)\\n cds_path = os.path.join(output_path_module_src, ConfigConstants.CDS_SOURCE_PATH)\\n\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_HANA, output_path_hana)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_MODULE, output_path_module)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_OUTPUT_PATH_MODULE_SRC, output_path_module_src)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_MODULE_TEMPLATE_PATH, module_template_path)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_GRANTS_PATH, grants_path) \\n self.config.add_entry( ConfigConstants.CONFIG_KEY_SYNONYMS_PATH, synonyms_path)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_PROCEDURES_PATH, procedures_path)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_ROLES_PATH, roles_path)\\n self.config.add_entry( ConfigConstants.CONFIG_KEY_CDS_PATH, cds_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fcf4697e4e8bb1027a2846d8c35eccd\",\n \"score\": \"0.5712404\",\n \"text\": \"def write(self):\\n\\t\\twith open(self.path) as configFile:\\n\\t\\t\\tself.config.write(configFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ffe75a9b57de50d035ea275566d5820\",\n \"score\": \"0.5706289\",\n \"text\": \"def export(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b65498eff9a17070436927e1cf01b9\",\n \"score\": \"0.568244\",\n \"text\": \"def _writeConfig(self, filename):\\n f = open(filename, \\\"wb\\\")\\n f.write(self._getConfig())\\n f.close()\\n\\n directory = os.path.dirname(filename)\\n for d in self.datasets:\\n d._writeGeneratedFiles(directory)\\n\\n print \\\"Wrote multicrab configuration to %s\\\" % filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6f0cdf46bcbc876d1480e08fd11004\",\n \"score\": \"0.5678342\",\n \"text\": \"def _write(self):\\n with open(self.path, 'w') as configfile:\\n self.cfg.write(configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d871944e0f24cf7eca38ed8dc296361\",\n \"score\": \"0.5660896\",\n \"text\": \"def _copy_config(self):\\n\\n\\t\\tout_config_filename = os.path.join(self.experiment_dir, 'config.json')\\n\\t\\ttempOrderedDict = collections.OrderedDict()\\n\\t\\tfor key in self.cfg.keys():\\n\\t\\t\\ttempOrderedDict[key] = self.cfg[key]\\n\\t\\twith open(out_config_filename, 'w') as outfile:\\n\\t\\t\\tjson.dump(tempOrderedDict, outfile)\\n\\t\\tthis_filename = sys.argv[0]\\n\\t\\tout_train_filename = os.path.join(self.experiment_dir, 'training_script.py')\\n\\t\\tshutil.copyfile(this_filename, out_train_filename)\\n\\t\\tout_architecture_filename = os.path.join(self.experiment_dir, 'architecture.json')\\n\\t\\tjson.dump(self.cfg['gqcnn_config']['architecture'], open(out_architecture_filename, 'w'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf29013e33e9947a3b7760c6f0e80eb\",\n \"score\": \"0.56565434\",\n \"text\": \"def setting_by_cfg_file(self, conf):\\n cfg = conf.get(\\\"default\\\")\\n check_dict = ConfigChecker.check_env(cfg, env_name=\\\"default\\\", update_default=True)\\n self.__dict__.update(check_dict)\\n \\n self.project_path = Path(self.project_path)\\n self.data_path = Path(self.data_path)\\n\\n if conf.get(\\\"experiment\\\") is not None:\\n cfg = conf.get(\\\"experiment\\\")\\n check_dict = ConfigChecker.check_env(cfg, env_name=\\\"experiment\\\", update_default=True)\\n self.__dict__.update(check_dict)\\n self.exp_path = self.project_path / \\\"checkpoints\\\" / self.eval_type\\n ConfigChecker.check_dir_exist(self.exp_path, file=False)\\n else:\\n print(\\\"No Configure for `experiment`\\\")\\n\\n if conf.get(\\\"postprocessor\\\") is not None:\\n cfg = conf.get(\\\"postprocessor\\\")\\n check_dict = ConfigChecker.check_env(cfg, env_name=\\\"postprocessor\\\", update_default=True)\\n else:\\n check_dict = ConfigChecker.checkdefault_generator(\\\"postprocessor\\\")\\n self.__dict__.update(check_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3517d4ce0dd0f8ea1bbec4ce1fa227af\",\n \"score\": \"0.56503063\",\n \"text\": \"def write(self):\\n for section in self.sections.itervalues():\\n section.write(self.config_parser)\\n\\n with open(self.file_path, 'wb') as config_file:\\n self.config_parser.write(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3fbe270e18077982e7c58c291af453b\",\n \"score\": \"0.56486994\",\n \"text\": \"def init_conf(argv):\\n conf = ConfigParser()\\n conf.read(argv[1])\\n CONF['logfile'] = conf.get('export', 'logfile')\\n CONF['magic_number'] = unhexlify(conf.get('export', 'magic_number'))\\n CONF['db'] = conf.getint('export', 'db')\\n CONF['debug'] = conf.getboolean('export', 'debug')\\n CONF['export_dir'] = conf.get('export', 'export_dir')\\n CONF['exclude_uagent_string'] = conf.get('export', 'exclude_uagent_string').strip().split(\\\"\\\\n\\\")\\n if not os.path.exists(CONF['export_dir']):\\n os.makedirs(CONF['export_dir'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852648a3bd469f9c3242d4c63854ced0\",\n \"score\": \"0.56245977\",\n \"text\": \"def __save_config(self) -> None:\\n #TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8935be3016765f0b523295938d3d351c\",\n \"score\": \"0.56119084\",\n \"text\": \"def setup_allowed_and_configs(cls, allowed_fname, configs_fname):\\n with open(allowed_fname, 'w') as out:\\n out.write('CONFIG_OLD_ONE')\\n with open(configs_fname, 'w') as out:\\n out.write('\\\\n'.join(['CONFIG_OLD_ONE', 'CONFIG_NEW_ONE',\\n 'CONFIG_MY_KCONFIG']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4913c679fda8bb45e65fe7242a88bf\",\n \"score\": \"0.5602432\",\n \"text\": \"def get_postprocess_config_files(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f77aae1b71a57050ab266e931af402\",\n \"score\": \"0.5593669\",\n \"text\": \"def write(self):\\n self.name = self[\\\"name\\\"]\\n PyConfig.write(self)\\n self.convirt_generated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2fd750df16c7e29fe2385e7140d8e9\",\n \"score\": \"0.5585339\",\n \"text\": \"def _prepare(self):\\n logging.info('-> copy configuration...')\\n\\n self._copy_config_to_expt('path_config')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ed173af5ebb7f68aac3d49cb681304\",\n \"score\": \"0.55760974\",\n \"text\": \"def __init__(self):\\n\\n configpath = self.get_configpath()\\n self.read(configpath)\\n if not os.path.exists(configpath):\\n self.write(configpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4a041ac993c728677ee719e8dcdd67\",\n \"score\": \"0.55397725\",\n \"text\": \"def __configure(self):\\n home_dir = os.path.expanduser('~')\\n stripped_root = os.path.basename(os.path.normpath(self.__root))\\n conf_path = os.path.join(home_dir, '.conf')\\n conf_path = os.path.join(conf_path, 'pysync')\\n self.__conf_path = os.path.join(conf_path, stripped_root)\\n os.makedirs(self.__conf_path, exist_ok=True)\\n self.__conf['MAC'] = ':'.join(re.findall('..', '%012x' % uuid.getnode())),\\n if self.__conf['backup']:\\n if self.__conf['backup_path'] == 'DEFAULT':\\n self.__conf['backup_path'] = os.path.join(self.__conf_path, 'backups')\\n os.makedirs(self.__conf['backup_path'], exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434b62d444eafa298b47cbdbcf7b53d7\",\n \"score\": \"0.55269283\",\n \"text\": \"def saveconf(self):\\n try:\\n self._config['SERIALS'] = {serial:0 for serial in self.config['serials']}\\n print (configuration_file)\\n self._config.write(open(configuration_file,'wt'))\\n # os.sys.exit(-1)\\n except Exception,e:\\n print e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb649a4dbf8261b32853e97b961a015\",\n \"score\": \"0.5522753\",\n \"text\": \"def ExportSetup(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470050f715da8b0ce73bacfefa4b957b\",\n \"score\": \"0.55209905\",\n \"text\": \"def write(self, path):\\n cp = ConfigParser.SafeConfigParser()\\n # XXX the use of the disabled_auth file can be removed when\\n # compatibility with the older code is no longer needed\\n da = ConfigParser.SafeConfigParser()\\n cp.optionxform = str # preserve option case\\n\\n # For compatibility, the preferred-publisher is written out\\n # as the preferred-authority. Modify a copy so that we don't\\n # change the in-memory copy.\\n props = self.properties.copy()\\n try:\\n del props[\\\"preferred-publisher\\\"]\\n except KeyError:\\n pass\\n props[\\\"preferred-authority\\\"] = str(self.__publisher_search_order[0])\\n props[\\\"publisher-search-order\\\"] = str(self.__publisher_search_order)\\n\\n cp.add_section(\\\"property\\\")\\n for p in props:\\n cp.set(\\\"property\\\", p, props[p].encode(\\\"utf-8\\\"))\\n\\n cp.add_section(\\\"variant\\\")\\n for f in self.variants:\\n cp.set(\\\"variant\\\", f, str(self.variants[f]))\\n\\n cp.add_section(\\\"facet\\\")\\n\\n for f in self.facets:\\n\\n cp.set(\\\"facet\\\", f, str(self.facets[f]))\\n\\n for prefix in self.__publishers:\\n pub = self.__publishers[prefix]\\n section = \\\"authority_%s\\\" % pub.prefix\\n\\n c = cp\\n if pub.disabled:\\n c = da\\n\\n c.add_section(section)\\n c.set(section, \\\"alias\\\", str(pub.alias))\\n c.set(section, \\\"prefix\\\", str(pub.prefix))\\n c.set(section, \\\"disabled\\\", str(pub.disabled))\\n c.set(section, \\\"sticky\\\", str(pub.sticky))\\n\\n repo = pub.selected_repository\\n\\n # For now, write out \\\"origin\\\" for compatibility with\\n # older clients in addition to \\\"origins\\\". Older\\n # clients may drop the \\\"origins\\\" when rewriting the\\n # configuration, but that doesn't really break\\n # anything.\\n c.set(section, \\\"origin\\\", repo.origins[0].uri)\\n\\n c.set(section, \\\"origins\\\",\\n str([u.uri for u in repo.origins]))\\n c.set(section, \\\"mirrors\\\",\\n str([u.uri for u in repo.mirrors]))\\n\\n if repo.system_repo:\\n c.set(section, \\\"sysrepo.uri\\\",\\n str(repo.system_repo))\\n c.set(section, \\\"sysrepo.sock_path\\\",\\n repo.system_repo.socket_path)\\n else:\\n c.set(section, \\\"sysrepo.uri\\\", \\\"None\\\")\\n c.set(section, \\\"sysrepo.sock_path\\\", \\\"None\\\")\\n\\n #\\n # For zones, where the reachability of an absolute path\\n # changes depending on whether you're in the zone or\\n # not. So we have a different policy: ssl_key and\\n # ssl_cert are treated as zone root relative.\\n #\\n ngz = self.variants.get(\\\"variant.opensolaris.zone\\\",\\n \\\"global\\\") == \\\"nonglobal\\\"\\n p = str(pub[\\\"ssl_key\\\"])\\n if ngz and self.__imgroot != os.sep and p != \\\"None\\\":\\n # Trim the imageroot from the path.\\n if p.startswith(self.__imgroot):\\n p = p[len(self.__imgroot):]\\n # XXX this should be per origin or mirror\\n c.set(section, \\\"ssl_key\\\", p)\\n p = str(pub[\\\"ssl_cert\\\"])\\n if ngz and self.__imgroot != os.sep and p != \\\"None\\\":\\n if p.startswith(self.__imgroot):\\n p = p[len(self.__imgroot):]\\n # XXX this should be per origin or mirror\\n c.set(section, \\\"ssl_cert\\\", p)\\n\\n # XXX this should really be client_uuid, but is being\\n # left with this name for compatibility with older\\n # clients.\\n c.set(section, \\\"uuid\\\", str(pub.client_uuid))\\n\\n # Write selected repository data.\\n # XXX this is temporary until a switch to a more\\n # expressive configuration format is made.\\n repo = pub.selected_repository\\n repo_data = {\\n \\\"collection_type\\\": repo.collection_type,\\n \\\"description\\\": repo.description,\\n \\\"legal_uris\\\": [u.uri for u in repo.legal_uris],\\n \\\"name\\\": repo.name,\\n \\\"refresh_seconds\\\": repo.refresh_seconds,\\n \\\"registered\\\": repo.registered,\\n \\\"registration_uri\\\": repo.registration_uri,\\n \\\"related_uris\\\": [u.uri for u in repo.related_uris],\\n \\\"sort_policy\\\": repo.sort_policy,\\n }\\n\\n for key, val in repo_data.iteritems():\\n c.set(section, \\\"repo.%s\\\" % key, str(val))\\n\\n # XXX Child images\\n\\n for afile, acp in [(CFG_FILE, cp), (DA_FILE, da)]:\\n thefile = os.path.join(path, afile)\\n if len(acp.sections()) == 0:\\n if os.path.exists(thefile):\\n portable.remove(thefile)\\n continue\\n try:\\n f = open(thefile, \\\"w\\\")\\n except EnvironmentError, e:\\n if e.errno == errno.EACCES:\\n raise api_errors.PermissionsException(\\n e.filename)\\n if e.errno == errno.EROFS:\\n raise api_errors.ReadOnlyFileSystemException(\\n e.filename)\\n raise\\n acp.write(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808e91790dd50e465812c84fb9060a44\",\n \"score\": \"0.5520405\",\n \"text\": \"def dump(self, conf: \\\"ONDict\\\", schema: Optional[\\\"ONDict\\\"] = None):\\n with open(self, \\\"w\\\") as f:\\n self.module.dump(conf, f, schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5531e9862a3c34e7deac9676fe0c727\",\n \"score\": \"0.55201316\",\n \"text\": \"def _save_conf_file(self):\\n path = os.path.join(self._result_log_base_path, \\\"config.json\\\")\\n with open(path, \\\"w\\\") as f:\\n f.write(json.dumps(self._conf, indent=4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2a59d2d3841a3aa23e206bce81afac\",\n \"score\": \"0.54912245\",\n \"text\": \"def save_config(self) -> None:\\n with open(self.SETUP_CONF, \\\"w\\\") as configfile:\\n self.config.write(configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465b135b22f7a92c60dd6f2c3ffd13bf\",\n \"score\": \"0.54902333\",\n \"text\": \"def write(self, config: Config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c420ab6d5693d174d45797da38c4b058\",\n \"score\": \"0.5472056\",\n \"text\": \"def _prepare(self):\\n logging.info('-> copy configuration...')\\n\\n self._copy_config_to_expt('path_config_bUnwarpJ')\\n if 'path_config_IJ_SIFT' in self.params:\\n self._copy_config_to_expt('path_config_IJ_SIFT')\\n if 'path_config_IJ_MOPS' in self.params:\\n self._copy_config_to_expt('path_config_IJ_MOPS')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9079180c2af6f1d241e28ad5de1418a5\",\n \"score\": \"0.54701686\",\n \"text\": \"def genconf(self, _conf: dict):\\n result = self.__gen_conf_file()\\n\\n if result:\\n print(f\\\"Made {', '.join(result)} successfully\\\")\\n else:\\n print(f\\\"There were configuration files already.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c22e80c0686a4fa96db8594c8b8c28\",\n \"score\": \"0.5469064\",\n \"text\": \"def writeConfigToStore(self):\\n with self.writeSharedFileStream(\\\"config.pickle\\\", isProtected=False) as fileHandle:\\n cPickle.dump(self.__config, fileHandle, cPickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45262bd712e231176fb3b65cce8b22d\",\n \"score\": \"0.54503626\",\n \"text\": \"def config_dump():\\n # TODO - This seems like a candidate to be part of Config class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21366e002c98428dd963858e1a24c77\",\n \"score\": \"0.5435241\",\n \"text\": \"def configs_to_export(context):\\n config = {}\\n hcpdiff_config = {\\\"config\\\": config}\\n for key in [\\\"RegName\\\", \\\"Subject\\\", \\\"DWIName\\\"]:\\n if key in context.config.keys():\\n config[key] = context.config[key]\\n\\n hcpdiff_config_filename = op.join(\\n context.work_dir,\\n context.config[\\\"Subject\\\"],\\n \\\"{}_{}_hcpfunc_config.json\\\".format(\\n context.config[\\\"Subject\\\"], context.config[\\\"DWIName\\\"]\\n ),\\n )\\n\\n return hcpdiff_config, hcpdiff_config_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3efa1a6ea33dbf2e1abc194440b2b71c\",\n \"score\": \"0.54306775\",\n \"text\": \"def write_config(self, filename):\\n # import json\\n # with open(filename, 'w') as fp:\\n # json.dump(self.config.dict(), fp)\\n self.config.filename = filename\\n self.config.write()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1218d74bfbc5e9f499ceee23eff096d5\",\n \"score\": \"0.5391969\",\n \"text\": \"def write(self):\\n # Not needed for gconf\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fccce06c1e500cb114e84b103dec368\",\n \"score\": \"0.5389813\",\n \"text\": \"def __init__(self):\\n self.config = RawConfigParser()\\n self.config.read('./configfiles/serialize_conf.cfg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51febfa66a99711ad312f7750fd824f\",\n \"score\": \"0.536987\",\n \"text\": \"def save(self):# WIP\\n logging.debug('Saving current configuration to self.config_path = {0!r}'.format(self.config_path))\\n # Collect data together.\\n config_data_out = {\\n 'csv_filepath': self.csv_filepath,\\n 'images_dir': self.images_dir,\\n 'zip_path': self.zip_path,\\n 'board_name': self.board_name,\\n }\\n logging.debug('Saving config_data_out = {0!r}'.format(config_data_out))\\n # Write data to file.\\n with open(self.config_path, 'wb') as save_f:\\n yaml.dump(\\n data=config_data_out,\\n stream=save_f,\\n explicit_start=True,# Begin with '---'\\n explicit_end=True,# End with '...'\\n default_flow_style=False# Output as multiple lines\\n )\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2030559d3eb108e495464bfad687562\",\n \"score\": \"0.5368343\",\n \"text\": \"def serialize(self):\\n\\t\\tassert ltrace(globals()['TRACE_' + self.name.upper()], '| serialize()')\\n\\t\\twith self.lock:\\n\\t\\t\\tif self.changed:\\n\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t#raise the hint level, so as the MOVED_TO event will make it\\n\\t\\t\\t\\t\\t# just back to the state where only ONE event will suffice to\\n\\t\\t\\t\\t\\t# trigger it again.\\n\\t\\t\\t\\t\\tself.conf_hint += 1\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tpass\\n\\n\\t\\t\\t\\t#print '>> serialize', self.conf_hint\\n\\n\\t\\t\\t\\tftemp, fpath = tempfile.mkstemp(dir=settings.config_dir)\\n\\n\\t\\t\\t\\tos.write(ftemp, '%s\\\\n' % '\\\\n'.join(sorted(self.iterkeys())))\\n\\t\\t\\t\\tos.fchmod(ftemp, 0644)\\n\\t\\t\\t\\tos.fchown(ftemp, 0, 0)\\n\\t\\t\\t\\tos.close(ftemp)\\n\\n\\t\\t\\t\\tos.rename(fpath, self.conf_file)\\n\\t\\t\\t\\tself.changed = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098c7289d4cfb0922d29179e5a726bc0\",\n \"score\": \"0.53549963\",\n \"text\": \"def config():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f23d51135e008df8bcbe1c48851137b\",\n \"score\": \"0.535434\",\n \"text\": \"def config(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272855bacbee19ab6a037f5cbc8ab885\",\n \"score\": \"0.53530526\",\n \"text\": \"def write(self):\\n with open(self.configfile, 'w') as cfgfile:\\n self.config.write(cfgfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ff1e890e82ba732b72cd8bd185910b\",\n \"score\": \"0.53521955\",\n \"text\": \"def __save_file(self, filename):\\n\\t\\ttry:\\n\\t\\t\\t# open temporary file for writing\\n\\t\\t\\treg_file = open(filename, 'w')\\n\\t\\t\\t# write data to file\\n\\t\\t\\treg_file.write('# univention_ base.conf\\\\n\\\\n')\\n\\t\\t\\treg_file.write(self.__str__())\\n\\t\\t\\t# flush (meta)data\\n\\t\\t\\treg_file.flush()\\n\\t\\t\\tos.fsync(reg_file.fileno())\\n\\t\\t\\t# close fd\\n\\t\\t\\treg_file.close()\\n\\t\\texcept EnvironmentError, ex:\\n\\t\\t\\t# suppress certain errors\\n\\t\\t\\tif ex.errno != errno.EACCES:\\n\\t\\t\\t\\traise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc10c0bdaa787641a2bf9ed40605cf43\",\n \"score\": \"0.5345368\",\n \"text\": \"def __init__(self):\\n # get directory name\\n basedir = os.path.dirname(os.path.abspath(__file__))\\n self.__filename = basedir + \\\"/etc/alarmforwarder.conf\\\"\\n self.__config = configparser.ConfigParser()\\n self.__config.read(self.__filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1a0be37effff52bc2a209c90b687c0\",\n \"score\": \"0.53442496\",\n \"text\": \"def SaveConfig(self):\\n LOG.info('Saving Config - Version:{}'.format(__version__))\\n for l_module in self.m_parts_apis.values():\\n l_module.SaveConfig()\\n for l_module in self.m_module_apis.values():\\n l_module.SaveConfig()\\n LOG.info(\\\"Saved Config.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da93943f4af4196fa82a91c1f5c6377\",\n \"score\": \"0.53321433\",\n \"text\": \"def write(self):\\n\\n self.configobj.write()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5bae1fc0afcb5cb2d99cf6787a98d37\",\n \"score\": \"0.5331288\",\n \"text\": \"def write(self):\\n return self.conf.write(open(self.path, 'w'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e713e083fa7ae6cc70c0e52980336a6\",\n \"score\": \"0.5325092\",\n \"text\": \"def load_from_conf(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59889e159f5e1c64639db9b23ed15209\",\n \"score\": \"0.53057945\",\n \"text\": \"def save_config(self):\\r\\n r = self.resources\\r\\n self.cfg_listeners.write(r.listeners.get_data())\\r\\n self.cfg_registration.write(r.registration.get_data())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0b1f03f523df6dc0986bc7d631af5e\",\n \"score\": \"0.52941704\",\n \"text\": \"def configure(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e4b994141c4364fb2695a7d3ad9eba8\",\n \"score\": \"0.52872485\",\n \"text\": \"def apply_config(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4143eaf752228a3e37a955ed1e14f45\",\n \"score\": \"0.52825373\",\n \"text\": \"def save(self):\\n logging.debug('Saving current configuration to self.config_path = {0!r}'.format(self.config_path))\\n # Collect data together.\\n config_data_out = {\\n 'csv_filepath': self.csv_filepath,\\n 'images_dir': self.images_dir,\\n 'zip_path': self.zip_path,\\n 'board_name': self.board_name,\\n }\\n logging.debug('Saving config_data_out = {0!r}'.format(config_data_out))\\n # Write data to file.\\n with open(self.config_path, 'wb') as save_f:\\n yaml.dump(\\n data=config_data_out,\\n stream=save_f,\\n explicit_start=True,# Begin with '---'\\n explicit_end=True,# End with '...'\\n default_flow_style=False# Output as multiple lines\\n )\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e77b7b2430052b32a911d854a16caf\",\n \"score\": \"0.52820677\",\n \"text\": \"def save(self):\\n t = self._file\\n with open(t, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n json.dump(self._config, f, ensure_ascii=True, indent=4)\\n\\n if not os.path.exists(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], 'project.ini')):\\n for x in self.project_ini.sections():\\n for y in self.project_ini.items(x):\\n v = str(self._config[x][y[0]])\\n self.project_ini[x][y[0]] = v\\n with open(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], 'project.ini'), 'w', encoding=\\\"utf-8\\\") as configfile:\\n self._ini_project.write(configfile)\\n\\n if not os.path.exists(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], 'secret.ini')):\\n for x in self.project_secret_ini.sections():\\n for y in self.project_secret_ini.items(x):\\n v = str(self._config[x][y[0]])\\n self.project_secret_ini[x][y[0]] = v\\n with open(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], 'secret.ini'), 'w', encoding=\\\"utf-8\\\") as configfile:\\n self._ini_secret.write(configfile)\\n\\n for a in self.backend_ini:\\n if not os.path.exists(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], \\\"backapps\\\", a, 'app.ini')):\\n for x in self.backend_ini[a].sections():\\n if x == \\\"BACKEND\\\":\\n for y in self.backend_ini[a].items(x):\\n v = str(self._config['BACKEND'][a][y[0]])\\n self.backend_ini[a][x][y[0]] = v\\n with open(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], \\\"backapps\\\", a, 'app.ini'), 'w', encoding=\\\"utf-8\\\") as configfile:\\n self.backend_ini[a].write(configfile)\\n\\n for a in self.frontend_ini:\\n if not os.path.exists(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], \\\"frontapps\\\", a, 'app.ini')):\\n for x in self.frontend_ini[a].sections():\\n if x == \\\"FRONTEND\\\":\\n for y in self.frontend_ini[a].items(x):\\n v = str(self._config['FRONTEND'][a][y[0]])\\n self.frontend_ini[a][x][y[0]] = v\\n with open(join(self._config[\\\"PROJECT\\\"][\\\"path\\\"], \\\"frontapps\\\", a, 'app.ini'), 'w', encoding=\\\"utf-8\\\") as configfile:\\n self.frontend_ini[a].write(configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2664c62237247e9723b27ec71c6e8e2e\",\n \"score\": \"0.52759707\",\n \"text\": \"def save(self):\\n self.config.write(self.file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad6beb440bd362835423396cfa41fc0\",\n \"score\": \"0.5269988\",\n \"text\": \"def write_conf(run_dir, param):\\n cname = param[\\\"sbatch\\\"].get(\\\"conf_name\\\", \\\"overwritable_conf\\\")\\n if not cname.endswith(\\\".yaml\\\"):\\n cname += \\\".yaml\\\"\\n\\n with open(run_dir / cname, \\\"w\\\") as f:\\n yaml.dump(param[\\\"config\\\"], f, default_flow_style=False)\\n return run_dir / cname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad00c974959668574cc9fe63b21a1cc\",\n \"score\": \"0.5261115\",\n \"text\": \"def pass_configuration(config):\\n global modules\\n\\n print 'Passing configuration'\\n for section in config:\\n module = config[section]['module']\\n modules[module].pass_config(section, config[section])\\n\\n print 'Running setup'\\n for key in modules:\\n modules[key].setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087623e1119f721bd3327faa7dde57ea\",\n \"score\": \"0.52575636\",\n \"text\": \"def write_config(self):\\n assert self.config_file_path is not None, 'config_file_path not set'\\n with open(self.config_file_path, 'wb') as config_file:\\n self.config.write(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb6b3642d38cb4c4ef4685538d60857e\",\n \"score\": \"0.5255247\",\n \"text\": \"def __gen_config_file(self):\\n \\n config_file = '# Platform: %s\\\\n' % platform.platform(aliased=True)\\n config_file += '# Processor: %s\\\\n' % platform.processor()\\n \\n try:\\n config_file += '# Number of CPUs: %s\\\\n' % os.sysconf('SC_NPROCESSORS_ONLN')\\n #config_file += '# Size of word: %s bits\\\\n' % os.sysconf('SC_WORD_BIT')\\n except:\\n pass\\n \\n config_file += '\\\\n\\\\n'\\n \\n # original file\\n first_column = ['* Original File *', '']\\n \\n # size of original file\\n second_column = ['* Size (KB) *', '']\\n \\n # include or not\\n third_column = ['* Include? *', '']\\n \\n # configuration file\\n #fourth_column = ['* Config File? *', '']\\n \\n # file in the reproducible folder\\n #fifth_column = ['* Package File *', '']\\n \\n ########################################################################\\n \\n # function used to include information inside the configuration file\\n def include_in_config(title, file_dict):\\n \\\"\\\"\\\"\\n 'title' -> class of file (main program, ...)\\n 'file_dict' -> dictionary with files\\n \\n Returns lists for first, second, third and fourth columns of\\n configuration file.\\n \\\"\\\"\\\"\\n first_column = []\\n second_column = []\\n third_column = []\\n #fourth_column = []\\n #fifth_column = []\\n \\n first_column.append('[%s]' %title)\\n second_column.append('')\\n third_column.append('')\\n #fourth_column.append('')\\n #fifth_column.append('')\\n \\n for file_ in file_dict:\\n size = ''\\n try:\\n size = '%0.2f' %(os.path.getsize(file_)/1024.0)\\n except:\\n size = 'N/A'\\n \\n first_column.append(file_)\\n second_column.append(size)\\n third_column.append('Y')\\n #fourth_column.append('N')\\n #fifth_column.append(file_dict[file_])\\n \\n if self.__symlink_to_target.has_key(file_):\\n size = ''\\n try:\\n size = '%0.2f' %(os.path.getsize(self.__symlink_to_target[file_])/1024.0)\\n except:\\n size = 'N/A'\\n \\n first_column.append(self.__symlink_to_target[file_])\\n second_column.append(size)\\n third_column.append('Y')\\n #fourth_column.append('N')\\n #fifth_column.append(self.__targets[self.__symlink_to_target[file_]])\\n \\n first_column.append('')\\n second_column.append('')\\n third_column.append('')\\n #fourth_column.append('')\\n #fifth_column.append('')\\n \\n return (first_column, second_column, third_column)\\n #return (first_column, second_column, third_column, fourth_column)\\n \\n ########################################################################\\n \\n # main program\\n if len(self.__main_program) == 1:\\n (first, second, third) = include_in_config('main program',\\n self.__main_program)\\n first_column += first\\n second_column += second\\n third_column += third\\n #fourth_column += fourth\\n \\n # child programs\\n if len(self.__child_programs) > 0:\\n (first, second, third) = include_in_config('other programs',\\n self.__child_programs)\\n first_column += first\\n second_column += second\\n third_column += third\\n #fourth_column += fourth\\n \\n # main input files\\n if len(self.__input_files) > 0:\\n (first, second, third) = include_in_config('main input files',\\n self.__input_files)\\n first_column += first\\n second_column += second\\n third_column += third\\n #fourth_column += fourth\\n \\n # child input files\\n if len(self.__child_input_files) > 0:\\n (first, second, third) = include_in_config('other input files',\\n self.__child_input_files)\\n first_column += first\\n second_column += second\\n third_column += third\\n #fourth_column += fourth\\n \\n # dependencies\\n if len(self.__dependencies) > 0:\\n (first, second, third) = include_in_config('dependencies',\\n self.__dependencies)\\n first_column += first\\n second_column += second\\n third_column += third\\n #fourth_column += fourth\\n \\n # exclude patterns\\n # these patterns should be written using Unix shell-style wildcards,\\n # otherwise it will not work\\n first_column.append('[exclude]')\\n second_column.append('')\\n third_column.append('')\\n #fourth_column.append('')\\n \\n # maximum lengths, for the purpose of formatting\\n max_first = str(len(max(first_column, key=len)))\\n max_second = str(len(max(second_column, key=len)))\\n max_third = str(len(max(third_column, key=len)))\\n #max_fourth = str(len(max(fourth_column, key=len)))\\n \\n format = '{:<%s}\\\\t{:^%s}\\\\t{:^%s}\\\\n' % (max_first, max_second, max_third)\\n for i in range(len(first_column)):\\n config_file += format.format(first_column[i],\\n second_column[i],\\n third_column[i])\\n \\n try:\\n f = open(reprozip.utils.config_path, 'w')\\n f.write(config_file)\\n f.close()\\n except:\\n reprozip.debug.error('Could not save configuration file: %s' %sys.exc_info()[1])\\n sys.exit(1)\\n \\n self.__config_mtime = os.path.getmtime(reprozip.utils.config_path)\\n \\n print '** Configuration file created in \\\"%s\\\" **' % reprozip.utils.config_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c949dc26c4351443e529a7af0dfe6af\",\n \"score\": \"0.5254118\",\n \"text\": \"def writeConf(self):\\n\\t\\tconfStr = \\\"#Start\\\\n[defaults]\\\\n\\\"\\n\\n\\t\\tfor key in self.defaults:\\n\\t\\t\\tconfStr += \\\"%s = %s\\\\n\\\" % (key, str(self.defaults[key]))\\n\\t\\t\\n\\t\\tconfStr += \\\"#End\\\\n\\\"\\n\\t\\t\\n\\t\\tpathName = os.path.expandvars(\\\"${HOME}\\\") + \\\"/.config/tint2/\\\"\\n\\t\\t\\n\\t\\tf = open(pathName+\\\"tintwizard.conf\\\", \\\"w\\\")\\n\\t\\t\\n\\t\\tf.write(confStr)\\n\\t\\t\\n\\t\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb3e87f6f45c613c415731a37db55201\",\n \"score\": \"0.5252355\",\n \"text\": \"def _write_config(self):\\n if not 'component_config.py' in bpy.data.texts.keys():\\n bpy.ops.text.new()\\n bpy.data.texts[-1].name = 'component_config.py'\\n cfg = bpy.data.texts['component_config.py']\\n cfg.clear()\\n cfg.write('component_mw = ' + json.dumps(cfg_middleware, indent=1) )\\n cfg.write('\\\\n')\\n cfg.write('component_modifier = ' + json.dumps(cfg_modifier, indent=1) )\\n cfg.write('\\\\n')\\n cfg.write('component_service = ' + json.dumps(cfg_service, indent=1) )\\n cfg.write('\\\\n')\\n cfg.write('overlays = ' + json.dumps(cfg_overlay, indent=1) )\\n cfg.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf7c0a298a2b72f7fb854f7987938e1\",\n \"score\": \"0.5250742\",\n \"text\": \"def __load_config_files(self):\\n # Parsed configuration files\\n # ==========================\\n\\n # Main Fields\\n self.__main_fields = ('U', 'p')\\n p_dict = ParsedParameterFile(self.config_path('0/p'))\\n U_dict = ParsedParameterFile(self.config_path('0/U'))\\n\\n turbulence_props = ParsedParameterFile(\\n self.config_path('constant/turbulenceProperties')\\n )\\n\\n # Material values\\n transport_props = ParsedParameterFile(\\n self.config_path('constant/transportProperties')\\n )\\n\\n # Controls\\n fv_schemes = ParsedParameterFile(self.config_path('system/fvSchemes'))\\n self.control_dict = ParsedParameterFile(self.config_path('system/controlDict'))\\n self.fv_solutions = ParsedParameterFile(self.config_path('system/fvSolution'))\\n self._decompose_par_dict = ParsedParameterFile(\\n self.config_path('system/decomposeParDict')\\n )\\n\\n # cellZone options\\n self.mrf_props = ParsedParameterFile(\\n self.config_path('constant/MRFProperties')\\n )\\n\\n # Post-Processing\\n self.function_objects_dict = ParsedParameterFile(\\n self.config_path('system/functionObjects'),\\n noHeader=True, preserveComments=False\\n )\\n\\n # Registered files\\n # ================\\n self.foam_file('0/p', p_dict)\\n self.foam_file('0/U', U_dict)\\n\\n self.foam_file('system/fvSolution', self.fv_solutions)\\n self.foam_file('system/fvSchemes', fv_schemes)\\n self.foam_file('system/controlDict', self.control_dict)\\n self.foam_file('system/decomposeParDict', self._decompose_par_dict)\\n\\n self.foam_file('constant/transportProperties', transport_props)\\n self.foam_file('constant/turbulenceProperties', turbulence_props)\\n\\n self.foam_file('constant/MRFProperties', self.mrf_props)\\n self.foam_file('system/functionObjects', self.function_objects_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b51e89eab7a58de3e0ee1b2c8cc89ae\",\n \"score\": \"0.5249466\",\n \"text\": \"def export_command(_args):\\n config = load_all_config()\\n ensure_clean_state(config)\\n for lst in augmented_listing(config):\\n export(config, lst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfb8b8b9aad9b541acda760ea33ec1e\",\n \"score\": \"0.5236535\",\n \"text\": \"def write(self) -> None:\\n if not self.new_actions:\\n return\\n\\n file_data = utils.load_yaml(path=self.path)\\n file_data.setdefault(self.module, {})\\n\\n for action_type, action_options in self.new_actions.items():\\n file_data[self.module].setdefault(\\n action_type,\\n [],\\n ).extend(action_options)\\n\\n utils.dump_yaml(\\n path=self.path,\\n data=file_data,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38aad65b6267b16d5762b112e95a38f\",\n \"score\": \"0.52214885\",\n \"text\": \"def assign_conf(self, conf):\\n\\n if isinstance(conf, str):\\n conf = read_conf(conf)\\n\\n self.conf = conf\\n\\n try:\\n self.archive_id = conf.getint('Archive', 'archive_id')\\n except ValueError:\\n print('Error in configuration file '\\n '([{}], {})'.format('Archive', attr))\\n except configparser.Error:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a80712f0ec344f3e18538c6df3cca05\",\n \"score\": \"0.52145886\",\n \"text\": \"def Convert_Conf_to_Multiple_Servers(self):\\n\\n # check if old settings exist\\n if self.__dict__.has_key(\\\"nagios_url\\\") and \\\\\\n self.__dict__.has_key(\\\"nagios_cgi_url\\\") and \\\\\\n self.__dict__.has_key(\\\"username\\\") and \\\\\\n self.__dict__.has_key(\\\"password\\\") and \\\\\\n self.__dict__.has_key(\\\"use_proxy_yes\\\") and \\\\\\n self.__dict__.has_key(\\\"use_proxy_no\\\"):\\n # create Server and fill it with old settings\\n server_name = \\\"Default\\\"\\n self.servers[server_name] = Server()\\n self.servers[server_name].name = server_name\\n self.servers[server_name].monitor_url = self.nagios_url\\n self.servers[server_name].monitor_cgi_url = self.nagios_cgi_url\\n self.servers[server_name].username = self.username\\n self.servers[server_name].password = self.password\\n # convert VERY old config files\\n try:\\n self.servers[server_name].use_proxy = self.use_proxy_yes\\n except:\\n self.servers[server_name].use_proxy = False\\n try:\\n self.servers[server_name].use_proxy_from_os = self.use_proxy_from_os_yes\\n except:\\n self.servers[server_name].use_proxy_from_os = False\\n # delete old settings from config\\n self.__dict__.pop(\\\"nagios_url\\\")\\n self.__dict__.pop(\\\"nagios_cgi_url\\\")\\n self.__dict__.pop(\\\"username\\\")\\n self.__dict__.pop(\\\"password\\\")\\n self.__dict__.pop(\\\"use_proxy_yes\\\")\\n self.__dict__.pop(\\\"use_proxy_no\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"735022bba88fc7a31f7c936e4d14cc96\",\n \"score\": \"0.5203702\",\n \"text\": \"async def _export(self, ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64a2756c95ee03b323b60f1c05d73ae\",\n \"score\": \"0.51988924\",\n \"text\": \"def setupMasternodeConfFile(server, label, masternodeConfFile, masternodePort, masternodeOutput, masternodeKey): \\r\\n print(\\\"Setup masternode conf file..\\\")\\r\\n \\r\\n # Append to the existing file\\r\\n with open(masternodeConfFile, 'a') as f:\\r\\n line = \\\"\\\\n{0} {1}:{2} {3} {4} {5}\\\".format(label, server, masternodePort, masternodeKey, masternodeOutput[\\\"txhash\\\"], masternodeOutput[\\\"outputidx\\\"])\\r\\n f.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f103611e081a157027980ef701cb7c1c\",\n \"score\": \"0.5196658\",\n \"text\": \"def write_conf(self):\\n f = open(self.output_filename, 'w')\\n print(self.t.render(prefixes=self.prefixes), file=f)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640a1aad4f95bf7188a9c27db156aaa8\",\n \"score\": \"0.5195018\",\n \"text\": \"def _write_files(self):\\n to_save = []\\n for file in self.files.values():\\n to_save.append(file.meta)\\n with open(os.path.join(self.output_directory, \\\"files.yaml\\\"), \\\"w\\\", encoding=\\\"utf8\\\") as f:\\n yaml.safe_dump(to_save, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c036ce47bfb037c32267a675ec5b74\",\n \"score\": \"0.51645446\",\n \"text\": \"def config_save(self):\\n \\n # Create the output directory if needed\\n if not os.path.exists(self.output_dir): os.mkdir(self.output_dir)\\n\\n # Save\\n with open(self.output_dir+'/config.pkl', 'wb') as pfile:\\n pickle.dump(self.__dict__, pfile, pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7054b20e252b419382fc4486b2d3e850\",\n \"score\": \"0.5157857\",\n \"text\": \"def configure(self, filename):\\n self.__filename = filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe82d81ade3eed55613745185762cd1c\",\n \"score\": \"0.51577884\",\n \"text\": \"def file(config):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e631a260a80478c63107b8cc521297d5\",\n \"score\": \"0.51501733\",\n \"text\": \"def saveConfig ():\\n global config\\n filename = os.path.join (configdir, \\\"{0}.config\\\".format (__name__))\\n outfile = open (filename, \\\"w\\\")\\n pickle.dump (config, outfile)\\n outfile.close ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976c4c26419cd5f425db354e0c484df2\",\n \"score\": \"0.5148247\",\n \"text\": \"def genConfFile():\\n conf_file_template = pkg_resources.resource_string(__name__,'rose.batch.conf')\\n f = open(\\\"rose.batch.conf\\\", \\\"w\\\")\\n f.write(conf_file_template)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3cbd18bdbd22f860d3d15bb837943bd\",\n \"score\": \"0.51464355\",\n \"text\": \"def to_file(self):\\r\\n with open('config.json', 'w') as f:\\r\\n f.write(self.to_json_string())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b59facd977d595b93656619b188e9df\",\n \"score\": \"0.51458484\",\n \"text\": \"def _writeHostConfig(self, host, out):\\n confFName = f\\\"{self.workDir}/{host}.yaml\\\"\\n with open(confFName, 'w', encoding='utf-8') as fd:\\n fd.write(yaml.dump(out))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b09cf7cecaa3e3f56dc7faf17734c28\",\n \"score\": \"0.51382\",\n \"text\": \"def write_config(self, filename, data):\\n conf = os.path.join(self.steam_config, filename)\\n text = vdf.dumps(data, pretty=True)\\n write_file(conf, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819c81fcd36a8e7923a19087b56cea92\",\n \"score\": \"0.5136384\",\n \"text\": \"def loadConf ( self ):\\n\\t\\tcp2 = ConfigParser.ConfigParser()\\n\\n\\t\\tself.workingpath = self.validateDir( os.getcwd() )\\n\\t\\tself.userpath = self.validateDir( os.path.expanduser(\\\"~\\\") )\\n\\n\\t\\tself.currentFile = None\\n\\t\\tnew = False\\n\\n\\t\\tif os.path.isfile('conf.dat') == False:\\n\\t\\t\\t#First time using PSPMan or just new conf\\n\\t\\t\\tnew = True\\n\\t\\t\\tcp2.add_section(_('Options'))\\n\\t\\t\\tcp2.set(_('Options'),'PSP_Path','/media/disk/')\\n\\t\\t\\tcp2.set(_('Options'),'ISO_Path',self.userpath + 'ISO/')\\n\\t\\t\\tcp2.set(_('Options'),'Save_Path',self.userpath + 'SAVEDATA/')\\n\\t\\t\\tcp2.set(_('Options'),'OSD', 0)\\n\\t\\t\\tcp2.set(_('Options'),'Bell', 0)\\n\\t\\t\\tcp2.set(_('Options'),'Expl', 'nautilus')\\n\\n\\t\\t\\twith open('conf.dat','w') as file:\\n\\t\\t\\t cp2.write(file)\\n\\t\\t\\t \\n\\n\\t\\tcp2.read('conf.dat')\\n\\n\\t\\tself.mspath = cp2.get(_('Options'), 'PSP_Path')\\n\\t\\tself.localiso = cp2.get(_('Options'), 'ISO_Path')\\n\\t\\tself.tSave = cp2.get(_('Options'),'Save_Path')\\n\\t\\tself.oOSD = cp2.get(_('Options'),'OSD')\\n\\t\\tself.oBell = cp2.get(_('Options'),'Bell')\\n\\t\\tself.Expl = cp2.get(_('Options'), 'Expl')\\n\\t\\t\\n\\t\\tself.oOSD = bool(int(self.oOSD))\\n\\t\\tself.oBell = bool(int(self.oBell))\\n\\n\\t\\tself.mspsp = self.mspath + 'ISO'\\n\\t\\tself.mssave = self.mspath + 'PSP/SAVEDATA'\\n\\t\\tself.busy = False\\n\\n\\t\\tif new:\\n\\t\\t\\tself.notify(_(\\\"This is your first run. Please, go to configuration first.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550cc411450da75926cf306d6f18236e\",\n \"score\": \"0.51336646\",\n \"text\": \"def _update_conf(conf:Config)->None:\\n\\n conf_common = get_conf_common(conf)\\n conf_dataset = get_conf_dataset(conf)\\n experiment_name = conf_common['experiment_name']\\n\\n # make sure dataroot exists\\n dataroot = conf_dataset['dataroot']\\n dataroot = utils.full_path(dataroot)\\n\\n # make sure logdir and expdir exists\\n logdir = conf_common['logdir']\\n if logdir:\\n logdir = utils.full_path(logdir)\\n expdir = os.path.join(logdir, experiment_name)\\n\\n # directory for non-master replica logs\\n distdir = os.path.join(expdir, 'dist')\\n else:\\n expdir = distdir = logdir\\n\\n # update conf so everyone gets expanded full paths from here on\\n # set environment variable so it can be referenced in paths used in config\\n conf_common['logdir'] = logdir\\n conf_dataset['dataroot'] = dataroot\\n conf_common['expdir'] = expdir\\n conf_common['distdir'] = distdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988f1fc1521444d216741f0aadb3b5cb\",\n \"score\": \"0.5130486\",\n \"text\": \"def _write_yaml(self):\\n # add environment variables\\n to_yaml = {}\\n to_yaml['ENVIRONMENT_VARIABLES'] = self.environment_variables\\n # add blacklist\\n plugin_packages = os.listdir(self.local_paths.plugins)\\n blacklist_as_dict = {}\\n for pkg in plugin_packages:\\n if pkg in self.blacklist:\\n blacklist_as_dict[pkg] = True\\n else:\\n blacklist_as_dict[pkg] = False\\n to_yaml['BLACKLIST'] = blacklist_as_dict\\n # dump all to yaml file\\n with open(self.yaml_path, 'w') as stream:\\n yaml.dump(to_yaml, stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648b88636bec6087ba4cb30b07466dcb\",\n \"score\": \"0.51302344\",\n \"text\": \"def __init__(self):\\n # check for directory path\\n if not os.path.exists(conf_dir):\\n os.mkdir(conf_dir)\\n if not os.path.exists(tor_data):\\n os.mkdir(tor_data)\\n\\n if not os.path.exists(config_file):\\n with open(config_file, 'w') as cfg:\\n # XXX. add keys with their default values.\\n cfg.write('dio: cane\\\\n')\\n\\n # load configuration\\n with open(config_file, 'r') as cfg:\\n for key, value in (yaml.load(cfg) or dict()).iteritems():\\n setattr(self, key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2fec3e98f45796cfffef4de89557709\",\n \"score\": \"0.5128848\",\n \"text\": \"def _cfg_import_export(cfg_interactor, cfg_, prefix='', mode='fill_parser'):\\r\\n for k in dir(cfg_):\\r\\n if k[0] == '_': continue # hidden\\r\\n v = getattr(cfg_, k)\\r\\n if type(v) in [float, str, int, bool]:\\r\\n if mode == 'fill_parser':\\r\\n cfg_interactor.add_argument('--{}{}'.format(prefix, k), type=type(v), help='default: {}'.format(v))\\r\\n elif mode == 'fill_dict':\\r\\n cfg_interactor['{}{}'.format(prefix, k)] = v\\r\\n elif mode == 'override':\\r\\n prek = '{}{}'.format(prefix, k)\\r\\n if prek in cfg_interactor:\\r\\n setattr(cfg_, k, getattr(cfg_interactor, prek))\\r\\n elif type(v) == Bunch: # recurse; descend into Bunch\\r\\n _cfg_import_export(cfg_interactor, v, prefix=prefix + k + '.', mode=mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b351f4bd4594e909ddcdc4fec7edea\",\n \"score\": \"0.51235884\",\n \"text\": \"def configure():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3624c6dfdec0e7e0894874ee0e1603b\",\n \"score\": \"0.5122384\",\n \"text\": \"def configure(self):\\n \\n # working directory of main program\\n # this directory will be used to find a common directory structure\\n # between programs and files\\n wdir = self.__prov_tree.root.execve_pwd\\n \\n ########################################################################\\n \\n # function that takes care of including the necessary files in the\\n # reproducible package\\n def add_rep_file(file, file_dict):\\n \\\"\\\"\\\"\\n file -> file to be added\\n file_dict -> dictionary containing the file\\n \\\"\\\"\\\"\\n \\n file_ = file\\n if file_.startswith(os.sep):\\n file_ = file_[1:]\\n file_dict[file] = os.path.join(reprozip.utils.exp_dir, file_)\\n \\n # symbolic links\\n if self.__symlink_to_target.has_key(file):\\n target = self.__symlink_to_target[file]\\n self.__targets[target] = None\\n add_rep_file(target, self.__targets)\\n \\n ########################################################################\\n \\n # child programs\\n reprozip.debug.verbose(self.verbose, 'Configuring child programs...')\\n for program in self.__child_programs:\\n add_rep_file(program, self.__child_programs)\\n \\n # main program\\n reprozip.debug.verbose(self.verbose, 'Configuring main program...')\\n add_rep_file(self.__main_program.keys()[0], self.__main_program)\\n \\n # main input files\\n reprozip.debug.verbose(self.verbose, 'Configuring main input files...')\\n for input_file in self.__input_files:\\n add_rep_file(input_file, self.__input_files)\\n \\n # child input files\\n reprozip.debug.verbose(self.verbose, 'Configuring child input files...')\\n for input_file in self.__child_input_files:\\n add_rep_file(input_file, self.__child_input_files)\\n \\n # directories\\n reprozip.debug.verbose(self.verbose, 'Configuring directories...')\\n for dir in self.__dirs:\\n add_rep_file(dir, self.__dirs)\\n \\n # trying to identify implicit input files among dependencies\\n # a dependency is considered an implicit input file if it is\\n # located together with other input files (considering the working\\n # directory of the main program as the common structure) \\n # implicit input files are put together with child input files\\n reprozip.debug.verbose(self.verbose, 'Configuring dependencies...')\\n \\n rm_dependencies = []\\n for dependency in self.__dependencies:\\n l = [dependency, wdir]\\n common_prefix = os.path.normpath(os.path.commonprefix(l))\\n if not os.path.exists(common_prefix):\\n common_prefix = os.path.dirname(common_prefix)\\n \\n # if there is no common_prefix\\n if (common_prefix == '') or (common_prefix == os.sep):\\n continue\\n \\n # if there is a common prefix\\n else:\\n self.__child_input_files[dependency] = None\\n rm_dependencies.append(dependency)\\n \\n add_rep_file(dependency, self.__child_input_files)\\n \\n for rm_dependency in rm_dependencies:\\n self.__dependencies.pop(rm_dependency)\\n\\n # dependencies\\n # dependencies are libraries and possible include files\\n for dependency in self.__dependencies:\\n add_rep_file(dependency, self.__dependencies)\\n \\n# self.verbose_(['input files: %s' %str(self.__input_files),\\n# 'output files: %s' %str(self.__output_files),\\n# 'main program: %s' %str(self.__main_program),\\n# 'child programs: %s' %str(self.__child_programs),\\n# 'child input files: %s' %str(self.__child_input_files),\\n# 'dependencies: %s' %str(self.__dependencies)])\\n\\n # generate configuration file\\n reprozip.debug.verbose(self.verbose, 'Writing configuration file...')\\n self.__gen_config_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a3d624364d4ccf35ed926cd1da33f0\",\n \"score\": \"0.5121893\",\n \"text\": \"def export(self, context):\\n context.writeDataFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009cb950ea482d0a40062eece34c54a6\",\n \"score\": \"0.5114487\",\n \"text\": \"def downloadConfAndMeta(api, project, destDir, api_user, api_password):\\n if not os.path.isdir(destDir):\\n os.makedirs(destDir)\\n for myFile in [\\\"_meta\\\", \\\"_config\\\"]:\\n fileUrl = urllib.quote(\\\"source/%s/%s\\\" % (project, myFile))\\n fileUrl = \\\"%s/%s\\\" % (api, fileUrl)\\n retCode = downloadFile(fileUrl, destDir, user=api_user, password=api_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6db13bc962ff97c037dc77380348dd\",\n \"score\": \"0.510468\",\n \"text\": \"async def write_config_to_yml():\\n from hummingbot.client.config.in_memory_config_map import in_memory_config_map\\n current_strategy = in_memory_config_map.get(\\\"strategy\\\").value\\n strategy_file_path = in_memory_config_map.get(\\\"strategy_file_path\\\").value\\n\\n if current_strategy is not None and strategy_file_path is not None:\\n strategy_config_map = get_strategy_config_map(current_strategy)\\n strategy_file_path = join(CONF_FILE_PATH, strategy_file_path)\\n await save_to_yml(strategy_file_path, strategy_config_map)\\n\\n await save_to_yml(GLOBAL_CONFIG_PATH, global_config_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77ecd2860b6db1f1c31e69ad334ee1c\",\n \"score\": \"0.51043546\",\n \"text\": \"def _create_new_config_file(self):\\n os.makedirs(os.path.dirname(self.path), exist_ok=True)\\n self.cfg = configparser.ConfigParser()\\n self._set_up_default_config()\\n self._write()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db07be9a7ebc87a60da2be3336c54b08\",\n \"score\": \"0.51019675\",\n \"text\": \"def writeFile(self): \\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc784122edba566772e2d21f70ec630\",\n \"score\": \"0.51015824\",\n \"text\": \"def load_conf(self):\\n self.net = self.load_net(self.cfgfilepath,\\n self.weightsfilepath,\\n 0)\\n self.meta = self.load_meta(self.datafilepath)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993695,"cells":{"query_id":{"kind":"string","value":"ee1cf8ae640d9d6335f1a2b91185bac0"},"query":{"kind":"string","value":"calculate min error for all learners"},"positive_passages":{"kind":"list like","value":[{"docid":"47cfa6338e4dcfde0b322f4f814868f3","score":"0.7466648","text":"def getMinError(self):\n return np.array([learner.getMinError()()\n for learner in self._learners])","title":""}],"string":"[\n {\n \"docid\": \"47cfa6338e4dcfde0b322f4f814868f3\",\n \"score\": \"0.7466648\",\n \"text\": \"def getMinError(self):\\n return np.array([learner.getMinError()()\\n for learner in self._learners])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"317a5e9d0284abde4cc9dd6f16d84f2a","score":"0.64385617","text":"def batch_training(self, num_minibatches):\n\n epoch_tr_err = 0 # initialize the training error for each epoch to zero\n\n if self.with_metrics:\n\n for l in range(num_minibatches): # minibatch loop\n _, batch_err = self.sess.run([self.opt, self.Rmse])\n # average msr error per minibatch\n epoch_tr_err += batch_err / num_minibatches\n\n else:\n for l in range(num_minibatches): # minibatch loop\n _ = self.sess.run(self.opt)\n\n return epoch_tr_err","title":""},{"docid":"610b4289607343eb2e06b52d65c93edc","score":"0.62677014","text":"def compute_errors(m, x_train, y_train, x_test, y_test, stds, one_hot_encoded):\n\n model = m()\n if(stds is None):\n for p in model.parameters():\n if len(p.size()) == 2:\n updated_tensor, std = xavier_normal_(p.data.normal_(0, 1), gain=1)\n print(\"Computed standard deviation according to 'Xavier initialization': {:.3f}\\n\".format(std))\n stds = [std]\n for std in stds:\n if std > 0:\n for p in model.parameters():\n p.data.normal_(0, std)\n\n train_model(model, x_train, y_train, one_hot_encoded)\n train_error = compute_nb_errors(model, x_train, y_train, one_hot_encoded)\n test_error = compute_nb_errors(model, x_test, y_test, one_hot_encoded)\n print('std {:f} {:s} train_error {:.02f}% test_error {:.02f}%'.format(\n std,\n m.__name__,\n train_error / x_train.size(0) * 100,\n test_error / x_test.size(0) * 100))","title":""},{"docid":"04621ad38fc674e2584e27d8371dfb7e","score":"0.62116385","text":"def getMaxError(self):\n return np.array([learner.getMaxError()()\n for learner in self._learners])","title":""},{"docid":"4f2cfb4f3b00df038a0e99c3faf8cd41","score":"0.61574125","text":"def cal_error(self):\n\n # calculate error of the train dataset and turn it into the seasonal\n # error\n self.trn_error = cal_error(\n self.dataset,\n self.model,\n data_index=self.dataset.split_list[0])\n self.sea_error = cal_season_error(\n self.trn_error, roll_win=14, agg='mean')\n print('max error', np.max(self.sea_error.values))","title":""},{"docid":"9f5849b746287adcea07faa7ee6b2385","score":"0.61223793","text":"def _get_sum_error(self, training_set):\n return sum([target_result - self.evaluate(inputs)\n for inputs, target_result in training_set])","title":""},{"docid":"d8ae5f90b92658825976eb6f27f2e9e7","score":"0.60142446","text":"def learing_rate(self, error_est):\r\n return 0.008*np.sqrt(error_est)+1","title":""},{"docid":"0095ee2df021a5c92f42358fc992881c","score":"0.600224","text":"def _calc_mlc_error(self):\n for picket in self.pickets:\n picket.fit_poly()\n picket.calc_mlc_errors()","title":""},{"docid":"b15c9b345a86b13b2f4fb04f3eff1596","score":"0.59789574","text":"def train_dist_error_model(self, xtrain, ytrain, k=5):\n\n if xtrain.shape[0] != ytrain.shape[0]:\n raise TypeError(\"Xtrain and Ytrain do not have same shape.\")\n\n dist_list = []\n for x0 in xtrain:\n\n # Change basis\n x0 = np.dot(self.L_mat, x0)\n\n # Get nearest neighbors in original training set\n dist, loc = self.transf_xtree.query(x0, k=k)\n # Weighted density in ball for NN\n # dist = np.array([np.max([1e-15,d]) for d in dist])\n # weight = 1.0/dist\n # dist_list.append(np.sum(weight))\n dist_list.append(np.mean(dist))\n\n dist_list = np.array(dist_list)\n scalar_error = np.mean(\n np.abs(ytrain), axis=1\n ) # TODO: is this a good error measure?\n\n assert dist_list.shape == scalar_error.shape\n\n def error_model(dist, a, b, c):\n return a * (dist) + b * (dist) ** c\n\n bestfit, cov = opt.curve_fit(\n error_model,\n dist_list,\n scalar_error,\n # bounds=((0.0,0.0,0.0),(np.inf,np.inf,np.inf)))\n bounds=((0.0, 0.0, 0.0), (1e1, 1e1, 1e1)),\n )\n\n # print(\"this is bestfit:\", bestfit)\n\n def new_error_model(xval):\n xval = np.dot(self.L_mat, xval)\n # Get nearest neighbors in original training set\n dist, loc = self.transf_xtree.query(xval, k=k)\n # Mean distance to NN\n dist = np.mean(dist)\n\n # dist = dist/bestfit[2]\n\n err_guess = bestfit[0] * dist + bestfit[1] * dist ** bestfit[2]\n # rand_sign = np.random.rand() - 0.5\n # err_guess *= 1.0 if rand_sign>0.0 else -1.0\n\n return err_guess\n\n # DEBUG\n # import matplotlib.pyplot as plt # type: ignore\n # plt.plot(dist_list, np.abs(ytrain),'bo')\n # plt.plot(dist_list, map(new_error_model,xtrain),'ro')\n # plt.show()\n\n return new_error_model","title":""},{"docid":"4a6d71cfba389e0db16f81a3750054a3","score":"0.5899873","text":"def compute_training_error(self, users : numpy.ndarray,\n movies : numpy.ndarray, user_ids : list,\n movie_ids : list, ratings : numpy.ndarray):\n error = 0.0\n\n for i in range(0, ratings.shape[0]):\n user_id = user_ids[i]\n movie_id = movie_ids[i]\n\n prediction = self.training_predict(user_id, movie_id, users, movies)\n error += math.fabs(prediction - ratings[i][0])\n\n return error / ratings.shape[0]","title":""},{"docid":"8e91eea44e99a5f1d9d3047c68bbb63e","score":"0.5858162","text":"def _compute_stats(self):\n # this code was adapted from fastai: https://github.com/fastai/fastai\n self.ml = np.argmin(self.losses)\n try:\n self.mg = (np.gradient(np.array(self.losses[32 : self.ml]))).argmin()\n except Exception as e:\n self.mg = None\n warnings.warn(str(e))\n return","title":""},{"docid":"35fbfadff3a17180ed59339be62c2b43","score":"0.5813214","text":"def train(self, training_set, learning_rate, pre_error=False):\n error = 0\n #pe = self._get_sum_error(training_set) if pre_error else None\n for inputs, target_result in training_set:\n error += (target_result - self.evaluate(inputs))**2\n if pre_error:\n evals = []\n for inputs, target_result in training_set:\n evals.append(self.evaluate(inputs))\n for i, weight in enumerate(self.weights):\n delta = 0\n for j,(inputs, target_result) in enumerate(training_set):\n inp = inputs + [1]\n delta += (target_result - evals[j])*inp[i]\n self.weights[i] += learning_rate*delta\n else:\n for inputs, target_result in training_set:\n self._alter_weight(inputs, target_result, learning_rate, None)\n return error / 2.","title":""},{"docid":"21feee7460cbac075f1121009e53bc97","score":"0.579272","text":"def calculate_error(self, predictions, targets):\r\n rss = 0\r\n for prediction, target in zip(predictions, targets):\r\n rss += ((prediction - target) ** 2)\r\n #print(\"Prediction: \", prediction, \" Target: \", target)\r\n return math.sqrt(rss/len(predictions))","title":""},{"docid":"fb144aa587600901b1cb850ba03c2d44","score":"0.5779556","text":"def eval_error(experimental_values: numpy.ndarray, prediction_values: numpy.ndarray) -> float:\n return math.sqrt(numpy.sum((experimental_values - prediction_values) ** 2) / len(experimental_values))","title":""},{"docid":"b7f9b8c4ac23a0e5388636fd9eee778b","score":"0.576412","text":"def error(self):\n\n sum, count = 0, 0\n for dim, center in enumerate(self.centers):\n for point in self.classes[dim]:\n sum += self._euclidean_distance(point, center)\n count += len(self.classes[dim])\n return sum / count","title":""},{"docid":"ef6f14cb469c57d4c2372149ac44833d","score":"0.57414156","text":"def q5_error_rate_on_test_set(self):\n test_data = [zip(*sentence) for sentence in self.test.tagged_sentences]\n self.model_tester = ModelTester(lambda x: self.model.decode(x), self.model.states, test_data)\n print(\"Q5: Error rate on model for test set:\", self.model_tester.get_error_rate())","title":""},{"docid":"1d857de1e1fe796eef90b23645c44d83","score":"0.57398325","text":"def ex_1_1_b(x_train, x_test, y_train, y_test):\n\n ## TODO\n nh = 5\n max_it = 10\n min_i_train = 11\n min_i_test = 11\n max_train = 0\n min_train = 1\n min_test = 11\n mean_train = 1\n std_train = 0\n mse = np.zeros(shape=(10))\n for i in range(0,max_it):\n nn = MLPRegressor(activation='logistic',solver='lbfgs',max_iter=5000,alpha= 0,hidden_layer_sizes=(nh,),random_state=i)\n nn.fit(x_train,y_train)\n mse_train = calculate_mse(nn, x_train, y_train)\n mse_test = calculate_mse(nn, x_test, y_test)\n mse[i] = mse_train\n print(i,'.)MSE train ',mse_train)\n # print(i,'.)MSE test ',mse_test)\n if(mse_train < min_train):\n min_train = mse_train\n min_i_train = i\n if(mse_test < min_test):\n min_test = mse_test\n min_i_test = i\n \n if(mse_train > max_train):\n max_train = mse_train\n \n print(\"Minimum Train: \",min_train)\n print(\"Maximum Train: \",max_train)\n mean_train = mse.mean()\n print(\"Mean Train: \",mean_train)\n std_train = np.std(mse)\n print(\"Standard Derivation Train: \",std_train)\n print(\"Min Index train: \",min_i_train,\" & test: \",min_i_test)\n pass","title":""},{"docid":"148f6bc1a1c069d412e2e005080db88b","score":"0.57361376","text":"def get_errors(self):\n\n self.restore_best_fit()\n\n if not self._migrad_has_converged():\n \n raise CannotComputeErrors(\"MIGRAD results not valid, cannot compute errors. Did you run the fit first ?\")\n\n try:\n\n self.minuit.minos()\n\n except:\n\n raise MINOSFailed(\"MINOS has failed. This is not necessarily a problem if:\\n\\n\"\n \"* There are unconstrained parameters (the error is undefined). This is usually signaled \"\n \"by an approximated error, printed after the fit, larger than the best fit value\\n\\n\"\n \"* The fit is very difficult, because of high correlation between parameters. This is \"\n \"signaled by values close to 1.0 or -1.0 in the correlation matrix printed after the \"\n \"fit step.\\n\\n\"\n \"In this cases you can check the contour plots with get_contours(). If you are using a \"\n \"user-defined model, you can also try to reformulate your model with less correlated \"\n \"parameters.\")\n\n # Make a list for the results\n\n errors = collections.OrderedDict()\n\n for k, par in self.parameters.iteritems():\n\n minuit_name = self._parameter_name_to_minuit_name(k)\n\n minus_error = self.minuit.merrors[(minuit_name, -1)]\n plus_error = self.minuit.merrors[(minuit_name, 1)]\n\n errors[k] = ((minus_error,plus_error))\n\n return errors","title":""},{"docid":"9776c7831f4d066f3d09444a544e3448","score":"0.56850666","text":"def roulette_selection_min(self, errors):\n sum_e = sum(errors)\n max_e = max(errors)\n min_e = min(errors)\n p = random.random() * sum_e\n t = max_e + min_e\n chosen_index = 0\n for idx, e in enumerate(errors):\n p -= (t - e)\n if p < 0:\n chosen_index = idx\n break\n return chosen_index","title":""},{"docid":"3158f5b8aa06c2c1afa683358dbd6426","score":"0.5648767","text":"def training_error(f,xx,yy):\n s = f(xx) - yy\n squareSum = 0\n for i in range(len(s)):\n squareSum = squareSum + s[i]**2\n return np.sqrt(squareSum)","title":""},{"docid":"c469b18f7f1d2cfa3dc99ae5b987498e","score":"0.5647742","text":"def mae(errors):\n return np.mean(np.abs(errors.flatten()))","title":""},{"docid":"89a46661a485a7473b1df9938facc0b3","score":"0.56444246","text":"def calc_errors(self):\n filt_type = self.filt_type\n\n fs = array([])\n MSEs = array([])\n UPREs = array([])\n DP2s = array([])\n GCVs = array([])\n if filt_type != 'TSVD': Lcs = array([])\n for alpha in self.rng:\n fs = append(fs, self.relative_error(alpha))\n MSEs = append(MSEs, self.MSE(alpha))\n UPREs = append(UPREs, self.UPRE(alpha))\n DP2s = append(DP2s, self.DP2(alpha))\n GCVs = append(GCVs, self.GCV(alpha))\n if filt_type != 'TSVD': Lcs = append(Lcs, self.Lcurve(alpha))\n self.fs = fs \n self.MSEs = MSEs \n self.UPREs = UPREs\n self.DP2s = DP2s \n self.GCVs = GCVs \n if filt_type != 'TSVD': self.Lcs = Lcs","title":""},{"docid":"4917c80f2119663d1dde0ea7ca859c05","score":"0.5641145","text":"def train_network(self):\n global_error_stats = []\n refined = False\n j = 0\n while j < self.EPOCH_LIMIT and not refined:\n j += 1\n # feed forward\n layer_outputs = [self.data[:self.TRAIN_RATIO]] # first layer output\n for i in range(1, len(self.layers)):\n output = self.activate(np.dot(layer_outputs[-1],self.layers[i].weights))\n layer_outputs.append(output)\n\n # get output layer error - how much did we miss the traget value\n layer_error = self.expected[:self.TRAIN_RATIO] - layer_outputs[-1]\n\n # mean of global output error\n global_err = np.mean(np.abs(layer_error))\n # track global error changing for statistics\n global_error_stats.append(global_err)\n # if global error is small enough, stop\n if global_err < 0.01: # TODO\n refined = True\n if j % 100 == 0:\n print(statistics.mean(global_error_stats[-99:]))\n\n # get weight error differences\n error_deltas = [layer_error * self.activate(layer_outputs[-1], deriv=True)] # last layer error delta\n for i in range(len(self.layers)-2, 0, -1):\n # how much did each prev layer output value contribute to the error (according to the weights)\n layer_error = np.dot(error_deltas[-1], self.layers[i+1].weights.T)\n # if prediction is confident, dont change much (change rate depends on the slope of the sigmoid)\n error_delta = layer_error * self.activate(layer_outputs[i], deriv=True)\n error_deltas.append(error_delta)\n\n # update weights\n error_deltas.reverse() # reverse this so easier to iterate with i\n layer_outputs.pop()\n for i in range(len(self.layers)-1, 0, -1):\n self.layers[i].weights += self.LEARNING_RATE * np.dot(layer_outputs.pop().T, error_deltas[i-1])\n\n global_error_stats = [*np.array_split(global_error_stats, j/100)]\n plt.plot([statistics.mean(sub) for sub in global_error_stats])\n plt.ylabel('global error in (0,1)')\n plt.xlabel('100 iterations')\n plt.show()","title":""},{"docid":"dd465a5d976c7afbe07433368dede393","score":"0.5634746","text":"def get_error_rate(digits,labelled_centroids):\n classified_incorrect = 0\n for (label,digit) in digits:\n classified_label = classify_digit(digit, labelled_centroids)\n if classified_label != label:\n classified_incorrect +=1\n error_rate = classified_incorrect / float(len(digits))\n return error_rate","title":""},{"docid":"870dcb066fc8bbbda0c916280321e27f","score":"0.56330866","text":"def _initialize_errors(self):\n \n # Initialize errors for each expert\n \n # The maximum left and right error for each expert\n self.max_error_left = np.zeros([self.num_experts])\n self.max_error_right = np.zeros([self.num_experts])\n \n # The minimum and maximum position predictions of each expert\n self.min_predict = (np.ones([self.num_experts]) * self._data_set.num_positions) - 1\n self.max_predict = np.zeros([self.num_experts])\n \n # The minimum and maximum true positions handled by each expert\n self.min_pos = (np.ones([self.num_experts]) * self._data_set.num_positions) - 1\n self.max_pos = np.zeros([self.num_experts])","title":""},{"docid":"4b7596de55884a8627bfe8e498437762","score":"0.5629915","text":"def training_score(self):\n # Try to calculate the mean absolute error if class instance contains all relevant information\n try:\n mae = mean_absolute_error(self.target.reshape(-1, 1), self.predictions)\n print('The mean absolute error is: {}'.format(mae))\n except AttributeError:\n print('There is no target vector present. Note that this method can only '\n 'be used by instances that were used for training ')","title":""},{"docid":"555cbb8e79a1ccf52fab4bd739258c93","score":"0.56187147","text":"def mse(errors):\n return np.mean(errors.flatten() ** 2)","title":""},{"docid":"8868cb9832652039e522070923c4e249","score":"0.5617532","text":"def _roc_error(self):\n s = self\n # r1 is number of negatives with each score,\n # r2 is number of positives rated higher than each score\n # r3 is number of positives with each score\n # r4 is number of negatives rated lower than each score\n r1 = s.NE\n r2 = s.TP - s.PE\n r3 = s.PE\n r4 = s.TN\n r5 = r1 * r2 + 0.5 * r1 * r3\n r6 = r3 * (r4**2 + r4*r1 + (r1**2)/3)\n r7 = r1 * (r2**2 + r2*r3 + (r3**2)/3)\n N = float(len(s.nscores))\n P = float(len(s.pscores))\n W = r5.sum() / (N*P)\n Q2 = r6.sum() / (P * N**2)\n Q1 = r7.sum() / (N * P**2)\n W_stderr = nx.sqrt((W*(1-W)+(P-1)*(Q1-W**2)+(N-1)*(Q2-W**2))/(P*N))\n #print W, Q1, Q2, W_stderr\n self.W = W\n self.W_stderr = W_stderr","title":""},{"docid":"80503037f1dbb1ece1868f6a0cc1b46f","score":"0.56028014","text":"def test_regression_learners(self):\n\n lrn = []\n num_exceptions = 0\n try:\n lrn.append(r.svr())\n lrn.append(r.elastic_net_regression())\n lrn.append(r.ridge_regression())\n lrn.append(r.ard_regression())\n lrn.append(r.regression_tree())\n # lrn.append( r.lasso_LARS() ) # complains about data scaling ?\n # lrn.append( r.sgd_regressor() )# complains about data scaling ?\n\n except Exception as e:\n num_exceptions += 1\n print(\"Exception: \" + str(e))\n\n self.assertIs(num_exceptions, 0)\n return lrn","title":""},{"docid":"eafb96359d83001e91d56e06219cd70f","score":"0.55901015","text":"def error_rate(self):\n error = 0.0\n rows, cols = self.reviews.shape\n for idx in xrange(rows):\n for jdx in xrange(cols):\n if self.reviews[idx, jdx] > 0:\n error += (self.model[idx, jdx] - self.reviews[idx, jdx]) ** 2\n print error\n return error","title":""},{"docid":"1b3143fd23f1d6ff18a127c0c26c8dd2","score":"0.5588403","text":"def mse(self):\n user_ids, item_ids, ratings = [], [], []\n for u,i,r in self.R:\n user_ids.append(u)\n item_ids.append(i)\n ratings.append(r)\n \n error = 0\n for u,i,r in self.R:\n user_attr = self.UserAttr[u] if self.fit_user_attributes else None\n item_attr = self.ItemAttr[i] if self.fit_item_attributes else None\n predicted = self._predict(u, i, user_attr, item_attr)\n error += pow(r - predicted, 2)\n return np.sqrt(error)","title":""},{"docid":"9560742e660554a2a613bb047e9006d8","score":"0.55805045","text":"def current_error(self):\n preds = self.predict_dataset(self.dataset)\n rmse = mean_squared_error(self.dataset[:, -1], preds) ** 0.5\n mae = mean_absolute_error(self.dataset[:, -1], preds)\n return rmse, mae","title":""},{"docid":"c29cc34f7384ef798c5642757b384fd6","score":"0.5580416","text":"def fit(self, train_set_x, train_set_y):\n self.w = (sum(train_set_x*train_set_y))/(sum(train_set_x*train_set_x))\n\n self.bias = (sum(train_set_y)-self.w*sum(train_set_x))/len(train_set_x)\n\n average_train_error = sum((train_set_x * self.w + self.bias - train_set_y) ** 2) / len(train_set_x)\n print('average squared training error:', average_train_error)","title":""},{"docid":"9fca8a9f0786859bc1add1bfcb91c394","score":"0.5575022","text":"def errors(self):\n a, p = self.actual, self.pred\n return [a[i] - p[i] for i in range(len(a))]","title":""},{"docid":"4015c848f0399c8a4e4b12a9bdba1fed","score":"0.5573774","text":"def NeuralNetErrorMetrics(resultsDF):\n # Turning columns to values\n testY = resultsDF['Trajanje'].values\n preds = resultsDF['Predictions'].values\n\n resultsDF['minValue'] = resultsDF.min(axis = 1)\n minValue = resultsDF['minValue'].values\n\n # Calculate difference between real and predicted values\n diff = np.abs((testY - preds) / testY) * 100\n diffProduction = np.abs((testY - preds) / minValue) * 100\n # test_y['diff'] = diff\n # test_y['diffProduction'] = diffProduction\n\n # Put values in DF\n diff = pd.DataFrame(data = diff, columns = ['Error %'])\n diffProduction = pd.DataFrame(data = diffProduction, columns = ['Error %'])\n \n return diff, diffProduction","title":""},{"docid":"1600cd31abea1aaf71b8a2f2b5f8f914","score":"0.55589885","text":"def errfunc(self, ynodes, x, y):\n return self.fitfunc(x, ynodes) - y","title":""},{"docid":"622d84ad60620427b6220b81d39deb00","score":"0.5557471","text":"def error(clf, X, y, ntrials=100, test_size=0.2) :\n\n ### ========== TODO : START ========== ###\n # compute cross-validation error using StratifiedShuffleSplit over ntrials\n # hint: use train_test_split (be careful of the parameters)\n \n # use stratified shuffle split over ntrials\n ss_split = StratifiedShuffleSplit(n_splits = ntrials, test_size = test_size, random_state = 0)\n\n # get accumulators set up\n acc_train = 0\n acc_test = 0\n acc_f1 = 0\n\n # get train and test\n for train, test in ss_split.split(X,y):\n # fit to training data\n clf.fit(X[train], y[train])\n\n # predict from the test data\n y_pred_train = clf.predict(X[train])\n y_pred_test = clf.predict(X[test])\n\n y_train_pred = clf.predict(X[train])\n y_test_pred = clf.predict(X[test])\n\n # get the testing error and add to accumulators\n acc_test += 1 - metrics.accuracy_score(y[test], y_pred_test, normalize = True)\n acc_train += 1 - metrics.accuracy_score(y[train], y_pred_train, normalize = True)\n acc_f1 += metrics.f1_score(y[test], y_pred_test, average = \"micro\")\n\n # set up return values\n train_error = acc_train / ntrials\n test_error = acc_test / ntrials\n f1_score = acc_f1 / ntrials\n \n ### ========== TODO : END ========== ###\n\n return train_error, test_error, f1_score","title":""},{"docid":"523c9a97d5514d8bd098f77ba072fc1d","score":"0.55494857","text":"def computeError(self): \n v = (self.u - self.old_u).flat\n return self.np.sqrt(self.np.dot(v, v))","title":""},{"docid":"cd11aa1b87f6efbf537076957f770ad7","score":"0.55462056","text":"def local_error_test(self):\n dataPtsOrg = [ 2.30, .373, .583, 1.880, 500]\n dataPtsCalc = [-1.21, -.445, .466, .226, 300]\n localErrors = [ 3.51, .818, .117, 1.654, 200]\n\n mad = MeanAbsoluteDeviationError()\n\n for idx in xrange(len(dataPtsOrg)):\n le = mad.local_error(dataPtsOrg[idx], dataPtsCalc[idx])\n ple = localErrors[idx]\n\n ## compare the strings due to accuracy\n #print le, ple\n assert str(le) == str(ple)","title":""},{"docid":"6d47a4f15726adf753acfd4e39183f21","score":"0.55422914","text":"def MSE_err(truth,pred):\r\n return np.mean((truth-pred)**2)","title":""},{"docid":"a9b418d485bb285eeee445c6ae630fe1","score":"0.553092","text":"def boot_error(fit_model):\n\n def one_sample(n):\n \"\"\"\n Helper function for error. Samples from the data, fits a model,\n and finds the loss on the outsample.\n\n Output:\n in_test_set - a 1D binary array with 1s indicating the \n presence of observation (i) in the test set.\n\n loss - a 1D array of the loss for observation (i), equal to 0 \n if (i) was not in the test set.\n \"\"\"\n in_test_set = np.zeros(n)\n loss = np.zeros(n)\n\n fit = 0\n while not fit:\n try:\n train = np.random.choice(n, n)\n test = np.setdiff1d(np.arange(n), train)\n\n fit = fit_model(x[train], y[train])\n except np.linalg.linalg.LinAlgError as e:\n print(\"lin_alg_error\")\n pass\n\n in_test_set[test] = 1\n y_hat = fit(x[test])\n\n loss[test] = loss_fun(y_hat, y[test])\n\n return np.concatenate([in_test_set, loss])\n\n n = len(x)\n q = math.pow(1 - 1/n, n)\n p = 1 - q\n\n time_sample = lambda x: timeit(lambda:one_sample(n), \"Running sample\")\n\n all_samples = reduce(operator.add, \n map(time_sample, range(num_samples)))\n in_test_set, loss = np.split(all_samples, 2)\n\n fit = timeit(lambda:fit_model(x, y), \"fitting overall model\")\n y_hat = fit(x)\n if metrics is not None: metrics(y_hat, y)\n\n if any(in_test_set == 0):\n i_in_test_set = lambda i: in_test_set[i] != 0\n good_indices = list(filter(i_in_test_set, range(n)))\n loss = loss[good_indices]\n in_test_set = in_test_set[good_indices]\n\n err_1 = np.mean(loss/in_test_set)\n err_bar = np.mean(loss_fun(y_hat, y))\n err_632 = q * err_bar + p * err_1\n\n if binary_outcome:\n p1 = sum(map(lambda t: t == 1, y))/n\n q1 = sum(map(lambda t: t == 1, y_hat))/n\n gamma = p1 * (1 - q1) + q1 * (1 - p1)\n else:\n # gamma = err_bar\n unary_loss = lambda a: loss_fun(*a)\n loss_vector = map(unary_loss, itertools.product(y_hat, y))\n gamma = sum(loss_vector)/n/n\n \n if err_1 > err_bar and gamma > err_bar:\n r = (err_1 - err_bar)/(gamma - err_bar)\n else:\n r = 0\n\n err1_ = min(err_1, gamma)\n print(err_632 + (err1_ - err_bar) * (p * q * r) / (1 - q * r))\n return err_632 + (err1_ - err_bar) * (p * q * r) / (1 - q * r)","title":""},{"docid":"ff0baec98ffcaff698ad055b7b2d7d51","score":"0.5517289","text":"def analyse_erreur(err_train, err_test):\n #AJOUTER CODE ICI","title":""},{"docid":"e7e749a664c378f07ba2863013efb5b3","score":"0.55152315","text":"def compute_error(self):\n pred = np.dot(self.M, self.C)\n self.error = math.sqrt(sum((pred - self.B) ** 2) / len(self.B))\n # print self.error","title":""},{"docid":"43b4f06170a3ebe8c27312579f7a9a02","score":"0.54949987","text":"def find_min_loss_acc(history):\n min_loss = 0\n min_loss_acc = 0\n try:\n print(\"Getting minimum loss and corresponding accuracy\")\n loss_list = history.history['val_loss']\n min_loss = min(loss_list)\n min_loss_idx = len(loss_list) - loss_list[::-1].index(min_loss) - 1\n min_loss_acc = history.history['val_acc'][min_loss_idx] * 100\n except Exception as e:\n print(\"Finding minimum loss failed: {}\".format(e))\n\n return min_loss, min_loss_acc","title":""},{"docid":"8bf6ea54b13169cd212780479ddfadb2","score":"0.54926234","text":"def _train(self) -> float:\n pass","title":""},{"docid":"8240448ac6221279960f9761e66b2c2e","score":"0.5484894","text":"def calculate_error(train, test, forecast):\n resolution = detect_resolution(train)\n return mase_loss(test, forecast, train, sp=int(timedelta(days=1)/resolution))","title":""},{"docid":"982ee56897c53550d0a5486a95b4100e","score":"0.54752505","text":"def train():\n global population_size, models, best\n # Create the population\n for _ in range(0, population_size):\n models.append(getLSTM())\n\n # Format: MSE, hyperparams, weights\n errors = []\n for j in range(0, len(models)):\n print(\"\\tTraining member \" + str(j) + \" out of \" + str(population_size))\n model = models[j]\n step(model)\n error = eval(model)\n if error < best[0]:\n print(\"New best error: \" + str(error) + \", hyperparams=\", model[1])\n best = [error, model[1], model[0].get_weights()]\n errors.append(error)\n # TODO: make better decisions\n if random.randint(0, 100) % 2 == 0:\n explore(j)\n else:\n exploit(j, best)\n\n return best","title":""},{"docid":"f87be4049e7e400b8c9e8fe680375f45","score":"0.5473502","text":"def get_local_minimum(self):\r\n\t\tcurrent_x = self.startPoint\r\n\t\tprevious_step_size = current_x\r\n\r\n\t\twhile previous_step_size > self.learnRate:\r\n\t\t\tprevious_x = current_x\r\n\t\t\tcurrent_x = current_x - (self.learnRate * self.get_grad_f(current_x))\r\n\t\t\tprevious_step_size = np.abs(current_x - previous_x)\r\n\t\treturn current_x","title":""},{"docid":"fe01fc350f4cb7292db86ef555855686","score":"0.54661894","text":"def show_errors(params, samples, y):\n global __errors__\n error_acum = 0\n error = 0\n\n # Optimized version\n hyp = calculateHyp(params,samples)\n error = numpy.vectorize(crossEntropy)(hyp,y)\n error_acum = error.sum()\n\n # PREVIOUS Implementation\n # for instance in range(len(samples)):\n # hyp = calculateHyp(params,samples.iloc[instance])\n # error = crossEntropy(hyp, y.iloc[instance])\n # error_acum = error_acum + error # this error is different from the one used to update, this is general for each sentence it is not for each individual param\n\t#print(\"acum error %f \" % (error_acum));\n mean_error_param = error_acum/len(samples)\n __errors__.append(mean_error_param)\n return mean_error_param","title":""},{"docid":"64449d87e4acf090b7e3f0a945e02533","score":"0.5461774","text":"def min_forest_mae(leaf_nodes, train_X, val_X, train_y, val_y):\n dict_mae = {}\n for node in leaf_nodes:\n model = DecisionTreeRegressor(max_leaf_nodes=node, random_state=1)\n model.fit(train_X, train_y)\n preds_val = model.predict(val_X)\n mae = mean_absolute_error(val_y, preds_val)\n dict_mae[node] = mae\n node = min(dict_mae, key=dict_mae.get)\n print(f'Best Fit Node modelTree: {node} - MAE: {dict_mae[node]:.0f}')\n return node","title":""},{"docid":"ef69f9b58633417eac4e1bbb6fc5924e","score":"0.54485613","text":"def get_train_test_error(classifier, X, y, num_iterations = 1, split = 0.25):\n\n train_error, test_error = 0.0, 0.0\n for i in range(num_iterations):\n X_train, X_test, y_train, y_test = train_test_split(X, y,\n test_size=split,\n random_state=i)\n classifier.fit(X_train, y_train)\n y_train_pred = classifier.predict(X_train)\n y_test_pred = classifier.predict(X_test)\n train_error+=1- metrics.accuracy_score(y_true=y_train,\n y_pred=y_train_pred,\n normalize=True)\n test_error+=1 - metrics.accuracy_score(y_true=y_test,\n y_pred=y_test_pred,\n normalize=True)\n train_error /=num_iterations\n test_error /=num_iterations\n return train_error, test_error","title":""},{"docid":"5a2a38f01c5fb7fad50117c18e376f31","score":"0.5447187","text":"def validate(self, epoch):\n losses = AverageMeter()\n accs = AverageMeter()\n\n glimpses = AverageMeter()\n\n for i, (x, y) in enumerate(self.valid_loader):\n if self.use_gpu:\n x, y = x.cuda(), y.cuda()\n x, y = Variable(x), Variable(y)\n loss, glm, acc = self.rollout(x, y)\n glimpses.update(glm)\n\n # store\n try:\n accs.update(acc.data.item())\n losses.update(loss.data[0], x.size()[0])\n except:\n losses.update(loss.data.item(), x.size()[0])\n accs.update(acc.data.item(), x.size()[0])\n\n return losses.avg, accs.avg, glimpses.avg","title":""},{"docid":"5433000ff39414061777fda10c6d67e9","score":"0.54465586","text":"def compute_error(data, user_features, item_features, nz):\n\n square_errors = []\n for i, j in nz:\n square_errors.append(np.square(data[i, j] - np.dot(item_features[i, :], user_features[j, :].T)))\n\n mse = np.mean(square_errors)\n rmse = np.sqrt(mse)\n return rmse","title":""},{"docid":"8754732e508c3deba3fbf801df88467b","score":"0.5446375","text":"def fit(self,maxiterations=50):\n self.fitError.append(self.sUpdate())\n return self.fitError[-1]","title":""},{"docid":"46102d112a7d3880b1dfcddd02a428e1","score":"0.5445701","text":"def compute_error(data, user_features, item_features, nz):\n mse = 0\n for row, col in nz:\n item_info = item_features[:, row]\n user_info = user_features[:, col]\n mse += (data[row, col] - user_info.T.dot(item_info)) ** 2\n return np.sqrt(1.0 * mse / len(nz))","title":""},{"docid":"dd48d3a77b254c8db0318af236f227a1","score":"0.5428743","text":"def _tol_mean_score(self):\n scores = []\n self.restart_model()\n for _ in range(1):\n self.model.fit(self.X, self.y)\n for _ in range(100):\n bag_idxs = np.random.choice(len(self.y_test), len(self.y_test))\n scores.append(self.value(self.model, metric=self.metric,\n X=self.X_test[bag_idxs], y=self.y_test[bag_idxs]))\n self.tol = np.std(scores)\n self.mean_score = np.mean(scores)","title":""},{"docid":"6b29af9118c6f70a8e8108ca7d9bd4fd","score":"0.5425718","text":"def min_tree_mae(leaf_nodes, train_X, val_X, train_y, val_y):\n dict_mae = {}\n for node in leaf_nodes:\n model = DecisionTreeRegressor(max_leaf_nodes=node, random_state=1)\n model.fit(train_X, train_y)\n preds_val = model.predict(val_X)\n mae = mean_absolute_error(val_y, preds_val)\n dict_mae[node] = mae\n node = min(dict_mae, key=dict_mae.get)\n print(f'Best Fit Node modelTree: {node} - MAE: {dict_mae[node]:.0f}')\n return node","title":""},{"docid":"ab219a647b147acc0ab483e9f874f106","score":"0.5425643","text":"def get_score(n_estimators):\n my_p = Pipeline(\n steps=[\n ('prep', SimpleImputer()),\n ('model', RandomForestRegressor(n_estimators=n_estimators, random_state=0))\n ])\n scores = -1 * cross_val_score(my_p, X, y,\n cv=3,\n scoring='neg_mean_absolute_error')\n return scores.mean()","title":""},{"docid":"0c0b434079894a15dfbd2303bb9210eb","score":"0.5424404","text":"def error(self) -> float:\n if self._error is None:\n self._error = (1 - self.averageRepresentativeness) \\\n * self.averageDistanceFromRepresentativeToNonRepresentative\n return self._error","title":""},{"docid":"163e6f06c7b36d803ec3aa1dc2459d34","score":"0.5417803","text":"def gen_error_1_NN(embedding, labels):\n model = KNeighborsClassifier(n_neighbors=1)\n loo = LeaveOneOut()\n loo.get_n_splits(embedding)\n scores = cross_val_score(model , X = embedding , y = labels, cv = loo)\n gen_error = (1 - np.mean(scores))\n \n return gen_error","title":""},{"docid":"41eb9b29cc2cd8b4096b6bf56ca86221","score":"0.54143006","text":"def get_trainable_model_error(model) -> float:\n\n # Check model validity -- see above for implementation details\n assert hasattr(model, \"trainable\")\n assert model.trainable is True\n assert hasattr(model, \"output_bias\")\n assert model.output_bias.shape == (1,)\n\n # The error is just absolute value of our scalar output bias\n return abs(float(model.output_bias[0]))","title":""},{"docid":"584fbb0a9221777956ae5f490e925d98","score":"0.5413518","text":"def observation_error(self: ModelData) -> Optional[float]:","title":""},{"docid":"a2f4b28a972b9d5bdbe3c4cf3aec6ca2","score":"0.54089165","text":"def get_current_error(self):\r\n current_output_layer_errors = [0] * len(self.output_layer)\r\n current_training_set_errors = []\r\n index = 0\r\n while index < len(self.input_layer_data):\r\n # get the input layer and the expected output layer from the training data set\r\n input_layer = self.input_layer_data[index]\r\n real_output_layer = self.output_layer_data[index]\r\n assert len(real_output_layer) == len(self.output_layer)\r\n\r\n # compute the hidden layers values by using the current weights in the network\r\n last_layer_data = input_layer\r\n for hidden_layer in self.hidden_layers:\r\n for neuron in hidden_layer:\r\n neuron.compute_value(input_values=last_layer_data)\r\n last_layer_data = numpy.array([neuron.value for neuron in hidden_layer])\r\n\r\n # compute the output layer values based on the current weights in the network\r\n for neuron in self.output_layer:\r\n neuron.compute_value(input_values=last_layer_data)\r\n\r\n # compute the current errors\r\n for neuron_index, neuron in enumerate(self.output_layer):\r\n current_output_layer_errors[neuron_index] = neuron.compute_value_error(real_output_layer[neuron_index])\r\n\r\n current_training_set_errors.append(sum(current_output_layer_errors))\r\n\r\n index += 1\r\n\r\n return sum(current_training_set_errors), sum(current_training_set_errors) / len(current_training_set_errors), len(current_training_set_errors)","title":""},{"docid":"b79872cfa732640a6b7ce87f943378f7","score":"0.5406132","text":"def _fit_no_arch_normal_errors_params(self) -> NDArray:","title":""},{"docid":"10ba0481d10e80dc5aeaa4b1750ad834","score":"0.5399917","text":"def _compute_error(self, target):\n\n tempWeights = None\n tempDerivatives = None\n\n for layer in reversed(self.layers):\n if layer == self._get_output_layer():\n layer.computeDerivative(self.loss.calculateDerivative(target, layer.outp), 1.0)\n else:\n layer.computeDerivative(tempDerivatives, tempWeights[1:])\n\n tempWeights = layer.weights\n tempDerivatives = layer.deltas\n\n output = self._get_output_layer().outp\n return self.loss.calculateError(target, output)","title":""},{"docid":"01f32756505f4cf54e5b9143fe00436a","score":"0.5395561","text":"def get_score(n_estimators):\n \n from sklearn.ensemble import RandomForestRegressor\n from sklearn.pipeline import Pipeline\n from sklearn.impute import SimpleImputer\n \n my_pipeline = Pipeline(steps=[\n ('preprocessor', SimpleImputer()),\n ('model', RandomForestRegressor(n_estimators=n_estimators, random_state=0))\n])\n \n \n from sklearn.model_selection import cross_val_score\n\n # Multiply by -1 since sklearn calculates *negative* MAE\n scores = -1 * cross_val_score(my_pipeline, X, y,\n cv=3,\n scoring='neg_mean_absolute_error')\n\n print(\"Average MAE score:\", scores.mean())\n return scores.mean()","title":""},{"docid":"e29ac3b5e880c9cdbd7e3362ddb4d313","score":"0.5394792","text":"def validateOneEpoch(self) -> Tuple[float, float]:\n self.model.eval()\n predictedLabels = []\n expectedLabels = []\n\n with torch.no_grad():\n for datapoints in self.validationDataLoader:\n image = datapoints[\"image\"].to(self.config.device)\n expectedLabel = datapoints[\"label\"].to(self.config.device)\n predicted = self.model(image)\n predicted = torch.nn.functional.softmax(predicted, dim=1)\n\n expectedLabels.extend(expectedLabel.cpu().numpy().tolist())\n predictedLabels.extend(torch.max(predicted, dim=1).indices.cpu().numpy().tolist())\n\n return f1_score(expectedLabels, predictedLabels), accuracy_score(expectedLabels, predictedLabels)","title":""},{"docid":"58010838074f1b03f9bf7817ce79a8e3","score":"0.53932995","text":"def get_error_dfs(df_pred, df_target, alpha=0.1):\n tmp = alpha * (df_target - df_pred).values\n return (np.exp(tmp) - tmp - np.ones_like(tmp)).mean()","title":""},{"docid":"d3d6cd9d2a3bfa4bc5a2c6902a57439c","score":"0.53902656","text":"def estimate_sigma_min(self):\n if self.sigma_min is None:\n sigma_min = 0\n for id, labels in enumerate(self.target):\n if len(labels) == 0:\n ts_subs = subsequences(self.data[id], min(self.windows))\n sigma_min = max(sigma_min, ts_subs.std(axis=1).max())\n print(\"sigma_min set to {}\".format(sigma_min))\n self.sigma_min = sigma_min\n shapelet_utils.sigma_min = self.sigma_min","title":""},{"docid":"2d396d72bb39a3b2c33fa95b304731ac","score":"0.53889966","text":"def get_error_dist(self):\n if self.error_dist:\n return self.error_dist\n self.n = float(self.n)\n if self.n > 1:\n self.error_dist = (1. / (self.n - 1) * ((self.dd) - 1. / self.n * (self.d) ** 2) ) ** 0.5\n return self.error_dist","title":""},{"docid":"64e693d0f0bdf007214389e0e0e17dfc","score":"0.5368556","text":"def MAE_err(truth,pred):\r\n return np.mean(abs(np.array(truth-pred)))","title":""},{"docid":"b3c60a058ebfa8409535d6f6795d6fc7","score":"0.5364231","text":"def error( inputs, params ):\n\ttotalError = 0\n\tfor i in range( 0, len( inputs ) ):\n\t\tx = inputs[i]\n\t\ttotalError += ( dPhi_t_dx( x, params ) + 0.2 * phi_t( x, params ) - np.exp( -x / 5.0 ) * np.cos( x ) ) ** 2\n\treturn totalError / float( len( points ) )","title":""},{"docid":"5b152868b34afcb79c2bfe9784d31b53","score":"0.53613174","text":"def min_k_rmse(list_k, model, X_train, X_test, y_train, y_test):\n dict_rmse = {}\n for k in list_k:\n SelectKBest(f_regression, k=k).fit_transform(X_train, y_train)\n model.fit(X_train, y_train)\n predictions = model.predict(X_test)\n rmse = mean_squared_error(y_test, predictions)\n dict_rmse[k] = rmse\n k_best = min(dict_rmse, key=dict_rmse.get)\n print(f'Best Fit k features modelLinear: {k_best} - RMSE: {dict_rmse[k_best]:.4f}')\n return dict_rmse","title":""},{"docid":"9f98ccdbe07d1fd75bf220b224b9ded8","score":"0.53490466","text":"def test_error_rate(estimator, clusterparams, gentype='default', runs=100):\n error_rate_list = list()\n\n # Do multiple runs\n for _x in range(runs):\n if gentype == 'default':\n samples, labels = make_blobs(**clusterparams)\n elif gentype == 'skewed':\n samples, labels = make_skewed_blobs(**clusterparams)\n elif gentype == 'unequal':\n samples, labels = make_unequal_blobs(**clusterparams)\n\n estimator.fit(samples)\n\n # Actual and estimated labels won't match so we must map them.\n # Each possibile mapping is tested and the lowest error rate is used.\n # e.g. for mapping, if there are three labels 0,1,2\n # -> mapping = (0,1,2), (0,2,1), (2,1,0), etc.\n # So if estimator label = 1 then we pretend it is the number at index 1 of mapping\n error_rate = np.inf\n for mapping in permutations(range(len(estimator.cluster_centers_))):\n error = 0\n for label, result in zip(labels, estimator.labels_):\n if label != mapping[result]:\n error += 1\n result = error/len(samples)\n if result < error_rate:\n error_rate = result\n error_rate_list.append(error_rate)\n\n mean = np.mean(error_rate_list)\n standard_deviation = np.std(error_rate_list)\n return {'mean': mean,\n 'std': standard_deviation,\n 'elabels': estimator.labels_,\n 'labels': labels,\n 'samples': samples,\n 'clusters': estimator.cluster_centers_}","title":""},{"docid":"ebf23b715deedf882cea97656a8a344c","score":"0.5340559","text":"def __compute_bnn_training_error(self):\n\t\texp = np.reshape(self.episode_buffer_bnn, (len(self.episode_buffer_bnn),-1))\n\t\tepisode_X = np.array([np.hstack([exp[tt,0],exp[tt,1]]) for tt in range(exp.shape[0])])\n\t\tepisode_Y = np.array([exp[tt,3] for tt in range(exp.shape[0])])\n\t\tif self.state_diffs:\n\t\t\t# subtract previous state\n\t\t\tepisode_Y -= episode_X[:,:self.num_dims]\n\t\tl2_errors = self.network.get_td_error(np.hstack([episode_X, np.tile(self.weight_set, (episode_X.shape[0],1))]), episode_Y, 0.0, 1.0)\n\t\tself.mean_episode_errors[self.instance_iter,self.episode_iter] = np.mean(l2_errors)\n\t\tself.std_episode_errors[self.instance_iter,self.episode_iter] = np.std(l2_errors)\n\t\tif self.print_output:\n\t\t\tprint('BNN Error: {}'.format(self.mean_episode_errors[self.instance_iter,self.episode_iter]))","title":""},{"docid":"b2e28937fe76f94245935f42fa7523d1","score":"0.5334064","text":"def predictor_metrics(self, outputs, prediction):\n try:\n a = self.regressor.feature_importances_\n except sklearn.utils.validation.NotFittedError:\n print \"Please fit the algorithm before calling this function.\"\n return\n return mean_squared_error(outputs, prediction)","title":""},{"docid":"5115c4401605603a510178cd4ea5dfa6","score":"0.5332098","text":"def getAvgError(self):\n warnings.warn(\"use getRate instead\", DeprecationWarning)\n with self.mutex:\n return self.getError()","title":""},{"docid":"a0eff74db08b3ba5a9515e1bb5d22e61","score":"0.53310543","text":"def get_error_parameter_test(self):\n data = [[0.0, 0.0], [1, 0.1], [2, 0.2], [3, 0.3], [4, 0.4]]\n tsOrg = TimeSeries.from_twodim_list(data)\n tsCalc = TimeSeries.from_twodim_list(data)\n\n bem = BaseErrorMeasure()\n\n bem_calculate = bem._calculate\n bem_local_error = bem.local_error\n \n def return_zero(ignoreMe, ignoreMeToo):\n return 0\n\n ## remove the NotImplementedErrors for initialization\n bem.local_error = return_zero\n bem._calculate = return_zero\n bem.initialize(tsOrg, tsCalc)\n\n bem.local_error = bem_local_error\n bem._calculate = bem_calculate\n\n try:\n bem.get_error(10.0, 90.0)\n except NotImplementedError:\n pass\n else:\n assert False # pragma: no cover\n\n for start in [-1.0, 80.0, 103.0]:\n for end in [-5.0, 10.0, 105.0]:\n try:\n bem.get_error(start, end)\n except ValueError:\n pass\n else:\n assert False # pragma: no cover","title":""},{"docid":"175b82572c994e7f937b448a2deffa2d","score":"0.5329545","text":"def err_ratio(learner, dataset, examples=None):\n examples = examples or dataset.examples\n if len(examples) == 0:\n return 0.0\n right = 0\n for example in examples:\n desired = example[dataset.target]\n output = learner(example)\n\n # Check if prediction is correct\n if isinstance(desired, str):\n correct = desired == output\n else:\n correct = np.allclose(output, desired)\n if correct:\n right += 1\n return 1 - (right / len(examples))","title":""},{"docid":"a41d8b79be5ca0bdfd2a9c38ab995197","score":"0.53274846","text":"def MinTemp(models, startyear, endyear):\n return sum(map(lambda m: models[m]['tasmin'].AverageThanExtreme(startyear,endyear,np.fmin), models)) /len(models)","title":""},{"docid":"04eb54885fd4998dcbb9a580f482ce54","score":"0.5322022","text":"def calc_rmin(self):\n sigma,n,m = self.sigma, self.n, self.m\n rmin = (n/m*sigma**(n-m))**(1./(n-m))\n return rmin","title":""},{"docid":"02d5ef3b50618a0ed2a8f35854e0dd34","score":"0.531001","text":"def compute_errors(self, benchmark='default', mcset='default', failoninc=True, verbose=False):\n if mcset == 'default':\n lsslist = self.data.keys()\n elif callable(mcset):\n # mcset is function that will generate subset of HRXN from sset(self)\n lsslist = [mc for mc in self.data.keys() if mc in mcset(self)] # untested\n else:\n # mcset is array containing modelchemistries\n lsslist = [mc for mc in self.data.keys() if mc in mcset]\n # assemble dict of qcdb.Reaction objects from array of reaction names\n lsset = OrderedDict()\n for mc in lsslist:\n lsset[mc] = self.data[mc]\n\n lbench = self.benchmark if benchmark == 'default' else benchmark\n try:\n mcGreater = self.data[lbench].value\n except KeyError as e:\n raise ValidationError(\"\"\"Reaction %s missing benchmark datum %s.\"\"\" % (self.name, str(e)))\n\n err = {}\n for label, datum in lsset.items():\n try:\n mcLesser = datum.value\n except KeyError as e:\n if failoninc:\n raise ValidationError(\"\"\"Reaction %s missing datum %s.\"\"\" % (label, str(e)))\n else:\n continue\n\n err[label] = [mcLesser - mcGreater,\n (mcLesser - mcGreater) / abs(mcGreater),\n (mcLesser - mcGreater) / abs(mcGreater)] # TODO define BER\n if verbose:\n print(\"\"\"p = %6.2f, pe = %6.1f%%, bpe = %6.1f%% modelchem %s.\"\"\" %\n (err[label][0], 100 * err[label][1], 100 * err[label][2], label))\n\n return err","title":""},{"docid":"1e5f1ae62e3183f2c29c4fb8c688892b","score":"0.53076226","text":"def oneError(y_test, probabilities):\n oneerror = 0.0\n ranking = rankingMatrix(probabilities)\n\n for i in range(y_test.shape[0]):\n index = np.argmin(ranking[i,:])\n if y_test[i,index] == 0:\n oneerror += 1.0\n \n oneerror = float(oneerror)/float(y_test.shape[0])\n\n return oneerror","title":""},{"docid":"8d592008aaf560bb7921aa6d3f70553d","score":"0.53075165","text":"def calc_min_max_errors(self,\n key_pos=None,\n batch_size=10000):\n\n if key_pos == None: # Use all keys\n\n # Initialize errors\n self._initialize_errors()\n\n # Use all keys and positions\n keys = self._data_set.keys\n true_positions = self._data_set.positions\n num_keys = self._data_set.num_keys\n\n else: # Use subset of keys\n\n # Only use keys and position specified by key_pos\n keys, true_positions = list(zip(*key_pos))\n keys = list(keys)\n true_positions = list(true_positions)\n num_keys = len(keys)\n \n \n # Calculate errors for each expert\n for step in range(0, num_keys, batch_size):\n \n positions, experts = self.run_inference(keys[step:(step+batch_size)])\n true_positions_batch = true_positions[step:(step+batch_size)]\n \n for idx in range(len(positions)):\n \n #pos = np.round(positions[idx])\n pos = positions[idx]\n expert = experts[idx]\n true_pos = true_positions_batch[idx]\n \n self.min_predict[expert] = np.minimum(self.min_predict[expert],\n pos)\n self.max_predict[expert] = np.maximum(self.max_predict[expert],\n pos)\n \n self.min_pos[expert] = np.minimum(self.min_pos[expert],\n true_pos)\n self.max_pos[expert] = np.maximum(self.max_pos[expert],\n true_pos)\n \n error = pos - true_pos\n if error > 0:\n self.max_error_left[expert] = np.maximum(self.max_error_left[expert],\n error)\n elif error < 0:\n self.max_error_right[expert] = np.maximum(self.max_error_right[expert],\n np.abs(error))\n\n for expert in range(self.max_error_left.shape[0]):\n print(\"expert %d left error: %.10f\" % (expert, self.max_error_left[expert]))\n for expert in range(self.max_error_right.shape[0]):\n print(\"expert %d right error: %.10f\" % (expert, self.max_error_right[expert]))","title":""},{"docid":"1b78e299cdb0f4cebf6b67f1e05ae76d","score":"0.53066504","text":"def scorer():\n return make_scorer(RMSE, greater_is_better=False)","title":""},{"docid":"b70eeee9a183a24db459575096c4cfb2","score":"0.5303065","text":"def ex_1_1_b(x_train, x_test, y_train, y_test):\n \n ## TODO\n #pass\n \n MSE_TEST = np.zeros(10, dtype=float)\n MSE_TRAIN = np.zeros(10, dtype=float)\n\n n_hidden = 5\n for j in range(10):\n reg = MLPRegressor(hidden_layer_sizes=(n_hidden,8), activation='logistic', solver='lbfgs', alpha=0,random_state=random.randint(0, 1000))\n reg.fit(x_train, y_train)\n mse = calculate_mse(reg, x_test, y_test)\n MSE_TEST[j] = mse\n \n mse = calculate_mse(reg, x_train, y_train)\n MSE_TRAIN[j] = mse\n \n mse_test_max = max(MSE_TEST)\n mse_test_min = min(MSE_TEST)\n mse_test_mean = np.mean(MSE_TEST)\n mse_test_std = np.std(MSE_TEST)\n \n print(mse_test_max, \"/\", mse_test_min, \"/\", mse_test_mean, \"/\", mse_test_std)\n \n mse_train_max = max(MSE_TRAIN)\n mse_train_min = min(MSE_TRAIN)\n mse_train_mean = np.mean(MSE_TRAIN)\n mse_train_std = np.std(MSE_TRAIN)\n \n print(mse_train_max, \"/\", mse_train_min, \"/\", mse_train_mean, \"/\", mse_train_std)","title":""},{"docid":"b2646c7f9c80c926502e95f17c6b14dc","score":"0.52926373","text":"def find_nearest_error(model, data_loader, device, pgd_iters):\n total = 0\n correct = 0\n d_err_min = 10\n model.eval()\n for i, (inputs, labels) in enumerate(data_loader):\n labels = labels.to(device)\n inputs = inputs.to(device)\n advex = inputs.clone()\n # search for nearest error d(E)\n for j in range(pgd_iters):\n advex = fgm_l2(model, advex, labels, 0.01, device)\n # project back on to manifold\n advex /= torch.unsqueeze(advex.norm(p=2, dim=1), dim=1)\n outputs = model(advex)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct_idx = predicted == labels\n correct += (correct_idx).sum().item()\n if (correct_idx == 0).sum() > 0:\n # compute nearest error on inner sphere\n d_err = (inputs[correct_idx == 0] - advex[correct_idx == 0]\n ).norm(p=2, dim=1).min().item()\n if d_err < d_err_min:\n print('itr %d got d = %f' % (j, d_err))\n d_err_min = d_err\n break\n print('%d of %d' % (i, len(data_loader)))\n return d_err_min","title":""},{"docid":"94a76af53a5f57b3935c1eadd5d037bf","score":"0.52888185","text":"def error(self, predicted, target, method='L2', alpha=1.):\n errors = target.ravel() - predicted.ravel()\n\n # Adjust for NaN and np.inf in predictions (unstable solution)\n if not np.all(np.isfinite(predicted)):\n # print(\"Warning: some predicted values are not finite\")\n errors = np.inf\n\n # Compute mean error\n if method == 'L2':\n error = np.linalg.norm(errors, ord=2) # Technically the same as rmse\n elif method == 'mse':\n error = np.mean(np.square(errors))\n elif method == 'tanh':\n error = alpha * np.tanh(np.mean(np.square(errors)) / alpha) # To 'squeeze' mse onto the interval (0, 1)\n elif method == 'rmse':\n error = np.sqrt(np.mean(np.square(errors)))\n elif method == 'nmse':\n error = np.mean(np.square(errors)) / np.square(target.ravel().std(ddof=1))\n elif method == 'nrmse':\n error = np.sqrt(np.mean(np.square(errors))) / target.ravel().std(ddof=1)\n elif method == 'tanh-nmse':\n nmse = np.mean(np.square(errors)) / np.square(target.ravel().std(ddof=1))\n error = alpha * np.tanh(nmse / alpha)\n else:\n raise ValueError('Scoring method not recognized')\n return error","title":""},{"docid":"f505010a169db4355f18eacd66772b61","score":"0.52881634","text":"def calc_error(self, loss, ytrue, yhat):\n if loss.name == 'gini':\n accuracy = [y == yhat[i] for i, y in enumerate(ytrue)]\n return accuracy.count(False)/len(ytrue)\n return mean_squared_error(ytrue, yhat)","title":""},{"docid":"705beee34b676c259b0411891a2c4ee9","score":"0.5281362","text":"def weak_learner(data, dist=None):\n dist = dist or [1 / len(data) for _ in range(len(data))]\n fns_and_errs = [(fn, error(fn, data, dist)) for fn in basis_functions]\n fns_and_errs = sorted(fns_and_errs, key=lambda x: x[1])\n min_err = fns_and_errs[0][1]\n result = [fn for fn, err in fns_and_errs if err <= min_err]\n return random.choice(result), min_err","title":""},{"docid":"991a51b37d23012ecf1cfa303ef26a75","score":"0.5276654","text":"def error_calculation_test(self):\n dataOrg = [[0,0], [1,1], [2,2], [3,3], [4,4], [5,5], [6, 6], [7,7], [8,8], [9,9]]\n dataCalc = [[0,1], [1,3], [2,5], [3,0], [4,3], [5,5], [6.1,6], [7,3], [8.1,8], [9,8]]\n # difference: 1 2 3 3 1 0 NA 4 NA 1\n # local errors: 1 4 9 9 1 0 NA 16 NA 1\n\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\n\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\n\n mse = MeanSquaredError(80.0)\n mse.initialize(tsOrg, tsCalc)\n\n assert str(mse.get_error()) == \"5.125\"","title":""},{"docid":"9a64521736ed38ee12ea7e0639631407","score":"0.5276397","text":"def getStatisticalError(self):\n return np.sqrt(self.stat)","title":""},{"docid":"3066781bec5b2c6dc88417277ade976d","score":"0.52720255","text":"def min(self):\n \n pass","title":""},{"docid":"2f71f5bcc7f7f058eaed475a7caa4893","score":"0.5267713","text":"def error_rate(predictions, labels):\n return 100.0 - (\n 100.0 *\n np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) /\n predictions.shape[0])","title":""},{"docid":"00c2feddabab35f7ee292c1b71d45e40","score":"0.52666175","text":"def compute_error_rate(df, ind_vars, seed):\n (n, p) = df.shape\n n_train = int(0.5 * n)\n\n # Split data into train and test sets.\n # Task (i)\n np.random.seed(seed)\n ind_train = np.sort(np.random.choice(n, n_train, replace=False))\n ind_test = np.setdiff1d(np.arange(n), ind_train)\n df_train = df.loc[ind_train]\n df_test = df.loc[ind_test]\n\n y_train = df_train['bool default']\n y_test = df_test['bool default']\n x_train = sm.add_constant(df_train[ind_vars])\n x_test = sm.add_constant(df_test[ind_vars])\n\n # Fit logistic regression model and predict error rate.\n # Task (ii)\n model = sm.Logit(y_train, x_train).fit()\n\n # Task (iii)\n pred_table = confusion_matrix(model, x_test, y_test)\n\n # Task (iv)\n error_rate = 1 - np.trace(pred_table) / np.sum(pred_table)\n return error_rate","title":""},{"docid":"85c524b2c9171a8f444c9465731d0edd","score":"0.52661973","text":"def train(mlp, inputs, targets, epochs, learning_rate):\n\n # The amount of times we will go\n # over the data.\n for i in range(epochs):\n\n # The overall error that we get\n # with the distance between the\n # MLP guess and the actual value.\n sum_error = 0\n for _input, _target in zip(inputs, targets):\n\n # [weights, activations, derivatives]\n # foward propagate and get the neural\n # network guess over the data\n output = forward_propagate(_input, mlp[0],\n mlp[1])\n\n # Calculate the difference between\n # the guess and the actual true value\n error = _target - output\n\n # Propagate the error trought the MLP\n back_propagate(error, mlp[0], mlp[1], mlp[2])\n\n # Go over the MLP and try to aproximate the\n # the minimum\n gradient_descent(mlp[0], mlp[2], learning_rate)\n\n # Accumulate the error\n sum_error += mse(_target, output)\n\n print(\"Target: {}, Output: {}\"\n .format(_target, output))\n print()\n print(\"Error: {} at epoch {}\"\n .format(sum_error / len(inputs), i))\n pass","title":""},{"docid":"05f766f86945b64f3d6e2ebbff0e4d37","score":"0.52660483","text":"def validate(self):\n self.set_model_mode('eval')\n self.evaluator.reset()\n losses = MetricMeter()\n\n print('Do evaluation on {} set'.format('valid set'))\n data_loader = self.val_loader\n assert data_loader is not None\n for batch_idx, batch in enumerate(data_loader):\n input, label = self.parse_batch_test(batch)\n loss = self.forward_backward(batch, backprob=False)\n losses.update(loss)\n # total_loss += loss['loss']\n output = self.model_inference(input)\n self.evaluator.process(output, label)\n\n results = self.evaluator.evaluate()\n # total_loss = losses.meters['total_loss'].avg\n total_loss = losses.meters['general_loss'].avg\n\n\n for k, v in results.items():\n tag = '{}/{}'.format('validation', k)\n self.write_scalar(tag, v, self.epoch)\n return [total_loss,losses.dict_results(),results]","title":""},{"docid":"cb19faa81a3e53cd805634af4292e997","score":"0.5261249","text":"def error(self, J_flat, gibbs_iter):\n np.random.seed(seed=1)\n J_flat = np.array(J_flat)\n self.gibbs_iter = gibbs_iter\n self.J = J_flat.reshape((self.m, self.m))\n cov_models = np.zeros((self.m**2, self.n_pools))\n batch_samples = np.array(self.pool.map(self.sample_preperation,\n range(0, self.n_pools)))\n self.batch_samples = np.moveaxis(batch_samples, 0, -1)\n n, m, k = np.shape(self.batch_samples)\n # Combine all montecarlo samples\n for i in range(0, k):\n cov_model_temp = np.dot(self.batch_samples[:, :, i].T,\n self.batch_samples[:, :, i])/n\n cov_models[:, i] = cov_model_temp.flatten()\n self.cov_model = np.sum(cov_models, axis=1)/self.n_pools\n # Calculate the difference in model correlation\n # and emperical correlation\n error_train = (-self.cov_model + self.emp_cov_train.flatten())\n\n if self.save_loss:\n self.train_error.append(np.max(abs(error_train)))\n\n if self.emp_cov_test is not None:\n error_test = (-self.cov_model + self.emp_cov_test.flatten())\n if self.save_loss:\n self.test_error.append(np.max(abs(error_test)))\n\n return error_train","title":""},{"docid":"76b723fb6339719e83b83347abda9458","score":"0.5260465","text":"def ex_1_1_b(x_train, x_test, y_train, y_test):\n\n ## TODO\n mse_trains = []\n mse_tests = []\n for index in range(0,10):\n regressor = MLPRegressor(\n hidden_layer_sizes=(8,),\n solver=\"lbfgs\",\n activation=\"logistic\",\n alpha=0.0,\n max_iter=200,\n random_state=randint(1,1000)\n )\n\n regressor.fit(x_train, y_train)\n mse_trains.append(calculate_mse(regressor, x_train, y_train))\n mse_tests.append(calculate_mse(regressor, x_test, y_test))\n\n print(\"min_train:\", np.min(mse_trains))\n print(\"min_test:\", np.min(mse_tests))\n\n print(\"max_train:\", np.max(mse_trains))\n print(\"max_test:\", np.max(mse_tests))\n\n print(\"mean_train:\", np.mean(mse_trains))\n print(\"mean_test:\", np.mean(mse_tests))\n\n print(\"std_train:\", np.std(mse_trains))\n print(\"std_test:\", np.std(mse_tests))\n\n pass","title":""},{"docid":"46d3197977dc9466173ceffdf39fb2e8","score":"0.5258542","text":"def select_best_model(self):\r\n \r\n self.best_model = min(self.mean_mse, key=self.mean_mse.get)","title":""},{"docid":"598bef602b4d81f518031cd3d984b175","score":"0.5256269","text":"def computeScore(self, kmers):\n if self.kmers.size > 0:\n #self.common = np.intersect1d(self.kmers, kmers, assume_unique = True)\n self.common = kmerizer.intersect(self.kmers, kmers)\n self.denominator = float(self.kmers.size)\n if self.denominator > 0:\n self.score = float(self.common.size) / self.denominator\n else:\n self.score = 0 \n else:\n self.score = 0","title":""}],"string":"[\n {\n \"docid\": \"317a5e9d0284abde4cc9dd6f16d84f2a\",\n \"score\": \"0.64385617\",\n \"text\": \"def batch_training(self, num_minibatches):\\n\\n epoch_tr_err = 0 # initialize the training error for each epoch to zero\\n\\n if self.with_metrics:\\n\\n for l in range(num_minibatches): # minibatch loop\\n _, batch_err = self.sess.run([self.opt, self.Rmse])\\n # average msr error per minibatch\\n epoch_tr_err += batch_err / num_minibatches\\n\\n else:\\n for l in range(num_minibatches): # minibatch loop\\n _ = self.sess.run(self.opt)\\n\\n return epoch_tr_err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"610b4289607343eb2e06b52d65c93edc\",\n \"score\": \"0.62677014\",\n \"text\": \"def compute_errors(m, x_train, y_train, x_test, y_test, stds, one_hot_encoded):\\n\\n model = m()\\n if(stds is None):\\n for p in model.parameters():\\n if len(p.size()) == 2:\\n updated_tensor, std = xavier_normal_(p.data.normal_(0, 1), gain=1)\\n print(\\\"Computed standard deviation according to 'Xavier initialization': {:.3f}\\\\n\\\".format(std))\\n stds = [std]\\n for std in stds:\\n if std > 0:\\n for p in model.parameters():\\n p.data.normal_(0, std)\\n\\n train_model(model, x_train, y_train, one_hot_encoded)\\n train_error = compute_nb_errors(model, x_train, y_train, one_hot_encoded)\\n test_error = compute_nb_errors(model, x_test, y_test, one_hot_encoded)\\n print('std {:f} {:s} train_error {:.02f}% test_error {:.02f}%'.format(\\n std,\\n m.__name__,\\n train_error / x_train.size(0) * 100,\\n test_error / x_test.size(0) * 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04621ad38fc674e2584e27d8371dfb7e\",\n \"score\": \"0.62116385\",\n \"text\": \"def getMaxError(self):\\n return np.array([learner.getMaxError()()\\n for learner in self._learners])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2cfb4f3b00df038a0e99c3faf8cd41\",\n \"score\": \"0.61574125\",\n \"text\": \"def cal_error(self):\\n\\n # calculate error of the train dataset and turn it into the seasonal\\n # error\\n self.trn_error = cal_error(\\n self.dataset,\\n self.model,\\n data_index=self.dataset.split_list[0])\\n self.sea_error = cal_season_error(\\n self.trn_error, roll_win=14, agg='mean')\\n print('max error', np.max(self.sea_error.values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5849b746287adcea07faa7ee6b2385\",\n \"score\": \"0.61223793\",\n \"text\": \"def _get_sum_error(self, training_set):\\n return sum([target_result - self.evaluate(inputs)\\n for inputs, target_result in training_set])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ae5f90b92658825976eb6f27f2e9e7\",\n \"score\": \"0.60142446\",\n \"text\": \"def learing_rate(self, error_est):\\r\\n return 0.008*np.sqrt(error_est)+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0095ee2df021a5c92f42358fc992881c\",\n \"score\": \"0.600224\",\n \"text\": \"def _calc_mlc_error(self):\\n for picket in self.pickets:\\n picket.fit_poly()\\n picket.calc_mlc_errors()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15c9b345a86b13b2f4fb04f3eff1596\",\n \"score\": \"0.59789574\",\n \"text\": \"def train_dist_error_model(self, xtrain, ytrain, k=5):\\n\\n if xtrain.shape[0] != ytrain.shape[0]:\\n raise TypeError(\\\"Xtrain and Ytrain do not have same shape.\\\")\\n\\n dist_list = []\\n for x0 in xtrain:\\n\\n # Change basis\\n x0 = np.dot(self.L_mat, x0)\\n\\n # Get nearest neighbors in original training set\\n dist, loc = self.transf_xtree.query(x0, k=k)\\n # Weighted density in ball for NN\\n # dist = np.array([np.max([1e-15,d]) for d in dist])\\n # weight = 1.0/dist\\n # dist_list.append(np.sum(weight))\\n dist_list.append(np.mean(dist))\\n\\n dist_list = np.array(dist_list)\\n scalar_error = np.mean(\\n np.abs(ytrain), axis=1\\n ) # TODO: is this a good error measure?\\n\\n assert dist_list.shape == scalar_error.shape\\n\\n def error_model(dist, a, b, c):\\n return a * (dist) + b * (dist) ** c\\n\\n bestfit, cov = opt.curve_fit(\\n error_model,\\n dist_list,\\n scalar_error,\\n # bounds=((0.0,0.0,0.0),(np.inf,np.inf,np.inf)))\\n bounds=((0.0, 0.0, 0.0), (1e1, 1e1, 1e1)),\\n )\\n\\n # print(\\\"this is bestfit:\\\", bestfit)\\n\\n def new_error_model(xval):\\n xval = np.dot(self.L_mat, xval)\\n # Get nearest neighbors in original training set\\n dist, loc = self.transf_xtree.query(xval, k=k)\\n # Mean distance to NN\\n dist = np.mean(dist)\\n\\n # dist = dist/bestfit[2]\\n\\n err_guess = bestfit[0] * dist + bestfit[1] * dist ** bestfit[2]\\n # rand_sign = np.random.rand() - 0.5\\n # err_guess *= 1.0 if rand_sign>0.0 else -1.0\\n\\n return err_guess\\n\\n # DEBUG\\n # import matplotlib.pyplot as plt # type: ignore\\n # plt.plot(dist_list, np.abs(ytrain),'bo')\\n # plt.plot(dist_list, map(new_error_model,xtrain),'ro')\\n # plt.show()\\n\\n return new_error_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a6d71cfba389e0db16f81a3750054a3\",\n \"score\": \"0.5899873\",\n \"text\": \"def compute_training_error(self, users : numpy.ndarray,\\n movies : numpy.ndarray, user_ids : list,\\n movie_ids : list, ratings : numpy.ndarray):\\n error = 0.0\\n\\n for i in range(0, ratings.shape[0]):\\n user_id = user_ids[i]\\n movie_id = movie_ids[i]\\n\\n prediction = self.training_predict(user_id, movie_id, users, movies)\\n error += math.fabs(prediction - ratings[i][0])\\n\\n return error / ratings.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e91eea44e99a5f1d9d3047c68bbb63e\",\n \"score\": \"0.5858162\",\n \"text\": \"def _compute_stats(self):\\n # this code was adapted from fastai: https://github.com/fastai/fastai\\n self.ml = np.argmin(self.losses)\\n try:\\n self.mg = (np.gradient(np.array(self.losses[32 : self.ml]))).argmin()\\n except Exception as e:\\n self.mg = None\\n warnings.warn(str(e))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35fbfadff3a17180ed59339be62c2b43\",\n \"score\": \"0.5813214\",\n \"text\": \"def train(self, training_set, learning_rate, pre_error=False):\\n error = 0\\n #pe = self._get_sum_error(training_set) if pre_error else None\\n for inputs, target_result in training_set:\\n error += (target_result - self.evaluate(inputs))**2\\n if pre_error:\\n evals = []\\n for inputs, target_result in training_set:\\n evals.append(self.evaluate(inputs))\\n for i, weight in enumerate(self.weights):\\n delta = 0\\n for j,(inputs, target_result) in enumerate(training_set):\\n inp = inputs + [1]\\n delta += (target_result - evals[j])*inp[i]\\n self.weights[i] += learning_rate*delta\\n else:\\n for inputs, target_result in training_set:\\n self._alter_weight(inputs, target_result, learning_rate, None)\\n return error / 2.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21feee7460cbac075f1121009e53bc97\",\n \"score\": \"0.579272\",\n \"text\": \"def calculate_error(self, predictions, targets):\\r\\n rss = 0\\r\\n for prediction, target in zip(predictions, targets):\\r\\n rss += ((prediction - target) ** 2)\\r\\n #print(\\\"Prediction: \\\", prediction, \\\" Target: \\\", target)\\r\\n return math.sqrt(rss/len(predictions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb144aa587600901b1cb850ba03c2d44\",\n \"score\": \"0.5779556\",\n \"text\": \"def eval_error(experimental_values: numpy.ndarray, prediction_values: numpy.ndarray) -> float:\\n return math.sqrt(numpy.sum((experimental_values - prediction_values) ** 2) / len(experimental_values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f9b8c4ac23a0e5388636fd9eee778b\",\n \"score\": \"0.576412\",\n \"text\": \"def error(self):\\n\\n sum, count = 0, 0\\n for dim, center in enumerate(self.centers):\\n for point in self.classes[dim]:\\n sum += self._euclidean_distance(point, center)\\n count += len(self.classes[dim])\\n return sum / count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6f14cb469c57d4c2372149ac44833d\",\n \"score\": \"0.57414156\",\n \"text\": \"def q5_error_rate_on_test_set(self):\\n test_data = [zip(*sentence) for sentence in self.test.tagged_sentences]\\n self.model_tester = ModelTester(lambda x: self.model.decode(x), self.model.states, test_data)\\n print(\\\"Q5: Error rate on model for test set:\\\", self.model_tester.get_error_rate())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d857de1e1fe796eef90b23645c44d83\",\n \"score\": \"0.57398325\",\n \"text\": \"def ex_1_1_b(x_train, x_test, y_train, y_test):\\n\\n ## TODO\\n nh = 5\\n max_it = 10\\n min_i_train = 11\\n min_i_test = 11\\n max_train = 0\\n min_train = 1\\n min_test = 11\\n mean_train = 1\\n std_train = 0\\n mse = np.zeros(shape=(10))\\n for i in range(0,max_it):\\n nn = MLPRegressor(activation='logistic',solver='lbfgs',max_iter=5000,alpha= 0,hidden_layer_sizes=(nh,),random_state=i)\\n nn.fit(x_train,y_train)\\n mse_train = calculate_mse(nn, x_train, y_train)\\n mse_test = calculate_mse(nn, x_test, y_test)\\n mse[i] = mse_train\\n print(i,'.)MSE train ',mse_train)\\n # print(i,'.)MSE test ',mse_test)\\n if(mse_train < min_train):\\n min_train = mse_train\\n min_i_train = i\\n if(mse_test < min_test):\\n min_test = mse_test\\n min_i_test = i\\n \\n if(mse_train > max_train):\\n max_train = mse_train\\n \\n print(\\\"Minimum Train: \\\",min_train)\\n print(\\\"Maximum Train: \\\",max_train)\\n mean_train = mse.mean()\\n print(\\\"Mean Train: \\\",mean_train)\\n std_train = np.std(mse)\\n print(\\\"Standard Derivation Train: \\\",std_train)\\n print(\\\"Min Index train: \\\",min_i_train,\\\" & test: \\\",min_i_test)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148f6bc1a1c069d412e2e005080db88b\",\n \"score\": \"0.57361376\",\n \"text\": \"def get_errors(self):\\n\\n self.restore_best_fit()\\n\\n if not self._migrad_has_converged():\\n \\n raise CannotComputeErrors(\\\"MIGRAD results not valid, cannot compute errors. Did you run the fit first ?\\\")\\n\\n try:\\n\\n self.minuit.minos()\\n\\n except:\\n\\n raise MINOSFailed(\\\"MINOS has failed. This is not necessarily a problem if:\\\\n\\\\n\\\"\\n \\\"* There are unconstrained parameters (the error is undefined). This is usually signaled \\\"\\n \\\"by an approximated error, printed after the fit, larger than the best fit value\\\\n\\\\n\\\"\\n \\\"* The fit is very difficult, because of high correlation between parameters. This is \\\"\\n \\\"signaled by values close to 1.0 or -1.0 in the correlation matrix printed after the \\\"\\n \\\"fit step.\\\\n\\\\n\\\"\\n \\\"In this cases you can check the contour plots with get_contours(). If you are using a \\\"\\n \\\"user-defined model, you can also try to reformulate your model with less correlated \\\"\\n \\\"parameters.\\\")\\n\\n # Make a list for the results\\n\\n errors = collections.OrderedDict()\\n\\n for k, par in self.parameters.iteritems():\\n\\n minuit_name = self._parameter_name_to_minuit_name(k)\\n\\n minus_error = self.minuit.merrors[(minuit_name, -1)]\\n plus_error = self.minuit.merrors[(minuit_name, 1)]\\n\\n errors[k] = ((minus_error,plus_error))\\n\\n return errors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9776c7831f4d066f3d09444a544e3448\",\n \"score\": \"0.56850666\",\n \"text\": \"def roulette_selection_min(self, errors):\\n sum_e = sum(errors)\\n max_e = max(errors)\\n min_e = min(errors)\\n p = random.random() * sum_e\\n t = max_e + min_e\\n chosen_index = 0\\n for idx, e in enumerate(errors):\\n p -= (t - e)\\n if p < 0:\\n chosen_index = idx\\n break\\n return chosen_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3158f5b8aa06c2c1afa683358dbd6426\",\n \"score\": \"0.5648767\",\n \"text\": \"def training_error(f,xx,yy):\\n s = f(xx) - yy\\n squareSum = 0\\n for i in range(len(s)):\\n squareSum = squareSum + s[i]**2\\n return np.sqrt(squareSum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c469b18f7f1d2cfa3dc99ae5b987498e\",\n \"score\": \"0.5647742\",\n \"text\": \"def mae(errors):\\n return np.mean(np.abs(errors.flatten()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a46661a485a7473b1df9938facc0b3\",\n \"score\": \"0.56444246\",\n \"text\": \"def calc_errors(self):\\n filt_type = self.filt_type\\n\\n fs = array([])\\n MSEs = array([])\\n UPREs = array([])\\n DP2s = array([])\\n GCVs = array([])\\n if filt_type != 'TSVD': Lcs = array([])\\n for alpha in self.rng:\\n fs = append(fs, self.relative_error(alpha))\\n MSEs = append(MSEs, self.MSE(alpha))\\n UPREs = append(UPREs, self.UPRE(alpha))\\n DP2s = append(DP2s, self.DP2(alpha))\\n GCVs = append(GCVs, self.GCV(alpha))\\n if filt_type != 'TSVD': Lcs = append(Lcs, self.Lcurve(alpha))\\n self.fs = fs \\n self.MSEs = MSEs \\n self.UPREs = UPREs\\n self.DP2s = DP2s \\n self.GCVs = GCVs \\n if filt_type != 'TSVD': self.Lcs = Lcs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4917c80f2119663d1dde0ea7ca859c05\",\n \"score\": \"0.5641145\",\n \"text\": \"def train_network(self):\\n global_error_stats = []\\n refined = False\\n j = 0\\n while j < self.EPOCH_LIMIT and not refined:\\n j += 1\\n # feed forward\\n layer_outputs = [self.data[:self.TRAIN_RATIO]] # first layer output\\n for i in range(1, len(self.layers)):\\n output = self.activate(np.dot(layer_outputs[-1],self.layers[i].weights))\\n layer_outputs.append(output)\\n\\n # get output layer error - how much did we miss the traget value\\n layer_error = self.expected[:self.TRAIN_RATIO] - layer_outputs[-1]\\n\\n # mean of global output error\\n global_err = np.mean(np.abs(layer_error))\\n # track global error changing for statistics\\n global_error_stats.append(global_err)\\n # if global error is small enough, stop\\n if global_err < 0.01: # TODO\\n refined = True\\n if j % 100 == 0:\\n print(statistics.mean(global_error_stats[-99:]))\\n\\n # get weight error differences\\n error_deltas = [layer_error * self.activate(layer_outputs[-1], deriv=True)] # last layer error delta\\n for i in range(len(self.layers)-2, 0, -1):\\n # how much did each prev layer output value contribute to the error (according to the weights)\\n layer_error = np.dot(error_deltas[-1], self.layers[i+1].weights.T)\\n # if prediction is confident, dont change much (change rate depends on the slope of the sigmoid)\\n error_delta = layer_error * self.activate(layer_outputs[i], deriv=True)\\n error_deltas.append(error_delta)\\n\\n # update weights\\n error_deltas.reverse() # reverse this so easier to iterate with i\\n layer_outputs.pop()\\n for i in range(len(self.layers)-1, 0, -1):\\n self.layers[i].weights += self.LEARNING_RATE * np.dot(layer_outputs.pop().T, error_deltas[i-1])\\n\\n global_error_stats = [*np.array_split(global_error_stats, j/100)]\\n plt.plot([statistics.mean(sub) for sub in global_error_stats])\\n plt.ylabel('global error in (0,1)')\\n plt.xlabel('100 iterations')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd465a5d976c7afbe07433368dede393\",\n \"score\": \"0.5634746\",\n \"text\": \"def get_error_rate(digits,labelled_centroids):\\n classified_incorrect = 0\\n for (label,digit) in digits:\\n classified_label = classify_digit(digit, labelled_centroids)\\n if classified_label != label:\\n classified_incorrect +=1\\n error_rate = classified_incorrect / float(len(digits))\\n return error_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870dcb066fc8bbbda0c916280321e27f\",\n \"score\": \"0.56330866\",\n \"text\": \"def _initialize_errors(self):\\n \\n # Initialize errors for each expert\\n \\n # The maximum left and right error for each expert\\n self.max_error_left = np.zeros([self.num_experts])\\n self.max_error_right = np.zeros([self.num_experts])\\n \\n # The minimum and maximum position predictions of each expert\\n self.min_predict = (np.ones([self.num_experts]) * self._data_set.num_positions) - 1\\n self.max_predict = np.zeros([self.num_experts])\\n \\n # The minimum and maximum true positions handled by each expert\\n self.min_pos = (np.ones([self.num_experts]) * self._data_set.num_positions) - 1\\n self.max_pos = np.zeros([self.num_experts])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7596de55884a8627bfe8e498437762\",\n \"score\": \"0.5629915\",\n \"text\": \"def training_score(self):\\n # Try to calculate the mean absolute error if class instance contains all relevant information\\n try:\\n mae = mean_absolute_error(self.target.reshape(-1, 1), self.predictions)\\n print('The mean absolute error is: {}'.format(mae))\\n except AttributeError:\\n print('There is no target vector present. Note that this method can only '\\n 'be used by instances that were used for training ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555cbb8e79a1ccf52fab4bd739258c93\",\n \"score\": \"0.56187147\",\n \"text\": \"def mse(errors):\\n return np.mean(errors.flatten() ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8868cb9832652039e522070923c4e249\",\n \"score\": \"0.5617532\",\n \"text\": \"def _roc_error(self):\\n s = self\\n # r1 is number of negatives with each score,\\n # r2 is number of positives rated higher than each score\\n # r3 is number of positives with each score\\n # r4 is number of negatives rated lower than each score\\n r1 = s.NE\\n r2 = s.TP - s.PE\\n r3 = s.PE\\n r4 = s.TN\\n r5 = r1 * r2 + 0.5 * r1 * r3\\n r6 = r3 * (r4**2 + r4*r1 + (r1**2)/3)\\n r7 = r1 * (r2**2 + r2*r3 + (r3**2)/3)\\n N = float(len(s.nscores))\\n P = float(len(s.pscores))\\n W = r5.sum() / (N*P)\\n Q2 = r6.sum() / (P * N**2)\\n Q1 = r7.sum() / (N * P**2)\\n W_stderr = nx.sqrt((W*(1-W)+(P-1)*(Q1-W**2)+(N-1)*(Q2-W**2))/(P*N))\\n #print W, Q1, Q2, W_stderr\\n self.W = W\\n self.W_stderr = W_stderr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80503037f1dbb1ece1868f6a0cc1b46f\",\n \"score\": \"0.56028014\",\n \"text\": \"def test_regression_learners(self):\\n\\n lrn = []\\n num_exceptions = 0\\n try:\\n lrn.append(r.svr())\\n lrn.append(r.elastic_net_regression())\\n lrn.append(r.ridge_regression())\\n lrn.append(r.ard_regression())\\n lrn.append(r.regression_tree())\\n # lrn.append( r.lasso_LARS() ) # complains about data scaling ?\\n # lrn.append( r.sgd_regressor() )# complains about data scaling ?\\n\\n except Exception as e:\\n num_exceptions += 1\\n print(\\\"Exception: \\\" + str(e))\\n\\n self.assertIs(num_exceptions, 0)\\n return lrn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafb96359d83001e91d56e06219cd70f\",\n \"score\": \"0.55901015\",\n \"text\": \"def error_rate(self):\\n error = 0.0\\n rows, cols = self.reviews.shape\\n for idx in xrange(rows):\\n for jdx in xrange(cols):\\n if self.reviews[idx, jdx] > 0:\\n error += (self.model[idx, jdx] - self.reviews[idx, jdx]) ** 2\\n print error\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3143fd23f1d6ff18a127c0c26c8dd2\",\n \"score\": \"0.5588403\",\n \"text\": \"def mse(self):\\n user_ids, item_ids, ratings = [], [], []\\n for u,i,r in self.R:\\n user_ids.append(u)\\n item_ids.append(i)\\n ratings.append(r)\\n \\n error = 0\\n for u,i,r in self.R:\\n user_attr = self.UserAttr[u] if self.fit_user_attributes else None\\n item_attr = self.ItemAttr[i] if self.fit_item_attributes else None\\n predicted = self._predict(u, i, user_attr, item_attr)\\n error += pow(r - predicted, 2)\\n return np.sqrt(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9560742e660554a2a613bb047e9006d8\",\n \"score\": \"0.55805045\",\n \"text\": \"def current_error(self):\\n preds = self.predict_dataset(self.dataset)\\n rmse = mean_squared_error(self.dataset[:, -1], preds) ** 0.5\\n mae = mean_absolute_error(self.dataset[:, -1], preds)\\n return rmse, mae\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29cc34f7384ef798c5642757b384fd6\",\n \"score\": \"0.5580416\",\n \"text\": \"def fit(self, train_set_x, train_set_y):\\n self.w = (sum(train_set_x*train_set_y))/(sum(train_set_x*train_set_x))\\n\\n self.bias = (sum(train_set_y)-self.w*sum(train_set_x))/len(train_set_x)\\n\\n average_train_error = sum((train_set_x * self.w + self.bias - train_set_y) ** 2) / len(train_set_x)\\n print('average squared training error:', average_train_error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fca8a9f0786859bc1add1bfcb91c394\",\n \"score\": \"0.5575022\",\n \"text\": \"def errors(self):\\n a, p = self.actual, self.pred\\n return [a[i] - p[i] for i in range(len(a))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4015c848f0399c8a4e4b12a9bdba1fed\",\n \"score\": \"0.5573774\",\n \"text\": \"def NeuralNetErrorMetrics(resultsDF):\\n # Turning columns to values\\n testY = resultsDF['Trajanje'].values\\n preds = resultsDF['Predictions'].values\\n\\n resultsDF['minValue'] = resultsDF.min(axis = 1)\\n minValue = resultsDF['minValue'].values\\n\\n # Calculate difference between real and predicted values\\n diff = np.abs((testY - preds) / testY) * 100\\n diffProduction = np.abs((testY - preds) / minValue) * 100\\n # test_y['diff'] = diff\\n # test_y['diffProduction'] = diffProduction\\n\\n # Put values in DF\\n diff = pd.DataFrame(data = diff, columns = ['Error %'])\\n diffProduction = pd.DataFrame(data = diffProduction, columns = ['Error %'])\\n \\n return diff, diffProduction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1600cd31abea1aaf71b8a2f2b5f8f914\",\n \"score\": \"0.55589885\",\n \"text\": \"def errfunc(self, ynodes, x, y):\\n return self.fitfunc(x, ynodes) - y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"622d84ad60620427b6220b81d39deb00\",\n \"score\": \"0.5557471\",\n \"text\": \"def error(clf, X, y, ntrials=100, test_size=0.2) :\\n\\n ### ========== TODO : START ========== ###\\n # compute cross-validation error using StratifiedShuffleSplit over ntrials\\n # hint: use train_test_split (be careful of the parameters)\\n \\n # use stratified shuffle split over ntrials\\n ss_split = StratifiedShuffleSplit(n_splits = ntrials, test_size = test_size, random_state = 0)\\n\\n # get accumulators set up\\n acc_train = 0\\n acc_test = 0\\n acc_f1 = 0\\n\\n # get train and test\\n for train, test in ss_split.split(X,y):\\n # fit to training data\\n clf.fit(X[train], y[train])\\n\\n # predict from the test data\\n y_pred_train = clf.predict(X[train])\\n y_pred_test = clf.predict(X[test])\\n\\n y_train_pred = clf.predict(X[train])\\n y_test_pred = clf.predict(X[test])\\n\\n # get the testing error and add to accumulators\\n acc_test += 1 - metrics.accuracy_score(y[test], y_pred_test, normalize = True)\\n acc_train += 1 - metrics.accuracy_score(y[train], y_pred_train, normalize = True)\\n acc_f1 += metrics.f1_score(y[test], y_pred_test, average = \\\"micro\\\")\\n\\n # set up return values\\n train_error = acc_train / ntrials\\n test_error = acc_test / ntrials\\n f1_score = acc_f1 / ntrials\\n \\n ### ========== TODO : END ========== ###\\n\\n return train_error, test_error, f1_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523c9a97d5514d8bd098f77ba072fc1d\",\n \"score\": \"0.55494857\",\n \"text\": \"def computeError(self): \\n v = (self.u - self.old_u).flat\\n return self.np.sqrt(self.np.dot(v, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd11aa1b87f6efbf537076957f770ad7\",\n \"score\": \"0.55462056\",\n \"text\": \"def local_error_test(self):\\n dataPtsOrg = [ 2.30, .373, .583, 1.880, 500]\\n dataPtsCalc = [-1.21, -.445, .466, .226, 300]\\n localErrors = [ 3.51, .818, .117, 1.654, 200]\\n\\n mad = MeanAbsoluteDeviationError()\\n\\n for idx in xrange(len(dataPtsOrg)):\\n le = mad.local_error(dataPtsOrg[idx], dataPtsCalc[idx])\\n ple = localErrors[idx]\\n\\n ## compare the strings due to accuracy\\n #print le, ple\\n assert str(le) == str(ple)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d47a4f15726adf753acfd4e39183f21\",\n \"score\": \"0.55422914\",\n \"text\": \"def MSE_err(truth,pred):\\r\\n return np.mean((truth-pred)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9b418d485bb285eeee445c6ae630fe1\",\n \"score\": \"0.553092\",\n \"text\": \"def boot_error(fit_model):\\n\\n def one_sample(n):\\n \\\"\\\"\\\"\\n Helper function for error. Samples from the data, fits a model,\\n and finds the loss on the outsample.\\n\\n Output:\\n in_test_set - a 1D binary array with 1s indicating the \\n presence of observation (i) in the test set.\\n\\n loss - a 1D array of the loss for observation (i), equal to 0 \\n if (i) was not in the test set.\\n \\\"\\\"\\\"\\n in_test_set = np.zeros(n)\\n loss = np.zeros(n)\\n\\n fit = 0\\n while not fit:\\n try:\\n train = np.random.choice(n, n)\\n test = np.setdiff1d(np.arange(n), train)\\n\\n fit = fit_model(x[train], y[train])\\n except np.linalg.linalg.LinAlgError as e:\\n print(\\\"lin_alg_error\\\")\\n pass\\n\\n in_test_set[test] = 1\\n y_hat = fit(x[test])\\n\\n loss[test] = loss_fun(y_hat, y[test])\\n\\n return np.concatenate([in_test_set, loss])\\n\\n n = len(x)\\n q = math.pow(1 - 1/n, n)\\n p = 1 - q\\n\\n time_sample = lambda x: timeit(lambda:one_sample(n), \\\"Running sample\\\")\\n\\n all_samples = reduce(operator.add, \\n map(time_sample, range(num_samples)))\\n in_test_set, loss = np.split(all_samples, 2)\\n\\n fit = timeit(lambda:fit_model(x, y), \\\"fitting overall model\\\")\\n y_hat = fit(x)\\n if metrics is not None: metrics(y_hat, y)\\n\\n if any(in_test_set == 0):\\n i_in_test_set = lambda i: in_test_set[i] != 0\\n good_indices = list(filter(i_in_test_set, range(n)))\\n loss = loss[good_indices]\\n in_test_set = in_test_set[good_indices]\\n\\n err_1 = np.mean(loss/in_test_set)\\n err_bar = np.mean(loss_fun(y_hat, y))\\n err_632 = q * err_bar + p * err_1\\n\\n if binary_outcome:\\n p1 = sum(map(lambda t: t == 1, y))/n\\n q1 = sum(map(lambda t: t == 1, y_hat))/n\\n gamma = p1 * (1 - q1) + q1 * (1 - p1)\\n else:\\n # gamma = err_bar\\n unary_loss = lambda a: loss_fun(*a)\\n loss_vector = map(unary_loss, itertools.product(y_hat, y))\\n gamma = sum(loss_vector)/n/n\\n \\n if err_1 > err_bar and gamma > err_bar:\\n r = (err_1 - err_bar)/(gamma - err_bar)\\n else:\\n r = 0\\n\\n err1_ = min(err_1, gamma)\\n print(err_632 + (err1_ - err_bar) * (p * q * r) / (1 - q * r))\\n return err_632 + (err1_ - err_bar) * (p * q * r) / (1 - q * r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0baec98ffcaff698ad055b7b2d7d51\",\n \"score\": \"0.5517289\",\n \"text\": \"def analyse_erreur(err_train, err_test):\\n #AJOUTER CODE ICI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e749a664c378f07ba2863013efb5b3\",\n \"score\": \"0.55152315\",\n \"text\": \"def compute_error(self):\\n pred = np.dot(self.M, self.C)\\n self.error = math.sqrt(sum((pred - self.B) ** 2) / len(self.B))\\n # print self.error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b4f06170a3ebe8c27312579f7a9a02\",\n \"score\": \"0.54949987\",\n \"text\": \"def find_min_loss_acc(history):\\n min_loss = 0\\n min_loss_acc = 0\\n try:\\n print(\\\"Getting minimum loss and corresponding accuracy\\\")\\n loss_list = history.history['val_loss']\\n min_loss = min(loss_list)\\n min_loss_idx = len(loss_list) - loss_list[::-1].index(min_loss) - 1\\n min_loss_acc = history.history['val_acc'][min_loss_idx] * 100\\n except Exception as e:\\n print(\\\"Finding minimum loss failed: {}\\\".format(e))\\n\\n return min_loss, min_loss_acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf6ea54b13169cd212780479ddfadb2\",\n \"score\": \"0.54926234\",\n \"text\": \"def _train(self) -> float:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8240448ac6221279960f9761e66b2c2e\",\n \"score\": \"0.5484894\",\n \"text\": \"def calculate_error(train, test, forecast):\\n resolution = detect_resolution(train)\\n return mase_loss(test, forecast, train, sp=int(timedelta(days=1)/resolution))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982ee56897c53550d0a5486a95b4100e\",\n \"score\": \"0.54752505\",\n \"text\": \"def train():\\n global population_size, models, best\\n # Create the population\\n for _ in range(0, population_size):\\n models.append(getLSTM())\\n\\n # Format: MSE, hyperparams, weights\\n errors = []\\n for j in range(0, len(models)):\\n print(\\\"\\\\tTraining member \\\" + str(j) + \\\" out of \\\" + str(population_size))\\n model = models[j]\\n step(model)\\n error = eval(model)\\n if error < best[0]:\\n print(\\\"New best error: \\\" + str(error) + \\\", hyperparams=\\\", model[1])\\n best = [error, model[1], model[0].get_weights()]\\n errors.append(error)\\n # TODO: make better decisions\\n if random.randint(0, 100) % 2 == 0:\\n explore(j)\\n else:\\n exploit(j, best)\\n\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87be4049e7e400b8c9e8fe680375f45\",\n \"score\": \"0.5473502\",\n \"text\": \"def get_local_minimum(self):\\r\\n\\t\\tcurrent_x = self.startPoint\\r\\n\\t\\tprevious_step_size = current_x\\r\\n\\r\\n\\t\\twhile previous_step_size > self.learnRate:\\r\\n\\t\\t\\tprevious_x = current_x\\r\\n\\t\\t\\tcurrent_x = current_x - (self.learnRate * self.get_grad_f(current_x))\\r\\n\\t\\t\\tprevious_step_size = np.abs(current_x - previous_x)\\r\\n\\t\\treturn current_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe01fc350f4cb7292db86ef555855686\",\n \"score\": \"0.54661894\",\n \"text\": \"def show_errors(params, samples, y):\\n global __errors__\\n error_acum = 0\\n error = 0\\n\\n # Optimized version\\n hyp = calculateHyp(params,samples)\\n error = numpy.vectorize(crossEntropy)(hyp,y)\\n error_acum = error.sum()\\n\\n # PREVIOUS Implementation\\n # for instance in range(len(samples)):\\n # hyp = calculateHyp(params,samples.iloc[instance])\\n # error = crossEntropy(hyp, y.iloc[instance])\\n # error_acum = error_acum + error # this error is different from the one used to update, this is general for each sentence it is not for each individual param\\n\\t#print(\\\"acum error %f \\\" % (error_acum));\\n mean_error_param = error_acum/len(samples)\\n __errors__.append(mean_error_param)\\n return mean_error_param\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64449d87e4acf090b7e3f0a945e02533\",\n \"score\": \"0.5461774\",\n \"text\": \"def min_forest_mae(leaf_nodes, train_X, val_X, train_y, val_y):\\n dict_mae = {}\\n for node in leaf_nodes:\\n model = DecisionTreeRegressor(max_leaf_nodes=node, random_state=1)\\n model.fit(train_X, train_y)\\n preds_val = model.predict(val_X)\\n mae = mean_absolute_error(val_y, preds_val)\\n dict_mae[node] = mae\\n node = min(dict_mae, key=dict_mae.get)\\n print(f'Best Fit Node modelTree: {node} - MAE: {dict_mae[node]:.0f}')\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef69f9b58633417eac4e1bbb6fc5924e\",\n \"score\": \"0.54485613\",\n \"text\": \"def get_train_test_error(classifier, X, y, num_iterations = 1, split = 0.25):\\n\\n train_error, test_error = 0.0, 0.0\\n for i in range(num_iterations):\\n X_train, X_test, y_train, y_test = train_test_split(X, y,\\n test_size=split,\\n random_state=i)\\n classifier.fit(X_train, y_train)\\n y_train_pred = classifier.predict(X_train)\\n y_test_pred = classifier.predict(X_test)\\n train_error+=1- metrics.accuracy_score(y_true=y_train,\\n y_pred=y_train_pred,\\n normalize=True)\\n test_error+=1 - metrics.accuracy_score(y_true=y_test,\\n y_pred=y_test_pred,\\n normalize=True)\\n train_error /=num_iterations\\n test_error /=num_iterations\\n return train_error, test_error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2a38f01c5fb7fad50117c18e376f31\",\n \"score\": \"0.5447187\",\n \"text\": \"def validate(self, epoch):\\n losses = AverageMeter()\\n accs = AverageMeter()\\n\\n glimpses = AverageMeter()\\n\\n for i, (x, y) in enumerate(self.valid_loader):\\n if self.use_gpu:\\n x, y = x.cuda(), y.cuda()\\n x, y = Variable(x), Variable(y)\\n loss, glm, acc = self.rollout(x, y)\\n glimpses.update(glm)\\n\\n # store\\n try:\\n accs.update(acc.data.item())\\n losses.update(loss.data[0], x.size()[0])\\n except:\\n losses.update(loss.data.item(), x.size()[0])\\n accs.update(acc.data.item(), x.size()[0])\\n\\n return losses.avg, accs.avg, glimpses.avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5433000ff39414061777fda10c6d67e9\",\n \"score\": \"0.54465586\",\n \"text\": \"def compute_error(data, user_features, item_features, nz):\\n\\n square_errors = []\\n for i, j in nz:\\n square_errors.append(np.square(data[i, j] - np.dot(item_features[i, :], user_features[j, :].T)))\\n\\n mse = np.mean(square_errors)\\n rmse = np.sqrt(mse)\\n return rmse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8754732e508c3deba3fbf801df88467b\",\n \"score\": \"0.5446375\",\n \"text\": \"def fit(self,maxiterations=50):\\n self.fitError.append(self.sUpdate())\\n return self.fitError[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46102d112a7d3880b1dfcddd02a428e1\",\n \"score\": \"0.5445701\",\n \"text\": \"def compute_error(data, user_features, item_features, nz):\\n mse = 0\\n for row, col in nz:\\n item_info = item_features[:, row]\\n user_info = user_features[:, col]\\n mse += (data[row, col] - user_info.T.dot(item_info)) ** 2\\n return np.sqrt(1.0 * mse / len(nz))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd48d3a77b254c8db0318af236f227a1\",\n \"score\": \"0.5428743\",\n \"text\": \"def _tol_mean_score(self):\\n scores = []\\n self.restart_model()\\n for _ in range(1):\\n self.model.fit(self.X, self.y)\\n for _ in range(100):\\n bag_idxs = np.random.choice(len(self.y_test), len(self.y_test))\\n scores.append(self.value(self.model, metric=self.metric,\\n X=self.X_test[bag_idxs], y=self.y_test[bag_idxs]))\\n self.tol = np.std(scores)\\n self.mean_score = np.mean(scores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b29af9118c6f70a8e8108ca7d9bd4fd\",\n \"score\": \"0.5425718\",\n \"text\": \"def min_tree_mae(leaf_nodes, train_X, val_X, train_y, val_y):\\n dict_mae = {}\\n for node in leaf_nodes:\\n model = DecisionTreeRegressor(max_leaf_nodes=node, random_state=1)\\n model.fit(train_X, train_y)\\n preds_val = model.predict(val_X)\\n mae = mean_absolute_error(val_y, preds_val)\\n dict_mae[node] = mae\\n node = min(dict_mae, key=dict_mae.get)\\n print(f'Best Fit Node modelTree: {node} - MAE: {dict_mae[node]:.0f}')\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab219a647b147acc0ab483e9f874f106\",\n \"score\": \"0.5425643\",\n \"text\": \"def get_score(n_estimators):\\n my_p = Pipeline(\\n steps=[\\n ('prep', SimpleImputer()),\\n ('model', RandomForestRegressor(n_estimators=n_estimators, random_state=0))\\n ])\\n scores = -1 * cross_val_score(my_p, X, y,\\n cv=3,\\n scoring='neg_mean_absolute_error')\\n return scores.mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0b434079894a15dfbd2303bb9210eb\",\n \"score\": \"0.5424404\",\n \"text\": \"def error(self) -> float:\\n if self._error is None:\\n self._error = (1 - self.averageRepresentativeness) \\\\\\n * self.averageDistanceFromRepresentativeToNonRepresentative\\n return self._error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163e6f06c7b36d803ec3aa1dc2459d34\",\n \"score\": \"0.5417803\",\n \"text\": \"def gen_error_1_NN(embedding, labels):\\n model = KNeighborsClassifier(n_neighbors=1)\\n loo = LeaveOneOut()\\n loo.get_n_splits(embedding)\\n scores = cross_val_score(model , X = embedding , y = labels, cv = loo)\\n gen_error = (1 - np.mean(scores))\\n \\n return gen_error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41eb9b29cc2cd8b4096b6bf56ca86221\",\n \"score\": \"0.54143006\",\n \"text\": \"def get_trainable_model_error(model) -> float:\\n\\n # Check model validity -- see above for implementation details\\n assert hasattr(model, \\\"trainable\\\")\\n assert model.trainable is True\\n assert hasattr(model, \\\"output_bias\\\")\\n assert model.output_bias.shape == (1,)\\n\\n # The error is just absolute value of our scalar output bias\\n return abs(float(model.output_bias[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"584fbb0a9221777956ae5f490e925d98\",\n \"score\": \"0.5413518\",\n \"text\": \"def observation_error(self: ModelData) -> Optional[float]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f4b28a972b9d5bdbe3c4cf3aec6ca2\",\n \"score\": \"0.54089165\",\n \"text\": \"def get_current_error(self):\\r\\n current_output_layer_errors = [0] * len(self.output_layer)\\r\\n current_training_set_errors = []\\r\\n index = 0\\r\\n while index < len(self.input_layer_data):\\r\\n # get the input layer and the expected output layer from the training data set\\r\\n input_layer = self.input_layer_data[index]\\r\\n real_output_layer = self.output_layer_data[index]\\r\\n assert len(real_output_layer) == len(self.output_layer)\\r\\n\\r\\n # compute the hidden layers values by using the current weights in the network\\r\\n last_layer_data = input_layer\\r\\n for hidden_layer in self.hidden_layers:\\r\\n for neuron in hidden_layer:\\r\\n neuron.compute_value(input_values=last_layer_data)\\r\\n last_layer_data = numpy.array([neuron.value for neuron in hidden_layer])\\r\\n\\r\\n # compute the output layer values based on the current weights in the network\\r\\n for neuron in self.output_layer:\\r\\n neuron.compute_value(input_values=last_layer_data)\\r\\n\\r\\n # compute the current errors\\r\\n for neuron_index, neuron in enumerate(self.output_layer):\\r\\n current_output_layer_errors[neuron_index] = neuron.compute_value_error(real_output_layer[neuron_index])\\r\\n\\r\\n current_training_set_errors.append(sum(current_output_layer_errors))\\r\\n\\r\\n index += 1\\r\\n\\r\\n return sum(current_training_set_errors), sum(current_training_set_errors) / len(current_training_set_errors), len(current_training_set_errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79872cfa732640a6b7ce87f943378f7\",\n \"score\": \"0.5406132\",\n \"text\": \"def _fit_no_arch_normal_errors_params(self) -> NDArray:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ba0481d10e80dc5aeaa4b1750ad834\",\n \"score\": \"0.5399917\",\n \"text\": \"def _compute_error(self, target):\\n\\n tempWeights = None\\n tempDerivatives = None\\n\\n for layer in reversed(self.layers):\\n if layer == self._get_output_layer():\\n layer.computeDerivative(self.loss.calculateDerivative(target, layer.outp), 1.0)\\n else:\\n layer.computeDerivative(tempDerivatives, tempWeights[1:])\\n\\n tempWeights = layer.weights\\n tempDerivatives = layer.deltas\\n\\n output = self._get_output_layer().outp\\n return self.loss.calculateError(target, output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f32756505f4cf54e5b9143fe00436a\",\n \"score\": \"0.5395561\",\n \"text\": \"def get_score(n_estimators):\\n \\n from sklearn.ensemble import RandomForestRegressor\\n from sklearn.pipeline import Pipeline\\n from sklearn.impute import SimpleImputer\\n \\n my_pipeline = Pipeline(steps=[\\n ('preprocessor', SimpleImputer()),\\n ('model', RandomForestRegressor(n_estimators=n_estimators, random_state=0))\\n])\\n \\n \\n from sklearn.model_selection import cross_val_score\\n\\n # Multiply by -1 since sklearn calculates *negative* MAE\\n scores = -1 * cross_val_score(my_pipeline, X, y,\\n cv=3,\\n scoring='neg_mean_absolute_error')\\n\\n print(\\\"Average MAE score:\\\", scores.mean())\\n return scores.mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e29ac3b5e880c9cdbd7e3362ddb4d313\",\n \"score\": \"0.5394792\",\n \"text\": \"def validateOneEpoch(self) -> Tuple[float, float]:\\n self.model.eval()\\n predictedLabels = []\\n expectedLabels = []\\n\\n with torch.no_grad():\\n for datapoints in self.validationDataLoader:\\n image = datapoints[\\\"image\\\"].to(self.config.device)\\n expectedLabel = datapoints[\\\"label\\\"].to(self.config.device)\\n predicted = self.model(image)\\n predicted = torch.nn.functional.softmax(predicted, dim=1)\\n\\n expectedLabels.extend(expectedLabel.cpu().numpy().tolist())\\n predictedLabels.extend(torch.max(predicted, dim=1).indices.cpu().numpy().tolist())\\n\\n return f1_score(expectedLabels, predictedLabels), accuracy_score(expectedLabels, predictedLabels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58010838074f1b03f9bf7817ce79a8e3\",\n \"score\": \"0.53932995\",\n \"text\": \"def get_error_dfs(df_pred, df_target, alpha=0.1):\\n tmp = alpha * (df_target - df_pred).values\\n return (np.exp(tmp) - tmp - np.ones_like(tmp)).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d6cd9d2a3bfa4bc5a2c6902a57439c\",\n \"score\": \"0.53902656\",\n \"text\": \"def estimate_sigma_min(self):\\n if self.sigma_min is None:\\n sigma_min = 0\\n for id, labels in enumerate(self.target):\\n if len(labels) == 0:\\n ts_subs = subsequences(self.data[id], min(self.windows))\\n sigma_min = max(sigma_min, ts_subs.std(axis=1).max())\\n print(\\\"sigma_min set to {}\\\".format(sigma_min))\\n self.sigma_min = sigma_min\\n shapelet_utils.sigma_min = self.sigma_min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d396d72bb39a3b2c33fa95b304731ac\",\n \"score\": \"0.53889966\",\n \"text\": \"def get_error_dist(self):\\n if self.error_dist:\\n return self.error_dist\\n self.n = float(self.n)\\n if self.n > 1:\\n self.error_dist = (1. / (self.n - 1) * ((self.dd) - 1. / self.n * (self.d) ** 2) ) ** 0.5\\n return self.error_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64e693d0f0bdf007214389e0e0e17dfc\",\n \"score\": \"0.5368556\",\n \"text\": \"def MAE_err(truth,pred):\\r\\n return np.mean(abs(np.array(truth-pred)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c60a058ebfa8409535d6f6795d6fc7\",\n \"score\": \"0.5364231\",\n \"text\": \"def error( inputs, params ):\\n\\ttotalError = 0\\n\\tfor i in range( 0, len( inputs ) ):\\n\\t\\tx = inputs[i]\\n\\t\\ttotalError += ( dPhi_t_dx( x, params ) + 0.2 * phi_t( x, params ) - np.exp( -x / 5.0 ) * np.cos( x ) ) ** 2\\n\\treturn totalError / float( len( points ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b152868b34afcb79c2bfe9784d31b53\",\n \"score\": \"0.53613174\",\n \"text\": \"def min_k_rmse(list_k, model, X_train, X_test, y_train, y_test):\\n dict_rmse = {}\\n for k in list_k:\\n SelectKBest(f_regression, k=k).fit_transform(X_train, y_train)\\n model.fit(X_train, y_train)\\n predictions = model.predict(X_test)\\n rmse = mean_squared_error(y_test, predictions)\\n dict_rmse[k] = rmse\\n k_best = min(dict_rmse, key=dict_rmse.get)\\n print(f'Best Fit k features modelLinear: {k_best} - RMSE: {dict_rmse[k_best]:.4f}')\\n return dict_rmse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f98ccdbe07d1fd75bf220b224b9ded8\",\n \"score\": \"0.53490466\",\n \"text\": \"def test_error_rate(estimator, clusterparams, gentype='default', runs=100):\\n error_rate_list = list()\\n\\n # Do multiple runs\\n for _x in range(runs):\\n if gentype == 'default':\\n samples, labels = make_blobs(**clusterparams)\\n elif gentype == 'skewed':\\n samples, labels = make_skewed_blobs(**clusterparams)\\n elif gentype == 'unequal':\\n samples, labels = make_unequal_blobs(**clusterparams)\\n\\n estimator.fit(samples)\\n\\n # Actual and estimated labels won't match so we must map them.\\n # Each possibile mapping is tested and the lowest error rate is used.\\n # e.g. for mapping, if there are three labels 0,1,2\\n # -> mapping = (0,1,2), (0,2,1), (2,1,0), etc.\\n # So if estimator label = 1 then we pretend it is the number at index 1 of mapping\\n error_rate = np.inf\\n for mapping in permutations(range(len(estimator.cluster_centers_))):\\n error = 0\\n for label, result in zip(labels, estimator.labels_):\\n if label != mapping[result]:\\n error += 1\\n result = error/len(samples)\\n if result < error_rate:\\n error_rate = result\\n error_rate_list.append(error_rate)\\n\\n mean = np.mean(error_rate_list)\\n standard_deviation = np.std(error_rate_list)\\n return {'mean': mean,\\n 'std': standard_deviation,\\n 'elabels': estimator.labels_,\\n 'labels': labels,\\n 'samples': samples,\\n 'clusters': estimator.cluster_centers_}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf23b715deedf882cea97656a8a344c\",\n \"score\": \"0.5340559\",\n \"text\": \"def __compute_bnn_training_error(self):\\n\\t\\texp = np.reshape(self.episode_buffer_bnn, (len(self.episode_buffer_bnn),-1))\\n\\t\\tepisode_X = np.array([np.hstack([exp[tt,0],exp[tt,1]]) for tt in range(exp.shape[0])])\\n\\t\\tepisode_Y = np.array([exp[tt,3] for tt in range(exp.shape[0])])\\n\\t\\tif self.state_diffs:\\n\\t\\t\\t# subtract previous state\\n\\t\\t\\tepisode_Y -= episode_X[:,:self.num_dims]\\n\\t\\tl2_errors = self.network.get_td_error(np.hstack([episode_X, np.tile(self.weight_set, (episode_X.shape[0],1))]), episode_Y, 0.0, 1.0)\\n\\t\\tself.mean_episode_errors[self.instance_iter,self.episode_iter] = np.mean(l2_errors)\\n\\t\\tself.std_episode_errors[self.instance_iter,self.episode_iter] = np.std(l2_errors)\\n\\t\\tif self.print_output:\\n\\t\\t\\tprint('BNN Error: {}'.format(self.mean_episode_errors[self.instance_iter,self.episode_iter]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e28937fe76f94245935f42fa7523d1\",\n \"score\": \"0.5334064\",\n \"text\": \"def predictor_metrics(self, outputs, prediction):\\n try:\\n a = self.regressor.feature_importances_\\n except sklearn.utils.validation.NotFittedError:\\n print \\\"Please fit the algorithm before calling this function.\\\"\\n return\\n return mean_squared_error(outputs, prediction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5115c4401605603a510178cd4ea5dfa6\",\n \"score\": \"0.5332098\",\n \"text\": \"def getAvgError(self):\\n warnings.warn(\\\"use getRate instead\\\", DeprecationWarning)\\n with self.mutex:\\n return self.getError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0eff74db08b3ba5a9515e1bb5d22e61\",\n \"score\": \"0.53310543\",\n \"text\": \"def get_error_parameter_test(self):\\n data = [[0.0, 0.0], [1, 0.1], [2, 0.2], [3, 0.3], [4, 0.4]]\\n tsOrg = TimeSeries.from_twodim_list(data)\\n tsCalc = TimeSeries.from_twodim_list(data)\\n\\n bem = BaseErrorMeasure()\\n\\n bem_calculate = bem._calculate\\n bem_local_error = bem.local_error\\n \\n def return_zero(ignoreMe, ignoreMeToo):\\n return 0\\n\\n ## remove the NotImplementedErrors for initialization\\n bem.local_error = return_zero\\n bem._calculate = return_zero\\n bem.initialize(tsOrg, tsCalc)\\n\\n bem.local_error = bem_local_error\\n bem._calculate = bem_calculate\\n\\n try:\\n bem.get_error(10.0, 90.0)\\n except NotImplementedError:\\n pass\\n else:\\n assert False # pragma: no cover\\n\\n for start in [-1.0, 80.0, 103.0]:\\n for end in [-5.0, 10.0, 105.0]:\\n try:\\n bem.get_error(start, end)\\n except ValueError:\\n pass\\n else:\\n assert False # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175b82572c994e7f937b448a2deffa2d\",\n \"score\": \"0.5329545\",\n \"text\": \"def err_ratio(learner, dataset, examples=None):\\n examples = examples or dataset.examples\\n if len(examples) == 0:\\n return 0.0\\n right = 0\\n for example in examples:\\n desired = example[dataset.target]\\n output = learner(example)\\n\\n # Check if prediction is correct\\n if isinstance(desired, str):\\n correct = desired == output\\n else:\\n correct = np.allclose(output, desired)\\n if correct:\\n right += 1\\n return 1 - (right / len(examples))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41d8b79be5ca0bdfd2a9c38ab995197\",\n \"score\": \"0.53274846\",\n \"text\": \"def MinTemp(models, startyear, endyear):\\n return sum(map(lambda m: models[m]['tasmin'].AverageThanExtreme(startyear,endyear,np.fmin), models)) /len(models)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04eb54885fd4998dcbb9a580f482ce54\",\n \"score\": \"0.5322022\",\n \"text\": \"def calc_rmin(self):\\n sigma,n,m = self.sigma, self.n, self.m\\n rmin = (n/m*sigma**(n-m))**(1./(n-m))\\n return rmin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d5ef3b50618a0ed2a8f35854e0dd34\",\n \"score\": \"0.531001\",\n \"text\": \"def compute_errors(self, benchmark='default', mcset='default', failoninc=True, verbose=False):\\n if mcset == 'default':\\n lsslist = self.data.keys()\\n elif callable(mcset):\\n # mcset is function that will generate subset of HRXN from sset(self)\\n lsslist = [mc for mc in self.data.keys() if mc in mcset(self)] # untested\\n else:\\n # mcset is array containing modelchemistries\\n lsslist = [mc for mc in self.data.keys() if mc in mcset]\\n # assemble dict of qcdb.Reaction objects from array of reaction names\\n lsset = OrderedDict()\\n for mc in lsslist:\\n lsset[mc] = self.data[mc]\\n\\n lbench = self.benchmark if benchmark == 'default' else benchmark\\n try:\\n mcGreater = self.data[lbench].value\\n except KeyError as e:\\n raise ValidationError(\\\"\\\"\\\"Reaction %s missing benchmark datum %s.\\\"\\\"\\\" % (self.name, str(e)))\\n\\n err = {}\\n for label, datum in lsset.items():\\n try:\\n mcLesser = datum.value\\n except KeyError as e:\\n if failoninc:\\n raise ValidationError(\\\"\\\"\\\"Reaction %s missing datum %s.\\\"\\\"\\\" % (label, str(e)))\\n else:\\n continue\\n\\n err[label] = [mcLesser - mcGreater,\\n (mcLesser - mcGreater) / abs(mcGreater),\\n (mcLesser - mcGreater) / abs(mcGreater)] # TODO define BER\\n if verbose:\\n print(\\\"\\\"\\\"p = %6.2f, pe = %6.1f%%, bpe = %6.1f%% modelchem %s.\\\"\\\"\\\" %\\n (err[label][0], 100 * err[label][1], 100 * err[label][2], label))\\n\\n return err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5f1ae62e3183f2c29c4fb8c688892b\",\n \"score\": \"0.53076226\",\n \"text\": \"def oneError(y_test, probabilities):\\n oneerror = 0.0\\n ranking = rankingMatrix(probabilities)\\n\\n for i in range(y_test.shape[0]):\\n index = np.argmin(ranking[i,:])\\n if y_test[i,index] == 0:\\n oneerror += 1.0\\n \\n oneerror = float(oneerror)/float(y_test.shape[0])\\n\\n return oneerror\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d592008aaf560bb7921aa6d3f70553d\",\n \"score\": \"0.53075165\",\n \"text\": \"def calc_min_max_errors(self,\\n key_pos=None,\\n batch_size=10000):\\n\\n if key_pos == None: # Use all keys\\n\\n # Initialize errors\\n self._initialize_errors()\\n\\n # Use all keys and positions\\n keys = self._data_set.keys\\n true_positions = self._data_set.positions\\n num_keys = self._data_set.num_keys\\n\\n else: # Use subset of keys\\n\\n # Only use keys and position specified by key_pos\\n keys, true_positions = list(zip(*key_pos))\\n keys = list(keys)\\n true_positions = list(true_positions)\\n num_keys = len(keys)\\n \\n \\n # Calculate errors for each expert\\n for step in range(0, num_keys, batch_size):\\n \\n positions, experts = self.run_inference(keys[step:(step+batch_size)])\\n true_positions_batch = true_positions[step:(step+batch_size)]\\n \\n for idx in range(len(positions)):\\n \\n #pos = np.round(positions[idx])\\n pos = positions[idx]\\n expert = experts[idx]\\n true_pos = true_positions_batch[idx]\\n \\n self.min_predict[expert] = np.minimum(self.min_predict[expert],\\n pos)\\n self.max_predict[expert] = np.maximum(self.max_predict[expert],\\n pos)\\n \\n self.min_pos[expert] = np.minimum(self.min_pos[expert],\\n true_pos)\\n self.max_pos[expert] = np.maximum(self.max_pos[expert],\\n true_pos)\\n \\n error = pos - true_pos\\n if error > 0:\\n self.max_error_left[expert] = np.maximum(self.max_error_left[expert],\\n error)\\n elif error < 0:\\n self.max_error_right[expert] = np.maximum(self.max_error_right[expert],\\n np.abs(error))\\n\\n for expert in range(self.max_error_left.shape[0]):\\n print(\\\"expert %d left error: %.10f\\\" % (expert, self.max_error_left[expert]))\\n for expert in range(self.max_error_right.shape[0]):\\n print(\\\"expert %d right error: %.10f\\\" % (expert, self.max_error_right[expert]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b78e299cdb0f4cebf6b67f1e05ae76d\",\n \"score\": \"0.53066504\",\n \"text\": \"def scorer():\\n return make_scorer(RMSE, greater_is_better=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70eeee9a183a24db459575096c4cfb2\",\n \"score\": \"0.5303065\",\n \"text\": \"def ex_1_1_b(x_train, x_test, y_train, y_test):\\n \\n ## TODO\\n #pass\\n \\n MSE_TEST = np.zeros(10, dtype=float)\\n MSE_TRAIN = np.zeros(10, dtype=float)\\n\\n n_hidden = 5\\n for j in range(10):\\n reg = MLPRegressor(hidden_layer_sizes=(n_hidden,8), activation='logistic', solver='lbfgs', alpha=0,random_state=random.randint(0, 1000))\\n reg.fit(x_train, y_train)\\n mse = calculate_mse(reg, x_test, y_test)\\n MSE_TEST[j] = mse\\n \\n mse = calculate_mse(reg, x_train, y_train)\\n MSE_TRAIN[j] = mse\\n \\n mse_test_max = max(MSE_TEST)\\n mse_test_min = min(MSE_TEST)\\n mse_test_mean = np.mean(MSE_TEST)\\n mse_test_std = np.std(MSE_TEST)\\n \\n print(mse_test_max, \\\"/\\\", mse_test_min, \\\"/\\\", mse_test_mean, \\\"/\\\", mse_test_std)\\n \\n mse_train_max = max(MSE_TRAIN)\\n mse_train_min = min(MSE_TRAIN)\\n mse_train_mean = np.mean(MSE_TRAIN)\\n mse_train_std = np.std(MSE_TRAIN)\\n \\n print(mse_train_max, \\\"/\\\", mse_train_min, \\\"/\\\", mse_train_mean, \\\"/\\\", mse_train_std)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2646c7f9c80c926502e95f17c6b14dc\",\n \"score\": \"0.52926373\",\n \"text\": \"def find_nearest_error(model, data_loader, device, pgd_iters):\\n total = 0\\n correct = 0\\n d_err_min = 10\\n model.eval()\\n for i, (inputs, labels) in enumerate(data_loader):\\n labels = labels.to(device)\\n inputs = inputs.to(device)\\n advex = inputs.clone()\\n # search for nearest error d(E)\\n for j in range(pgd_iters):\\n advex = fgm_l2(model, advex, labels, 0.01, device)\\n # project back on to manifold\\n advex /= torch.unsqueeze(advex.norm(p=2, dim=1), dim=1)\\n outputs = model(advex)\\n _, predicted = torch.max(outputs.data, 1)\\n total += labels.size(0)\\n correct_idx = predicted == labels\\n correct += (correct_idx).sum().item()\\n if (correct_idx == 0).sum() > 0:\\n # compute nearest error on inner sphere\\n d_err = (inputs[correct_idx == 0] - advex[correct_idx == 0]\\n ).norm(p=2, dim=1).min().item()\\n if d_err < d_err_min:\\n print('itr %d got d = %f' % (j, d_err))\\n d_err_min = d_err\\n break\\n print('%d of %d' % (i, len(data_loader)))\\n return d_err_min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a76af53a5f57b3935c1eadd5d037bf\",\n \"score\": \"0.52888185\",\n \"text\": \"def error(self, predicted, target, method='L2', alpha=1.):\\n errors = target.ravel() - predicted.ravel()\\n\\n # Adjust for NaN and np.inf in predictions (unstable solution)\\n if not np.all(np.isfinite(predicted)):\\n # print(\\\"Warning: some predicted values are not finite\\\")\\n errors = np.inf\\n\\n # Compute mean error\\n if method == 'L2':\\n error = np.linalg.norm(errors, ord=2) # Technically the same as rmse\\n elif method == 'mse':\\n error = np.mean(np.square(errors))\\n elif method == 'tanh':\\n error = alpha * np.tanh(np.mean(np.square(errors)) / alpha) # To 'squeeze' mse onto the interval (0, 1)\\n elif method == 'rmse':\\n error = np.sqrt(np.mean(np.square(errors)))\\n elif method == 'nmse':\\n error = np.mean(np.square(errors)) / np.square(target.ravel().std(ddof=1))\\n elif method == 'nrmse':\\n error = np.sqrt(np.mean(np.square(errors))) / target.ravel().std(ddof=1)\\n elif method == 'tanh-nmse':\\n nmse = np.mean(np.square(errors)) / np.square(target.ravel().std(ddof=1))\\n error = alpha * np.tanh(nmse / alpha)\\n else:\\n raise ValueError('Scoring method not recognized')\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f505010a169db4355f18eacd66772b61\",\n \"score\": \"0.52881634\",\n \"text\": \"def calc_error(self, loss, ytrue, yhat):\\n if loss.name == 'gini':\\n accuracy = [y == yhat[i] for i, y in enumerate(ytrue)]\\n return accuracy.count(False)/len(ytrue)\\n return mean_squared_error(ytrue, yhat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705beee34b676c259b0411891a2c4ee9\",\n \"score\": \"0.5281362\",\n \"text\": \"def weak_learner(data, dist=None):\\n dist = dist or [1 / len(data) for _ in range(len(data))]\\n fns_and_errs = [(fn, error(fn, data, dist)) for fn in basis_functions]\\n fns_and_errs = sorted(fns_and_errs, key=lambda x: x[1])\\n min_err = fns_and_errs[0][1]\\n result = [fn for fn, err in fns_and_errs if err <= min_err]\\n return random.choice(result), min_err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991a51b37d23012ecf1cfa303ef26a75\",\n \"score\": \"0.5276654\",\n \"text\": \"def error_calculation_test(self):\\n dataOrg = [[0,0], [1,1], [2,2], [3,3], [4,4], [5,5], [6, 6], [7,7], [8,8], [9,9]]\\n dataCalc = [[0,1], [1,3], [2,5], [3,0], [4,3], [5,5], [6.1,6], [7,3], [8.1,8], [9,8]]\\n # difference: 1 2 3 3 1 0 NA 4 NA 1\\n # local errors: 1 4 9 9 1 0 NA 16 NA 1\\n\\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\\n\\n tsOrg = TimeSeries.from_twodim_list(dataOrg)\\n tsCalc = TimeSeries.from_twodim_list(dataCalc)\\n\\n mse = MeanSquaredError(80.0)\\n mse.initialize(tsOrg, tsCalc)\\n\\n assert str(mse.get_error()) == \\\"5.125\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a64521736ed38ee12ea7e0639631407\",\n \"score\": \"0.5276397\",\n \"text\": \"def getStatisticalError(self):\\n return np.sqrt(self.stat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3066781bec5b2c6dc88417277ade976d\",\n \"score\": \"0.52720255\",\n \"text\": \"def min(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f71f5bcc7f7f058eaed475a7caa4893\",\n \"score\": \"0.5267713\",\n \"text\": \"def error_rate(predictions, labels):\\n return 100.0 - (\\n 100.0 *\\n np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) /\\n predictions.shape[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c2feddabab35f7ee292c1b71d45e40\",\n \"score\": \"0.52666175\",\n \"text\": \"def compute_error_rate(df, ind_vars, seed):\\n (n, p) = df.shape\\n n_train = int(0.5 * n)\\n\\n # Split data into train and test sets.\\n # Task (i)\\n np.random.seed(seed)\\n ind_train = np.sort(np.random.choice(n, n_train, replace=False))\\n ind_test = np.setdiff1d(np.arange(n), ind_train)\\n df_train = df.loc[ind_train]\\n df_test = df.loc[ind_test]\\n\\n y_train = df_train['bool default']\\n y_test = df_test['bool default']\\n x_train = sm.add_constant(df_train[ind_vars])\\n x_test = sm.add_constant(df_test[ind_vars])\\n\\n # Fit logistic regression model and predict error rate.\\n # Task (ii)\\n model = sm.Logit(y_train, x_train).fit()\\n\\n # Task (iii)\\n pred_table = confusion_matrix(model, x_test, y_test)\\n\\n # Task (iv)\\n error_rate = 1 - np.trace(pred_table) / np.sum(pred_table)\\n return error_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c524b2c9171a8f444c9465731d0edd\",\n \"score\": \"0.52661973\",\n \"text\": \"def train(mlp, inputs, targets, epochs, learning_rate):\\n\\n # The amount of times we will go\\n # over the data.\\n for i in range(epochs):\\n\\n # The overall error that we get\\n # with the distance between the\\n # MLP guess and the actual value.\\n sum_error = 0\\n for _input, _target in zip(inputs, targets):\\n\\n # [weights, activations, derivatives]\\n # foward propagate and get the neural\\n # network guess over the data\\n output = forward_propagate(_input, mlp[0],\\n mlp[1])\\n\\n # Calculate the difference between\\n # the guess and the actual true value\\n error = _target - output\\n\\n # Propagate the error trought the MLP\\n back_propagate(error, mlp[0], mlp[1], mlp[2])\\n\\n # Go over the MLP and try to aproximate the\\n # the minimum\\n gradient_descent(mlp[0], mlp[2], learning_rate)\\n\\n # Accumulate the error\\n sum_error += mse(_target, output)\\n\\n print(\\\"Target: {}, Output: {}\\\"\\n .format(_target, output))\\n print()\\n print(\\\"Error: {} at epoch {}\\\"\\n .format(sum_error / len(inputs), i))\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f766f86945b64f3d6e2ebbff0e4d37\",\n \"score\": \"0.52660483\",\n \"text\": \"def validate(self):\\n self.set_model_mode('eval')\\n self.evaluator.reset()\\n losses = MetricMeter()\\n\\n print('Do evaluation on {} set'.format('valid set'))\\n data_loader = self.val_loader\\n assert data_loader is not None\\n for batch_idx, batch in enumerate(data_loader):\\n input, label = self.parse_batch_test(batch)\\n loss = self.forward_backward(batch, backprob=False)\\n losses.update(loss)\\n # total_loss += loss['loss']\\n output = self.model_inference(input)\\n self.evaluator.process(output, label)\\n\\n results = self.evaluator.evaluate()\\n # total_loss = losses.meters['total_loss'].avg\\n total_loss = losses.meters['general_loss'].avg\\n\\n\\n for k, v in results.items():\\n tag = '{}/{}'.format('validation', k)\\n self.write_scalar(tag, v, self.epoch)\\n return [total_loss,losses.dict_results(),results]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb19faa81a3e53cd805634af4292e997\",\n \"score\": \"0.5261249\",\n \"text\": \"def error(self, J_flat, gibbs_iter):\\n np.random.seed(seed=1)\\n J_flat = np.array(J_flat)\\n self.gibbs_iter = gibbs_iter\\n self.J = J_flat.reshape((self.m, self.m))\\n cov_models = np.zeros((self.m**2, self.n_pools))\\n batch_samples = np.array(self.pool.map(self.sample_preperation,\\n range(0, self.n_pools)))\\n self.batch_samples = np.moveaxis(batch_samples, 0, -1)\\n n, m, k = np.shape(self.batch_samples)\\n # Combine all montecarlo samples\\n for i in range(0, k):\\n cov_model_temp = np.dot(self.batch_samples[:, :, i].T,\\n self.batch_samples[:, :, i])/n\\n cov_models[:, i] = cov_model_temp.flatten()\\n self.cov_model = np.sum(cov_models, axis=1)/self.n_pools\\n # Calculate the difference in model correlation\\n # and emperical correlation\\n error_train = (-self.cov_model + self.emp_cov_train.flatten())\\n\\n if self.save_loss:\\n self.train_error.append(np.max(abs(error_train)))\\n\\n if self.emp_cov_test is not None:\\n error_test = (-self.cov_model + self.emp_cov_test.flatten())\\n if self.save_loss:\\n self.test_error.append(np.max(abs(error_test)))\\n\\n return error_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b723fb6339719e83b83347abda9458\",\n \"score\": \"0.5260465\",\n \"text\": \"def ex_1_1_b(x_train, x_test, y_train, y_test):\\n\\n ## TODO\\n mse_trains = []\\n mse_tests = []\\n for index in range(0,10):\\n regressor = MLPRegressor(\\n hidden_layer_sizes=(8,),\\n solver=\\\"lbfgs\\\",\\n activation=\\\"logistic\\\",\\n alpha=0.0,\\n max_iter=200,\\n random_state=randint(1,1000)\\n )\\n\\n regressor.fit(x_train, y_train)\\n mse_trains.append(calculate_mse(regressor, x_train, y_train))\\n mse_tests.append(calculate_mse(regressor, x_test, y_test))\\n\\n print(\\\"min_train:\\\", np.min(mse_trains))\\n print(\\\"min_test:\\\", np.min(mse_tests))\\n\\n print(\\\"max_train:\\\", np.max(mse_trains))\\n print(\\\"max_test:\\\", np.max(mse_tests))\\n\\n print(\\\"mean_train:\\\", np.mean(mse_trains))\\n print(\\\"mean_test:\\\", np.mean(mse_tests))\\n\\n print(\\\"std_train:\\\", np.std(mse_trains))\\n print(\\\"std_test:\\\", np.std(mse_tests))\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d3197977dc9466173ceffdf39fb2e8\",\n \"score\": \"0.5258542\",\n \"text\": \"def select_best_model(self):\\r\\n \\r\\n self.best_model = min(self.mean_mse, key=self.mean_mse.get)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598bef602b4d81f518031cd3d984b175\",\n \"score\": \"0.5256269\",\n \"text\": \"def computeScore(self, kmers):\\n if self.kmers.size > 0:\\n #self.common = np.intersect1d(self.kmers, kmers, assume_unique = True)\\n self.common = kmerizer.intersect(self.kmers, kmers)\\n self.denominator = float(self.kmers.size)\\n if self.denominator > 0:\\n self.score = float(self.common.size) / self.denominator\\n else:\\n self.score = 0 \\n else:\\n self.score = 0\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993696,"cells":{"query_id":{"kind":"string","value":"b12f72f1722d41320902c9e5d681fc56"},"query":{"kind":"string","value":"performs costeffectiveness and costbenefit analyses"},"positive_passages":{"kind":"list like","value":[{"docid":"7289a2b99a2ee2c83553060f08c9e11c","score":"0.0","text":"def report_CEA_CBA(multi_cohort_outcomes_SOC, multi_cohort_outcomes_NSB):\n\n # define two strategies\n SOC_diagnostic_strategy = Econ.Strategy(\n name='SOC Diagnostic',\n cost_obs=multi_cohort_outcomes_SOC.meanCosts,\n effect_obs=multi_cohort_outcomes_SOC.totalYLL,\n color='green'\n )\n NSB_diagnostic_strategy = Econ.Strategy(\n name='NSB Diagnostic',\n cost_obs=multi_cohort_outcomes_NSB.meanCosts,\n effect_obs=multi_cohort_outcomes_NSB.totalYLL,\n color='blue'\n )\n\n # do CEA\n CEA = Econ.CEA(\n strategies=[SOC_diagnostic_strategy, NSB_diagnostic_strategy],\n if_paired=True,\n health_measure='d'\n )\n\n # show the cost-effectiveness plane\n show_ce_figure(CEA=CEA)\n\n # report the CE table\n CEA.build_CE_table(\n interval_type='p', # prediction intervals\n alpha=D.ALPHA,\n cost_digits=0,\n effect_digits=2,\n icer_digits=2)\n\n # CBA\n NBA = Econ.CBA(\n strategies=[SOC_diagnostic_strategy, NSB_diagnostic_strategy],\n if_paired=True,\n health_measure='d'\n )\n # show the net monetary benefit figure\n NBA.graph_incremental_NMBs(\n min_wtp=0,\n max_wtp=50000,\n title='Cost-Benefit Analysis',\n x_label='Willingness-To-Pay for One Additional QALY ($)',\n y_label='Incremental Net Monetary Benefit ($)',\n interval_type='p',\n show_legend=True,\n figure_size=(6, 5)\n )","title":""}],"string":"[\n {\n \"docid\": \"7289a2b99a2ee2c83553060f08c9e11c\",\n \"score\": \"0.0\",\n \"text\": \"def report_CEA_CBA(multi_cohort_outcomes_SOC, multi_cohort_outcomes_NSB):\\n\\n # define two strategies\\n SOC_diagnostic_strategy = Econ.Strategy(\\n name='SOC Diagnostic',\\n cost_obs=multi_cohort_outcomes_SOC.meanCosts,\\n effect_obs=multi_cohort_outcomes_SOC.totalYLL,\\n color='green'\\n )\\n NSB_diagnostic_strategy = Econ.Strategy(\\n name='NSB Diagnostic',\\n cost_obs=multi_cohort_outcomes_NSB.meanCosts,\\n effect_obs=multi_cohort_outcomes_NSB.totalYLL,\\n color='blue'\\n )\\n\\n # do CEA\\n CEA = Econ.CEA(\\n strategies=[SOC_diagnostic_strategy, NSB_diagnostic_strategy],\\n if_paired=True,\\n health_measure='d'\\n )\\n\\n # show the cost-effectiveness plane\\n show_ce_figure(CEA=CEA)\\n\\n # report the CE table\\n CEA.build_CE_table(\\n interval_type='p', # prediction intervals\\n alpha=D.ALPHA,\\n cost_digits=0,\\n effect_digits=2,\\n icer_digits=2)\\n\\n # CBA\\n NBA = Econ.CBA(\\n strategies=[SOC_diagnostic_strategy, NSB_diagnostic_strategy],\\n if_paired=True,\\n health_measure='d'\\n )\\n # show the net monetary benefit figure\\n NBA.graph_incremental_NMBs(\\n min_wtp=0,\\n max_wtp=50000,\\n title='Cost-Benefit Analysis',\\n x_label='Willingness-To-Pay for One Additional QALY ($)',\\n y_label='Incremental Net Monetary Benefit ($)',\\n interval_type='p',\\n show_legend=True,\\n figure_size=(6, 5)\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"15992c3ac7d433e6ebac76decbf55a3c","score":"0.7366483","text":"def compute_costs_benefits(self):\n\n # unit costs\n if self.verbose:\n print(\"Preparing unit values...\")\n self._create_unit_cost_matrix()\n\n # costs\n if self.verbose:\n print(\"Computing costs...\")\n self.compute_capex()\n self.compute_opex()\n self._compute_deterioration()\n self.compute_replacements()\n self.compute_residual_value()\n\n # benefits\n if self.verbose:\n print(\"Computing benefits...\")\n self._create_length_matrix()\n self._create_fuel_ratio_matrix()\n self._compute_travel_time_matrix()\n\n self._compute_vtts()\n if self.include_freight_time:\n self._compute_vfts()\n self._compute_voc()\n self._compute_fuel_consumption()\n self._compute_fuel_cost()\n self._compute_greenhouse()\n self._compute_emissions()\n self._compute_noise()\n self._compute_accidents()\n self._compute_toll()","title":""},{"docid":"982a66b9054117f6bce45298e6afeb61","score":"0.73594654","text":"def _benefit_cost_analysis(self, world):\n bc = -10 #Benefit - Cost\n for i in self.vicinity['good_id']:\n a = self._benefit(world, i) - self._cost(world, i)\n if a > bc:\n bc = a\n self.objective = i\n \n if bc <= -10:\n self.objective = None","title":""},{"docid":"81b6df4d165cfe4bd04ebe566c17cb12","score":"0.66118425","text":"def perform_economic_analysis(self):\n self.prepare_parameters()\n self.compute_costs_benefits()\n self.compute_economic_indicators()","title":""},{"docid":"3a1ff87a5494b0feca36e366c0f1b8f2","score":"0.6390947","text":"def compute_costs(self, car):\n self.compute_price(car)\n self.compute_commission()","title":""},{"docid":"38c6505625f6dc26bed4e28fe260b9f8","score":"0.63891405","text":"def effective():","title":""},{"docid":"302d40f575dbe32480bc86f8ed1a060a","score":"0.6338564","text":"def cost ( self, x ):\n \n x_dict = self._unpack_to_dict ( x )\n # Store the parameter dictionary in case we need it later for e.g.\n # crossvalidation\n self.parameter_dictionary = x_dict\n aggr_cost = 0\n aggr_der_cost = x*0.0\n self.cost_components = {}\n start_time = time.time()\n \n# import os\n# c_dir = os.getcwd()\n# f_obj = open(c_dir + '/object_func.dat', 'w')\n\n for op_name, the_op in self.operators.iteritems():\n \n cost, der_cost = the_op.der_cost ( x_dict, self.state_config )\n #!!!Max Edit!!!\n #print 'cost before weighting = ', cost\n cost = cost / the_op.cost_weight\n der_cost = der_cost / the_op.cost_weight\n self.f_obj.write('%.4f '%(cost))\n #!!!!!!!!!!!!!!\n aggr_cost = aggr_cost + cost\n aggr_der_cost = aggr_der_cost + der_cost\n self.cost_components[op_name] = der_cost\n if self.verbose:\n print \"\\t%s %8.3e\" % ( op_name, cost )\n self.f_obj.write('%.4f\\n'%(aggr_cost))\n self.the_cost = aggr_cost\n\n\n \n if self.verbose:\n print \"Total cost: %8.3e\" % aggr_cost\n print 'Elapsed: %.2f seconds' % (time.time() - start_time)\n \n \n return aggr_cost, aggr_der_cost","title":""},{"docid":"c555691b1c809177c76d7c47ebe502fe","score":"0.63324463","text":"def caluculate_cost(self):\n self.error = self.hypothesis - self.y\n self.cost = np.dot(self.error.transpose(), self.error) / (2 * self.m)\n self.cost_record.append(self.cost[0]) # add a number not a list","title":""},{"docid":"80776e9469e0fcc8cf102e5af08d354b","score":"0.62639254","text":"def compute_cost(AL, Y):\n # todo\n pass","title":""},{"docid":"32e142ca3068b634ca558400654cf45c","score":"0.62561446","text":"def main():\n\titem_cost = calculate_discount(100, 10, 30)\n\tprint item_cost\n\n\titem_cost = calculate_discount(-100, 10, 30)\n\tprint item_cost\n\n\titem_cost = calculate_discount(100, 20000, 30)\n\tprint item_cost\n\n\titem_cost = calculate_discount(100, 10, 1000)\n\tprint item_cost","title":""},{"docid":"2e3db9fdc5eda307ac39ebd049f42910","score":"0.6250568","text":"def compute_cost(self, previous_state, state, label_to_id, action, others={}):\n pass","title":""},{"docid":"c21f7213a26f41bc79ad5cf3ae86ccdb","score":"0.62449455","text":"def AB_sensitivity(A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base, num_options):\n itin = 0\n A_cost_record = []\n B_cost_record = []\n x_optimum_record = []\n x_optimum_index_record = []\n value_optimum_record = []\n # Largest increase or decrease that will be tested\n p1 = 0.6\n p2 = 0.5\n # Step by which will by value of cost looped over\n s1 = 5\n s2 = 5\n A_cost_range = np.arange(np.rint(A_cost * (1 - p1)), A_cost * (1 + p1) + s1, s1)\n B_cost_range = np.arange(B_cost * (1 - p2), B_cost * (1 + p2) + s2, s2)\n print(A_cost_range, B_cost_range)\n for A_cost_i in A_cost_range:\n for B_cost_i in B_cost_range:\n print(A_cost_i, B_cost_i)\n itin += 1\n x_optimum = optimise_total_costs(A_quantity, B_quantity, C_change, A_cost_i, B_cost_i, C_cost, C_base, num_options)\n value_optimum = evaluate_total_costs(x_optimum, A_quantity, B_quantity, C_change, A_cost_i, B_cost_i, C_cost, C_base)\n A_cost_record.append(A_cost_i)\n B_cost_record.append(B_cost_i)\n x_optimum_record.append(x_optimum)\n x_optimum_index_record.append((np.nonzero(x_optimum) + np.array([1,1,1])).tolist())\n value_optimum_record.append(value_optimum) \n print(itin)\n return pd.DataFrame.from_dict({\"A_cost\": A_cost_record, \"B_cost\": B_cost_record, \"x_optimum\": x_optimum_record, \"options_selected\": x_optimum_index_record, \"costs\": value_optimum_record})","title":""},{"docid":"074bb595116024602281aa80798ea585","score":"0.6206656","text":"def test_compute_cost(self):\n\n # Definition of a test decision variables vector\n states = np.array([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2]])\n controls = np.array([[0, 0, 0, 0, 0, ]])\n controls_col = np.empty(0)\n f_prm = np.array([1., 2.])\n t_i, t_f = 0, 10\n\n dvv = utils.make_decision_variable_vector(\n states, controls, controls_col, t_i, t_f, f_prm, self.cost_obj.options)\n\n # Cost computation\n cpt_cost = self.cost_obj.compute_cost(dvv)\n cor_cost = 52.0\n\n # Equality verification\n self.assertEqual(cpt_cost, cor_cost)","title":""},{"docid":"540146ce3f2b80bad17c4f685ed33aac","score":"0.6203327","text":"def apply_cost_benefit(change_posterior, true_positive=1.0, true_negative=1.0, false_negative=1.0, false_positive=1.0):\n\n lick_benefit = change_posterior * true_positive\n lick_cost = change_posterior * false_positive\n no_lick_benefit = (1 - change_posterior) * true_negative\n no_lick_cost = (1 - change_posterior) * false_negative\n\n prob_lick = (lick_benefit + no_lick_cost - lick_cost - no_lick_benefit) / (true_positive +\n true_negative + false_negative + false_positive)\n\n return prob_lick","title":""},{"docid":"cbe2c270c17bf5cb0fe1f467f56d4bf1","score":"0.6193927","text":"def cost_individual_measurements(self,studies_results):\n costs = []\n for study,study_results in studies_results.items():\n for ID, ID_results in study_results.items():\n for target,target_results in ID_results.items():\n for i in range(len(target_results)):\n sim = target_results[i]\n exp = self.observations[study][ID]['expectations'][target]['mean'][i]\n mean = (sim+exp)/2\n diff = abs(sim-exp)\n error = diff/mean\n costs.append(error)\n return np.mean(costs)","title":""},{"docid":"0c089c3e6e5812ff529af42708668159","score":"0.61890495","text":"def assess_effects(cost_types, maps_dir, cost_correction):\r\n measure_names = [f for f in os.listdir(maps_dir)\r\n if os.path.isdir(os.path.join(maps_dir,f))]\r\n\r\n cost_list = []\r\n std_list = []\r\n for msr_name in measure_names[:]:\r\n print(msr_name)\r\n measure_pathname = os.path.join(maps_dir, msr_name)\r\n measure = read_measure(measure_pathname)\r\n cost_all_types, std_all_types = assess_costs(cost_types, measure)\r\n cost_list.append(cost_all_types)\r\n std_list.append(std_all_types)\r\n\r\n cost_all_msrs = pd.concat(cost_list)\r\n std_all_msrs = pd.concat(std_list)\r\n cost_all_msrs.index.name = 'id'\r\n std_all_msrs.index.name = 'id'\r\n return cost_all_msrs, std_all_msrs","title":""},{"docid":"9c53ba49c90ade5335b54e27541055df","score":"0.6158172","text":"def compute_cost_symb(self, pred_batch, tg_batch):\n raise NotImplementedError","title":""},{"docid":"59270fa265ad5a1baf08da149f09135f","score":"0.6155377","text":"def uniformCostSearch(problem):\n\n # *** Your Code Here ***\n util.raiseNotDefined()","title":""},{"docid":"e7ece7abf40c5c76d81a74e6f8241667","score":"0.6143789","text":"def calculateEfficiency(self,n_triggers,n_valid):\n efficiency, error = 0, 0\n #TODO implement\n return efficiency, error","title":""},{"docid":"626969664782890d6e0566766e458b36","score":"0.61013293","text":"def test(self,dataset):\n outputs = self.use(dataset)\n costs = zeros(len(dataset),1)\n for o,c in zip(outputs,costs):\n c[0] = -o[0]\n\n return outputs,costs","title":""},{"docid":"e41fd3885ad5b121046c14c10fb7c356","score":"0.609207","text":"def uniformCostSearch(problem):\n \"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\"\n util.raiseNotDefined()","title":""},{"docid":"d7ffc8449bf9be685f88e3bbdd658389","score":"0.6090788","text":"def calculate_logistics_cost(*, logistics: models.OrderLogistics, status: str) -> dict:\n\n logistics_estimated_cost = 0\n cost_food_internal = 0\n cost_food_external = 0\n cost_beverage = 0\n cost_labor = 0\n cost_rentals = 0\n\n if status == \"review\":\n addons = logistics.addons_staff.all().filter(package__isnull=True)\n elif status == \"is_active\":\n addons = logistics.addons_staff.is_active().filter(package__isnull=True)\n elif status == \"is_draft\":\n addons = logistics.addons_staff.is_draft().filter(package__isnull=True)\n else:\n addons = logistics.addons_staff.all().filter(package__isnull=True)\n\n combined_per_person_cost = 0\n combined_fixed_cost = 0\n\n # ––– ADDONS ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––\n addons_staff = []\n\n for addon in addons:\n temp = {}\n\n temp[\"id\"] = addon.id\n temp[\"name\"] = addon.name\n temp[\"price_numeric\"] = addon.price_numeric\n temp[\"price_numeric_fixed\"] = addon.price_numeric_fixed\n\n addon_cost = (\n addon.price_numeric * logistics.guest_count\n ) + addon.price_numeric_fixed\n temp[\"cost\"] = addon_cost\n\n temp[\"cost_type_id\"] = addon.cost_type_id\n temp[\"package_id\"] = addon.package_id\n temp[\"logistics_id\"] = addon.logistics_id\n\n if addon.note:\n temp[\"note\"] = addon.note\n else:\n if (addon.price_numeric > 0) and (addon.price_numeric_fixed > 0):\n temp[\"price_descriptive\"] = \"{0} per person, plus {1} fee\".format(\n addon.price_numeric, addon.price_numeric_fixed\n )\n elif addon.price_numeric == 1:\n temp[\"price_descriptive\"] = \"{0} guest, at {1} per person\".format(\n logistics.guest_count, addon.price_numeric\n )\n elif addon.price_numeric > 1:\n temp[\"price_descriptive\"] = \"{0} guests, at {1} per person\".format(\n logistics.guest_count, addon.price_numeric\n )\n else:\n temp[\"price_descriptive\"] = \"{0} fee\".format(addon.price_numeric_fixed)\n\n addons_staff.append(temp)\n\n if addon.cost_type.name == \"Food (Internal)\":\n cost_food_internal += addon_cost\n elif addon.cost_type.name == \"Food (External)\":\n cost_food_external += addon_cost\n elif addon.cost_type.name == \"Alcohol and NA Beverages\":\n cost_beverage += addon_cost\n elif addon.cost_type.name == \"Labor\":\n cost_labor += addon_cost\n elif addon.cost_type.name == \"Equipment and Rentals\":\n cost_rentals += addon_cost\n\n return {\n \"addons_staff\": addons_staff,\n \"cost_food_internal\": cost_food_internal,\n \"cost_food_external\": cost_food_external,\n \"cost_beverage\": cost_beverage,\n \"cost_labor\": cost_labor,\n \"cost_rentals\": cost_rentals,\n }","title":""},{"docid":"04c4afa23459b865015cc200daf19ec8","score":"0.6086555","text":"def efficiency_cost(self, waypt):\n\t\tprev_waypt = waypt[0:7]\n\t\tcurr_waypt = waypt[7:14]\n\t\tfeature = self.environment.efficiency_features(curr_waypt,prev_waypt)\n\t\tfeature_idx = self.feat_list.index('efficiency')\n\t\treturn feature*self.weights[feature_idx]","title":""},{"docid":"caf0d434601c31f7cd4245cf022e626b","score":"0.6077921","text":"def power_analysis_does_yhat_reduce_effect_of_income(non_image_dataset, pval_thresh, n_iterates, dv):\n\n all_results = []\n assert dv in ['koos_pain_subscore', 'womac_pain_subscore']\n knee_pain_scores = non_image_dataset.processed_dataframes['all_knee_pain_scores']\n clinical_ratings = non_image_dataset.processed_dataframes['kxr_sq_bu']\n df_to_use = get_baseline_scores(get_combined_dataframe(non_image_dataset, clinical_ratings)) \n print(\"Length of baseline data\")\n print(len(df_to_use))\n iv = 'binarized_income_at_least_50k'\n\n pain_subscores = ['koos_pain_subscore', 'womac_pain_subscore']\n assert dv in pain_subscores\n for k in pain_subscores:\n df_to_use[k] = (df_to_use[k] - df_to_use[k].mean()) / df_to_use[k].std()\n\n clinical_controls = '+'.join(['C(%s)' % a for a in non_image_dataset.clinical_xray_semiquantitative_cols])\n\n for noise_param in [3, 5, 8, 10]:\n for disparity_param in [.2]:\n print(\"Noise param: %2.3f; disparity param: %2.3f\" % (noise_param, disparity_param))\n # as disparity param increases in magnitude, yhat gets more correlated with SES. \n # as noise_param increases in magnitude, yhat gets less correlated with y and SES. \n\n if dv == 'womac_pain_subscore':\n # higher scores indicate worse pain on the womac\n # so if you have a higher SES we want you to have lower predicted Yhat. \n disparity_param = -disparity_param\n df_to_use['yhat'] = df_to_use[dv] + df_to_use[iv] * disparity_param + noise_param * np.random.randn(len(df_to_use),)\n df_to_use = df_to_use.dropna(subset=[dv, iv])\n print(df_to_use[[iv, 'yhat']].groupby(iv).agg(['mean', 'std']))\n for subset_size in list(range(250, 2000, 250)):\n for _ in range(n_iterates):\n people = set(random.sample(non_image_dataset.all_ids, subset_size))\n people_idxs = df_to_use['id'].map(lambda x:x in people).values\n\n model_without_yhat = sm.OLS.from_formula('%s ~ %s + %s' % (dv, iv, clinical_controls), df_to_use.loc[people_idxs]).fit()\n model_with_yhat = sm.OLS.from_formula('%s ~ %s + %s + yhat' % (dv, iv, clinical_controls), df_to_use.loc[people_idxs]).fit()\n\n\n change_in_iv_coef = model_with_yhat.params[iv] - model_without_yhat.params[iv]\n # Note: \n ## To get estimate of noise variance for a model, the following 3 are all the same.\n # this is sigma_hat SQUARED, not sigma_hat.\n # 1. np.sum(model_without_yhat.resid ** 2) / model_without_yhat.df_resid)\n # 2. model_without_yhat.mse_resid\n # 3. model_without_yhat.scale\n\n squared_error_on_change = (model_with_yhat.bse[iv] ** 2 - \n model_without_yhat.bse[iv] ** 2 * model_with_yhat.scale / model_without_yhat.scale)\n assert squared_error_on_change > 0\n error_on_change = np.sqrt(squared_error_on_change)\n zscore = change_in_iv_coef/error_on_change\n if (model_with_yhat.params[iv] > 0) != (model_without_yhat.params[iv] > 0): \n # if the sign of the coefficient changes that is weird. It should just get smaller. \n print(\"Warning: coefficient changed sign from %2.3f to %2.3f\" % (model_without_yhat.params[iv], model_with_yhat.params[iv]))\n results = {'r2_with_yhat':model_with_yhat.rsquared, \n 'r2_without_yhat':model_without_yhat.rsquared, \n 'beta_with_yhat':model_with_yhat.params[iv], \n 'beta_without_yhat':model_without_yhat.params[iv], \n 'change_in_IV_coef':change_in_iv_coef, \n 'error_on_change':error_on_change, \n 'zscore':zscore, \n 'p_change':2*(1 - norm.cdf(abs(zscore))), # two-tailed p-value. \n 'yhat_iv_corr':pearsonr(df_to_use['yhat'], df_to_use[iv])[0], \n 'yhat_dv_corr':pearsonr(df_to_use['yhat'], df_to_use[dv])[0], \n 'subset_size':subset_size}\n all_results.append(results)\n \n # now make plot. \n all_results = pd.DataFrame(all_results)\n for iv_corr in sorted(list(set(all_results['yhat_iv_corr'])), key=lambda x:abs(x)):\n for dv_corr in sorted(list(set(all_results['yhat_dv_corr'])), key=lambda x:abs(x)):\n idxs = ((all_results['yhat_iv_corr'] == iv_corr) & \n (all_results['yhat_dv_corr'] == dv_corr))\n if idxs.sum() == 0:\n continue\n x = []\n y = []\n for subset_size in sorted(list(set(all_results['subset_size']))):\n x.append(subset_size)\n results = all_results.loc[idxs & (all_results['subset_size'] == subset_size), \n 'p_change'] < pval_thresh\n assert len(results) == n_iterates\n y.append(results.mean())\n \n plt.plot(x, y, label='IV (income/educ): r=%2.3f, DV (pain): r=%2.3f' % (abs(iv_corr), \n abs(dv_corr)))\n \n plt.legend(bbox_to_anchor=(1.1, 1.05))\n plt.xlabel(\"Test set size\")\n plt.ylabel(\"Fraction of simulations\\nwhich are significant @ $p = %2.2f$\" % pval_thresh)\n plt.title(\"Significance test is for change in income/educ coef\")\n plt.savefig('power_analysis_does_yhat_reduce_effect_of_ses.png')","title":""},{"docid":"973ba84c794cc67ec691e1b56877c001","score":"0.6056363","text":"def _calculate_benefit_cost(self):\n # merge final incidence rate\n self.frame = self.frame.merge(\n self.incidence_rates[[\"AGE_ATTAINED\", \"INCIDENCE_RATE\"]],\n how=\"left\",\n on=[\"AGE_ATTAINED\"],\n )\n\n # add modeled claim cost (i.e., DLR)\n self.frame[\"DLR\"] = [df[\"DLR\"].iat[0] for df in self.modeled_claim_cost.values()]\n\n # calculate benefit cost\n self.frame[\"BENEFIT_COST\"] = self.frame[\"DLR\"] * self.frame[\"INCIDENCE_RATE\"]","title":""},{"docid":"5e2ff2060be58e8ccdaa2b60ecfe3b59","score":"0.6026856","text":"def CSAnalysis(self,env):\r\n # Creates the attribute Generality List\r\n bitLength = env.getAttributeLength()\r\n wildCount = self.characterizePop()\r\n self.attributeGenList = self.condenseToAttributes(wildCount,bitLength) \r\n \r\n genSum = 0\r\n fitGenSum = 0\r\n fitSum = 0 \r\n for cl in self.clSet:\r\n genSum = genSum + cl.getClassifierGenerality() * cl.getNumerosity()\r\n fitGenSum = fitGenSum + cl.getClassifierGenerality() * cl.getNumerosity() * cl.getFitness()\r\n fitSum = fitSum + cl.getNumerosity() * cl.getFitness()\r\n \r\n self.generality = genSum / float(self.numerositySum)\r\n self.weightedGenerality = fitGenSum / float(fitSum)","title":""},{"docid":"9712975ef61c2f16232c02419f45f844","score":"0.6002524","text":"def compute_cost(self, X, Y_true):\n Y_pred, act_h = self.evaluate(X)\n num_exampl = X.shape[1]\n rglz = self.labda * np.sum(self.w1**2) + self.labda * np.sum(self.w2**2)\n cross_ent = self.cross_entropy(Y_true, Y_pred)\n cost = cross_ent / num_exampl + rglz\n return cost","title":""},{"docid":"5b8c836e191289499ea72657070fbbb2","score":"0.5991674","text":"def _compute_total_cost_maint(self):\n total = 0\n for data in self:\n for data_1 in data.cost_id:\n total += data_1.cost\n data.main_cost = total","title":""},{"docid":"04a4fd8f0210f120661deee71f47867b","score":"0.59876204","text":"def CostFunction(xo, grad):\n\n grad[:] = 0.\n Set.J[:] = 0.\n\n ### Minimize PSF = xo\n if Set.costfunction_type == 0:\n\n #<< testing and debugging block >>#\n #try:\n #\n # xo /= N.sum(xo) ## normalize and scale PSF\n #except:\n #\n # pass\n\n grad.shape = xo.shape = Set.shape\n fftw.rfft(a=xo, af=Set.OTF, inplace=False)\n fn = Calculate_h_J_and_gradJ(grad) #@\n\n ### Minimize Object = 'xo'\n elif Set.costfunction_type == 1:\n\n #<< testing and debugging line >>#\n #Set.J[0] = Set.J[1] = 0.\n \n grad.shape = xo.shape = Set.shape\n fftw.rfft(a=xo, af=Set.OBJECT, inplace=False)\n fn = Calculate_o_J_and_gradJ(xo, grad) #@\n\n ### Minimize PSF = 'xo' in the Presence of Nobjects\n elif Set.costfunction_type == 2:\n\n #<< testing and debugging block >>#\n #Set.J[0] = Set.J[2] = 0.\n #try:\n #\n # xo /= N.sum(xo) ## normalize and scale PSF\n #except:\n #\n # pass\n\n grad.shape = xo.shape = Set.shape\n fn = 0.\n fftw.rfft(a=xo, af=Set.OTF, inplace=False)\n\n if Set.memory_usage_level > 0:\n\n for i in range(len(Set.image_list)):\n\n Set.image = Set.image_array[i]\n Set.inv_w = Set.inv_w_array[i]\n Set.OBJECT = Set.OBJECT_array[i]\n ## cost function is calculated as sum over each PSF estimate\n fn += Calculate_h_J_and_gradJ(grad) #@\n else:\n\n for i in range(len(Set.image_list)):\n\n Set.image = Set.image_array[i]\n Set.inv_w = Set.inv_w_array[i]\n fftw.rfft(a=Set.object_array[i], af=Set.OBJECT, inplace=False)\n ## cost function is calculated as sum over each PSF estimate\n fn += Calculate_h_J_and_gradJ(grad) #@\n\n ### Minimize Object = 'xo' in the Presence of Npsfs\n elif Set.costfunction_type == 3:\n\n #<< testing and debugging line >>#\n #Set.J[0] = Set.J[1] = 0.\n \n grad.shape = xo.shape = Set.shape\n fn = 0.\n fftw.rfft(a=xo, af=Set.OBJECT, inplace=False)\n\n if Set.memory_usage_level > 0:\n\n for i in range(len(Set.image_list)):\n\n Set.image = Set.image_array[i]\n Set.inv_w = Set.inv_w_array[i]\n Set.inv_theta = Set.inv_theta_center_array[i]\n Set.lambda_object = Set.lambda_object_Narray[i]\n Set.mu = Set.mu_Narray[i]\n Set.OTF = Set.OTF_array[i]\n ## cost function is calculated as sum over each object estimate\n fn += Calculate_o_J_and_gradJ(xo, grad) #@\n else:\n\n for i in range(len(Set.image_list)):\n\n Set.image = Set.image_array[i]\n Set.inv_w = Set.inv_w_array[i]\n Set.inv_theta = Set.inv_theta_center_array[i]\n Set.lambda_object = Set.lambda_object_Narray[i]\n Set.mu = Set.mu_Narray[i]\n fftw.rfft(a=Set.PSF_array[i], af=Set.OTF,\n inplace=False)\n ## cost function is calculated as sum over each object estimate\n fn += Calculate_o_J_and_gradJ(xo, grad) #@\n\n#<< vvv BELOW IS TEMPORARY UNTIL WE INTEGRATE IT WITH SI CODE vvv >>#\n ### Minimize Object = 'xo' for Structured Illumination\n elif Set.costfunction_type == 4:\n\n grad.shape = xo.shape = Set.shape\n #fftw.rfft(a=xo,af=Set.OBJECT, inplace=False)\n fn = Calculate_o_J_and_gradJ_SI(xo, grad) #@\n\n ### Minimize PSF = 'xo' for Structured Illumination\n elif Set.costfunction_type == 5:\n\n grad.shape = xo.shape = Set.shape_small\n fftw.rfft(a=xo, af=Set.OTF_array[Set.PSForder], inplace=False)\n fn = Calculate_h_J_and_gradJ_SI(grad) #@\n#<< ^^^^^^ >>#\n\n else:\n\n print(\"Error!! Check `type` input flag!\")\n\n fn = 0\n\n iqflag = 0 # dummy flag in this case\n\n return (fn, iqflag)","title":""},{"docid":"0e0650bb264d435ea71f6dd2bd5264ca","score":"0.5979624","text":"def asset_calcs(params, asset_data):\n # grab the constant values from the parameters dictionary\n inflation_rate = params['inflation rate']\n stat_tax = params['tax rate']\n discount_rate = params['discount rate']\n save_rate = params['return to savers']\n r_prime = params['after-tax rate']\n inv_credit = params['inv_credit']\n w = params['prop tax']\n z = params['depr allow']\n Y_v = params['Y_v']\n phi = params['phi']\n expense_inventory = params['expense_inventory']\n financing_list = params['financing_list']\n entity_list = params['entity_list']\n asset_dict = params['asset_dict']\n major_asset_groups = params['major_asset_groups']\n\n # initialize dataframe - start w/ z output\n output_by_asset = z.copy()\n\n # Drop religious buildings and IP of nonprofits\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\n 'Religious'].copy()\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\n 'Private universities and colleges'].copy()\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\n 'Other nonprofit institutions'].copy()\n\n # calculate the cost of capital, metr, mettr\n for i in range(save_rate.shape[0]):\n for j in range(save_rate.shape[1]):\n output_by_asset['rho' + entity_list[j] + financing_list[i]] = \\\n ((((discount_rate[i, j] - inflation_rate) +\n output_by_asset['delta']) *\n (1 - inv_credit - (stat_tax[j] *\n output_by_asset['z' +\n entity_list[j] +\n financing_list[i]])) /\n (1 - stat_tax[j])) + w - output_by_asset['delta'])\n if not expense_inventory:\n output_by_asset.loc[output_by_asset['Asset Type'] ==\n \"Inventories\", 'rho' +\n entity_list[j] +\n financing_list[i]] = \\\n ((phi * (((1 / Y_v) *\n np.log((np.exp(discount_rate[i, j] * Y_v) -\n stat_tax[j]) / (1 - stat_tax[j]))) -\n inflation_rate)) +\n ((1 - phi) * (((1 / Y_v) *\n np.log((np.exp((discount_rate[i, j] -\n inflation_rate) * Y_v) -\n stat_tax[j]) /\n (1 - stat_tax[j]))))))\n output_by_asset['metr' + entity_list[j] + financing_list[i]] = \\\n ((output_by_asset['rho' + entity_list[j] + financing_list[i]] -\n (r_prime[i, j] - inflation_rate)) /\n output_by_asset['rho' + entity_list[j] + financing_list[i]])\n output_by_asset['mettr' + entity_list[j] + financing_list[i]] = \\\n ((output_by_asset['rho' + entity_list[j] + financing_list[i]] -\n save_rate[i, j]) /\n output_by_asset['rho' + entity_list[j] + financing_list[i]])\n\n # create asset category variable\n output_by_asset['asset_category'] = output_by_asset['Asset Type']\n output_by_asset['asset_category'].replace(asset_dict, inplace=True)\n\n # Drop IP (for now - need to better figure out how depreciate)\n # output_by_asset = output_by_asset[output_by_asset['asset_category'] !=\n # 'Intellectual Property'].copy()\n # output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\n # 'Land'].copy()\n\n # merge in dollar value of assets - sep for corp and non-corp\n # should be able to do this better with pivot table\n bea_corp = asset_data[asset_data['tax_treat'] == 'corporate'].copy()\n bea_noncorp = asset_data[asset_data['tax_treat'] == 'non-corporate'].copy()\n bea_corp_assets = (pd.DataFrame({'assets': bea_corp.\n groupby('bea_asset_code')['assets'].\n sum()})).reset_index()\n bea_noncorp_assets = (pd.DataFrame({'assets': bea_noncorp.\n groupby('bea_asset_code')['assets'].\n sum()})).reset_index()\n bea_corp_assets.rename(columns={\"assets\": \"assets_c\"}, inplace=True)\n bea_noncorp_assets.rename(columns={\"assets\": \"assets_nc\"}, inplace=True)\n\n output_by_asset = pd.merge(output_by_asset, bea_corp_assets,\n how='left', left_on=['bea_asset_code'],\n right_on=['bea_asset_code'],\n left_index=False, right_index=False,\n sort=False, copy=True)\n output_by_asset = pd.merge(output_by_asset, bea_noncorp_assets,\n how='left', left_on=['bea_asset_code'],\n right_on=['bea_asset_code'],\n left_index=False, right_index=False,\n sort=False, copy=True)\n\n # Add major asset groups\n output_by_asset['major_asset_group'] = output_by_asset['Asset Type']\n output_by_asset['major_asset_group'].replace(major_asset_groups,\n inplace=True)\n\n # Now compute METR and other output by major asset group\n # create weighted averages by major asset group/tax treatment\n by_major_asset =\\\n pd.DataFrame({'delta': output_by_asset.\n groupby(['major_asset_group'] ).\n apply(wavg, \"delta\", \"assets_c\")}).reset_index()\n corp_list = ['z_c', 'z_c_d', 'z_c_e', 'rho_c', 'rho_c_d', 'rho_c_e']\n noncorp_list = ['z_nc', 'z_nc_d', 'z_nc_e', 'rho_nc', 'rho_nc_d',\n 'rho_nc_e']\n for item in corp_list:\n by_major_asset[item] =\\\n (pd.DataFrame({item: output_by_asset.\n groupby(['major_asset_group']).\n apply(wavg, item, \"assets_c\")})).reset_index()[item]\n for item in noncorp_list:\n by_major_asset[item] =\\\n (pd.DataFrame({item: output_by_asset.\n groupby(['major_asset_group']).\n apply(wavg, item, \"assets_nc\")})).reset_index()[item]\n\n by_major_asset['assets_c'] =\\\n (pd.DataFrame({'assets_c': output_by_asset.\n groupby(['major_asset_group'])['assets_c'].\n sum()})).reset_index()['assets_c']\n by_major_asset['assets_nc'] =\\\n (pd.DataFrame({'assets_nc': output_by_asset.\n groupby(['major_asset_group'])['assets_nc'].\n sum()})).reset_index()['assets_nc']\n\n # calculate the cost of capital, metr, mettr\n for i in range(save_rate.shape[0]):\n for j in range(save_rate.shape[1]):\n by_major_asset['metr' + entity_list[j] + financing_list[i]] = \\\n ((by_major_asset['rho' + entity_list[j] + financing_list[i]] -\n (r_prime[i, j] - inflation_rate)) /\n (by_major_asset['rho' + entity_list[j] + financing_list[i]]))\n by_major_asset['mettr' + entity_list[j] + financing_list[i]] = \\\n ((by_major_asset['rho' + entity_list[j] + financing_list[i]] -\n save_rate[i, j]) /\n (by_major_asset['rho' + entity_list[j] + financing_list[i]]))\n\n\n\n # make asset type = major asset group in by_major_asset\n by_major_asset['Asset'] = by_major_asset['major_asset_group']\n by_major_asset['Asset Type'] = by_major_asset['major_asset_group']\n\n # make calculation for overall rates\n corp_list = ['z_c', 'z_c_d', 'z_c_e', 'rho_c', 'rho_c_d', 'rho_c_e']\n noncorp_list = ['z_nc', 'z_nc_d', 'z_nc_e', 'rho_nc', 'rho_nc_d',\n 'rho_nc_e']\n overall = pd.DataFrame({'delta': ((output_by_asset['delta'] *\n output_by_asset['assets_c']).sum()\n / output_by_asset['assets_c'].sum())},\n index=[0])\n overall['assets_c'] = output_by_asset['assets_c'].sum()\n overall['assets_nc'] = output_by_asset['assets_nc'].sum()\n # overall =\\\n # pd.DataFrame({'delta_nc': ((output_by_asset['delta'] *\n # output_by_asset['assets_nc']).sum()\n # / output_by_asset['assets_nc'].\n # sum())}).reset_index()\n overall['Asset'] = 'All Investments'\n overall['Asset Type'] = 'All Investments'\n overall['major_asset_group'] = 'All Investments'\n for item in corp_list:\n overall[item] = ((output_by_asset[item] *\n output_by_asset['assets_c']).sum() /\n output_by_asset['assets_c'].sum())\n for item in noncorp_list:\n overall[item] = ((output_by_asset[item] *\n output_by_asset['assets_nc']).sum() /\n output_by_asset['assets_nc'].sum())\n for i in range(save_rate.shape[0]):\n for j in range(save_rate.shape[1]):\n overall['metr' + entity_list[j] + financing_list[i]] = \\\n ((overall['rho' + entity_list[j] + financing_list[i]] -\n (r_prime[i, j] - inflation_rate)) / (overall['rho' + entity_list[j] + financing_list[i]]))\n overall['mettr' + entity_list[j] + financing_list[i]] = \\\n ((overall['rho' + entity_list[j] + financing_list[i]] -\n save_rate[i, j]) / (overall['rho' + entity_list[j] + financing_list[i]]))\n\n # append by_major_asset to output_by_asset\n # drop asset types that are only one in major group\n by_major_asset = by_major_asset[by_major_asset['major_asset_group'] !=\n 'Inventories'].copy()\n by_major_asset = by_major_asset[by_major_asset['major_asset_group'] !=\n 'Land'].copy()\n output_by_asset = (output_by_asset.append([by_major_asset,overall],\n ignore_index=True)).copy().reset_index()\n output_by_asset.drop('index', axis=1, inplace=True)\n\n # sort output_by_asset dataframe\n # output_by_asset = (output_by_asset.sort_values(['Asset'], inplace=True)).copy().reset_index()\n output_by_asset.sort_values(['Asset'], inplace=True)\n output_by_asset.reset_index(drop=True, inplace=True)\n\n return output_by_asset","title":""},{"docid":"a7bc35285f33797517960d42794584bf","score":"0.5973494","text":"def optimise_total_costs(A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base, num_options):\n # Define the decision variable array\n x = cp.Variable(10, boolean = True)\n # Define constraints on decision variable\n constraints = [0 <= x, x <= 1, sum(x) == num_options]\n # Objective function \n total_costs = cp.sum(A_cost * cp.multiply(A_quantity, x)) + cp.sum(B_cost * cp.multiply(B_quantity, x)) + (C_cost * - cp.minimum(C_base + cp.sum(cp.multiply(C_change, x)), 0))\n # Goal of optimisation\n objective = cp.Minimize(total_costs)\n # Solve the optimisation problem \n problem = cp.Problem(objective, constraints)\n result = problem.solve(solver = cp.ECOS_BB)\n return np.abs(np.rint(x.value))","title":""},{"docid":"0057fd19d6a9f2a90b0e6e377a99b36f","score":"0.59511423","text":"def compute_cost_scores(techDB, system_size, app_type, target_cost):\n if app_type == 'Power':\n cost_scores = techDB[f'Cost at {system_size} ($/kW)'].apply(normalize_by_target, tc=target_cost).fillna(value=0)\n elif app_type == 'Energy':\n kWh_costs = techDB[f'Cost at {system_size} ($/kW)']/techDB['Discharge duration (hours)']\n cost_scores = kWh_costs.apply(normalize_by_target, tc=target_cost).fillna(value=0)\n\n return cost_scores","title":""},{"docid":"b7e6ec4fe9066b49a77660fc0110bfc5","score":"0.59428144","text":"def compute_cost(self):\n for task in self:\n resource = self.env['resource.resource'].sudo().search(\n [\n ('user_id', '=', task.user_id.id)\n ]\n )\n employee = self.env['hr.employee'].sudo().search(\n [\n ('resource_id', '=', resource.id)\n ]\n )\n cost = 0\n\n if resource and employee and employee.contract_id:\n hour_cost = employee.contract_id.wage\n cost = hour_cost * task.planned_hours\n\n task.cost = cost","title":""},{"docid":"adb51a5c764917eb772158ad36523e0a","score":"0.5937327","text":"def __cost(training_set, test_set, knn_function, solution, adaptative=False):\n\n # Dado a solucao (configuracao de pesos/caracteristicas), transforma os , calcula o k-NN e retorna a taxa de acerto\n return training_machine(training_set, test_set, knn_function, solution, adaptative)","title":""},{"docid":"c26acc33bf32985e58bdf75d9ca35d7d","score":"0.59194976","text":"def main():\r\n\r\n \"\"\"General input\"\"\"\r\n # final Time\r\n T = 100\r\n # Number of elements\r\n M = 10000\r\n # initial susceptible, infected and recovered\r\n S0, I0, R0 = 700, 165, 90\r\n # Number of people\r\n N = S0 + I0 + R0\r\n # birt and death rate, recover rate, and transmission coefficient\r\n v, gamma, beta = 0.41, 0.56, 0.002\r\n # the used tolerance for norm(u_{k+1}-u_k)_{L2} < tol\r\n tol = 1e-6\r\n\r\n # a invalid choice\r\n choice = -1\r\n # as long as choice is not 0, meaning end loop\r\n while choice != 0:\r\n choice = get_choice()\r\n\r\n if choice == 1:\r\n\r\n print(\"Running program with the original costfunctional\")\r\n # coefficient A_1 and A_2 in cost functional\r\n A1, A2 = 0.050, 0.075\r\n # coefficient in front of u (0 <= tau <= N)\r\n tau = 15\r\n # setup\r\n CP1 = ControlProblem(N, S0, I0, R0, A1, A2, tau, v, gamma, beta, T, M)\r\n\r\n u1, y1, p1, iterations, u_list1, fu_list1 = ProjectedGradientMethod(CP1, tol=tol)\r\n\r\n plotSIRu(CP1.CostFunc.tvec, y1, u1, \"SIR_u_CP1\", save=save, pdf=pdf)\r\n plotAdjoint(CP1.CostFunc.tvec, p1, \"Adjoint_CP1\", save=save, pdf=pdf)\r\n plotRel(CP1.CostFunc.tvec, u_list1, fu_list1, \"RelErrs_CP1\", save=save, pdf=pdf)\r\n\r\n elif choice == 2:\r\n\r\n print(\"Running program with the alternative costfunctional\")\r\n # coefficient A_1 and A_2 in cost functional\r\n A1, A2 = 0.050 / N * 2.95, 0.075 / N * 2.95\r\n # coefficient in front of u (0 <= tau <= N)\r\n tau = 14\r\n # setup\r\n CP2 = ControlProblem(N, S0, I0, R0, A1, A2, tau, v, gamma, beta, T, M, use_alt_costfunc=True)\r\n\r\n u2, y2, p2, iterations2, u_list2, fu_list2 = ProjectedGradientMethod(CP2, tol=tol)\r\n\r\n plotSIRu(CP2.CostFunc.tvec, y2, u2, \"SIR_u_CP2\", save=save, pdf=pdf)\r\n plotAdjoint(CP2.CostFunc.tvec, p2, \"Adjoint_CP2\", save=save, pdf=pdf)\r\n plotRel(CP2.CostFunc.tvec, u_list2, fu_list2, \"RelErrs_CP2\", save=save, pdf=pdf)","title":""},{"docid":"a8d0ff4fe3974c90e86ac819d7fa5593","score":"0.59185445","text":"def uniform_cost_search(problem):\n \"*** YOUR CODE HERE ***\"\n util.raiseNotDefined()","title":""},{"docid":"5292eca13dca9bcdd914353417a4aa01","score":"0.5918371","text":"def costs(self):\n raise ValueError('costs not implemented yet.')","title":""},{"docid":"a931de7d216a262546c62a05a1e35bec","score":"0.5909073","text":"def efficiency(self, *args, **kwargs):\n pass","title":""},{"docid":"dc4af3fcae96fc8ea1c48711de8849b1","score":"0.5904193","text":"def calc_costs(region, cost_structure, backhaul, backhaul_quantity,\n global_parameters, country_parameters):\n all_sites = region['upgraded_mno_sites'] + region['new_mno_sites']\n geotype = region['geotype'].split(' ')[0]\n\n total_cost = 0\n cost_by_asset = []\n\n for asset_name1, cost in cost_structure.items():\n for asset_name2, type_of_cost in COST_TYPE.items():\n if asset_name1 == asset_name2:\n\n if asset_name1 == 'backhaul' and backhaul_quantity == 0:\n continue\n\n if asset_name1 == 'regional_node' and backhaul == 'microwave':\n continue\n\n if asset_name1 == 'regional_edge' and backhaul == 'microwave':\n continue\n\n if type_of_cost == 'capex_and_opex':\n\n cost = discount_capex_and_opex(cost, global_parameters,\n country_parameters)\n\n if asset_name1 == 'single_sector_antenna':\n cost = cost * global_parameters['sectorization']\n\n if asset_name1 == 'cots_processing':\n\n split = 'cots_processing_split_{}'.format(geotype)\n quantity = int(math.ceil(all_sites / global_parameters[split]))\n cost = cost * quantity\n\n if asset_name1 == 'low_latency_switch':\n quantity = int(math.ceil(all_sites /\n global_parameters['low_latency_switch_split']))\n cost = cost * quantity\n\n if asset_name1 == 'rack':\n quantity = int(math.ceil(all_sites /\n global_parameters['rack_split']))\n cost = cost * quantity\n\n if asset_name1 == 'cloud_power_supply_converter':\n quantity = int(math.ceil(all_sites /\n global_parameters['cloud_power_supply_converter_split']))\n cost = cost * quantity\n\n if asset_name1 == 'cloud_backhaul':\n quantity = int(math.ceil(all_sites /\n global_parameters['cloud_backhaul_split']))\n cost = (cost * quantity) / all_sites\n\n elif type_of_cost == 'capex':\n cost = cost * (1 + (country_parameters['financials']['wacc'] / 100))\n\n elif type_of_cost == 'opex':\n cost = discount_opex(cost, global_parameters, country_parameters)\n\n else:\n return 'Did not recognize cost type'\n\n total_cost += cost\n\n cost_by_asset.append({\n 'asset': asset_name1,\n 'cost': cost,\n })\n\n cost_by_asset = {item['asset']: item['cost'] for item in cost_by_asset}\n\n ran = [\n 'single_sector_antenna',\n 'single_remote_radio_unit',\n 'io_fronthaul',\n 'processing',\n 'io_s1_x2',\n 'control_unit',\n 'cooling_fans',\n 'distributed_power_supply_converter',\n 'bbu_cabinet',\n 'cots_processing',\n 'io_n2_n3',\n 'low_latency_switch',\n 'rack',\n 'cloud_power_supply_converter',\n 'power',\n ]\n\n backhaul_fronthaul = [\n 'fronthaul',\n 'backhaul',\n 'cloud_backhaul',\n ]\n\n civils = [\n 'tower',\n 'civil_materials',\n 'transportation',\n 'installation',\n 'site_rental',\n 'power_generator_battery_system',\n ]\n\n core = [\n 'regional_node',\n 'regional_edge',\n 'core_node',\n 'core_edge',\n ]\n\n ran_cost = 0\n backhaul_fronthaul_cost = 0\n civils_cost = 0\n core_cost = 0\n\n for key, value in cost_by_asset.items():\n if key in ran:\n ran_cost += value\n if key in backhaul_fronthaul:\n backhaul_fronthaul_cost += value\n if key in civils:\n civils_cost += value\n if key in core:\n core_cost += value\n\n cost_by_asset = {\n 'ran': ran_cost,\n 'backhaul_fronthaul': backhaul_fronthaul_cost,\n 'civils': civils_cost,\n 'core_network': core_cost,\n }\n\n return int(round(total_cost)), cost_by_asset","title":""},{"docid":"92eaf5e630ffe6455f6603e0b22c4001","score":"0.58884954","text":"def compute(self, config, budget, *args, **kwargs):\n if config['data'] == 'pubmed':\n config['num_out_heads'] = 8\n config['weight-decay'] = 0.001\n elif config['data'] in ['cora', 'citeseer']:\n config['num_out_heads'] = 1\n config['weight-decay'] = 5e-4\n\n config = self.add_config(config, budget)\n config.__dict__.update(self.standard_config)\n print(config)\n best_test_perf, results = main_ctgr(config)\n best_epoch, best_test_perf, best_valid_perf, att_on_noisy_edge = results\n\n best_results = ({\n 'loss': 1 - best_test_perf,\n 'info': {\n 'best_epoch': best_epoch,\n 'best_test_perf': best_test_perf,\n 'best_valid_perf': best_valid_perf,\n 'att_on_noisy_edge': att_on_noisy_edge,\n }\n })\n all_results = {'config': config.__dict__, 'results': best_results}\n self.append_pickle('tmp_results.txt', all_results)\n return best_results","title":""},{"docid":"cbe72cc07f067e57d4639564616c1866","score":"0.5871775","text":"def measure(g, beliefs):\n#################CHANGES###############\n #measure a specific subset of beliefs\n beliefs = [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12)]\n########################################\n\n res = {} # dictionary to collect measurements\n\n # build a matrix of who (rows) is susceptible to what beliefs (columns)\n suscep = pd.DataFrame(index=g.nodes(), columns=[tuple(b) for b in beliefs])\n for agent in g:\n for belief in suscep.columns:\n suscep.at[agent, belief] = susceptible(g, agent, belief)\n res['% susceptible'] = suscep.mean().mean() # average susceptible fraction across all beliefs\n\n # build a matrix of who (rows) holds what beliefs (columns)\n adopt = pd.DataFrame(index=g.nodes(), columns=[tuple(b) for b in beliefs])\n for agent in g:\n for belief in adopt.columns:\n adopt.at[agent, belief] = g.nodes[agent]['M'].has_edge(*belief)\n res['% adopted'] = adopt.mean().mean() # average adopting fraction across all beliefs\n\n n_agents = len(adopt.index)\n corrs = adopt.astype(float).T.corr().mask(np.tri(n_agents, n_agents, 0, dtype='bool')).stack()\n res['95% similarity'] = np.percentile(corrs, 95)\n res['5% similarity'] = np.percentile(corrs, 5)\n res['90% similarity'] = np.percentile(corrs, 90)\n res['10% similarity'] = np.percentile(corrs, 10)\n res['85% similarity'] = np.percentile(corrs, 85)\n res['15% similarity'] = np.percentile(corrs, 15)\n res['80% similarity'] = np.percentile(corrs, 80)\n res['20% similarity'] = np.percentile(corrs, 20)\n res['75% similarity'] = np.percentile(corrs, 75)\n res['25% similarity'] = np.percentile(corrs, 25)\n res['std similarity'] = np.std(corrs)\n\n pca = PCA(n_components=1)\n pca.fit(adopt)\n res['PC1 percent of variance'] = pca.explained_variance_ratio_[0] * 100\n\n###### Changes\n # Measure difference from expected value\n e95 = []\n e5 = []\n e10 = []\n e15 = []\n e20 = []\n e25 = []\n e75 = []\n e80 = []\n e85 = []\n e90 = []\n estd = []\n ePC1 = []\n for _ in range(100):\n shuffle_adopt = pd.DataFrame()\n for col in adopt.columns:\n shuffle_adopt[col] = np.random.permutation(adopt[col].values)\n\n n_agents = len(shuffle_adopt.index)\n corrs = shuffle_adopt.astype(float).T.corr().mask(np.tri(n_agents, n_agents, 0, dtype='bool')).stack()\n e95.append(np.percentile(corrs, 95))\n e5.append(np.percentile(corrs, 5))\n\n e10.append(np.percentile(corrs, 10))\n e15.append(np.percentile(corrs, 15))\n e20.append(np.percentile(corrs, 20))\n e25.append(np.percentile(corrs, 25))\n e75.append(np.percentile(corrs, 75))\n e80.append(np.percentile(corrs, 80))\n e85.append(np.percentile(corrs, 85))\n e90.append(np.percentile(corrs, 90))\n estd.append(np.std(corrs))\n\n pca = PCA(n_components=1)\n pca.fit(shuffle_adopt)\n ePC1.append(pca.explained_variance_ratio_[0] * 100)\n\n res['expected PC1 percent of variance'] = np.mean(ePC1)\n res['expected 95% similarity'] = np.mean(e95)\n res['expected 5% similarity'] = np.mean(e5)\n\n res['expected 10% similarity'] = np.mean(e10)\n res['expected 15% similarity'] = np.mean(e15)\n res['expected 20% similarity'] = np.mean(e20)\n res['expected 25% similarity'] = np.mean(e25)\n res['expected 75% similarity'] = np.mean(e75)\n res['expected 80% similarity'] = np.mean(e80)\n res['expected 85% similarity'] = np.mean(e85)\n res['expected 90% similarity'] = np.mean(e90)\n res['expected std similarity'] = np.mean(estd)\n\n res['net PC1 percent of variance'] = res['PC1 percent of variance'] - res['expected PC1 percent of variance']\n res['net 95% similarity'] = res['95% similarity'] - res['expected 95% similarity']\n res['net 5% similarity'] = res['5% similarity'] - res['expected 5% similarity']\n\n res['net 10% similarity'] = res['10% similarity'] - res['expected 10% similarity']\n res['net 15% similarity'] = res['15% similarity'] - res['expected 15% similarity']\n res['net 20% similarity'] = res['20% similarity'] - res['expected 20% similarity']\n res['net 25% similarity'] = res['25% similarity'] - res['expected 25% similarity']\n res['net 75% similarity'] = res['75% similarity'] - res['expected 75% similarity']\n res['net 80% similarity'] = res['90% similarity'] - res['expected 80% similarity']\n res['net 85% similarity'] = res['85% similarity'] - res['expected 85% similarity']\n res['net 90% similarity'] = res['90% similarity'] - res['expected 90% similarity']\n res['net std similarity'] = res['std similarity'] - res['expected std similarity']\n#########\n return res","title":""},{"docid":"944c90c042297bc5468bf1481951dbfd","score":"0.5866878","text":"def main():\n\n experiment_number = 15 # run the experiment 15 times\n\n temp_cost = []\n # run 30 times to calculate the mean cost of each alpha value\n for i in range(experiment_number):\n best_cost = launch(df='gr96-tsp.txt')\n temp_cost.append(best_cost)\n cost_mean = sum(temp_cost)/len(temp_cost)\n\n print('Cost: ', cost_mean)","title":""},{"docid":"1e621bc22545066c3cc7524f25b985b5","score":"0.5862649","text":"def cost_function(self, actions):\n # trajectory_cost = 0\n # for i in range(len(actions)):\n # trajectory_cost += cost_function(states[i], actions[i], next_states[i])\n # return trajectory_cost\n actions = actions.reshape((-1, self.horizon, self.action_dim)) # [pop size, horizon, action_dim]\n actions = np.tile(actions, (self.particle, 1, 1))\n\n costs = np.zeros(self.popsize*self.particle)\n state = np.repeat(self.state.reshape(1, -1), self.popsize*self.particle, axis=0)\n\n for t in range(self.horizon):\n action = actions[:, t, :] # numpy array (batch_size x action dim)\n\n state_next = self.predict(state, action) + state\n\n cost = -self.cost_predict(state_next, action) # compute cost\n # cost = cost.reshape(costs.shape)\n costs += cost[:, 0] * self.gamma**t\n state = copy.deepcopy(state_next)\n\n # average between particles\n costs = np.mean(costs.reshape((self.particle, -1)), axis=0)\n return costs","title":""},{"docid":"4c07ea7a819360a80c054331961de93a","score":"0.58428305","text":"def run (self, scalers = {'capital costs':1.0}):\n tag = self.cd['file id'].split('+')\n\n self.was_run = True\n self.reason = \"OK\"\n\n if len(tag) > 1 and tag[1] != 'water-wastewater':\n self.was_run = False\n self.reason = \"Not a water/wastewater project.\"\n return\n #~ print self.comp_specs['data']['assumption type used']\n if self.comp_specs['data']['assumption type used'] == 'UNKNOWN':\n self.was_run = False\n self.reason = \"Water/wastewater system type unknown.\"\n #~ print self.reason\n return\n\n if self.cd[\"model electricity\"]:\n self.calc_baseline_kWh_consumption()\n self.calc_proposed_kWh_consumption()\n self.calc_savings_kWh_consumption()\n\n if self.cd[\"model heating fuel\"]:\n self.calc_baseline_HF_consumption()\n self.calc_proposed_HF_consumption()\n self.calc_savings_HF_consumption()\n\n #~ years = range(self.start_year,self.end_year)\n #~ self.forecast.add_heating_fuel_column(\\\n #~ \"heating_fuel_water-wastewater_consumed [gallons/year]\",\n #~ years,\n #~ self.baseline_HF_consumption*constants.mmbtu_to_gal_HF)\n #~ self.forecast.add_heating_fuel_column(\\\n #~ \"heating_fuel_water-wastewater_consumed [mmbtu/year]\", years,\n #~ self.baseline_HF_consumption)\n\n #~ self.forecast.add_heat_demand_column(\\\n #~ \"heat_energy_demand_water-wastewater [mmbtu/year]\",\n #~ years, self.baseline_HF_consumption)\n\n if self.cd[\"model financial\"]:\n self.calc_capital_costs()\n\n self.get_diesel_prices()\n self.calc_annual_electric_savings()\n self.calc_annual_heating_savings()\n self.calc_annual_total_savings()\n\n self.calc_annual_costs(self.cd['interest rate'],\n scalers['capital costs'])\n self.calc_annual_net_benefit()\n self.calc_npv(self.cd['discount rate'], self.cd[\"current year\"])\n self.calc_levelized_costs(0)\n #~ self.levelized_cost_of_energy['MMBtu'] *= .5\n #~ self.levelized_cost_of_energy['kWh'] *= .5","title":""},{"docid":"17313a723ae8e681e56f4acad03668b0","score":"0.58413917","text":"def prCost(args):\n\t#hours\n\ti = 0\n\ttaskFrontHours = args[i]\n\ti = i + 1\n\ttaskMLHours = args[i]\n\ti = i + 1\n\ttaskMLLeadParticipation = args[i]\n\ti = i + 1\n\ttaskDeployHours = args[i]\n\ti = i + 1\n\ttaskProjMgmtHours = args[i]\n\t\n\t#unexpected\n\ti = i + 1\n\tunexpected = args[i]\n\ti = i + 1\n\tunexpectedHours = args[i]\n\n\t#interruptions\n\telapsedHours = 0.8 * (taskFrontHours + taskMLHours + taskDeployHours + taskProjMgmtHours)\n\telapsedDays = int(elapsedHours / 8) + 1\n\tintrCounts = args[i+1:i+7]\n\t#print(str(intrCounts))\n\n\n\ti = i + 7\n\tproj = args[i]\n\t\n\ttcost = 0.0\n\ttcost += proj.taskCost(proj.taskFront, taskFrontHours)\n\totherParticipation = 100.0 - taskMLLeadParticipation - 10.0\n\ttaskML = {\"KD\" : taskMLLeadParticipation, \"PL\" : otherParticipation, \"SP\" : 10.0}\n\ttcost += proj.taskCost(taskML, taskMLHours)\n\ttcost += proj.taskCost(proj.taskDeploy, taskDeployHours) \n\ttcost += proj.taskCost(proj.taskProjMgmt, taskProjMgmtHours) \n\t\n\tif unexpected:\n\t\ttcost += unexpectedHours * proj.replCost\n\n\ttcost += proj.intrCost(intrCounts, elapsedDays)\n\t\n\treturn tcost","title":""},{"docid":"b6008bfb865213cf52a074355102b86d","score":"0.58405036","text":"def determine_cost(self, solution):\n\n cost = 0\n #print 'begin cost'\n #print solution\n #print solution.__len__()\n if self.metric == 1:\n for i in range(solution.__len__()-1):\n j = solution[i]\n k = solution[i + 1]\n #print j , k\n cost += self.cost_matrix[j][k]\n if self.metric == 2:\n #print(\"In speed\")\n for i in range(solution.__len__() - 1):\n j = solution[i]\n k = solution[i + 1]\n # print j , k\n cost += self.cost_matrix[j][k]/self.speed_matrix[j][k]\n #print cost\n #print 'end cost'\n return cost","title":""},{"docid":"f03fd973027f8e17c412ae9500f36718","score":"0.5839229","text":"def cost_function(features, target, weights):\n ##########################################################################\n # TODO\n # hint:\n # np.exp, np.log, np.sum\n ##########################################################################\n\n\n ##########################################################################\n # END OF YOUR CODE\n ##########################################################################","title":""},{"docid":"9f04c89282626a840d3a2a04e37d6d8c","score":"0.5832934","text":"def cost_func(self, emp_cov):\n cost = -self.log_likelihood(emp_cov)\n cost += self.penalty()\n return cost","title":""},{"docid":"30c68c8b8dd65a68d035a2766065e948","score":"0.5826755","text":"def compute_test_statistics(self):","title":""},{"docid":"03bb2946e2439d8d852fcc9c5edc0b6f","score":"0.58233416","text":"def cost(self, predicted_classes, actual_classes):\r\n _, accuracy, MK = get_metrics(actual_classes, predicted_classes)\r\n cost = 2-(accuracy + MK)\r\n cost = cost*10\r\n return cost","title":""},{"docid":"eba6b59f82f9ea634ebfa7b7c6cdebe8","score":"0.580647","text":"def do_stress_computation(params, inputs, disp_points=(), strain_points=()):\n\n print(\"Beginning stress calculation.\");\n print(\"Number of sources: %d \" % len(inputs.source_object));\n print(\"Number of receivers: %d \" % len(inputs.receiver_object));\n subfaulted_inputs = split_subfault_receivers(params, inputs);\n\n # Computes here.\n [x, y, x2d, y2d, u_disps, v_disps, w_disps] = compute_grid_def(subfaulted_inputs, params);\n model_disp_points = compute_ll_def(subfaulted_inputs, params, disp_points);\n strain_tensor_results = compute_ll_strain(subfaulted_inputs, params.alpha, strain_points);\n [receiver_normal, receiver_shear, receiver_coulomb] = compute_strains_stresses(params, subfaulted_inputs);\n receiver_profile_results = compute_stresses_horiz_profile(params, subfaulted_inputs);\n\n MyOutObject = cc.Out_object(x=x, y=y, x2d=x2d, y2d=y2d, u_disp=u_disps, v_disp=v_disps, w_disp=w_disps,\n strains=strain_tensor_results, model_disp_points=model_disp_points,\n zerolon=inputs.zerolon, zerolat=inputs.zerolat,\n source_object=inputs.source_object, receiver_object=subfaulted_inputs.receiver_object,\n receiver_normal=receiver_normal, receiver_shear=receiver_shear,\n receiver_coulomb=receiver_coulomb, receiver_profile=receiver_profile_results);\n return MyOutObject;","title":""},{"docid":"5da8efad61e003f763987481c633c08f","score":"0.58060473","text":"def PostProcess(abc_df, Summary, data, q=0.5, weighted=True):\n # take accpted values output from ABC\n df_accepted = abc_df[abc_df['accept'] == 1]\n accepted_count = len(df_accepted.index)\n if accepted_count < 5:\n \tprint accepted_count, \"is number of accepted thetas\"\n\n if accepted_count < 2:\n \tprint \"Post processing failed; too few accepted values.\"\n \treturn 0\n\n df_accepted.statistics_diff = np.subtract(df_accepted.statistics,Summary(data)) # here a list of vectors\n df_accepted.statistics_diff_abs = np.power(np.sum(np.square(df_accepted.statistics_diff)), 0.5) # we want a vector of distances\n \n #df_accepted.statistics_diff_abs.hist(bins=100)\n quantile = df_accepted.statistics_diff_abs.quantile(q) # shouled be ok\n #plt.show()\n\n #create column with kernal transform\n df_accepted.kernel = df_accepted.statistics_diff_abs.apply(lambda x: EpanechnikovKernel(x, delta=quantile)) #diff_abs\n\n #print df_accepted.kernel[df_accepted.kernel > 0.0]\n\n mod = LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n X = np.array(df_accepted.statistics_diff) #list of vector\n X.shape = (shape(X)[0],1) #the number of cols is equal to the lenght of the data\n y = np.array(df_accepted.theta)\n y.shape = (shape(y)[0],1)\n weights = np.array(df_accepted.kernel)\n \n if weighted:\n res = mod.fit(X, y, sample_weight=weights)\n else: \n res = mod.fit(X, y)\n #alpha = res.intercept_[0]\n beta = res.coef_[0][0] #dimension equal to the size of the data\n beta_stats_diff = beta*df_accepted.statistics_diff # multiplication of: vector * matrix\n beta_stats_diff_array = np.array(beta_stats_diff) #change the size\n beta_stats_diff_array.shape = (shape(beta_stats_diff_array)[0],1) #change the size\n theta_star = np.subtract(y, beta_stats_diff_array)\n theta_star = np.reshape(theta_star, theta_star.shape[0])\n df_accepted['theta_star'] = theta_star\n \n return df_accepted","title":""},{"docid":"f97af8b2a75082ae2c91b1d079c7b500","score":"0.5772366","text":"def evaluate_total_costs(x, A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base):\n x = np.rint(x)\n A_total_costs = np.sum(A_cost * A_quantity * x)\n B_total_costs = np.sum(B_cost * B_quantity * x)\n C_total_costs = C_cost * - min(C_base + np.sum(C_change * x), 0)\n return A_total_costs + B_total_costs + C_total_costs","title":""},{"docid":"c318f61af9a47aece3f8be45003f772b","score":"0.57631046","text":"def _process_results(self):\n m = self.model\n\n pcha = [m.pcha[n].value for n in m.time]\n pdis = [m.pdis[n].value for n in m.time]\n ptot = [m.pnet[n] + m.pcha[n].value - m.pdis[n].value for n in m.time]\n soc = [m.s[n].value for n in m.time]\n pfpk_without_es = max(m.pnet)\n ptpk_without_es = [max(m.pnet[n]*m.mask_ds[p][n] for n in m.time) for p in m.period]\n \n demand_charge_with_es=m.pfpk.value*m.flt_dr+sum(m.ptpk[p].value*m.tou_dr[p] for p in m.period)\n demand_charge_without_es=pfpk_without_es*m.flt_dr+sum(ptpk_without_es[p]*m.tou_dr[p] for p in m.period)\n \n energy_charge_with_es=sum(max(0,ptot[n])*m.tou_er[n] for n in m.time)\n energy_charge_without_es=sum(max(0,m.pnet[n])*m.tou_er[n] for n in m.time)\n \n nem_charge_with_es=sum(min(0,ptot[n])*m.nem_sr[n] for n in m.time) #negative since it is credit\n nem_charge_without_es=sum(min(0,m.pnet[n])*m.nem_sr[n] for n in m.time) #negative since it is credit\n \n tot_bill_with_es=demand_charge_with_es + energy_charge_with_es + nem_charge_with_es\n tot_bill_without_es=demand_charge_without_es + energy_charge_without_es + nem_charge_without_es\n \n run_results = {'time': m.time, 'Pload': m.pld, 'Ppv': m.ppv, 'Pcharge': pcha, 'Pdischarge': pdis, 'Ptotal': ptot,\n 'state of charge': soc, 'energy_charge_with_es': energy_charge_with_es,'nem_charge_with_es': nem_charge_with_es, \n 'demand_charge_with_es':demand_charge_with_es, 'total_bill_with_es': tot_bill_with_es, \n 'energy_charge_without_es': energy_charge_without_es,'nem_charge_without_es': nem_charge_without_es, \n 'demand_charge_without_es':demand_charge_without_es, 'total_bill_without_es': tot_bill_without_es }\n self.results = pd.DataFrame(run_results)\n\n self.total_bill_with_es = tot_bill_with_es\n self.total_bill_without_es = tot_bill_without_es\n\n self.demand_charge_with_es = demand_charge_with_es\n self.demand_charge_without_es = demand_charge_without_es\n\n self.energy_charge_with_es = energy_charge_with_es\n self.energy_charge_without_es = energy_charge_without_es\n\n self.nem_charge_with_es = nem_charge_with_es\n self.nem_charge_without_es = nem_charge_without_es","title":""},{"docid":"ff1dcb4b1eaff72e52c8ac19db197f1d","score":"0.57603204","text":"def evaluate_cost(\n self,\n results,\n name='',\n vectorise=False,\n **kwargs\n ):\n return self.evaluate_cost_and_std(\n results, name=name, vectorise=vectorise, **kwargs)[0]","title":""},{"docid":"3552864300a9cd6b62d76019ecc8c501","score":"0.5756126","text":"def _costfunction(self, newpos=None):\n if newpos is not None:\n self.movemotor(newpos)\n img = url2array(self.url, navg=self.navg)\n img = img[self._slice_0, self._slice_1]\n if self.stretch:\n img = stretch_contrast(img, *self.stretch)\n self._image = img\n img_contrast = contrast(img, self.contrast)\n residual = 1./img_contrast\n print(\"Value: %f\"%residual)\n return residual","title":""},{"docid":"46920153088fd6972cb2908ecc2aa23b","score":"0.5750806","text":"def linecosts(self,candidates,image,cseg_guess=None,transcript_guess=None):\n threshold = scipy.stats.scoreatpercentile([bestcost(x) for x in candidates],per=25)\n print \"threshold\",threshold\n best = [x for x in candidates if bestcost(x)<threshold]\n if len(best)<4: best = candidates\n base = median([x.bbox.y0 for x in best])\n scale = estimate_xheight(best)\n print \"base\",base,\"scale\",scale\n for x in candidates:\n x0,y0,x1,y1 = x.bbox.tuple()\n aspect = (y1-y0)*1.0/(x1-x0)\n costs = {}\n for cls,cost in x.outputs:\n costs[cls] = cost\n for a in costs:\n if a==\"~\": continue\n ac = ncost(aspect,self.aspects[a])\n if costs[a]<2 and ac>1.0:\n print \"adjusting\",a,costs[a],ac,\"aspect\"\n costs[cls] += ac\n for a,b in loc_confusions:\n if abs(costs[a]-costs[b])>1.0: continue\n ac = ncost((y0-base)/scale,self.ys[a])\n bc = ncost((y0-base)/scale,self.ys[b])\n if costs[a]<10:\n print \"adjusting\",a,b,costs[a],costs[b],\"loc\"\n if ac<bc: costs[b] += 2.0\n else: costs[a] += 2.0\n for a,b in size_confusions:\n if abs(costs[a]-costs[b])>1.0: continue\n ac = ncost((y1-y0)/scale,self.heights[a])\n bc = ncost((y1-y0)/scale,self.heights[b])\n if costs[a]<10:\n print \"adjusting\",a,b,costs[a],costs[b],\"size\"\n if ac<bc: costs[b] += 2.0\n else: costs[a] += 2.0\n for a in small_height:\n ac = ncost((y1-y0)/scale,self.heights[a])\n if ac<1.0: continue\n if costs[a]<10:\n print \"penalizing\",a,costs[a],ac,\"small\"\n costs[a] += ac\n x.outputs = [(cls,cost) for cls,cost in costs.items()]","title":""},{"docid":"ca586c9eb3e78ab5e428c94b6c97866c","score":"0.5747674","text":"def compute_cost_function(errors, weights, l2_penalty):\n\treturn np.dot(errors,errors) + l2_penalty * (np.dot(weights,weights) - weights[0] ** 2)","title":""},{"docid":"6e5e3b5bb821cfe5d76ea6bfdf5c34e6","score":"0.5743608","text":"def total_cost(self,data):\n cost = 0.0\n for x, y in data:\n a = self.feed_forward(x)\n cost += self.cost.fn(a,y)/len(data)\n # add regularization\n cost += 0.5*(self.parameters['regParam']/len(data))*sum( np.linalg.norm(w)**2 for w in self.weights )\n return cost","title":""},{"docid":"523b7799d373e24c2fbe414d3a876d86","score":"0.57361656","text":"def cost(X,Y,W1,b1,W2,b2):\n ### START YOUR CODE ###\n m = X.shape[1]\n yPred = predict(X, W1, b1, W2, b2)\n J = 1/(2*m)*np.sum((yPred - Y)**2)\n ### END YOUR CODE ###\n return J","title":""},{"docid":"6a69abe65016b57eee110f3a8a2d32a2","score":"0.57349277","text":"def display_costfunc():\r\n\r\n # The beta variables will represent the range of values on the x axis, which are the possible\r\n # beta coefficients for the independent variable (years of education for this example)\r\n beta_low = 0\r\n beta_high = 6000\r\n # The alpha variables will represent the possible values for the intercept.\r\n alpha_low = 13000\r\n alpha_high = 15000\r\n # NOTE: I set the above values via a process of trial and error and by observing the coefficients from\r\n # the previously executed regression models.\r\n\r\n # Define our sampling grid for slopes and intercepts\r\n # My betas and alphas variables will be two dimensionsal ndarrays containing the points on the x (betas)\r\n # and y (alphas) axis.\r\n # 100 was an arbitrary value that I inserted just to have enough variation in the visual\r\n betas, alphas = np.meshgrid(np.linspace(beta_low, beta_high, 100),\r\n np.linspace(alpha_low, alpha_high, 100))\r\n\r\n # My cost function will work with a numpy ndarray, so I am converting my dependent variable and my\r\n # independent variable from the Series within the DataFrame to a numpy ndarray.\r\n # I should be fine with the default shape that results from the to_numpy() function.\r\n # If not, I would have had to reshape it (to go down instead of going across).\r\n x = my_data['yearseducation'].to_numpy()\r\n y = my_data['currentsalary'].to_numpy()\r\n\r\n # Our cost function: Here, I compute the natural logarithm of the standard\r\n # l2-norm of the model residuals (MSE) to enhance the scale.\r\n # l2-norm is the mean square error (MSE).\r\n # In this equation, y is the true value\r\n # m is the beta coefficient for our independent variable\r\n # x is an observed value for the independent variable\r\n # b is the intercept.\r\n def cost(m, b):\r\n return np.log(np.sum((y - m * x - b)**2))\r\n\r\n # Now vectorize our function, which turns our cost function into a vectorized function\r\n # that can be broadcast to a NumPy array. This simplifies our code as we remove the need for an explicit loop.\r\n # Said differently, the purpose of np.vectorize is to transform functions that are not\r\n # numpy-aware (e.g. take floats as input and return floats as output) into functions\r\n # that can operate on (and return) numpy arrays.\r\n v_cost = np.vectorize(cost)\r\n\r\n # Our vectorized cost function sampled at every grid point\r\n epsilons = v_cost(betas, alphas)\r\n\r\n # Now, let's attempt to plot/graph/visualize the result\r\n fig, ax = plt.subplots()\r\n\r\n # First we draw the OLS result\r\n ax.plot(slope, intercept, marker='o', color='r', markersize=5)\r\n\r\n # Now plot the sampled grid as an image\r\n # https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.axes.Axes.imshow.html\r\n # If we want to blur the image to reduce image noise and reduce detail add the following:\r\n #interpolation=\"gaussian\"\r\n ax.imshow(epsilons, origin='lower', extent=[beta_low, beta_high, alpha_low, alpha_high],\r\n aspect='auto', interpolation='gaussian')\r\n\r\n # clean up the final plot\r\n ax.set_xlabel(r\"$\\beta$\", fontsize=14)\r\n ax.set_ylabel(r\"$\\alpha$\", fontsize=14)\r\n ax.set_title(r\"Cost function for $y = \\beta*x + \\alpha$\", fontsize=18)\r\n sns.despine(offset=0, trim=True)","title":""},{"docid":"7a8deee13062d64fb12f94b893fb09d3","score":"0.572154","text":"def mtf_cost_core_main(true_tan, true_sag, sim_tan, sim_sag):\n difference_t = true_tan - sim_tan\n difference_s = true_sag - sim_sag\n return difference_t, difference_s","title":""},{"docid":"314edc02517f3703e09af0a41aea6021","score":"0.5711565","text":"def main():\n\n logging.info(\"Calculating distinctiveness data...\")\n distinctiveness_dataset.calculate_distinctiveness()\n distinctiveness_dataset.calculate_persistent_stream_distinctiveness()\n distinctiveness_dataset.calculate_distinctiveness_dataset_complexities()\n logging.info(\"Distinctiveness data OK!\")","title":""},{"docid":"fa514a2af64d35762054cd86ec440382","score":"0.5708623","text":"def compute_performance_statistics (y_true_f, y_pred_f):\n \n y_true = np.load(y_true_f)\n y_pred = np.load(y_pred_f)\n \n y_true_o = np.load(y_true_f)\n y_pred_o = np.load(y_pred_f)\n #print (y_true.shape, y_pred.shape)\n y_true = y_true.flatten()\n y_pred = y_pred.flatten()\n \n sample_weights = np.copy(y_true)\n sample_weights[sample_weights == 1] = 1.\n sample_weights[sample_weights == 0] = .2\n \n \n epsilon = 1e-7\n y_pred[y_pred<=0.] = epsilon\n y_pred[y_pred>=1.] = 1. -epsilon\n \n #print (y_true.shape, y_pred.shape)\n smooth = 1.\n intersection = np.sum(y_true * y_pred)\n dice_coef = (2. * intersection + smooth) / (np.sum(y_true) + np.sum(y_pred) + smooth)\n\n score = log_loss (y_true, y_pred)\n score2 = log_loss (y_true, y_pred, sample_weight = sample_weights)\n acc = math.exp(-score)\n acc2 = math.exp(-score2)\n y_pred = np.round(y_pred)\n\n prec = precision_score(y_true, y_pred, average=\"binary\")\n rec = recall_score(y_true, y_pred, average=\"binary\")\n f1 = f1_score(y_true, y_pred, average=\"binary\")\n\n \n cm = confusion_matrix(y_true, y_pred)\n #cm.print_stats()\n true_p = cm[1][1]\n false_p = cm[0][1]\n true_n = cm[0][0]\n false_n = cm[1][0]\n\n \n #perf = {}\n \n# keys = [\"samples\", \"logloss\", \"weighted_logloss\",\"accuracy\", \"weighted_accuracy\", \"dice_coef\", \"precision\",\"recall\", \"f1_score\", \"true_positive\", \\\n# \"false_positive\",\"true_negative\",\"false_negative\", \"zero_contour_labels\", \"zero_contour_pred\", \\\n# \"missed_pred_lt_05\", \"missed_pred_gt_25\", \"missed_pred_gt_50\", \"missed_pred_eq_100\"]\n perf = OrderedDict.fromkeys(perf_keys)\n \n perf[\"logloss\"] = score\n perf[\"weighted_logloss\"] = score2\n perf[\"accuracy\"] = acc\n perf[\"weighted_accuracy\"] = acc2\n\n perf[\"dice_coef\"] = dice_coef\n perf[\"precision\"] = prec\n perf[\"recall\"] = rec\n perf[\"f1_score\"] = f1\n perf[\"true_positive\"] = int(cm[1][1])\n perf[\"false_positive\"] = int(cm[0][1])\n perf[\"true_negative\"] = int(cm[0][0])\n perf[\"false_negative\"] = int(cm[1][0])\n \n y_true = y_true_o\n y_pred = np.round(y_pred_o)\n samples, x, y, z = y_pred.shape\n y_true_sum = y_true.sum(axis=(1, 2), keepdims=True).reshape(samples)\n y_pred_sum = y_pred.sum(axis=(1, 2), keepdims=True).reshape(samples) \n lb0 = (np.where(y_true_sum == 0))\n pd0 = (np.where(y_pred_sum == 0))\n lb0 = list(lb0[0])\n pd0 = list(pd0[0])\n perf[\"samples\"] = samples\n perf[\"zero_contour_labels\"] = len(lb0)\n perf[\"zero_contour_pred\"] = len(pd0)\n \n pix_diff = (abs(y_true_sum - y_pred_sum))/(y_true_sum + epsilon)\n px1 = np.where(pix_diff <.0005)\n px1 = list(px1[0])\n px25 = np.where(pix_diff>.25)\n px25 = list(px25[0])\n px50 = np.where(pix_diff>.5)\n px50 = list(px50[0])\n px100 = np.where(pix_diff >= 1.0) \n px100 = list(px100[0])\n perf[\"missed_pred_lt_05\"] = len(px1)\n perf[\"missed_pred_gt_25\"] = len(px25)\n perf[\"missed_pred_gt_50\"] = len(px50)\n perf[\"missed_pred_eq_100\"] = len(px100)\n return perf","title":""},{"docid":"5319dde6efb7d39bcb89a3a0d0959ce1","score":"0.5701224","text":"def main():\n arrays = []\n if sys.argv[1] == \"1\":\n arrays = file_to_arrays('random_vs_al_accuracy.txt', 3)\n elif sys.argv[1] == \"2\":\n arrays = file_to_arrays('scaling_accuracy.txt', 3)\n elif sys.argv[1] == \"3\":\n arrays = file_to_arrays('heuristics_accuracy', 3)\n averaged_arrays = []\n for arr in arrays:\n averaged_arrays.append(np.divide(np.sum(arr, axis=0), 3))\n for i in range(len(averaged_arrays)):\n print(compute_deficiency(averaged_arrays[0], averaged_arrays[i]))","title":""},{"docid":"5ff18448737607acb70034dc0a1c0a37","score":"0.5692014","text":"def compute_risk(self, *args, **kwargs):\n\n pass","title":""},{"docid":"e497dc053db97c7a4669400d8d6a7272","score":"0.5691597","text":"def calculate_efficiency(plot_dict) :\n t1 = ROOT.TText(0.28,0.185,\"MICE preliminary [simulation]\")\n\tt2 = ROOT.TText(0.28,0.15,\"ISIS Cycle 2015/04\")\n\tt1.SetNDC(1)\n\tt1.SetTextSize(0.04)\n\tt1.SetTextFont(42)\n\tt2.SetNDC(1)\n\tt2.SetTextSize(0.03)\n\tt2.SetTextFont(42)\n\tf = ROOT.TFile(\"tracker_resolution_plots_\"+str(TOF_ll)+\".root\",\"RECREATE\")\n\tpEffx = ROOT.TEfficiency()\n pEffx = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_x'],plot_dict['downstream']['MC_theta_x'])\n\tc5 = ROOT.TCanvas()\n plot_dict['downstream']['efficiency_scat_x'] = pEffx.CreateGraph()\n\tpEffx_graph = pEffx.CreateGraph()\n\tpEffx_graph.SetName(\"Effx_graph\")\n\t#pEffx_graph.SetTitle(\"Acceptance plot #theta_x\")\n\tpEffx_graph.GetXaxis().SetTitle(\"#theta_{x} (mrad)\")\n\tpEffx_graph.GetYaxis().SetTitle(\"Efficiency\")\n\tpEffx_graph.Draw()\n\tf1 = ROOT.TF1(\"f1\",\"pol2\",-0.040,0.040)\n\t#f1.SetParameters(1,1)\n\t#pEffx_graph.Fit(\"f1\",\"R\")\n\t#pEffx_graph.SetRangeUser(-0.60,0.60)\n\tt1.Draw(\"same\")\n\tt2.Draw(\"same\")\n\tt1.Paint()\n\tt2.Paint()\n\tc5.SaveAs(\"pEff_x.pdf\")\n\tpEffx_graph.Write()\n\n pEffy = ROOT.TEfficiency()\n pEffy = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_y'],plot_dict['downstream']['MC_theta_y'])\n\tc7 = ROOT.TCanvas()\n plot_dict['downstream']['efficiency_scat_y'] = pEffy.CreateGraph()\n\tpEffy_graph = pEffy.CreateGraph()\n\tpEffy_graph.SetName(\"Effy_graph\")\n\t#pEffy_graph.SetTitle(\"Acceptance plot #theta_y\")\n\tpEffy_graph.GetXaxis().SetTitle(\"#theta_{y} (mrad)\")\n\tpEffy_graph.GetYaxis().SetTitle(\"Efficiency\")\n\tpEffy_graph.Draw()\n\t#pEffy_graph.Fit(\"f1\",\"R\")\n\tt1.Draw(\"same\")\n\tt2.Draw(\"same\")\n\tt1.Paint()\n\tt2.Paint()\n\tc7.SaveAs(\"pEff_y.pdf\")\n\tpEffy_graph.Write()\n\n pEffscatt = ROOT.TEfficiency()\n pEffscatt = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_scatt'],plot_dict['downstream']['MC_theta_scatt'])\n\tc17 = ROOT.TCanvas()\n plot_dict['downstream']['efficiency_scat_scatt'] = pEffscatt.CreateGraph()\n\tpEffscatt.Draw()\n\tc17.SaveAs(\"pEff_scatt.pdf\")\n\tpEffscatt_graph = pEffscatt.CreateGraph()\n\tpEffscatt_graph.SetName(\"Effscatt_graph\")\n\tpEffscatt_graph.Write()\n\n pEff2scatt = ROOT.TEfficiency()\n pEff2scatt = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_2scatt'],plot_dict['downstream']['MC_theta_2scatt'])\n\tc17 = ROOT.TCanvas()\n plot_dict['downstream']['efficiency_scat_2scatt'] = pEff2scatt.CreateGraph()\n\tpEff2scatt.Draw()\n\tc17.SaveAs(\"pEff_2scatt.pdf\")\n\tpEff2scatt_graph = pEff2scatt.CreateGraph()\n\tpEff2scatt_graph.SetName(\"Eff2scatt_graph\")\n\tpEff2scatt_graph.Write()\n\tf.Close()\n\n\t'''\n\tc3 = ROOT.TCanvas()\n\tplot_dict['downstream']['efficiency_scat'].Divide(plot_dict['downstream']['recon_theta'].Draw(),ROOT.TH1(plot_dict['downstream']['MC_theta'].Draw()))\n\tplot_dict['downstream']['recon_theta'].Draw()\n\tline = ROOT.TLine(-0.06,1,0.06,1)\n\tline.SetLineColor(22)\n\tline.Draw()\n\tc3.SaveAs('effi.pdf')\n '''\n\tc1 = ROOT.TCanvas()\n\tplot_dict['downstream']['recon_theta_x'].Draw()\n\tplot_dict['downstream']['MC_theta_x'].Draw(\"SAMES\")\n\tplot_dict['downstream']['MC_theta_x'].SetLineColor(2)\n\tplot_dict['downstream']['MC_theta_x'].SetLineStyle(2)\n\tc1.SaveAs('recon_theta.pdf')\n\tc1.Clear()\n\tc4= ROOT.TCanvas()\n #recon_theta_hist = plot_dict['downstream']['recon_theta'].Draw()\n\t#recon_theta_hist.Sumw2()\n\t#c4.SaveAs('recon_theta_hist.pdf')\n\tc2 = ROOT.TCanvas()\n\tMC_theta = plot_dict['downstream']['MC_theta_x'].Draw()\n\tc2.SaveAs('MC_theta.pdf')\n\n\t#plot_dict['downstream']['efficiency_scat'] = recon_theta_hist\n\t#plot_dict['downstream']['efficiency_scat'].Divide(MC_theta)\n\t#plot_dict['downstream']['efficiency_scat'].Draw()","title":""},{"docid":"4ab83de6076d298725c39d6c83f1c328","score":"0.5690338","text":"def cost_fn(params):\n return circuit(params[0], params[1:], w=0) ** 2","title":""},{"docid":"b63eee6e53890da3c0ae13efcdf0e1cd","score":"0.56772316","text":"def costFunction( params, controlData, Xprior=None, Xuncert=None ):\n \n n=len(controlData)\n Ymodel=np.zeros(n)\n Yobs=np.zeros(n)\n R=np.zeros((n,n))\n \n #compute the modelled albedo/fAPAR values\n for (i,data) in enumerate(controlData):\n Ymodel[i]=julesRT_wrapper( params, data )\n Yobs[i]=data.obsVal\n R[i,i]=1./(data.obsUncert**2)\n \n \n #VAR term one (obs):\n diff=Ymodel-Yobs\n cost=0.5*np.dot(diff,np.dot(R,diff.T))\n \n if Xprior != None:\n #compute B matrix\n B=np.diag(1./(np.array(Xuncert)**2))\n \n #VAR term two:\n diff=np.array(params)-np.array(Xprior) \n cost+=0.5*np.dot(diff,np.dot(B,diff.T))\n \n return cost","title":""},{"docid":"ebc0214dd6ae6badb515e91b084d7d0a","score":"0.5670897","text":"def _compute_specific(self, logits, targets):","title":""},{"docid":"cda1c2a1aa41d2b02e503d676b1c6f17","score":"0.56691784","text":"def compute_cost(data_set, m=0, c=0):\n cost = 0.0\n for i in data_set:\n x, y = i[0], i[1]\n y_diff = (y - (m*x + c))**2\n cost += y_diff\n return cost / len(data_set)","title":""},{"docid":"4b77be95a236f8eedc9f126c5832bd40","score":"0.5659344","text":"def calculate():\n\tdf_trust = pd.read_csv(trust_params.OUTPUT_MATRIX_FILE_NAME, index_col=0)\n\tdf_ideal = pd.read_csv(trust_params.IDEAL_MATRIX_FILE_NAME, index_col=0)\n\n\ttotal_count = 0\n\ttotal_diff = 0\n\tfor index, row in df_trust.iterrows():\n\t\tprint(\"Index row: \", index)\n\t\tfor col, value in row.iteritems():\n\t\t\tdiff_value = round(abs(value - df_ideal.loc[index,col]),4)\n\t\t\tif(index == col):\n\t\t\t\ttotal_count += 4\n\t\t\t\ttotal_diff += 4*diff_value\n\t\t\telse:\n\t\t\t\ttotal_count += 1\n\t\t\t\ttotal_diff += 1*diff_value\n\n\t\t\tprint(col, value, df_ideal.loc[index,col], diff_value)\n\n\tprint(\"Total count: \", total_count)\n\tprint(\"Total diff: \", total_diff)\n\tprint(\"Model trust score: \", total_diff/total_count*1.0)","title":""},{"docid":"3ce08f1c5f60e59057a7c120ec15aa68","score":"0.565901","text":"def test_cost_function(self):\n\n # Use seed for deterministic testing\n np.random.seed(42)\n\n def check(D, N, K,\n mu=None,\n Lambda=None,\n rho=None):\n\n if mu is None:\n mu = np.zeros(D)\n if Lambda is None:\n Lambda = np.identity(D)\n if rho is None:\n rho = np.ones(D)\n\n V = np.identity(D) + np.ones((D,D))\n\n # Construct model\n B = GaussianARD(3, 5,\n shape=(D,K),\n plates=(1,D))\n S = GaussianARD(2, 4,\n shape=(K,),\n plates=(N,1))\n A = SumMultiply('dk,k->d', B, S)\n X = GaussianMarkovChain(mu,\n Lambda,\n A,\n rho,\n n=N+1,\n initialize=False)\n Y = Gaussian(X,\n V,\n initialize=False)\n\n # Posterior estimation\n Y.observe(np.random.randn(N+1,D))\n X.update()\n B.update()\n S.update()\n try:\n mu.update()\n except:\n pass\n try:\n Lambda.update()\n except:\n pass\n try:\n rho.update()\n except:\n pass\n\n # Construct rotator\n rotB = RotateGaussianARD(B, axis=-2)\n rotX = RotateVaryingMarkovChain(X, B, S, rotB)\n\n # Rotation\n true_cost0 = X.lower_bound_contribution()\n rotX.setup()\n I = np.identity(D)\n R = np.random.randn(D, D)\n rot_cost0 = rotX.get_bound_terms(I)\n rot_cost1 = rotX.get_bound_terms(R)\n self.assertAllClose(sum(rot_cost0.values()),\n rotX.bound(I)[0],\n msg=\"Bound terms and total bound differ\")\n self.assertAllClose(sum(rot_cost1.values()),\n rotX.bound(R)[0],\n msg=\"Bound terms and total bound differ\")\n rotX.rotate(R)\n true_cost1 = X.lower_bound_contribution()\n self.assertAllClose(true_cost1 - true_cost0,\n rot_cost1[X] - rot_cost0[X],\n msg=\"Incorrect rotation cost for X\")\n \n return\n\n self._run_checks(check)\n \n pass","title":""},{"docid":"af62b2ffe17bf8e771b5e0f6eca91082","score":"0.5656809","text":"def compile(self):\n self.markov_blanket = self.compute_markov_blanket()\n self.parent_multiplier = self.compute_parent_multipliers()\n self.distributions = self.compute_distributions()\n self.compiled = True","title":""},{"docid":"98d4d0a445741ad25d0139f1b9537a39","score":"0.5654857","text":"def cal_performance(pred_gen,\n pred_cp,\n p_gen,\n gold,\n smoothing=False):\n # print(\"gold1\", gold)\n # print(gold.shape)\n loss, pred = cal_loss_cp(pred_gen,\n pred_cp,\n p_gen,\n gold)\n\n pred = pred.max(1)[1]\n gold = gold.contiguous().view(-1)\n non_pad_mask = gold.ne(Constants.PAD)\n n_correct = pred.eq(gold)\n n_correct = n_correct.masked_select(non_pad_mask).sum().item()\n\n return loss, n_correct, pred","title":""},{"docid":"874322e5ae7ce6c85bd21edf17e57a61","score":"0.5653596","text":"def cost_loop(self, coefs, X, y):\n acc = 0\n m = X.shape[0]\n\n for i in range(m):\n if y[i] == 1:\n acc += -np.log(self.hypothesis(coefs, X[i]))\n else:\n acc += -np.log(1-self.hypothesis(coefs, X[i]))\n return acc/m + self.regul/(2*m) * np.sum(np.square(coefs[1:]))","title":""},{"docid":"d74ee629fdbde0a2a4b7ee73be6ea693","score":"0.56526184","text":"def cost_func(es_y, gt_y):\n s = 0\n for e, g in zip(es_y, gt_y):\n s += (e - g) ** 2\n m = len(es_y)\n error = s / (2 * m)\n return error","title":""},{"docid":"9fa01c7a2337adee9c23c0400265e710","score":"0.5648534","text":"def compute(self,seaDepth,machineRating,hubHt,rtrDiam,tcc, turbine_number, year, month,verbose=0.0):\n\n lPrmtsCostCoeff1 = 9.94E-04 \n lPrmtsCostCoeff2 = 20.31 \n oPrmtsCostFactor = 37.0 # $/kW (2003)\n scourCostFactor = 55.0 # $/kW (2003)\n ptstgCostFactor = 20.0 # $/kW (2003)\n ossElCostFactor = 260.0 # $/kW (2003) shallow\n ostElCostFactor = 290.0 # $/kW (2003) transitional\n ostSTransFactor = 25.0 # $/kW (2003)\n ostTTransFactor = 77.0 # $/kW (2003)\n osInstallFactor = 100.0 # $/kW (2003) shallow & trans\n suppInstallFactor = 330.0 # $/kW (2003) trans additional\n paiCost = 60000.0 # per turbine\n \n suretyBRate = 0.03 # 3% of ICC\n suretyBond = 0.0\n\n #set variables\n if seaDepth == 0: # type of plant # 1: Land, 2: < 30m, 3: < 60m, 4: >= 60m\n iDepth = 1\n elif seaDepth < 30:\n iDepth = 2\n elif seaDepth < 60:\n iDepth = 3\n else:\n iDepth = 4\n\n # foundation cost calculations\n self.fdn.compute(machineRating, hubHt, rtrDiam, seaDepth, year, month)\n\n # initialize self.ppi index calculator\n if iDepth == 1:\n ref_yr = 2002 \n ref_mon = 9\n else:\n ref_yr = 2003\n ref_mon = 9\n ppi.ref_yr = ref_yr\n ppi.ref_mon = ref_mon\n ppi.curr_yr = year\n ppi.curr_mon = month\n \n # cost calculations\n tpC1 =0.00001581\n tpC2 =-0.0375\n tpInt =54.7\n tFact = tpC1*machineRating*machineRating + tpC2*machineRating + tpInt \n\n if (iDepth == 1):\n self.engPermits = (lPrmtsCostCoeff1 * machineRating * machineRating) + \\\n (lPrmtsCostCoeff2 * machineRating)\n ppi.ref_mon = 3\n self.engPermits *= ppi.compute('IPPI_LPM') * turbine_number\n ppi.ref_mon = 9\n \n elC1 = 3.49E-06\n elC2 = -0.0221\n elInt = 109.7\n eFact = elC1*machineRating*machineRating + elC2*machineRating + elInt\n self.electrical = machineRating * eFact * ppi.compute('IPPI_LEL') * turbine_number\n \n rcC1 = 2.17E-06\n rcC2 = -0.0145\n rcInt =69.54\n rFact = rcC1*machineRating*machineRating + rcC2*machineRating + rcInt\n self.roadsCivil = machineRating * rFact * ppi.compute('IPPI_RDC') * turbine_number\n \n iCoeff = 1.965\n iExp = 1.1736\n self.installation = iCoeff * ((hubHt*rtrDiam)**iExp) * ppi.compute('IPPI_LAI') * turbine_number\n \n self.transportation = machineRating * tFact * ppi.compute('IPPI_TPT') * turbine_number\n \n pass\n elif (iDepth == 2): # offshore shallow\n ppi.ref_yr = 2003\n self.pai = paiCost * ppi.compute('IPPI_PAE') * turbine_number\n self.portStaging = ptstgCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number # 1.415538133\n self.engPermits = oPrmtsCostFactor * machineRating * ppi.compute('IPPI_OPM') * turbine_number\n self.scour = scourCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number # 1.415538133#\n self.installation = osInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number \n self.electrical = ossElCostFactor * machineRating * ppi.compute('IPPI_OEL') * turbine_number\n ppi.ref_yr = 2002 \n self.transportation = machineRating * tFact * ppi.compute('IPPI_TPT') * turbine_number\n ppi.ref_yr = 2003\n\n pass \n elif (iDepth == 3): # offshore transitional depth\n ppi.ref_yr = 2003\n self.turbInstall = osInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\n self.supportInstall = suppInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\n self.installation = self.turbInstall + self.supportInstall\n self.pai = paiCost * ppi.compute('IPPI_PAE') * turbine_number\n self.electrical = ostElCostFactor * machineRating * ppi.compute('IPPI_OEL') * turbine_number\n self.portStaging = ptstgCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number\n self.engPermits = oPrmtsCostFactor * machineRating * ppi.compute('IPPI_OPM') * turbine_number\n self.scour = scourCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number\n ppi.ref_yr = 2002\n self.turbTrans = ostTTransFactor * machineRating * ppi.compute('IPPI_TPT') * turbine_number\n ppi.ref_yr = 2003\n self.supportTrans = ostSTransFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\n self.transportation = self.turbTrans + self.supportTrans\n \n elif (iDepth == 4): # offshore deep\n print \"\\ncsmBOS: Add costCat 4 code\\n\\n\"\n pass\n \n self.foundation = self.fdn.getCost() * turbine_number\n self.cost = self.foundation + \\\n self.transportation + \\\n self.roadsCivil + \\\n self.portStaging + \\\n self.installation + \\\n self.electrical + \\\n self.engPermits + \\\n self.pai + \\\n self.scour\n\n if (iDepth > 1):\n self.suretyBond = suretyBRate * (tcc + self.cost/ turbine_number)\n self.cost += self.suretyBond * turbine_number\n else:\n self.suretyBond = 0.0\n\n if (verbose > 0):\n self.dump()\n \n ppi.ref_yr = 2002","title":""},{"docid":"ec99e71992e7db783771a5e5102f4b7c","score":"0.5634694","text":"def cost_fn(self, states, actions, next_states):\n if len(next_states.shape) == 1:\n next_states = np.expand_dims(next_states, 0)\n # xyz_pos = next_states[:, 14:17]\n # desired_xyz_pos = self.multitask_goal[14:17] * np.ones_like(xyz_pos)\n # diff = xyz_pos - desired_xyz_pos\n next_joint_angles = next_states[:, :7]\n desired_joint_angles = (\n self.multitask_goal[:7] * np.ones_like(next_joint_angles)\n )\n diff = next_joint_angles - desired_joint_angles\n return (diff**2).sum(1, keepdims=True)","title":""},{"docid":"7c023b709caf5d34aebdeaacc6e5479a","score":"0.5631135","text":"def ga_optimization():\n pass","title":""},{"docid":"d2921a4dd31554ae6eac383e8dcd81db","score":"0.56286204","text":"def estimate_cost(self, board):\n pass","title":""},{"docid":"7a3b88c522d9508a8764c82dbb4a98c5","score":"0.56272495","text":"def calculate_reward(self):\n t=self.period\n \n \n \n if self.unsatisfied_demand == 'backorders':\n #print('Back order State:',np.array(self.BO[t]))\n backorder_costs = np.sum(np.array(self.BO[t]) * np.array(self.bo_costs))\n hc=self.holding_costs\n holding_costs = np.sum(np.array(self.INV[t]) * np.array(hc))\n revenue=np.sum(np.array(self.TotalSales[t]) * np.array(self.selling_price))\n cost_of_goods=np.sum(np.array(self.TotalCOGS[t]) * np.array(self.cost_price))\n #self.cost_price=self.cost_price\n #self.selling_price=self.selling_price\n \n ## Penalty applying\n if t>0:\n if np.sum(np.array(self.BO[t]))>np.sum(np.array(self.BO[t-1])):\n backorder_costs=backorder_costs+(t+1/t)\n if np.sum(np.array(self.INV[t]))>np.sum(np.array(self.INV[t-1])):\n holding_costs=holding_costs+(t+1/t)\n else:\n backorder_costs=backorder_costs\n holding_costs=holding_costs\n lost_sales_costs=0\n \n elif self.unsatisfied_demand != 'backorders':\n #print('Back order State:',np.array(self.BO[t]))\n lost_sales_costs = np.sum(np.array(self.LO[t]) * np.array(self.lo_costs))\n hc=self.holding_costs\n holding_costs = np.sum(np.array(self.INV[t]) * np.array(hc))\n revenue=np.sum(np.array(self.TotalSales[t]) * np.array(self.selling_price))\n cost_of_goods=np.sum(np.array(self.TotalCOGS[t]) * np.array(self.cost_price))\n #self.cost_price=self.cost_price\n #self.selling_price=self.selling_price\n \n ## Penalty applying\n if t>0:\n if np.sum(np.array(self.LO[t]))>np.sum(np.array(self.LO[t-1])):\n lost_sales_costs=lost_sales_costs+(t+1/t)\n if np.sum(np.array(self.INV[t]))>np.sum(np.array(self.INV[t-1])):\n holding_costs=holding_costs+(t+1/t)\n else:\n lost_sales_costs=lost_sales_costs\n holding_costs=holding_costs\n backorder_costs=0\n \n return holding_costs, backorder_costs,lost_sales_costs,revenue,cost_of_goods","title":""},{"docid":"4eef6df526514f0724c4d124d6aecfd5","score":"0.56261903","text":"def _construct_adv_cost(self):\n match_cost = self.GN.compute_log_prob(Xd=self.match_target)\n adv_cost = -T.sum(match_cost) / self.obs_count\n return adv_cost","title":""},{"docid":"cafb27efeff8f9c992d10e219de5f849","score":"0.562235","text":"def evaluate_cost_and_std(\n self,\n results,\n name='',\n vectorise=False,\n **kwargs\n ):\n\n # we make it possible to instance a CrossFidelity obj without a\n # comparison_results dict so that we can easily generate the\n # comparison data using the same setup (e.g. seed, prefix). But\n # in that case cannote evaluate the cost.\n if self._comparison_results is None:\n raise ValueError('No comparison results set has been passed to'\n + ' CrossFidelity obj.')\n\n def circ_namesA(idx):\n return name + self._rand_meas_handler.circ_name(idx)\n\n def circ_namesB(idx):\n return self._prefixB + f'{idx}'\n\n if self._num_bootstraps > 0:\n\n # case using bootstrapping to estimate standard error from dist \n # of U's\n\n dist_tr_rhoA_rhoB = _crosscorrelation_per_u(\n results, self._comparison_results, self.nb_random,\n circ_namesA=circ_namesA, circ_namesB=circ_namesB,\n vectorise=vectorise,\n )\n dist_tr_rhoA_2 = _purity_per_u(\n results, self.nb_random, names=circ_namesA, vectorise=vectorise)\n dist_tr_rhoB_2 = _purity_per_u(\n self._comparison_results, self.nb_random, names=circ_namesB, \n vectorise=vectorise)\n\n # bootstrap resample for means and std-errs\n tr_rhoA_rhoB, tr_rhoA_rhoB_err = bootstrap_resample(\n np.mean, dist_tr_rhoA_rhoB, self._num_bootstraps,\n random_seed=self.seed,\n )\n tr_rhoA_2, tr_rhoA_2_err = bootstrap_resample(\n np.mean, dist_tr_rhoA_2, self._num_bootstraps,\n random_seed=self.seed,\n )\n tr_rhoB_2, tr_rhoB_2_err = bootstrap_resample(\n np.mean, dist_tr_rhoB_2, self._num_bootstraps,\n random_seed=self.seed,\n )\n\n else:\n\n # case without bootstrapping, this only includes standard error\n # arising from counts spread (currently not even that)\n\n if isinstance(results, FastCountsResult):\n tmp_results = results\n elif isinstance(results, Result):\n tmp_results = FastCountsResult(results)\n else:\n raise TypeError(\n 'results type not recognised: '+f'{type(results)}')\n tmp_results.combine_counts([\n circ_namesA(idx) for idx in range(self.nb_random)],\n circ_namesA('')\n )\n\n if isinstance(self._comparison_results, FastCountsResult):\n tmp_comparison_results = self._comparison_results\n elif isinstance(self._comparison_results, Result):\n tmp_comparison_results = FastCountsResult(\n self._comparison_results)\n else:\n raise TypeError(\n 'results type not recognised: '\n + f'{type(self._comparison_results)}')\n tmp_comparison_results.combine_counts([\n circ_namesB(idx) for idx in range(self.nb_random)],\n circ_namesB('')\n )\n\n def tmp_circ_namesA(idx):\n return circ_namesA('')\n\n def tmp_circ_namesB(idx):\n return circ_namesB('')\n\n tr_rhoA_rhoB = _crosscorrelation_per_u(\n tmp_results, tmp_comparison_results, 1,\n circ_namesA=tmp_circ_namesA, circ_namesB=tmp_circ_namesB,\n vectorise=vectorise,\n )[0]\n tr_rhoA_2 = _purity_per_u(\n tmp_results, 1, names=tmp_circ_namesA, vectorise=vectorise)[0]\n tr_rhoB_2 = _purity_per_u(\n tmp_comparison_results, 1, names=tmp_circ_namesB, \n vectorise=vectorise)[0]\n\n tr_rhoA_rhoB_err, tr_rhoA_2_err, tr_rhoB_2_err = 0, 0, 0\n\n # divide by largest\n if tr_rhoA_2 > tr_rhoB_2:\n mean = tr_rhoA_rhoB / tr_rhoA_2\n std = (mean**2) * (\n (tr_rhoA_rhoB_err**2) / (tr_rhoA_rhoB)**2\n + (tr_rhoA_2_err)**2 / (tr_rhoA_2)**2\n )\n else:\n mean = tr_rhoA_rhoB / tr_rhoB_2\n std = (mean**2) * (\n (tr_rhoA_rhoB_err**2) / (tr_rhoA_rhoB)**2\n + (tr_rhoB_2_err)**2 / (tr_rhoB_2)**2\n )\n\n # store results of evaluation\n self.last_evaluation = {\n 'tr_rhoA_rhoB': tr_rhoA_rhoB,\n 'tr_rhoA_rhoB_err': tr_rhoA_rhoB_err,\n 'tr_rhoA_2': tr_rhoA_2,\n 'tr_rhoA_2_err': tr_rhoA_2_err,\n 'tr_rhoB_2': tr_rhoB_2,\n 'tr_rhoB_2_err': tr_rhoB_2_err,\n 'mean': mean,\n 'std': std,\n }\n\n return mean, std","title":""},{"docid":"19fcc29a2120a244d1c43d4967696cc7","score":"0.56170565","text":"def efficiency(self):\n return 0.9","title":""},{"docid":"4717c0565581871da05e316916f12f01","score":"0.56154263","text":"def contestEvaluationFunc(currentGameState):\n def closest_dot(cur_pos, food_pos):\n food_distances = []\n for food in food_pos:\n food_distances.append(util.manhattanDistance(food, cur_pos))\n return min(food_distances) if len(food_distances) > 0 else 1\n\n def closest_ghost(cur_pos, ghosts):\n food_distances = []\n for food in ghosts:\n food_distances.append(util.manhattanDistance(food.getPosition(), cur_pos))\n return min(food_distances) if len(food_distances) > 0 else 1\n\n\n def ghost_stuff(cur_pos, ghost_states, radius, scores):\n num_ghosts = 0\n for ghost in ghost_states:\n if util.manhattanDistance(ghost.getPosition(), cur_pos) <= radius:\n scores -= 30\n num_ghosts += 1\n return scores\n\n def food_stuff(cur_pos, food_positions):\n food_distances = []\n for food in food_positions:\n food_distances.append(util.manhattanDistance(food, cur_pos))\n return sum(food_distances)\n\n def num_food(cur_pos, food):\n return len(food)\n\n def closest_capsule(cur_pos, caps_pos):\n capsule_distances = []\n for caps in caps_pos:\n capsule_distances.append(util.manhattanDistance(caps, cur_pos))\n return min(capsule_distances) if len(capsule_distances) > 0 else 9999999\n\n def scaredghosts(ghost_states, cur_pos, scores):\n scoreslist = []\n for ghost in ghost_states:\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 4:\n scoreslist.append(scores + 50)\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 3:\n scoreslist.append(scores + 60)\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 2:\n scoreslist.append(scores + 70)\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 1:\n scoreslist.append(scores + 90)\n #if ghost.scaredTimer > 0 and util.manhattanDistance(ghost.getPosition(), cur_pos) < 1:\n # scoreslist.append(scores + 100)\n return max(scoreslist) if len(scoreslist) > 0 else scores\n\n def ghostattack(ghost_states, cur_pos, scores):\n scoreslist = []\n for ghost in ghost_states:\n if ghost.scaredTimer == 0:\n scoreslist.append(scores - util.manhattanDistance(ghost.getPosition(), cur_pos) - 10)\n return max(scoreslist) if len(scoreslist) > 0 else scores\n\n def scoreagent(cur_pos, food_pos, ghost_states, caps_pos, score):\n if closest_capsule(cur_pos, caps_pos) < closest_ghost(cur_pos, ghost_states):\n return score + 40\n if closest_dot(cur_pos, food_pos) < closest_ghost(cur_pos, ghost_states) + 3:\n return score + 20\n if closest_capsule(cur_pos, caps_pos) < closest_dot(cur_pos, food_pos) + 3:\n return score + 30\n else:\n return score\n\n\n capsule_pos = currentGameState.getCapsules()\n pacman_pos = currentGameState.getPacmanPosition()\n score = currentGameState.getScore()\n food = currentGameState.getFood().asList()\n ghosts = currentGameState.getGhostStates()\n\n #score = score * 2 if closest_dot(pacman_pos, food) < closest_ghost(pacman_pos, ghosts) + 3 else score\n #score = score * 1.5 if closest_capsule(pacman_pos, capsule_pos) < closest_dot(pacman_pos, food) + 4 else score\n score = scoreagent(pacman_pos, food, ghosts, capsule_pos, score)\n score = scaredghosts(ghosts, pacman_pos, score)\n score = ghostattack(ghosts, pacman_pos, score)\n score -= .35 * food_stuff(pacman_pos, food)\n return score","title":""},{"docid":"256facda13367c15eb8a7eda7244ef2f","score":"0.56154144","text":"def _compute(self, predicted, targets, track):","title":""},{"docid":"ef446cf6d4ca3458b97836a744e99828","score":"0.56115925","text":"def profit_curve():\n cost_TP = 8000; cost_FP = -2000; cost_FN = 0; cost_TN = 0\n cb_matrix = np.array([[cost_TN, cost_FP], [cost_FN, cost_TP]])\n\n profits_gb= []\n profits_rf= []\n thresholds = []\n cfs = []\n cfs2 = []\n count = 0\n models = [model, gb_model]\n for threshold in np.arange(0.0,1.0,0.01):\n thresholds.append(threshold)\n for model_type in models:\n count += 1\n predicted_proba = model_type.predict_proba(X_test2)\n predicted = (predicted_proba [:,1] >= threshold).astype('int')\n accuracy = metrics.accuracy_score(y_test2, predicted)\n #recall_score_1 = recall_score(y_test, y_pred)\n if count % 2 ==1 :\n cf1 = confusion_matrix(y_test, predicted)\n cf12 = (cf1[0,1] + cf1[1,1]) / cf1.sum()\n cfs2.append(cf12)\n full_matrix_rf = (confusion_matrix(y_test, predicted) * cb_matrix)\n profits_rf.append(np.sum(full_matrix_rf))\n #print('Cost matrix sum for RF:', np.sum(full_matrix_rf), 'Threshold:', threshold)\n #print(f'Accuracy score for RF: {accuracy}')\n #print(\"Recall:\", recall_score1)\n \n else:\n cf = confusion_matrix(y_test2, predicted)\n cf2 = (cf[0,1] + cf[1,1]) / cf.sum()\n cfs.append(cf2)\n full_matrix_gb = confusion_matrix(y_test2, predicted) * cb_matrix\n profits_gb.append(np.sum(full_matrix_gb))\n #print(f'Accuracy score for GB: {accuracy}')\n #print('Cost matrix sum for GB:', np.sum(full_matrix_gb),'Threshold:', threshold)\n\n _, ax = plt.subplots(figsize=(12,8))\n ax.plot(cfs2, profits_rf, label='Random Forest', c='r')\n ax.plot(cfs, profits_gb, label='Gradient Boost', c='b')\n plt.title('Profit Curve', size=26)\n #ax.yaxis.zoom(3) \n plt.xlabel('Loan Acceptance Rate', size=26)\n plt.ylabel('Profit', size=26)\n plt.xticks(size=20)\n plt.yticks(size=20)\n ax.xaxis.set_major_formatter(mtick.PercentFormatter())\n plt.tight_layout()\n plt.legend()\n plt.savefig('../images/profit_curve_for_git.png')\n plt.show()","title":""},{"docid":"0ebed9fe0896e34395197f02b55c6d02","score":"0.5611045","text":"def evaluate_performances(self, population, offsprings, pool=None):\n return","title":""},{"docid":"0089c3556d94dff3ce4ca2bbd81da621","score":"0.5608713","text":"def stguide():\n\n def payoff_fn(**kwargs):\n values = kwargs[\"values\"]\n bid = values[\"bid\"] if \"bid\" in values.keys() else 0\n competitor_bid = (\n values[\"competitor_bid\"] if \"competitor_bid\" in values.keys() else 0\n )\n cost = values[\"cost\"] if \"cost\" in values.keys() else 0\n return (bid - cost) * (1 if bid < competitor_bid else 0)\n\n nodes = DataNodes()\n nodes.add_decision(\n name=\"bid\",\n branches=[\n (\"low\", 500, \"competitor_bid\"),\n (\"high\", 700, \"competitor_bid\"),\n ],\n maximize=True,\n )\n nodes.add_chance(\n name=\"competitor_bid\",\n branches=[\n (\"low\", 0.35, 400, \"cost\"),\n (\"medium\", 0.50, 600, \"cost\"),\n (\"high\", 0.15, 800, \"cost\"),\n ],\n )\n nodes.add_chance(\n name=\"cost\",\n branches=[\n (\"low\", 0.25, 200, \"profit\"),\n (\"medium\", 0.50, 400, \"profit\"),\n (\"high\", 0.25, 600, \"profit\"),\n ],\n )\n nodes.add_terminal(name=\"profit\", payoff_fn=payoff_fn)\n\n return nodes","title":""},{"docid":"c1c9521546b23c669c01d6b2cd42dd0a","score":"0.55957615","text":"def get_expected_cost(self):\n raise NotImplementedError(\"This is an abstract method and needs to implemented in derived classes.\")","title":""},{"docid":"044f3446541075f1f032ecd33f37ee7a","score":"0.55898297","text":"def evaluate(predictor, test_ds):\n coco = convert_to_coco_api(test_ds)\n coco_evaluator = CocoEvaluator(coco)\n evaluator_times = []\n proc_times = []\n for i in range(len(test_ds)):\n image, targets = test_ds[i]\n init = time.time()\n boxes, labels, probs = predictor.predict(image, 10 , 0.2)\n proc_times.append(time.time() - init)\n if boxes.size()[0] == 0:\n continue\n outputs = {'boxes': boxes,\n 'labels':labels,\n 'scores': probs}\n res = {targets['image_id'].item(): outputs}\n evaluator_time = time.time()\n coco_evaluator.update(res)\n evaluator_times.append(time.time() - evaluator_time) \n \n print(\"Averaged stats:\", np.mean(evaluator_times))\n print(\"Averaged proc time:\", np.mean(proc_times))\n coco_evaluator.synchronize_between_processes()\n # accumulate predictions from all images\n coco_evaluator.accumulate()\n coco_evaluator.summarize() \n return coco_evaluator","title":""},{"docid":"6a45cc1e2d49b9ab4fcedf21bfd7ce1c","score":"0.5584052","text":"def _compute(self):\n\n # unless clause D checks are used, test one literal at a time\n # and add it either to satisfied of backbone assumptions\n i = 0\n while i < len(self.setd):\n if self.ucld:\n self.do_cld_check(self.setd[i:])\n i = 0\n\n if self.setd:\n # if may be empty after the clause D check\n\n self.ss_assumps.append(self.setd[i])\n if not self.oracle.solve(assumptions=self.ss_assumps + self.bb_assumps):\n self.ss_assumps.pop()\n self.bb_assumps.append(-self.setd[i])\n\n i += 1","title":""},{"docid":"8d9409000c5660df50b37f6a3ac6a3f0","score":"0.5582407","text":"def cost(self, data_X, data_Y):\n return self.cost_function(data_Y, self.predict(data_X))","title":""},{"docid":"05028ca83a0a236617f504313ce66b10","score":"0.5576904","text":"def compute_performance(self):\n # TODO Track fixation breaks here? Also in the remote?\n\n if self.trial_data:\n data = pd.DataFrame([t for t, _ in self.trial_data])\n mean_acc = data[\"correct\"].mean()\n responses = data[\"responded\"].sum()\n return mean_acc, responses\n else:\n return None, None","title":""},{"docid":"7a37e28c3700639fb48fb89aa3704876","score":"0.5576557","text":"def compute_common_data(self):\n self.filter_expression_and_priors()\n print('Creating design and response matrix ... ')\n self.design_response_driver.delTmin = self.delTmin\n self.design_response_driver.delTmax = self.delTmax\n self.design_response_driver.tau = self.tau\n (self.design, self.response) = self.design_response_driver.run(self.expression_matrix, self.meta_data)\n\n # compute half_tau_response\n print('Setting up TFA specific response matrix ... ')\n self.design_response_driver.tau = self.tau / 2\n (self.design, self.half_tau_response) = self.design_response_driver.run(self.expression_matrix, self.meta_data)","title":""},{"docid":"1ee2525f7cfdeb1c9e5176e68829e221","score":"0.55751634","text":"def _compute_dci(mus_train, ys_train, mus_test, ys_test):\n scores = {}\n importance_matrix, train_err, test_err = compute_importance_gbt(\n mus_train, ys_train, mus_test, ys_test)\n assert importance_matrix.shape[0] == mus_train.shape[0]\n assert importance_matrix.shape[1] == ys_train.shape[0]\n scores[\"informativeness_train\"] = train_err\n scores[\"informativeness_test\"] = test_err\n scores[\"disentanglement\"] = disentanglement(importance_matrix)\n scores[\"completeness\"] = completeness(importance_matrix)\n return scores","title":""},{"docid":"0597dfef712c767ea648feecb122e59a","score":"0.55748093","text":"def calculate(self, results):\n self.results = results\n self.original_candidates_ppl = []\n self.successful_candidates_ppl = []\n\n for i, result in enumerate(self.results):\n if isinstance(result, FailedAttackResult):\n continue\n elif isinstance(result, SkippedAttackResult):\n continue\n else:\n self.original_candidates.append(\n result.original_result.attacked_text.text.lower()\n )\n self.successful_candidates.append(\n result.perturbed_result.attacked_text.text.lower()\n )\n\n ppl_orig = self.calc_ppl(self.original_candidates)\n ppl_attack = self.calc_ppl(self.successful_candidates)\n\n self.all_metrics[\"avg_original_perplexity\"] = round(ppl_orig[0], 2)\n self.all_metrics[\"original_perplexity_list\"] = ppl_orig[1]\n\n self.all_metrics[\"avg_attack_perplexity\"] = round(ppl_attack[0], 2)\n self.all_metrics[\"attack_perplexity_list\"] = ppl_attack[1]\n\n return self.all_metrics","title":""},{"docid":"47203e7cbdd83fedd19cd6b2096c4649","score":"0.5573001","text":"def _calcUsage(self):\n pass","title":""},{"docid":"bc28f5ebd05b9986f887b9163841c36b","score":"0.5566476","text":"def cost(self, module):\n module.resetDerivatives()\n error = 0.0\n ponderation = 0.0\n shuffledSequences = []\n for seq in self.ds._provideSequences():\n shuffledSequences.append(seq)\n shuffle(shuffledSequences)\n for seq in shuffledSequences:\n e, p = self._calcDerivs(module, seq)\n error += e\n ponderation += p\n grad = module.derivs.copy()\n error /= ponderation\n # Calculate the scaling parameter of the gradient to make the elements\n # sum to 1.0.\n norm = absolute(grad).sum()\n if norm > 10.0:\n scale = 1.0 / norm\n else:\n scale = 1.0\n return error, -grad * scale","title":""},{"docid":"7306e008dcc9808da59f9ce9f9ba8250","score":"0.55659544","text":"def compute():\n\n body = request.get_json(force=True)\n\n # TODO: Finish this method.\n\n # Note: I've spoken to one of the FE engineers and it wouldn't be a\n # great experience to have our endpoint slow. Can we speed up or do\n # something about the baseline risk API?\n\n raise NotImplementedError()","title":""},{"docid":"b7428f49862761119421b4b4d36cce3e","score":"0.5565938","text":"def compute_stress(state):\n K=state.aux[1,:,:]\n stress_rel=state.aux[2,:,:]\n eps=state.q[0,:,:]\n state.p[0,:,:] = np.where(stress_rel==1,1,0) * K*eps \\\n +np.where(stress_rel==2,1,0) * (np.exp(eps*K)-1) \\\n +np.where(stress_rel==3,1,0) * K*eps+K**2*eps**2","title":""}],"string":"[\n {\n \"docid\": \"15992c3ac7d433e6ebac76decbf55a3c\",\n \"score\": \"0.7366483\",\n \"text\": \"def compute_costs_benefits(self):\\n\\n # unit costs\\n if self.verbose:\\n print(\\\"Preparing unit values...\\\")\\n self._create_unit_cost_matrix()\\n\\n # costs\\n if self.verbose:\\n print(\\\"Computing costs...\\\")\\n self.compute_capex()\\n self.compute_opex()\\n self._compute_deterioration()\\n self.compute_replacements()\\n self.compute_residual_value()\\n\\n # benefits\\n if self.verbose:\\n print(\\\"Computing benefits...\\\")\\n self._create_length_matrix()\\n self._create_fuel_ratio_matrix()\\n self._compute_travel_time_matrix()\\n\\n self._compute_vtts()\\n if self.include_freight_time:\\n self._compute_vfts()\\n self._compute_voc()\\n self._compute_fuel_consumption()\\n self._compute_fuel_cost()\\n self._compute_greenhouse()\\n self._compute_emissions()\\n self._compute_noise()\\n self._compute_accidents()\\n self._compute_toll()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982a66b9054117f6bce45298e6afeb61\",\n \"score\": \"0.73594654\",\n \"text\": \"def _benefit_cost_analysis(self, world):\\n bc = -10 #Benefit - Cost\\n for i in self.vicinity['good_id']:\\n a = self._benefit(world, i) - self._cost(world, i)\\n if a > bc:\\n bc = a\\n self.objective = i\\n \\n if bc <= -10:\\n self.objective = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81b6df4d165cfe4bd04ebe566c17cb12\",\n \"score\": \"0.66118425\",\n \"text\": \"def perform_economic_analysis(self):\\n self.prepare_parameters()\\n self.compute_costs_benefits()\\n self.compute_economic_indicators()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1ff87a5494b0feca36e366c0f1b8f2\",\n \"score\": \"0.6390947\",\n \"text\": \"def compute_costs(self, car):\\n self.compute_price(car)\\n self.compute_commission()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c6505625f6dc26bed4e28fe260b9f8\",\n \"score\": \"0.63891405\",\n \"text\": \"def effective():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302d40f575dbe32480bc86f8ed1a060a\",\n \"score\": \"0.6338564\",\n \"text\": \"def cost ( self, x ):\\n \\n x_dict = self._unpack_to_dict ( x )\\n # Store the parameter dictionary in case we need it later for e.g.\\n # crossvalidation\\n self.parameter_dictionary = x_dict\\n aggr_cost = 0\\n aggr_der_cost = x*0.0\\n self.cost_components = {}\\n start_time = time.time()\\n \\n# import os\\n# c_dir = os.getcwd()\\n# f_obj = open(c_dir + '/object_func.dat', 'w')\\n\\n for op_name, the_op in self.operators.iteritems():\\n \\n cost, der_cost = the_op.der_cost ( x_dict, self.state_config )\\n #!!!Max Edit!!!\\n #print 'cost before weighting = ', cost\\n cost = cost / the_op.cost_weight\\n der_cost = der_cost / the_op.cost_weight\\n self.f_obj.write('%.4f '%(cost))\\n #!!!!!!!!!!!!!!\\n aggr_cost = aggr_cost + cost\\n aggr_der_cost = aggr_der_cost + der_cost\\n self.cost_components[op_name] = der_cost\\n if self.verbose:\\n print \\\"\\\\t%s %8.3e\\\" % ( op_name, cost )\\n self.f_obj.write('%.4f\\\\n'%(aggr_cost))\\n self.the_cost = aggr_cost\\n\\n\\n \\n if self.verbose:\\n print \\\"Total cost: %8.3e\\\" % aggr_cost\\n print 'Elapsed: %.2f seconds' % (time.time() - start_time)\\n \\n \\n return aggr_cost, aggr_der_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c555691b1c809177c76d7c47ebe502fe\",\n \"score\": \"0.63324463\",\n \"text\": \"def caluculate_cost(self):\\n self.error = self.hypothesis - self.y\\n self.cost = np.dot(self.error.transpose(), self.error) / (2 * self.m)\\n self.cost_record.append(self.cost[0]) # add a number not a list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80776e9469e0fcc8cf102e5af08d354b\",\n \"score\": \"0.62639254\",\n \"text\": \"def compute_cost(AL, Y):\\n # todo\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e142ca3068b634ca558400654cf45c\",\n \"score\": \"0.62561446\",\n \"text\": \"def main():\\n\\titem_cost = calculate_discount(100, 10, 30)\\n\\tprint item_cost\\n\\n\\titem_cost = calculate_discount(-100, 10, 30)\\n\\tprint item_cost\\n\\n\\titem_cost = calculate_discount(100, 20000, 30)\\n\\tprint item_cost\\n\\n\\titem_cost = calculate_discount(100, 10, 1000)\\n\\tprint item_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3db9fdc5eda307ac39ebd049f42910\",\n \"score\": \"0.6250568\",\n \"text\": \"def compute_cost(self, previous_state, state, label_to_id, action, others={}):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21f7213a26f41bc79ad5cf3ae86ccdb\",\n \"score\": \"0.62449455\",\n \"text\": \"def AB_sensitivity(A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base, num_options):\\n itin = 0\\n A_cost_record = []\\n B_cost_record = []\\n x_optimum_record = []\\n x_optimum_index_record = []\\n value_optimum_record = []\\n # Largest increase or decrease that will be tested\\n p1 = 0.6\\n p2 = 0.5\\n # Step by which will by value of cost looped over\\n s1 = 5\\n s2 = 5\\n A_cost_range = np.arange(np.rint(A_cost * (1 - p1)), A_cost * (1 + p1) + s1, s1)\\n B_cost_range = np.arange(B_cost * (1 - p2), B_cost * (1 + p2) + s2, s2)\\n print(A_cost_range, B_cost_range)\\n for A_cost_i in A_cost_range:\\n for B_cost_i in B_cost_range:\\n print(A_cost_i, B_cost_i)\\n itin += 1\\n x_optimum = optimise_total_costs(A_quantity, B_quantity, C_change, A_cost_i, B_cost_i, C_cost, C_base, num_options)\\n value_optimum = evaluate_total_costs(x_optimum, A_quantity, B_quantity, C_change, A_cost_i, B_cost_i, C_cost, C_base)\\n A_cost_record.append(A_cost_i)\\n B_cost_record.append(B_cost_i)\\n x_optimum_record.append(x_optimum)\\n x_optimum_index_record.append((np.nonzero(x_optimum) + np.array([1,1,1])).tolist())\\n value_optimum_record.append(value_optimum) \\n print(itin)\\n return pd.DataFrame.from_dict({\\\"A_cost\\\": A_cost_record, \\\"B_cost\\\": B_cost_record, \\\"x_optimum\\\": x_optimum_record, \\\"options_selected\\\": x_optimum_index_record, \\\"costs\\\": value_optimum_record})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074bb595116024602281aa80798ea585\",\n \"score\": \"0.6206656\",\n \"text\": \"def test_compute_cost(self):\\n\\n # Definition of a test decision variables vector\\n states = np.array([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2]])\\n controls = np.array([[0, 0, 0, 0, 0, ]])\\n controls_col = np.empty(0)\\n f_prm = np.array([1., 2.])\\n t_i, t_f = 0, 10\\n\\n dvv = utils.make_decision_variable_vector(\\n states, controls, controls_col, t_i, t_f, f_prm, self.cost_obj.options)\\n\\n # Cost computation\\n cpt_cost = self.cost_obj.compute_cost(dvv)\\n cor_cost = 52.0\\n\\n # Equality verification\\n self.assertEqual(cpt_cost, cor_cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540146ce3f2b80bad17c4f685ed33aac\",\n \"score\": \"0.6203327\",\n \"text\": \"def apply_cost_benefit(change_posterior, true_positive=1.0, true_negative=1.0, false_negative=1.0, false_positive=1.0):\\n\\n lick_benefit = change_posterior * true_positive\\n lick_cost = change_posterior * false_positive\\n no_lick_benefit = (1 - change_posterior) * true_negative\\n no_lick_cost = (1 - change_posterior) * false_negative\\n\\n prob_lick = (lick_benefit + no_lick_cost - lick_cost - no_lick_benefit) / (true_positive +\\n true_negative + false_negative + false_positive)\\n\\n return prob_lick\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe2c270c17bf5cb0fe1f467f56d4bf1\",\n \"score\": \"0.6193927\",\n \"text\": \"def cost_individual_measurements(self,studies_results):\\n costs = []\\n for study,study_results in studies_results.items():\\n for ID, ID_results in study_results.items():\\n for target,target_results in ID_results.items():\\n for i in range(len(target_results)):\\n sim = target_results[i]\\n exp = self.observations[study][ID]['expectations'][target]['mean'][i]\\n mean = (sim+exp)/2\\n diff = abs(sim-exp)\\n error = diff/mean\\n costs.append(error)\\n return np.mean(costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c089c3e6e5812ff529af42708668159\",\n \"score\": \"0.61890495\",\n \"text\": \"def assess_effects(cost_types, maps_dir, cost_correction):\\r\\n measure_names = [f for f in os.listdir(maps_dir)\\r\\n if os.path.isdir(os.path.join(maps_dir,f))]\\r\\n\\r\\n cost_list = []\\r\\n std_list = []\\r\\n for msr_name in measure_names[:]:\\r\\n print(msr_name)\\r\\n measure_pathname = os.path.join(maps_dir, msr_name)\\r\\n measure = read_measure(measure_pathname)\\r\\n cost_all_types, std_all_types = assess_costs(cost_types, measure)\\r\\n cost_list.append(cost_all_types)\\r\\n std_list.append(std_all_types)\\r\\n\\r\\n cost_all_msrs = pd.concat(cost_list)\\r\\n std_all_msrs = pd.concat(std_list)\\r\\n cost_all_msrs.index.name = 'id'\\r\\n std_all_msrs.index.name = 'id'\\r\\n return cost_all_msrs, std_all_msrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c53ba49c90ade5335b54e27541055df\",\n \"score\": \"0.6158172\",\n \"text\": \"def compute_cost_symb(self, pred_batch, tg_batch):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59270fa265ad5a1baf08da149f09135f\",\n \"score\": \"0.6155377\",\n \"text\": \"def uniformCostSearch(problem):\\n\\n # *** Your Code Here ***\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ece7abf40c5c76d81a74e6f8241667\",\n \"score\": \"0.6143789\",\n \"text\": \"def calculateEfficiency(self,n_triggers,n_valid):\\n efficiency, error = 0, 0\\n #TODO implement\\n return efficiency, error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626969664782890d6e0566766e458b36\",\n \"score\": \"0.61013293\",\n \"text\": \"def test(self,dataset):\\n outputs = self.use(dataset)\\n costs = zeros(len(dataset),1)\\n for o,c in zip(outputs,costs):\\n c[0] = -o[0]\\n\\n return outputs,costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41fd3885ad5b121046c14c10fb7c356\",\n \"score\": \"0.609207\",\n \"text\": \"def uniformCostSearch(problem):\\n \\\"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\\\"\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ffc8449bf9be685f88e3bbdd658389\",\n \"score\": \"0.6090788\",\n \"text\": \"def calculate_logistics_cost(*, logistics: models.OrderLogistics, status: str) -> dict:\\n\\n logistics_estimated_cost = 0\\n cost_food_internal = 0\\n cost_food_external = 0\\n cost_beverage = 0\\n cost_labor = 0\\n cost_rentals = 0\\n\\n if status == \\\"review\\\":\\n addons = logistics.addons_staff.all().filter(package__isnull=True)\\n elif status == \\\"is_active\\\":\\n addons = logistics.addons_staff.is_active().filter(package__isnull=True)\\n elif status == \\\"is_draft\\\":\\n addons = logistics.addons_staff.is_draft().filter(package__isnull=True)\\n else:\\n addons = logistics.addons_staff.all().filter(package__isnull=True)\\n\\n combined_per_person_cost = 0\\n combined_fixed_cost = 0\\n\\n # ––– ADDONS ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––\\n addons_staff = []\\n\\n for addon in addons:\\n temp = {}\\n\\n temp[\\\"id\\\"] = addon.id\\n temp[\\\"name\\\"] = addon.name\\n temp[\\\"price_numeric\\\"] = addon.price_numeric\\n temp[\\\"price_numeric_fixed\\\"] = addon.price_numeric_fixed\\n\\n addon_cost = (\\n addon.price_numeric * logistics.guest_count\\n ) + addon.price_numeric_fixed\\n temp[\\\"cost\\\"] = addon_cost\\n\\n temp[\\\"cost_type_id\\\"] = addon.cost_type_id\\n temp[\\\"package_id\\\"] = addon.package_id\\n temp[\\\"logistics_id\\\"] = addon.logistics_id\\n\\n if addon.note:\\n temp[\\\"note\\\"] = addon.note\\n else:\\n if (addon.price_numeric > 0) and (addon.price_numeric_fixed > 0):\\n temp[\\\"price_descriptive\\\"] = \\\"{0} per person, plus {1} fee\\\".format(\\n addon.price_numeric, addon.price_numeric_fixed\\n )\\n elif addon.price_numeric == 1:\\n temp[\\\"price_descriptive\\\"] = \\\"{0} guest, at {1} per person\\\".format(\\n logistics.guest_count, addon.price_numeric\\n )\\n elif addon.price_numeric > 1:\\n temp[\\\"price_descriptive\\\"] = \\\"{0} guests, at {1} per person\\\".format(\\n logistics.guest_count, addon.price_numeric\\n )\\n else:\\n temp[\\\"price_descriptive\\\"] = \\\"{0} fee\\\".format(addon.price_numeric_fixed)\\n\\n addons_staff.append(temp)\\n\\n if addon.cost_type.name == \\\"Food (Internal)\\\":\\n cost_food_internal += addon_cost\\n elif addon.cost_type.name == \\\"Food (External)\\\":\\n cost_food_external += addon_cost\\n elif addon.cost_type.name == \\\"Alcohol and NA Beverages\\\":\\n cost_beverage += addon_cost\\n elif addon.cost_type.name == \\\"Labor\\\":\\n cost_labor += addon_cost\\n elif addon.cost_type.name == \\\"Equipment and Rentals\\\":\\n cost_rentals += addon_cost\\n\\n return {\\n \\\"addons_staff\\\": addons_staff,\\n \\\"cost_food_internal\\\": cost_food_internal,\\n \\\"cost_food_external\\\": cost_food_external,\\n \\\"cost_beverage\\\": cost_beverage,\\n \\\"cost_labor\\\": cost_labor,\\n \\\"cost_rentals\\\": cost_rentals,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c4afa23459b865015cc200daf19ec8\",\n \"score\": \"0.6086555\",\n \"text\": \"def efficiency_cost(self, waypt):\\n\\t\\tprev_waypt = waypt[0:7]\\n\\t\\tcurr_waypt = waypt[7:14]\\n\\t\\tfeature = self.environment.efficiency_features(curr_waypt,prev_waypt)\\n\\t\\tfeature_idx = self.feat_list.index('efficiency')\\n\\t\\treturn feature*self.weights[feature_idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf0d434601c31f7cd4245cf022e626b\",\n \"score\": \"0.6077921\",\n \"text\": \"def power_analysis_does_yhat_reduce_effect_of_income(non_image_dataset, pval_thresh, n_iterates, dv):\\n\\n all_results = []\\n assert dv in ['koos_pain_subscore', 'womac_pain_subscore']\\n knee_pain_scores = non_image_dataset.processed_dataframes['all_knee_pain_scores']\\n clinical_ratings = non_image_dataset.processed_dataframes['kxr_sq_bu']\\n df_to_use = get_baseline_scores(get_combined_dataframe(non_image_dataset, clinical_ratings)) \\n print(\\\"Length of baseline data\\\")\\n print(len(df_to_use))\\n iv = 'binarized_income_at_least_50k'\\n\\n pain_subscores = ['koos_pain_subscore', 'womac_pain_subscore']\\n assert dv in pain_subscores\\n for k in pain_subscores:\\n df_to_use[k] = (df_to_use[k] - df_to_use[k].mean()) / df_to_use[k].std()\\n\\n clinical_controls = '+'.join(['C(%s)' % a for a in non_image_dataset.clinical_xray_semiquantitative_cols])\\n\\n for noise_param in [3, 5, 8, 10]:\\n for disparity_param in [.2]:\\n print(\\\"Noise param: %2.3f; disparity param: %2.3f\\\" % (noise_param, disparity_param))\\n # as disparity param increases in magnitude, yhat gets more correlated with SES. \\n # as noise_param increases in magnitude, yhat gets less correlated with y and SES. \\n\\n if dv == 'womac_pain_subscore':\\n # higher scores indicate worse pain on the womac\\n # so if you have a higher SES we want you to have lower predicted Yhat. \\n disparity_param = -disparity_param\\n df_to_use['yhat'] = df_to_use[dv] + df_to_use[iv] * disparity_param + noise_param * np.random.randn(len(df_to_use),)\\n df_to_use = df_to_use.dropna(subset=[dv, iv])\\n print(df_to_use[[iv, 'yhat']].groupby(iv).agg(['mean', 'std']))\\n for subset_size in list(range(250, 2000, 250)):\\n for _ in range(n_iterates):\\n people = set(random.sample(non_image_dataset.all_ids, subset_size))\\n people_idxs = df_to_use['id'].map(lambda x:x in people).values\\n\\n model_without_yhat = sm.OLS.from_formula('%s ~ %s + %s' % (dv, iv, clinical_controls), df_to_use.loc[people_idxs]).fit()\\n model_with_yhat = sm.OLS.from_formula('%s ~ %s + %s + yhat' % (dv, iv, clinical_controls), df_to_use.loc[people_idxs]).fit()\\n\\n\\n change_in_iv_coef = model_with_yhat.params[iv] - model_without_yhat.params[iv]\\n # Note: \\n ## To get estimate of noise variance for a model, the following 3 are all the same.\\n # this is sigma_hat SQUARED, not sigma_hat.\\n # 1. np.sum(model_without_yhat.resid ** 2) / model_without_yhat.df_resid)\\n # 2. model_without_yhat.mse_resid\\n # 3. model_without_yhat.scale\\n\\n squared_error_on_change = (model_with_yhat.bse[iv] ** 2 - \\n model_without_yhat.bse[iv] ** 2 * model_with_yhat.scale / model_without_yhat.scale)\\n assert squared_error_on_change > 0\\n error_on_change = np.sqrt(squared_error_on_change)\\n zscore = change_in_iv_coef/error_on_change\\n if (model_with_yhat.params[iv] > 0) != (model_without_yhat.params[iv] > 0): \\n # if the sign of the coefficient changes that is weird. It should just get smaller. \\n print(\\\"Warning: coefficient changed sign from %2.3f to %2.3f\\\" % (model_without_yhat.params[iv], model_with_yhat.params[iv]))\\n results = {'r2_with_yhat':model_with_yhat.rsquared, \\n 'r2_without_yhat':model_without_yhat.rsquared, \\n 'beta_with_yhat':model_with_yhat.params[iv], \\n 'beta_without_yhat':model_without_yhat.params[iv], \\n 'change_in_IV_coef':change_in_iv_coef, \\n 'error_on_change':error_on_change, \\n 'zscore':zscore, \\n 'p_change':2*(1 - norm.cdf(abs(zscore))), # two-tailed p-value. \\n 'yhat_iv_corr':pearsonr(df_to_use['yhat'], df_to_use[iv])[0], \\n 'yhat_dv_corr':pearsonr(df_to_use['yhat'], df_to_use[dv])[0], \\n 'subset_size':subset_size}\\n all_results.append(results)\\n \\n # now make plot. \\n all_results = pd.DataFrame(all_results)\\n for iv_corr in sorted(list(set(all_results['yhat_iv_corr'])), key=lambda x:abs(x)):\\n for dv_corr in sorted(list(set(all_results['yhat_dv_corr'])), key=lambda x:abs(x)):\\n idxs = ((all_results['yhat_iv_corr'] == iv_corr) & \\n (all_results['yhat_dv_corr'] == dv_corr))\\n if idxs.sum() == 0:\\n continue\\n x = []\\n y = []\\n for subset_size in sorted(list(set(all_results['subset_size']))):\\n x.append(subset_size)\\n results = all_results.loc[idxs & (all_results['subset_size'] == subset_size), \\n 'p_change'] < pval_thresh\\n assert len(results) == n_iterates\\n y.append(results.mean())\\n \\n plt.plot(x, y, label='IV (income/educ): r=%2.3f, DV (pain): r=%2.3f' % (abs(iv_corr), \\n abs(dv_corr)))\\n \\n plt.legend(bbox_to_anchor=(1.1, 1.05))\\n plt.xlabel(\\\"Test set size\\\")\\n plt.ylabel(\\\"Fraction of simulations\\\\nwhich are significant @ $p = %2.2f$\\\" % pval_thresh)\\n plt.title(\\\"Significance test is for change in income/educ coef\\\")\\n plt.savefig('power_analysis_does_yhat_reduce_effect_of_ses.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973ba84c794cc67ec691e1b56877c001\",\n \"score\": \"0.6056363\",\n \"text\": \"def _calculate_benefit_cost(self):\\n # merge final incidence rate\\n self.frame = self.frame.merge(\\n self.incidence_rates[[\\\"AGE_ATTAINED\\\", \\\"INCIDENCE_RATE\\\"]],\\n how=\\\"left\\\",\\n on=[\\\"AGE_ATTAINED\\\"],\\n )\\n\\n # add modeled claim cost (i.e., DLR)\\n self.frame[\\\"DLR\\\"] = [df[\\\"DLR\\\"].iat[0] for df in self.modeled_claim_cost.values()]\\n\\n # calculate benefit cost\\n self.frame[\\\"BENEFIT_COST\\\"] = self.frame[\\\"DLR\\\"] * self.frame[\\\"INCIDENCE_RATE\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2ff2060be58e8ccdaa2b60ecfe3b59\",\n \"score\": \"0.6026856\",\n \"text\": \"def CSAnalysis(self,env):\\r\\n # Creates the attribute Generality List\\r\\n bitLength = env.getAttributeLength()\\r\\n wildCount = self.characterizePop()\\r\\n self.attributeGenList = self.condenseToAttributes(wildCount,bitLength) \\r\\n \\r\\n genSum = 0\\r\\n fitGenSum = 0\\r\\n fitSum = 0 \\r\\n for cl in self.clSet:\\r\\n genSum = genSum + cl.getClassifierGenerality() * cl.getNumerosity()\\r\\n fitGenSum = fitGenSum + cl.getClassifierGenerality() * cl.getNumerosity() * cl.getFitness()\\r\\n fitSum = fitSum + cl.getNumerosity() * cl.getFitness()\\r\\n \\r\\n self.generality = genSum / float(self.numerositySum)\\r\\n self.weightedGenerality = fitGenSum / float(fitSum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9712975ef61c2f16232c02419f45f844\",\n \"score\": \"0.6002524\",\n \"text\": \"def compute_cost(self, X, Y_true):\\n Y_pred, act_h = self.evaluate(X)\\n num_exampl = X.shape[1]\\n rglz = self.labda * np.sum(self.w1**2) + self.labda * np.sum(self.w2**2)\\n cross_ent = self.cross_entropy(Y_true, Y_pred)\\n cost = cross_ent / num_exampl + rglz\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b8c836e191289499ea72657070fbbb2\",\n \"score\": \"0.5991674\",\n \"text\": \"def _compute_total_cost_maint(self):\\n total = 0\\n for data in self:\\n for data_1 in data.cost_id:\\n total += data_1.cost\\n data.main_cost = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a4fd8f0210f120661deee71f47867b\",\n \"score\": \"0.59876204\",\n \"text\": \"def CostFunction(xo, grad):\\n\\n grad[:] = 0.\\n Set.J[:] = 0.\\n\\n ### Minimize PSF = xo\\n if Set.costfunction_type == 0:\\n\\n #<< testing and debugging block >>#\\n #try:\\n #\\n # xo /= N.sum(xo) ## normalize and scale PSF\\n #except:\\n #\\n # pass\\n\\n grad.shape = xo.shape = Set.shape\\n fftw.rfft(a=xo, af=Set.OTF, inplace=False)\\n fn = Calculate_h_J_and_gradJ(grad) #@\\n\\n ### Minimize Object = 'xo'\\n elif Set.costfunction_type == 1:\\n\\n #<< testing and debugging line >>#\\n #Set.J[0] = Set.J[1] = 0.\\n \\n grad.shape = xo.shape = Set.shape\\n fftw.rfft(a=xo, af=Set.OBJECT, inplace=False)\\n fn = Calculate_o_J_and_gradJ(xo, grad) #@\\n\\n ### Minimize PSF = 'xo' in the Presence of Nobjects\\n elif Set.costfunction_type == 2:\\n\\n #<< testing and debugging block >>#\\n #Set.J[0] = Set.J[2] = 0.\\n #try:\\n #\\n # xo /= N.sum(xo) ## normalize and scale PSF\\n #except:\\n #\\n # pass\\n\\n grad.shape = xo.shape = Set.shape\\n fn = 0.\\n fftw.rfft(a=xo, af=Set.OTF, inplace=False)\\n\\n if Set.memory_usage_level > 0:\\n\\n for i in range(len(Set.image_list)):\\n\\n Set.image = Set.image_array[i]\\n Set.inv_w = Set.inv_w_array[i]\\n Set.OBJECT = Set.OBJECT_array[i]\\n ## cost function is calculated as sum over each PSF estimate\\n fn += Calculate_h_J_and_gradJ(grad) #@\\n else:\\n\\n for i in range(len(Set.image_list)):\\n\\n Set.image = Set.image_array[i]\\n Set.inv_w = Set.inv_w_array[i]\\n fftw.rfft(a=Set.object_array[i], af=Set.OBJECT, inplace=False)\\n ## cost function is calculated as sum over each PSF estimate\\n fn += Calculate_h_J_and_gradJ(grad) #@\\n\\n ### Minimize Object = 'xo' in the Presence of Npsfs\\n elif Set.costfunction_type == 3:\\n\\n #<< testing and debugging line >>#\\n #Set.J[0] = Set.J[1] = 0.\\n \\n grad.shape = xo.shape = Set.shape\\n fn = 0.\\n fftw.rfft(a=xo, af=Set.OBJECT, inplace=False)\\n\\n if Set.memory_usage_level > 0:\\n\\n for i in range(len(Set.image_list)):\\n\\n Set.image = Set.image_array[i]\\n Set.inv_w = Set.inv_w_array[i]\\n Set.inv_theta = Set.inv_theta_center_array[i]\\n Set.lambda_object = Set.lambda_object_Narray[i]\\n Set.mu = Set.mu_Narray[i]\\n Set.OTF = Set.OTF_array[i]\\n ## cost function is calculated as sum over each object estimate\\n fn += Calculate_o_J_and_gradJ(xo, grad) #@\\n else:\\n\\n for i in range(len(Set.image_list)):\\n\\n Set.image = Set.image_array[i]\\n Set.inv_w = Set.inv_w_array[i]\\n Set.inv_theta = Set.inv_theta_center_array[i]\\n Set.lambda_object = Set.lambda_object_Narray[i]\\n Set.mu = Set.mu_Narray[i]\\n fftw.rfft(a=Set.PSF_array[i], af=Set.OTF,\\n inplace=False)\\n ## cost function is calculated as sum over each object estimate\\n fn += Calculate_o_J_and_gradJ(xo, grad) #@\\n\\n#<< vvv BELOW IS TEMPORARY UNTIL WE INTEGRATE IT WITH SI CODE vvv >>#\\n ### Minimize Object = 'xo' for Structured Illumination\\n elif Set.costfunction_type == 4:\\n\\n grad.shape = xo.shape = Set.shape\\n #fftw.rfft(a=xo,af=Set.OBJECT, inplace=False)\\n fn = Calculate_o_J_and_gradJ_SI(xo, grad) #@\\n\\n ### Minimize PSF = 'xo' for Structured Illumination\\n elif Set.costfunction_type == 5:\\n\\n grad.shape = xo.shape = Set.shape_small\\n fftw.rfft(a=xo, af=Set.OTF_array[Set.PSForder], inplace=False)\\n fn = Calculate_h_J_and_gradJ_SI(grad) #@\\n#<< ^^^^^^ >>#\\n\\n else:\\n\\n print(\\\"Error!! Check `type` input flag!\\\")\\n\\n fn = 0\\n\\n iqflag = 0 # dummy flag in this case\\n\\n return (fn, iqflag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e0650bb264d435ea71f6dd2bd5264ca\",\n \"score\": \"0.5979624\",\n \"text\": \"def asset_calcs(params, asset_data):\\n # grab the constant values from the parameters dictionary\\n inflation_rate = params['inflation rate']\\n stat_tax = params['tax rate']\\n discount_rate = params['discount rate']\\n save_rate = params['return to savers']\\n r_prime = params['after-tax rate']\\n inv_credit = params['inv_credit']\\n w = params['prop tax']\\n z = params['depr allow']\\n Y_v = params['Y_v']\\n phi = params['phi']\\n expense_inventory = params['expense_inventory']\\n financing_list = params['financing_list']\\n entity_list = params['entity_list']\\n asset_dict = params['asset_dict']\\n major_asset_groups = params['major_asset_groups']\\n\\n # initialize dataframe - start w/ z output\\n output_by_asset = z.copy()\\n\\n # Drop religious buildings and IP of nonprofits\\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\\n 'Religious'].copy()\\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\\n 'Private universities and colleges'].copy()\\n output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\\n 'Other nonprofit institutions'].copy()\\n\\n # calculate the cost of capital, metr, mettr\\n for i in range(save_rate.shape[0]):\\n for j in range(save_rate.shape[1]):\\n output_by_asset['rho' + entity_list[j] + financing_list[i]] = \\\\\\n ((((discount_rate[i, j] - inflation_rate) +\\n output_by_asset['delta']) *\\n (1 - inv_credit - (stat_tax[j] *\\n output_by_asset['z' +\\n entity_list[j] +\\n financing_list[i]])) /\\n (1 - stat_tax[j])) + w - output_by_asset['delta'])\\n if not expense_inventory:\\n output_by_asset.loc[output_by_asset['Asset Type'] ==\\n \\\"Inventories\\\", 'rho' +\\n entity_list[j] +\\n financing_list[i]] = \\\\\\n ((phi * (((1 / Y_v) *\\n np.log((np.exp(discount_rate[i, j] * Y_v) -\\n stat_tax[j]) / (1 - stat_tax[j]))) -\\n inflation_rate)) +\\n ((1 - phi) * (((1 / Y_v) *\\n np.log((np.exp((discount_rate[i, j] -\\n inflation_rate) * Y_v) -\\n stat_tax[j]) /\\n (1 - stat_tax[j]))))))\\n output_by_asset['metr' + entity_list[j] + financing_list[i]] = \\\\\\n ((output_by_asset['rho' + entity_list[j] + financing_list[i]] -\\n (r_prime[i, j] - inflation_rate)) /\\n output_by_asset['rho' + entity_list[j] + financing_list[i]])\\n output_by_asset['mettr' + entity_list[j] + financing_list[i]] = \\\\\\n ((output_by_asset['rho' + entity_list[j] + financing_list[i]] -\\n save_rate[i, j]) /\\n output_by_asset['rho' + entity_list[j] + financing_list[i]])\\n\\n # create asset category variable\\n output_by_asset['asset_category'] = output_by_asset['Asset Type']\\n output_by_asset['asset_category'].replace(asset_dict, inplace=True)\\n\\n # Drop IP (for now - need to better figure out how depreciate)\\n # output_by_asset = output_by_asset[output_by_asset['asset_category'] !=\\n # 'Intellectual Property'].copy()\\n # output_by_asset = output_by_asset[output_by_asset['Asset Type'] !=\\n # 'Land'].copy()\\n\\n # merge in dollar value of assets - sep for corp and non-corp\\n # should be able to do this better with pivot table\\n bea_corp = asset_data[asset_data['tax_treat'] == 'corporate'].copy()\\n bea_noncorp = asset_data[asset_data['tax_treat'] == 'non-corporate'].copy()\\n bea_corp_assets = (pd.DataFrame({'assets': bea_corp.\\n groupby('bea_asset_code')['assets'].\\n sum()})).reset_index()\\n bea_noncorp_assets = (pd.DataFrame({'assets': bea_noncorp.\\n groupby('bea_asset_code')['assets'].\\n sum()})).reset_index()\\n bea_corp_assets.rename(columns={\\\"assets\\\": \\\"assets_c\\\"}, inplace=True)\\n bea_noncorp_assets.rename(columns={\\\"assets\\\": \\\"assets_nc\\\"}, inplace=True)\\n\\n output_by_asset = pd.merge(output_by_asset, bea_corp_assets,\\n how='left', left_on=['bea_asset_code'],\\n right_on=['bea_asset_code'],\\n left_index=False, right_index=False,\\n sort=False, copy=True)\\n output_by_asset = pd.merge(output_by_asset, bea_noncorp_assets,\\n how='left', left_on=['bea_asset_code'],\\n right_on=['bea_asset_code'],\\n left_index=False, right_index=False,\\n sort=False, copy=True)\\n\\n # Add major asset groups\\n output_by_asset['major_asset_group'] = output_by_asset['Asset Type']\\n output_by_asset['major_asset_group'].replace(major_asset_groups,\\n inplace=True)\\n\\n # Now compute METR and other output by major asset group\\n # create weighted averages by major asset group/tax treatment\\n by_major_asset =\\\\\\n pd.DataFrame({'delta': output_by_asset.\\n groupby(['major_asset_group'] ).\\n apply(wavg, \\\"delta\\\", \\\"assets_c\\\")}).reset_index()\\n corp_list = ['z_c', 'z_c_d', 'z_c_e', 'rho_c', 'rho_c_d', 'rho_c_e']\\n noncorp_list = ['z_nc', 'z_nc_d', 'z_nc_e', 'rho_nc', 'rho_nc_d',\\n 'rho_nc_e']\\n for item in corp_list:\\n by_major_asset[item] =\\\\\\n (pd.DataFrame({item: output_by_asset.\\n groupby(['major_asset_group']).\\n apply(wavg, item, \\\"assets_c\\\")})).reset_index()[item]\\n for item in noncorp_list:\\n by_major_asset[item] =\\\\\\n (pd.DataFrame({item: output_by_asset.\\n groupby(['major_asset_group']).\\n apply(wavg, item, \\\"assets_nc\\\")})).reset_index()[item]\\n\\n by_major_asset['assets_c'] =\\\\\\n (pd.DataFrame({'assets_c': output_by_asset.\\n groupby(['major_asset_group'])['assets_c'].\\n sum()})).reset_index()['assets_c']\\n by_major_asset['assets_nc'] =\\\\\\n (pd.DataFrame({'assets_nc': output_by_asset.\\n groupby(['major_asset_group'])['assets_nc'].\\n sum()})).reset_index()['assets_nc']\\n\\n # calculate the cost of capital, metr, mettr\\n for i in range(save_rate.shape[0]):\\n for j in range(save_rate.shape[1]):\\n by_major_asset['metr' + entity_list[j] + financing_list[i]] = \\\\\\n ((by_major_asset['rho' + entity_list[j] + financing_list[i]] -\\n (r_prime[i, j] - inflation_rate)) /\\n (by_major_asset['rho' + entity_list[j] + financing_list[i]]))\\n by_major_asset['mettr' + entity_list[j] + financing_list[i]] = \\\\\\n ((by_major_asset['rho' + entity_list[j] + financing_list[i]] -\\n save_rate[i, j]) /\\n (by_major_asset['rho' + entity_list[j] + financing_list[i]]))\\n\\n\\n\\n # make asset type = major asset group in by_major_asset\\n by_major_asset['Asset'] = by_major_asset['major_asset_group']\\n by_major_asset['Asset Type'] = by_major_asset['major_asset_group']\\n\\n # make calculation for overall rates\\n corp_list = ['z_c', 'z_c_d', 'z_c_e', 'rho_c', 'rho_c_d', 'rho_c_e']\\n noncorp_list = ['z_nc', 'z_nc_d', 'z_nc_e', 'rho_nc', 'rho_nc_d',\\n 'rho_nc_e']\\n overall = pd.DataFrame({'delta': ((output_by_asset['delta'] *\\n output_by_asset['assets_c']).sum()\\n / output_by_asset['assets_c'].sum())},\\n index=[0])\\n overall['assets_c'] = output_by_asset['assets_c'].sum()\\n overall['assets_nc'] = output_by_asset['assets_nc'].sum()\\n # overall =\\\\\\n # pd.DataFrame({'delta_nc': ((output_by_asset['delta'] *\\n # output_by_asset['assets_nc']).sum()\\n # / output_by_asset['assets_nc'].\\n # sum())}).reset_index()\\n overall['Asset'] = 'All Investments'\\n overall['Asset Type'] = 'All Investments'\\n overall['major_asset_group'] = 'All Investments'\\n for item in corp_list:\\n overall[item] = ((output_by_asset[item] *\\n output_by_asset['assets_c']).sum() /\\n output_by_asset['assets_c'].sum())\\n for item in noncorp_list:\\n overall[item] = ((output_by_asset[item] *\\n output_by_asset['assets_nc']).sum() /\\n output_by_asset['assets_nc'].sum())\\n for i in range(save_rate.shape[0]):\\n for j in range(save_rate.shape[1]):\\n overall['metr' + entity_list[j] + financing_list[i]] = \\\\\\n ((overall['rho' + entity_list[j] + financing_list[i]] -\\n (r_prime[i, j] - inflation_rate)) / (overall['rho' + entity_list[j] + financing_list[i]]))\\n overall['mettr' + entity_list[j] + financing_list[i]] = \\\\\\n ((overall['rho' + entity_list[j] + financing_list[i]] -\\n save_rate[i, j]) / (overall['rho' + entity_list[j] + financing_list[i]]))\\n\\n # append by_major_asset to output_by_asset\\n # drop asset types that are only one in major group\\n by_major_asset = by_major_asset[by_major_asset['major_asset_group'] !=\\n 'Inventories'].copy()\\n by_major_asset = by_major_asset[by_major_asset['major_asset_group'] !=\\n 'Land'].copy()\\n output_by_asset = (output_by_asset.append([by_major_asset,overall],\\n ignore_index=True)).copy().reset_index()\\n output_by_asset.drop('index', axis=1, inplace=True)\\n\\n # sort output_by_asset dataframe\\n # output_by_asset = (output_by_asset.sort_values(['Asset'], inplace=True)).copy().reset_index()\\n output_by_asset.sort_values(['Asset'], inplace=True)\\n output_by_asset.reset_index(drop=True, inplace=True)\\n\\n return output_by_asset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7bc35285f33797517960d42794584bf\",\n \"score\": \"0.5973494\",\n \"text\": \"def optimise_total_costs(A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base, num_options):\\n # Define the decision variable array\\n x = cp.Variable(10, boolean = True)\\n # Define constraints on decision variable\\n constraints = [0 <= x, x <= 1, sum(x) == num_options]\\n # Objective function \\n total_costs = cp.sum(A_cost * cp.multiply(A_quantity, x)) + cp.sum(B_cost * cp.multiply(B_quantity, x)) + (C_cost * - cp.minimum(C_base + cp.sum(cp.multiply(C_change, x)), 0))\\n # Goal of optimisation\\n objective = cp.Minimize(total_costs)\\n # Solve the optimisation problem \\n problem = cp.Problem(objective, constraints)\\n result = problem.solve(solver = cp.ECOS_BB)\\n return np.abs(np.rint(x.value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0057fd19d6a9f2a90b0e6e377a99b36f\",\n \"score\": \"0.59511423\",\n \"text\": \"def compute_cost_scores(techDB, system_size, app_type, target_cost):\\n if app_type == 'Power':\\n cost_scores = techDB[f'Cost at {system_size} ($/kW)'].apply(normalize_by_target, tc=target_cost).fillna(value=0)\\n elif app_type == 'Energy':\\n kWh_costs = techDB[f'Cost at {system_size} ($/kW)']/techDB['Discharge duration (hours)']\\n cost_scores = kWh_costs.apply(normalize_by_target, tc=target_cost).fillna(value=0)\\n\\n return cost_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e6ec4fe9066b49a77660fc0110bfc5\",\n \"score\": \"0.59428144\",\n \"text\": \"def compute_cost(self):\\n for task in self:\\n resource = self.env['resource.resource'].sudo().search(\\n [\\n ('user_id', '=', task.user_id.id)\\n ]\\n )\\n employee = self.env['hr.employee'].sudo().search(\\n [\\n ('resource_id', '=', resource.id)\\n ]\\n )\\n cost = 0\\n\\n if resource and employee and employee.contract_id:\\n hour_cost = employee.contract_id.wage\\n cost = hour_cost * task.planned_hours\\n\\n task.cost = cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb51a5c764917eb772158ad36523e0a\",\n \"score\": \"0.5937327\",\n \"text\": \"def __cost(training_set, test_set, knn_function, solution, adaptative=False):\\n\\n # Dado a solucao (configuracao de pesos/caracteristicas), transforma os , calcula o k-NN e retorna a taxa de acerto\\n return training_machine(training_set, test_set, knn_function, solution, adaptative)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c26acc33bf32985e58bdf75d9ca35d7d\",\n \"score\": \"0.59194976\",\n \"text\": \"def main():\\r\\n\\r\\n \\\"\\\"\\\"General input\\\"\\\"\\\"\\r\\n # final Time\\r\\n T = 100\\r\\n # Number of elements\\r\\n M = 10000\\r\\n # initial susceptible, infected and recovered\\r\\n S0, I0, R0 = 700, 165, 90\\r\\n # Number of people\\r\\n N = S0 + I0 + R0\\r\\n # birt and death rate, recover rate, and transmission coefficient\\r\\n v, gamma, beta = 0.41, 0.56, 0.002\\r\\n # the used tolerance for norm(u_{k+1}-u_k)_{L2} < tol\\r\\n tol = 1e-6\\r\\n\\r\\n # a invalid choice\\r\\n choice = -1\\r\\n # as long as choice is not 0, meaning end loop\\r\\n while choice != 0:\\r\\n choice = get_choice()\\r\\n\\r\\n if choice == 1:\\r\\n\\r\\n print(\\\"Running program with the original costfunctional\\\")\\r\\n # coefficient A_1 and A_2 in cost functional\\r\\n A1, A2 = 0.050, 0.075\\r\\n # coefficient in front of u (0 <= tau <= N)\\r\\n tau = 15\\r\\n # setup\\r\\n CP1 = ControlProblem(N, S0, I0, R0, A1, A2, tau, v, gamma, beta, T, M)\\r\\n\\r\\n u1, y1, p1, iterations, u_list1, fu_list1 = ProjectedGradientMethod(CP1, tol=tol)\\r\\n\\r\\n plotSIRu(CP1.CostFunc.tvec, y1, u1, \\\"SIR_u_CP1\\\", save=save, pdf=pdf)\\r\\n plotAdjoint(CP1.CostFunc.tvec, p1, \\\"Adjoint_CP1\\\", save=save, pdf=pdf)\\r\\n plotRel(CP1.CostFunc.tvec, u_list1, fu_list1, \\\"RelErrs_CP1\\\", save=save, pdf=pdf)\\r\\n\\r\\n elif choice == 2:\\r\\n\\r\\n print(\\\"Running program with the alternative costfunctional\\\")\\r\\n # coefficient A_1 and A_2 in cost functional\\r\\n A1, A2 = 0.050 / N * 2.95, 0.075 / N * 2.95\\r\\n # coefficient in front of u (0 <= tau <= N)\\r\\n tau = 14\\r\\n # setup\\r\\n CP2 = ControlProblem(N, S0, I0, R0, A1, A2, tau, v, gamma, beta, T, M, use_alt_costfunc=True)\\r\\n\\r\\n u2, y2, p2, iterations2, u_list2, fu_list2 = ProjectedGradientMethod(CP2, tol=tol)\\r\\n\\r\\n plotSIRu(CP2.CostFunc.tvec, y2, u2, \\\"SIR_u_CP2\\\", save=save, pdf=pdf)\\r\\n plotAdjoint(CP2.CostFunc.tvec, p2, \\\"Adjoint_CP2\\\", save=save, pdf=pdf)\\r\\n plotRel(CP2.CostFunc.tvec, u_list2, fu_list2, \\\"RelErrs_CP2\\\", save=save, pdf=pdf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d0ff4fe3974c90e86ac819d7fa5593\",\n \"score\": \"0.59185445\",\n \"text\": \"def uniform_cost_search(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5292eca13dca9bcdd914353417a4aa01\",\n \"score\": \"0.5918371\",\n \"text\": \"def costs(self):\\n raise ValueError('costs not implemented yet.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a931de7d216a262546c62a05a1e35bec\",\n \"score\": \"0.5909073\",\n \"text\": \"def efficiency(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4af3fcae96fc8ea1c48711de8849b1\",\n \"score\": \"0.5904193\",\n \"text\": \"def calc_costs(region, cost_structure, backhaul, backhaul_quantity,\\n global_parameters, country_parameters):\\n all_sites = region['upgraded_mno_sites'] + region['new_mno_sites']\\n geotype = region['geotype'].split(' ')[0]\\n\\n total_cost = 0\\n cost_by_asset = []\\n\\n for asset_name1, cost in cost_structure.items():\\n for asset_name2, type_of_cost in COST_TYPE.items():\\n if asset_name1 == asset_name2:\\n\\n if asset_name1 == 'backhaul' and backhaul_quantity == 0:\\n continue\\n\\n if asset_name1 == 'regional_node' and backhaul == 'microwave':\\n continue\\n\\n if asset_name1 == 'regional_edge' and backhaul == 'microwave':\\n continue\\n\\n if type_of_cost == 'capex_and_opex':\\n\\n cost = discount_capex_and_opex(cost, global_parameters,\\n country_parameters)\\n\\n if asset_name1 == 'single_sector_antenna':\\n cost = cost * global_parameters['sectorization']\\n\\n if asset_name1 == 'cots_processing':\\n\\n split = 'cots_processing_split_{}'.format(geotype)\\n quantity = int(math.ceil(all_sites / global_parameters[split]))\\n cost = cost * quantity\\n\\n if asset_name1 == 'low_latency_switch':\\n quantity = int(math.ceil(all_sites /\\n global_parameters['low_latency_switch_split']))\\n cost = cost * quantity\\n\\n if asset_name1 == 'rack':\\n quantity = int(math.ceil(all_sites /\\n global_parameters['rack_split']))\\n cost = cost * quantity\\n\\n if asset_name1 == 'cloud_power_supply_converter':\\n quantity = int(math.ceil(all_sites /\\n global_parameters['cloud_power_supply_converter_split']))\\n cost = cost * quantity\\n\\n if asset_name1 == 'cloud_backhaul':\\n quantity = int(math.ceil(all_sites /\\n global_parameters['cloud_backhaul_split']))\\n cost = (cost * quantity) / all_sites\\n\\n elif type_of_cost == 'capex':\\n cost = cost * (1 + (country_parameters['financials']['wacc'] / 100))\\n\\n elif type_of_cost == 'opex':\\n cost = discount_opex(cost, global_parameters, country_parameters)\\n\\n else:\\n return 'Did not recognize cost type'\\n\\n total_cost += cost\\n\\n cost_by_asset.append({\\n 'asset': asset_name1,\\n 'cost': cost,\\n })\\n\\n cost_by_asset = {item['asset']: item['cost'] for item in cost_by_asset}\\n\\n ran = [\\n 'single_sector_antenna',\\n 'single_remote_radio_unit',\\n 'io_fronthaul',\\n 'processing',\\n 'io_s1_x2',\\n 'control_unit',\\n 'cooling_fans',\\n 'distributed_power_supply_converter',\\n 'bbu_cabinet',\\n 'cots_processing',\\n 'io_n2_n3',\\n 'low_latency_switch',\\n 'rack',\\n 'cloud_power_supply_converter',\\n 'power',\\n ]\\n\\n backhaul_fronthaul = [\\n 'fronthaul',\\n 'backhaul',\\n 'cloud_backhaul',\\n ]\\n\\n civils = [\\n 'tower',\\n 'civil_materials',\\n 'transportation',\\n 'installation',\\n 'site_rental',\\n 'power_generator_battery_system',\\n ]\\n\\n core = [\\n 'regional_node',\\n 'regional_edge',\\n 'core_node',\\n 'core_edge',\\n ]\\n\\n ran_cost = 0\\n backhaul_fronthaul_cost = 0\\n civils_cost = 0\\n core_cost = 0\\n\\n for key, value in cost_by_asset.items():\\n if key in ran:\\n ran_cost += value\\n if key in backhaul_fronthaul:\\n backhaul_fronthaul_cost += value\\n if key in civils:\\n civils_cost += value\\n if key in core:\\n core_cost += value\\n\\n cost_by_asset = {\\n 'ran': ran_cost,\\n 'backhaul_fronthaul': backhaul_fronthaul_cost,\\n 'civils': civils_cost,\\n 'core_network': core_cost,\\n }\\n\\n return int(round(total_cost)), cost_by_asset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92eaf5e630ffe6455f6603e0b22c4001\",\n \"score\": \"0.58884954\",\n \"text\": \"def compute(self, config, budget, *args, **kwargs):\\n if config['data'] == 'pubmed':\\n config['num_out_heads'] = 8\\n config['weight-decay'] = 0.001\\n elif config['data'] in ['cora', 'citeseer']:\\n config['num_out_heads'] = 1\\n config['weight-decay'] = 5e-4\\n\\n config = self.add_config(config, budget)\\n config.__dict__.update(self.standard_config)\\n print(config)\\n best_test_perf, results = main_ctgr(config)\\n best_epoch, best_test_perf, best_valid_perf, att_on_noisy_edge = results\\n\\n best_results = ({\\n 'loss': 1 - best_test_perf,\\n 'info': {\\n 'best_epoch': best_epoch,\\n 'best_test_perf': best_test_perf,\\n 'best_valid_perf': best_valid_perf,\\n 'att_on_noisy_edge': att_on_noisy_edge,\\n }\\n })\\n all_results = {'config': config.__dict__, 'results': best_results}\\n self.append_pickle('tmp_results.txt', all_results)\\n return best_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe72cc07f067e57d4639564616c1866\",\n \"score\": \"0.5871775\",\n \"text\": \"def measure(g, beliefs):\\n#################CHANGES###############\\n #measure a specific subset of beliefs\\n beliefs = [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12)]\\n########################################\\n\\n res = {} # dictionary to collect measurements\\n\\n # build a matrix of who (rows) is susceptible to what beliefs (columns)\\n suscep = pd.DataFrame(index=g.nodes(), columns=[tuple(b) for b in beliefs])\\n for agent in g:\\n for belief in suscep.columns:\\n suscep.at[agent, belief] = susceptible(g, agent, belief)\\n res['% susceptible'] = suscep.mean().mean() # average susceptible fraction across all beliefs\\n\\n # build a matrix of who (rows) holds what beliefs (columns)\\n adopt = pd.DataFrame(index=g.nodes(), columns=[tuple(b) for b in beliefs])\\n for agent in g:\\n for belief in adopt.columns:\\n adopt.at[agent, belief] = g.nodes[agent]['M'].has_edge(*belief)\\n res['% adopted'] = adopt.mean().mean() # average adopting fraction across all beliefs\\n\\n n_agents = len(adopt.index)\\n corrs = adopt.astype(float).T.corr().mask(np.tri(n_agents, n_agents, 0, dtype='bool')).stack()\\n res['95% similarity'] = np.percentile(corrs, 95)\\n res['5% similarity'] = np.percentile(corrs, 5)\\n res['90% similarity'] = np.percentile(corrs, 90)\\n res['10% similarity'] = np.percentile(corrs, 10)\\n res['85% similarity'] = np.percentile(corrs, 85)\\n res['15% similarity'] = np.percentile(corrs, 15)\\n res['80% similarity'] = np.percentile(corrs, 80)\\n res['20% similarity'] = np.percentile(corrs, 20)\\n res['75% similarity'] = np.percentile(corrs, 75)\\n res['25% similarity'] = np.percentile(corrs, 25)\\n res['std similarity'] = np.std(corrs)\\n\\n pca = PCA(n_components=1)\\n pca.fit(adopt)\\n res['PC1 percent of variance'] = pca.explained_variance_ratio_[0] * 100\\n\\n###### Changes\\n # Measure difference from expected value\\n e95 = []\\n e5 = []\\n e10 = []\\n e15 = []\\n e20 = []\\n e25 = []\\n e75 = []\\n e80 = []\\n e85 = []\\n e90 = []\\n estd = []\\n ePC1 = []\\n for _ in range(100):\\n shuffle_adopt = pd.DataFrame()\\n for col in adopt.columns:\\n shuffle_adopt[col] = np.random.permutation(adopt[col].values)\\n\\n n_agents = len(shuffle_adopt.index)\\n corrs = shuffle_adopt.astype(float).T.corr().mask(np.tri(n_agents, n_agents, 0, dtype='bool')).stack()\\n e95.append(np.percentile(corrs, 95))\\n e5.append(np.percentile(corrs, 5))\\n\\n e10.append(np.percentile(corrs, 10))\\n e15.append(np.percentile(corrs, 15))\\n e20.append(np.percentile(corrs, 20))\\n e25.append(np.percentile(corrs, 25))\\n e75.append(np.percentile(corrs, 75))\\n e80.append(np.percentile(corrs, 80))\\n e85.append(np.percentile(corrs, 85))\\n e90.append(np.percentile(corrs, 90))\\n estd.append(np.std(corrs))\\n\\n pca = PCA(n_components=1)\\n pca.fit(shuffle_adopt)\\n ePC1.append(pca.explained_variance_ratio_[0] * 100)\\n\\n res['expected PC1 percent of variance'] = np.mean(ePC1)\\n res['expected 95% similarity'] = np.mean(e95)\\n res['expected 5% similarity'] = np.mean(e5)\\n\\n res['expected 10% similarity'] = np.mean(e10)\\n res['expected 15% similarity'] = np.mean(e15)\\n res['expected 20% similarity'] = np.mean(e20)\\n res['expected 25% similarity'] = np.mean(e25)\\n res['expected 75% similarity'] = np.mean(e75)\\n res['expected 80% similarity'] = np.mean(e80)\\n res['expected 85% similarity'] = np.mean(e85)\\n res['expected 90% similarity'] = np.mean(e90)\\n res['expected std similarity'] = np.mean(estd)\\n\\n res['net PC1 percent of variance'] = res['PC1 percent of variance'] - res['expected PC1 percent of variance']\\n res['net 95% similarity'] = res['95% similarity'] - res['expected 95% similarity']\\n res['net 5% similarity'] = res['5% similarity'] - res['expected 5% similarity']\\n\\n res['net 10% similarity'] = res['10% similarity'] - res['expected 10% similarity']\\n res['net 15% similarity'] = res['15% similarity'] - res['expected 15% similarity']\\n res['net 20% similarity'] = res['20% similarity'] - res['expected 20% similarity']\\n res['net 25% similarity'] = res['25% similarity'] - res['expected 25% similarity']\\n res['net 75% similarity'] = res['75% similarity'] - res['expected 75% similarity']\\n res['net 80% similarity'] = res['90% similarity'] - res['expected 80% similarity']\\n res['net 85% similarity'] = res['85% similarity'] - res['expected 85% similarity']\\n res['net 90% similarity'] = res['90% similarity'] - res['expected 90% similarity']\\n res['net std similarity'] = res['std similarity'] - res['expected std similarity']\\n#########\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944c90c042297bc5468bf1481951dbfd\",\n \"score\": \"0.5866878\",\n \"text\": \"def main():\\n\\n experiment_number = 15 # run the experiment 15 times\\n\\n temp_cost = []\\n # run 30 times to calculate the mean cost of each alpha value\\n for i in range(experiment_number):\\n best_cost = launch(df='gr96-tsp.txt')\\n temp_cost.append(best_cost)\\n cost_mean = sum(temp_cost)/len(temp_cost)\\n\\n print('Cost: ', cost_mean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e621bc22545066c3cc7524f25b985b5\",\n \"score\": \"0.5862649\",\n \"text\": \"def cost_function(self, actions):\\n # trajectory_cost = 0\\n # for i in range(len(actions)):\\n # trajectory_cost += cost_function(states[i], actions[i], next_states[i])\\n # return trajectory_cost\\n actions = actions.reshape((-1, self.horizon, self.action_dim)) # [pop size, horizon, action_dim]\\n actions = np.tile(actions, (self.particle, 1, 1))\\n\\n costs = np.zeros(self.popsize*self.particle)\\n state = np.repeat(self.state.reshape(1, -1), self.popsize*self.particle, axis=0)\\n\\n for t in range(self.horizon):\\n action = actions[:, t, :] # numpy array (batch_size x action dim)\\n\\n state_next = self.predict(state, action) + state\\n\\n cost = -self.cost_predict(state_next, action) # compute cost\\n # cost = cost.reshape(costs.shape)\\n costs += cost[:, 0] * self.gamma**t\\n state = copy.deepcopy(state_next)\\n\\n # average between particles\\n costs = np.mean(costs.reshape((self.particle, -1)), axis=0)\\n return costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c07ea7a819360a80c054331961de93a\",\n \"score\": \"0.58428305\",\n \"text\": \"def run (self, scalers = {'capital costs':1.0}):\\n tag = self.cd['file id'].split('+')\\n\\n self.was_run = True\\n self.reason = \\\"OK\\\"\\n\\n if len(tag) > 1 and tag[1] != 'water-wastewater':\\n self.was_run = False\\n self.reason = \\\"Not a water/wastewater project.\\\"\\n return\\n #~ print self.comp_specs['data']['assumption type used']\\n if self.comp_specs['data']['assumption type used'] == 'UNKNOWN':\\n self.was_run = False\\n self.reason = \\\"Water/wastewater system type unknown.\\\"\\n #~ print self.reason\\n return\\n\\n if self.cd[\\\"model electricity\\\"]:\\n self.calc_baseline_kWh_consumption()\\n self.calc_proposed_kWh_consumption()\\n self.calc_savings_kWh_consumption()\\n\\n if self.cd[\\\"model heating fuel\\\"]:\\n self.calc_baseline_HF_consumption()\\n self.calc_proposed_HF_consumption()\\n self.calc_savings_HF_consumption()\\n\\n #~ years = range(self.start_year,self.end_year)\\n #~ self.forecast.add_heating_fuel_column(\\\\\\n #~ \\\"heating_fuel_water-wastewater_consumed [gallons/year]\\\",\\n #~ years,\\n #~ self.baseline_HF_consumption*constants.mmbtu_to_gal_HF)\\n #~ self.forecast.add_heating_fuel_column(\\\\\\n #~ \\\"heating_fuel_water-wastewater_consumed [mmbtu/year]\\\", years,\\n #~ self.baseline_HF_consumption)\\n\\n #~ self.forecast.add_heat_demand_column(\\\\\\n #~ \\\"heat_energy_demand_water-wastewater [mmbtu/year]\\\",\\n #~ years, self.baseline_HF_consumption)\\n\\n if self.cd[\\\"model financial\\\"]:\\n self.calc_capital_costs()\\n\\n self.get_diesel_prices()\\n self.calc_annual_electric_savings()\\n self.calc_annual_heating_savings()\\n self.calc_annual_total_savings()\\n\\n self.calc_annual_costs(self.cd['interest rate'],\\n scalers['capital costs'])\\n self.calc_annual_net_benefit()\\n self.calc_npv(self.cd['discount rate'], self.cd[\\\"current year\\\"])\\n self.calc_levelized_costs(0)\\n #~ self.levelized_cost_of_energy['MMBtu'] *= .5\\n #~ self.levelized_cost_of_energy['kWh'] *= .5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17313a723ae8e681e56f4acad03668b0\",\n \"score\": \"0.58413917\",\n \"text\": \"def prCost(args):\\n\\t#hours\\n\\ti = 0\\n\\ttaskFrontHours = args[i]\\n\\ti = i + 1\\n\\ttaskMLHours = args[i]\\n\\ti = i + 1\\n\\ttaskMLLeadParticipation = args[i]\\n\\ti = i + 1\\n\\ttaskDeployHours = args[i]\\n\\ti = i + 1\\n\\ttaskProjMgmtHours = args[i]\\n\\t\\n\\t#unexpected\\n\\ti = i + 1\\n\\tunexpected = args[i]\\n\\ti = i + 1\\n\\tunexpectedHours = args[i]\\n\\n\\t#interruptions\\n\\telapsedHours = 0.8 * (taskFrontHours + taskMLHours + taskDeployHours + taskProjMgmtHours)\\n\\telapsedDays = int(elapsedHours / 8) + 1\\n\\tintrCounts = args[i+1:i+7]\\n\\t#print(str(intrCounts))\\n\\n\\n\\ti = i + 7\\n\\tproj = args[i]\\n\\t\\n\\ttcost = 0.0\\n\\ttcost += proj.taskCost(proj.taskFront, taskFrontHours)\\n\\totherParticipation = 100.0 - taskMLLeadParticipation - 10.0\\n\\ttaskML = {\\\"KD\\\" : taskMLLeadParticipation, \\\"PL\\\" : otherParticipation, \\\"SP\\\" : 10.0}\\n\\ttcost += proj.taskCost(taskML, taskMLHours)\\n\\ttcost += proj.taskCost(proj.taskDeploy, taskDeployHours) \\n\\ttcost += proj.taskCost(proj.taskProjMgmt, taskProjMgmtHours) \\n\\t\\n\\tif unexpected:\\n\\t\\ttcost += unexpectedHours * proj.replCost\\n\\n\\ttcost += proj.intrCost(intrCounts, elapsedDays)\\n\\t\\n\\treturn tcost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6008bfb865213cf52a074355102b86d\",\n \"score\": \"0.58405036\",\n \"text\": \"def determine_cost(self, solution):\\n\\n cost = 0\\n #print 'begin cost'\\n #print solution\\n #print solution.__len__()\\n if self.metric == 1:\\n for i in range(solution.__len__()-1):\\n j = solution[i]\\n k = solution[i + 1]\\n #print j , k\\n cost += self.cost_matrix[j][k]\\n if self.metric == 2:\\n #print(\\\"In speed\\\")\\n for i in range(solution.__len__() - 1):\\n j = solution[i]\\n k = solution[i + 1]\\n # print j , k\\n cost += self.cost_matrix[j][k]/self.speed_matrix[j][k]\\n #print cost\\n #print 'end cost'\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03fd973027f8e17c412ae9500f36718\",\n \"score\": \"0.5839229\",\n \"text\": \"def cost_function(features, target, weights):\\n ##########################################################################\\n # TODO\\n # hint:\\n # np.exp, np.log, np.sum\\n ##########################################################################\\n\\n\\n ##########################################################################\\n # END OF YOUR CODE\\n ##########################################################################\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f04c89282626a840d3a2a04e37d6d8c\",\n \"score\": \"0.5832934\",\n \"text\": \"def cost_func(self, emp_cov):\\n cost = -self.log_likelihood(emp_cov)\\n cost += self.penalty()\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30c68c8b8dd65a68d035a2766065e948\",\n \"score\": \"0.5826755\",\n \"text\": \"def compute_test_statistics(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03bb2946e2439d8d852fcc9c5edc0b6f\",\n \"score\": \"0.58233416\",\n \"text\": \"def cost(self, predicted_classes, actual_classes):\\r\\n _, accuracy, MK = get_metrics(actual_classes, predicted_classes)\\r\\n cost = 2-(accuracy + MK)\\r\\n cost = cost*10\\r\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba6b59f82f9ea634ebfa7b7c6cdebe8\",\n \"score\": \"0.580647\",\n \"text\": \"def do_stress_computation(params, inputs, disp_points=(), strain_points=()):\\n\\n print(\\\"Beginning stress calculation.\\\");\\n print(\\\"Number of sources: %d \\\" % len(inputs.source_object));\\n print(\\\"Number of receivers: %d \\\" % len(inputs.receiver_object));\\n subfaulted_inputs = split_subfault_receivers(params, inputs);\\n\\n # Computes here.\\n [x, y, x2d, y2d, u_disps, v_disps, w_disps] = compute_grid_def(subfaulted_inputs, params);\\n model_disp_points = compute_ll_def(subfaulted_inputs, params, disp_points);\\n strain_tensor_results = compute_ll_strain(subfaulted_inputs, params.alpha, strain_points);\\n [receiver_normal, receiver_shear, receiver_coulomb] = compute_strains_stresses(params, subfaulted_inputs);\\n receiver_profile_results = compute_stresses_horiz_profile(params, subfaulted_inputs);\\n\\n MyOutObject = cc.Out_object(x=x, y=y, x2d=x2d, y2d=y2d, u_disp=u_disps, v_disp=v_disps, w_disp=w_disps,\\n strains=strain_tensor_results, model_disp_points=model_disp_points,\\n zerolon=inputs.zerolon, zerolat=inputs.zerolat,\\n source_object=inputs.source_object, receiver_object=subfaulted_inputs.receiver_object,\\n receiver_normal=receiver_normal, receiver_shear=receiver_shear,\\n receiver_coulomb=receiver_coulomb, receiver_profile=receiver_profile_results);\\n return MyOutObject;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da8efad61e003f763987481c633c08f\",\n \"score\": \"0.58060473\",\n \"text\": \"def PostProcess(abc_df, Summary, data, q=0.5, weighted=True):\\n # take accpted values output from ABC\\n df_accepted = abc_df[abc_df['accept'] == 1]\\n accepted_count = len(df_accepted.index)\\n if accepted_count < 5:\\n \\tprint accepted_count, \\\"is number of accepted thetas\\\"\\n\\n if accepted_count < 2:\\n \\tprint \\\"Post processing failed; too few accepted values.\\\"\\n \\treturn 0\\n\\n df_accepted.statistics_diff = np.subtract(df_accepted.statistics,Summary(data)) # here a list of vectors\\n df_accepted.statistics_diff_abs = np.power(np.sum(np.square(df_accepted.statistics_diff)), 0.5) # we want a vector of distances\\n \\n #df_accepted.statistics_diff_abs.hist(bins=100)\\n quantile = df_accepted.statistics_diff_abs.quantile(q) # shouled be ok\\n #plt.show()\\n\\n #create column with kernal transform\\n df_accepted.kernel = df_accepted.statistics_diff_abs.apply(lambda x: EpanechnikovKernel(x, delta=quantile)) #diff_abs\\n\\n #print df_accepted.kernel[df_accepted.kernel > 0.0]\\n\\n mod = LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\\n X = np.array(df_accepted.statistics_diff) #list of vector\\n X.shape = (shape(X)[0],1) #the number of cols is equal to the lenght of the data\\n y = np.array(df_accepted.theta)\\n y.shape = (shape(y)[0],1)\\n weights = np.array(df_accepted.kernel)\\n \\n if weighted:\\n res = mod.fit(X, y, sample_weight=weights)\\n else: \\n res = mod.fit(X, y)\\n #alpha = res.intercept_[0]\\n beta = res.coef_[0][0] #dimension equal to the size of the data\\n beta_stats_diff = beta*df_accepted.statistics_diff # multiplication of: vector * matrix\\n beta_stats_diff_array = np.array(beta_stats_diff) #change the size\\n beta_stats_diff_array.shape = (shape(beta_stats_diff_array)[0],1) #change the size\\n theta_star = np.subtract(y, beta_stats_diff_array)\\n theta_star = np.reshape(theta_star, theta_star.shape[0])\\n df_accepted['theta_star'] = theta_star\\n \\n return df_accepted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f97af8b2a75082ae2c91b1d079c7b500\",\n \"score\": \"0.5772366\",\n \"text\": \"def evaluate_total_costs(x, A_quantity, B_quantity, C_change, A_cost, B_cost, C_cost, C_base):\\n x = np.rint(x)\\n A_total_costs = np.sum(A_cost * A_quantity * x)\\n B_total_costs = np.sum(B_cost * B_quantity * x)\\n C_total_costs = C_cost * - min(C_base + np.sum(C_change * x), 0)\\n return A_total_costs + B_total_costs + C_total_costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c318f61af9a47aece3f8be45003f772b\",\n \"score\": \"0.57631046\",\n \"text\": \"def _process_results(self):\\n m = self.model\\n\\n pcha = [m.pcha[n].value for n in m.time]\\n pdis = [m.pdis[n].value for n in m.time]\\n ptot = [m.pnet[n] + m.pcha[n].value - m.pdis[n].value for n in m.time]\\n soc = [m.s[n].value for n in m.time]\\n pfpk_without_es = max(m.pnet)\\n ptpk_without_es = [max(m.pnet[n]*m.mask_ds[p][n] for n in m.time) for p in m.period]\\n \\n demand_charge_with_es=m.pfpk.value*m.flt_dr+sum(m.ptpk[p].value*m.tou_dr[p] for p in m.period)\\n demand_charge_without_es=pfpk_without_es*m.flt_dr+sum(ptpk_without_es[p]*m.tou_dr[p] for p in m.period)\\n \\n energy_charge_with_es=sum(max(0,ptot[n])*m.tou_er[n] for n in m.time)\\n energy_charge_without_es=sum(max(0,m.pnet[n])*m.tou_er[n] for n in m.time)\\n \\n nem_charge_with_es=sum(min(0,ptot[n])*m.nem_sr[n] for n in m.time) #negative since it is credit\\n nem_charge_without_es=sum(min(0,m.pnet[n])*m.nem_sr[n] for n in m.time) #negative since it is credit\\n \\n tot_bill_with_es=demand_charge_with_es + energy_charge_with_es + nem_charge_with_es\\n tot_bill_without_es=demand_charge_without_es + energy_charge_without_es + nem_charge_without_es\\n \\n run_results = {'time': m.time, 'Pload': m.pld, 'Ppv': m.ppv, 'Pcharge': pcha, 'Pdischarge': pdis, 'Ptotal': ptot,\\n 'state of charge': soc, 'energy_charge_with_es': energy_charge_with_es,'nem_charge_with_es': nem_charge_with_es, \\n 'demand_charge_with_es':demand_charge_with_es, 'total_bill_with_es': tot_bill_with_es, \\n 'energy_charge_without_es': energy_charge_without_es,'nem_charge_without_es': nem_charge_without_es, \\n 'demand_charge_without_es':demand_charge_without_es, 'total_bill_without_es': tot_bill_without_es }\\n self.results = pd.DataFrame(run_results)\\n\\n self.total_bill_with_es = tot_bill_with_es\\n self.total_bill_without_es = tot_bill_without_es\\n\\n self.demand_charge_with_es = demand_charge_with_es\\n self.demand_charge_without_es = demand_charge_without_es\\n\\n self.energy_charge_with_es = energy_charge_with_es\\n self.energy_charge_without_es = energy_charge_without_es\\n\\n self.nem_charge_with_es = nem_charge_with_es\\n self.nem_charge_without_es = nem_charge_without_es\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1dcb4b1eaff72e52c8ac19db197f1d\",\n \"score\": \"0.57603204\",\n \"text\": \"def evaluate_cost(\\n self,\\n results,\\n name='',\\n vectorise=False,\\n **kwargs\\n ):\\n return self.evaluate_cost_and_std(\\n results, name=name, vectorise=vectorise, **kwargs)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3552864300a9cd6b62d76019ecc8c501\",\n \"score\": \"0.5756126\",\n \"text\": \"def _costfunction(self, newpos=None):\\n if newpos is not None:\\n self.movemotor(newpos)\\n img = url2array(self.url, navg=self.navg)\\n img = img[self._slice_0, self._slice_1]\\n if self.stretch:\\n img = stretch_contrast(img, *self.stretch)\\n self._image = img\\n img_contrast = contrast(img, self.contrast)\\n residual = 1./img_contrast\\n print(\\\"Value: %f\\\"%residual)\\n return residual\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46920153088fd6972cb2908ecc2aa23b\",\n \"score\": \"0.5750806\",\n \"text\": \"def linecosts(self,candidates,image,cseg_guess=None,transcript_guess=None):\\n threshold = scipy.stats.scoreatpercentile([bestcost(x) for x in candidates],per=25)\\n print \\\"threshold\\\",threshold\\n best = [x for x in candidates if bestcost(x)<threshold]\\n if len(best)<4: best = candidates\\n base = median([x.bbox.y0 for x in best])\\n scale = estimate_xheight(best)\\n print \\\"base\\\",base,\\\"scale\\\",scale\\n for x in candidates:\\n x0,y0,x1,y1 = x.bbox.tuple()\\n aspect = (y1-y0)*1.0/(x1-x0)\\n costs = {}\\n for cls,cost in x.outputs:\\n costs[cls] = cost\\n for a in costs:\\n if a==\\\"~\\\": continue\\n ac = ncost(aspect,self.aspects[a])\\n if costs[a]<2 and ac>1.0:\\n print \\\"adjusting\\\",a,costs[a],ac,\\\"aspect\\\"\\n costs[cls] += ac\\n for a,b in loc_confusions:\\n if abs(costs[a]-costs[b])>1.0: continue\\n ac = ncost((y0-base)/scale,self.ys[a])\\n bc = ncost((y0-base)/scale,self.ys[b])\\n if costs[a]<10:\\n print \\\"adjusting\\\",a,b,costs[a],costs[b],\\\"loc\\\"\\n if ac<bc: costs[b] += 2.0\\n else: costs[a] += 2.0\\n for a,b in size_confusions:\\n if abs(costs[a]-costs[b])>1.0: continue\\n ac = ncost((y1-y0)/scale,self.heights[a])\\n bc = ncost((y1-y0)/scale,self.heights[b])\\n if costs[a]<10:\\n print \\\"adjusting\\\",a,b,costs[a],costs[b],\\\"size\\\"\\n if ac<bc: costs[b] += 2.0\\n else: costs[a] += 2.0\\n for a in small_height:\\n ac = ncost((y1-y0)/scale,self.heights[a])\\n if ac<1.0: continue\\n if costs[a]<10:\\n print \\\"penalizing\\\",a,costs[a],ac,\\\"small\\\"\\n costs[a] += ac\\n x.outputs = [(cls,cost) for cls,cost in costs.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca586c9eb3e78ab5e428c94b6c97866c\",\n \"score\": \"0.5747674\",\n \"text\": \"def compute_cost_function(errors, weights, l2_penalty):\\n\\treturn np.dot(errors,errors) + l2_penalty * (np.dot(weights,weights) - weights[0] ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5e3b5bb821cfe5d76ea6bfdf5c34e6\",\n \"score\": \"0.5743608\",\n \"text\": \"def total_cost(self,data):\\n cost = 0.0\\n for x, y in data:\\n a = self.feed_forward(x)\\n cost += self.cost.fn(a,y)/len(data)\\n # add regularization\\n cost += 0.5*(self.parameters['regParam']/len(data))*sum( np.linalg.norm(w)**2 for w in self.weights )\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523b7799d373e24c2fbe414d3a876d86\",\n \"score\": \"0.57361656\",\n \"text\": \"def cost(X,Y,W1,b1,W2,b2):\\n ### START YOUR CODE ###\\n m = X.shape[1]\\n yPred = predict(X, W1, b1, W2, b2)\\n J = 1/(2*m)*np.sum((yPred - Y)**2)\\n ### END YOUR CODE ###\\n return J\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a69abe65016b57eee110f3a8a2d32a2\",\n \"score\": \"0.57349277\",\n \"text\": \"def display_costfunc():\\r\\n\\r\\n # The beta variables will represent the range of values on the x axis, which are the possible\\r\\n # beta coefficients for the independent variable (years of education for this example)\\r\\n beta_low = 0\\r\\n beta_high = 6000\\r\\n # The alpha variables will represent the possible values for the intercept.\\r\\n alpha_low = 13000\\r\\n alpha_high = 15000\\r\\n # NOTE: I set the above values via a process of trial and error and by observing the coefficients from\\r\\n # the previously executed regression models.\\r\\n\\r\\n # Define our sampling grid for slopes and intercepts\\r\\n # My betas and alphas variables will be two dimensionsal ndarrays containing the points on the x (betas)\\r\\n # and y (alphas) axis.\\r\\n # 100 was an arbitrary value that I inserted just to have enough variation in the visual\\r\\n betas, alphas = np.meshgrid(np.linspace(beta_low, beta_high, 100),\\r\\n np.linspace(alpha_low, alpha_high, 100))\\r\\n\\r\\n # My cost function will work with a numpy ndarray, so I am converting my dependent variable and my\\r\\n # independent variable from the Series within the DataFrame to a numpy ndarray.\\r\\n # I should be fine with the default shape that results from the to_numpy() function.\\r\\n # If not, I would have had to reshape it (to go down instead of going across).\\r\\n x = my_data['yearseducation'].to_numpy()\\r\\n y = my_data['currentsalary'].to_numpy()\\r\\n\\r\\n # Our cost function: Here, I compute the natural logarithm of the standard\\r\\n # l2-norm of the model residuals (MSE) to enhance the scale.\\r\\n # l2-norm is the mean square error (MSE).\\r\\n # In this equation, y is the true value\\r\\n # m is the beta coefficient for our independent variable\\r\\n # x is an observed value for the independent variable\\r\\n # b is the intercept.\\r\\n def cost(m, b):\\r\\n return np.log(np.sum((y - m * x - b)**2))\\r\\n\\r\\n # Now vectorize our function, which turns our cost function into a vectorized function\\r\\n # that can be broadcast to a NumPy array. This simplifies our code as we remove the need for an explicit loop.\\r\\n # Said differently, the purpose of np.vectorize is to transform functions that are not\\r\\n # numpy-aware (e.g. take floats as input and return floats as output) into functions\\r\\n # that can operate on (and return) numpy arrays.\\r\\n v_cost = np.vectorize(cost)\\r\\n\\r\\n # Our vectorized cost function sampled at every grid point\\r\\n epsilons = v_cost(betas, alphas)\\r\\n\\r\\n # Now, let's attempt to plot/graph/visualize the result\\r\\n fig, ax = plt.subplots()\\r\\n\\r\\n # First we draw the OLS result\\r\\n ax.plot(slope, intercept, marker='o', color='r', markersize=5)\\r\\n\\r\\n # Now plot the sampled grid as an image\\r\\n # https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.axes.Axes.imshow.html\\r\\n # If we want to blur the image to reduce image noise and reduce detail add the following:\\r\\n #interpolation=\\\"gaussian\\\"\\r\\n ax.imshow(epsilons, origin='lower', extent=[beta_low, beta_high, alpha_low, alpha_high],\\r\\n aspect='auto', interpolation='gaussian')\\r\\n\\r\\n # clean up the final plot\\r\\n ax.set_xlabel(r\\\"$\\\\beta$\\\", fontsize=14)\\r\\n ax.set_ylabel(r\\\"$\\\\alpha$\\\", fontsize=14)\\r\\n ax.set_title(r\\\"Cost function for $y = \\\\beta*x + \\\\alpha$\\\", fontsize=18)\\r\\n sns.despine(offset=0, trim=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a8deee13062d64fb12f94b893fb09d3\",\n \"score\": \"0.572154\",\n \"text\": \"def mtf_cost_core_main(true_tan, true_sag, sim_tan, sim_sag):\\n difference_t = true_tan - sim_tan\\n difference_s = true_sag - sim_sag\\n return difference_t, difference_s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314edc02517f3703e09af0a41aea6021\",\n \"score\": \"0.5711565\",\n \"text\": \"def main():\\n\\n logging.info(\\\"Calculating distinctiveness data...\\\")\\n distinctiveness_dataset.calculate_distinctiveness()\\n distinctiveness_dataset.calculate_persistent_stream_distinctiveness()\\n distinctiveness_dataset.calculate_distinctiveness_dataset_complexities()\\n logging.info(\\\"Distinctiveness data OK!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa514a2af64d35762054cd86ec440382\",\n \"score\": \"0.5708623\",\n \"text\": \"def compute_performance_statistics (y_true_f, y_pred_f):\\n \\n y_true = np.load(y_true_f)\\n y_pred = np.load(y_pred_f)\\n \\n y_true_o = np.load(y_true_f)\\n y_pred_o = np.load(y_pred_f)\\n #print (y_true.shape, y_pred.shape)\\n y_true = y_true.flatten()\\n y_pred = y_pred.flatten()\\n \\n sample_weights = np.copy(y_true)\\n sample_weights[sample_weights == 1] = 1.\\n sample_weights[sample_weights == 0] = .2\\n \\n \\n epsilon = 1e-7\\n y_pred[y_pred<=0.] = epsilon\\n y_pred[y_pred>=1.] = 1. -epsilon\\n \\n #print (y_true.shape, y_pred.shape)\\n smooth = 1.\\n intersection = np.sum(y_true * y_pred)\\n dice_coef = (2. * intersection + smooth) / (np.sum(y_true) + np.sum(y_pred) + smooth)\\n\\n score = log_loss (y_true, y_pred)\\n score2 = log_loss (y_true, y_pred, sample_weight = sample_weights)\\n acc = math.exp(-score)\\n acc2 = math.exp(-score2)\\n y_pred = np.round(y_pred)\\n\\n prec = precision_score(y_true, y_pred, average=\\\"binary\\\")\\n rec = recall_score(y_true, y_pred, average=\\\"binary\\\")\\n f1 = f1_score(y_true, y_pred, average=\\\"binary\\\")\\n\\n \\n cm = confusion_matrix(y_true, y_pred)\\n #cm.print_stats()\\n true_p = cm[1][1]\\n false_p = cm[0][1]\\n true_n = cm[0][0]\\n false_n = cm[1][0]\\n\\n \\n #perf = {}\\n \\n# keys = [\\\"samples\\\", \\\"logloss\\\", \\\"weighted_logloss\\\",\\\"accuracy\\\", \\\"weighted_accuracy\\\", \\\"dice_coef\\\", \\\"precision\\\",\\\"recall\\\", \\\"f1_score\\\", \\\"true_positive\\\", \\\\\\n# \\\"false_positive\\\",\\\"true_negative\\\",\\\"false_negative\\\", \\\"zero_contour_labels\\\", \\\"zero_contour_pred\\\", \\\\\\n# \\\"missed_pred_lt_05\\\", \\\"missed_pred_gt_25\\\", \\\"missed_pred_gt_50\\\", \\\"missed_pred_eq_100\\\"]\\n perf = OrderedDict.fromkeys(perf_keys)\\n \\n perf[\\\"logloss\\\"] = score\\n perf[\\\"weighted_logloss\\\"] = score2\\n perf[\\\"accuracy\\\"] = acc\\n perf[\\\"weighted_accuracy\\\"] = acc2\\n\\n perf[\\\"dice_coef\\\"] = dice_coef\\n perf[\\\"precision\\\"] = prec\\n perf[\\\"recall\\\"] = rec\\n perf[\\\"f1_score\\\"] = f1\\n perf[\\\"true_positive\\\"] = int(cm[1][1])\\n perf[\\\"false_positive\\\"] = int(cm[0][1])\\n perf[\\\"true_negative\\\"] = int(cm[0][0])\\n perf[\\\"false_negative\\\"] = int(cm[1][0])\\n \\n y_true = y_true_o\\n y_pred = np.round(y_pred_o)\\n samples, x, y, z = y_pred.shape\\n y_true_sum = y_true.sum(axis=(1, 2), keepdims=True).reshape(samples)\\n y_pred_sum = y_pred.sum(axis=(1, 2), keepdims=True).reshape(samples) \\n lb0 = (np.where(y_true_sum == 0))\\n pd0 = (np.where(y_pred_sum == 0))\\n lb0 = list(lb0[0])\\n pd0 = list(pd0[0])\\n perf[\\\"samples\\\"] = samples\\n perf[\\\"zero_contour_labels\\\"] = len(lb0)\\n perf[\\\"zero_contour_pred\\\"] = len(pd0)\\n \\n pix_diff = (abs(y_true_sum - y_pred_sum))/(y_true_sum + epsilon)\\n px1 = np.where(pix_diff <.0005)\\n px1 = list(px1[0])\\n px25 = np.where(pix_diff>.25)\\n px25 = list(px25[0])\\n px50 = np.where(pix_diff>.5)\\n px50 = list(px50[0])\\n px100 = np.where(pix_diff >= 1.0) \\n px100 = list(px100[0])\\n perf[\\\"missed_pred_lt_05\\\"] = len(px1)\\n perf[\\\"missed_pred_gt_25\\\"] = len(px25)\\n perf[\\\"missed_pred_gt_50\\\"] = len(px50)\\n perf[\\\"missed_pred_eq_100\\\"] = len(px100)\\n return perf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5319dde6efb7d39bcb89a3a0d0959ce1\",\n \"score\": \"0.5701224\",\n \"text\": \"def main():\\n arrays = []\\n if sys.argv[1] == \\\"1\\\":\\n arrays = file_to_arrays('random_vs_al_accuracy.txt', 3)\\n elif sys.argv[1] == \\\"2\\\":\\n arrays = file_to_arrays('scaling_accuracy.txt', 3)\\n elif sys.argv[1] == \\\"3\\\":\\n arrays = file_to_arrays('heuristics_accuracy', 3)\\n averaged_arrays = []\\n for arr in arrays:\\n averaged_arrays.append(np.divide(np.sum(arr, axis=0), 3))\\n for i in range(len(averaged_arrays)):\\n print(compute_deficiency(averaged_arrays[0], averaged_arrays[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff18448737607acb70034dc0a1c0a37\",\n \"score\": \"0.5692014\",\n \"text\": \"def compute_risk(self, *args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e497dc053db97c7a4669400d8d6a7272\",\n \"score\": \"0.5691597\",\n \"text\": \"def calculate_efficiency(plot_dict) :\\n t1 = ROOT.TText(0.28,0.185,\\\"MICE preliminary [simulation]\\\")\\n\\tt2 = ROOT.TText(0.28,0.15,\\\"ISIS Cycle 2015/04\\\")\\n\\tt1.SetNDC(1)\\n\\tt1.SetTextSize(0.04)\\n\\tt1.SetTextFont(42)\\n\\tt2.SetNDC(1)\\n\\tt2.SetTextSize(0.03)\\n\\tt2.SetTextFont(42)\\n\\tf = ROOT.TFile(\\\"tracker_resolution_plots_\\\"+str(TOF_ll)+\\\".root\\\",\\\"RECREATE\\\")\\n\\tpEffx = ROOT.TEfficiency()\\n pEffx = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_x'],plot_dict['downstream']['MC_theta_x'])\\n\\tc5 = ROOT.TCanvas()\\n plot_dict['downstream']['efficiency_scat_x'] = pEffx.CreateGraph()\\n\\tpEffx_graph = pEffx.CreateGraph()\\n\\tpEffx_graph.SetName(\\\"Effx_graph\\\")\\n\\t#pEffx_graph.SetTitle(\\\"Acceptance plot #theta_x\\\")\\n\\tpEffx_graph.GetXaxis().SetTitle(\\\"#theta_{x} (mrad)\\\")\\n\\tpEffx_graph.GetYaxis().SetTitle(\\\"Efficiency\\\")\\n\\tpEffx_graph.Draw()\\n\\tf1 = ROOT.TF1(\\\"f1\\\",\\\"pol2\\\",-0.040,0.040)\\n\\t#f1.SetParameters(1,1)\\n\\t#pEffx_graph.Fit(\\\"f1\\\",\\\"R\\\")\\n\\t#pEffx_graph.SetRangeUser(-0.60,0.60)\\n\\tt1.Draw(\\\"same\\\")\\n\\tt2.Draw(\\\"same\\\")\\n\\tt1.Paint()\\n\\tt2.Paint()\\n\\tc5.SaveAs(\\\"pEff_x.pdf\\\")\\n\\tpEffx_graph.Write()\\n\\n pEffy = ROOT.TEfficiency()\\n pEffy = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_y'],plot_dict['downstream']['MC_theta_y'])\\n\\tc7 = ROOT.TCanvas()\\n plot_dict['downstream']['efficiency_scat_y'] = pEffy.CreateGraph()\\n\\tpEffy_graph = pEffy.CreateGraph()\\n\\tpEffy_graph.SetName(\\\"Effy_graph\\\")\\n\\t#pEffy_graph.SetTitle(\\\"Acceptance plot #theta_y\\\")\\n\\tpEffy_graph.GetXaxis().SetTitle(\\\"#theta_{y} (mrad)\\\")\\n\\tpEffy_graph.GetYaxis().SetTitle(\\\"Efficiency\\\")\\n\\tpEffy_graph.Draw()\\n\\t#pEffy_graph.Fit(\\\"f1\\\",\\\"R\\\")\\n\\tt1.Draw(\\\"same\\\")\\n\\tt2.Draw(\\\"same\\\")\\n\\tt1.Paint()\\n\\tt2.Paint()\\n\\tc7.SaveAs(\\\"pEff_y.pdf\\\")\\n\\tpEffy_graph.Write()\\n\\n pEffscatt = ROOT.TEfficiency()\\n pEffscatt = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_scatt'],plot_dict['downstream']['MC_theta_scatt'])\\n\\tc17 = ROOT.TCanvas()\\n plot_dict['downstream']['efficiency_scat_scatt'] = pEffscatt.CreateGraph()\\n\\tpEffscatt.Draw()\\n\\tc17.SaveAs(\\\"pEff_scatt.pdf\\\")\\n\\tpEffscatt_graph = pEffscatt.CreateGraph()\\n\\tpEffscatt_graph.SetName(\\\"Effscatt_graph\\\")\\n\\tpEffscatt_graph.Write()\\n\\n pEff2scatt = ROOT.TEfficiency()\\n pEff2scatt = ROOT.TEfficiency(plot_dict['downstream']['recon_theta_2scatt'],plot_dict['downstream']['MC_theta_2scatt'])\\n\\tc17 = ROOT.TCanvas()\\n plot_dict['downstream']['efficiency_scat_2scatt'] = pEff2scatt.CreateGraph()\\n\\tpEff2scatt.Draw()\\n\\tc17.SaveAs(\\\"pEff_2scatt.pdf\\\")\\n\\tpEff2scatt_graph = pEff2scatt.CreateGraph()\\n\\tpEff2scatt_graph.SetName(\\\"Eff2scatt_graph\\\")\\n\\tpEff2scatt_graph.Write()\\n\\tf.Close()\\n\\n\\t'''\\n\\tc3 = ROOT.TCanvas()\\n\\tplot_dict['downstream']['efficiency_scat'].Divide(plot_dict['downstream']['recon_theta'].Draw(),ROOT.TH1(plot_dict['downstream']['MC_theta'].Draw()))\\n\\tplot_dict['downstream']['recon_theta'].Draw()\\n\\tline = ROOT.TLine(-0.06,1,0.06,1)\\n\\tline.SetLineColor(22)\\n\\tline.Draw()\\n\\tc3.SaveAs('effi.pdf')\\n '''\\n\\tc1 = ROOT.TCanvas()\\n\\tplot_dict['downstream']['recon_theta_x'].Draw()\\n\\tplot_dict['downstream']['MC_theta_x'].Draw(\\\"SAMES\\\")\\n\\tplot_dict['downstream']['MC_theta_x'].SetLineColor(2)\\n\\tplot_dict['downstream']['MC_theta_x'].SetLineStyle(2)\\n\\tc1.SaveAs('recon_theta.pdf')\\n\\tc1.Clear()\\n\\tc4= ROOT.TCanvas()\\n #recon_theta_hist = plot_dict['downstream']['recon_theta'].Draw()\\n\\t#recon_theta_hist.Sumw2()\\n\\t#c4.SaveAs('recon_theta_hist.pdf')\\n\\tc2 = ROOT.TCanvas()\\n\\tMC_theta = plot_dict['downstream']['MC_theta_x'].Draw()\\n\\tc2.SaveAs('MC_theta.pdf')\\n\\n\\t#plot_dict['downstream']['efficiency_scat'] = recon_theta_hist\\n\\t#plot_dict['downstream']['efficiency_scat'].Divide(MC_theta)\\n\\t#plot_dict['downstream']['efficiency_scat'].Draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab83de6076d298725c39d6c83f1c328\",\n \"score\": \"0.5690338\",\n \"text\": \"def cost_fn(params):\\n return circuit(params[0], params[1:], w=0) ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b63eee6e53890da3c0ae13efcdf0e1cd\",\n \"score\": \"0.56772316\",\n \"text\": \"def costFunction( params, controlData, Xprior=None, Xuncert=None ):\\n \\n n=len(controlData)\\n Ymodel=np.zeros(n)\\n Yobs=np.zeros(n)\\n R=np.zeros((n,n))\\n \\n #compute the modelled albedo/fAPAR values\\n for (i,data) in enumerate(controlData):\\n Ymodel[i]=julesRT_wrapper( params, data )\\n Yobs[i]=data.obsVal\\n R[i,i]=1./(data.obsUncert**2)\\n \\n \\n #VAR term one (obs):\\n diff=Ymodel-Yobs\\n cost=0.5*np.dot(diff,np.dot(R,diff.T))\\n \\n if Xprior != None:\\n #compute B matrix\\n B=np.diag(1./(np.array(Xuncert)**2))\\n \\n #VAR term two:\\n diff=np.array(params)-np.array(Xprior) \\n cost+=0.5*np.dot(diff,np.dot(B,diff.T))\\n \\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc0214dd6ae6badb515e91b084d7d0a\",\n \"score\": \"0.5670897\",\n \"text\": \"def _compute_specific(self, logits, targets):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda1c2a1aa41d2b02e503d676b1c6f17\",\n \"score\": \"0.56691784\",\n \"text\": \"def compute_cost(data_set, m=0, c=0):\\n cost = 0.0\\n for i in data_set:\\n x, y = i[0], i[1]\\n y_diff = (y - (m*x + c))**2\\n cost += y_diff\\n return cost / len(data_set)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b77be95a236f8eedc9f126c5832bd40\",\n \"score\": \"0.5659344\",\n \"text\": \"def calculate():\\n\\tdf_trust = pd.read_csv(trust_params.OUTPUT_MATRIX_FILE_NAME, index_col=0)\\n\\tdf_ideal = pd.read_csv(trust_params.IDEAL_MATRIX_FILE_NAME, index_col=0)\\n\\n\\ttotal_count = 0\\n\\ttotal_diff = 0\\n\\tfor index, row in df_trust.iterrows():\\n\\t\\tprint(\\\"Index row: \\\", index)\\n\\t\\tfor col, value in row.iteritems():\\n\\t\\t\\tdiff_value = round(abs(value - df_ideal.loc[index,col]),4)\\n\\t\\t\\tif(index == col):\\n\\t\\t\\t\\ttotal_count += 4\\n\\t\\t\\t\\ttotal_diff += 4*diff_value\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttotal_count += 1\\n\\t\\t\\t\\ttotal_diff += 1*diff_value\\n\\n\\t\\t\\tprint(col, value, df_ideal.loc[index,col], diff_value)\\n\\n\\tprint(\\\"Total count: \\\", total_count)\\n\\tprint(\\\"Total diff: \\\", total_diff)\\n\\tprint(\\\"Model trust score: \\\", total_diff/total_count*1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce08f1c5f60e59057a7c120ec15aa68\",\n \"score\": \"0.565901\",\n \"text\": \"def test_cost_function(self):\\n\\n # Use seed for deterministic testing\\n np.random.seed(42)\\n\\n def check(D, N, K,\\n mu=None,\\n Lambda=None,\\n rho=None):\\n\\n if mu is None:\\n mu = np.zeros(D)\\n if Lambda is None:\\n Lambda = np.identity(D)\\n if rho is None:\\n rho = np.ones(D)\\n\\n V = np.identity(D) + np.ones((D,D))\\n\\n # Construct model\\n B = GaussianARD(3, 5,\\n shape=(D,K),\\n plates=(1,D))\\n S = GaussianARD(2, 4,\\n shape=(K,),\\n plates=(N,1))\\n A = SumMultiply('dk,k->d', B, S)\\n X = GaussianMarkovChain(mu,\\n Lambda,\\n A,\\n rho,\\n n=N+1,\\n initialize=False)\\n Y = Gaussian(X,\\n V,\\n initialize=False)\\n\\n # Posterior estimation\\n Y.observe(np.random.randn(N+1,D))\\n X.update()\\n B.update()\\n S.update()\\n try:\\n mu.update()\\n except:\\n pass\\n try:\\n Lambda.update()\\n except:\\n pass\\n try:\\n rho.update()\\n except:\\n pass\\n\\n # Construct rotator\\n rotB = RotateGaussianARD(B, axis=-2)\\n rotX = RotateVaryingMarkovChain(X, B, S, rotB)\\n\\n # Rotation\\n true_cost0 = X.lower_bound_contribution()\\n rotX.setup()\\n I = np.identity(D)\\n R = np.random.randn(D, D)\\n rot_cost0 = rotX.get_bound_terms(I)\\n rot_cost1 = rotX.get_bound_terms(R)\\n self.assertAllClose(sum(rot_cost0.values()),\\n rotX.bound(I)[0],\\n msg=\\\"Bound terms and total bound differ\\\")\\n self.assertAllClose(sum(rot_cost1.values()),\\n rotX.bound(R)[0],\\n msg=\\\"Bound terms and total bound differ\\\")\\n rotX.rotate(R)\\n true_cost1 = X.lower_bound_contribution()\\n self.assertAllClose(true_cost1 - true_cost0,\\n rot_cost1[X] - rot_cost0[X],\\n msg=\\\"Incorrect rotation cost for X\\\")\\n \\n return\\n\\n self._run_checks(check)\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af62b2ffe17bf8e771b5e0f6eca91082\",\n \"score\": \"0.5656809\",\n \"text\": \"def compile(self):\\n self.markov_blanket = self.compute_markov_blanket()\\n self.parent_multiplier = self.compute_parent_multipliers()\\n self.distributions = self.compute_distributions()\\n self.compiled = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d4d0a445741ad25d0139f1b9537a39\",\n \"score\": \"0.5654857\",\n \"text\": \"def cal_performance(pred_gen,\\n pred_cp,\\n p_gen,\\n gold,\\n smoothing=False):\\n # print(\\\"gold1\\\", gold)\\n # print(gold.shape)\\n loss, pred = cal_loss_cp(pred_gen,\\n pred_cp,\\n p_gen,\\n gold)\\n\\n pred = pred.max(1)[1]\\n gold = gold.contiguous().view(-1)\\n non_pad_mask = gold.ne(Constants.PAD)\\n n_correct = pred.eq(gold)\\n n_correct = n_correct.masked_select(non_pad_mask).sum().item()\\n\\n return loss, n_correct, pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874322e5ae7ce6c85bd21edf17e57a61\",\n \"score\": \"0.5653596\",\n \"text\": \"def cost_loop(self, coefs, X, y):\\n acc = 0\\n m = X.shape[0]\\n\\n for i in range(m):\\n if y[i] == 1:\\n acc += -np.log(self.hypothesis(coefs, X[i]))\\n else:\\n acc += -np.log(1-self.hypothesis(coefs, X[i]))\\n return acc/m + self.regul/(2*m) * np.sum(np.square(coefs[1:]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74ee629fdbde0a2a4b7ee73be6ea693\",\n \"score\": \"0.56526184\",\n \"text\": \"def cost_func(es_y, gt_y):\\n s = 0\\n for e, g in zip(es_y, gt_y):\\n s += (e - g) ** 2\\n m = len(es_y)\\n error = s / (2 * m)\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa01c7a2337adee9c23c0400265e710\",\n \"score\": \"0.5648534\",\n \"text\": \"def compute(self,seaDepth,machineRating,hubHt,rtrDiam,tcc, turbine_number, year, month,verbose=0.0):\\n\\n lPrmtsCostCoeff1 = 9.94E-04 \\n lPrmtsCostCoeff2 = 20.31 \\n oPrmtsCostFactor = 37.0 # $/kW (2003)\\n scourCostFactor = 55.0 # $/kW (2003)\\n ptstgCostFactor = 20.0 # $/kW (2003)\\n ossElCostFactor = 260.0 # $/kW (2003) shallow\\n ostElCostFactor = 290.0 # $/kW (2003) transitional\\n ostSTransFactor = 25.0 # $/kW (2003)\\n ostTTransFactor = 77.0 # $/kW (2003)\\n osInstallFactor = 100.0 # $/kW (2003) shallow & trans\\n suppInstallFactor = 330.0 # $/kW (2003) trans additional\\n paiCost = 60000.0 # per turbine\\n \\n suretyBRate = 0.03 # 3% of ICC\\n suretyBond = 0.0\\n\\n #set variables\\n if seaDepth == 0: # type of plant # 1: Land, 2: < 30m, 3: < 60m, 4: >= 60m\\n iDepth = 1\\n elif seaDepth < 30:\\n iDepth = 2\\n elif seaDepth < 60:\\n iDepth = 3\\n else:\\n iDepth = 4\\n\\n # foundation cost calculations\\n self.fdn.compute(machineRating, hubHt, rtrDiam, seaDepth, year, month)\\n\\n # initialize self.ppi index calculator\\n if iDepth == 1:\\n ref_yr = 2002 \\n ref_mon = 9\\n else:\\n ref_yr = 2003\\n ref_mon = 9\\n ppi.ref_yr = ref_yr\\n ppi.ref_mon = ref_mon\\n ppi.curr_yr = year\\n ppi.curr_mon = month\\n \\n # cost calculations\\n tpC1 =0.00001581\\n tpC2 =-0.0375\\n tpInt =54.7\\n tFact = tpC1*machineRating*machineRating + tpC2*machineRating + tpInt \\n\\n if (iDepth == 1):\\n self.engPermits = (lPrmtsCostCoeff1 * machineRating * machineRating) + \\\\\\n (lPrmtsCostCoeff2 * machineRating)\\n ppi.ref_mon = 3\\n self.engPermits *= ppi.compute('IPPI_LPM') * turbine_number\\n ppi.ref_mon = 9\\n \\n elC1 = 3.49E-06\\n elC2 = -0.0221\\n elInt = 109.7\\n eFact = elC1*machineRating*machineRating + elC2*machineRating + elInt\\n self.electrical = machineRating * eFact * ppi.compute('IPPI_LEL') * turbine_number\\n \\n rcC1 = 2.17E-06\\n rcC2 = -0.0145\\n rcInt =69.54\\n rFact = rcC1*machineRating*machineRating + rcC2*machineRating + rcInt\\n self.roadsCivil = machineRating * rFact * ppi.compute('IPPI_RDC') * turbine_number\\n \\n iCoeff = 1.965\\n iExp = 1.1736\\n self.installation = iCoeff * ((hubHt*rtrDiam)**iExp) * ppi.compute('IPPI_LAI') * turbine_number\\n \\n self.transportation = machineRating * tFact * ppi.compute('IPPI_TPT') * turbine_number\\n \\n pass\\n elif (iDepth == 2): # offshore shallow\\n ppi.ref_yr = 2003\\n self.pai = paiCost * ppi.compute('IPPI_PAE') * turbine_number\\n self.portStaging = ptstgCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number # 1.415538133\\n self.engPermits = oPrmtsCostFactor * machineRating * ppi.compute('IPPI_OPM') * turbine_number\\n self.scour = scourCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number # 1.415538133#\\n self.installation = osInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number \\n self.electrical = ossElCostFactor * machineRating * ppi.compute('IPPI_OEL') * turbine_number\\n ppi.ref_yr = 2002 \\n self.transportation = machineRating * tFact * ppi.compute('IPPI_TPT') * turbine_number\\n ppi.ref_yr = 2003\\n\\n pass \\n elif (iDepth == 3): # offshore transitional depth\\n ppi.ref_yr = 2003\\n self.turbInstall = osInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\\n self.supportInstall = suppInstallFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\\n self.installation = self.turbInstall + self.supportInstall\\n self.pai = paiCost * ppi.compute('IPPI_PAE') * turbine_number\\n self.electrical = ostElCostFactor * machineRating * ppi.compute('IPPI_OEL') * turbine_number\\n self.portStaging = ptstgCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number\\n self.engPermits = oPrmtsCostFactor * machineRating * ppi.compute('IPPI_OPM') * turbine_number\\n self.scour = scourCostFactor * machineRating * ppi.compute('IPPI_STP') * turbine_number\\n ppi.ref_yr = 2002\\n self.turbTrans = ostTTransFactor * machineRating * ppi.compute('IPPI_TPT') * turbine_number\\n ppi.ref_yr = 2003\\n self.supportTrans = ostSTransFactor * machineRating * ppi.compute('IPPI_OAI') * turbine_number\\n self.transportation = self.turbTrans + self.supportTrans\\n \\n elif (iDepth == 4): # offshore deep\\n print \\\"\\\\ncsmBOS: Add costCat 4 code\\\\n\\\\n\\\"\\n pass\\n \\n self.foundation = self.fdn.getCost() * turbine_number\\n self.cost = self.foundation + \\\\\\n self.transportation + \\\\\\n self.roadsCivil + \\\\\\n self.portStaging + \\\\\\n self.installation + \\\\\\n self.electrical + \\\\\\n self.engPermits + \\\\\\n self.pai + \\\\\\n self.scour\\n\\n if (iDepth > 1):\\n self.suretyBond = suretyBRate * (tcc + self.cost/ turbine_number)\\n self.cost += self.suretyBond * turbine_number\\n else:\\n self.suretyBond = 0.0\\n\\n if (verbose > 0):\\n self.dump()\\n \\n ppi.ref_yr = 2002\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec99e71992e7db783771a5e5102f4b7c\",\n \"score\": \"0.5634694\",\n \"text\": \"def cost_fn(self, states, actions, next_states):\\n if len(next_states.shape) == 1:\\n next_states = np.expand_dims(next_states, 0)\\n # xyz_pos = next_states[:, 14:17]\\n # desired_xyz_pos = self.multitask_goal[14:17] * np.ones_like(xyz_pos)\\n # diff = xyz_pos - desired_xyz_pos\\n next_joint_angles = next_states[:, :7]\\n desired_joint_angles = (\\n self.multitask_goal[:7] * np.ones_like(next_joint_angles)\\n )\\n diff = next_joint_angles - desired_joint_angles\\n return (diff**2).sum(1, keepdims=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c023b709caf5d34aebdeaacc6e5479a\",\n \"score\": \"0.5631135\",\n \"text\": \"def ga_optimization():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2921a4dd31554ae6eac383e8dcd81db\",\n \"score\": \"0.56286204\",\n \"text\": \"def estimate_cost(self, board):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3b88c522d9508a8764c82dbb4a98c5\",\n \"score\": \"0.56272495\",\n \"text\": \"def calculate_reward(self):\\n t=self.period\\n \\n \\n \\n if self.unsatisfied_demand == 'backorders':\\n #print('Back order State:',np.array(self.BO[t]))\\n backorder_costs = np.sum(np.array(self.BO[t]) * np.array(self.bo_costs))\\n hc=self.holding_costs\\n holding_costs = np.sum(np.array(self.INV[t]) * np.array(hc))\\n revenue=np.sum(np.array(self.TotalSales[t]) * np.array(self.selling_price))\\n cost_of_goods=np.sum(np.array(self.TotalCOGS[t]) * np.array(self.cost_price))\\n #self.cost_price=self.cost_price\\n #self.selling_price=self.selling_price\\n \\n ## Penalty applying\\n if t>0:\\n if np.sum(np.array(self.BO[t]))>np.sum(np.array(self.BO[t-1])):\\n backorder_costs=backorder_costs+(t+1/t)\\n if np.sum(np.array(self.INV[t]))>np.sum(np.array(self.INV[t-1])):\\n holding_costs=holding_costs+(t+1/t)\\n else:\\n backorder_costs=backorder_costs\\n holding_costs=holding_costs\\n lost_sales_costs=0\\n \\n elif self.unsatisfied_demand != 'backorders':\\n #print('Back order State:',np.array(self.BO[t]))\\n lost_sales_costs = np.sum(np.array(self.LO[t]) * np.array(self.lo_costs))\\n hc=self.holding_costs\\n holding_costs = np.sum(np.array(self.INV[t]) * np.array(hc))\\n revenue=np.sum(np.array(self.TotalSales[t]) * np.array(self.selling_price))\\n cost_of_goods=np.sum(np.array(self.TotalCOGS[t]) * np.array(self.cost_price))\\n #self.cost_price=self.cost_price\\n #self.selling_price=self.selling_price\\n \\n ## Penalty applying\\n if t>0:\\n if np.sum(np.array(self.LO[t]))>np.sum(np.array(self.LO[t-1])):\\n lost_sales_costs=lost_sales_costs+(t+1/t)\\n if np.sum(np.array(self.INV[t]))>np.sum(np.array(self.INV[t-1])):\\n holding_costs=holding_costs+(t+1/t)\\n else:\\n lost_sales_costs=lost_sales_costs\\n holding_costs=holding_costs\\n backorder_costs=0\\n \\n return holding_costs, backorder_costs,lost_sales_costs,revenue,cost_of_goods\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eef6df526514f0724c4d124d6aecfd5\",\n \"score\": \"0.56261903\",\n \"text\": \"def _construct_adv_cost(self):\\n match_cost = self.GN.compute_log_prob(Xd=self.match_target)\\n adv_cost = -T.sum(match_cost) / self.obs_count\\n return adv_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafb27efeff8f9c992d10e219de5f849\",\n \"score\": \"0.562235\",\n \"text\": \"def evaluate_cost_and_std(\\n self,\\n results,\\n name='',\\n vectorise=False,\\n **kwargs\\n ):\\n\\n # we make it possible to instance a CrossFidelity obj without a\\n # comparison_results dict so that we can easily generate the\\n # comparison data using the same setup (e.g. seed, prefix). But\\n # in that case cannote evaluate the cost.\\n if self._comparison_results is None:\\n raise ValueError('No comparison results set has been passed to'\\n + ' CrossFidelity obj.')\\n\\n def circ_namesA(idx):\\n return name + self._rand_meas_handler.circ_name(idx)\\n\\n def circ_namesB(idx):\\n return self._prefixB + f'{idx}'\\n\\n if self._num_bootstraps > 0:\\n\\n # case using bootstrapping to estimate standard error from dist \\n # of U's\\n\\n dist_tr_rhoA_rhoB = _crosscorrelation_per_u(\\n results, self._comparison_results, self.nb_random,\\n circ_namesA=circ_namesA, circ_namesB=circ_namesB,\\n vectorise=vectorise,\\n )\\n dist_tr_rhoA_2 = _purity_per_u(\\n results, self.nb_random, names=circ_namesA, vectorise=vectorise)\\n dist_tr_rhoB_2 = _purity_per_u(\\n self._comparison_results, self.nb_random, names=circ_namesB, \\n vectorise=vectorise)\\n\\n # bootstrap resample for means and std-errs\\n tr_rhoA_rhoB, tr_rhoA_rhoB_err = bootstrap_resample(\\n np.mean, dist_tr_rhoA_rhoB, self._num_bootstraps,\\n random_seed=self.seed,\\n )\\n tr_rhoA_2, tr_rhoA_2_err = bootstrap_resample(\\n np.mean, dist_tr_rhoA_2, self._num_bootstraps,\\n random_seed=self.seed,\\n )\\n tr_rhoB_2, tr_rhoB_2_err = bootstrap_resample(\\n np.mean, dist_tr_rhoB_2, self._num_bootstraps,\\n random_seed=self.seed,\\n )\\n\\n else:\\n\\n # case without bootstrapping, this only includes standard error\\n # arising from counts spread (currently not even that)\\n\\n if isinstance(results, FastCountsResult):\\n tmp_results = results\\n elif isinstance(results, Result):\\n tmp_results = FastCountsResult(results)\\n else:\\n raise TypeError(\\n 'results type not recognised: '+f'{type(results)}')\\n tmp_results.combine_counts([\\n circ_namesA(idx) for idx in range(self.nb_random)],\\n circ_namesA('')\\n )\\n\\n if isinstance(self._comparison_results, FastCountsResult):\\n tmp_comparison_results = self._comparison_results\\n elif isinstance(self._comparison_results, Result):\\n tmp_comparison_results = FastCountsResult(\\n self._comparison_results)\\n else:\\n raise TypeError(\\n 'results type not recognised: '\\n + f'{type(self._comparison_results)}')\\n tmp_comparison_results.combine_counts([\\n circ_namesB(idx) for idx in range(self.nb_random)],\\n circ_namesB('')\\n )\\n\\n def tmp_circ_namesA(idx):\\n return circ_namesA('')\\n\\n def tmp_circ_namesB(idx):\\n return circ_namesB('')\\n\\n tr_rhoA_rhoB = _crosscorrelation_per_u(\\n tmp_results, tmp_comparison_results, 1,\\n circ_namesA=tmp_circ_namesA, circ_namesB=tmp_circ_namesB,\\n vectorise=vectorise,\\n )[0]\\n tr_rhoA_2 = _purity_per_u(\\n tmp_results, 1, names=tmp_circ_namesA, vectorise=vectorise)[0]\\n tr_rhoB_2 = _purity_per_u(\\n tmp_comparison_results, 1, names=tmp_circ_namesB, \\n vectorise=vectorise)[0]\\n\\n tr_rhoA_rhoB_err, tr_rhoA_2_err, tr_rhoB_2_err = 0, 0, 0\\n\\n # divide by largest\\n if tr_rhoA_2 > tr_rhoB_2:\\n mean = tr_rhoA_rhoB / tr_rhoA_2\\n std = (mean**2) * (\\n (tr_rhoA_rhoB_err**2) / (tr_rhoA_rhoB)**2\\n + (tr_rhoA_2_err)**2 / (tr_rhoA_2)**2\\n )\\n else:\\n mean = tr_rhoA_rhoB / tr_rhoB_2\\n std = (mean**2) * (\\n (tr_rhoA_rhoB_err**2) / (tr_rhoA_rhoB)**2\\n + (tr_rhoB_2_err)**2 / (tr_rhoB_2)**2\\n )\\n\\n # store results of evaluation\\n self.last_evaluation = {\\n 'tr_rhoA_rhoB': tr_rhoA_rhoB,\\n 'tr_rhoA_rhoB_err': tr_rhoA_rhoB_err,\\n 'tr_rhoA_2': tr_rhoA_2,\\n 'tr_rhoA_2_err': tr_rhoA_2_err,\\n 'tr_rhoB_2': tr_rhoB_2,\\n 'tr_rhoB_2_err': tr_rhoB_2_err,\\n 'mean': mean,\\n 'std': std,\\n }\\n\\n return mean, std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fcc29a2120a244d1c43d4967696cc7\",\n \"score\": \"0.56170565\",\n \"text\": \"def efficiency(self):\\n return 0.9\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4717c0565581871da05e316916f12f01\",\n \"score\": \"0.56154263\",\n \"text\": \"def contestEvaluationFunc(currentGameState):\\n def closest_dot(cur_pos, food_pos):\\n food_distances = []\\n for food in food_pos:\\n food_distances.append(util.manhattanDistance(food, cur_pos))\\n return min(food_distances) if len(food_distances) > 0 else 1\\n\\n def closest_ghost(cur_pos, ghosts):\\n food_distances = []\\n for food in ghosts:\\n food_distances.append(util.manhattanDistance(food.getPosition(), cur_pos))\\n return min(food_distances) if len(food_distances) > 0 else 1\\n\\n\\n def ghost_stuff(cur_pos, ghost_states, radius, scores):\\n num_ghosts = 0\\n for ghost in ghost_states:\\n if util.manhattanDistance(ghost.getPosition(), cur_pos) <= radius:\\n scores -= 30\\n num_ghosts += 1\\n return scores\\n\\n def food_stuff(cur_pos, food_positions):\\n food_distances = []\\n for food in food_positions:\\n food_distances.append(util.manhattanDistance(food, cur_pos))\\n return sum(food_distances)\\n\\n def num_food(cur_pos, food):\\n return len(food)\\n\\n def closest_capsule(cur_pos, caps_pos):\\n capsule_distances = []\\n for caps in caps_pos:\\n capsule_distances.append(util.manhattanDistance(caps, cur_pos))\\n return min(capsule_distances) if len(capsule_distances) > 0 else 9999999\\n\\n def scaredghosts(ghost_states, cur_pos, scores):\\n scoreslist = []\\n for ghost in ghost_states:\\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 4:\\n scoreslist.append(scores + 50)\\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 3:\\n scoreslist.append(scores + 60)\\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 2:\\n scoreslist.append(scores + 70)\\n if ghost.scaredTimer > 8 and util.manhattanDistance(ghost.getPosition(), cur_pos) <= 1:\\n scoreslist.append(scores + 90)\\n #if ghost.scaredTimer > 0 and util.manhattanDistance(ghost.getPosition(), cur_pos) < 1:\\n # scoreslist.append(scores + 100)\\n return max(scoreslist) if len(scoreslist) > 0 else scores\\n\\n def ghostattack(ghost_states, cur_pos, scores):\\n scoreslist = []\\n for ghost in ghost_states:\\n if ghost.scaredTimer == 0:\\n scoreslist.append(scores - util.manhattanDistance(ghost.getPosition(), cur_pos) - 10)\\n return max(scoreslist) if len(scoreslist) > 0 else scores\\n\\n def scoreagent(cur_pos, food_pos, ghost_states, caps_pos, score):\\n if closest_capsule(cur_pos, caps_pos) < closest_ghost(cur_pos, ghost_states):\\n return score + 40\\n if closest_dot(cur_pos, food_pos) < closest_ghost(cur_pos, ghost_states) + 3:\\n return score + 20\\n if closest_capsule(cur_pos, caps_pos) < closest_dot(cur_pos, food_pos) + 3:\\n return score + 30\\n else:\\n return score\\n\\n\\n capsule_pos = currentGameState.getCapsules()\\n pacman_pos = currentGameState.getPacmanPosition()\\n score = currentGameState.getScore()\\n food = currentGameState.getFood().asList()\\n ghosts = currentGameState.getGhostStates()\\n\\n #score = score * 2 if closest_dot(pacman_pos, food) < closest_ghost(pacman_pos, ghosts) + 3 else score\\n #score = score * 1.5 if closest_capsule(pacman_pos, capsule_pos) < closest_dot(pacman_pos, food) + 4 else score\\n score = scoreagent(pacman_pos, food, ghosts, capsule_pos, score)\\n score = scaredghosts(ghosts, pacman_pos, score)\\n score = ghostattack(ghosts, pacman_pos, score)\\n score -= .35 * food_stuff(pacman_pos, food)\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256facda13367c15eb8a7eda7244ef2f\",\n \"score\": \"0.56154144\",\n \"text\": \"def _compute(self, predicted, targets, track):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef446cf6d4ca3458b97836a744e99828\",\n \"score\": \"0.56115925\",\n \"text\": \"def profit_curve():\\n cost_TP = 8000; cost_FP = -2000; cost_FN = 0; cost_TN = 0\\n cb_matrix = np.array([[cost_TN, cost_FP], [cost_FN, cost_TP]])\\n\\n profits_gb= []\\n profits_rf= []\\n thresholds = []\\n cfs = []\\n cfs2 = []\\n count = 0\\n models = [model, gb_model]\\n for threshold in np.arange(0.0,1.0,0.01):\\n thresholds.append(threshold)\\n for model_type in models:\\n count += 1\\n predicted_proba = model_type.predict_proba(X_test2)\\n predicted = (predicted_proba [:,1] >= threshold).astype('int')\\n accuracy = metrics.accuracy_score(y_test2, predicted)\\n #recall_score_1 = recall_score(y_test, y_pred)\\n if count % 2 ==1 :\\n cf1 = confusion_matrix(y_test, predicted)\\n cf12 = (cf1[0,1] + cf1[1,1]) / cf1.sum()\\n cfs2.append(cf12)\\n full_matrix_rf = (confusion_matrix(y_test, predicted) * cb_matrix)\\n profits_rf.append(np.sum(full_matrix_rf))\\n #print('Cost matrix sum for RF:', np.sum(full_matrix_rf), 'Threshold:', threshold)\\n #print(f'Accuracy score for RF: {accuracy}')\\n #print(\\\"Recall:\\\", recall_score1)\\n \\n else:\\n cf = confusion_matrix(y_test2, predicted)\\n cf2 = (cf[0,1] + cf[1,1]) / cf.sum()\\n cfs.append(cf2)\\n full_matrix_gb = confusion_matrix(y_test2, predicted) * cb_matrix\\n profits_gb.append(np.sum(full_matrix_gb))\\n #print(f'Accuracy score for GB: {accuracy}')\\n #print('Cost matrix sum for GB:', np.sum(full_matrix_gb),'Threshold:', threshold)\\n\\n _, ax = plt.subplots(figsize=(12,8))\\n ax.plot(cfs2, profits_rf, label='Random Forest', c='r')\\n ax.plot(cfs, profits_gb, label='Gradient Boost', c='b')\\n plt.title('Profit Curve', size=26)\\n #ax.yaxis.zoom(3) \\n plt.xlabel('Loan Acceptance Rate', size=26)\\n plt.ylabel('Profit', size=26)\\n plt.xticks(size=20)\\n plt.yticks(size=20)\\n ax.xaxis.set_major_formatter(mtick.PercentFormatter())\\n plt.tight_layout()\\n plt.legend()\\n plt.savefig('../images/profit_curve_for_git.png')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ebed9fe0896e34395197f02b55c6d02\",\n \"score\": \"0.5611045\",\n \"text\": \"def evaluate_performances(self, population, offsprings, pool=None):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0089c3556d94dff3ce4ca2bbd81da621\",\n \"score\": \"0.5608713\",\n \"text\": \"def stguide():\\n\\n def payoff_fn(**kwargs):\\n values = kwargs[\\\"values\\\"]\\n bid = values[\\\"bid\\\"] if \\\"bid\\\" in values.keys() else 0\\n competitor_bid = (\\n values[\\\"competitor_bid\\\"] if \\\"competitor_bid\\\" in values.keys() else 0\\n )\\n cost = values[\\\"cost\\\"] if \\\"cost\\\" in values.keys() else 0\\n return (bid - cost) * (1 if bid < competitor_bid else 0)\\n\\n nodes = DataNodes()\\n nodes.add_decision(\\n name=\\\"bid\\\",\\n branches=[\\n (\\\"low\\\", 500, \\\"competitor_bid\\\"),\\n (\\\"high\\\", 700, \\\"competitor_bid\\\"),\\n ],\\n maximize=True,\\n )\\n nodes.add_chance(\\n name=\\\"competitor_bid\\\",\\n branches=[\\n (\\\"low\\\", 0.35, 400, \\\"cost\\\"),\\n (\\\"medium\\\", 0.50, 600, \\\"cost\\\"),\\n (\\\"high\\\", 0.15, 800, \\\"cost\\\"),\\n ],\\n )\\n nodes.add_chance(\\n name=\\\"cost\\\",\\n branches=[\\n (\\\"low\\\", 0.25, 200, \\\"profit\\\"),\\n (\\\"medium\\\", 0.50, 400, \\\"profit\\\"),\\n (\\\"high\\\", 0.25, 600, \\\"profit\\\"),\\n ],\\n )\\n nodes.add_terminal(name=\\\"profit\\\", payoff_fn=payoff_fn)\\n\\n return nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c9521546b23c669c01d6b2cd42dd0a\",\n \"score\": \"0.55957615\",\n \"text\": \"def get_expected_cost(self):\\n raise NotImplementedError(\\\"This is an abstract method and needs to implemented in derived classes.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044f3446541075f1f032ecd33f37ee7a\",\n \"score\": \"0.55898297\",\n \"text\": \"def evaluate(predictor, test_ds):\\n coco = convert_to_coco_api(test_ds)\\n coco_evaluator = CocoEvaluator(coco)\\n evaluator_times = []\\n proc_times = []\\n for i in range(len(test_ds)):\\n image, targets = test_ds[i]\\n init = time.time()\\n boxes, labels, probs = predictor.predict(image, 10 , 0.2)\\n proc_times.append(time.time() - init)\\n if boxes.size()[0] == 0:\\n continue\\n outputs = {'boxes': boxes,\\n 'labels':labels,\\n 'scores': probs}\\n res = {targets['image_id'].item(): outputs}\\n evaluator_time = time.time()\\n coco_evaluator.update(res)\\n evaluator_times.append(time.time() - evaluator_time) \\n \\n print(\\\"Averaged stats:\\\", np.mean(evaluator_times))\\n print(\\\"Averaged proc time:\\\", np.mean(proc_times))\\n coco_evaluator.synchronize_between_processes()\\n # accumulate predictions from all images\\n coco_evaluator.accumulate()\\n coco_evaluator.summarize() \\n return coco_evaluator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a45cc1e2d49b9ab4fcedf21bfd7ce1c\",\n \"score\": \"0.5584052\",\n \"text\": \"def _compute(self):\\n\\n # unless clause D checks are used, test one literal at a time\\n # and add it either to satisfied of backbone assumptions\\n i = 0\\n while i < len(self.setd):\\n if self.ucld:\\n self.do_cld_check(self.setd[i:])\\n i = 0\\n\\n if self.setd:\\n # if may be empty after the clause D check\\n\\n self.ss_assumps.append(self.setd[i])\\n if not self.oracle.solve(assumptions=self.ss_assumps + self.bb_assumps):\\n self.ss_assumps.pop()\\n self.bb_assumps.append(-self.setd[i])\\n\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9409000c5660df50b37f6a3ac6a3f0\",\n \"score\": \"0.5582407\",\n \"text\": \"def cost(self, data_X, data_Y):\\n return self.cost_function(data_Y, self.predict(data_X))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05028ca83a0a236617f504313ce66b10\",\n \"score\": \"0.5576904\",\n \"text\": \"def compute_performance(self):\\n # TODO Track fixation breaks here? Also in the remote?\\n\\n if self.trial_data:\\n data = pd.DataFrame([t for t, _ in self.trial_data])\\n mean_acc = data[\\\"correct\\\"].mean()\\n responses = data[\\\"responded\\\"].sum()\\n return mean_acc, responses\\n else:\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a37e28c3700639fb48fb89aa3704876\",\n \"score\": \"0.5576557\",\n \"text\": \"def compute_common_data(self):\\n self.filter_expression_and_priors()\\n print('Creating design and response matrix ... ')\\n self.design_response_driver.delTmin = self.delTmin\\n self.design_response_driver.delTmax = self.delTmax\\n self.design_response_driver.tau = self.tau\\n (self.design, self.response) = self.design_response_driver.run(self.expression_matrix, self.meta_data)\\n\\n # compute half_tau_response\\n print('Setting up TFA specific response matrix ... ')\\n self.design_response_driver.tau = self.tau / 2\\n (self.design, self.half_tau_response) = self.design_response_driver.run(self.expression_matrix, self.meta_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ee2525f7cfdeb1c9e5176e68829e221\",\n \"score\": \"0.55751634\",\n \"text\": \"def _compute_dci(mus_train, ys_train, mus_test, ys_test):\\n scores = {}\\n importance_matrix, train_err, test_err = compute_importance_gbt(\\n mus_train, ys_train, mus_test, ys_test)\\n assert importance_matrix.shape[0] == mus_train.shape[0]\\n assert importance_matrix.shape[1] == ys_train.shape[0]\\n scores[\\\"informativeness_train\\\"] = train_err\\n scores[\\\"informativeness_test\\\"] = test_err\\n scores[\\\"disentanglement\\\"] = disentanglement(importance_matrix)\\n scores[\\\"completeness\\\"] = completeness(importance_matrix)\\n return scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0597dfef712c767ea648feecb122e59a\",\n \"score\": \"0.55748093\",\n \"text\": \"def calculate(self, results):\\n self.results = results\\n self.original_candidates_ppl = []\\n self.successful_candidates_ppl = []\\n\\n for i, result in enumerate(self.results):\\n if isinstance(result, FailedAttackResult):\\n continue\\n elif isinstance(result, SkippedAttackResult):\\n continue\\n else:\\n self.original_candidates.append(\\n result.original_result.attacked_text.text.lower()\\n )\\n self.successful_candidates.append(\\n result.perturbed_result.attacked_text.text.lower()\\n )\\n\\n ppl_orig = self.calc_ppl(self.original_candidates)\\n ppl_attack = self.calc_ppl(self.successful_candidates)\\n\\n self.all_metrics[\\\"avg_original_perplexity\\\"] = round(ppl_orig[0], 2)\\n self.all_metrics[\\\"original_perplexity_list\\\"] = ppl_orig[1]\\n\\n self.all_metrics[\\\"avg_attack_perplexity\\\"] = round(ppl_attack[0], 2)\\n self.all_metrics[\\\"attack_perplexity_list\\\"] = ppl_attack[1]\\n\\n return self.all_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47203e7cbdd83fedd19cd6b2096c4649\",\n \"score\": \"0.5573001\",\n \"text\": \"def _calcUsage(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc28f5ebd05b9986f887b9163841c36b\",\n \"score\": \"0.5566476\",\n \"text\": \"def cost(self, module):\\n module.resetDerivatives()\\n error = 0.0\\n ponderation = 0.0\\n shuffledSequences = []\\n for seq in self.ds._provideSequences():\\n shuffledSequences.append(seq)\\n shuffle(shuffledSequences)\\n for seq in shuffledSequences:\\n e, p = self._calcDerivs(module, seq)\\n error += e\\n ponderation += p\\n grad = module.derivs.copy()\\n error /= ponderation\\n # Calculate the scaling parameter of the gradient to make the elements\\n # sum to 1.0.\\n norm = absolute(grad).sum()\\n if norm > 10.0:\\n scale = 1.0 / norm\\n else:\\n scale = 1.0\\n return error, -grad * scale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7306e008dcc9808da59f9ce9f9ba8250\",\n \"score\": \"0.55659544\",\n \"text\": \"def compute():\\n\\n body = request.get_json(force=True)\\n\\n # TODO: Finish this method.\\n\\n # Note: I've spoken to one of the FE engineers and it wouldn't be a\\n # great experience to have our endpoint slow. Can we speed up or do\\n # something about the baseline risk API?\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7428f49862761119421b4b4d36cce3e\",\n \"score\": \"0.5565938\",\n \"text\": \"def compute_stress(state):\\n K=state.aux[1,:,:]\\n stress_rel=state.aux[2,:,:]\\n eps=state.q[0,:,:]\\n state.p[0,:,:] = np.where(stress_rel==1,1,0) * K*eps \\\\\\n +np.where(stress_rel==2,1,0) * (np.exp(eps*K)-1) \\\\\\n +np.where(stress_rel==3,1,0) * K*eps+K**2*eps**2\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993697,"cells":{"query_id":{"kind":"string","value":"462594201d5d2bf56be495970fb8cac8"},"query":{"kind":"string","value":"This just calls drop_series on the characters specified."},"positive_passages":{"kind":"list like","value":[{"docid":"b8ba7aeb9c2e2bbbeefc466fdee26036","score":"0.7531891","text":"def drop_series_list(item: pd.Series, chars_to_drop=None) -> pd.Series:\n if chars_to_drop is None:\n chars_to_drop = [\"-\", None, \"/\", \"\"]\n for char_to_drop in chars_to_drop:\n item = drop_series(item, char_to_drop)\n return item","title":""}],"string":"[\n {\n \"docid\": \"b8ba7aeb9c2e2bbbeefc466fdee26036\",\n \"score\": \"0.7531891\",\n \"text\": \"def drop_series_list(item: pd.Series, chars_to_drop=None) -> pd.Series:\\n if chars_to_drop is None:\\n chars_to_drop = [\\\"-\\\", None, \\\"/\\\", \\\"\\\"]\\n for char_to_drop in chars_to_drop:\\n item = drop_series(item, char_to_drop)\\n return item\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f83bca0f7ea21874cf2e6713ce456691","score":"0.7848441","text":"def drop_series(item: pd.Series, char_to_drop) -> pd.Series:\n return item[item != char_to_drop]","title":""},{"docid":"2d396762a72c513b02da28942e414a14","score":"0.6176747","text":"def remove_symbols(self, symbols):\n self.df = self.df.drop(symbols, level='symbol')\n self.df.index = self.df.index.remove_unused_levels()","title":""},{"docid":"6a7baa14184dd79429e28c2086008150","score":"0.55239195","text":"def remove(text, chars):\r\n for c in chars:\r\n text = text.replace(c, \"\")\r\n return text","title":""},{"docid":"a534d79894bdfed1e2d891d2f2027424","score":"0.5481985","text":"def deselect_all(self):\n for i in range(len(characters)):\n self.deselect(i)","title":""},{"docid":"0421080da449c8492e50fef967f438ee","score":"0.54818064","text":"def ignore_columns(self, columns, dataframe):\n print(\"Holoclean cannot use dataframes with strings \"\n \"more than 255 characters\")\n columns_message = \" and \".join(columns)\n message = \"The columns \" + columns_message + \" will be dropped\"\n print (message)\n for column in columns:\n dataframe = dataframe.drop(column)\n return dataframe","title":""},{"docid":"2cd8306a8b4e343e335d482ede450f8a","score":"0.5374376","text":"def unicode_filter_df_series(self, df_series):\n SL = [] # char list\n\n for i in xrange(0,len(df_series)): # turns string into list of chars\n sl = list (df_series[i])\n SL.append(sl)\n \n for i in xrange(0, len(SL)): # filters chars \n for j in xrange(0,len(SL[i])):\n SL[i][j] = self.filter_unicode(SL[i][j])\n\n return np.array(SL)","title":""},{"docid":"cfd93a831b8bef3cc6a1423a657c7f0d","score":"0.5322538","text":"def clean_series(series, *args, **kwargs):\n if not series.dtype == np.dtype('O'):\n return series\n if any_generated((isinstance(v, datetime.datetime) for v in series)):\n series = series.apply(clip_datetime)\n if any_generated((isinstance(v, basestring) for v in series)):\n series = series.apply(encode)\n return series","title":""},{"docid":"a7b9a1310733bea164720da0b511263a","score":"0.5312601","text":"def delete_characters(self, count=None):\r\n count = count or 1\r\n\r\n for _ in range(min(self.columns - self.cursor.x, count)):\r\n self[self.cursor.y].pop(self.cursor.x)\r\n self[self.cursor.y].append(self.cursor.attrs)","title":""},{"docid":"550732926ea93e37aeacaed862f18116","score":"0.5282036","text":"def remove_invalid_chars(seq):\n\t\tfor letter in INVALID_VALUE_CHARS:\n\t\t\tseq = seq.replace(letter, '')\n\t\treturn seq","title":""},{"docid":"6e8c39f8fa606193751e8ccaed832944","score":"0.52656263","text":"def drop_str_cols(df) -> pd.DataFrame:\n\n return df.select_dtypes(exclude=['object'])","title":""},{"docid":"a58dd8f103bdb08bbc18fcfe30797fbe","score":"0.5241149","text":"def strip(self, chars=None):\r\n return ''","title":""},{"docid":"eb160528be8c898f4ab1c66fcfc44873","score":"0.52129316","text":"def drop(self, *args, **kwargs):\n raise NotImplementedError('To be implemented')","title":""},{"docid":"7398b2c31634670aaaefd828599c6028","score":"0.5209102","text":"def clean_data(column:pd.Series) -> pd.Series:\n column = column.copy()\n column = column.str.replace(r\"[(),:!?@&\\'\\`\\\"\\_]\", \"\")\n column = column.str.replace(r\"[\\n]\", \" \")\n column = column.str.replace(r\"[...]\", \"\")\n column = column.str.replace(r\"[\\d]\", \"\") \n column = column.str.replace(r\"[-]\", \"\") \n column = column.str.replace(r\"[\", \"\")\n column = column.str.replace(r\"]\", \"\")\n column = column.str.lower()\n return column","title":""},{"docid":"e1180af190f8f7b6b968fa276ff9078f","score":"0.51874286","text":"def deleteChar(c, s):\n pass","title":""},{"docid":"b3629ee58238a19bec09a109ee179bd5","score":"0.5170022","text":"def drop_pfam(df, dropList):\n \n df.drop(dropList, axis=1, inplace=True)\n return df","title":""},{"docid":"8345cabd68b43c5d557af9d122589026","score":"0.5140048","text":"def dropNames(events):\n to_drop = ['subEventName', 'eventName']\n print(\"\\tDropping subEventName and eventName columns\")\n events.drop(columns=to_drop, inplace=True)","title":""},{"docid":"0cd207c3ddd2f32fd6e0d8969b9215ee","score":"0.51045287","text":"def _strip_chars(word, chars_to_remove):\n for char in chars_to_remove:\n word = word.replace(char, '')\n return word","title":""},{"docid":"f0d1611b8068599c3463e4c138a42a6c","score":"0.50480825","text":"def dropData(mimeType, text):\n ...","title":""},{"docid":"fc911fc18a9e24c214af82172e1711d8","score":"0.5009979","text":"def sdc_pandas_dataframe_drop(df, labels=None, axis=0, index=None, columns=None, level=None, inplace=False,\n errors='raise'):\n\n _func_name = 'drop'\n\n ty_checker = TypeChecker(f'Method {_func_name}().')\n ty_checker.check(df, DataFrameType)\n\n if not isinstance(labels, types.Omitted) and labels is not None:\n ty_checker.raise_exc(labels, 'None', 'labels')\n\n if not isinstance(axis, (int, types.Omitted)):\n ty_checker.raise_exc(axis, 'int', 'axis')\n\n if not isinstance(index, types.Omitted) and index is not None:\n ty_checker.raise_exc(index, 'None', 'index')\n\n if not isinstance(columns, (types.Omitted, types.Tuple, types.Literal)):\n ty_checker.raise_exc(columns, 'str, tuple of str', 'columns')\n\n if not isinstance(level, (types.Omitted, types.Literal)) and level is not None:\n ty_checker.raise_exc(level, 'None', 'level')\n\n if not isinstance(inplace, (bool, types.Omitted)) and inplace:\n ty_checker.raise_exc(inplace, 'bool', 'inplace')\n\n if not isinstance(errors, (str, types.Omitted, types.Literal)):\n ty_checker.raise_exc(errors, 'str', 'errors')\n\n args = {'labels': None, 'axis': 0, 'index': None, 'columns': None, 'level': None, 'inplace': False,\n 'errors': f'\"raise\"'}\n\n def sdc_pandas_dataframe_drop_impl(df, _func_name, args, columns):\n func_args = ['df']\n for key, value in args.items():\n if key not in func_args:\n if isinstance(value, types.Literal):\n value = value.literal_value\n func_args.append(f'{key}={value}')\n\n if isinstance(columns, types.StringLiteral):\n drop_cols = (columns.literal_value,)\n elif isinstance(columns, types.Tuple):\n drop_cols = tuple(column.literal_value for column in columns)\n else:\n raise ValueError('Only drop by one column or tuple of columns is currently supported in df.drop()')\n\n func_def, global_vars = sdc_pandas_dataframe_drop_codegen(_func_name, func_args, df, drop_cols)\n loc_vars = {}\n exec(func_def, global_vars, loc_vars)\n _drop_impl = loc_vars['sdc_pandas_dataframe_drop_impl']\n return _drop_impl\n\n return sdc_pandas_dataframe_drop_impl(df, _func_name, args, columns)","title":""},{"docid":"85acab321dc8416e031fa1734adeb83a","score":"0.5002741","text":"def remove():\n series = configuration['series']\n\n if len(series) == 0:\n click.echo('No series to remove!')\n return\n\n click.echo(\"Which series shall be removed?\")\n for idx, current in enumerate(series):\n click.echo(\"{0}. {1}\".format(idx, current['name']))\n\n while 1:\n value = click.prompt('Enter the number of the series to remove',\n type=int)\n if value >= 0 and value < len(series):\n break\n else:\n click.echo('OOps, that\\'s not a valid option. Choose a value '\n 'between 0 and {0}!'.format(len(series)-1))\n\n name = series[value]['name']\n fn.remove_series(configuration, series[value])\n click.echo('Series {0} removed!'.format(name))","title":""},{"docid":"d0834fb275457b33d2dc354ad00e5b6b","score":"0.49970055","text":"def rstrip(self, chars=None):\r\n return ''","title":""},{"docid":"9e07610865b681d21e89d605218438ed","score":"0.49880996","text":"def remove_characters(data):\n\t# Reading data containing list of important characters only and their IDs\n\tchar_id_index = pd.read_csv('.\\\\characters.csv')\n\t# Creating aa dict containing character IDs for each character\n\tnames = {old:new for (old, new) in zip(char_id_index['Character Name'], char_id_index['Character ID'])}\n\t# Converting the data dict into a pandas data frame for easy handling\n\tchar_movie_matrix = pd.DataFrame(data)\n\t# Renaming characters with their character IDs\n\tchar_movie_matrix.rename(index=names, inplace=True)\n\t# Creating a list of unimportant characters to be removed\n\tpop_list = [name for name in char_movie_matrix.index if name not in list(char_id_index['Character ID'])]\n\t# Removing all unimportant characters from the data frame\n\tchar_movie_matrix = char_movie_matrix.drop(pop_list)\n\t# Combine rows with same index\n\tchar_movie_matrix = combine_rows(char_movie_matrix)\n\t# Considering movies in the Infinity Saga only\n\tdel char_movie_matrix['Spider-Man: Far from Home']\n\t# Returning the character-movie-run_time matrix in the form of a pandas dataframe\n\treturn char_movie_matrix","title":""},{"docid":"792e840595c527467e128704577abc2e","score":"0.4965961","text":"def _drop_columns(data, columns):\n logger.info(\"{} 필드를 데이터에서 제외시킵니다\".format(columns))\n data = data.drop(columns=[columns], axis=1)\n return data","title":""},{"docid":"a2bb1b005b303922d4e7dfbb8db8431e","score":"0.49580613","text":"def filter_stocks(dataframe):\n dataframe.Symbol = dataframe.Symbol.astype('str')\n dataframe.Symbol = dataframe.Symbol.map(lambda x: x.strip())\n dataframe = dataframe[dataframe.Symbol != 'ACCP']\n print(\"Number of tickers before trim:\", len(dataframe))\n dataframe = dataframe[dataframe.Symbol.str.isalpha()]\n print(\"Number of tickers after trim:\", len(dataframe))\n dataframe = dataframe.set_index('Symbol')\n return dataframe","title":""},{"docid":"20165b73b9e9290a61bd6060fbba5828","score":"0.49577388","text":"def erase_characters(self, count=None):\r\n count = count or 1\r\n\r\n for column in range(self.cursor.x,\r\n min(self.cursor.x + count, self.columns)):\r\n self[self.cursor.y][column] = self.cursor.attrs","title":""},{"docid":"c441579147d607a994bb69058374915d","score":"0.49402955","text":"def dropPLZ(path, drop_plz):\n df = gpd.read_file(path)\n # Drop all not necessary plz\n df.drop(df[~df['plz'].str.contains('|'.join(str(x) for x in\n drop_plz))].index, axis=0, inplace=True)\n #df.drop(['note'], axis=1, inplace = True)\n df['plz'] = df['plz'].astype(int)\n\n return df","title":""},{"docid":"94d69b8575ac30fe5d162854d5954017","score":"0.493667","text":"def drop_features(df, features):\n return df.drop(columns=features)","title":""},{"docid":"d0e9ea85d1cce428a5fa4a039a03943f","score":"0.49306646","text":"def deleteSpecChars(text, specialChars):\r\n for char in specialChars:\r\n text = text.replace(char, '') \r\n return text","title":""},{"docid":"f3d62a471f5c60ec30aaf15afbac476c","score":"0.49301264","text":"def lstrip(self, chars=None):\r\n return ''","title":""},{"docid":"40b2c8bf6d0a69af862056b29990b3f1","score":"0.49283543","text":"def remove_stop_words(series):\n regex_stop = re.compile(r'\\b(' + r'|'.join(stopwords.words('english')) + r')\\b\\s*')\n series = series.str.replace(regex_stop, '')\n series = series.str.replace('realdonaldtrump', '')\n series = series.str.replace('thank', '')\n series = series.str.replace('trump', '')\n series = series.str.replace('donald', '')\n series = series.str.replace('amp', '')\n\n return series","title":""},{"docid":"ee4e2b8b204f5ed0e6d592855e00b949","score":"0.49270698","text":"def test_keep_drop_columns(d):\n columns = list(d.dtype.names)\n selected_data = strax.apply_selection(d, keep_columns=columns[1:])\n\n # Check we din't loose anything of the original array\n assert columns == list(d.dtype.names)\n\n for c in columns[1:]:\n assert np.all(selected_data[c] == d[c])\n for c in columns[:1]:\n assert c not in selected_data.dtype.names\n \n # Repeat test but for drop columns:\n selected_data = strax.apply_selection(d, drop_columns=columns[:1])\n for c in columns[1:]:\n assert np.all(selected_data[c] == d[c])\n for c in columns[:1]:\n assert c not in selected_data.dtype.names","title":""},{"docid":"a94a21bfc36e44ecfa51a52d159a031f","score":"0.49227712","text":"def clean_data(cfg,df):\n \n df.drop(['Date/Time'],axis=1,inplace=True) \n return df","title":""},{"docid":"9c8d858d4df2736f26ec67e3fc2a1184","score":"0.4914771","text":"def removeNonAlphanumericCharacter(self):\n\n\t\tnonAlphaNumericCharacter = set(\"`~!@#$%^&*()_+-={}|[]\\:\\\"<>?;',./'\u0013\u0019\u0018\\xc2\")\n\t\ttemp = []\n\n\t\tfor item in self.characterMap:\n\t\t\tif not any(( c in nonAlphaNumericCharacter ) for c in item[1]):\n\t\t\t\ttemp.append(item)\n\n\t\tself.cleanCharacterMap = temp","title":""},{"docid":"96be73ad46667d6ba65a7b68d65bcada","score":"0.49120212","text":"def drop_columns(data, attributes):\n return data.drop(attributes, axis=1)","title":""},{"docid":"069564257d5095f4d03e39836cb8b720","score":"0.4910914","text":"def _delchars(p_str, chars):\n # Translate demands a mapping string of 256 characters;\n # whip up a string that will leave all characters unmolested.\n identity = ''.join([chr(x) for x in range(256)])\n return p_str.translate(identity, chars)","title":""},{"docid":"a872c41c38febb94172673c5e8ba5eac","score":"0.49096254","text":"def drop_column_suffix(frame, suffix):\r\n frame.columns = [col if not col.endswith(\r\n suffix) else col[:-len(suffix)] for col in frame.columns]\r\n return frame","title":""},{"docid":"4d237be40a951924fcb1f4d67d01bd03","score":"0.4901735","text":"def drop_features(df, feature_list):\n df = df.drop(feature_list, 1)\n return df","title":""},{"docid":"f47f9d5adbc1f3ff2ba04bdcb723c4cb","score":"0.48922855","text":"def drop(self):\n list_of_high_corr = [1, 3, 4, 8, 10, 14, 18, 23, 27, 30, 32, 33, 34, 36, 41, 44, 45, 47, 48]\n self.data = self.data.drop(['feature_'+str(x) for x in list_of_high_corr], axis=1)","title":""},{"docid":"7cf073afbc4ac26a57e0c873830b7e81","score":"0.48867807","text":"def remove( str, chars ):\n if chars: return remove( str.replace(chars[0], \"\"), chars[1:] )\n return str","title":""},{"docid":"fc4f39370e006aa7cd6d2286923579da","score":"0.48802772","text":"def remove_problem_chars(x):\n #print x\n chars = [\"\\)\", \"\\(\", \"\\[\", \"\\]\", \"\\:\"]\n for c in chars:\n x = re.sub(c, \"\", x)\n return x","title":""},{"docid":"6605961ecbdcd6a6f39736808b56a2ec","score":"0.48673037","text":"def setify_drop_nones(series):\n\n def drop_nones(x):\n x.discard(None)\n return x\n\n return series.apply(lambda x: set(x)).apply(drop_nones)","title":""},{"docid":"16f4b18b51e3f09cd32d241f52b75976","score":"0.4849629","text":"def RemoveCharacterExtension(self,pExt):\n pass","title":""},{"docid":"91cb13f123050e77f4cba8ca00ad235f","score":"0.4839863","text":"def drop_columns(self, columns): \n self.data.drop(columns, axis=1, inplace=True) \n return self.data","title":""},{"docid":"6c5ed089f51a7db44014fb9d8e89567d","score":"0.48382556","text":"def strip(s, chars=None):\r\n return s.strip(chars)","title":""},{"docid":"79342c4033937072a2701a3a67754c59","score":"0.4827922","text":"def drop_option(option, cmd, drop_value=False):\n to_drop = []\n for i, word in enumerate(cmd):\n if word == option:\n if drop_value is True:\n to_drop += [i, i + 1]\n elif is_string(drop_value):\n if cmd[i + 1].startswith(drop_value):\n to_drop += [i, i + 1]\n else:\n # do not drop option\n pass\n else:\n to_drop += [i]\n continue\n if word.startswith(option + \"=\"):\n to_drop += [i]\n continue\n for idx in sorted(to_drop, reverse=True):\n del cmd[idx]\n return cmd","title":""},{"docid":"47580b45a4046bab5acd46bdc68df332","score":"0.4826921","text":"def remove_char(string,\n iterable):\n for i in iterable:\n string = string.replace(i, \"\")\n return string","title":""},{"docid":"f3ec8389b9138a8ef7808296f95ba076","score":"0.4798504","text":"def clean_rogue_chars(self, s: bytes) -> bytes:\n if self.rogue_chars:\n for cleaner in self.rogue_char_cleaners:\n s = cleaner(s)\n return s","title":""},{"docid":"1895f17692428dc3d07e106d48303ae9","score":"0.47975427","text":"def delete_junk_data(self, junk_string):\n while junk_string in self.temp:\n self.temp.remove(junk_string)","title":""},{"docid":"d3d4b742d15b6cf9cec21eaaffe62635","score":"0.4783758","text":"def drop_columns(df, drop_cols):\n df = df.drop(drop_cols, 1)\n return df","title":""},{"docid":"a2252b4d7c6ed56b1e2a904b4e93ee52","score":"0.47530732","text":"def test_stripDegenerate(self):\n s = RnaMolType.stripDegenerate\n self.assertEqual(s('UCAG-'), 'UCAG-')\n self.assertEqual(s('NRYSW'), '')\n self.assertEqual(s('USNG'), 'UG')","title":""},{"docid":"606e2a7d79ddd4d700ead44d7532f699","score":"0.47489426","text":"def remove_direct_identifier(self, df):\n direct_identifiers = self.__config.get_direct_identifiers()\n df = df.drop(columns=direct_identifiers)\n logger.info(\"Dropped direct identifying attributes %s\", \", \".join(direct_identifiers))\n return df","title":""},{"docid":"9c35c60284ea66cb9f05c3c7b0c16cad","score":"0.47382987","text":"def _clean_barcode(row: pd.Series):\n barcode = row['barcode_raw']\n barcode_clean = barcode.split('-')[0]\n\n return barcode_clean","title":""},{"docid":"eda559869e640dd4fb4db7eefc579867","score":"0.4710356","text":"def test_strip_degenerate(self):\n s = RnaMolType.strip_degenerate\n self.assertEqual(s(\"UCAG-\"), \"UCAG-\")\n self.assertEqual(s(\"NRYSW\"), \"\")\n self.assertEqual(s(\"USNG\"), \"UG\")","title":""},{"docid":"b7cf6229280a7b7dc7df055bb5eae26d","score":"0.4673311","text":"def clean_from_symbols(column, list_of_cleaned_values):\n\n symbols = [',', '-', '.', ' ', ';', ':', '!', \"*\"]\n\n for value in column:\n value = ''.join(i for i in value if i not in symbols)\n if value.isnumeric():\n list_of_cleaned_values.append(int(value))\n else:\n list_of_cleaned_values.append(value)\n\n return list_of_cleaned_values","title":""},{"docid":"39d52402e598bffcc6c147fc2ad73eea","score":"0.4670337","text":"def discard(self, n: int):\n self.replace(n, '')","title":""},{"docid":"dbf78e9ab8c12caf5c204e3515b114db","score":"0.46509728","text":"def delete_stress(s):\n try: s = re.sub('\\d','',s)\n except TypeError:\n pass\n return s","title":""},{"docid":"9b5c7dde82497bde834d3f80571d8832","score":"0.4637092","text":"def removeToneNumbersFromText(text):\n\t\treturn ''.join([i for i in text if not i.isdigit()])","title":""},{"docid":"f6869c12f798dc43d720f6bd015974aa","score":"0.46365967","text":"def dropColumns(self, column_list):\n\n cols = [col if col not in self._col_map else self._col_map[col] for col in column_list]\n self._df = self._df.drop(columns=cols)","title":""},{"docid":"9c1cffb031c5ce85b8b60db81e51252b","score":"0.46308625","text":"def remove_chars(s: str, keep: Union[str, re.Pattern] = LEGAL_CHARS) -> str:\n return ' '.join([re.sub(keep, '', i) for i in s.split()])","title":""},{"docid":"da08fa0a51137eaf74107376ad9d84a7","score":"0.46243876","text":"def cleanup_supervised_data(supervised_data):\n supervised_data.drop(\n supervised_data.columns[[9, 10, 11, 12, 13, 14, 15]], axis=1, inplace=True)\n\n print(\"supervised datasets after cleanup unused column\\n{}\".format(\n supervised_data.head()))","title":""},{"docid":"1fce5735a202a723fa5e0d34e52e08fb","score":"0.46220335","text":"def remove_bad(self, disp=['FP','EB','NEB','BEB','SV','BD','NPC','SB1','SB2','FA']):\n for bad in disp:\n self.sample.query(\"disp != '%s'\"%bad, inplace=True)\n self.sample.query(\"drop == False\", inplace=True)\n self.sample.query(\"finish == False\", inplace=True)","title":""},{"docid":"b404c6d3244663b3e92891a71e450fff","score":"0.4618967","text":"def test_strip_bad(self):\n s = RnaMolType.strip_bad\n self.assertEqual(s(\"UCAGwsnyrHBND-D\"), \"UCAGwsnyrHBND-D\")\n self.assertEqual(s(\"@#^*($@!#&()!@QZX\"), \"\")\n self.assertEqual(s(\"aaa ggg ---!ccc\"), \"aaaggg---ccc\")","title":""},{"docid":"5f44a53b8e2000762c4af87389c54b4f","score":"0.46145","text":"def del_letters(self):\n if self.backspace_timer <= 0:\n self.usr_input = self.usr_input[:-1]\n self.backspace_timer = 2","title":""},{"docid":"1538e337f1f505204b5e8c7baf69deb8","score":"0.46123657","text":"def SetCharacterizeOff(self):\n pass","title":""},{"docid":"7dee06ac56ebb64778652354c49e4ac0","score":"0.46115652","text":"def _dropChunk(self, x, z):\n del self.chunkCache[(x, z)]\n del self.timestamps[(x, z)]\n self.cachedChunks -= 1","title":""},{"docid":"6aa2f1217997e57c756ba9a67b11104e","score":"0.4611185","text":"def drop(self, drop=None, like=None):\n\n from pyranges.methods.drop import _drop\n return _drop(self, drop, like)","title":""},{"docid":"d4ccb205f3b706b00eaea8a7364be916","score":"0.46108228","text":"def drop(self):\n self.s.account.execute_cmd('give/drop %s' % self.o.get_display_name(self.s, plain=True))","title":""},{"docid":"52bd2e79d3dd657f77280ce14be30f4d","score":"0.46059892","text":"def _remove(df, years, months=range(1, 13), days=range(1, 32)):\r\n if isinstance(days, int):\r\n days = [days]\r\n if isinstance(months, int):\r\n months = [months]\r\n if isinstance(years, int):\r\n years = [years]\r\n for day in days:\r\n for month in months:\r\n for year in years:\r\n # print(f'day: {day}, month: {month}, year: {year}')\r\n df = df[~((df['DAY'] == day) & (df['MONTH'] == month) &\r\n (df['YEAR'] == year))]\r\n return df","title":""},{"docid":"e5cf71753578216b1cf9281be083348a","score":"0.46005324","text":"def drop_data(data, drop_ranges=None):\n if not drop_ranges:\n drop_ranges=[(0, 50000), (300000, 450000), (700000, 800000)]\n\n data_to_drop = []\n for drop_range in drop_ranges:\n data_to_drop += list(range(drop_range[0],drop_range[1]))\n\n return data.drop(labels=data_to_drop).reset_index(drop=True)","title":""},{"docid":"d077ff4d4df2e90362e15c6ab484a684","score":"0.45974976","text":"def __drop_df__(self):\n self.df_income = self.filtered_df.loc[self.filtered_df['category'] == 'Einkommen']\n self.filtered_df = self.filtered_df.drop(self.filtered_df[self.filtered_df.category == 'Einkommen'].index)\n self.filtered_df = self.filtered_df.drop(self.filtered_df[self.filtered_df.category == 'Ignorieren'].index)","title":""},{"docid":"cdda96a8657a1c91379e1038f70fcde2","score":"0.45927387","text":"def strip(lst=None,chars=None):\n if lst is None: raise ValueError(\"lst must be input\")\n if type(lst) is str: return lst.strip(chars)\n return [o.strip(chars) for o in np.array(lst,ndmin=1)]","title":""},{"docid":"9b925aa64b1e2d71531035d3df55766f","score":"0.45908934","text":"def _strip_index(symbol_name):\n return \"_\".join(symbol_name.split(\"_\")[:-1])","title":""},{"docid":"9b925aa64b1e2d71531035d3df55766f","score":"0.45908934","text":"def _strip_index(symbol_name):\n return \"_\".join(symbol_name.split(\"_\")[:-1])","title":""},{"docid":"4ee13e125b1f273bea8a6e6a10eaf78e","score":"0.45868257","text":"def remove_mean_and_player(team_players_9cat_stats_dataframe, player_to_drop):\n team_players_9cat_stats_dataframe = team_players_9cat_stats_dataframe.drop(MEAN_ROW)\n team_players_9cat_stats_dataframe = \\\n team_players_9cat_stats_dataframe[~team_players_9cat_stats_dataframe[\n PLAYER_COLUMN].str.contains(player_to_drop)]\n return team_players_9cat_stats_dataframe","title":""},{"docid":"6cc54eebae2d8c7b600ab5ab3e8a81b2","score":"0.45858672","text":"def _clean_non_chars(text):\r\n # should this also remove everything after the first tab?\r\n\r\n # remove non alphanumeric characters\r\n return _non_word_chars.sub(\"\", text)","title":""},{"docid":"100f6fcf67ac46c4fe51303539b88276","score":"0.45856407","text":"def drop_columns(data):\n # Load in Pickle file.\n feats2delete = pickle.load(open('reuseables/feats_to_delete.pkl', 'rb'))\n\n # Loop over the columns defined in the Pickle file and drop each from the dataset.\n for col in feats2delete:\n data.pop(col)\n\n filter_cols = [col for col in data if col.startswith('comp')]\n\n for col in filter_cols:\n data.pop(col)\n\n logging.info(\n f'The following features have been removed from the original dataset: {feats2delete} and {filter_cols}.')\n\n return data","title":""},{"docid":"1170ef25fec4258aaab1d149b7367612","score":"0.4577277","text":"def clean_chunk(self, txt: str):\n txt = self.remove_non_greek(txt)\n return self.remove_multiple_space(txt)","title":""},{"docid":"f827feb946ae979b1168fce0db082f1a","score":"0.45708227","text":"def drop_na(x):\n a = x.strip().split(\"\\001\")\n filtered_data = [a[i] for i in xrange(len(a)) if i not in na_col.value]\n indice = range(len(filtered_data)-1)\n #return float(filtered_data[0]), np.array(indice, dtype=np.int32), np.array(filtered_data[1:],dtype=np.float64)\n return filtered_data[0], np.array(indice, dtype=np.int32), np.array([el.strip() for el in filtered_data[1:]],dtype=np.dtype(Decimal))","title":""},{"docid":"57df202c7545cfe1df339a4af908e8d0","score":"0.4568766","text":"def test_badChars(self):\n c = CharFilter('abc')\n d = CharFilter('abc',invert_charset=True)\n #should automatically strip whitespace\n s2 = ' aceacef '\n self.assertEqual(c.badChars(s2), set('ef'))\n self.assertEqual(d.badChars(s2), set('ac'))","title":""},{"docid":"ab8e2796c4df3292d821e448da5dc339","score":"0.456675","text":"def drop_key(dict):\n\t\n\t# Remove articles, personal pronouns, prepositions, etc\n\tdrop = [ \n\t\t\"the\", \"a\", \"A\", \"As\", \"as\", \"at\", \"At\", \"Against\", \"About\", \"By\", \"With\", \"It\", \"Is\", \"to\", \"To\", \"TO\", \"in\", \"In\", \n\t\t\"MARKETS:\", \"BRIEFING\", \"IN\", \"Into\", \"for\", \"For\", \"THE\", \"The\", \"An\", \"an\", \"and\", \"And\", \"Are\", \"After\", \"Of\", \"OF\",\n\t\t\"of\", \"Off\", \"May\", \"On\", \"on\", \"or\", 'OR', 'Out', \"One\", \"Over\", \"by\", \"her\", \"Her\", \"his\", \"His\", \"From\", \"That\", \"This\", \n\t\t\"No\", \"but\", \"GUIDE\", \"But\", \"Not\", \"Paid\", \"Notice:\", \"Deaths\", \"New\", \"Corrections\", \"Memorials\", \"Back\", \"October\", \n\t\t\"First\", \"York\", \"You\", \"Your\", \"Times;\", \"Their\", \"It's\", \"Its\", \"How\", \"Two\", \"Up\", \"Who\", \"What\", \"When\", \"Where\", \"We\", \n\t\t\"They\", \"vs.\", \"Journal.\", \"Books\", \"Sports\", \"Big\", \"Say\", \"Do\", \"Be\", \"Sports\", \"Will\", \"I\", \"Has\", \"Your\", \"SUMMARY\",\n\t\t\"TRANSACTIONS\", \"QUOTATION\"]\n\n\t# Remove number in range [0,100], ascii characters, years and months.\n\tdrop.extend(str(i) for i in range(101))\n\tdrop.extend(letter+'.' for letter in ascii_uppercase)\n\tdrop.extend(month_name[i] for i in range(1,13))\n\tdrop.extend(string.printable[i] for i in range(62,94))\n\tdrop.extend(str(year) for year in range(2000,2019))\n\n\n\t# Remove keys in dictionary\n\tfor word in drop:\n\t dict.pop(word, None)\n\t \n\treturn dict","title":""},{"docid":"662faffb795c1224be9c15c5c32a9acb","score":"0.45604455","text":"def clean_column(df, col, use_dict=1):\n df[col] = df[col].str.replace(\"-\", ' ')\n df[col] = df[col].apply(clean_text, use_dict=use_dict)\n df[col] = df[col].str.replace('\\d+', '')\n return df","title":""},{"docid":"f83a2ade34ad834032465ad063688647","score":"0.4546585","text":"def remove_ascii_words(df):\n our_special_word = 'qwerty'\n non_ascii_words = []\n for i in range(len(df)):\n for word in df.loc[i, 'text'].split(' '):\n if any([ord(character) >= 128 for character in word]):\n non_ascii_words.append(word)\n df.loc[i, 'text'] = df.loc[i, 'text'].replace(word, our_special_word)\n return non_ascii_words","title":""},{"docid":"fdd7df6a53a30ec14cfda39251877c86","score":"0.45455316","text":"def _clean_cards(self, cardnames):\n cardnames = self._split_two_part_cards(cardnames)\n cardnames = self._fix_weird_characters(cardnames)\n cardnames = self._apply_generic_remapping(cardnames)\n return cardnames","title":""},{"docid":"e2236bd9043ba614707e3e618c942ec5","score":"0.45453298","text":"def temp_remove (self):\n self.tempstr = self.tempstr[:-1]\n self.delete (1)\n self.set_completions (self.completions, self.tempstr)","title":""},{"docid":"a09f0a935919e23e711bc44f876a7b9a","score":"0.4544507","text":"def clean(ticker):\n\n replace_with_blank = [\"NASDAQ\", \"NYSE\", \"*\", \":\",\n \"/\", \" \", \"[\", \"]\", '\"'] # manual filtering\n ticker = ticker.replace(\".\", \"-\") # todo: replace this with an actual dictionary of replacements\n ticker = ticker.replace(\"CRDA CRDB\", \"CRDA\")\n ticker = ticker.replace(\"CRDA-CRDB\", \"CRDB\")\n ticker = ticker.replace('FCE-A/FCEB', \"FCEA\")\n ticker = ticker.replace('FCEA/FCEB', \"FCEB\")\n ticker = ticker.replace('BFA, BFB', \"BFB\")\n for symbol in replace_with_blank:\n ticker = ticker.replace(symbol, \"\")\n\n return ticker","title":""},{"docid":"c8dd594d087189906f3ec69d7500c4f4","score":"0.45400095","text":"def dropping_unnescessary_columns(self, dataframe):\n logging.info('Dropping columns.')\n return dataframe.drop(self.unused_columns, axis=1)","title":""},{"docid":"fe911cc48f3cb4f2ba65b6e3e13732d9","score":"0.4539785","text":"def deleteChar(name):\n os.remove(buildPath('data/chars/' + name + '.json'))","title":""},{"docid":"dc82f8d8733cdadf2eb78a7d7c73099d","score":"0.45324293","text":"def remove_space(toks:Collection):\n res = []\n for t in toks:\n if t!=' ': res.append(t)\n return res","title":""},{"docid":"09ce40178c6ceb530c73d18b65f01434","score":"0.45319062","text":"def test_strip_bad_and_gaps(self):\n s = RnaMolType.strip_bad_and_gaps\n self.assertEqual(s(\"UCAGwsnyrHBND-D\"), \"UCAGwsnyrHBNDD\")\n self.assertEqual(s(\"@#^*($@!#&()!@QZX\"), \"\")\n self.assertEqual(s(\"aaa ggg ---!ccc\"), \"aaagggccc\")","title":""},{"docid":"d07c7559f5a1f0d3dd0d77216d525cf5","score":"0.4530663","text":"def drop_column(self, table_name, column_name):\n raise NotImplementedError","title":""},{"docid":"4b0f2a062c4c93be652aa84fec2c855d","score":"0.45285517","text":"def strip(self, scope = 'all'):\n if scope == 'all':\n if self._peaks is None:\n # decode, just in case ...\n self.peaks\n self._tmzSet = None\n self._tmassSet = None\n self._transformedMzWithError = None\n self._transformedPeaks = None\n self._transformed_deconvolutedPeaks = None\n self._transformedMassWithError = None\n if 'encodedData' in self.keys():\n del self['encodedData']\n del self['PY:0000000']\n # this is the ID tag corresponding to 'encodedData'\n else:\n print(\"Dont understand strip request \", file = sys.stderr)","title":""},{"docid":"f9831f52e6c639fb4a09c1c529773f46","score":"0.4524795","text":"def drop_sids(self, to_drop):\n self.sids = pd.Index(\n sorted(self.sids - _ensure_index(to_drop)),\n )\n self._realign_sids()","title":""},{"docid":"e706d7a772989dc5dc3ef96dd99d5011","score":"0.4519591","text":"def strip(s):\r\n return \"\".join(i for i in s if ord(i)<128)","title":""},{"docid":"2c7c9663d12a8aa1f373e400d44cf40a","score":"0.45141435","text":"def demark_seq(values: pd.Series) -> pd.DataFrame:\n return ta.td_seq(values, asint=True)","title":""},{"docid":"754de207f5514da8d7d806d2e9232500","score":"0.45130885","text":"def removeUTF(self, s):\n res = s.replace(\"_utf8'\", \"'\")\n\n \"\"\"Postgres speciffic syntax\"\"\"\n return res.replace(\"::text\", \" \")","title":""},{"docid":"bcda3383697b503505d648410ada80b3","score":"0.45106238","text":"def deleteFirstChar(c,s):\n pass","title":""},{"docid":"30e9ed1597caf3e66692ff61a51af247","score":"0.45054317","text":"def drop_column(self, *columns):\n for column in columns:\n self.table.drop_column(column)\n\n return self","title":""},{"docid":"b1a4d077517de10fc1d6b4c2898f4373","score":"0.45034474","text":"def remove_character(self, character_to_remove):\n if character_to_remove in self.characters:\n self.characters.remove(character_to_remove)","title":""},{"docid":"a0ca0fd4277d1fdf0770abf1f8a355ff","score":"0.4502169","text":"def remove_unnecessary_symbols(text):\n text = re.sub(r'[\\(\\)\\[\\]\\<\\>\\\"]+', '', text)\n return text","title":""},{"docid":"b2b21f471eb5583c1a6483683cf51a64","score":"0.4496638","text":"def strip_special(w):\n\treturn ''.join(c for c in w if ord(c) < 128)","title":""}],"string":"[\n {\n \"docid\": \"f83bca0f7ea21874cf2e6713ce456691\",\n \"score\": \"0.7848441\",\n \"text\": \"def drop_series(item: pd.Series, char_to_drop) -> pd.Series:\\n return item[item != char_to_drop]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d396762a72c513b02da28942e414a14\",\n \"score\": \"0.6176747\",\n \"text\": \"def remove_symbols(self, symbols):\\n self.df = self.df.drop(symbols, level='symbol')\\n self.df.index = self.df.index.remove_unused_levels()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7baa14184dd79429e28c2086008150\",\n \"score\": \"0.55239195\",\n \"text\": \"def remove(text, chars):\\r\\n for c in chars:\\r\\n text = text.replace(c, \\\"\\\")\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a534d79894bdfed1e2d891d2f2027424\",\n \"score\": \"0.5481985\",\n \"text\": \"def deselect_all(self):\\n for i in range(len(characters)):\\n self.deselect(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0421080da449c8492e50fef967f438ee\",\n \"score\": \"0.54818064\",\n \"text\": \"def ignore_columns(self, columns, dataframe):\\n print(\\\"Holoclean cannot use dataframes with strings \\\"\\n \\\"more than 255 characters\\\")\\n columns_message = \\\" and \\\".join(columns)\\n message = \\\"The columns \\\" + columns_message + \\\" will be dropped\\\"\\n print (message)\\n for column in columns:\\n dataframe = dataframe.drop(column)\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd8306a8b4e343e335d482ede450f8a\",\n \"score\": \"0.5374376\",\n \"text\": \"def unicode_filter_df_series(self, df_series):\\n SL = [] # char list\\n\\n for i in xrange(0,len(df_series)): # turns string into list of chars\\n sl = list (df_series[i])\\n SL.append(sl)\\n \\n for i in xrange(0, len(SL)): # filters chars \\n for j in xrange(0,len(SL[i])):\\n SL[i][j] = self.filter_unicode(SL[i][j])\\n\\n return np.array(SL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd93a831b8bef3cc6a1423a657c7f0d\",\n \"score\": \"0.5322538\",\n \"text\": \"def clean_series(series, *args, **kwargs):\\n if not series.dtype == np.dtype('O'):\\n return series\\n if any_generated((isinstance(v, datetime.datetime) for v in series)):\\n series = series.apply(clip_datetime)\\n if any_generated((isinstance(v, basestring) for v in series)):\\n series = series.apply(encode)\\n return series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b9a1310733bea164720da0b511263a\",\n \"score\": \"0.5312601\",\n \"text\": \"def delete_characters(self, count=None):\\r\\n count = count or 1\\r\\n\\r\\n for _ in range(min(self.columns - self.cursor.x, count)):\\r\\n self[self.cursor.y].pop(self.cursor.x)\\r\\n self[self.cursor.y].append(self.cursor.attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550732926ea93e37aeacaed862f18116\",\n \"score\": \"0.5282036\",\n \"text\": \"def remove_invalid_chars(seq):\\n\\t\\tfor letter in INVALID_VALUE_CHARS:\\n\\t\\t\\tseq = seq.replace(letter, '')\\n\\t\\treturn seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8c39f8fa606193751e8ccaed832944\",\n \"score\": \"0.52656263\",\n \"text\": \"def drop_str_cols(df) -> pd.DataFrame:\\n\\n return df.select_dtypes(exclude=['object'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58dd8f103bdb08bbc18fcfe30797fbe\",\n \"score\": \"0.5241149\",\n \"text\": \"def strip(self, chars=None):\\r\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb160528be8c898f4ab1c66fcfc44873\",\n \"score\": \"0.52129316\",\n \"text\": \"def drop(self, *args, **kwargs):\\n raise NotImplementedError('To be implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7398b2c31634670aaaefd828599c6028\",\n \"score\": \"0.5209102\",\n \"text\": \"def clean_data(column:pd.Series) -> pd.Series:\\n column = column.copy()\\n column = column.str.replace(r\\\"[(),:!?@&\\\\'\\\\`\\\\\\\"\\\\_]\\\", \\\"\\\")\\n column = column.str.replace(r\\\"[\\\\n]\\\", \\\" \\\")\\n column = column.str.replace(r\\\"[...]\\\", \\\"\\\")\\n column = column.str.replace(r\\\"[\\\\d]\\\", \\\"\\\") \\n column = column.str.replace(r\\\"[-]\\\", \\\"\\\") \\n column = column.str.replace(r\\\"[\\\", \\\"\\\")\\n column = column.str.replace(r\\\"]\\\", \\\"\\\")\\n column = column.str.lower()\\n return column\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1180af190f8f7b6b968fa276ff9078f\",\n \"score\": \"0.51874286\",\n \"text\": \"def deleteChar(c, s):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3629ee58238a19bec09a109ee179bd5\",\n \"score\": \"0.5170022\",\n \"text\": \"def drop_pfam(df, dropList):\\n \\n df.drop(dropList, axis=1, inplace=True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8345cabd68b43c5d557af9d122589026\",\n \"score\": \"0.5140048\",\n \"text\": \"def dropNames(events):\\n to_drop = ['subEventName', 'eventName']\\n print(\\\"\\\\tDropping subEventName and eventName columns\\\")\\n events.drop(columns=to_drop, inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd207c3ddd2f32fd6e0d8969b9215ee\",\n \"score\": \"0.51045287\",\n \"text\": \"def _strip_chars(word, chars_to_remove):\\n for char in chars_to_remove:\\n word = word.replace(char, '')\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0d1611b8068599c3463e4c138a42a6c\",\n \"score\": \"0.50480825\",\n \"text\": \"def dropData(mimeType, text):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc911fc18a9e24c214af82172e1711d8\",\n \"score\": \"0.5009979\",\n \"text\": \"def sdc_pandas_dataframe_drop(df, labels=None, axis=0, index=None, columns=None, level=None, inplace=False,\\n errors='raise'):\\n\\n _func_name = 'drop'\\n\\n ty_checker = TypeChecker(f'Method {_func_name}().')\\n ty_checker.check(df, DataFrameType)\\n\\n if not isinstance(labels, types.Omitted) and labels is not None:\\n ty_checker.raise_exc(labels, 'None', 'labels')\\n\\n if not isinstance(axis, (int, types.Omitted)):\\n ty_checker.raise_exc(axis, 'int', 'axis')\\n\\n if not isinstance(index, types.Omitted) and index is not None:\\n ty_checker.raise_exc(index, 'None', 'index')\\n\\n if not isinstance(columns, (types.Omitted, types.Tuple, types.Literal)):\\n ty_checker.raise_exc(columns, 'str, tuple of str', 'columns')\\n\\n if not isinstance(level, (types.Omitted, types.Literal)) and level is not None:\\n ty_checker.raise_exc(level, 'None', 'level')\\n\\n if not isinstance(inplace, (bool, types.Omitted)) and inplace:\\n ty_checker.raise_exc(inplace, 'bool', 'inplace')\\n\\n if not isinstance(errors, (str, types.Omitted, types.Literal)):\\n ty_checker.raise_exc(errors, 'str', 'errors')\\n\\n args = {'labels': None, 'axis': 0, 'index': None, 'columns': None, 'level': None, 'inplace': False,\\n 'errors': f'\\\"raise\\\"'}\\n\\n def sdc_pandas_dataframe_drop_impl(df, _func_name, args, columns):\\n func_args = ['df']\\n for key, value in args.items():\\n if key not in func_args:\\n if isinstance(value, types.Literal):\\n value = value.literal_value\\n func_args.append(f'{key}={value}')\\n\\n if isinstance(columns, types.StringLiteral):\\n drop_cols = (columns.literal_value,)\\n elif isinstance(columns, types.Tuple):\\n drop_cols = tuple(column.literal_value for column in columns)\\n else:\\n raise ValueError('Only drop by one column or tuple of columns is currently supported in df.drop()')\\n\\n func_def, global_vars = sdc_pandas_dataframe_drop_codegen(_func_name, func_args, df, drop_cols)\\n loc_vars = {}\\n exec(func_def, global_vars, loc_vars)\\n _drop_impl = loc_vars['sdc_pandas_dataframe_drop_impl']\\n return _drop_impl\\n\\n return sdc_pandas_dataframe_drop_impl(df, _func_name, args, columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85acab321dc8416e031fa1734adeb83a\",\n \"score\": \"0.5002741\",\n \"text\": \"def remove():\\n series = configuration['series']\\n\\n if len(series) == 0:\\n click.echo('No series to remove!')\\n return\\n\\n click.echo(\\\"Which series shall be removed?\\\")\\n for idx, current in enumerate(series):\\n click.echo(\\\"{0}. {1}\\\".format(idx, current['name']))\\n\\n while 1:\\n value = click.prompt('Enter the number of the series to remove',\\n type=int)\\n if value >= 0 and value < len(series):\\n break\\n else:\\n click.echo('OOps, that\\\\'s not a valid option. Choose a value '\\n 'between 0 and {0}!'.format(len(series)-1))\\n\\n name = series[value]['name']\\n fn.remove_series(configuration, series[value])\\n click.echo('Series {0} removed!'.format(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0834fb275457b33d2dc354ad00e5b6b\",\n \"score\": \"0.49970055\",\n \"text\": \"def rstrip(self, chars=None):\\r\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e07610865b681d21e89d605218438ed\",\n \"score\": \"0.49880996\",\n \"text\": \"def remove_characters(data):\\n\\t# Reading data containing list of important characters only and their IDs\\n\\tchar_id_index = pd.read_csv('.\\\\\\\\characters.csv')\\n\\t# Creating aa dict containing character IDs for each character\\n\\tnames = {old:new for (old, new) in zip(char_id_index['Character Name'], char_id_index['Character ID'])}\\n\\t# Converting the data dict into a pandas data frame for easy handling\\n\\tchar_movie_matrix = pd.DataFrame(data)\\n\\t# Renaming characters with their character IDs\\n\\tchar_movie_matrix.rename(index=names, inplace=True)\\n\\t# Creating a list of unimportant characters to be removed\\n\\tpop_list = [name for name in char_movie_matrix.index if name not in list(char_id_index['Character ID'])]\\n\\t# Removing all unimportant characters from the data frame\\n\\tchar_movie_matrix = char_movie_matrix.drop(pop_list)\\n\\t# Combine rows with same index\\n\\tchar_movie_matrix = combine_rows(char_movie_matrix)\\n\\t# Considering movies in the Infinity Saga only\\n\\tdel char_movie_matrix['Spider-Man: Far from Home']\\n\\t# Returning the character-movie-run_time matrix in the form of a pandas dataframe\\n\\treturn char_movie_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792e840595c527467e128704577abc2e\",\n \"score\": \"0.4965961\",\n \"text\": \"def _drop_columns(data, columns):\\n logger.info(\\\"{} 필드를 데이터에서 제외시킵니다\\\".format(columns))\\n data = data.drop(columns=[columns], axis=1)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bb1b005b303922d4e7dfbb8db8431e\",\n \"score\": \"0.49580613\",\n \"text\": \"def filter_stocks(dataframe):\\n dataframe.Symbol = dataframe.Symbol.astype('str')\\n dataframe.Symbol = dataframe.Symbol.map(lambda x: x.strip())\\n dataframe = dataframe[dataframe.Symbol != 'ACCP']\\n print(\\\"Number of tickers before trim:\\\", len(dataframe))\\n dataframe = dataframe[dataframe.Symbol.str.isalpha()]\\n print(\\\"Number of tickers after trim:\\\", len(dataframe))\\n dataframe = dataframe.set_index('Symbol')\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20165b73b9e9290a61bd6060fbba5828\",\n \"score\": \"0.49577388\",\n \"text\": \"def erase_characters(self, count=None):\\r\\n count = count or 1\\r\\n\\r\\n for column in range(self.cursor.x,\\r\\n min(self.cursor.x + count, self.columns)):\\r\\n self[self.cursor.y][column] = self.cursor.attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c441579147d607a994bb69058374915d\",\n \"score\": \"0.49402955\",\n \"text\": \"def dropPLZ(path, drop_plz):\\n df = gpd.read_file(path)\\n # Drop all not necessary plz\\n df.drop(df[~df['plz'].str.contains('|'.join(str(x) for x in\\n drop_plz))].index, axis=0, inplace=True)\\n #df.drop(['note'], axis=1, inplace = True)\\n df['plz'] = df['plz'].astype(int)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d69b8575ac30fe5d162854d5954017\",\n \"score\": \"0.493667\",\n \"text\": \"def drop_features(df, features):\\n return df.drop(columns=features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e9ea85d1cce428a5fa4a039a03943f\",\n \"score\": \"0.49306646\",\n \"text\": \"def deleteSpecChars(text, specialChars):\\r\\n for char in specialChars:\\r\\n text = text.replace(char, '') \\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3d62a471f5c60ec30aaf15afbac476c\",\n \"score\": \"0.49301264\",\n \"text\": \"def lstrip(self, chars=None):\\r\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b2c8bf6d0a69af862056b29990b3f1\",\n \"score\": \"0.49283543\",\n \"text\": \"def remove_stop_words(series):\\n regex_stop = re.compile(r'\\\\b(' + r'|'.join(stopwords.words('english')) + r')\\\\b\\\\s*')\\n series = series.str.replace(regex_stop, '')\\n series = series.str.replace('realdonaldtrump', '')\\n series = series.str.replace('thank', '')\\n series = series.str.replace('trump', '')\\n series = series.str.replace('donald', '')\\n series = series.str.replace('amp', '')\\n\\n return series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4e2b8b204f5ed0e6d592855e00b949\",\n \"score\": \"0.49270698\",\n \"text\": \"def test_keep_drop_columns(d):\\n columns = list(d.dtype.names)\\n selected_data = strax.apply_selection(d, keep_columns=columns[1:])\\n\\n # Check we din't loose anything of the original array\\n assert columns == list(d.dtype.names)\\n\\n for c in columns[1:]:\\n assert np.all(selected_data[c] == d[c])\\n for c in columns[:1]:\\n assert c not in selected_data.dtype.names\\n \\n # Repeat test but for drop columns:\\n selected_data = strax.apply_selection(d, drop_columns=columns[:1])\\n for c in columns[1:]:\\n assert np.all(selected_data[c] == d[c])\\n for c in columns[:1]:\\n assert c not in selected_data.dtype.names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94a21bfc36e44ecfa51a52d159a031f\",\n \"score\": \"0.49227712\",\n \"text\": \"def clean_data(cfg,df):\\n \\n df.drop(['Date/Time'],axis=1,inplace=True) \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8d858d4df2736f26ec67e3fc2a1184\",\n \"score\": \"0.4914771\",\n \"text\": \"def removeNonAlphanumericCharacter(self):\\n\\n\\t\\tnonAlphaNumericCharacter = set(\\\"`~!@#$%^&*()_+-={}|[]\\\\:\\\\\\\"<>?;',./'\\u0013\\u0019\\u0018\\\\xc2\\\")\\n\\t\\ttemp = []\\n\\n\\t\\tfor item in self.characterMap:\\n\\t\\t\\tif not any(( c in nonAlphaNumericCharacter ) for c in item[1]):\\n\\t\\t\\t\\ttemp.append(item)\\n\\n\\t\\tself.cleanCharacterMap = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96be73ad46667d6ba65a7b68d65bcada\",\n \"score\": \"0.49120212\",\n \"text\": \"def drop_columns(data, attributes):\\n return data.drop(attributes, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069564257d5095f4d03e39836cb8b720\",\n \"score\": \"0.4910914\",\n \"text\": \"def _delchars(p_str, chars):\\n # Translate demands a mapping string of 256 characters;\\n # whip up a string that will leave all characters unmolested.\\n identity = ''.join([chr(x) for x in range(256)])\\n return p_str.translate(identity, chars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a872c41c38febb94172673c5e8ba5eac\",\n \"score\": \"0.49096254\",\n \"text\": \"def drop_column_suffix(frame, suffix):\\r\\n frame.columns = [col if not col.endswith(\\r\\n suffix) else col[:-len(suffix)] for col in frame.columns]\\r\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d237be40a951924fcb1f4d67d01bd03\",\n \"score\": \"0.4901735\",\n \"text\": \"def drop_features(df, feature_list):\\n df = df.drop(feature_list, 1)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f47f9d5adbc1f3ff2ba04bdcb723c4cb\",\n \"score\": \"0.48922855\",\n \"text\": \"def drop(self):\\n list_of_high_corr = [1, 3, 4, 8, 10, 14, 18, 23, 27, 30, 32, 33, 34, 36, 41, 44, 45, 47, 48]\\n self.data = self.data.drop(['feature_'+str(x) for x in list_of_high_corr], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf073afbc4ac26a57e0c873830b7e81\",\n \"score\": \"0.48867807\",\n \"text\": \"def remove( str, chars ):\\n if chars: return remove( str.replace(chars[0], \\\"\\\"), chars[1:] )\\n return str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4f39370e006aa7cd6d2286923579da\",\n \"score\": \"0.48802772\",\n \"text\": \"def remove_problem_chars(x):\\n #print x\\n chars = [\\\"\\\\)\\\", \\\"\\\\(\\\", \\\"\\\\[\\\", \\\"\\\\]\\\", \\\"\\\\:\\\"]\\n for c in chars:\\n x = re.sub(c, \\\"\\\", x)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6605961ecbdcd6a6f39736808b56a2ec\",\n \"score\": \"0.48673037\",\n \"text\": \"def setify_drop_nones(series):\\n\\n def drop_nones(x):\\n x.discard(None)\\n return x\\n\\n return series.apply(lambda x: set(x)).apply(drop_nones)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f4b18b51e3f09cd32d241f52b75976\",\n \"score\": \"0.4849629\",\n \"text\": \"def RemoveCharacterExtension(self,pExt):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91cb13f123050e77f4cba8ca00ad235f\",\n \"score\": \"0.4839863\",\n \"text\": \"def drop_columns(self, columns): \\n self.data.drop(columns, axis=1, inplace=True) \\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5ed089f51a7db44014fb9d8e89567d\",\n \"score\": \"0.48382556\",\n \"text\": \"def strip(s, chars=None):\\r\\n return s.strip(chars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79342c4033937072a2701a3a67754c59\",\n \"score\": \"0.4827922\",\n \"text\": \"def drop_option(option, cmd, drop_value=False):\\n to_drop = []\\n for i, word in enumerate(cmd):\\n if word == option:\\n if drop_value is True:\\n to_drop += [i, i + 1]\\n elif is_string(drop_value):\\n if cmd[i + 1].startswith(drop_value):\\n to_drop += [i, i + 1]\\n else:\\n # do not drop option\\n pass\\n else:\\n to_drop += [i]\\n continue\\n if word.startswith(option + \\\"=\\\"):\\n to_drop += [i]\\n continue\\n for idx in sorted(to_drop, reverse=True):\\n del cmd[idx]\\n return cmd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47580b45a4046bab5acd46bdc68df332\",\n \"score\": \"0.4826921\",\n \"text\": \"def remove_char(string,\\n iterable):\\n for i in iterable:\\n string = string.replace(i, \\\"\\\")\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ec8389b9138a8ef7808296f95ba076\",\n \"score\": \"0.4798504\",\n \"text\": \"def clean_rogue_chars(self, s: bytes) -> bytes:\\n if self.rogue_chars:\\n for cleaner in self.rogue_char_cleaners:\\n s = cleaner(s)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1895f17692428dc3d07e106d48303ae9\",\n \"score\": \"0.47975427\",\n \"text\": \"def delete_junk_data(self, junk_string):\\n while junk_string in self.temp:\\n self.temp.remove(junk_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d4b742d15b6cf9cec21eaaffe62635\",\n \"score\": \"0.4783758\",\n \"text\": \"def drop_columns(df, drop_cols):\\n df = df.drop(drop_cols, 1)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2252b4d7c6ed56b1e2a904b4e93ee52\",\n \"score\": \"0.47530732\",\n \"text\": \"def test_stripDegenerate(self):\\n s = RnaMolType.stripDegenerate\\n self.assertEqual(s('UCAG-'), 'UCAG-')\\n self.assertEqual(s('NRYSW'), '')\\n self.assertEqual(s('USNG'), 'UG')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606e2a7d79ddd4d700ead44d7532f699\",\n \"score\": \"0.47489426\",\n \"text\": \"def remove_direct_identifier(self, df):\\n direct_identifiers = self.__config.get_direct_identifiers()\\n df = df.drop(columns=direct_identifiers)\\n logger.info(\\\"Dropped direct identifying attributes %s\\\", \\\", \\\".join(direct_identifiers))\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c35c60284ea66cb9f05c3c7b0c16cad\",\n \"score\": \"0.47382987\",\n \"text\": \"def _clean_barcode(row: pd.Series):\\n barcode = row['barcode_raw']\\n barcode_clean = barcode.split('-')[0]\\n\\n return barcode_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda559869e640dd4fb4db7eefc579867\",\n \"score\": \"0.4710356\",\n \"text\": \"def test_strip_degenerate(self):\\n s = RnaMolType.strip_degenerate\\n self.assertEqual(s(\\\"UCAG-\\\"), \\\"UCAG-\\\")\\n self.assertEqual(s(\\\"NRYSW\\\"), \\\"\\\")\\n self.assertEqual(s(\\\"USNG\\\"), \\\"UG\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7cf6229280a7b7dc7df055bb5eae26d\",\n \"score\": \"0.4673311\",\n \"text\": \"def clean_from_symbols(column, list_of_cleaned_values):\\n\\n symbols = [',', '-', '.', ' ', ';', ':', '!', \\\"*\\\"]\\n\\n for value in column:\\n value = ''.join(i for i in value if i not in symbols)\\n if value.isnumeric():\\n list_of_cleaned_values.append(int(value))\\n else:\\n list_of_cleaned_values.append(value)\\n\\n return list_of_cleaned_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d52402e598bffcc6c147fc2ad73eea\",\n \"score\": \"0.4670337\",\n \"text\": \"def discard(self, n: int):\\n self.replace(n, '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf78e9ab8c12caf5c204e3515b114db\",\n \"score\": \"0.46509728\",\n \"text\": \"def delete_stress(s):\\n try: s = re.sub('\\\\d','',s)\\n except TypeError:\\n pass\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b5c7dde82497bde834d3f80571d8832\",\n \"score\": \"0.4637092\",\n \"text\": \"def removeToneNumbersFromText(text):\\n\\t\\treturn ''.join([i for i in text if not i.isdigit()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6869c12f798dc43d720f6bd015974aa\",\n \"score\": \"0.46365967\",\n \"text\": \"def dropColumns(self, column_list):\\n\\n cols = [col if col not in self._col_map else self._col_map[col] for col in column_list]\\n self._df = self._df.drop(columns=cols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1cffb031c5ce85b8b60db81e51252b\",\n \"score\": \"0.46308625\",\n \"text\": \"def remove_chars(s: str, keep: Union[str, re.Pattern] = LEGAL_CHARS) -> str:\\n return ' '.join([re.sub(keep, '', i) for i in s.split()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da08fa0a51137eaf74107376ad9d84a7\",\n \"score\": \"0.46243876\",\n \"text\": \"def cleanup_supervised_data(supervised_data):\\n supervised_data.drop(\\n supervised_data.columns[[9, 10, 11, 12, 13, 14, 15]], axis=1, inplace=True)\\n\\n print(\\\"supervised datasets after cleanup unused column\\\\n{}\\\".format(\\n supervised_data.head()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fce5735a202a723fa5e0d34e52e08fb\",\n \"score\": \"0.46220335\",\n \"text\": \"def remove_bad(self, disp=['FP','EB','NEB','BEB','SV','BD','NPC','SB1','SB2','FA']):\\n for bad in disp:\\n self.sample.query(\\\"disp != '%s'\\\"%bad, inplace=True)\\n self.sample.query(\\\"drop == False\\\", inplace=True)\\n self.sample.query(\\\"finish == False\\\", inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b404c6d3244663b3e92891a71e450fff\",\n \"score\": \"0.4618967\",\n \"text\": \"def test_strip_bad(self):\\n s = RnaMolType.strip_bad\\n self.assertEqual(s(\\\"UCAGwsnyrHBND-D\\\"), \\\"UCAGwsnyrHBND-D\\\")\\n self.assertEqual(s(\\\"@#^*($@!#&()!@QZX\\\"), \\\"\\\")\\n self.assertEqual(s(\\\"aaa ggg ---!ccc\\\"), \\\"aaaggg---ccc\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f44a53b8e2000762c4af87389c54b4f\",\n \"score\": \"0.46145\",\n \"text\": \"def del_letters(self):\\n if self.backspace_timer <= 0:\\n self.usr_input = self.usr_input[:-1]\\n self.backspace_timer = 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1538e337f1f505204b5e8c7baf69deb8\",\n \"score\": \"0.46123657\",\n \"text\": \"def SetCharacterizeOff(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dee06ac56ebb64778652354c49e4ac0\",\n \"score\": \"0.46115652\",\n \"text\": \"def _dropChunk(self, x, z):\\n del self.chunkCache[(x, z)]\\n del self.timestamps[(x, z)]\\n self.cachedChunks -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa2f1217997e57c756ba9a67b11104e\",\n \"score\": \"0.4611185\",\n \"text\": \"def drop(self, drop=None, like=None):\\n\\n from pyranges.methods.drop import _drop\\n return _drop(self, drop, like)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ccb205f3b706b00eaea8a7364be916\",\n \"score\": \"0.46108228\",\n \"text\": \"def drop(self):\\n self.s.account.execute_cmd('give/drop %s' % self.o.get_display_name(self.s, plain=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52bd2e79d3dd657f77280ce14be30f4d\",\n \"score\": \"0.46059892\",\n \"text\": \"def _remove(df, years, months=range(1, 13), days=range(1, 32)):\\r\\n if isinstance(days, int):\\r\\n days = [days]\\r\\n if isinstance(months, int):\\r\\n months = [months]\\r\\n if isinstance(years, int):\\r\\n years = [years]\\r\\n for day in days:\\r\\n for month in months:\\r\\n for year in years:\\r\\n # print(f'day: {day}, month: {month}, year: {year}')\\r\\n df = df[~((df['DAY'] == day) & (df['MONTH'] == month) &\\r\\n (df['YEAR'] == year))]\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5cf71753578216b1cf9281be083348a\",\n \"score\": \"0.46005324\",\n \"text\": \"def drop_data(data, drop_ranges=None):\\n if not drop_ranges:\\n drop_ranges=[(0, 50000), (300000, 450000), (700000, 800000)]\\n\\n data_to_drop = []\\n for drop_range in drop_ranges:\\n data_to_drop += list(range(drop_range[0],drop_range[1]))\\n\\n return data.drop(labels=data_to_drop).reset_index(drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d077ff4d4df2e90362e15c6ab484a684\",\n \"score\": \"0.45974976\",\n \"text\": \"def __drop_df__(self):\\n self.df_income = self.filtered_df.loc[self.filtered_df['category'] == 'Einkommen']\\n self.filtered_df = self.filtered_df.drop(self.filtered_df[self.filtered_df.category == 'Einkommen'].index)\\n self.filtered_df = self.filtered_df.drop(self.filtered_df[self.filtered_df.category == 'Ignorieren'].index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdda96a8657a1c91379e1038f70fcde2\",\n \"score\": \"0.45927387\",\n \"text\": \"def strip(lst=None,chars=None):\\n if lst is None: raise ValueError(\\\"lst must be input\\\")\\n if type(lst) is str: return lst.strip(chars)\\n return [o.strip(chars) for o in np.array(lst,ndmin=1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b925aa64b1e2d71531035d3df55766f\",\n \"score\": \"0.45908934\",\n \"text\": \"def _strip_index(symbol_name):\\n return \\\"_\\\".join(symbol_name.split(\\\"_\\\")[:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b925aa64b1e2d71531035d3df55766f\",\n \"score\": \"0.45908934\",\n \"text\": \"def _strip_index(symbol_name):\\n return \\\"_\\\".join(symbol_name.split(\\\"_\\\")[:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee13e125b1f273bea8a6e6a10eaf78e\",\n \"score\": \"0.45868257\",\n \"text\": \"def remove_mean_and_player(team_players_9cat_stats_dataframe, player_to_drop):\\n team_players_9cat_stats_dataframe = team_players_9cat_stats_dataframe.drop(MEAN_ROW)\\n team_players_9cat_stats_dataframe = \\\\\\n team_players_9cat_stats_dataframe[~team_players_9cat_stats_dataframe[\\n PLAYER_COLUMN].str.contains(player_to_drop)]\\n return team_players_9cat_stats_dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc54eebae2d8c7b600ab5ab3e8a81b2\",\n \"score\": \"0.45858672\",\n \"text\": \"def _clean_non_chars(text):\\r\\n # should this also remove everything after the first tab?\\r\\n\\r\\n # remove non alphanumeric characters\\r\\n return _non_word_chars.sub(\\\"\\\", text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100f6fcf67ac46c4fe51303539b88276\",\n \"score\": \"0.45856407\",\n \"text\": \"def drop_columns(data):\\n # Load in Pickle file.\\n feats2delete = pickle.load(open('reuseables/feats_to_delete.pkl', 'rb'))\\n\\n # Loop over the columns defined in the Pickle file and drop each from the dataset.\\n for col in feats2delete:\\n data.pop(col)\\n\\n filter_cols = [col for col in data if col.startswith('comp')]\\n\\n for col in filter_cols:\\n data.pop(col)\\n\\n logging.info(\\n f'The following features have been removed from the original dataset: {feats2delete} and {filter_cols}.')\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1170ef25fec4258aaab1d149b7367612\",\n \"score\": \"0.4577277\",\n \"text\": \"def clean_chunk(self, txt: str):\\n txt = self.remove_non_greek(txt)\\n return self.remove_multiple_space(txt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f827feb946ae979b1168fce0db082f1a\",\n \"score\": \"0.45708227\",\n \"text\": \"def drop_na(x):\\n a = x.strip().split(\\\"\\\\001\\\")\\n filtered_data = [a[i] for i in xrange(len(a)) if i not in na_col.value]\\n indice = range(len(filtered_data)-1)\\n #return float(filtered_data[0]), np.array(indice, dtype=np.int32), np.array(filtered_data[1:],dtype=np.float64)\\n return filtered_data[0], np.array(indice, dtype=np.int32), np.array([el.strip() for el in filtered_data[1:]],dtype=np.dtype(Decimal))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57df202c7545cfe1df339a4af908e8d0\",\n \"score\": \"0.4568766\",\n \"text\": \"def test_badChars(self):\\n c = CharFilter('abc')\\n d = CharFilter('abc',invert_charset=True)\\n #should automatically strip whitespace\\n s2 = ' aceacef '\\n self.assertEqual(c.badChars(s2), set('ef'))\\n self.assertEqual(d.badChars(s2), set('ac'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8e2796c4df3292d821e448da5dc339\",\n \"score\": \"0.456675\",\n \"text\": \"def drop_key(dict):\\n\\t\\n\\t# Remove articles, personal pronouns, prepositions, etc\\n\\tdrop = [ \\n\\t\\t\\\"the\\\", \\\"a\\\", \\\"A\\\", \\\"As\\\", \\\"as\\\", \\\"at\\\", \\\"At\\\", \\\"Against\\\", \\\"About\\\", \\\"By\\\", \\\"With\\\", \\\"It\\\", \\\"Is\\\", \\\"to\\\", \\\"To\\\", \\\"TO\\\", \\\"in\\\", \\\"In\\\", \\n\\t\\t\\\"MARKETS:\\\", \\\"BRIEFING\\\", \\\"IN\\\", \\\"Into\\\", \\\"for\\\", \\\"For\\\", \\\"THE\\\", \\\"The\\\", \\\"An\\\", \\\"an\\\", \\\"and\\\", \\\"And\\\", \\\"Are\\\", \\\"After\\\", \\\"Of\\\", \\\"OF\\\",\\n\\t\\t\\\"of\\\", \\\"Off\\\", \\\"May\\\", \\\"On\\\", \\\"on\\\", \\\"or\\\", 'OR', 'Out', \\\"One\\\", \\\"Over\\\", \\\"by\\\", \\\"her\\\", \\\"Her\\\", \\\"his\\\", \\\"His\\\", \\\"From\\\", \\\"That\\\", \\\"This\\\", \\n\\t\\t\\\"No\\\", \\\"but\\\", \\\"GUIDE\\\", \\\"But\\\", \\\"Not\\\", \\\"Paid\\\", \\\"Notice:\\\", \\\"Deaths\\\", \\\"New\\\", \\\"Corrections\\\", \\\"Memorials\\\", \\\"Back\\\", \\\"October\\\", \\n\\t\\t\\\"First\\\", \\\"York\\\", \\\"You\\\", \\\"Your\\\", \\\"Times;\\\", \\\"Their\\\", \\\"It's\\\", \\\"Its\\\", \\\"How\\\", \\\"Two\\\", \\\"Up\\\", \\\"Who\\\", \\\"What\\\", \\\"When\\\", \\\"Where\\\", \\\"We\\\", \\n\\t\\t\\\"They\\\", \\\"vs.\\\", \\\"Journal.\\\", \\\"Books\\\", \\\"Sports\\\", \\\"Big\\\", \\\"Say\\\", \\\"Do\\\", \\\"Be\\\", \\\"Sports\\\", \\\"Will\\\", \\\"I\\\", \\\"Has\\\", \\\"Your\\\", \\\"SUMMARY\\\",\\n\\t\\t\\\"TRANSACTIONS\\\", \\\"QUOTATION\\\"]\\n\\n\\t# Remove number in range [0,100], ascii characters, years and months.\\n\\tdrop.extend(str(i) for i in range(101))\\n\\tdrop.extend(letter+'.' for letter in ascii_uppercase)\\n\\tdrop.extend(month_name[i] for i in range(1,13))\\n\\tdrop.extend(string.printable[i] for i in range(62,94))\\n\\tdrop.extend(str(year) for year in range(2000,2019))\\n\\n\\n\\t# Remove keys in dictionary\\n\\tfor word in drop:\\n\\t dict.pop(word, None)\\n\\t \\n\\treturn dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662faffb795c1224be9c15c5c32a9acb\",\n \"score\": \"0.45604455\",\n \"text\": \"def clean_column(df, col, use_dict=1):\\n df[col] = df[col].str.replace(\\\"-\\\", ' ')\\n df[col] = df[col].apply(clean_text, use_dict=use_dict)\\n df[col] = df[col].str.replace('\\\\d+', '')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83a2ade34ad834032465ad063688647\",\n \"score\": \"0.4546585\",\n \"text\": \"def remove_ascii_words(df):\\n our_special_word = 'qwerty'\\n non_ascii_words = []\\n for i in range(len(df)):\\n for word in df.loc[i, 'text'].split(' '):\\n if any([ord(character) >= 128 for character in word]):\\n non_ascii_words.append(word)\\n df.loc[i, 'text'] = df.loc[i, 'text'].replace(word, our_special_word)\\n return non_ascii_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd7df6a53a30ec14cfda39251877c86\",\n \"score\": \"0.45455316\",\n \"text\": \"def _clean_cards(self, cardnames):\\n cardnames = self._split_two_part_cards(cardnames)\\n cardnames = self._fix_weird_characters(cardnames)\\n cardnames = self._apply_generic_remapping(cardnames)\\n return cardnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2236bd9043ba614707e3e618c942ec5\",\n \"score\": \"0.45453298\",\n \"text\": \"def temp_remove (self):\\n self.tempstr = self.tempstr[:-1]\\n self.delete (1)\\n self.set_completions (self.completions, self.tempstr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09f0a935919e23e711bc44f876a7b9a\",\n \"score\": \"0.4544507\",\n \"text\": \"def clean(ticker):\\n\\n replace_with_blank = [\\\"NASDAQ\\\", \\\"NYSE\\\", \\\"*\\\", \\\":\\\",\\n \\\"/\\\", \\\" \\\", \\\"[\\\", \\\"]\\\", '\\\"'] # manual filtering\\n ticker = ticker.replace(\\\".\\\", \\\"-\\\") # todo: replace this with an actual dictionary of replacements\\n ticker = ticker.replace(\\\"CRDA CRDB\\\", \\\"CRDA\\\")\\n ticker = ticker.replace(\\\"CRDA-CRDB\\\", \\\"CRDB\\\")\\n ticker = ticker.replace('FCE-A/FCEB', \\\"FCEA\\\")\\n ticker = ticker.replace('FCEA/FCEB', \\\"FCEB\\\")\\n ticker = ticker.replace('BFA, BFB', \\\"BFB\\\")\\n for symbol in replace_with_blank:\\n ticker = ticker.replace(symbol, \\\"\\\")\\n\\n return ticker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8dd594d087189906f3ec69d7500c4f4\",\n \"score\": \"0.45400095\",\n \"text\": \"def dropping_unnescessary_columns(self, dataframe):\\n logging.info('Dropping columns.')\\n return dataframe.drop(self.unused_columns, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe911cc48f3cb4f2ba65b6e3e13732d9\",\n \"score\": \"0.4539785\",\n \"text\": \"def deleteChar(name):\\n os.remove(buildPath('data/chars/' + name + '.json'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc82f8d8733cdadf2eb78a7d7c73099d\",\n \"score\": \"0.45324293\",\n \"text\": \"def remove_space(toks:Collection):\\n res = []\\n for t in toks:\\n if t!=' ': res.append(t)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ce40178c6ceb530c73d18b65f01434\",\n \"score\": \"0.45319062\",\n \"text\": \"def test_strip_bad_and_gaps(self):\\n s = RnaMolType.strip_bad_and_gaps\\n self.assertEqual(s(\\\"UCAGwsnyrHBND-D\\\"), \\\"UCAGwsnyrHBNDD\\\")\\n self.assertEqual(s(\\\"@#^*($@!#&()!@QZX\\\"), \\\"\\\")\\n self.assertEqual(s(\\\"aaa ggg ---!ccc\\\"), \\\"aaagggccc\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07c7559f5a1f0d3dd0d77216d525cf5\",\n \"score\": \"0.4530663\",\n \"text\": \"def drop_column(self, table_name, column_name):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0f2a062c4c93be652aa84fec2c855d\",\n \"score\": \"0.45285517\",\n \"text\": \"def strip(self, scope = 'all'):\\n if scope == 'all':\\n if self._peaks is None:\\n # decode, just in case ...\\n self.peaks\\n self._tmzSet = None\\n self._tmassSet = None\\n self._transformedMzWithError = None\\n self._transformedPeaks = None\\n self._transformed_deconvolutedPeaks = None\\n self._transformedMassWithError = None\\n if 'encodedData' in self.keys():\\n del self['encodedData']\\n del self['PY:0000000']\\n # this is the ID tag corresponding to 'encodedData'\\n else:\\n print(\\\"Dont understand strip request \\\", file = sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9831f52e6c639fb4a09c1c529773f46\",\n \"score\": \"0.4524795\",\n \"text\": \"def drop_sids(self, to_drop):\\n self.sids = pd.Index(\\n sorted(self.sids - _ensure_index(to_drop)),\\n )\\n self._realign_sids()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e706d7a772989dc5dc3ef96dd99d5011\",\n \"score\": \"0.4519591\",\n \"text\": \"def strip(s):\\r\\n return \\\"\\\".join(i for i in s if ord(i)<128)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7c9663d12a8aa1f373e400d44cf40a\",\n \"score\": \"0.45141435\",\n \"text\": \"def demark_seq(values: pd.Series) -> pd.DataFrame:\\n return ta.td_seq(values, asint=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754de207f5514da8d7d806d2e9232500\",\n \"score\": \"0.45130885\",\n \"text\": \"def removeUTF(self, s):\\n res = s.replace(\\\"_utf8'\\\", \\\"'\\\")\\n\\n \\\"\\\"\\\"Postgres speciffic syntax\\\"\\\"\\\"\\n return res.replace(\\\"::text\\\", \\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcda3383697b503505d648410ada80b3\",\n \"score\": \"0.45106238\",\n \"text\": \"def deleteFirstChar(c,s):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e9ed1597caf3e66692ff61a51af247\",\n \"score\": \"0.45054317\",\n \"text\": \"def drop_column(self, *columns):\\n for column in columns:\\n self.table.drop_column(column)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a4d077517de10fc1d6b4c2898f4373\",\n \"score\": \"0.45034474\",\n \"text\": \"def remove_character(self, character_to_remove):\\n if character_to_remove in self.characters:\\n self.characters.remove(character_to_remove)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ca0fd4277d1fdf0770abf1f8a355ff\",\n \"score\": \"0.4502169\",\n \"text\": \"def remove_unnecessary_symbols(text):\\n text = re.sub(r'[\\\\(\\\\)\\\\[\\\\]\\\\<\\\\>\\\\\\\"]+', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b21f471eb5583c1a6483683cf51a64\",\n \"score\": \"0.4496638\",\n \"text\": \"def strip_special(w):\\n\\treturn ''.join(c for c in w if ord(c) < 128)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993698,"cells":{"query_id":{"kind":"string","value":"1a9b779989c37015734fc8703de0b700"},"query":{"kind":"string","value":"Return to the user their total earnings so far."},"positive_passages":{"kind":"list like","value":[{"docid":"6b4c43b9b62b044df327c9b90569703c","score":"0.7550638","text":"def calculate_total_earnings():\n all_earnings = worksheet_update.col_values(7)\n del all_earnings[0]\n # Used method 3 to turn a list of strings into a list of integers\n # https://www.geeksforgeeks.org/python-converting-all-strings-in-list-to-integers/\n all_earnings_int = list(map(int, all_earnings))\n\n earnings_total = sum(all_earnings_int)\n print(f\"Total earnings to date: {Fore.GREEN}£{earnings_total}\")\n print(\"\\033[39m\")\n print(Fore.LIGHTMAGENTA_EX)\n print(\"Thank you, goodbye for now...\")\n print(\"\\033[39m\")","title":""}],"string":"[\n {\n \"docid\": \"6b4c43b9b62b044df327c9b90569703c\",\n \"score\": \"0.7550638\",\n \"text\": \"def calculate_total_earnings():\\n all_earnings = worksheet_update.col_values(7)\\n del all_earnings[0]\\n # Used method 3 to turn a list of strings into a list of integers\\n # https://www.geeksforgeeks.org/python-converting-all-strings-in-list-to-integers/\\n all_earnings_int = list(map(int, all_earnings))\\n\\n earnings_total = sum(all_earnings_int)\\n print(f\\\"Total earnings to date: {Fore.GREEN}£{earnings_total}\\\")\\n print(\\\"\\\\033[39m\\\")\\n print(Fore.LIGHTMAGENTA_EX)\\n print(\\\"Thank you, goodbye for now...\\\")\\n print(\\\"\\\\033[39m\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c65615b462a8b663f0d3f469f92bd2ec","score":"0.78068686","text":"def total_earnings(self):\n return self.total_balance - self.net_invested","title":""},{"docid":"a7832efbb787ce74a2c76cc6a4c42a3e","score":"0.66952634","text":"def update_total(self):\n\n for item in self.item_list:\n if item == \"pumpkin slice\":\n self.total += Pie.pumpkin_pie_slice_price\n elif item == \"cherry slice\":\n self.total += Pie.cherry_pie_slice_price\n elif item == \"whole apple pie\":\n self.total += Pie.whole_apple_pie_price\n\n print(f\"{self.username}'s total: ${round(self.total, 2)}\")\n return round(self.total, 2)","title":""},{"docid":"b624c59ce2878c301a0f696e54092094","score":"0.66274047","text":"def update_total(self):\n\n for item in self.item_list:\n if item == \"white cake\":\n self.total += Cake.white_cake_price\n elif item == \"crepe cake\":\n self.total += Cake.crepe_cake_price\n elif item == \"birthday cake\":\n self.total += Cake.birthday_cake_price\n\n print(f\"{self.username}'s total: ${round(self.total, 2)}\")\n return round(self.total, 2)","title":""},{"docid":"712b0a3b93db9a6d2488838f6ea2f611","score":"0.644746","text":"def update_total(self):\n\n for ingredient in self.ingredients:\n if ingredient == \"patty\":\n self.total += Burger.patty_cost\n elif ingredient == \"lettuce\":\n self.total += Burger.lettuce_cost\n elif ingredient == \"pickle\":\n self.total += Burger.pickle_cost\n elif ingredient == \"tomato\":\n self.total += Burger.tomato_cost\n else:\n self.total += Burger.cheese_cost\n\n print(f\"{self.username}'s total: ${round(self.total, 2)}\")\n return round(self.total, 2)","title":""},{"docid":"5d24fefd92ba8b7f7df2523d67a4f839","score":"0.6444193","text":"def earn_wage(self) -> None:\r\n payment = 0 # start with 0 payment\r\n for task in self.task_manager:\r\n payment += task.payment\r\n task.zero_payment() # reset payment value\r\n self.wallet.currency_dict[1] += payment # add payment to wallet (1 is the key for the first currency)\r\n print('[game_manager/Game/earn_wage]: - earned wage: ', payment)\r\n self.save_game()","title":""},{"docid":"4f2230623cc95ac81e10153bc75cbcd2","score":"0.63954026","text":"def calc_earned(self):\n return self.paid_participants() * self.price","title":""},{"docid":"a60cc8aa5f2706cfc841c11ae7da6b48","score":"0.63836026","text":"def get_total_savings(self):\n\n return sum(p.savings for p in self.people)","title":""},{"docid":"6e8f024967e2b7aeba9937fceebbff38","score":"0.6366007","text":"def update_total(self):\n\n for item in self.item_list:\n if item == \"slice\":\n self.total += Pizza.slice_price\n elif item == \"10in\":\n self.total += Pizza.ten_in_price\n else:\n self.total += Pizza.the_jeremiah_special_price\n\n print(f\"{self.username}'s total: ${round(self.total, 2)}\")\n return round(self.total, 2)","title":""},{"docid":"eff80a5b0c3e4d8c8007b0cb4eee6df7","score":"0.6352608","text":"def get_total_spending(self):\n total_spending = 0\n for meal in self.meals.all():\n total_spending += meal.get_meal_spending()\n return total_spending","title":""},{"docid":"d5384eaab09fb07924f19a0363b586b7","score":"0.6264039","text":"def total_sells(self):\n return self.sum_orders(self.sell_orders)","title":""},{"docid":"bf85c9310e2eda41bb751db4c17e244a","score":"0.62436634","text":"def get_total(self):\n\t\treturn self.amount, self.nonvat, self.taxable, self.vat, self.profit","title":""},{"docid":"88c2f354d6a0e841d7a77b8112d34362","score":"0.62204516","text":"def _compute_total_amount(self):\n total = 0\n active_id = self._context.get('active_id')\n purchase_id = self.env['purchase.order'].browse(active_id)\n for pay in purchase_id.payment_history_ids:\n total += pay.advance_amount\n return abs(total)","title":""},{"docid":"f31955b5792d9d413e97ddda2aadf030","score":"0.62175107","text":"def total(self, request):\n total = self.total_withdrawal(request)\n return Response({'total': total})","title":""},{"docid":"f7746971888cbd64aa14b5b92eca46cc","score":"0.6215908","text":"def total_salary(self):\n print(\"\\nTotal Salary at the end of the month after all bonuses and deductions have been made: \")\n self.salary =((self.salary * 0.1) + self.salary) - (self.salary * 0.2)\n return self.salary","title":""},{"docid":"0b1c85783be9906567f2dc2ea2c1e148","score":"0.6210936","text":"def calculate_fees(self):\n total = 0\n for course_code, fee in self._enrolments:\n total += fee\n return total","title":""},{"docid":"f4c0f379b280814a65e0db6ee67caf17","score":"0.62084264","text":"def get_user_score(self, username):\n user = User.nodes.get(username=username)\n completed_dares = user.completed_dare.all()\n total = 0\n for dare in completed_dares:\n total += dare.score\n return total","title":""},{"docid":"e0f34bd051fe13cfbe859b587fe10cbf","score":"0.61893326","text":"def calculate_total(self):\n self.total_cost = self.product_price * self.rental_days\n self.save()","title":""},{"docid":"abdc8dffa4159a23113d959a8edce6f0","score":"0.6146693","text":"def get_user_winnings(user):\n total_amount = 0.0\n prizes = Prize.objects.filter(winning_ticket__purchased_by=user)\n num_prizes = prizes.count()\n for prize in prizes:\n if prize.amount_paid:\n total_amount += float(prize.amount_paid)\n else:\n # payment is pending but add in the the amount\n # that will be paid\n total_amount += float(prize.amount_to_pay)\n return num_prizes, Decimal(total_amount)","title":""},{"docid":"034ee1c9b8502bf7532d64ee0bfb471f","score":"0.6100508","text":"def percent_earnings_loss(self):\r\n original_cost = self.purchase_prices\r\n current_value = self.current_prices \r\n num_shares = self.no_shares\r\n #(current value – purchase price) x number of shares\r\n percent_yield = (current_value - original_cost) * num_shares * 100\r\n return percent_yield","title":""},{"docid":"11ef05de8bca745a0e59d7923ad39c26","score":"0.60802895","text":"def _calculate_total_income(self):\n self.logger.debug('%s: calculating total income.', self.name)\n income_columns = [\n self.INTEREST_PAYMENT,\n self.LATE_FEE_PAYMENT,\n self.BUYBACK_INTEREST_PAYMENT,\n self.BONUS_PAYMENT,\n self.DEFAULTS]\n self.df[self.TOTAL_INCOME] = 0.\n for col in [col for col in self.df.columns if col in income_columns]:\n self.df[self.TOTAL_INCOME] += self.df[col]\n self.logger.debug('%s: finished calculating total income.', self.name)","title":""},{"docid":"b69dede81adb63f0f8a2b9bcf2b98a85","score":"0.60638815","text":"def get_total_amount(self) -> Decimal:\n return Decimal(sum(assignment.get_total_amount() for assignment in self.assignments))","title":""},{"docid":"601bd2146bf1ba9b4498059729a5c01f","score":"0.60631245","text":"def calculate_sum(self):\n return (\n self.total_invoice_amount + self.total_duty_amount +\n self.total_fright_amount + self.total_clearance_amount\n )","title":""},{"docid":"7742048eb99fbb98d762eed28781601b","score":"0.6058161","text":"def getTotalNeeded(self):","title":""},{"docid":"4ee2233bed23dcf1d761b4bb97f4c621","score":"0.60502625","text":"def _total_value(self, row):\n total_value = self._share_value(row)\n if pf.TradeLog.cash > 0:\n total_value += pf.TradeLog.cash\n return total_value","title":""},{"docid":"7234cbd0a2d4c382ac325ac76c89d4e5","score":"0.6038182","text":"def get_amount(self, data):\n tot_amount = 0.0\n rent_amt = data.total_rent\n for line in data.rent_schedule_ids:\n if line.paid is True:\n tot_amount = tot_amount + line.amount\n return tot_amount","title":""},{"docid":"c13ad2c823f1a65b32b78301c2cc71c1","score":"0.6034645","text":"def calculate_total_profit():\n\n df = pd.read_csv(filepath_or_buffer=\"sales_file.csv\", index_col=0)\n total_price = float(df[['price']].sum())\n tk.messagebox.showinfo(\"Hot Dog World | Total Profit\", \"The total profits are: \" + str(total_price))\n return total_price","title":""},{"docid":"f5d10bddacbcc6f34b252f2adca8245f","score":"0.6025272","text":"def _calculate_total(self):\n print(\"_calculate_total\")\n total = 0.0\n for line in self._line_list:\n # If there is a row where calculation has not yet happened\n try:\n price_final = line.values[\"price_final\"]\n except KeyError:\n return\n\n total += price_final\n\n total = self._float2str(total)\n self._root_objects.labels[\"total_var\"].object.config(text=total)","title":""},{"docid":"a2e2bd6844cd62e107aa859e83934a3b","score":"0.6015662","text":"def get_total_profit(self) -> int:\n return self.__profit","title":""},{"docid":"b293214a80b6414126f278f3e9f9d416","score":"0.6013858","text":"def get_total_balance(self):\n self.balance = sum(m.money for m in self.members.values())\n return self.balance","title":""},{"docid":"913616433141117d9792980ed6020a93","score":"0.6013291","text":"def add_earnings(username, amount):\n df = pd.read_csv('database/Developer.csv')\n df.loc[df['username'] == username, 'earnings'] += amount\n df.to_csv('database/Developer.csv', index=False)","title":""},{"docid":"a24ed8de67e6ad6acd252387eab5a47b","score":"0.6011141","text":"def cal_total(self):\n self.total = self.boxes_needed() * self.box_price\n return self.total","title":""},{"docid":"d09e9b8384390b5caa6f03444c8890ed","score":"0.6006917","text":"def expenses(self):\n return sum([t.delta_balance for t in\n self.transactions.filter(delta_balance__lt=0)])","title":""},{"docid":"f4414897858afde425d8b3af7231cb37","score":"0.6003653","text":"def totalExpenses(self):\n if not isinstance(self.amount, list):\n\n return False\n\n return sum(map(int, self.amount))","title":""},{"docid":"59a865363847dc56ed5361301c965e0d","score":"0.5986198","text":"def calculate_earnings():\n\n lesson_price = prices.col_values(2)\n del lesson_price[0]\n\n price = int(lesson_price[duration_index])\n\n lesson_earnings = price * attendance_total\n\n # I've placed the below print statement here as input\n # data should not push back into the worksheet if it\n # isn't valid\n print(f\"\\n{Fore.LIGHTGREEN_EX}Data input is valid!\\n\")\n print(\"\\033[39m\")\n\n print(\n f\"Total earnings for this class is: {Fore.GREEN}£{lesson_earnings} \\n\")\n print(\"\\033[39m\")\n # append calculation to a list of user inputs\n new_lesson_data.append(lesson_earnings)","title":""},{"docid":"5ea2ba2ebe4b5f8e08ba3e11eac00f38","score":"0.59809273","text":"def overall_total(self):\r\n if self.profile[\"overall_total\"] is None:\r\n self._fetch_runemetrics()\r\n return self.profile[\"overall_total\"]","title":""},{"docid":"672ceac762919bc9c788d498861e8b28","score":"0.5965296","text":"def total(self):\n href = '{0}/{1}'.format(self.api.base_url, self.entity)\n params = {'limit': 0, 'offset': 0}\n resp = self.api.get(href, params)\n\n return resp['total']","title":""},{"docid":"7b0057b6b64d41039bcac942c3bda356","score":"0.59367865","text":"def get_table_spending(self):\n total = 0\n meals = self.meals.all()\n for meal in meals:\n total += meal.get_meal_spending()\n return total","title":""},{"docid":"199b34a4ec4301302c233a7b9303a665","score":"0.59159696","text":"def getPriceGross(self):\n total = 0.0 \n for item in self.item_management.getItems():\n total += item.getPriceGross()\n total -= item.getDiscountGross()\n\n total += self.context.getPaymentPriceGross()\n total += self.context.getShippingPriceGross()\n\n return total","title":""},{"docid":"9a5bbcc64c0c4fc45b92450a44307ec8","score":"0.5904075","text":"def saldo_total():\n pass","title":""},{"docid":"761555e21e91a8a1c70328272268905a","score":"0.589524","text":"async def payouts_total():\n # memoized historical sum. To update:\n # SELECT SUM(payout) FROM hive_posts_cache\n # WHERE is_paidout = 1 AND payout_at <= precalc_date\n precalc_date = '2017-08-30 00:00:00'\n precalc_sum = Decimal('19358777.541')\n\n # sum all payouts since `precalc_date`\n sql = \"\"\"\n SELECT SUM(payout) FROM hive_posts_cache\n WHERE is_paidout = '1' AND payout_at > '%s'\n \"\"\" % (precalc_date)\n\n return float(precalc_sum + DB.query_one(sql)) #TODO: decimal","title":""},{"docid":"bc98ae977430ebe20f0e798ce1c10e71","score":"0.58830047","text":"def total(self):\n params = {'limit': 0, 'offset': 0}\n resp = self._api.get(self._entity, params=params)\n\n return resp['total']","title":""},{"docid":"cb2c1b54b9b152bdfa7726d464d1b35c","score":"0.5880711","text":"def set_total(self) -> float:\n return self.total","title":""},{"docid":"a4f703927068af1e794864bb1d2f0ed9","score":"0.58772","text":"def total(self):\n return sum((item.amount * item.price) for item in self._items)","title":""},{"docid":"8452f2b642c7ba71b66d49e2298a9f1f","score":"0.5860056","text":"def calculateFinances(self):\n for employee in self.employees:\n try:\n employee.setAttendanceHalf(self.month, self.half)\n # Problem only arises when trying to calculate pay for employee who has no attendance record.\n employee.calculatePay()\n except:\n pass\n self.totalsalary = 0\n self.totalbalance = 0\n self.totalloans = 0\n for employee in self.employees:\n if employee.salary_interval == \"Monthly\" and not self.name == \"Staff\" :\n self.totalsalary += employee.overtimepay\n else:\n self.totalsalary += employee.totalpay\n self.totalloans += employee.loans\n self.totalbalance = self.totalsalary - self.totalloans","title":""},{"docid":"93ef0f832db9339a1fb156009eb2fd4a","score":"0.5857614","text":"def _get_advance_payments_total(self):\n for line in self:\n line.advance_payments_total = sum(payment.amount for payment in line.advance_payment_ids)","title":""},{"docid":"641400682774bc5b7c3c96b2daa32fec","score":"0.58502144","text":"def profit(self):\n return max(0, self.total_recovered - self.purchase_price)","title":""},{"docid":"a15c9e9829a83cf851ddf6e7dee49ff3","score":"0.58501595","text":"def full_total(self):\n return self.full_price - self.multiple_purchase_discount - self.credit_applied","title":""},{"docid":"865ff87f12d2b5e686553da46120f57f","score":"0.5849784","text":"def karma_total_user(self):\n if self.__karma_stats.total_user is not None:\n return self.__karma_stats.total_user\n\n total_plus = 0\n total_minus = 0\n for user_id, user in get_user_database().root.iteritems():\n total_plus += user.karma_plus_received()\n total_minus += user.karma_minus_received()\n\n self.__karma_stats.total_user = (total_plus, total_minus)\n qon.log.stats_info('KarmaStats\\tuser:%d,%d' % self.__karma_stats.total_user)\n return self.__karma_stats.total_user","title":""},{"docid":"7fd46876463c5e51095947afe60ee5b4","score":"0.5848427","text":"def total_buys(self):\n return self.sum_orders(self.buy_orders)","title":""},{"docid":"6527bb7baf4580b5be84259d75f23fcb","score":"0.5837438","text":"def total_sold(self):\n return abs(sum(t.delta_quantity for t in self.outbound_transactions))","title":""},{"docid":"2e7e02c66365afa42003c5536d2a2ec9","score":"0.5829619","text":"def total_given(self, donations):\n total_sum = sum(donations) #sum all entries in the obj._donation list\n return total_sum","title":""},{"docid":"5ebcdcbf12c1b6e0c4b12fc23dd99f13","score":"0.5827203","text":"def total_value(self) -> Tuple[float, Mapping[str, int], float, str]:\n value = 0\n coll_items = {}\n for user in self.users:\n value += user.value\n for item, amount in user.inventory.items():\n if item in coll_items:\n coll_items[item] += amount\n else:\n coll_items[item] = amount\n # do the same for the pot.\n value += self.pot.value\n for item, amount in self.pot.inventory.items():\n if item in coll_items:\n coll_items[item] += amount\n else:\n coll_items[item] = amount\n\n total_value = value + sum([amount*self.library.library[item] if self.library.library[item] >= 0 else 0\n for item, amount in coll_items.items()])\n items_str = ''\n for item, amount in coll_items.items():\n items_str += '%s:%d, ' % (item, amount)\n items_str = items_str[:-2]\n ret = 'Everyone together holds %d and %s for a total value of %d.\\n' % (value, items_str, total_value)\n return value, coll_items, total_value, ret","title":""},{"docid":"8e3702498cab56723212a0101137b8d5","score":"0.58269405","text":"def to_consume(self, central, r, year, month):\n money = sum(m.grab_money() for m in self.members.values())\n permanent_income = self.permanent_income(central, r)\n # Having loans will impact on a lower long-run permanent income consumption and on a monthly strongly\n # reduction of consumption. However, the price of the house may be appreciating in the market.\n # If cash at hand is positive consume it capped to permanent income\n money_to_spend = None\n if money > 0:\n if money > permanent_income > 0:\n money_to_spend = permanent_income\n # Deposit family money above that of permanent income\n self.savings += money - permanent_income\n else:\n money_to_spend = money\n # If there is no cash available, withdraw at most permanent income from savings\n elif self.savings > permanent_income > 0:\n self.savings -= permanent_income\n money_to_spend = permanent_income\n elif self.savings > 0:\n money_to_spend = self.savings\n self.savings = 0\n else:\n # If there is no cash and no savings withdraw from any long-term deposits if any\n if central.wallet[self]:\n cash = self.grab_savings(central, year, month)\n if cash > permanent_income:\n cash -= permanent_income\n money_to_spend = permanent_income\n else:\n money_to_spend = cash\n return money_to_spend","title":""},{"docid":"34559e36b7782504c172836f9ab69ca0","score":"0.58247757","text":"def annual_total_section_seven_expenses(responses, derived):\n\n try:\n return float(responses.get('annual_total_section_seven_expenses', 0))\n except ValueError:\n return 0","title":""},{"docid":"6f5fbdaeb7743aab0843996cb6ca2b99","score":"0.5809489","text":"def calculateAmount(self):\n sum_1 = [float(str(i).split(', ')[1]) for i in self.items]\n return sum(sum_1)","title":""},{"docid":"d509632b4c992cae4937687e829f36d8","score":"0.58066624","text":"def get_total(self):\n t_total = 0.0\n for i in self.orders:\n t_total += i.get_total()\n return t_total","title":""},{"docid":"7c8681738d9f2e3b8cde39e5e9d4a03f","score":"0.5805168","text":"def total(self):\n return self.response[-1] - self.query","title":""},{"docid":"ea9e63f8e162b7128e0d1e85cce023be","score":"0.57943785","text":"def get_total_winnings():\n total_amount = 0.0\n prizes = Prize.objects.filter(winning_ticket__isnull=False)\n num_prizes = prizes.count()\n num_winners = len(set(\n [prize.winning_ticket.purchased_by for prize in prizes]))\n for prize in prizes:\n if prize.amount_paid:\n total_amount += float(prize.amount_paid)\n else:\n # payment is pending but add in the the amount\n # that will be paid\n total_amount += float(prize.amount_to_pay)\n return num_prizes, num_winners, Decimal(total_amount)","title":""},{"docid":"350bd994bd6f181242d2b2a9e329038c","score":"0.57908016","text":"def total_section_seven_expenses(responses, derived):\n\n try:\n return float(responses.get('total_section_seven_expenses', 0))\n except ValueError:\n return 0","title":""},{"docid":"58f7b26e559a2b0ce818d7785260e433","score":"0.5782383","text":"def episode_total_score(self):\n return self.score","title":""},{"docid":"b59235f0b4c41d515131fd975f4b2498","score":"0.5773943","text":"def total_expense(self, time_units):\n return self.price * time_units","title":""},{"docid":"2dc80b587e5dc1f1ae5d0caf04b0a1c0","score":"0.57713234","text":"def total_score(self):\n return Task.objects.filter(task_type__event=self).aggregate(\n total_score=Sum(F('num_people')*F('score')))['total_score'] or 0","title":""},{"docid":"ad5e061c382607e2ef074a66da6ca9d5","score":"0.57709074","text":"def getTotalPrice(self):\n totalPrice = 0\n for orderedItem in self.orderedItems:\n totalPrice += orderedItem.productStock.price\n if orderedItem.deliveryOption == -1:\n raise DevilveryOptionNotSetExeption()\n totalPrice += + orderedItem.deliveryOption.price\n return totalPrice","title":""},{"docid":"ea4a649d31ed4b718bf6b80e848280e3","score":"0.5767469","text":"def getTotal(self):\n u, q = self.getRate(), self.getQuantity()\n if u and q:\n return u * q\n else:\n return Money('0.00', self.getInvoiceCurrency())","title":""},{"docid":"9412c1db7062ae1070512660b3e61d6e","score":"0.57668346","text":"def calculate_total(self):\n return round_half_up(self.calculate_subtotal() - self.calculate_discount(), 2)","title":""},{"docid":"46beedfa2576c9775fe7417a1fa79209","score":"0.5764895","text":"def account_potential(self) -> float:\n stocks = self._rh.securities_owned()['results']\n potential_sum = float(self._rh.portfolios()['withdrawable_amount'])\n for stock in stocks:\n # Make quantity a float as the API may change when I buy fractional shares.\n quantity = float(stock['quantity'])\n buy_price = float(stock['average_buy_price'])\n potential_sum += quantity * buy_price\n return potential_sum","title":""},{"docid":"c5bc5b86f96cee41ca846c6518826023","score":"0.57634187","text":"def total():\n return price() + tax()","title":""},{"docid":"dd685e5304338999ee0e1b1cf2691bc3","score":"0.57587683","text":"def get_total_items(self):\n return self.total_items","title":""},{"docid":"b8dae457bbd38f549031ff9189f3ffdb","score":"0.5754589","text":"def updateTotal(self):\n\n\t\ta = [] # List that contains numerical values of all the players' cards. (EG: Jack has a value of 11)\n\n\t\tfor b in self.cards: # Adding all the cards to 'a'\n\n\t\t\tif(b[0] < 11): # if a number card, add its face value\n\n\t\t\t\ta.append(b[0])\n\n\t\t\telif(b[0] >= 11 and b[0] != 14): # if a face card (but not an Ace) add 10\n\n\t\t\t\ta.append(10)\n\n\t\t\telse: # if an Ace, add 1 or 11 depending on which will NOT bust the player\n\n\t\t\t\tif(self.currentTotal + 11) > 21:\n\n\t\t\t\t\ta.append(1)\n\n\t\t\t\telse:\n\n\t\t\t\t\ta.append(11)\n\n\t\tself.currentTotal = sum(a) # Updating the total to be the sum of all the values","title":""},{"docid":"eefa502f41e1dda8ac4906fb6bd0f062","score":"0.5751323","text":"def total_used_minerals_economy(self):\n return self.proto.total_used_minerals.economy","title":""},{"docid":"9817e2a9f507a7280a7025bd5a15ca42","score":"0.5748139","text":"def calc_pending_earned(self):\n return self.not_paid_participants() * self.price","title":""},{"docid":"89c14030c278ce3ad0da3ea6db4960b8","score":"0.5744632","text":"def update_current_earning_balance(self):\n if self.for_trade and self.subscribed_live_api:\n upstox_user = self.get_upstox_user()\n balance = upstox_user.get_balance()\n current_balance = balance.get(\"equity\").get(\"available_margin\")\n if self.bank.current_balance != current_balance:\n pl = None\n try:\n pl = Earning.objects.get(user=self, date=get_local_time().date() - timedelta(1))\n except:\n pl = Earning.objects.create(user=self, date=get_local_time().date() - timedelta(1), opening_balance=self.bank.current_balance)\n pl.profit_loss = float(current_balance) - float(pl.opening_balance)\n pl.save()\n self.bank.current_balance = current_balance\n self.bank.save(update_fields=[\"current_balance\"])","title":""},{"docid":"7e5d9f7667482e3eb48d1f5dbb0fe91d","score":"0.5742514","text":"def total_paid(self):\n return self.payment_set.aggregate(\n sum=Sum('amount')\n )['sum'] or Decimal(0)","title":""},{"docid":"2f9853116c214497e5dee814de0c1091","score":"0.5734045","text":"def get_wealth(self, bank):\n estate_value = sum(h.price for h in self.owned_houses)\n return self.savings + estate_value + bank.sum_deposits(self) - bank.loan_balance(self.id)","title":""},{"docid":"995bd8a2a28dd6c5e24f2e96dfd11f0c","score":"0.57313603","text":"def get_total(self):\n raise NotImplementedError()","title":""},{"docid":"c4aa1882fe1ce606edf409a86492d62b","score":"0.57272375","text":"def get_total_price(self):\n return self.price","title":""},{"docid":"8e48ac3609c9e8ab6f30aa755247831d","score":"0.5723626","text":"def getTotal(self):\n total = 0\n for eachProduct in self.itemsInCart.all():\n total += eachProduct.getLineTotal()\n #return total\n return '${:,.2f}'.format(total)","title":""},{"docid":"c31b5522e7ab3377d995d7f1bbc3b724","score":"0.571877","text":"def _get_total(self, property, partner_id=None):\n if partner_id is None:\n total = D('0.00')\n for line in self.all_lines():\n try:\n total += getattr(line, property)\n # except ObjectDoesNotExist:\n # # Handle situation where the product may have been deleted\n # pass\n except TypeError:\n # Handle Unavailable products with no known price\n info = self.get_stock_info(line.product, line.attributes.all())\n if info.availability.is_available_to_buy:\n raise\n pass\n return total\n else:\n total = D('0.00')\n for line in self.partner_lines(partner_id):\n try:\n total += getattr(line, property)\n except ObjectDoesNotExist:\n # Handle situation where the product may have been deleted\n pass\n except TypeError:\n # Handle Unavailable products with no known price\n info = self.get_stock_info(line.product, line.attributes.all())\n if info.availability.is_available_to_buy:\n raise\n pass\n return total","title":""},{"docid":"a52724dbb5fb1b775c5254dc19131ecb","score":"0.5717645","text":"def get_total(self):\n total = super().get_total()\n\n # International fee for orders of less than 10\n if self.qty < 10:\n return total + 3 \n else:\n return total","title":""},{"docid":"03369bb4868a48636df79a85ed358947","score":"0.57161427","text":"def total( self ):\n return Decimal( self.itemPrice) * Decimal(self.units)","title":""},{"docid":"6cfe448b626d5924d264ccb0dcef7233","score":"0.571474","text":"def test_total(self):\n # Run forecast:\n self.forecast(self.available)\n\n # Regardless of setup, all available money (i.e. $3000)\n # should be contributed.\n self.assertAlmostEqual(self.forecast.total, Money(3000))","title":""},{"docid":"f79cdfc0c5a92e0a34758cbb947edc92","score":"0.5707575","text":"def get_total(self, offers=None):\n\n amounts = []\n for item in self.items:\n \n # Cart total without any offers applied\n line_total = item.get_line_total(self.product_store)\n\n if offers is not None:\n for offer in offers:\n if offer.target_product == item.product:\n offer_total = offer.calculate_line_total(\n item, self.product_store, self)\n\n if offer_total < line_total:\n line_total = offer_total\n\n amounts.append(line_total)\n \n cart_total = float(sum(amounts))\n if cart_total <= 0.0:\n cart_total = 0\n \n return cart_total","title":""},{"docid":"d647dff27edc10e6fad17014a4a74cc5","score":"0.57025427","text":"def total(self):\n return (self.galleons * 17 * 29) + (self.sickles * 29) + (self.knuts)","title":""},{"docid":"8bf31bea62ad1652cb2a2ac600b5c942","score":"0.569522","text":"def total(self) -> Optional[int]:\n return pulumi.get(self, \"total\")","title":""},{"docid":"26a3bfb0df93ff0288fabfd5a1057c2d","score":"0.56816375","text":"def yearly_earnings(self):\r\n value = self.current_values\r\n price = self.purchase_prices\r\n m, d, y = self.purchase_dates.split('/')\r\n purc_date = datetime.date(int(y),int(m),int(d))\r\n today = datetime.date(2017,8,2)\r\n years = (today-purc_date).days / 365\r\n #((((current value – purchase price)/purchase price)/(current date – purchase date)))*100\r\n return (value - price)/price / years * 100","title":""},{"docid":"1b027cfe684c2f8762f7afdc5b8ea906","score":"0.567855","text":"def getTotalReward(self):\n return self.cumreward","title":""},{"docid":"5a47f54af62def1f6aa1d9b373134fe8","score":"0.5671748","text":"def calcs_total(self):\n total = decimal.Decimal(0.00)\n for related_product in self.products_in_request:\n for product in self.products:\n if product.get('product_id') == related_product.get(\n 'product_id'):\n total += product.get('price') * related_product.get(\n 'quantity')\n self.total = total","title":""},{"docid":"272b3fd75ff086a34fe25eada3087c9a","score":"0.5664258","text":"def total_used_vespene_economy(self):\n return self.proto.total_used_vespene.economy","title":""},{"docid":"e6dd388d72cad0b285a8bc31bdb5672a","score":"0.5658225","text":"def TotalGrade(self):\n return sum([r.Grade*r.Category.Weight for r in self.results.all()]) / 100","title":""},{"docid":"c46a99ef631ccf52d29ee4aa578e383f","score":"0.5656973","text":"def earnings_yield(self,earnings_per_share = None, stock_price = None):\n try:\n if earnings_per_share != None and stock_price != None:\n self.earnings_per_share = earnings_per_share\n self.stock_price = stock_price\n \n if self.earnings_per_share == None or self.stock_price == None:\n ratioval = np.nan\n else:\n ratioval = self.earnings_per_share / self.stock_price\n \n return ratioval\n except:\n pass","title":""},{"docid":"53f26bb446f364092322d94439e388af","score":"0.5651384","text":"def get_totalscore(self):\n scores = Roundscore.query.filter_by(round_id=self.id)\n totalscore = 0\n for score in scores:\n totalscore = totalscore + score.score\n return totalscore","title":""},{"docid":"aaf37514b2850382d9a9ded4b27e89fb","score":"0.5647695","text":"def _compute_sale_total(self):\n for service in self:\n service.sale_total = service.qty * service.list_price or 0.0","title":""},{"docid":"512f7bc7d7abd72f9fb7f5cc98701954","score":"0.56422186","text":"def total_recovered(self):\n return sum(t.delta_balance for t in self.outbound_transactions)","title":""},{"docid":"3e3408aa76898ecf1cf0c977ff7ed287","score":"0.5640393","text":"def assignment_6():\n \n earnings_per_staff = Payment.objects.values(\"staff\").annotate(Sum(\"amount\"))\n return [(element['staff'], element['amount__sum']) for element in earnings_per_staff]","title":""},{"docid":"ed9bd90e49bb0f00efbee262cff3f22b","score":"0.56392443","text":"def income(self):\n return sum([t.delta_balance for t in\n self.transactions.filter(delta_balance__gt=0)])","title":""},{"docid":"ee32386bedb881c4ddb18be6bff491d1","score":"0.563837","text":"def get_amount_users() -> User:\n return User.objects.all().count()","title":""},{"docid":"0aa896c0e50ed55ca19a9711e6b3f4f1","score":"0.5638367","text":"def get_total_amount(self) -> Decimal:\n total = Decimal(0)\n for t in self.transactions:\n iter_amount = getattr(t, 'amount', None)\n if iter_amount:\n total += iter_amount\n return total","title":""},{"docid":"a9f6ba3481b5f55b451eb3d3214faeef","score":"0.56246233","text":"def daily_expenses_price(dte, user_id):\n expenses = Expenses.objects.filter(add_on=dte) if user_id == 0 else Expenses.objects.filter(\n Q(add_on=dte) & Q(warehouse_id=user_id))\n total_expense = 0\n\n for expense in expenses.values():\n total_expense += expense['price']\n\n return total_expense","title":""},{"docid":"36c2520b22b7a15bc8b579609286d216","score":"0.56227934","text":"def students_total():\n total_students = Student.objects.all().count()\n return total_students","title":""},{"docid":"debde732130d8babfb2757014e1c5fb4","score":"0.5619387","text":"def calculate_total(self):\r\n\t\tlogging.info('calculating total of the 5 subjects')\r\n\t\tself.total = self.english + self.maths + self.tamil + self.science + self.social\r\n\t\tprint(\"Total=\" , self.total )","title":""}],"string":"[\n {\n \"docid\": \"c65615b462a8b663f0d3f469f92bd2ec\",\n \"score\": \"0.78068686\",\n \"text\": \"def total_earnings(self):\\n return self.total_balance - self.net_invested\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7832efbb787ce74a2c76cc6a4c42a3e\",\n \"score\": \"0.66952634\",\n \"text\": \"def update_total(self):\\n\\n for item in self.item_list:\\n if item == \\\"pumpkin slice\\\":\\n self.total += Pie.pumpkin_pie_slice_price\\n elif item == \\\"cherry slice\\\":\\n self.total += Pie.cherry_pie_slice_price\\n elif item == \\\"whole apple pie\\\":\\n self.total += Pie.whole_apple_pie_price\\n\\n print(f\\\"{self.username}'s total: ${round(self.total, 2)}\\\")\\n return round(self.total, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b624c59ce2878c301a0f696e54092094\",\n \"score\": \"0.66274047\",\n \"text\": \"def update_total(self):\\n\\n for item in self.item_list:\\n if item == \\\"white cake\\\":\\n self.total += Cake.white_cake_price\\n elif item == \\\"crepe cake\\\":\\n self.total += Cake.crepe_cake_price\\n elif item == \\\"birthday cake\\\":\\n self.total += Cake.birthday_cake_price\\n\\n print(f\\\"{self.username}'s total: ${round(self.total, 2)}\\\")\\n return round(self.total, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712b0a3b93db9a6d2488838f6ea2f611\",\n \"score\": \"0.644746\",\n \"text\": \"def update_total(self):\\n\\n for ingredient in self.ingredients:\\n if ingredient == \\\"patty\\\":\\n self.total += Burger.patty_cost\\n elif ingredient == \\\"lettuce\\\":\\n self.total += Burger.lettuce_cost\\n elif ingredient == \\\"pickle\\\":\\n self.total += Burger.pickle_cost\\n elif ingredient == \\\"tomato\\\":\\n self.total += Burger.tomato_cost\\n else:\\n self.total += Burger.cheese_cost\\n\\n print(f\\\"{self.username}'s total: ${round(self.total, 2)}\\\")\\n return round(self.total, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d24fefd92ba8b7f7df2523d67a4f839\",\n \"score\": \"0.6444193\",\n \"text\": \"def earn_wage(self) -> None:\\r\\n payment = 0 # start with 0 payment\\r\\n for task in self.task_manager:\\r\\n payment += task.payment\\r\\n task.zero_payment() # reset payment value\\r\\n self.wallet.currency_dict[1] += payment # add payment to wallet (1 is the key for the first currency)\\r\\n print('[game_manager/Game/earn_wage]: - earned wage: ', payment)\\r\\n self.save_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2230623cc95ac81e10153bc75cbcd2\",\n \"score\": \"0.63954026\",\n \"text\": \"def calc_earned(self):\\n return self.paid_participants() * self.price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60cc8aa5f2706cfc841c11ae7da6b48\",\n \"score\": \"0.63836026\",\n \"text\": \"def get_total_savings(self):\\n\\n return sum(p.savings for p in self.people)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8f024967e2b7aeba9937fceebbff38\",\n \"score\": \"0.6366007\",\n \"text\": \"def update_total(self):\\n\\n for item in self.item_list:\\n if item == \\\"slice\\\":\\n self.total += Pizza.slice_price\\n elif item == \\\"10in\\\":\\n self.total += Pizza.ten_in_price\\n else:\\n self.total += Pizza.the_jeremiah_special_price\\n\\n print(f\\\"{self.username}'s total: ${round(self.total, 2)}\\\")\\n return round(self.total, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff80a5b0c3e4d8c8007b0cb4eee6df7\",\n \"score\": \"0.6352608\",\n \"text\": \"def get_total_spending(self):\\n total_spending = 0\\n for meal in self.meals.all():\\n total_spending += meal.get_meal_spending()\\n return total_spending\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5384eaab09fb07924f19a0363b586b7\",\n \"score\": \"0.6264039\",\n \"text\": \"def total_sells(self):\\n return self.sum_orders(self.sell_orders)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf85c9310e2eda41bb751db4c17e244a\",\n \"score\": \"0.62436634\",\n \"text\": \"def get_total(self):\\n\\t\\treturn self.amount, self.nonvat, self.taxable, self.vat, self.profit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c2f354d6a0e841d7a77b8112d34362\",\n \"score\": \"0.62204516\",\n \"text\": \"def _compute_total_amount(self):\\n total = 0\\n active_id = self._context.get('active_id')\\n purchase_id = self.env['purchase.order'].browse(active_id)\\n for pay in purchase_id.payment_history_ids:\\n total += pay.advance_amount\\n return abs(total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f31955b5792d9d413e97ddda2aadf030\",\n \"score\": \"0.62175107\",\n \"text\": \"def total(self, request):\\n total = self.total_withdrawal(request)\\n return Response({'total': total})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7746971888cbd64aa14b5b92eca46cc\",\n \"score\": \"0.6215908\",\n \"text\": \"def total_salary(self):\\n print(\\\"\\\\nTotal Salary at the end of the month after all bonuses and deductions have been made: \\\")\\n self.salary =((self.salary * 0.1) + self.salary) - (self.salary * 0.2)\\n return self.salary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1c85783be9906567f2dc2ea2c1e148\",\n \"score\": \"0.6210936\",\n \"text\": \"def calculate_fees(self):\\n total = 0\\n for course_code, fee in self._enrolments:\\n total += fee\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c0f379b280814a65e0db6ee67caf17\",\n \"score\": \"0.62084264\",\n \"text\": \"def get_user_score(self, username):\\n user = User.nodes.get(username=username)\\n completed_dares = user.completed_dare.all()\\n total = 0\\n for dare in completed_dares:\\n total += dare.score\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f34bd051fe13cfbe859b587fe10cbf\",\n \"score\": \"0.61893326\",\n \"text\": \"def calculate_total(self):\\n self.total_cost = self.product_price * self.rental_days\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abdc8dffa4159a23113d959a8edce6f0\",\n \"score\": \"0.6146693\",\n \"text\": \"def get_user_winnings(user):\\n total_amount = 0.0\\n prizes = Prize.objects.filter(winning_ticket__purchased_by=user)\\n num_prizes = prizes.count()\\n for prize in prizes:\\n if prize.amount_paid:\\n total_amount += float(prize.amount_paid)\\n else:\\n # payment is pending but add in the the amount\\n # that will be paid\\n total_amount += float(prize.amount_to_pay)\\n return num_prizes, Decimal(total_amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034ee1c9b8502bf7532d64ee0bfb471f\",\n \"score\": \"0.6100508\",\n \"text\": \"def percent_earnings_loss(self):\\r\\n original_cost = self.purchase_prices\\r\\n current_value = self.current_prices \\r\\n num_shares = self.no_shares\\r\\n #(current value – purchase price) x number of shares\\r\\n percent_yield = (current_value - original_cost) * num_shares * 100\\r\\n return percent_yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ef05de8bca745a0e59d7923ad39c26\",\n \"score\": \"0.60802895\",\n \"text\": \"def _calculate_total_income(self):\\n self.logger.debug('%s: calculating total income.', self.name)\\n income_columns = [\\n self.INTEREST_PAYMENT,\\n self.LATE_FEE_PAYMENT,\\n self.BUYBACK_INTEREST_PAYMENT,\\n self.BONUS_PAYMENT,\\n self.DEFAULTS]\\n self.df[self.TOTAL_INCOME] = 0.\\n for col in [col for col in self.df.columns if col in income_columns]:\\n self.df[self.TOTAL_INCOME] += self.df[col]\\n self.logger.debug('%s: finished calculating total income.', self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69dede81adb63f0f8a2b9bcf2b98a85\",\n \"score\": \"0.60638815\",\n \"text\": \"def get_total_amount(self) -> Decimal:\\n return Decimal(sum(assignment.get_total_amount() for assignment in self.assignments))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601bd2146bf1ba9b4498059729a5c01f\",\n \"score\": \"0.60631245\",\n \"text\": \"def calculate_sum(self):\\n return (\\n self.total_invoice_amount + self.total_duty_amount +\\n self.total_fright_amount + self.total_clearance_amount\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7742048eb99fbb98d762eed28781601b\",\n \"score\": \"0.6058161\",\n \"text\": \"def getTotalNeeded(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee2233bed23dcf1d761b4bb97f4c621\",\n \"score\": \"0.60502625\",\n \"text\": \"def _total_value(self, row):\\n total_value = self._share_value(row)\\n if pf.TradeLog.cash > 0:\\n total_value += pf.TradeLog.cash\\n return total_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7234cbd0a2d4c382ac325ac76c89d4e5\",\n \"score\": \"0.6038182\",\n \"text\": \"def get_amount(self, data):\\n tot_amount = 0.0\\n rent_amt = data.total_rent\\n for line in data.rent_schedule_ids:\\n if line.paid is True:\\n tot_amount = tot_amount + line.amount\\n return tot_amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13ad2c823f1a65b32b78301c2cc71c1\",\n \"score\": \"0.6034645\",\n \"text\": \"def calculate_total_profit():\\n\\n df = pd.read_csv(filepath_or_buffer=\\\"sales_file.csv\\\", index_col=0)\\n total_price = float(df[['price']].sum())\\n tk.messagebox.showinfo(\\\"Hot Dog World | Total Profit\\\", \\\"The total profits are: \\\" + str(total_price))\\n return total_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d10bddacbcc6f34b252f2adca8245f\",\n \"score\": \"0.6025272\",\n \"text\": \"def _calculate_total(self):\\n print(\\\"_calculate_total\\\")\\n total = 0.0\\n for line in self._line_list:\\n # If there is a row where calculation has not yet happened\\n try:\\n price_final = line.values[\\\"price_final\\\"]\\n except KeyError:\\n return\\n\\n total += price_final\\n\\n total = self._float2str(total)\\n self._root_objects.labels[\\\"total_var\\\"].object.config(text=total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e2bd6844cd62e107aa859e83934a3b\",\n \"score\": \"0.6015662\",\n \"text\": \"def get_total_profit(self) -> int:\\n return self.__profit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b293214a80b6414126f278f3e9f9d416\",\n \"score\": \"0.6013858\",\n \"text\": \"def get_total_balance(self):\\n self.balance = sum(m.money for m in self.members.values())\\n return self.balance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913616433141117d9792980ed6020a93\",\n \"score\": \"0.6013291\",\n \"text\": \"def add_earnings(username, amount):\\n df = pd.read_csv('database/Developer.csv')\\n df.loc[df['username'] == username, 'earnings'] += amount\\n df.to_csv('database/Developer.csv', index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24ed8de67e6ad6acd252387eab5a47b\",\n \"score\": \"0.6011141\",\n \"text\": \"def cal_total(self):\\n self.total = self.boxes_needed() * self.box_price\\n return self.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09e9b8384390b5caa6f03444c8890ed\",\n \"score\": \"0.6006917\",\n \"text\": \"def expenses(self):\\n return sum([t.delta_balance for t in\\n self.transactions.filter(delta_balance__lt=0)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4414897858afde425d8b3af7231cb37\",\n \"score\": \"0.6003653\",\n \"text\": \"def totalExpenses(self):\\n if not isinstance(self.amount, list):\\n\\n return False\\n\\n return sum(map(int, self.amount))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a865363847dc56ed5361301c965e0d\",\n \"score\": \"0.5986198\",\n \"text\": \"def calculate_earnings():\\n\\n lesson_price = prices.col_values(2)\\n del lesson_price[0]\\n\\n price = int(lesson_price[duration_index])\\n\\n lesson_earnings = price * attendance_total\\n\\n # I've placed the below print statement here as input\\n # data should not push back into the worksheet if it\\n # isn't valid\\n print(f\\\"\\\\n{Fore.LIGHTGREEN_EX}Data input is valid!\\\\n\\\")\\n print(\\\"\\\\033[39m\\\")\\n\\n print(\\n f\\\"Total earnings for this class is: {Fore.GREEN}£{lesson_earnings} \\\\n\\\")\\n print(\\\"\\\\033[39m\\\")\\n # append calculation to a list of user inputs\\n new_lesson_data.append(lesson_earnings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea2ba2ebe4b5f8e08ba3e11eac00f38\",\n \"score\": \"0.59809273\",\n \"text\": \"def overall_total(self):\\r\\n if self.profile[\\\"overall_total\\\"] is None:\\r\\n self._fetch_runemetrics()\\r\\n return self.profile[\\\"overall_total\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672ceac762919bc9c788d498861e8b28\",\n \"score\": \"0.5965296\",\n \"text\": \"def total(self):\\n href = '{0}/{1}'.format(self.api.base_url, self.entity)\\n params = {'limit': 0, 'offset': 0}\\n resp = self.api.get(href, params)\\n\\n return resp['total']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b0057b6b64d41039bcac942c3bda356\",\n \"score\": \"0.59367865\",\n \"text\": \"def get_table_spending(self):\\n total = 0\\n meals = self.meals.all()\\n for meal in meals:\\n total += meal.get_meal_spending()\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199b34a4ec4301302c233a7b9303a665\",\n \"score\": \"0.59159696\",\n \"text\": \"def getPriceGross(self):\\n total = 0.0 \\n for item in self.item_management.getItems():\\n total += item.getPriceGross()\\n total -= item.getDiscountGross()\\n\\n total += self.context.getPaymentPriceGross()\\n total += self.context.getShippingPriceGross()\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a5bbcc64c0c4fc45b92450a44307ec8\",\n \"score\": \"0.5904075\",\n \"text\": \"def saldo_total():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761555e21e91a8a1c70328272268905a\",\n \"score\": \"0.589524\",\n \"text\": \"async def payouts_total():\\n # memoized historical sum. To update:\\n # SELECT SUM(payout) FROM hive_posts_cache\\n # WHERE is_paidout = 1 AND payout_at <= precalc_date\\n precalc_date = '2017-08-30 00:00:00'\\n precalc_sum = Decimal('19358777.541')\\n\\n # sum all payouts since `precalc_date`\\n sql = \\\"\\\"\\\"\\n SELECT SUM(payout) FROM hive_posts_cache\\n WHERE is_paidout = '1' AND payout_at > '%s'\\n \\\"\\\"\\\" % (precalc_date)\\n\\n return float(precalc_sum + DB.query_one(sql)) #TODO: decimal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc98ae977430ebe20f0e798ce1c10e71\",\n \"score\": \"0.58830047\",\n \"text\": \"def total(self):\\n params = {'limit': 0, 'offset': 0}\\n resp = self._api.get(self._entity, params=params)\\n\\n return resp['total']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb2c1b54b9b152bdfa7726d464d1b35c\",\n \"score\": \"0.5880711\",\n \"text\": \"def set_total(self) -> float:\\n return self.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f703927068af1e794864bb1d2f0ed9\",\n \"score\": \"0.58772\",\n \"text\": \"def total(self):\\n return sum((item.amount * item.price) for item in self._items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8452f2b642c7ba71b66d49e2298a9f1f\",\n \"score\": \"0.5860056\",\n \"text\": \"def calculateFinances(self):\\n for employee in self.employees:\\n try:\\n employee.setAttendanceHalf(self.month, self.half)\\n # Problem only arises when trying to calculate pay for employee who has no attendance record.\\n employee.calculatePay()\\n except:\\n pass\\n self.totalsalary = 0\\n self.totalbalance = 0\\n self.totalloans = 0\\n for employee in self.employees:\\n if employee.salary_interval == \\\"Monthly\\\" and not self.name == \\\"Staff\\\" :\\n self.totalsalary += employee.overtimepay\\n else:\\n self.totalsalary += employee.totalpay\\n self.totalloans += employee.loans\\n self.totalbalance = self.totalsalary - self.totalloans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ef0f832db9339a1fb156009eb2fd4a\",\n \"score\": \"0.5857614\",\n \"text\": \"def _get_advance_payments_total(self):\\n for line in self:\\n line.advance_payments_total = sum(payment.amount for payment in line.advance_payment_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"641400682774bc5b7c3c96b2daa32fec\",\n \"score\": \"0.58502144\",\n \"text\": \"def profit(self):\\n return max(0, self.total_recovered - self.purchase_price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15c9e9829a83cf851ddf6e7dee49ff3\",\n \"score\": \"0.58501595\",\n \"text\": \"def full_total(self):\\n return self.full_price - self.multiple_purchase_discount - self.credit_applied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865ff87f12d2b5e686553da46120f57f\",\n \"score\": \"0.5849784\",\n \"text\": \"def karma_total_user(self):\\n if self.__karma_stats.total_user is not None:\\n return self.__karma_stats.total_user\\n\\n total_plus = 0\\n total_minus = 0\\n for user_id, user in get_user_database().root.iteritems():\\n total_plus += user.karma_plus_received()\\n total_minus += user.karma_minus_received()\\n\\n self.__karma_stats.total_user = (total_plus, total_minus)\\n qon.log.stats_info('KarmaStats\\\\tuser:%d,%d' % self.__karma_stats.total_user)\\n return self.__karma_stats.total_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd46876463c5e51095947afe60ee5b4\",\n \"score\": \"0.5848427\",\n \"text\": \"def total_buys(self):\\n return self.sum_orders(self.buy_orders)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6527bb7baf4580b5be84259d75f23fcb\",\n \"score\": \"0.5837438\",\n \"text\": \"def total_sold(self):\\n return abs(sum(t.delta_quantity for t in self.outbound_transactions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7e02c66365afa42003c5536d2a2ec9\",\n \"score\": \"0.5829619\",\n \"text\": \"def total_given(self, donations):\\n total_sum = sum(donations) #sum all entries in the obj._donation list\\n return total_sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ebcdcbf12c1b6e0c4b12fc23dd99f13\",\n \"score\": \"0.5827203\",\n \"text\": \"def total_value(self) -> Tuple[float, Mapping[str, int], float, str]:\\n value = 0\\n coll_items = {}\\n for user in self.users:\\n value += user.value\\n for item, amount in user.inventory.items():\\n if item in coll_items:\\n coll_items[item] += amount\\n else:\\n coll_items[item] = amount\\n # do the same for the pot.\\n value += self.pot.value\\n for item, amount in self.pot.inventory.items():\\n if item in coll_items:\\n coll_items[item] += amount\\n else:\\n coll_items[item] = amount\\n\\n total_value = value + sum([amount*self.library.library[item] if self.library.library[item] >= 0 else 0\\n for item, amount in coll_items.items()])\\n items_str = ''\\n for item, amount in coll_items.items():\\n items_str += '%s:%d, ' % (item, amount)\\n items_str = items_str[:-2]\\n ret = 'Everyone together holds %d and %s for a total value of %d.\\\\n' % (value, items_str, total_value)\\n return value, coll_items, total_value, ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3702498cab56723212a0101137b8d5\",\n \"score\": \"0.58269405\",\n \"text\": \"def to_consume(self, central, r, year, month):\\n money = sum(m.grab_money() for m in self.members.values())\\n permanent_income = self.permanent_income(central, r)\\n # Having loans will impact on a lower long-run permanent income consumption and on a monthly strongly\\n # reduction of consumption. However, the price of the house may be appreciating in the market.\\n # If cash at hand is positive consume it capped to permanent income\\n money_to_spend = None\\n if money > 0:\\n if money > permanent_income > 0:\\n money_to_spend = permanent_income\\n # Deposit family money above that of permanent income\\n self.savings += money - permanent_income\\n else:\\n money_to_spend = money\\n # If there is no cash available, withdraw at most permanent income from savings\\n elif self.savings > permanent_income > 0:\\n self.savings -= permanent_income\\n money_to_spend = permanent_income\\n elif self.savings > 0:\\n money_to_spend = self.savings\\n self.savings = 0\\n else:\\n # If there is no cash and no savings withdraw from any long-term deposits if any\\n if central.wallet[self]:\\n cash = self.grab_savings(central, year, month)\\n if cash > permanent_income:\\n cash -= permanent_income\\n money_to_spend = permanent_income\\n else:\\n money_to_spend = cash\\n return money_to_spend\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34559e36b7782504c172836f9ab69ca0\",\n \"score\": \"0.58247757\",\n \"text\": \"def annual_total_section_seven_expenses(responses, derived):\\n\\n try:\\n return float(responses.get('annual_total_section_seven_expenses', 0))\\n except ValueError:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5fbdaeb7743aab0843996cb6ca2b99\",\n \"score\": \"0.5809489\",\n \"text\": \"def calculateAmount(self):\\n sum_1 = [float(str(i).split(', ')[1]) for i in self.items]\\n return sum(sum_1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d509632b4c992cae4937687e829f36d8\",\n \"score\": \"0.58066624\",\n \"text\": \"def get_total(self):\\n t_total = 0.0\\n for i in self.orders:\\n t_total += i.get_total()\\n return t_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8681738d9f2e3b8cde39e5e9d4a03f\",\n \"score\": \"0.5805168\",\n \"text\": \"def total(self):\\n return self.response[-1] - self.query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea9e63f8e162b7128e0d1e85cce023be\",\n \"score\": \"0.57943785\",\n \"text\": \"def get_total_winnings():\\n total_amount = 0.0\\n prizes = Prize.objects.filter(winning_ticket__isnull=False)\\n num_prizes = prizes.count()\\n num_winners = len(set(\\n [prize.winning_ticket.purchased_by for prize in prizes]))\\n for prize in prizes:\\n if prize.amount_paid:\\n total_amount += float(prize.amount_paid)\\n else:\\n # payment is pending but add in the the amount\\n # that will be paid\\n total_amount += float(prize.amount_to_pay)\\n return num_prizes, num_winners, Decimal(total_amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350bd994bd6f181242d2b2a9e329038c\",\n \"score\": \"0.57908016\",\n \"text\": \"def total_section_seven_expenses(responses, derived):\\n\\n try:\\n return float(responses.get('total_section_seven_expenses', 0))\\n except ValueError:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f7b26e559a2b0ce818d7785260e433\",\n \"score\": \"0.5782383\",\n \"text\": \"def episode_total_score(self):\\n return self.score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b59235f0b4c41d515131fd975f4b2498\",\n \"score\": \"0.5773943\",\n \"text\": \"def total_expense(self, time_units):\\n return self.price * time_units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc80b587e5dc1f1ae5d0caf04b0a1c0\",\n \"score\": \"0.57713234\",\n \"text\": \"def total_score(self):\\n return Task.objects.filter(task_type__event=self).aggregate(\\n total_score=Sum(F('num_people')*F('score')))['total_score'] or 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5e061c382607e2ef074a66da6ca9d5\",\n \"score\": \"0.57709074\",\n \"text\": \"def getTotalPrice(self):\\n totalPrice = 0\\n for orderedItem in self.orderedItems:\\n totalPrice += orderedItem.productStock.price\\n if orderedItem.deliveryOption == -1:\\n raise DevilveryOptionNotSetExeption()\\n totalPrice += + orderedItem.deliveryOption.price\\n return totalPrice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4a649d31ed4b718bf6b80e848280e3\",\n \"score\": \"0.5767469\",\n \"text\": \"def getTotal(self):\\n u, q = self.getRate(), self.getQuantity()\\n if u and q:\\n return u * q\\n else:\\n return Money('0.00', self.getInvoiceCurrency())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9412c1db7062ae1070512660b3e61d6e\",\n \"score\": \"0.57668346\",\n \"text\": \"def calculate_total(self):\\n return round_half_up(self.calculate_subtotal() - self.calculate_discount(), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46beedfa2576c9775fe7417a1fa79209\",\n \"score\": \"0.5764895\",\n \"text\": \"def account_potential(self) -> float:\\n stocks = self._rh.securities_owned()['results']\\n potential_sum = float(self._rh.portfolios()['withdrawable_amount'])\\n for stock in stocks:\\n # Make quantity a float as the API may change when I buy fractional shares.\\n quantity = float(stock['quantity'])\\n buy_price = float(stock['average_buy_price'])\\n potential_sum += quantity * buy_price\\n return potential_sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5bc5b86f96cee41ca846c6518826023\",\n \"score\": \"0.57634187\",\n \"text\": \"def total():\\n return price() + tax()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd685e5304338999ee0e1b1cf2691bc3\",\n \"score\": \"0.57587683\",\n \"text\": \"def get_total_items(self):\\n return self.total_items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8dae457bbd38f549031ff9189f3ffdb\",\n \"score\": \"0.5754589\",\n \"text\": \"def updateTotal(self):\\n\\n\\t\\ta = [] # List that contains numerical values of all the players' cards. (EG: Jack has a value of 11)\\n\\n\\t\\tfor b in self.cards: # Adding all the cards to 'a'\\n\\n\\t\\t\\tif(b[0] < 11): # if a number card, add its face value\\n\\n\\t\\t\\t\\ta.append(b[0])\\n\\n\\t\\t\\telif(b[0] >= 11 and b[0] != 14): # if a face card (but not an Ace) add 10\\n\\n\\t\\t\\t\\ta.append(10)\\n\\n\\t\\t\\telse: # if an Ace, add 1 or 11 depending on which will NOT bust the player\\n\\n\\t\\t\\t\\tif(self.currentTotal + 11) > 21:\\n\\n\\t\\t\\t\\t\\ta.append(1)\\n\\n\\t\\t\\t\\telse:\\n\\n\\t\\t\\t\\t\\ta.append(11)\\n\\n\\t\\tself.currentTotal = sum(a) # Updating the total to be the sum of all the values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefa502f41e1dda8ac4906fb6bd0f062\",\n \"score\": \"0.5751323\",\n \"text\": \"def total_used_minerals_economy(self):\\n return self.proto.total_used_minerals.economy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9817e2a9f507a7280a7025bd5a15ca42\",\n \"score\": \"0.5748139\",\n \"text\": \"def calc_pending_earned(self):\\n return self.not_paid_participants() * self.price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c14030c278ce3ad0da3ea6db4960b8\",\n \"score\": \"0.5744632\",\n \"text\": \"def update_current_earning_balance(self):\\n if self.for_trade and self.subscribed_live_api:\\n upstox_user = self.get_upstox_user()\\n balance = upstox_user.get_balance()\\n current_balance = balance.get(\\\"equity\\\").get(\\\"available_margin\\\")\\n if self.bank.current_balance != current_balance:\\n pl = None\\n try:\\n pl = Earning.objects.get(user=self, date=get_local_time().date() - timedelta(1))\\n except:\\n pl = Earning.objects.create(user=self, date=get_local_time().date() - timedelta(1), opening_balance=self.bank.current_balance)\\n pl.profit_loss = float(current_balance) - float(pl.opening_balance)\\n pl.save()\\n self.bank.current_balance = current_balance\\n self.bank.save(update_fields=[\\\"current_balance\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e5d9f7667482e3eb48d1f5dbb0fe91d\",\n \"score\": \"0.5742514\",\n \"text\": \"def total_paid(self):\\n return self.payment_set.aggregate(\\n sum=Sum('amount')\\n )['sum'] or Decimal(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9853116c214497e5dee814de0c1091\",\n \"score\": \"0.5734045\",\n \"text\": \"def get_wealth(self, bank):\\n estate_value = sum(h.price for h in self.owned_houses)\\n return self.savings + estate_value + bank.sum_deposits(self) - bank.loan_balance(self.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995bd8a2a28dd6c5e24f2e96dfd11f0c\",\n \"score\": \"0.57313603\",\n \"text\": \"def get_total(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4aa1882fe1ce606edf409a86492d62b\",\n \"score\": \"0.57272375\",\n \"text\": \"def get_total_price(self):\\n return self.price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e48ac3609c9e8ab6f30aa755247831d\",\n \"score\": \"0.5723626\",\n \"text\": \"def getTotal(self):\\n total = 0\\n for eachProduct in self.itemsInCart.all():\\n total += eachProduct.getLineTotal()\\n #return total\\n return '${:,.2f}'.format(total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31b5522e7ab3377d995d7f1bbc3b724\",\n \"score\": \"0.571877\",\n \"text\": \"def _get_total(self, property, partner_id=None):\\n if partner_id is None:\\n total = D('0.00')\\n for line in self.all_lines():\\n try:\\n total += getattr(line, property)\\n # except ObjectDoesNotExist:\\n # # Handle situation where the product may have been deleted\\n # pass\\n except TypeError:\\n # Handle Unavailable products with no known price\\n info = self.get_stock_info(line.product, line.attributes.all())\\n if info.availability.is_available_to_buy:\\n raise\\n pass\\n return total\\n else:\\n total = D('0.00')\\n for line in self.partner_lines(partner_id):\\n try:\\n total += getattr(line, property)\\n except ObjectDoesNotExist:\\n # Handle situation where the product may have been deleted\\n pass\\n except TypeError:\\n # Handle Unavailable products with no known price\\n info = self.get_stock_info(line.product, line.attributes.all())\\n if info.availability.is_available_to_buy:\\n raise\\n pass\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a52724dbb5fb1b775c5254dc19131ecb\",\n \"score\": \"0.5717645\",\n \"text\": \"def get_total(self):\\n total = super().get_total()\\n\\n # International fee for orders of less than 10\\n if self.qty < 10:\\n return total + 3 \\n else:\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03369bb4868a48636df79a85ed358947\",\n \"score\": \"0.57161427\",\n \"text\": \"def total( self ):\\n return Decimal( self.itemPrice) * Decimal(self.units)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfe448b626d5924d264ccb0dcef7233\",\n \"score\": \"0.571474\",\n \"text\": \"def test_total(self):\\n # Run forecast:\\n self.forecast(self.available)\\n\\n # Regardless of setup, all available money (i.e. $3000)\\n # should be contributed.\\n self.assertAlmostEqual(self.forecast.total, Money(3000))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f79cdfc0c5a92e0a34758cbb947edc92\",\n \"score\": \"0.5707575\",\n \"text\": \"def get_total(self, offers=None):\\n\\n amounts = []\\n for item in self.items:\\n \\n # Cart total without any offers applied\\n line_total = item.get_line_total(self.product_store)\\n\\n if offers is not None:\\n for offer in offers:\\n if offer.target_product == item.product:\\n offer_total = offer.calculate_line_total(\\n item, self.product_store, self)\\n\\n if offer_total < line_total:\\n line_total = offer_total\\n\\n amounts.append(line_total)\\n \\n cart_total = float(sum(amounts))\\n if cart_total <= 0.0:\\n cart_total = 0\\n \\n return cart_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d647dff27edc10e6fad17014a4a74cc5\",\n \"score\": \"0.57025427\",\n \"text\": \"def total(self):\\n return (self.galleons * 17 * 29) + (self.sickles * 29) + (self.knuts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf31bea62ad1652cb2a2ac600b5c942\",\n \"score\": \"0.569522\",\n \"text\": \"def total(self) -> Optional[int]:\\n return pulumi.get(self, \\\"total\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26a3bfb0df93ff0288fabfd5a1057c2d\",\n \"score\": \"0.56816375\",\n \"text\": \"def yearly_earnings(self):\\r\\n value = self.current_values\\r\\n price = self.purchase_prices\\r\\n m, d, y = self.purchase_dates.split('/')\\r\\n purc_date = datetime.date(int(y),int(m),int(d))\\r\\n today = datetime.date(2017,8,2)\\r\\n years = (today-purc_date).days / 365\\r\\n #((((current value – purchase price)/purchase price)/(current date – purchase date)))*100\\r\\n return (value - price)/price / years * 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b027cfe684c2f8762f7afdc5b8ea906\",\n \"score\": \"0.567855\",\n \"text\": \"def getTotalReward(self):\\n return self.cumreward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a47f54af62def1f6aa1d9b373134fe8\",\n \"score\": \"0.5671748\",\n \"text\": \"def calcs_total(self):\\n total = decimal.Decimal(0.00)\\n for related_product in self.products_in_request:\\n for product in self.products:\\n if product.get('product_id') == related_product.get(\\n 'product_id'):\\n total += product.get('price') * related_product.get(\\n 'quantity')\\n self.total = total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272b3fd75ff086a34fe25eada3087c9a\",\n \"score\": \"0.5664258\",\n \"text\": \"def total_used_vespene_economy(self):\\n return self.proto.total_used_vespene.economy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dd388d72cad0b285a8bc31bdb5672a\",\n \"score\": \"0.5658225\",\n \"text\": \"def TotalGrade(self):\\n return sum([r.Grade*r.Category.Weight for r in self.results.all()]) / 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46a99ef631ccf52d29ee4aa578e383f\",\n \"score\": \"0.5656973\",\n \"text\": \"def earnings_yield(self,earnings_per_share = None, stock_price = None):\\n try:\\n if earnings_per_share != None and stock_price != None:\\n self.earnings_per_share = earnings_per_share\\n self.stock_price = stock_price\\n \\n if self.earnings_per_share == None or self.stock_price == None:\\n ratioval = np.nan\\n else:\\n ratioval = self.earnings_per_share / self.stock_price\\n \\n return ratioval\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f26bb446f364092322d94439e388af\",\n \"score\": \"0.5651384\",\n \"text\": \"def get_totalscore(self):\\n scores = Roundscore.query.filter_by(round_id=self.id)\\n totalscore = 0\\n for score in scores:\\n totalscore = totalscore + score.score\\n return totalscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf37514b2850382d9a9ded4b27e89fb\",\n \"score\": \"0.5647695\",\n \"text\": \"def _compute_sale_total(self):\\n for service in self:\\n service.sale_total = service.qty * service.list_price or 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512f7bc7d7abd72f9fb7f5cc98701954\",\n \"score\": \"0.56422186\",\n \"text\": \"def total_recovered(self):\\n return sum(t.delta_balance for t in self.outbound_transactions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e3408aa76898ecf1cf0c977ff7ed287\",\n \"score\": \"0.5640393\",\n \"text\": \"def assignment_6():\\n \\n earnings_per_staff = Payment.objects.values(\\\"staff\\\").annotate(Sum(\\\"amount\\\"))\\n return [(element['staff'], element['amount__sum']) for element in earnings_per_staff]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9bd90e49bb0f00efbee262cff3f22b\",\n \"score\": \"0.56392443\",\n \"text\": \"def income(self):\\n return sum([t.delta_balance for t in\\n self.transactions.filter(delta_balance__gt=0)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee32386bedb881c4ddb18be6bff491d1\",\n \"score\": \"0.563837\",\n \"text\": \"def get_amount_users() -> User:\\n return User.objects.all().count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa896c0e50ed55ca19a9711e6b3f4f1\",\n \"score\": \"0.5638367\",\n \"text\": \"def get_total_amount(self) -> Decimal:\\n total = Decimal(0)\\n for t in self.transactions:\\n iter_amount = getattr(t, 'amount', None)\\n if iter_amount:\\n total += iter_amount\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f6ba3481b5f55b451eb3d3214faeef\",\n \"score\": \"0.56246233\",\n \"text\": \"def daily_expenses_price(dte, user_id):\\n expenses = Expenses.objects.filter(add_on=dte) if user_id == 0 else Expenses.objects.filter(\\n Q(add_on=dte) & Q(warehouse_id=user_id))\\n total_expense = 0\\n\\n for expense in expenses.values():\\n total_expense += expense['price']\\n\\n return total_expense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c2520b22b7a15bc8b579609286d216\",\n \"score\": \"0.56227934\",\n \"text\": \"def students_total():\\n total_students = Student.objects.all().count()\\n return total_students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"debde732130d8babfb2757014e1c5fb4\",\n \"score\": \"0.5619387\",\n \"text\": \"def calculate_total(self):\\r\\n\\t\\tlogging.info('calculating total of the 5 subjects')\\r\\n\\t\\tself.total = self.english + self.maths + self.tamil + self.science + self.social\\r\\n\\t\\tprint(\\\"Total=\\\" , self.total )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993699,"cells":{"query_id":{"kind":"string","value":"1e8ad7e43807b35e02ff996c8e179da4"},"query":{"kind":"string","value":"get user object from json"},"positive_passages":{"kind":"list like","value":[{"docid":"8860bb34b741f37fe997ccdaf086faa1","score":"0.0","text":"def from_json(cls, json_data):\r\n user = User(json_data['username'])\r\n tasks = []\r\n\r\n for task_data in json_data['tasks']:\r\n tasks.append(Task.from_json(task_data))\r\n user.tasks = tasks\r\n\r\n return user","title":""}],"string":"[\n {\n \"docid\": \"8860bb34b741f37fe997ccdaf086faa1\",\n \"score\": \"0.0\",\n \"text\": \"def from_json(cls, json_data):\\r\\n user = User(json_data['username'])\\r\\n tasks = []\\r\\n\\r\\n for task_data in json_data['tasks']:\\r\\n tasks.append(Task.from_json(task_data))\\r\\n user.tasks = tasks\\r\\n\\r\\n return user\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f302e7e62f562844057606b1fa43a3bf","score":"0.7866385","text":"def get_tweet_user(json):\n return get_user(json)","title":""},{"docid":"5c448dd5475ebe6a26e1a8f723653225","score":"0.7602358","text":"def getuser(id):\n user = singleuser(id)\n user_json = jsonify(**user)\n return user_json","title":""},{"docid":"751f004d7c213637ae9f989299893a2d","score":"0.72072","text":"def getUser(self, user):\n if self.auth is None:\n raise Exception('Login required')\n\n ret = self.auth.session.get(self.auth.host + '/user' + user)\n ret.raise_for_status()\n return json.loads(ret.text)","title":""},{"docid":"f1e5ea04cf1dcc900fc51b42c98f646f","score":"0.71645534","text":"def singleuser(id):\n usr = openfile(\"userdata/\"+ str(id) + \".json\")\n return json.loads(usr)","title":""},{"docid":"7f75106c130c71d856daa93614dfac0d","score":"0.7116977","text":"def get_user(user_id):\n estado = storage.get('User', user_id)\n if estado:\n return jsonify(estado.to_dict())\n else:\n abort(404)","title":""},{"docid":"fd6c876862f8c7bfb2baab773624c2e0","score":"0.7113923","text":"def get_a_user(user_id):\n one_user = storage.get(\"User\", user_id)\n if one_user is None:\n abort(404)\n return (jsonify(one_user.to_json()))","title":""},{"docid":"6c3e1730bdc0ff23ab5d189a84ef1980","score":"0.7050663","text":"def get_one_user(user_id):\n user = storage.get(User, user_id)\n if user is None:\n abort(404)\n return jsonify(user.to_dict())","title":""},{"docid":"5a0eb7d335d450e6707d30f682a1a0fb","score":"0.703134","text":"def get_user(user_id):\n user = storage.get(\"User\", id=user_id)\n if user:\n return jsonify(user.to_dict())\n abort(404)","title":""},{"docid":"4442f45be4b05203c844251eea38cfcd","score":"0.7024546","text":"def deserialize(data):\n return User(**json.loads(data))","title":""},{"docid":"dda2163b923278a91d072cbc79936556","score":"0.7015862","text":"def get_user(self, id):\n return self._fetch_json(\"users/\" + id)","title":""},{"docid":"ca00d6adfe3880724997296edcbdeecb","score":"0.69837904","text":"def get_user():\n \n url = \"%s/me\" % (TOGGL_URL)\n global options\n if options.verbose:\n print url\n r = requests.get(url, auth=AUTH)\n r.raise_for_status() # raise exception on error\n return json.loads(r.text)","title":""},{"docid":"be37b448d28363a0699d420071f6776c","score":"0.69768006","text":"def get(self, user_id):\n\t\tu = get_user()\n\t\tuser = abort_if_user_doesnt_exit(user_id)\n\t\treturn user.to_json(u), 200","title":""},{"docid":"43217520f38141ba2c0f5c7334112a48","score":"0.69618434","text":"def create_user_from_json(json_value: str) -> User:\n tweet_dict = json.loads(json_value)\n return create_user_from_dict(tweet_dict)","title":""},{"docid":"5521d0dde6e262dc7d2dd3e754a234c1","score":"0.695885","text":"def load_user(payload):\n if payload['username']:\n g.user = mongo.User.find_one({\"username\": payload['username']})\n return g.user","title":""},{"docid":"75bbf1d5d9b4a46e221bb0ca3a6d6517","score":"0.6914342","text":"def _convert_from_json(cls, user_json: dict): # TODO -> return\n return cls(\n gender=user_json[\"gender\"],\n email=user_json[\"email\"],\n phone=user_json[\"phone\"],\n first_name=user_json[\"name\"][\"first\"],\n last_name=user_json[\"name\"][\"last\"],\n street_name=user_json[\"location\"][\"street\"][\"name\"],\n city=user_json[\"location\"][\"city\"],\n state=user_json[\"location\"][\"state\"],\n country=user_json[\"location\"][\"country\"],\n postcode=user_json[\"location\"][\"postcode\"],\n portrait_large=user_json[\"picture\"][\"large\"],\n portrait_thumbnail=user_json[\"picture\"][\"thumbnail\"],\n )","title":""},{"docid":"a6e62868bef6ace86be642aa43e8d43c","score":"0.69076896","text":"def get_user():\n user_record = flask.g.session.query(User).filter(User.email == flask.session['FLASK_FAS_OPENID_USER']['email']).one_or_none()\n user = convert_row_to_dict(user_record)\n return jsonify(user), 200","title":""},{"docid":"551898e85154df04a87da6d9038a56a5","score":"0.69011563","text":"def get_user_info(self, id):\n\n\t\tuser_info = None\n\n\t\ttry:\n\t\t\tf = urllib2.urlopen(url=\"%s%s%s.json\" % (self.base_uri, 'mumble/users/', urllib2.quote(\"%s\" % id, '')))\n\t\t\tuser_info = json.read(f.read())\n\t\texcept urllib2.URLError, e:\n\t\t\tprint \"Could not connect to authentication server: %s\" % e.reason\n\t\texcept urllib2.HTTPError, e:\n\t\t\tprint \"Unhandled authentication server response %d.\" % e.code\n\n\t\tif not (isinstance(user_info, dict) and 'id' in user_info and 'name' in user_info):\n\t\t\traise UserNotFoundError(\"Could not retrieve user information for %s\" % id)\n\n\t\treturn user_info","title":""},{"docid":"196d013b26b4a619dbb9fdb4b481f186","score":"0.68876106","text":"def get_user(self, api_key):\n return json.load(self._redis.hget(\"user.info\", api_key))","title":""},{"docid":"9ce1af86282819dabb301d7faf9fb53e","score":"0.6877681","text":"def get_user():\n return jsonify(dict(session))","title":""},{"docid":"dc350807f6ad3cad607ea4f54e00a4a5","score":"0.68705463","text":"def get(user_id):\n if not user_id:\n return None\n try:\n with open(PROFILES,'r',encoding='utf-8') as f:\n user_profiles = json.load(f)\n for user_name, profile in user_profiles.items():\n if profile[1] == user_id:\n return User(user_name)\n except Exception as e:\n return None\n return None","title":""},{"docid":"97ef5e1bdf8f999292a6ca4940249032","score":"0.6858497","text":"def get_user():\n data = request.get_json()\n if (not data):\n return\n user = data.get('username',None)\n password = data.get('password',None)\n return check_and_send(user, password)","title":""},{"docid":"364f9fde3693c9064c82f5e3ada13a80","score":"0.68557876","text":"def get_user(user_id: str):\n user = User.lookup(user_id)\n return make_response(jsonify(user.dump()), HTTPStatus.OK)","title":""},{"docid":"bcf36dbced10a36160f76ed5617ccdf6","score":"0.68362856","text":"def a_user(id):\n user = storage.get(User, id)\n if user is not None:\n return jsonify(user.to_dict())\n abort(404)","title":""},{"docid":"20bf2ebbd462560f587ad03351d97c3d","score":"0.68232137","text":"def get_user(user_id):\n if not user_id:\n list_objs = [v.to_dict() for v in storage.all(model).values()]\n return jsonify(list_objs)\n\n return get_model(model, user_id)","title":""},{"docid":"cb26df19f40c1cd321e01af61ee54c90","score":"0.6804018","text":"def load_logged_in_user():\n logger.debug(request.json)\n user_id = session.get('user_id')\n\n if user_id is None:\n g.user = None\n else:\n try:\n connection = get_connection()\n except InterfaceError as e:\n return e.msg\n\n cursor = connection.cursor(buffered=True)\n cursor.execute(\n 'SELECT user_id, username FROM users WHERE user_id = %(user_id)s', {'user_id': user_id}\n )\n g.user = cursor.fetchone()","title":""},{"docid":"6cd78011ae8ca58c8f2cb8b7caf5b483","score":"0.6801826","text":"def getusertable():\n userobj = json.loads(openfile(\"users.json\"))\n return userobj","title":""},{"docid":"2a02a6428c4a4bbde1f1475bf05ac0d1","score":"0.67947906","text":"def get_user_by_id(user_id):\n user = storage.get('User', user_id)\n if user is None:\n return jsonify({\"error\": \"Not found\"}), 404\n return jsonify(user.to_dict())","title":""},{"docid":"ddeb272a9b171c7cba6e9978e264bc1a","score":"0.67804986","text":"def get(self, context, id):\n user_obj = dbapi.users_get_by_id(context, id)\n return jsonutils.to_primitive(user_obj), 200, None","title":""},{"docid":"2b8d0599027c6fc1c0bbc696cdee2545","score":"0.6759025","text":"def get_user(data, user_id):\n # WORK\n user = User(user_id)\n user = data.obtener_usuario(user)\n return user","title":""},{"docid":"6db0724608f19c6f79b2a8dba6f0f793","score":"0.6758492","text":"def return_user(user_id):\n user = storage.get(User, user_id)\n if user is None:\n abort(404)\n return jsonify(user.to_dict())","title":""},{"docid":"8be560fb93eff636f93e7b34eb8aa809","score":"0.6753813","text":"def get_user_by_id(id):\n r = requests.get(base_url + \"/user/\" + str(id))\n user = r.json()\n return user","title":""},{"docid":"93300e6393022a3e5261c8eab967cb1b","score":"0.6749558","text":"def get_user(self, obj):\n return obj.user","title":""},{"docid":"1e326a29775ff1ee8b9a04f75bc1c1b1","score":"0.67435724","text":"def from_json(json_data):\n u = User()\n u.id = int(json_data[\"id\"])\n u.app_id = int(json_data[\"app_id\"])\n if \"pos\" in json_data:\n u.mobility = mobility.from_json(json_data[\"pos\"])\n\n return u","title":""},{"docid":"efb95a0ede654a698b15d0cd274660af","score":"0.6732571","text":"def getUserObject(login, user_id, brain):","title":""},{"docid":"3cc750541d92e1e070f0f2341c18a5c0","score":"0.673194","text":"def get_user_by_id(user_id=None):\n data_id = storage.get(\"User\", user_id)\n if data_id is None:\n abort(404)\n else:\n return jsonify(data_id.to_dict())","title":""},{"docid":"bc38ffc7d9c9043c88a655a7d39d01f1","score":"0.6731438","text":"def userinfo():\n token = request.json.get('token')\n user = request.json.get('user')\n\n if User.query.filter_by(token=token).first() is not None:\n username = User.query.filter_by(id=user).first()\n return jsonify({\"username\": username.username})\n\n return jsonify({\"error\": \"Access Denied!\"})","title":""},{"docid":"f3c857bf00263699049e3187552eb596","score":"0.67226195","text":"def me():\n user = request.oauth.user\n return jsonify(email=user.email, name=user.name)","title":""},{"docid":"6891259771f08ad352c60fadc5dcc67f","score":"0.67155737","text":"def get_user(uname: str) -> User:\n res = req.get(urls.USER_INFO.format(uname))\n if res.status_code != 200:\n return None\n obj = json.loads(res.text)\n return User(obj['avatarFileName'], obj['followerCount']+obj['anonymousFollowerCount'], obj['followingCount'], \n obj['id'], obj['displayName'], obj['username'], obj['aboutMe'], obj['likesCount'], obj['answerCount'], \n obj['tellCount'], obj['isVerified'])","title":""},{"docid":"ab720a7377d6360e1410a37443b4a0a7","score":"0.6713259","text":"def get_user(self, request):\n try:\n frontend = JSONParser().parse(request)\n\n user_query = User.objects.get(id = frontend['user_ID'])\n\n full_info = GetUsersSerializer(user_query)\n\n return JsonResponse(full_info.data, safe=False)\n except Exception as e:\n print(e)\n return JsonResponse({\"Error\":\"Error\"}, status=status.HTTP_400_BAD_REQUEST,safe=False)","title":""},{"docid":"86403142922ae717721148df45f0cd2b","score":"0.6712211","text":"def get_user_info(self, user_id: str) -> dict:","title":""},{"docid":"c372386e1ba6b396057b7bd2af0347a5","score":"0.6706261","text":"def user():\n return jsonify({'id': request.parsed_data['args']['id'], 'name': 'test'})","title":""},{"docid":"f10832de26f5e008abb0358daa0e7575","score":"0.6692014","text":"def get_user(user_id=None):\n user = storage.get(User, user_id)\n if user is None:\n abort(404)\n\n if request.method == 'DELETE':\n storage.delete(user)\n storage.save()\n return jsonify({}), 200\n\n elif request.method == 'PUT':\n data = request.get_json()\n if not data:\n return Response(\"Not a JSON\", 400)\n data['id'] = user.id\n data['email'] = user.email\n data['created_at'] = user.created_at\n user.__init__(**data)\n user.save()\n return jsonify(user.to_dict()), 200\n\n return jsonify(user.to_dict())","title":""},{"docid":"2c93505a58dd5f7e532db4518f77870f","score":"0.6684807","text":"def user_object(uid):\n instance = User.objects.filter(id=uid)[0]\n user = {\n 'id': instance.id,\n 'email': instance.email,\n 'username': instance.username,\n }\n\n try:\n user.bio = instance.bio\n except:\n pass\n try:\n user.following = instance.following\n except:\n pass\n try:\n user.image = instance.image\n except:\n pass\n return user","title":""},{"docid":"8c44838764ef43687e6ef25e9784bb00","score":"0.6679922","text":"def get_user(id):\n user = User.query.get(id)\n if not user:\n abort(400)\n return jsonify({'username': user.username})","title":""},{"docid":"52b5dfdb333f58ad83dd759387b07f31","score":"0.6668864","text":"def test_get_single_user(test_client):\n add_user(test_client)\n response = test_client.get(prefix+'/users/1')\n assert response.status_code == 200\n data = response.get_json()\n print(data)\n assert 'data' in data\n user = data.get('data')[0]\n assert 'id' in user and user['id'] == 1\n assert 'firstname' in user and user['firstname'] == 'king'","title":""},{"docid":"84ed7deba855726e33043a25793c8169","score":"0.6667568","text":"def read_user(username):\n if not g.user.is_authenticated: abort(401, \"Unauthorized\")\n return jsonify(Users.read(username))","title":""},{"docid":"6e3377249e463148af242a7bbbd09fdd","score":"0.66553366","text":"def get_user():\n\n # gets current user from session\n user_id = session['logged_in_user_id']\n user = User.query.get(user_id)\n\n # creates a dictionary to send to prepopulate edit profile modal form\n user_info = {\n 'first_name': user.first_name,\n 'last_name': user.last_name,\n 'email': user.email,\n 'zipcode': user.zipcode,\n }\n\n return jsonify(user_info)","title":""},{"docid":"36fc362aeded87086a8ed97ca3a8b466","score":"0.665219","text":"def get_person(self, id):\n try:\n url = HOST + '/user/%s' % id\n r = requests.get(url, headers=self.headers)\n return r.json()\n except requests.exceptions.RequestException as e:\n print(\"Something went wrong. Could not get that person:\", e)","title":""},{"docid":"1f199e1a0beb20fa1ab89f48cdc52384","score":"0.6640967","text":"def get_user(username):\n user = User.query.filter(User.username == username).first()\n if user is None:\n return abort(404)\n return jsonify(**user.__json__())","title":""},{"docid":"711a1f96acce85a26a842cbae7a4e880","score":"0.6625576","text":"def __init__(self, user_json):\n super(User, self).__init__(user_json)","title":""},{"docid":"e27dc419f866f999957466bef51a0556","score":"0.66180706","text":"def get_user(user_id):\n user_mongo = notifications_mongo_engine.find_user(user_id)\n if user_mongo:\n user_json = user_mongo.to_json()\n return user_json\n else:\n return Response(\"User not found\",status=400)","title":""},{"docid":"90a61003805b0f506d16ebcdadfe26e1","score":"0.6616714","text":"def get_user(user_id):\n user = User.query.get_or_404(user_id)\n return jsonify({'user': user.to_json()})","title":""},{"docid":"8782fb37817802bfec213ce5cc1a19bd","score":"0.66090107","text":"def get_user(login):\n user = db.session.query(User).filter_by(login=f\"{login}\").first()\n if user is None:\n abort(404, description=\"Resource not found\")\n return jsonify(user.to_dict(only=USER_FIELDS))","title":""},{"docid":"ac60a68a2fa25667169f1e0155211758","score":"0.6601462","text":"def _get_user(self):\n from flask import request\n\n user_identity = OptionalIdentityHeaders().load(request.headers)\n if user_identity.get(\"user_id\", False):\n return {\n \"logged\": True,\n \"id\": user_identity[\"user_id\"],\n \"email\": user_identity.get(\"email\", \"N/A\"),\n \"fullname\": user_identity.get(\"fullname\", \"N/A\"),\n }\n return {\"logged\": False}","title":""},{"docid":"ce6604e505b23f2c726cc1138e29c0d1","score":"0.65979505","text":"def handle_user_get():\n try:\n auth0_id = g.user['sub']\n user = User(auth0_id)\n return user.dump()\n except Exception as e:\n return default_error_responce(e)","title":""},{"docid":"02cce91f6d1cc664487b56f0747a7c93","score":"0.6597754","text":"def user_by_id(user_id):\n users = storage.all(\"User\").values()\n user = next(filter(lambda x: x.id == user_id, users), None)\n return jsonify(user.to_dict()) if user else abort(404)","title":""},{"docid":"21dc4d8e942b09a6c62d9d16e065cc09","score":"0.65960354","text":"def get_user(user_name):\n if auth.username() == user_name:\n user = {\"user_name\": user_name}\n body = {\"success\": True, \"user\": user}\n return jsonify(body)\n else:\n return jsonify({\"success\": False}), 403","title":""},{"docid":"20c977585d0bbd5a5adfd7e0cb0fa050","score":"0.65943664","text":"def get(username):\n user = None\n user_node = user_idx['username'][username].single\n if user_node:\n user = User(username, user_node['password'])\n user.name = user_node['name']\n user.gender = user_node['gender']\n user.hometown = user_node['hometown']\n return user","title":""},{"docid":"cf63391307267376a1be29a43e1c5a1d","score":"0.6590191","text":"def load_user(payload):\n _id = payload['username']\n user = Admin.get_one(_id)\n return user","title":""},{"docid":"f4b86d8738defe55a075dfae8f95f6d3","score":"0.6583974","text":"def get(user_id):\n user = UserModel.find_user_by_id(user_id)\n if user:\n logging.info(user.json())\n return user.json()\n\n logging.info(\"User not found\")\n return {\n \"message\": \"User not found\"\n }, 404","title":""},{"docid":"a417ac7a072a09650b418c021a1dba76","score":"0.6581847","text":"def get(self):\n user = UserRepository.get(public_id)\n return jsonify({\"user\": user.json})","title":""},{"docid":"642c9be4a7aa43da1b8a893ae8c1a679","score":"0.65767163","text":"def get_user_by_key(self, key):\n\n url = self.root_url + \"/users/\" + key\n\n data = {\n 'client_id': '{}'.format(self.client_id)\n }\n\n r = requests.get(url, params=data)\n http_error_handler(r.status_code)\n raw_json = r.json()\n return raw_json","title":""},{"docid":"d4180398f42984577e92c1653fcb43b8","score":"0.65766615","text":"def get_user_details():\n username = request.args[\"username\"]\n u = UserAuthentication().get_user_detail_from_username(username)\n return jsonify(u);","title":""},{"docid":"8467f089f22ea22e3b1fa6efb1638f3f","score":"0.65724736","text":"def load_user_info(user_id):\n \n filename = f\"{user_id}.json\"\n try:\n with open(filename) as f:\n user_info = json.load(f)\n except FileNotFoundError:\n return None\n else:\n return user_info","title":""},{"docid":"a0aae013abb1e50fd4b1eefead6a147c","score":"0.65688884","text":"def test_get_user_info(self, fasjson_user, client):\n username = fasjson_user[\"username\"]\n response = client.get(f\"{self.path}/{username}/info\")\n assert response.status_code == status.HTTP_200_OK\n assert response.json() == fasjson_user","title":""},{"docid":"b1d122967e76f0e7cc415f4760d0b506","score":"0.655065","text":"def user(self, user_id):\n return self._request('GET', '/users/{}'.format(user_id)).get('data')","title":""},{"docid":"852693f149721851f616f14e7e855b4c","score":"0.65504354","text":"def get_user(self, id_or_username):\n return self._request_json(\"get\", _GET_USER_URL.format(id_or_username))","title":""},{"docid":"39dc84e25d1033b5c83b13988686c215","score":"0.65478283","text":"def get_user_json(self):\n with open(\"src/db/users.json\", \"r\") as file:\n data = json.load(file)\n for user in data[\"users\"]:\n if user.get(\"username\") == self.username:\n return True","title":""},{"docid":"a3bfba587e81e5f5a5b59aa8f5e8dfa5","score":"0.6546176","text":"def performWithJSON(self, json=None, user=None):\r\n if user:\r\n return {'user':self.toJson(user)}\r\n return Errors.NO_USER.toJSON()","title":""},{"docid":"b295f5778ee86124aba1087167b762f1","score":"0.65411717","text":"def get_user(self):\n info = self.api.get_oauth2_user_info()\n usr = User(info['id'], client=self)\n for k in info:\n setattr(usr, k, info[k])\n return usr","title":""},{"docid":"a6e006526e38679dbf819898680da40d","score":"0.6531481","text":"def get_user_from_data(self, data):\n rfid = data['rfid']\n\n self.logger.info(\"Getting user %s\" % rfid)\n\n return self.user_from_rfid(rfid)","title":""},{"docid":"836df5f7456e5e1bebc5e869e2840c3d","score":"0.65255827","text":"def instagram_profile_obj(username):\n json_data = instagram_profile_json(username)\n try:\n return json.loads(json_data) if json_data else None\n except (KeyError, TypeError):\n while KeyError == 'ProfilePage':\n pass\n logger.exception(\"Profile timeout\")","title":""},{"docid":"6248a482d9c33006f6f833036536922e","score":"0.6520007","text":"def fetch_user_info(cls, redis_conn, config):\n user_str = redis_conn.get(config['cloud_id'])\n\n if user_str == None:\n raise ValueError('No user with cloud_id '+str(config['cloud_id'])+' exists.')\n\n logging.info(\"Fetching user info...\")\n logging.debug(\"\\tuser_str:\"+user_str)\n user_obj = json.loads(user_str)\n\n return user_obj","title":""},{"docid":"725c4054c4884c93e9aec472348261b0","score":"0.6518654","text":"def from_json(cls, json, user=None):\n user_id = json['user_id']\n email = json['email']\n user = user or users.User(email)\n\n user_data = cls(\n key_name=cls.key_for(user_id),\n user=user,\n current_user=user,\n user_id=user_id,\n user_email=email,\n moderator=False,\n joined=util.parse_iso8601(json['joined']),\n last_activity=util.parse_iso8601(json['last_activity']),\n last_badge_review=util.parse_iso8601(json['last_badge_review']),\n start_consecutive_activity_date=util.parse_iso8601(json['start_consecutive_activity_date']),\n need_to_reassess=True,\n points=int(json['points']),\n nickname=json['nickname'],\n coaches=['test@example.com'],\n total_seconds_watched=int(json['total_seconds_watched']),\n all_proficient_exercises=json['all_proficient_exercises'],\n proficient_exercises=json['proficient_exercises'],\n suggested_exercises=json['suggested_exercises'],\n )\n return user_data","title":""},{"docid":"7415e4f0eef7b2be039d3561dbec4cd1","score":"0.651649","text":"def test_user_get_api_json(self):\n resp = self.api_client.get('/api/v1/meme/', format='json')\n self.assertValidJSONResponse(resp)","title":""},{"docid":"1f598af6243489b8b31d21d3d6fed11a","score":"0.6516231","text":"def request_user(id):\n url = \"https://jsonplaceholder.typicode.com/users/{}\".format(id)\n response = requests.get(url)\n data = response.json()\n\n return data","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.6512226","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.6512226","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.6512226","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.6512226","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"57bd29eff35271d2a574fce31eb29d96","score":"0.6499525","text":"def get_user():\n\n if session.get(\"user\",0):\n user = crud.get_user_by_id(session[\"user\"])\n data = {\"user_id\": user.user_id,\n \"username\": user.username, \n \"firstname\": user.first_name, \n \"lastname\": user.last_name,\n \"image_path\": user.image_path,\n \"email\": user.email,\n \"password\": user.password}\n\n return jsonify(data)\n \n return \"None\"","title":""},{"docid":"027fead0a8f833440c2e4de587e299dc","score":"0.6497626","text":"def get_single_user(user_id):\r\n if len(user_id) == 0:\r\n return get_user_list()\r\n found_user_int = -1\r\n for user_position in range(0, len(user_list)):\r\n if user_list[user_position]['id'] == int(user_id):\r\n found_user_int = user_position\r\n\r\n if found_user_int >= 0:\r\n user = user_list[found_user_int]\r\n return jsonify({\"data\": user})\r\n else:\r\n return \"missing user\", 404","title":""},{"docid":"d8432c5250a6ad602d4d16dd0fc5103c","score":"0.648495","text":"def get_user(user_id=None):\n user = User.query.filter_by(id=user_id).first()\n if not user:\n abort(404, {'code': 'Not found', 'message': 'User not found'})\n\n return jsonify(user.to_dict())","title":""},{"docid":"abe3717fb29404958d3c59f75fd51680","score":"0.64828455","text":"def get_user_details(self, response):\n return {\n \"username\": response.get(\"username\"),\n \"email\": response.get(\"email\"),\n \"first_name\": response.get(\"first_name\"),\n \"last_name\": response.get(\"last_name\"),\n }","title":""},{"docid":"6dfd8bc4250ffba3328ecb395e213bfc","score":"0.6477704","text":"def get(self, **kwargs):\n user = kwargs.get('user')\n user_info = model.User.query.get(user['user_id'])\n return marshal(user_info, model.User.fields['extended'])","title":""},{"docid":"79ab6a22e23bcec63a0d1205e6c7dacf","score":"0.64722157","text":"def get_user(username):\n user = Account.find(username)\n\n if not user:\n return {\"error\": \"No user found\"}, 404\n\n return {\"user\": user.get_dict()}","title":""},{"docid":"d6d796f56840f1338b8aa545329e3b26","score":"0.6470981","text":"def get(user_id):\n db = get_db()\n user = db.execute(\n \"SELECT * FROM user WHERE id = ?\", (user_id,)\n ).fetchone()\n if not user:\n return None\n\n user = User(\n id_=user[0], name=user[1], email=user[2], profile_pic=user[3]\n )\n return user","title":""},{"docid":"8313afed1dc0e70d934582d0eb953ac8","score":"0.6459722","text":"def get_user_by_id(id):\n user = User.query.get(id)\n result = userSchema.dump(user)\n return jsonify(result)","title":""},{"docid":"5288510e06703fc7af4ff8f70df55b97","score":"0.645449","text":"def get_user(username):\n try:\n user = User.query.filter_by(username=username).one() \n return jsonify(user.to_dict())\n except NoResultFound:\n abort(404)","title":""},{"docid":"170dbae69a33abbfa4d2ee4c86652dca","score":"0.64514977","text":"def get_user(self):\n query = \"\"\"\n query {\n user {\n username\n banned\n assignedEthAddress\n availableNmr\n availableUsd\n email\n id\n mfaEnabled\n status\n insertedAt\n apiTokens {\n name\n public_id\n scopes\n }\n }\n }\n \"\"\"\n data = self.raw_query(query, authorization=True)['data']['user']\n return data","title":""},{"docid":"20cba0c5a0716db9b01e895e2a8cbeb7","score":"0.6447289","text":"def user(self, username):\n params = {\n \"f\" : \"json\",\n \"token\" : self._securityHandler.token\n }\n return self._do_get(\n url = self._url + \"/%s\" % username,\n param_dict=params,\n proxy_url=self._proxy_url,\n proxy_port=self._proxy_port)","title":""},{"docid":"f4ff34086e04cf9a74f7d822e90be23d","score":"0.64470685","text":"def load_user(user_id):\n temp = return_members_list_from_flat_file()\n for memb in temp:\n if memb['ID'] == int(user_id):\n loaded_member = get_member_object(temp,memb['EMAIL'])\n return loaded_member\n return jsonify(\"User not loaded\")","title":""},{"docid":"82d580c5b02fb050586e87feb5fa44b2","score":"0.64454395","text":"def fetch_one(user_id):\n user_detail = [x for x in data if x['id'] == int(user_id)]\n user_detail = user_detail[0] if user_detail else {}\n return jsonify(user_detail)","title":""},{"docid":"cbff70fefa6a02de64857b52fd7c34a2","score":"0.64366436","text":"def handleGetUserbyId(id):\n user = jsonify(userAsDict(m.getUserById(id)))\n return make_response(user, 200)","title":""},{"docid":"d01818eb7d2e904a194a7d0e94b458f9","score":"0.6434432","text":"def get(self, _id: int) -> tuple:\n current_user = get_jwt_identity()\n user = self.model.find_by_id(_id)\n if current_user and user and current_user == user.id:\n return user.json(), 200\n else:\n return {'error': {'message': 'Forbidden request.'}}, 403","title":""},{"docid":"58a08c045ba745658fec193a1646908b","score":"0.64340544","text":"def get(self, user_id: str) -> User:\n\n pass","title":""},{"docid":"b50d37565aacae48397230fda5766f45","score":"0.6432807","text":"def get_user(self, username):\n user = self.__get_user(username)\n return user['user']","title":""},{"docid":"d1238f8131cab7739dd606bc8649e455","score":"0.6432776","text":"def load_login_data(self):\r\n try:\r\n with open(USERS_JSON_PATH) as json_file:\r\n return json.load(json_file)\r\n except:\r\n print(\"Error opening json data\")","title":""},{"docid":"ff7d18175f740cab3885ded1d684683b","score":"0.64299655","text":"def get_current_user(self):\n u = self.get_secure_cookie('user')\n u = u and json_decode(u)\n if not isinstance(u, dict):\n raise TypeError('cracked cookie, unknown type of user-object: ' + str(u))\n\n return u","title":""},{"docid":"514598cd05fd3865f881d6234e714c96","score":"0.64146626","text":"def retrieve(self, id):\n user = get_pk(User, id)\n return jsonify(\n **SUCCESS_RESP,\n data=dump_data(UserSchema, user, nested_user=True)\n )","title":""},{"docid":"e4bc621f22105cf81c0f7f9b27aee966","score":"0.6414115","text":"def get_user(self):\n response = self._make_request('GET', '/user')\n return response.entity","title":""}],"string":"[\n {\n \"docid\": \"f302e7e62f562844057606b1fa43a3bf\",\n \"score\": \"0.7866385\",\n \"text\": \"def get_tweet_user(json):\\n return get_user(json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c448dd5475ebe6a26e1a8f723653225\",\n \"score\": \"0.7602358\",\n \"text\": \"def getuser(id):\\n user = singleuser(id)\\n user_json = jsonify(**user)\\n return user_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751f004d7c213637ae9f989299893a2d\",\n \"score\": \"0.72072\",\n \"text\": \"def getUser(self, user):\\n if self.auth is None:\\n raise Exception('Login required')\\n\\n ret = self.auth.session.get(self.auth.host + '/user' + user)\\n ret.raise_for_status()\\n return json.loads(ret.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e5ea04cf1dcc900fc51b42c98f646f\",\n \"score\": \"0.71645534\",\n \"text\": \"def singleuser(id):\\n usr = openfile(\\\"userdata/\\\"+ str(id) + \\\".json\\\")\\n return json.loads(usr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f75106c130c71d856daa93614dfac0d\",\n \"score\": \"0.7116977\",\n \"text\": \"def get_user(user_id):\\n estado = storage.get('User', user_id)\\n if estado:\\n return jsonify(estado.to_dict())\\n else:\\n abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6c876862f8c7bfb2baab773624c2e0\",\n \"score\": \"0.7113923\",\n \"text\": \"def get_a_user(user_id):\\n one_user = storage.get(\\\"User\\\", user_id)\\n if one_user is None:\\n abort(404)\\n return (jsonify(one_user.to_json()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3e1730bdc0ff23ab5d189a84ef1980\",\n \"score\": \"0.7050663\",\n \"text\": \"def get_one_user(user_id):\\n user = storage.get(User, user_id)\\n if user is None:\\n abort(404)\\n return jsonify(user.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a0eb7d335d450e6707d30f682a1a0fb\",\n \"score\": \"0.703134\",\n \"text\": \"def get_user(user_id):\\n user = storage.get(\\\"User\\\", id=user_id)\\n if user:\\n return jsonify(user.to_dict())\\n abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4442f45be4b05203c844251eea38cfcd\",\n \"score\": \"0.7024546\",\n \"text\": \"def deserialize(data):\\n return User(**json.loads(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda2163b923278a91d072cbc79936556\",\n \"score\": \"0.7015862\",\n \"text\": \"def get_user(self, id):\\n return self._fetch_json(\\\"users/\\\" + id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca00d6adfe3880724997296edcbdeecb\",\n \"score\": \"0.69837904\",\n \"text\": \"def get_user():\\n \\n url = \\\"%s/me\\\" % (TOGGL_URL)\\n global options\\n if options.verbose:\\n print url\\n r = requests.get(url, auth=AUTH)\\n r.raise_for_status() # raise exception on error\\n return json.loads(r.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be37b448d28363a0699d420071f6776c\",\n \"score\": \"0.69768006\",\n \"text\": \"def get(self, user_id):\\n\\t\\tu = get_user()\\n\\t\\tuser = abort_if_user_doesnt_exit(user_id)\\n\\t\\treturn user.to_json(u), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43217520f38141ba2c0f5c7334112a48\",\n \"score\": \"0.69618434\",\n \"text\": \"def create_user_from_json(json_value: str) -> User:\\n tweet_dict = json.loads(json_value)\\n return create_user_from_dict(tweet_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5521d0dde6e262dc7d2dd3e754a234c1\",\n \"score\": \"0.695885\",\n \"text\": \"def load_user(payload):\\n if payload['username']:\\n g.user = mongo.User.find_one({\\\"username\\\": payload['username']})\\n return g.user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75bbf1d5d9b4a46e221bb0ca3a6d6517\",\n \"score\": \"0.6914342\",\n \"text\": \"def _convert_from_json(cls, user_json: dict): # TODO -> return\\n return cls(\\n gender=user_json[\\\"gender\\\"],\\n email=user_json[\\\"email\\\"],\\n phone=user_json[\\\"phone\\\"],\\n first_name=user_json[\\\"name\\\"][\\\"first\\\"],\\n last_name=user_json[\\\"name\\\"][\\\"last\\\"],\\n street_name=user_json[\\\"location\\\"][\\\"street\\\"][\\\"name\\\"],\\n city=user_json[\\\"location\\\"][\\\"city\\\"],\\n state=user_json[\\\"location\\\"][\\\"state\\\"],\\n country=user_json[\\\"location\\\"][\\\"country\\\"],\\n postcode=user_json[\\\"location\\\"][\\\"postcode\\\"],\\n portrait_large=user_json[\\\"picture\\\"][\\\"large\\\"],\\n portrait_thumbnail=user_json[\\\"picture\\\"][\\\"thumbnail\\\"],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e62868bef6ace86be642aa43e8d43c\",\n \"score\": \"0.69076896\",\n \"text\": \"def get_user():\\n user_record = flask.g.session.query(User).filter(User.email == flask.session['FLASK_FAS_OPENID_USER']['email']).one_or_none()\\n user = convert_row_to_dict(user_record)\\n return jsonify(user), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"551898e85154df04a87da6d9038a56a5\",\n \"score\": \"0.69011563\",\n \"text\": \"def get_user_info(self, id):\\n\\n\\t\\tuser_info = None\\n\\n\\t\\ttry:\\n\\t\\t\\tf = urllib2.urlopen(url=\\\"%s%s%s.json\\\" % (self.base_uri, 'mumble/users/', urllib2.quote(\\\"%s\\\" % id, '')))\\n\\t\\t\\tuser_info = json.read(f.read())\\n\\t\\texcept urllib2.URLError, e:\\n\\t\\t\\tprint \\\"Could not connect to authentication server: %s\\\" % e.reason\\n\\t\\texcept urllib2.HTTPError, e:\\n\\t\\t\\tprint \\\"Unhandled authentication server response %d.\\\" % e.code\\n\\n\\t\\tif not (isinstance(user_info, dict) and 'id' in user_info and 'name' in user_info):\\n\\t\\t\\traise UserNotFoundError(\\\"Could not retrieve user information for %s\\\" % id)\\n\\n\\t\\treturn user_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"196d013b26b4a619dbb9fdb4b481f186\",\n \"score\": \"0.68876106\",\n \"text\": \"def get_user(self, api_key):\\n return json.load(self._redis.hget(\\\"user.info\\\", api_key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce1af86282819dabb301d7faf9fb53e\",\n \"score\": \"0.6877681\",\n \"text\": \"def get_user():\\n return jsonify(dict(session))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc350807f6ad3cad607ea4f54e00a4a5\",\n \"score\": \"0.68705463\",\n \"text\": \"def get(user_id):\\n if not user_id:\\n return None\\n try:\\n with open(PROFILES,'r',encoding='utf-8') as f:\\n user_profiles = json.load(f)\\n for user_name, profile in user_profiles.items():\\n if profile[1] == user_id:\\n return User(user_name)\\n except Exception as e:\\n return None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ef5e1bdf8f999292a6ca4940249032\",\n \"score\": \"0.6858497\",\n \"text\": \"def get_user():\\n data = request.get_json()\\n if (not data):\\n return\\n user = data.get('username',None)\\n password = data.get('password',None)\\n return check_and_send(user, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364f9fde3693c9064c82f5e3ada13a80\",\n \"score\": \"0.68557876\",\n \"text\": \"def get_user(user_id: str):\\n user = User.lookup(user_id)\\n return make_response(jsonify(user.dump()), HTTPStatus.OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf36dbced10a36160f76ed5617ccdf6\",\n \"score\": \"0.68362856\",\n \"text\": \"def a_user(id):\\n user = storage.get(User, id)\\n if user is not None:\\n return jsonify(user.to_dict())\\n abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20bf2ebbd462560f587ad03351d97c3d\",\n \"score\": \"0.68232137\",\n \"text\": \"def get_user(user_id):\\n if not user_id:\\n list_objs = [v.to_dict() for v in storage.all(model).values()]\\n return jsonify(list_objs)\\n\\n return get_model(model, user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb26df19f40c1cd321e01af61ee54c90\",\n \"score\": \"0.6804018\",\n \"text\": \"def load_logged_in_user():\\n logger.debug(request.json)\\n user_id = session.get('user_id')\\n\\n if user_id is None:\\n g.user = None\\n else:\\n try:\\n connection = get_connection()\\n except InterfaceError as e:\\n return e.msg\\n\\n cursor = connection.cursor(buffered=True)\\n cursor.execute(\\n 'SELECT user_id, username FROM users WHERE user_id = %(user_id)s', {'user_id': user_id}\\n )\\n g.user = cursor.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd78011ae8ca58c8f2cb8b7caf5b483\",\n \"score\": \"0.6801826\",\n \"text\": \"def getusertable():\\n userobj = json.loads(openfile(\\\"users.json\\\"))\\n return userobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a02a6428c4a4bbde1f1475bf05ac0d1\",\n \"score\": \"0.67947906\",\n \"text\": \"def get_user_by_id(user_id):\\n user = storage.get('User', user_id)\\n if user is None:\\n return jsonify({\\\"error\\\": \\\"Not found\\\"}), 404\\n return jsonify(user.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddeb272a9b171c7cba6e9978e264bc1a\",\n \"score\": \"0.67804986\",\n \"text\": \"def get(self, context, id):\\n user_obj = dbapi.users_get_by_id(context, id)\\n return jsonutils.to_primitive(user_obj), 200, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8d0599027c6fc1c0bbc696cdee2545\",\n \"score\": \"0.6759025\",\n \"text\": \"def get_user(data, user_id):\\n # WORK\\n user = User(user_id)\\n user = data.obtener_usuario(user)\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db0724608f19c6f79b2a8dba6f0f793\",\n \"score\": \"0.6758492\",\n \"text\": \"def return_user(user_id):\\n user = storage.get(User, user_id)\\n if user is None:\\n abort(404)\\n return jsonify(user.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be560fb93eff636f93e7b34eb8aa809\",\n \"score\": \"0.6753813\",\n \"text\": \"def get_user_by_id(id):\\n r = requests.get(base_url + \\\"/user/\\\" + str(id))\\n user = r.json()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93300e6393022a3e5261c8eab967cb1b\",\n \"score\": \"0.6749558\",\n \"text\": \"def get_user(self, obj):\\n return obj.user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e326a29775ff1ee8b9a04f75bc1c1b1\",\n \"score\": \"0.67435724\",\n \"text\": \"def from_json(json_data):\\n u = User()\\n u.id = int(json_data[\\\"id\\\"])\\n u.app_id = int(json_data[\\\"app_id\\\"])\\n if \\\"pos\\\" in json_data:\\n u.mobility = mobility.from_json(json_data[\\\"pos\\\"])\\n\\n return u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb95a0ede654a698b15d0cd274660af\",\n \"score\": \"0.6732571\",\n \"text\": \"def getUserObject(login, user_id, brain):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc750541d92e1e070f0f2341c18a5c0\",\n \"score\": \"0.673194\",\n \"text\": \"def get_user_by_id(user_id=None):\\n data_id = storage.get(\\\"User\\\", user_id)\\n if data_id is None:\\n abort(404)\\n else:\\n return jsonify(data_id.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc38ffc7d9c9043c88a655a7d39d01f1\",\n \"score\": \"0.6731438\",\n \"text\": \"def userinfo():\\n token = request.json.get('token')\\n user = request.json.get('user')\\n\\n if User.query.filter_by(token=token).first() is not None:\\n username = User.query.filter_by(id=user).first()\\n return jsonify({\\\"username\\\": username.username})\\n\\n return jsonify({\\\"error\\\": \\\"Access Denied!\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c857bf00263699049e3187552eb596\",\n \"score\": \"0.67226195\",\n \"text\": \"def me():\\n user = request.oauth.user\\n return jsonify(email=user.email, name=user.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6891259771f08ad352c60fadc5dcc67f\",\n \"score\": \"0.67155737\",\n \"text\": \"def get_user(uname: str) -> User:\\n res = req.get(urls.USER_INFO.format(uname))\\n if res.status_code != 200:\\n return None\\n obj = json.loads(res.text)\\n return User(obj['avatarFileName'], obj['followerCount']+obj['anonymousFollowerCount'], obj['followingCount'], \\n obj['id'], obj['displayName'], obj['username'], obj['aboutMe'], obj['likesCount'], obj['answerCount'], \\n obj['tellCount'], obj['isVerified'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab720a7377d6360e1410a37443b4a0a7\",\n \"score\": \"0.6713259\",\n \"text\": \"def get_user(self, request):\\n try:\\n frontend = JSONParser().parse(request)\\n\\n user_query = User.objects.get(id = frontend['user_ID'])\\n\\n full_info = GetUsersSerializer(user_query)\\n\\n return JsonResponse(full_info.data, safe=False)\\n except Exception as e:\\n print(e)\\n return JsonResponse({\\\"Error\\\":\\\"Error\\\"}, status=status.HTTP_400_BAD_REQUEST,safe=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86403142922ae717721148df45f0cd2b\",\n \"score\": \"0.6712211\",\n \"text\": \"def get_user_info(self, user_id: str) -> dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c372386e1ba6b396057b7bd2af0347a5\",\n \"score\": \"0.6706261\",\n \"text\": \"def user():\\n return jsonify({'id': request.parsed_data['args']['id'], 'name': 'test'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10832de26f5e008abb0358daa0e7575\",\n \"score\": \"0.6692014\",\n \"text\": \"def get_user(user_id=None):\\n user = storage.get(User, user_id)\\n if user is None:\\n abort(404)\\n\\n if request.method == 'DELETE':\\n storage.delete(user)\\n storage.save()\\n return jsonify({}), 200\\n\\n elif request.method == 'PUT':\\n data = request.get_json()\\n if not data:\\n return Response(\\\"Not a JSON\\\", 400)\\n data['id'] = user.id\\n data['email'] = user.email\\n data['created_at'] = user.created_at\\n user.__init__(**data)\\n user.save()\\n return jsonify(user.to_dict()), 200\\n\\n return jsonify(user.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c93505a58dd5f7e532db4518f77870f\",\n \"score\": \"0.6684807\",\n \"text\": \"def user_object(uid):\\n instance = User.objects.filter(id=uid)[0]\\n user = {\\n 'id': instance.id,\\n 'email': instance.email,\\n 'username': instance.username,\\n }\\n\\n try:\\n user.bio = instance.bio\\n except:\\n pass\\n try:\\n user.following = instance.following\\n except:\\n pass\\n try:\\n user.image = instance.image\\n except:\\n pass\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c44838764ef43687e6ef25e9784bb00\",\n \"score\": \"0.6679922\",\n \"text\": \"def get_user(id):\\n user = User.query.get(id)\\n if not user:\\n abort(400)\\n return jsonify({'username': user.username})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b5dfdb333f58ad83dd759387b07f31\",\n \"score\": \"0.6668864\",\n \"text\": \"def test_get_single_user(test_client):\\n add_user(test_client)\\n response = test_client.get(prefix+'/users/1')\\n assert response.status_code == 200\\n data = response.get_json()\\n print(data)\\n assert 'data' in data\\n user = data.get('data')[0]\\n assert 'id' in user and user['id'] == 1\\n assert 'firstname' in user and user['firstname'] == 'king'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ed7deba855726e33043a25793c8169\",\n \"score\": \"0.6667568\",\n \"text\": \"def read_user(username):\\n if not g.user.is_authenticated: abort(401, \\\"Unauthorized\\\")\\n return jsonify(Users.read(username))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3377249e463148af242a7bbbd09fdd\",\n \"score\": \"0.66553366\",\n \"text\": \"def get_user():\\n\\n # gets current user from session\\n user_id = session['logged_in_user_id']\\n user = User.query.get(user_id)\\n\\n # creates a dictionary to send to prepopulate edit profile modal form\\n user_info = {\\n 'first_name': user.first_name,\\n 'last_name': user.last_name,\\n 'email': user.email,\\n 'zipcode': user.zipcode,\\n }\\n\\n return jsonify(user_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36fc362aeded87086a8ed97ca3a8b466\",\n \"score\": \"0.665219\",\n \"text\": \"def get_person(self, id):\\n try:\\n url = HOST + '/user/%s' % id\\n r = requests.get(url, headers=self.headers)\\n return r.json()\\n except requests.exceptions.RequestException as e:\\n print(\\\"Something went wrong. Could not get that person:\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f199e1a0beb20fa1ab89f48cdc52384\",\n \"score\": \"0.6640967\",\n \"text\": \"def get_user(username):\\n user = User.query.filter(User.username == username).first()\\n if user is None:\\n return abort(404)\\n return jsonify(**user.__json__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"711a1f96acce85a26a842cbae7a4e880\",\n \"score\": \"0.6625576\",\n \"text\": \"def __init__(self, user_json):\\n super(User, self).__init__(user_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27dc419f866f999957466bef51a0556\",\n \"score\": \"0.66180706\",\n \"text\": \"def get_user(user_id):\\n user_mongo = notifications_mongo_engine.find_user(user_id)\\n if user_mongo:\\n user_json = user_mongo.to_json()\\n return user_json\\n else:\\n return Response(\\\"User not found\\\",status=400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a61003805b0f506d16ebcdadfe26e1\",\n \"score\": \"0.6616714\",\n \"text\": \"def get_user(user_id):\\n user = User.query.get_or_404(user_id)\\n return jsonify({'user': user.to_json()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8782fb37817802bfec213ce5cc1a19bd\",\n \"score\": \"0.66090107\",\n \"text\": \"def get_user(login):\\n user = db.session.query(User).filter_by(login=f\\\"{login}\\\").first()\\n if user is None:\\n abort(404, description=\\\"Resource not found\\\")\\n return jsonify(user.to_dict(only=USER_FIELDS))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac60a68a2fa25667169f1e0155211758\",\n \"score\": \"0.6601462\",\n \"text\": \"def _get_user(self):\\n from flask import request\\n\\n user_identity = OptionalIdentityHeaders().load(request.headers)\\n if user_identity.get(\\\"user_id\\\", False):\\n return {\\n \\\"logged\\\": True,\\n \\\"id\\\": user_identity[\\\"user_id\\\"],\\n \\\"email\\\": user_identity.get(\\\"email\\\", \\\"N/A\\\"),\\n \\\"fullname\\\": user_identity.get(\\\"fullname\\\", \\\"N/A\\\"),\\n }\\n return {\\\"logged\\\": False}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6604e505b23f2c726cc1138e29c0d1\",\n \"score\": \"0.65979505\",\n \"text\": \"def handle_user_get():\\n try:\\n auth0_id = g.user['sub']\\n user = User(auth0_id)\\n return user.dump()\\n except Exception as e:\\n return default_error_responce(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02cce91f6d1cc664487b56f0747a7c93\",\n \"score\": \"0.6597754\",\n \"text\": \"def user_by_id(user_id):\\n users = storage.all(\\\"User\\\").values()\\n user = next(filter(lambda x: x.id == user_id, users), None)\\n return jsonify(user.to_dict()) if user else abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21dc4d8e942b09a6c62d9d16e065cc09\",\n \"score\": \"0.65960354\",\n \"text\": \"def get_user(user_name):\\n if auth.username() == user_name:\\n user = {\\\"user_name\\\": user_name}\\n body = {\\\"success\\\": True, \\\"user\\\": user}\\n return jsonify(body)\\n else:\\n return jsonify({\\\"success\\\": False}), 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c977585d0bbd5a5adfd7e0cb0fa050\",\n \"score\": \"0.65943664\",\n \"text\": \"def get(username):\\n user = None\\n user_node = user_idx['username'][username].single\\n if user_node:\\n user = User(username, user_node['password'])\\n user.name = user_node['name']\\n user.gender = user_node['gender']\\n user.hometown = user_node['hometown']\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf63391307267376a1be29a43e1c5a1d\",\n \"score\": \"0.6590191\",\n \"text\": \"def load_user(payload):\\n _id = payload['username']\\n user = Admin.get_one(_id)\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b86d8738defe55a075dfae8f95f6d3\",\n \"score\": \"0.6583974\",\n \"text\": \"def get(user_id):\\n user = UserModel.find_user_by_id(user_id)\\n if user:\\n logging.info(user.json())\\n return user.json()\\n\\n logging.info(\\\"User not found\\\")\\n return {\\n \\\"message\\\": \\\"User not found\\\"\\n }, 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a417ac7a072a09650b418c021a1dba76\",\n \"score\": \"0.6581847\",\n \"text\": \"def get(self):\\n user = UserRepository.get(public_id)\\n return jsonify({\\\"user\\\": user.json})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642c9be4a7aa43da1b8a893ae8c1a679\",\n \"score\": \"0.65767163\",\n \"text\": \"def get_user_by_key(self, key):\\n\\n url = self.root_url + \\\"/users/\\\" + key\\n\\n data = {\\n 'client_id': '{}'.format(self.client_id)\\n }\\n\\n r = requests.get(url, params=data)\\n http_error_handler(r.status_code)\\n raw_json = r.json()\\n return raw_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4180398f42984577e92c1653fcb43b8\",\n \"score\": \"0.65766615\",\n \"text\": \"def get_user_details():\\n username = request.args[\\\"username\\\"]\\n u = UserAuthentication().get_user_detail_from_username(username)\\n return jsonify(u);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8467f089f22ea22e3b1fa6efb1638f3f\",\n \"score\": \"0.65724736\",\n \"text\": \"def load_user_info(user_id):\\n \\n filename = f\\\"{user_id}.json\\\"\\n try:\\n with open(filename) as f:\\n user_info = json.load(f)\\n except FileNotFoundError:\\n return None\\n else:\\n return user_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0aae013abb1e50fd4b1eefead6a147c\",\n \"score\": \"0.65688884\",\n \"text\": \"def test_get_user_info(self, fasjson_user, client):\\n username = fasjson_user[\\\"username\\\"]\\n response = client.get(f\\\"{self.path}/{username}/info\\\")\\n assert response.status_code == status.HTTP_200_OK\\n assert response.json() == fasjson_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d122967e76f0e7cc415f4760d0b506\",\n \"score\": \"0.655065\",\n \"text\": \"def user(self, user_id):\\n return self._request('GET', '/users/{}'.format(user_id)).get('data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852693f149721851f616f14e7e855b4c\",\n \"score\": \"0.65504354\",\n \"text\": \"def get_user(self, id_or_username):\\n return self._request_json(\\\"get\\\", _GET_USER_URL.format(id_or_username))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39dc84e25d1033b5c83b13988686c215\",\n \"score\": \"0.65478283\",\n \"text\": \"def get_user_json(self):\\n with open(\\\"src/db/users.json\\\", \\\"r\\\") as file:\\n data = json.load(file)\\n for user in data[\\\"users\\\"]:\\n if user.get(\\\"username\\\") == self.username:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3bfba587e81e5f5a5b59aa8f5e8dfa5\",\n \"score\": \"0.6546176\",\n \"text\": \"def performWithJSON(self, json=None, user=None):\\r\\n if user:\\r\\n return {'user':self.toJson(user)}\\r\\n return Errors.NO_USER.toJSON()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b295f5778ee86124aba1087167b762f1\",\n \"score\": \"0.65411717\",\n \"text\": \"def get_user(self):\\n info = self.api.get_oauth2_user_info()\\n usr = User(info['id'], client=self)\\n for k in info:\\n setattr(usr, k, info[k])\\n return usr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e006526e38679dbf819898680da40d\",\n \"score\": \"0.6531481\",\n \"text\": \"def get_user_from_data(self, data):\\n rfid = data['rfid']\\n\\n self.logger.info(\\\"Getting user %s\\\" % rfid)\\n\\n return self.user_from_rfid(rfid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"836df5f7456e5e1bebc5e869e2840c3d\",\n \"score\": \"0.65255827\",\n \"text\": \"def instagram_profile_obj(username):\\n json_data = instagram_profile_json(username)\\n try:\\n return json.loads(json_data) if json_data else None\\n except (KeyError, TypeError):\\n while KeyError == 'ProfilePage':\\n pass\\n logger.exception(\\\"Profile timeout\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6248a482d9c33006f6f833036536922e\",\n \"score\": \"0.6520007\",\n \"text\": \"def fetch_user_info(cls, redis_conn, config):\\n user_str = redis_conn.get(config['cloud_id'])\\n\\n if user_str == None:\\n raise ValueError('No user with cloud_id '+str(config['cloud_id'])+' exists.')\\n\\n logging.info(\\\"Fetching user info...\\\")\\n logging.debug(\\\"\\\\tuser_str:\\\"+user_str)\\n user_obj = json.loads(user_str)\\n\\n return user_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725c4054c4884c93e9aec472348261b0\",\n \"score\": \"0.6518654\",\n \"text\": \"def from_json(cls, json, user=None):\\n user_id = json['user_id']\\n email = json['email']\\n user = user or users.User(email)\\n\\n user_data = cls(\\n key_name=cls.key_for(user_id),\\n user=user,\\n current_user=user,\\n user_id=user_id,\\n user_email=email,\\n moderator=False,\\n joined=util.parse_iso8601(json['joined']),\\n last_activity=util.parse_iso8601(json['last_activity']),\\n last_badge_review=util.parse_iso8601(json['last_badge_review']),\\n start_consecutive_activity_date=util.parse_iso8601(json['start_consecutive_activity_date']),\\n need_to_reassess=True,\\n points=int(json['points']),\\n nickname=json['nickname'],\\n coaches=['test@example.com'],\\n total_seconds_watched=int(json['total_seconds_watched']),\\n all_proficient_exercises=json['all_proficient_exercises'],\\n proficient_exercises=json['proficient_exercises'],\\n suggested_exercises=json['suggested_exercises'],\\n )\\n return user_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7415e4f0eef7b2be039d3561dbec4cd1\",\n \"score\": \"0.651649\",\n \"text\": \"def test_user_get_api_json(self):\\n resp = self.api_client.get('/api/v1/meme/', format='json')\\n self.assertValidJSONResponse(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f598af6243489b8b31d21d3d6fed11a\",\n \"score\": \"0.6516231\",\n \"text\": \"def request_user(id):\\n url = \\\"https://jsonplaceholder.typicode.com/users/{}\\\".format(id)\\n response = requests.get(url)\\n data = response.json()\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.6512226\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.6512226\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.6512226\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.6512226\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57bd29eff35271d2a574fce31eb29d96\",\n \"score\": \"0.6499525\",\n \"text\": \"def get_user():\\n\\n if session.get(\\\"user\\\",0):\\n user = crud.get_user_by_id(session[\\\"user\\\"])\\n data = {\\\"user_id\\\": user.user_id,\\n \\\"username\\\": user.username, \\n \\\"firstname\\\": user.first_name, \\n \\\"lastname\\\": user.last_name,\\n \\\"image_path\\\": user.image_path,\\n \\\"email\\\": user.email,\\n \\\"password\\\": user.password}\\n\\n return jsonify(data)\\n \\n return \\\"None\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027fead0a8f833440c2e4de587e299dc\",\n \"score\": \"0.6497626\",\n \"text\": \"def get_single_user(user_id):\\r\\n if len(user_id) == 0:\\r\\n return get_user_list()\\r\\n found_user_int = -1\\r\\n for user_position in range(0, len(user_list)):\\r\\n if user_list[user_position]['id'] == int(user_id):\\r\\n found_user_int = user_position\\r\\n\\r\\n if found_user_int >= 0:\\r\\n user = user_list[found_user_int]\\r\\n return jsonify({\\\"data\\\": user})\\r\\n else:\\r\\n return \\\"missing user\\\", 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8432c5250a6ad602d4d16dd0fc5103c\",\n \"score\": \"0.648495\",\n \"text\": \"def get_user(user_id=None):\\n user = User.query.filter_by(id=user_id).first()\\n if not user:\\n abort(404, {'code': 'Not found', 'message': 'User not found'})\\n\\n return jsonify(user.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe3717fb29404958d3c59f75fd51680\",\n \"score\": \"0.64828455\",\n \"text\": \"def get_user_details(self, response):\\n return {\\n \\\"username\\\": response.get(\\\"username\\\"),\\n \\\"email\\\": response.get(\\\"email\\\"),\\n \\\"first_name\\\": response.get(\\\"first_name\\\"),\\n \\\"last_name\\\": response.get(\\\"last_name\\\"),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dfd8bc4250ffba3328ecb395e213bfc\",\n \"score\": \"0.6477704\",\n \"text\": \"def get(self, **kwargs):\\n user = kwargs.get('user')\\n user_info = model.User.query.get(user['user_id'])\\n return marshal(user_info, model.User.fields['extended'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ab6a22e23bcec63a0d1205e6c7dacf\",\n \"score\": \"0.64722157\",\n \"text\": \"def get_user(username):\\n user = Account.find(username)\\n\\n if not user:\\n return {\\\"error\\\": \\\"No user found\\\"}, 404\\n\\n return {\\\"user\\\": user.get_dict()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6d796f56840f1338b8aa545329e3b26\",\n \"score\": \"0.6470981\",\n \"text\": \"def get(user_id):\\n db = get_db()\\n user = db.execute(\\n \\\"SELECT * FROM user WHERE id = ?\\\", (user_id,)\\n ).fetchone()\\n if not user:\\n return None\\n\\n user = User(\\n id_=user[0], name=user[1], email=user[2], profile_pic=user[3]\\n )\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8313afed1dc0e70d934582d0eb953ac8\",\n \"score\": \"0.6459722\",\n \"text\": \"def get_user_by_id(id):\\n user = User.query.get(id)\\n result = userSchema.dump(user)\\n return jsonify(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5288510e06703fc7af4ff8f70df55b97\",\n \"score\": \"0.645449\",\n \"text\": \"def get_user(username):\\n try:\\n user = User.query.filter_by(username=username).one() \\n return jsonify(user.to_dict())\\n except NoResultFound:\\n abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170dbae69a33abbfa4d2ee4c86652dca\",\n \"score\": \"0.64514977\",\n \"text\": \"def get_user(self):\\n query = \\\"\\\"\\\"\\n query {\\n user {\\n username\\n banned\\n assignedEthAddress\\n availableNmr\\n availableUsd\\n email\\n id\\n mfaEnabled\\n status\\n insertedAt\\n apiTokens {\\n name\\n public_id\\n scopes\\n }\\n }\\n }\\n \\\"\\\"\\\"\\n data = self.raw_query(query, authorization=True)['data']['user']\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cba0c5a0716db9b01e895e2a8cbeb7\",\n \"score\": \"0.6447289\",\n \"text\": \"def user(self, username):\\n params = {\\n \\\"f\\\" : \\\"json\\\",\\n \\\"token\\\" : self._securityHandler.token\\n }\\n return self._do_get(\\n url = self._url + \\\"/%s\\\" % username,\\n param_dict=params,\\n proxy_url=self._proxy_url,\\n proxy_port=self._proxy_port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ff34086e04cf9a74f7d822e90be23d\",\n \"score\": \"0.64470685\",\n \"text\": \"def load_user(user_id):\\n temp = return_members_list_from_flat_file()\\n for memb in temp:\\n if memb['ID'] == int(user_id):\\n loaded_member = get_member_object(temp,memb['EMAIL'])\\n return loaded_member\\n return jsonify(\\\"User not loaded\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d580c5b02fb050586e87feb5fa44b2\",\n \"score\": \"0.64454395\",\n \"text\": \"def fetch_one(user_id):\\n user_detail = [x for x in data if x['id'] == int(user_id)]\\n user_detail = user_detail[0] if user_detail else {}\\n return jsonify(user_detail)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbff70fefa6a02de64857b52fd7c34a2\",\n \"score\": \"0.64366436\",\n \"text\": \"def handleGetUserbyId(id):\\n user = jsonify(userAsDict(m.getUserById(id)))\\n return make_response(user, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01818eb7d2e904a194a7d0e94b458f9\",\n \"score\": \"0.6434432\",\n \"text\": \"def get(self, _id: int) -> tuple:\\n current_user = get_jwt_identity()\\n user = self.model.find_by_id(_id)\\n if current_user and user and current_user == user.id:\\n return user.json(), 200\\n else:\\n return {'error': {'message': 'Forbidden request.'}}, 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a08c045ba745658fec193a1646908b\",\n \"score\": \"0.64340544\",\n \"text\": \"def get(self, user_id: str) -> User:\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50d37565aacae48397230fda5766f45\",\n \"score\": \"0.6432807\",\n \"text\": \"def get_user(self, username):\\n user = self.__get_user(username)\\n return user['user']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1238f8131cab7739dd606bc8649e455\",\n \"score\": \"0.6432776\",\n \"text\": \"def load_login_data(self):\\r\\n try:\\r\\n with open(USERS_JSON_PATH) as json_file:\\r\\n return json.load(json_file)\\r\\n except:\\r\\n print(\\\"Error opening json data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7d18175f740cab3885ded1d684683b\",\n \"score\": \"0.64299655\",\n \"text\": \"def get_current_user(self):\\n u = self.get_secure_cookie('user')\\n u = u and json_decode(u)\\n if not isinstance(u, dict):\\n raise TypeError('cracked cookie, unknown type of user-object: ' + str(u))\\n\\n return u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514598cd05fd3865f881d6234e714c96\",\n \"score\": \"0.64146626\",\n \"text\": \"def retrieve(self, id):\\n user = get_pk(User, id)\\n return jsonify(\\n **SUCCESS_RESP,\\n data=dump_data(UserSchema, user, nested_user=True)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bc621f22105cf81c0f7f9b27aee966\",\n \"score\": \"0.6414115\",\n \"text\": \"def get_user(self):\\n response = self._make_request('GET', '/user')\\n return response.entity\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
075a35f2320504820c45ef19fc858b29 | List the projects owned by the user. | [
{
"docid": "3b810c68cb5045c116a0e9381e9d7819",
"score": "0.6284729",
"text": "def list():\n gitlab = get_gitlab()\n for p in gitlab.get_projects():\n print ': '.join([p.name, p.ssh_url_to_repo])",
"title": ""
}
] | [
{
"docid": "24b1e582667a627343187825412f0ba0",
"score": "0.7759531",
"text": "def get_queryset(self):\n current_user = self.request.user\n return Project.objects.filter(owner__username=current_user)",
"title": ""
},
{
"docid": "68ee9ba0ccb4ef62b16e3b4334167bc6",
"score": "0... |
03c198cd855a63cc12034c00a3c839f5 | Function to obtain angle of orientation for contour line segments Found the following orientations to work best for aligning contours by trialanderror | [
{
"docid": "079fac25c70ef97b8dff5c7801a69bf0",
"score": "0.6582767",
"text": "def getContourOrientation(shearAngle):\n if shearAngle>0:\n ori = (shearAngle*15.9)\n else:\n ori=-np.abs(shearAngle)*45.9\n return ori",
"title": ""
}
] | [
{
"docid": "692dc4f2cdf33cfc12615a4ffd05f68c",
"score": "0.67531836",
"text": "def line_orientation(line):\n dx = line[END][0] - line[BGN][0]\n dy = line[END][1] - line[BGN][1]\n \n orientation = atan2(dy, dx)\n while (orientation < 0.0):\n orientation += pi\n \n return degre... |
b1526616bf685606824ede0c16309166 | create authentitaciton object from a JSON string | [
{
"docid": "989d48cc2a2181c1b544f59a25706ce4",
"score": "0.6706865",
"text": "def create_authentication_from_json(values):\n key_id = values.get('publicKey')\n authentication_type = values.get('type')\n if not key_id:\n raise ValueError('Invalid authentication definition,... | [
{
"docid": "985b400e20d778d78c323bc9f56bbed9",
"score": "0.7011966",
"text": "def create_authentication_from_json(values):\n key_id = values.get('publicKey')\n authentication_type = values.get('type')\n if not key_id:\n raise ValueError(\n f'Invalid authent... |
8977f1dbe444e2edda9cf2214c9a9080 | Returns a set of models ids for the specific query by the user. | [
{
"docid": "ca5ed7fbf2f0a98fb45262a4739351d0",
"score": "0.57983947",
"text": "def model_ids(self, protocol=None, groups=None, subworld=None, gender=None):\n\n return [client.id for client in self.clients(protocol, groups, subworld, gender)]",
"title": ""
}
] | [
{
"docid": "f419055486ed09701d58f214611c9a45",
"score": "0.61304605",
"text": "def user_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"user_ids\")",
"title": ""
},
{
"docid": "f419055486ed09701d58f214611c9a45",
"score": "0.61304605",
... |
f0a2a8b4472b452da6fd438bae9b38c3 | Returns search results from given `fromIndex` to given `toIndex` from the search with the given identifier. | [
{
"docid": "4273b053fedf425194c5a7947e32fdcc",
"score": "0.70739555",
"text": "def getSearchResults(\n self, searchId: str, fromIndex: int, toIndex: int\n ) -> Awaitable[Dict]:\n return self.client.send(\n \"DOM.getSearchResults\",\n {\"searchId\": searchId, \"from... | [
{
"docid": "782b6f5ad52583406788d850941f453b",
"score": "0.5331863",
"text": "def searchIndicators(fromDate='', query='', size=100, page=0, toDate='', value='', searchAfter=None,\n populateFields=None):\n return {}",
"title": ""
},
{
"docid": "ea0c90fbb8a93f13649c867cc... |
a21a8165654d11123527e42bafdb6b58 | Measures how well the availability of a mentor's and team's availabilities overlap Assumes the mentor uses input transit type, which is an integer in range(numTransitTypes) Finds the total amount of overlap (minus travel time for the mentor), but an overlap must be for at least minMeetingTime to count Value is then tot... | [
{
"docid": "80ae39227eb6d8d35c0d8e09f4fc774a",
"score": "0.7607376",
"text": "def getSingleOverlapValue(mentor, team, transitType):\n\ttotalOverlap = 0\n\tfor day in range(7):\n\t\t# at the beginning of a day, reset all counters for contiguous blocks\n\t\tmentorAvailabilityBeforeOverlap = 0 # mentor may... | [
{
"docid": "acf0891f9117c591656f8e0a35002cf9",
"score": "0.6468681",
"text": "def getPairOverlapValue(mentor1, mentor2, team):\n\ttotalOverlap = 0\n\tfor day in range(7):\n\t\tfor slotNum in range(slotsPerDay[day]):\n\t\t\tif mentor1.availability[day][slotNum] and mentor2.availability[day][slotNum] and ... |
b998911a668d0cd73cf46f4ea01aa101 | Returns true if both objects are equal | [
{
"docid": "2d64952e8551313e4d8ed48cc5c2cf36",
"score": "0.0",
"text": "def __eq__(self, other):\n if not isinstance(other, PutStockMovementsStockMovement):\n return False\n\n return self.to_dict() == other.to_dict()",
"title": ""
}
] | [
{
"docid": "854419fa0140bfcc5ad035f5827bde30",
"score": "0.85366243",
"text": "def is_two_object_has_same_value(a, b) -> bool:\n return a==b",
"title": ""
},
{
"docid": "11e011dc5ef6138ce990f879c93f1324",
"score": "0.85108745",
"text": "def is_two_objects_is_the_same_objects(a, b)... |
28195f6b56ae38fee88892537b956af4 | self & other. Causes pypeline to be run in parallel. | [
{
"docid": "07d966b63166bc63e1baed802e70d2d8",
"score": "0.54058367",
"text": "def __and__(self, other):\n if self.parallel is not None:\n raise Exception('Trying to set parallel twice for {}'.format(self))\n return self | other",
"title": ""
}
] | [
{
"docid": "97946f6de4a25643755186b8ccbe4d6c",
"score": "0.55980474",
"text": "def parar(self):\n pass",
"title": ""
},
{
"docid": "db762242ace18fe0a80bf3aa8a16ef08",
"score": "0.5586443",
"text": "def compose(self, other):\n raise NotImplementedError",
"title": ""
... |
d8af9f2d44794fc9a341f30e63a3d0bb | Validates the inputs for the scattergather node. | [
{
"docid": "6a7aebcfaec59eb545a5b47c654216a6",
"score": "0.5940705",
"text": "def validate_inputs(\n cls,\n *,\n silo_configs: List[FederatedLearningSilo],\n silo_component: Component,\n aggregation_component: Component,\n shared_silo_kwargs: Dict,\n aggr... | [
{
"docid": "3c11efe19061bbe805d87331cdc3299a",
"score": "0.676984",
"text": "def validate_input(self):\n pass",
"title": ""
},
{
"docid": "eda9bae52e295a987ac58bfd01bc5fcf",
"score": "0.6701751",
"text": "def check_input_param(self):\n for check_shape in (self.dgate_sha... |
e632f9b2c5871ec2883f82cb7e06d9a2 | preprocesses the data model | [
{
"docid": "7d677ad89f9814c088230be46a4aa880",
"score": "0.0",
"text": "def preprocess_data(X, Y):\n #x_train_p, y_train_oh = preprocess_data(x_train, y_train)\n #x_valid_p, y_valid_oh = preprocess_data(x_valid, y_valid)\n\n X = K.applications.xception.preprocess_input(X)\n Y = K.utils.to_ca... | [
{
"docid": "90c992a34a910951d198cf6e24ea01f3",
"score": "0.79803115",
"text": "def preprocess_data(self):\r\n pass",
"title": ""
},
{
"docid": "e70db494b3aedbf6d196b4c709726984",
"score": "0.78846335",
"text": "def preprocess_data(self):\n\tpass",
"title": ""
},
{
... |
591091bbd347cc062850b5dfa7c7b59a | Send mail to raj454raj.com about all the users who register | [
{
"docid": "37f00a3611f74cc92d46b8956b5cae99",
"score": "0.67834276",
"text": "def register_callback(form):\n\n # Send mail to raj454raj@gmail.com\n to = \"raj454raj@gmail.com\"\n subject = \"New user registered\"\n message = \"\"\"\nName: %s %s\nEmail: %s\nInstitute: %s\nStopStalk handle: %... | [
{
"docid": "eda30d744987685e968c7f06465b4901",
"score": "0.70978504",
"text": "def send_registration_email(self):\n\n self.send_email(\"registration\", {})",
"title": ""
},
{
"docid": "6a9e924ca5d72ade217330eeb11feb74",
"score": "0.70088774",
"text": "def save(self):\n ... |
511322456b28daf09f904f5bd6d6dc7d | Retorna una lista de peliculas con mas de n generos | [
{
"docid": "9196b144bec963995e4c3ba1724fecdc",
"score": "0.0",
"text": "def with_genres(movies, n):\n return filter(lambda x: len(x.genres) >= n, movies)",
"title": ""
}
] | [
{
"docid": "270cdd27b34b548d04fd984351274702",
"score": "0.6722946",
"text": "def _gen_n_puzzles(num_puzzles, m, final_state):\n puzzle_list = []\n for i in range(num_puzzles):\n puzzle = gen_puzzle_n(m)\n while not is_solvable(puzzle, final_state, m):\n puzzle = gen_puzzl... |
8bae83e48dc4b2ada3d16560033b9e1a | Resize eye image and normalize intensities. | [
{
"docid": "975e1a3e5d2ba66de424788c79a31faa",
"score": "0.5238271",
"text": "def preprocess_entry(self, entry):\n oh, ow = self._eye_image_shape\n left_eye = entry['leftEyeData']\n right_eye = entry['rightEyeData']\n gaze_point = entry['gazeData']\n\n\n #left_eye = cv... | [
{
"docid": "911c4113158b15bd6ec13eda97f5c0cd",
"score": "0.60649246",
"text": "def _preprocess(self):\n # resize input image to same shape as hog window\n self.img = cv2.resize(self.img, self._winSize)",
"title": ""
},
{
"docid": "bc3aeb10d7ca424f0d2c2e558faad672",
"score":... |
eda6c9acb9a56e2f04b22d62b266a830 | Save image to txt files. Input img image, 3 x h x w txtPaths txt path, 3 x fmt format | [
{
"docid": "bfa53aa23451a1162af9752abb24a108",
"score": "0.8077553",
"text": "def imgSvTxt(img, txtPaths, fmt='%.2f'):\n # dimension\n d = len(txtPaths)\n\n # load matrix\n for i in range(d):\n np.savetxt(txtPaths[i], img[i], fmt)",
"title": ""
}
] | [
{
"docid": "07441f888606f5a6e753569c12e03132",
"score": "0.63446015",
"text": "def saveImageAndHog(self, mag, hog, lbp, path):\n pathSplit = path.split('/')\n currImage = pathSplit[-1]\n imageName, imageExt = currImage.split('.')\n updatedImage = '.'.join([imageName+'_mag',im... |
59491e40c58c01c427603b59cd8e8130 | Constructs a R2Plus1D50 model. | [
{
"docid": "b13b892aaf831a3e6f726139e70d0611",
"score": "0.74719286",
"text": "def r2plus1d50(**kwargs):\n model = R2Plus1D(Bottleneck, [3, 4, 6, 3], **kwargs)\n return model",
"title": ""
}
] | [
{
"docid": "5ca825d253d46b723d0e20f89cb10738",
"score": "0.676142",
"text": "def r2plus1d10(**kwargs):\n model = R2Plus1D(BasicBlock, [1, 1, 1, 1], **kwargs)\n return model",
"title": ""
},
{
"docid": "fb930da44e2f08a76d43c1e3a9523626",
"score": "0.6616595",
"text": "def r2plus... |
94188f27c625d12f0c691c2c23b66238 | Calculate the [X/H] value for this collection of stars. This is calculated in the following way. | [
{
"docid": "5167f9c772f084634b075caf43b00be6",
"score": "0.64352614",
"text": "def x_on_h_total(self, element):\n # get the metal mass fractions\n f_Ia = self.yields_Ia.mass_fraction(element, self.Z_Ia)\n f_II = self.yields_II.mass_fraction(element, self.Z_II)\n star_num = np... | [
{
"docid": "8252ecf68ddf9d0e45c63b8160ca555c",
"score": "0.6941615",
"text": "def _h(self, x):\n num = (x - self.d)**2 * (x + self.d)**2\n den = (self.d**4 / self.H) + x**2\n return num / den",
"title": ""
},
{
"docid": "cf3dc263acf45e853b62f41315eb974a",
"score": "0... |
bf2190db35a2d620b620938d60bda927 | Format message to be used when file a new file is generated. | [
{
"docid": "344b2c2872c245d3eb555585268358f9",
"score": "0.6486107",
"text": "def build_file_generation_message(env, filename):\n return (\n c(PAINT_PURPLE, '---------------------------') +\n LINE_BREAK +\n c(PAINT_CYAN, 'Environment: {} '.format(env)) +\n LINE_BREAK +\n ... | [
{
"docid": "a9dfd7f1e12a8c7dea824234b4b44fca",
"score": "0.6390881",
"text": "def _create_log(self) -> str:\n logger_msg = \"Creating file {}\".format(\n self.data['filename']\n )\n return logger_msg",
"title": ""
},
{
"docid": "65acbf96b2162529218989b5da8601f... |
a22370a5f56f41c01cb5640145e8c984 | Symbolically render rays starting with raster_space according to geometry e defined by | [
{
"docid": "6f2396489687753e62c370020ecac963",
"score": "0.5525302",
"text": "def make_ro(r, raster_space, width, height):\n nmatrices = r.shape[0]\n resolution = np.array([width, height], dtype=floatX)\n # Normalise it to be bound between 0 1\n norm_raster_space = raster_space / resolution\... | [
{
"docid": "e655306b5cf230461104c6c0e721790f",
"score": "0.6136116",
"text": "def find_intersection_rays(self, rays):\n x = rays[:, 0, :]\n xp = rays[:, 1, :]\n data = rays[:, 2, :]\n n0 = rays[0, 2, 1] # Assume all rays have the same refractive index\n l = rays[0... |
55fab12e6ce4d71eac3a4486ba4cb6b6 | Tests that the code runs and that the resulting matrix is symmetric | [
{
"docid": "4322188f4cbe411df771bb0a6ebfd8d4",
"score": "0.0",
"text": "def test_coherence_mlab(): \n\n t = np.linspace(0,16*np.pi,1024)\n x = np.sin(t) + np.sin(2*t) + np.sin(3*t) + np.random.rand(t.shape[-1])\n y = x + np.random.rand(t.shape[-1])\n\n method = {\"this_method\":'mlab',\n ... | [
{
"docid": "410f2d6aa853cfb6c68773315cb8ba83",
"score": "0.7260345",
"text": "def test_symmetric_samples(self):\n np.random.seed(42)\n n = 3\n A = np.random.uniform(size=(n, n))\n A = 0.5 * (A + A.T) + n * np.eye(n)\n dist = prob.Normal(\n mean=np.eye(A.shap... |
829d8e3ea172d0f845a7d5701e35f994 | Get the base name of a variable, without the time index. Given a variable "FooBar_10", the last component is the time index (if present), so we want to strip the "_10". This does no error checking. You should not be passing in a variable whose name starts with "Seq_"; this is for usergenerated names, not the internal n... | [
{
"docid": "44eb9ae47664072c30750831e60ff797",
"score": "0.788898",
"text": "def _basename_of_variable(varname):\n \n comps = varname.split(\"_\")\n\n # handle variables that aren't time-varying\n if len(comps) == 1:\n return varname\n \n # this is kind of a heuristic; assume an... | [
{
"docid": "3727ce35b289c0fa37ee658cc8e5c040",
"score": "0.67316073",
"text": "def _get_name(self, var_id):\n return var_id[0] + '_' + str(var_id[1])",
"title": ""
},
{
"docid": "7808bb9d3f93a629c8b9eb45b2bcaa86",
"score": "0.6664792",
"text": "def getMangledName(self):\r\n ... |
e429f504150027a970a8981961829726 | Evaluate data against the model created with given number of features. | [
{
"docid": "aefc172c8380c8262596349849a53a18",
"score": "0.0",
"text": "def evaluate_all_players(self, feature_qty=47, model='LR'):\n data = self.pca[feature_qty]\n x_test = (skpre.StandardScaler()\n .fit_transform(data.subset.drop('response', axis=1)))\n y_test = d... | [
{
"docid": "e0510b66ecf5fe71dbd3fdad303674f0",
"score": "0.6462505",
"text": "def evaluate_models(self, evaluations=10):\n self.seed = None\n optimal_features = []\n for n in range(int(evaluations)):\n self.get_feature_subsets()\n logger.info(f'Evaluation:\\t{n... |
17fd39280cbd14235c14808097b0e83c | Handle response data of type JSON | [
{
"docid": "b66f0440d32140ad8b1ce08c2a2afa40",
"score": "0.0",
"text": "def _request_process_json(self, response):\n data = []\n try:\n if self._api_branch == 'bulk':\n response_data = self._request_bulk(response)\n else:\n response_data ... | [
{
"docid": "4d78f8661e854683b2b972b735895d20",
"score": "0.7780941",
"text": "def process_response(response):\r\n return response.json()",
"title": ""
},
{
"docid": "b50cecc059978420c2281c56129d6f41",
"score": "0.7539139",
"text": "def normal_json_response(data, *args, **kwargs):\... |
87d394aab08c4337d28ff1e90764e159 | setup database in PostgreSQL | [
{
"docid": "07927ad478d6d7a977c8fcdff563df03",
"score": "0.81652486",
"text": "def _setup_db(self):\n conn = psycopg2.connect(f\"host={DBHOST} user={DBUSER} password={DBPASSWORD}\")\n conn.set_session(autocommit=True)\n cur = conn.cursor()\n cur.execute(f\"DROP DATABASE IF EX... | [
{
"docid": "66575ae369e538a87935f26a2c7e64b4",
"score": "0.8368186",
"text": "def db_setup():\n # Symlink our own config file and restart PostgreSQL\n with cd(\"/etc/postgresql/8.4/main/\"):\n # remove the existing config file\n if exists(\"pg_hba.conf\"): \n run(\"rm pg_h... |
1d523ef15fd8cc0237678f5ab2b67155 | Test the right directional links between heads | [
{
"docid": "cb943cec3364795a541eb60576574447",
"score": "0.61844957",
"text": "def test_matrix_head_links_right(self):\n\t\ttest_matrix = Matrix([ [1, 0, 1, 1], [0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 0, 0] ])\n\t\tcol = test_matrix.columns\n\t\ttest_head = col[0][0].left\n\t\tnext_head = test_head.right\n\t... | [
{
"docid": "a2b43f42cdaee4692fca5b53f4fb5fba",
"score": "0.61282086",
"text": "def test_reversed_direction(self):\n # Create a harmonic oscillator system to test.\n compound_state, sampler_state = self.get_harmonic_oscillator()\n mcmc_move = self.get_langevin_dynamics_move()\n\n ... |
51e958855f0dca8db98c1de9be4bb748 | verifica que el formulario solo con el dato 'Ciudad' no es valido | [
{
"docid": "6c20da4db0026cf549f37d6940d4e762",
"score": "0.0",
"text": "def test_FotoForm_onlyoneitem(self):\n response = self.client.post(\n '/', {'username': 'pepe', 'password': '123456'})\n self.assertEqual(response.status_code, 302)\n data = {'Ciudad': \"cordoba\"}\n ... | [
{
"docid": "5299859ea146747269c9691566036e19",
"score": "0.6833197",
"text": "def test_simple_invalid_form(self):\n invalid_data = dict(self._data, codigo_de_area='a11')\n f = DatosDeEnvioForm(invalid_data)\n self.assert_(not f.is_valid())\n self.assert_(f.errors)",
"titl... |
a8dc8bc8bbf9a2d52bce166494c532b5 | private function, tries to find a file ``version.txt`` which should contains the version number (if svn is not present) path folder to look, it will look to the the path of this file, some parents directories and finally this path the version number If ``version.txt`` was not found, it throws an exception. | [
{
"docid": "734c207ad010d6a79b3fdf61a0620b95",
"score": "0.7988161",
"text": "def __get_version_from_version_txt(path) :\n file = os.path.split(__file__)[0]\n paths = [ file,\n os.path.join(file, \"..\"),\n os.path.join(file, \"..\", \"..\"),\n os.path.joi... | [
{
"docid": "68a313325d3a6311c212c2b85fc3ef07",
"score": "0.6514272",
"text": "def test_get_version_searching_for_version_file(mock_open_fix,\n mock_is_dir_true,\n mock_is_file_false,\n ... |
3795a77c3a6a97511d9e337e76e4c557 | Start timestamp of the query range. | [
{
"docid": "431c9641bb2acecb93e4624fd0dcf638",
"score": "0.649369",
"text": "def start_timestamp(self) -> str:\n return pulumi.get(self, \"start_timestamp\")",
"title": ""
}
] | [
{
"docid": "0ed6fc12aa8903e09ac03e37826a6d71",
"score": "0.70146394",
"text": "def start_time(self):\n return self._current_edit_range[0]",
"title": ""
},
{
"docid": "5c19bb6a8cb906189a9b0bd31ae8693c",
"score": "0.68969023",
"text": "def start_timestamp(self) -> datetime:\n ... |
b77313869068b1bf5e6a7fbfb2830ef4 | Initialize CronTabber with current user's crontab. | [
{
"docid": "6360535a11c311dbc9280cea8e053ee1",
"score": "0.85432917",
"text": "def __init__(self):\n self.crontab = CronTab(user=True)",
"title": ""
}
] | [
{
"docid": "4603756c86af606dca587e990dc8079c",
"score": "0.67277706",
"text": "def crontab():\n if env.host != 'local':\n output = \"/tmp/crontab\"\n local_crontab_file = render_to_file(\"crontab\", output)\n\n put(local_crontab_file, '/tmp/crontab')\n run('crontab /tmp/cr... |
6a23c8331c89e102131c676da1b71a82 | Checks to make sure a BAM file has an index, if the index does not exist it is created Usage undefined if file does not exist (check is made earlier in program) bamfile a path to a bam file Returns 1 | [
{
"docid": "ccd54fb2110fde7106e81a21dbcbe5e8",
"score": "0.85391355",
"text": "def check_for_index(bamfile):\n\n if not os.path.exists(bamfile):\n raise NameError(\"file %s does not exist\" % (bamfile))\n \n if os.path.exists(bamfile + \".bai\"):\n return 1\n else:\n ver... | [
{
"docid": "e94e4c7568a006bd03256cbbd6675f5d",
"score": "0.79619974",
"text": "def bam_index(path, bam_file):\n\t\n\tcmd3 = 'samtools index %s'%(path+bam_file)\n\tprint cmd3\n\tprint os.getcwd()\n\tif os.path.exists(path+bam_file) and os.path.exists('%s.bai'% (path+bam_file[:-4])) == False:\n\t\t\tres3 ... |
436179f3ed80932612fd4765048ab885 | Perform the functions in a satisfactory order. | [
{
"docid": "2d5d300624d0141dfcf0b5256889e871",
"score": "0.0",
"text": "def main():\n my_cipher = Cipher()\n print(my_cipher.encrypt())",
"title": ""
}
] | [
{
"docid": "30d79b1ff7399778240afe1f93913455",
"score": "0.6933145",
"text": "def all(self,*args):\n for f in self.anyorder:\n t1 = time.time()\n res = f(*args)\n t2 = time.time()\n self.update_stats(f,t2-t1,res)\n if not res:\n ... |
5efd19762b31660385ccad243d1bcdd8 | Setup initialize the runtime setup, which may require for the `gef` to be not None. | [
{
"docid": "0f3abe335a3caf0ffac2eed3d7dc1f84",
"score": "0.6264551",
"text": "def setup(self) -> None:\n self.reinitialize_managers()\n self.gdb = GefCommand()\n self.gdb.setup()\n tempdir = self.config[\"gef.tempdir\"]\n gef_makedirs(tempdir)\n gdb.execute(f\"s... | [
{
"docid": "76fbc9bd6e190f18c3b9ef1cf27d2a74",
"score": "0.6606635",
"text": "def _setup(self) -> None:",
"title": ""
},
{
"docid": "22e71f90696515479fa10af0304fd3b8",
"score": "0.6592227",
"text": "def setup(self) -> None:\n pass",
"title": ""
},
{
"docid": "22e71... |
8dac01e27ce535e660ebe70b2057cca8 | function get_user_email is an helper function to retrieve the email associated with the user | [
{
"docid": "7c09e3b8fa3fa96a9c15770ba9917374",
"score": "0.8517654",
"text": "def get_user_email(self):\n return self.user.email",
"title": ""
}
] | [
{
"docid": "ab547faa913138ec02b4ed088c1d569b",
"score": "0.78623545",
"text": "def get_emailuser(self, email):\n return ccnet_threaded_rpc.get_emailuser(email)",
"title": ""
},
{
"docid": "04b5507431338094d8baa567a4611598",
"score": "0.7805862",
"text": "def user_email(self) -... |
914bc926f0a367e9a585944129d16abf | invoke contract controller to react to failures | [
{
"docid": "207fe5b73b8f41ae6c958a0344e16c30",
"score": "0.0",
"text": "def react_to(failures, controller = 'greedy'):\n nonlocal current_state_id\n global strategy_dict\n #init_state, guart = options['init_contract']\n init_state = strategy_dict[current_state_id]\n as... | [
{
"docid": "ba1c78f6af1c27e09f89164b41e851e5",
"score": "0.6214747",
"text": "def __failed__(self, lifecycle, result):",
"title": ""
},
{
"docid": "c865a8e0d996d1b6d662b2cfcdff99ce",
"score": "0.5804121",
"text": "def _ebRequestFailed(self, reason):\n log.err(Exception('Fa... |
035bde00b0ca8428ebaf37bbe68bfda6 | Return the position of atom d with cd length r, bcd angle theta, and abcd dihedral phi, all in radians. | [
{
"docid": "dd2b059a6a8dbc01bb0300da5ebdb2f7",
"score": "0.53789735",
"text": "def ApplyDihedralGeometry(a, b, c, r, theta, phi):\n if not isinstance(a, numpy.ndarray):\n a = a.pos\n if not isinstance(b, numpy.ndarray):\n b = b.pos\n if not isinstance(c, numpy.ndarray):\n c... | [
{
"docid": "aca83763c75cf49a35332fb844c19e36",
"score": "0.5987134",
"text": "def CalcDihedral(a, b, c, d):\n if not isinstance(a, numpy.ndarray):\n a = a.pos\n if not isinstance(b, numpy.ndarray):\n b = b.pos\n if not isinstance(c, numpy.ndarray):\n c = c.pos\n if not i... |
754cbfec894ae5c0aa682bac3a7d2dd2 | Reset a key override. Any overrides applied to the specified key will be removed, allowing the value in the parent to be seen again. | [
{
"docid": "f41dae190abb6204987b37634b086063",
"score": "0.65187776",
"text": "def reset(self, key=_unpassed):\n\n if key is _unpassed:\n # OK, clear everything\n self._deleted = set()\n self._values = {}\n else:\n # Clear just the one key\n ... | [
{
"docid": "000d1e2400590ef0cab64e5b91afa7ff",
"score": "0.70527697",
"text": "def reset(self, key):\n\t\tsuper().__setitem__(key, self._metadata[key].default)",
"title": ""
},
{
"docid": "55fb9dde8823953ab5c2efa428d7ba34",
"score": "0.69658613",
"text": "def reset(self, key):\n ... |
87f8d7c1ae54134b2bbef3b161c7e971 | Simple utility function to look up pixelscale from apername | [
{
"docid": "98ba67c052d85002aa186988874a25f0",
"score": "0.80523163",
"text": "def _get_pixelscale_from_apername(self, apername):\n ap = self.siaf[apername]\n # Here we make the simplifying assumption of **square** pixels, which is true within 0.5%.\n # The slight departures from th... | [
{
"docid": "9357ce8b2f57c7121652c3bdffda79a8",
"score": "0.6847892",
"text": "def get_scale_factor(data_dir):\n reg_match = re.search(\"sf[0-9]+\", data_dir).group(0)\n return int(reg_match[2:])",
"title": ""
},
{
"docid": "17ecfd771ea5ba9c9f574d934a712861",
"score": "0.65308654",
... |
28be2400800ce2ba8cec46a109f27143 | Request the result for some text on DBPediaSpotlight. | [
{
"docid": "e9a76dfdddc8cc843a1d78359f838b24",
"score": "0.55680686",
"text": "def req(self, text, delay=0.5):\n self.post_data['text'] = filter_emoji(text)\n r = req_using_pool(self.pool, self.page, self.post_data)\n\n try:\n return json.loads(r.data.decode('utf8'))\n ... | [
{
"docid": "c6fe392535aacb3d10b4c68acd56990d",
"score": "0.6499329",
"text": "def get_result():\n r = request.args.get('text')\n texthtml = internet_sock.internet_get(r)\n return texthtml",
"title": ""
},
{
"docid": "399a772a789fb2581e6816bb2b16eb42",
"score": "0.6037484",
"... |
d6e5264081c833836abf3869d042c92f | Multiply numbers with uncertainties. | [
{
"docid": "6af62adac81de4b15a92b4bf8d717efc",
"score": "0.0",
"text": "def mul(x_sigma,y_sigma):\n\n x, dx = x_sigma\n y, dy = y_sigma\n\n z = x*y\n\n dz = z* np.sqrt((dx/x)**2+(dy/y)**2)\n\n return (z,dz)",
"title": ""
}
] | [
{
"docid": "8a456cd91fd61058e4174ab38f25bcaf",
"score": "0.7515985",
"text": "def multiply(self, n):\n\t\tpass",
"title": ""
},
{
"docid": "bb3d6cc4269c3bae4604f1aad9e34ff2",
"score": "0.706079",
"text": "def product(numbers):\n return reduce(mul, numbers, 1)",
"title": ""
}... |
00b4a1d13a8195d7b2260e3558faa6a2 | The network configuration for customer VPC connectivity. | [
{
"docid": "64bdf2b7efa844f4cd4b8ae714660da5",
"score": "0.72619134",
"text": "def network_configuration(self) -> pulumi.Output[Optional['outputs.ApplicationNetworkConfiguration']]:\n return pulumi.get(self, \"network_configuration\")",
"title": ""
}
] | [
{
"docid": "ad1e2b9fba1bd8e594ca5dc31ab570ea",
"score": "0.74247205",
"text": "def network_config(self) -> 'outputs.NetworkConfigResponse':\n return pulumi.get(self, \"network_config\")",
"title": ""
},
{
"docid": "f43649116f006ce921d40e40b5fa4dc3",
"score": "0.71666706",
"tex... |
a709a5737a6fd7504500981de36ec180 | Returns when the server is ready. | [
{
"docid": "3f1ce331c4c086998b209f1ddcc470f0",
"score": "0.0",
"text": "def ping():\n return ''",
"title": ""
}
] | [
{
"docid": "02aebb7dadf0c3c96bf82e8e5c3ec298",
"score": "0.78574795",
"text": "def is_ready(self):\n return True",
"title": ""
},
{
"docid": "613f497ac14caa0f96b71b382aaf4e4f",
"score": "0.7741623",
"text": "def is_ready(self) -> bool:\n pass",
"title": ""
},
{
... |
5bc5adcae699b537310df6aa90bf7cfc | Adds a transformer while taking care of the input type. | [
{
"docid": "f19412c69e1e533cb0769e028f40ed7a",
"score": "0.7337975",
"text": "def _add_step(self, transformer):\n\n name = None\n if isinstance(transformer, tuple):\n name, transformer = transformer\n\n # If the step is a function then wrap it in a FuncTransformer\n ... | [
{
"docid": "8f793780ba6e60795d160bf8e4fe527a",
"score": "0.7657541",
"text": "def register_transformer(cls, t):\n cls.subtransformers.append(t)\n return t",
"title": ""
},
{
"docid": "125899613a5f526ab07c399827738074",
"score": "0.6968655",
"text": "def transformer(self... |
77fd442f746fdcf07467b7de6a04bf10 | Detect objects and extract feature of objects for the given data | [
{
"docid": "c4ebb71575ebcb6b4a458e8ccfb9ee7b",
"score": "0.587448",
"text": "def extract_features(self, data,\n class_score_threshold=0.5,\n nms_threshold=0.3,\n layer=\"roi_pool5\"):\n\n if not self._executor_with_feature:\n ... | [
{
"docid": "8d00c68a120eada03135b0af77f65937",
"score": "0.65555567",
"text": "def detect_objects(interpreter, image, threshold):\n labels = load_labels()\n set_input_tensor(interpreter, image)\n interpreter.invoke()\n # Get all output details\n boxes = get_output_tensor(interpreter, 0)\n classes ... |
e31d1ba68aac9221b4a220599f630da9 | Creates a new occurrence counter with the given initial_value | [
{
"docid": "e3aad9b94abe74734550295156bf9d3f",
"score": "0.8686415",
"text": "def _create_occurrence_counter(self, initial_value=1):\n self._occurrences.append(initial_value)",
"title": ""
}
] | [
{
"docid": "cb31c80e9e0c8ea37c2a746140ffe318",
"score": "0.6011263",
"text": "def update_counter(galini, name, amount, initial_value=0.0):\n telemetry = galini.telemetry\n counter = telemetry.get_counter(name)\n if counter is None:\n counter = telemetry.create_counter(name, initial_value... |
8a02f31d65fa554429f78bc0ad1a4ea0 | Show the app's homepage | [
{
"docid": "a270a8112af1a21e18685e7e40fad310",
"score": "0.79977566",
"text": "def show_homepage():\n return render_template('homepage.html')",
"title": ""
}
] | [
{
"docid": "8de335c3b36ea442f058b097175f8a14",
"score": "0.8029172",
"text": "def homepage():\n\n\t\treturn render_template(\"homepage.html\")",
"title": ""
},
{
"docid": "533f390e18b8659416c5d69da75caaa2",
"score": "0.7907027",
"text": "def homepage():\r\n\r\n return render_templ... |
56e07490a9cad56c291b18f52911b22c | Analyze method separates channels into subbands using MST This method has been provided. | [
{
"docid": "6b300dd3cef8e5ed2e17af5a71f4a85f",
"score": "0.0",
"text": "def analyze(self, x):\n w = np.empty_like(x, dtype=np.int32)\n w[:, 0] = x[:, 0].astype(np.int32) + x[:, 1]\n w[:, 1] = x[:, 0].astype(np.int32) - x[:, 1]\n return w",
"title": ""
}
] | [
{
"docid": "3e052f69502990dbb46ce5e645990d6a",
"score": "0.59247506",
"text": "def construct_subbands(self):\n logger.info(\"Grouping individual channels into consecutive subbands (if possible)\")\n\n # check if the 128,129 channel pair exists and note to user that the subband will be divi... |
8eea9ec82e5d7ba7d39014e3ca250694 | Deserialize datetime object into string form for JSON processing. | [
{
"docid": "f5dd25b6ff54428d7f7d2b56e1c23735",
"score": "0.5695315",
"text": "def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]",
"title": ""
}
] | [
{
"docid": "48bc111e66ce1322720b65617c8c65eb",
"score": "0.7181511",
"text": "def _jsonify(o):\n if isinstance(o, datetime.datetime):\n return o.__str__()",
"title": ""
},
{
"docid": "aae7583788ec0c63b995f058043130d5",
"score": "0.70708674",
"text": "def convert_for... |
2a2039d67a02f77ba1a50da8311f93c5 | Check if an instance of SQL Server is running | [
{
"docid": "1ce17a3bed7df40df040cdf062d2cf7f",
"score": "0.80247015",
"text": "def checkRunningInstance():\n\n ret = runScript(checkRunningInstanceScript)\n\n if (ret == 0):\n print (_(\"An instance of SQL Server is running. Please stop the SQL Server service\"))\n print (_(\"using t... | [
{
"docid": "5bbde1c586f394e8f31f9fe15892b959",
"score": "0.6767707",
"text": "def is_server_running(self):\n try:\n return self.call([\"BackgroundScheduler\", \"state\"]) == 1\n except ConnectionRefusedError:\n return False",
"title": ""
},
{
"docid": "5bb... |
b299f3551c1bdeebecdd99f771313935 | Add new Elo rating for two charts | [
{
"docid": "0a55ea45e50d0925243aae78f06f5b52",
"score": "0.6828308",
"text": "def elo_rate_charts(chart1_id, chart2_id, user, draw=False, rate_type=0):\n rate_type_display = 'elo_rating_hc' if rate_type else 'elo_rating'\n with transaction.atomic():\n win_chart = Chart.objects.get(pk=chart1... | [
{
"docid": "c6e9c53253159ad07ae97821f12e0352",
"score": "0.5908906",
"text": "def co_rating():",
"title": ""
},
{
"docid": "808ee4b759d70cc7cd42947850ee29dc",
"score": "0.55668575",
"text": "def test_add_rating(self):\n\n self.client.login(username=\"test_user\", password=\"se... |
1ccdd9914d21f3a0dfba57e04eef4320 | Test decorator syntax and runtime errors. | [
{
"docid": "b3bead0185132cb747f6fc2a1f67d2e0",
"score": "0.0",
"text": "async def test_decorator_errors(hass, caplog):\n notify_q = asyncio.Queue(0)\n await setup_script(\n hass,\n notify_q,\n [dt(2020, 7, 1, 11, 59, 59, 999999)],\n \"\"\"\nseq_num = 0\n\ndef add_startu... | [
{
"docid": "57bc1f9e7e71137303dd94c17747c463",
"score": "0.6926897",
"text": "def test_runner_syntax_error():\n source = utils.codeblock(\n r'''\n def demo_parsetime_syntax_error1():\n \"\"\"\n Example:\n >>> from __future__ import print_func... |
6e2e0af482a440b25957ed5a8bae28ae | Internal fuction for euler_pole_calc | [
{
"docid": "7e489797bc19b610cd6677934ca80f58",
"score": "0.0",
"text": "def topo2dm(coords):\n n = len(coords);\n dm = np.zeros([2 * n,3]) \n for index in range (0,2*n-1,2): \n dm[index,0] = np.sin(coords[int((index+1)/2)][1]);\n dm[index,1] = -np.cos(coords[int((inde... | [
{
"docid": "86e30aaea8125cdd2499d560eb590dfa",
"score": "0.6845522",
"text": "def euler_totient_function(q, p):\n return (p - 1) * (q - 1)",
"title": ""
},
{
"docid": "0d1d7b3009d3188c2c7233c7ad4ef410",
"score": "0.63748366",
"text": "def euler_step(u, f, dt):\n \n return u ... |
7e78f227c5197e91a14e5a4bcff0b34d | Only works for catmaid1 server | [
{
"docid": "da06323c18f45583e3ab9d8f4096a901",
"score": "0.0",
"text": "def user_stats(self, project=None):\n pid = self.find_pid(project)\n return self.fetchJSON('/{}/stats-user-history'.format(pid))",
"title": ""
}
] | [
{
"docid": "b3be7b907a62d7b576d1b1be427a2787",
"score": "0.6278678",
"text": "def server(self) -> str:",
"title": ""
},
{
"docid": "60e6e10c636d07e9a88f29835cd8e5a6",
"score": "0.62576324",
"text": "def client():",
"title": ""
},
{
"docid": "9e96ac74a686435c643ae2138d0d62... |
d0e38cbd29e337d66dd596b20da802a4 | get popular image from dbase | [
{
"docid": "ce3e49e4f324c8308a4485dcca636ee1",
"score": "0.53152233",
"text": "def tophits():\n data = [i for i in db.image.find().sort(\"hits\", -1).limit(24)]\n return render_template(\"top.html\", data=data)",
"title": ""
}
] | [
{
"docid": "5363c0ac1800c09cef388513e2c21ad9",
"score": "0.6300238",
"text": "def most_similar_image():\n index = 61\n return index",
"title": ""
},
{
"docid": "e4dc38acaca09a5f811d78565bfe2ba1",
"score": "0.6279305",
"text": "def getimgtags():\n\n # rows = db(db.tagref.ta... |
5a5b819850d44a495d33ccfcf33de05d | Plots the learning curves | [
{
"docid": "d2a7481ddc4e390f360be1d5d9329b40",
"score": "0.70601207",
"text": "def plot_learning_curves(P, A, dataset='adult'):\r\n if dataset == 'crime':\r\n metric = 'MSE'\r\n else:\r\n metric = 'accuracy'\r\n\r\n fig, axs = plt.subplots(4, 1)\r\n axs[0].plot(np.arange(1, len... | [
{
"docid": "b6947415b67c04e531ee5b4c39f5317e",
"score": "0.8397364",
"text": "def plot_learning_curve(training_losses): \n plt.ylabel('Loss')\n plt.xlabel('Training Steps')\n plt.plot(training_losses)",
"title": ""
},
{
"docid": "b6947415b67c04e531ee5b4c39f5317e",
"score": "0.8... |
c0bf431b5bb65dce6e8ec4464b854b29 | New() > itkScalarImageKmeansImageFilterIUC2IUC2 Create a new object of the class itkScalarImageKmeansImageFilterIUC2IUC2 and set the input and the parameters if some named or nonnamed arguments are passed to that method. New() tries to assign all the non named parameters to the input of the new objects the first non na... | [
{
"docid": "8b814376fb0646d52782c57e642d3d6e",
"score": "0.8396783",
"text": "def New(*args, **kargs):\n obj = itkScalarImageKmeansImageFilterIUC2IUC2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
}
] | [
{
"docid": "ad809e014d8beb68f1fe6746da40e2b6",
"score": "0.823897",
"text": "def New(*args, **kargs):\n obj = itkScalarImageKmeansImageFilterIF2IUC2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
},
{
"docid":... |
c7b23e125f1f62bca2ea5c3a31158832 | Mark the feed as unread | [
{
"docid": "12f42e18e196ebc716deb7230414b4ca",
"score": "0.72663385",
"text": "def do_action(self):\n self.params['object'].dist_mark_as_unread()",
"title": ""
}
] | [
{
"docid": "eabef77bc25456bcaa79fe62863d497f",
"score": "0.71112126",
"text": "def unread(self, data):\n self._unread.append(data)",
"title": ""
},
{
"docid": "655f29a9f0d07f3a536fee64f03e76a5",
"score": "0.69926304",
"text": "def mark_all_notifications_as_read(self):\n for... |
f7cd3021d1999fa76ccf5c86cf1717ac | Sets the api_volume of this AccountLimits. | [
{
"docid": "2c99e473038f12f9d60462698fab6989",
"score": "0.7204298",
"text": "def api_volume(self, api_volume):\n if self.local_vars_configuration.client_side_validation and api_volume is None: # noqa: E501\n raise ValueError(\"Invalid value for `api_volume`, must not be `None`\") # ... | [
{
"docid": "eed3926e5bd301eeeec00de8ac8790b4",
"score": "0.6478817",
"text": "def api_level(self, api_level):\n\n self._api_level = api_level",
"title": ""
},
{
"docid": "bc941602964cbf205dd4978b8f99af04",
"score": "0.6474038",
"text": "def set_volume(self, zone: int, volume: ... |
23c6163a29c59109a813f9b12ef4e807 | Generate OAuth request to authorize token. | [
{
"docid": "25400348fd1c456d45605c164cd54361",
"score": "0.7670326",
"text": "def oauth_authorization_request(self, token):\n params = self.auth_extra_arguments() or {}\n params['api_key'] = BODYMEDIA_CONSUMER_KEY\n params.update(self.get_scope_argument())\n return OAuthReque... | [
{
"docid": "e42d4e79f6b42e1fedfe35a79128ae27",
"score": "0.7870929",
"text": "def oauth_authorization_request_(self, token):\n \n request = OAuthRequest.from_token_and_callback(\n token=token,\n http_url=self.AUTHORIZATION_URL,\n )\n request.is_form_enc... |
7880705b73e3a8660667158a6cbff765 | Getter method for host_interface_block, mapped from YANG variable /components/component/integrated_circuit/pipeline_counters/errors/host_interface_block (container) | [
{
"docid": "6fe199218d3b2417cb24f68b8603a1d1",
"score": "0.63888496",
"text": "def _get_host_interface_block(self):\n return self.__host_interface_block",
"title": ""
}
] | [
{
"docid": "531d4d11351c733b4946010bc3d08cae",
"score": "0.8094548",
"text": "def _set_host_interface_block(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_host_interface_block_openconfig_platform__components_component_integrated_c... |
9cad2ae063d763b88d9bc4a37d4cb9ed | The API key for accessing the Azure ML model endpoint. | [
{
"docid": "9e3e7e716c563cd19c3e51686203dfb4",
"score": "0.66521347",
"text": "def api_key(self) -> Any:\n return pulumi.get(self, \"api_key\")",
"title": ""
}
] | [
{
"docid": "fc85cd047dcb67f1dc883f75f78624ab",
"score": "0.67123985",
"text": "def api_key(self) -> str:\n return self._api_key",
"title": ""
},
{
"docid": "c39011b0d3b7489ab87b877729408b7b",
"score": "0.66387504",
"text": "def api_key(self):\n return self.__api_key",
... |
8179533704ade3fe1adb506fcef3d4f8 | Getter method for start_value, mapped from YANG variable /nsd_catalog/nsd/parameter_pool/range/start_value (uint32) | [
{
"docid": "9a214c747ecaf1da1c2869665c91cf0c",
"score": "0.64253175",
"text": "def _get_start_value(self):\n return self.__start_value",
"title": ""
}
] | [
{
"docid": "38b6d1df5d5d2e8ea7fe23c699921fad",
"score": "0.7645604",
"text": "def _set_start_value(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_... |
94d3221a8e2c7d0f96a7a5fecda208d9 | Load saved user data into Privacy Badger after a restart | [
{
"docid": "58b68adf2ad67bba16f62448c58de135",
"score": "0.63841015",
"text": "def load_user_data(self, data):\n self.load_extension_page()\n\n self.driver.execute_async_script((\n \"let done = arguments[arguments.length - 1];\"\n \"chrome.runtime.sendMessage({\"\n ... | [
{
"docid": "cdc6627c211ed872c955b517d2315718",
"score": "0.6293094",
"text": "def load_preferences():\n Preferences.load_settings()\n current_app.logger.info('Successfully loaded user preferences')",
"title": ""
},
{
"docid": "44c4e663882c612c13d872bb9b8c0a7e",
"score": "0.61983794... |
eba952d294cab4cbe3a8d5753a2b9925 | Parse command line arguments. | [
{
"docid": "93651595aa9142eaafcc7ae2c39e755e",
"score": "0.69388056",
"text": "def parse_args():\n parser = argparse.ArgumentParser(description=\"Performs class mapping inference.\")\n parser.add_argument(\n \"--organs_dir_path\",\n type=str,\n default=\"data/data_organs\",\n ... | [
{
"docid": "32c340fd2874fc6e01daf9f4e48bbb98",
"score": "0.7483945",
"text": "def _parse_args():\n _log('Parsing script arguments')\n args = copy.copy(sys.argv)\n args.pop(0) # Remove Python file path arg\n\n for arg in args:\n if arg[:7] == '--tags=':\n tags.append(arg)\n... |
4908cd16f708abcd37d08f81ea1bba19 | Request contains values that can be converted to the expected types. | [
{
"docid": "b0054c6bc11700f69139c60b1907caec",
"score": "0.0",
"text": "def test_pass_compatible_types(self):\n txn =\\\n ProposedTransaction(\n address = Address(self.trytes_3),\n value = 42,\n )\n\n filter_ =\\\n self._filter({\n # ``changeAddress`` can be a... | [
{
"docid": "091fd7d94953fa72a9cca4483b2f8870",
"score": "0.6157311",
"text": "def _convert_param_type(self):\n\n def convert_type(input_dict):\n for k, v in input_dict.items():\n if k == \"TvgRangeCorrection\":\n if v not in self.TvgRangeCorrection_all... |
9bbc9a3819836ec84bb24d39c964bfd1 | if you are calculating ir spectra, you have to have the dipole information for the molecule available in the simulated trajectory. that is realized by FORCE_EVAL%DFT%LOCALIZE | [
{
"docid": "c790371a37939879a1eb1935fdd9d667",
"score": "0.53871095",
"text": "def ir_spectra(self):\n self.set_section_status({\n \"force_eval-dft-localize\": True,\n })",
"title": ""
}
] | [
{
"docid": "8f1dac93b623de3c8c0179766b20c5c6",
"score": "0.57128525",
"text": "def ofdm_modulate(sys_parameters, waveform_info, grid):\n # Get dimensionality information derived from the system parameters\n waveform_info.set_info(sys_parameters)\n # Cache the main dims\n nsc = waveform_info.... |
2eb26ab344ab264b2d77ff0ba5f10ec4 | Given a 4 letter code, use SUBDIV_CODE_MAP, and COUNTY_CODE_MAP to produce subdivision and county fields | [
{
"docid": "e76b01c1e8df8b8637878da01cb73389",
"score": "0.7424413",
"text": "def parse_county_subdiv_code(code):\n county_code = code[:2].lower()\n subdiv_code = code[2:].lower().split('/')[0]\n county = COUNTY_CODE_MAP[county_code]\n subdiv = SUBDIV_CODE_MAP[subdiv_code]\n return county... | [
{
"docid": "c5742bd9491761cb50a2e4f87937a78b",
"score": "0.6208167",
"text": "def CountyCode():\n TableView(DOTRoads+\"\\NG911_County\", \"NG911_County\")\n JoinTbl(lyr,\"COUNTY_L\",\"NG911_County\", \"CountyName\", \"KEEP_COMMON\")\n CalcField(lyr,\"KDOT_COUNTY_L\",\"!NG911_County.CountyNumber... |
f74b0919a21ecee83c477aaf34df065a | get optimal control input for the next timestep. xFull Fullorder state measurement. xRedFlag True if the state is already reduced. | [
{
"docid": "992ea67314a4974baabd6e198bb8df44",
"score": "0.5942547",
"text": "def getUopt(self,xFull,xRedFlag = False):\n if xRedFlag == False:\n xRed = np.dot(self.T,xFull) # Get reduced state.\n else:\n xRed = np.zeros((xFull.shape[0] + self.nAux))\n xRed... | [
{
"docid": "2f6046ec082165f9eba24f95045fb8fd",
"score": "0.5990941",
"text": "def get_optimal_action(self, state):\n # TODO : Implement solving linear system, replace tmp with correct control\n control = matrix_opt.fit_control(self.target_point, state, self.A, self.B)\n if self.limi... |
650a9e122d71ed83cde50c8118e77292 | Update a Solr entry identified by its id using (key,value) pairs. | [
{
"docid": "79dbce15d2f0f767c50ccaf3520a41da",
"score": "0.63010025",
"text": "def pavicsupdate(solr_server, update_dict):\n\n # Get all the information of the current Solr document\n if 'id' in update_dict:\n my_search = \"q=id:{0}&wt=json\".format(update_dict['id'])\n elif 'dataset_id'... | [
{
"docid": "ec7dfe4631b7ba0e1372afcb6d34cb53",
"score": "0.6533157",
"text": "def update(self, record_id, **kwargs):\n arg_list = []\n value_list = []\n for key, value in kwargs.items():\n arg_list.append('{} = %s'.format(key))\n value_list.append(value)\n ... |
c1349abebbe8c83dc65e044b45deb10b | Add new keyword argument `hello` >>> PetriNet('N').hello() Hello from N >>> PetriNet('N', hello='Hi! This is %s...').hello() Hi! This is N... | [
{
"docid": "1e1c7dd30de7f3faf27d2bb7cde0875d",
"score": "0.47194985",
"text": "def __init__ (self, name, **args) :\n print(type(self))\n self._hello = args.pop(\"hello\", \"Hello from %s\")\n module.PetriNet.__init__(self, name, **args)\n print(self, type(self... | [
{
"docid": "6ff834fb381665f7b6a5cb2205bd8640",
"score": "0.61344844",
"text": "def get_hello(self, arg):\n return 'Hello %s' % arg",
"title": ""
},
{
"docid": "e231c1786b45af6be78007a724a57d10",
"score": "0.59566647",
"text": "def hello(name = 'keisatou'):\n print(\"hello, ... |
e07f1b50b16d81105dea0c8e95ed9246 | If user's input is a greeting, return a greeting response | [
{
"docid": "d479f4fb6f9e8e7ffc7c8074f358e593",
"score": "0.6962479",
"text": "def greeting(sentence):\n for word in sentence.split():\n if word.lower() in GREETING_INPUTS:\n return random.choice(GREETING_RESPONSES)",
"title": ""
}
] | [
{
"docid": "1f8d9f9a7b9a1fa754ea526a9517246a",
"score": "0.6959173",
"text": "def greeting(sentence):\n global GREETING_INPUTS\n global GREETING_RESPONSES\n for word in sentence.split():\n if word.lower() in GREETING_INPUTS:\n return random.choice(GREETING_RESPONSES)",
"ti... |
8f65bc1c0c4a28abfb913472ef62654d | Print the training configuration. | [
{
"docid": "ff532fbd9c7161a7913c7ce953996510",
"score": "0.73269385",
"text": "def print_configuration(flags, aher_params, data_sources, save_dir=None):\n def print_config(stream=None):\n print('\\n# =========================================================================== #', file=stream)\n... | [
{
"docid": "daa844beed1e2c214318b2a0ddc675ab",
"score": "0.732663",
"text": "def print_training_setting(self, settings):\n\n self.logger.info('\\nTraining details:')\n for k in settings[self.session_id]:\n if isinstance(settings[self.session_id][k], dict):\n self.... |
552f104a5e73fdfe68b949f38e7d083b | Displays a table of the habit(s) with the longest streak. | [
{
"docid": "043cd964882e5334c8094fca0e88f3ff",
"score": "0.7788809",
"text": "def habit_longest_streak(self):\n # Clean up the console\n self.clear_console()\n # Prints the name of the application and instructions to the main menu\n self.back_to_menu_info()\n print(\"\... | [
{
"docid": "f369f93a8f922afc1d7a798692823975",
"score": "0.70373034",
"text": "def test_longest_streak_all_habits():\n habits_trackings_table = analytics.habits_trackings_table()\n habit_info_longest_streak = analytics.habit_info_longest_streak(habits_trackings_table)\n assert analytics.name_ha... |
cbaae9bee5fa3c1d29a0637244ecdee2 | Converts to dictionary compatible with MATLAB | [
{
"docid": "2babb405052d42dc481d52f8200f977e",
"score": "0.0",
"text": "def to_mdict(self, name):\n mdict = {'poses': self._poses,\n 'frame_ids': self._frame_ids.items(),\n 'descriptors': None,\n 'name': [name],\n 'forward': [],\n ... | [
{
"docid": "cd24214198d90e08997c285f8bc68664",
"score": "0.6664811",
"text": "def get_dict():",
"title": ""
},
{
"docid": "7550910142e0a82f7792a2ddcef64df3",
"score": "0.6607125",
"text": "def recarray2dict(ra):\n d = { key:val for key, val in zip(ra.dtype.names, ra.tolist()) }\n ... |
6266c973d06d9a3db2f5dafb0c142c72 | Downloads documentation associated with IRS SOI Historical Table 2 | [
{
"docid": "190874280eeb34d573fc7d73a5b564a5",
"score": "0.62471473",
"text": "def get_docs(self):\n \n if self.year < self.mincompleteyr:\n raise ValueError(\"Documentation only available 2012 and onwards.\")\n\n filename = '{}incmdocguide'.format(self.yy)\n urlst... | [
{
"docid": "cdd5c1affe96db172149ff6160bcf39d",
"score": "0.61644614",
"text": "def get_doc(logya_inst, url):\n\n return logya_inst.docs.get(url)",
"title": ""
},
{
"docid": "4c0cbf68e7aca92819f0785808bf4984",
"score": "0.5851935",
"text": "def get_documentation(self):\n # c... |
783f32bd4d6c58ca4ec0767ead5e1768 | A previously established connection was found. This will be the default connection to start. | [
{
"docid": "b8e6b341e69a7e5fbd22ce439bef94c5",
"score": "0.5911767",
"text": "def found_previous_connection(self,\n app: Application,\n server: Server,\n ) -> NetworkState:\n app.interface_transitio... | [
{
"docid": "fd56e177497fcb7c271f65d8369b0672",
"score": "0.6363437",
"text": "def _reconnect(self):\n if self._connection.closed > 0:\n self._connection = self._connect()",
"title": ""
},
{
"docid": "acf5400480e3255106f644dd5c197160",
"score": "0.6337093",
"text": "... |
f460811000e7ba45eb2e7399a0b30c77 | Build context for view. | [
{
"docid": "e23049e8eb5b78724a9d00684fc7c34e",
"score": "0.0",
"text": "def get_context_data(self, **kwargs):\n context = super(PublicProfileView, self).get_context_data(**kwargs)\n request_user = User.objects.filter(username=self.kwargs['request_username'])\n imager_profile = Image... | [
{
"docid": "f5cfbe026e23b36604f93ffcd667d129",
"score": "0.6692236",
"text": "def generate_context(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "2c20dcefe742d6f2b76e08f388cf1e1a",
"score": "0.6588604",
"text": "def get_context(self, request, context, **kwargs... |
a301f3f1e7d6be942b7976f14714974d | Update the scraper config and name by sending PUT request. | [
{
"docid": "52b21767f5464fe5c27ed2ef92b4fc98",
"score": "0.74320465",
"text": "def update(self, id_or_name, name, config):\n data = {'name': name, 'config': config}\n return self.client.put('/scrapers/{}'.format(id_or_name), data)",
"title": ""
}
] | [
{
"docid": "e8c57175ec238a519b8d1a42dd3983f9",
"score": "0.6280583",
"text": "def put(self, name):\n data = rest_utils.get_json_and_verify_params({\n 'value': {},\n 'force': {'type': bool, 'optional': True}\n })\n value = data['value']\n force = data.get... |
6a10b8f3ca995c7dceea823c5601d9aa | Used inside transformed functions, recognized by , and the k is automatically supplied. Cannot be called in return position. Use thusly | [
{
"docid": "64df00d179c567bff1d455e16d845c99",
"score": "0.0",
"text": "def raise_Ef(x, k):\n x._k = k\n return x",
"title": ""
}
] | [
{
"docid": "2fd55c928bad1935d7a01e1647003a6d",
"score": "0.70614517",
"text": "def S(self, k):\n # TODO: 1 lines missing.\n raise NotImplementedError(\"Implement function body\")",
"title": ""
},
{
"docid": "7ab7375f0830bbb72d7dd22ad27d5089",
"score": "0.6948748",
"text... |
f588c36eb70338466bac35b9f64a9027 | Sets the value at a given indentation level. | [
{
"docid": "95ad4bcc077bf144a6dc4e3781251024",
"score": "0.0",
"text": "def __setitem__(self, index, value):\n if index < 0:\n raise ValueError\n while len(self.all) <= index:\n self.all.append(None)\n self.all[index+1:] = [value]",
"title": ""
}
] | [
{
"docid": "ad542c91c2251023fd1a5f6148c37cc5",
"score": "0.6587817",
"text": "def indent(self, value):\n self._indent += value",
"title": ""
},
{
"docid": "8c3976f43ef81edb18565c4fc254a835",
"score": "0.6459699",
"text": "def level(self, value):\n self._level = value",
... |
e7452dc48b784be27fd3650a5f4fb04f | Creates the Data Frame objects and returns back in array | [
{
"docid": "c68653d6a73539c71116e0adefe4a58b",
"score": "0.0",
"text": "def __initiate_vars(self,file_path, delimiter):\n df_pandas = pd.read_csv(file_path,delimiter=delimiter)\n rzt_data = RZTData(cntx.experiment)\n df_rzt = rzt_data.read({\n 'path':file_path,\n ... | [
{
"docid": "f3cac4bb3a80488c58c07f43841fc51f",
"score": "0.7516557",
"text": "def create_dataframe(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "f3c404fb5f07c549abab0f693980de90",
"score": "0.71988475",
"text": "def _get_data(self):\n # TODO implement ... |
a3a1c554473d8173e7ec8040362da842 | Gets details about a specific instance | [
{
"docid": "da8eb30b9db6664135e2b15ec7c0e2e5",
"score": "0.6746519",
"text": "def get_instance(self, instance_name):\n try:\n return self.driver.vms.get(name=instance_name)\n except Exception as e:\n return None",
"title": ""
}
] | [
{
"docid": "69447ec0eda44af05f3fe5ebfa6b9d16",
"score": "0.776595",
"text": "def show(self, instance):\n \n return self._list(\"/mgmt/instances/%s\" % base.getid(instance),\n 'instance')",
"title": ""
},
{
"docid": "f4d8eef76f2bdfe57093deb69f579519",
"score": "0.... |
7070bd69164b7992eb7f720077323f67 | Defines the hessian matrix for the cost function | [
{
"docid": "b536ae7f28eb22e92c4e3d9a75c5bf3c",
"score": "0.58042645",
"text": "def f_hess(x, *args):\n D, N, m_deg, nk = args[0], args[1], args[2], args[3]\n hess = [[0 for i in range(N)] for j in range(N)]\n n = sum(nk)\n for i in range(N):\n for j in range(N):\n hij_1 = 0... | [
{
"docid": "70b133178455eb1f704a65c04f1163e3",
"score": "0.7606198",
"text": "def hessian(self, x):\r\n return numpy.array([[10, 8], [8, 10]], dtype = numpy.float)",
"title": ""
},
{
"docid": "45c382621e4d63d7ac2a62c425605811",
"score": "0.74376804",
"text": "def hessian(X,Etha):\... |
200254087000ab1eccf0b7aeca357dcc | Generate network layers based on configs | [
{
"docid": "6c90944aa679d129f8a5e0ecfcb59f05",
"score": "0.0",
"text": "def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):\n downsample = None\n expanded_in_channels = in_channels * self.block.expansion\n expanded_out_channels = out_channels * self.block.expansi... | [
{
"docid": "c03843b0a4bb02acb8811d8b04215eaa",
"score": "0.738548",
"text": "def _make_layers(self, configs: list):\r\n layers = []\r\n for config in configs:\r\n layer = self._make_layer(config['class'], config['args'])\r\n layers.append(layer)\r\n\r\n self.ne... |
c9a569b56de5dbd8f8539da9ecd33b6d | Save data in dic to a hd5 file. | [
{
"docid": "bff8019ca851b8d1a94fed35da0c33ba",
"score": "0.75301886",
"text": "def _save_h5(fp, dic):\n import warnings\n warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning)\n\n jutil.create_dir(fp)\n h5 = pd.HDFStore(fp, complevel=9, complib='blosc'... | [
{
"docid": "e613c24abca0917289bc6962b1238918",
"score": "0.71980035",
"text": "def save_hdf5(file_path, dictionary, compression=False):\n f = h5py.File(file_path, 'a')\n for key in dictionary:\n if key in f:\n del f[key]\n if compression:\n f.create_dataset(key,... |
0bc4addec4b5366a097d76a134a5529d | Insert a row with values into table 'tname'. When the row exists it will be replaced with the new values. vallist must be in the same sequence as the table cols | [
{
"docid": "6aa4a825926fef1adda63853720b384d",
"score": "0.76797056",
"text": "def insert_or_replace(self, tname, vallist):\n s = u'REPLACE INTO {0:s} values'.format(tname)\n qs = ['?'] * len(vallist)\n qs = \",\".join(qs)\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\n ... | [
{
"docid": "bdcc00bfd47dbcffa0f28bd39ea7408b",
"score": "0.7562238",
"text": "def add_row(self, tname, vallist, commit=True):\n s = u'INSERT INTO {0:s} values'.format(tname)\n qs = ['?'] * len(vallist)\n qs = \",\".join(qs)\n\n try:\n self.c.execute('''%s (%s)''' %... |
594bfddb6d92d27142ddf9e2757fb099 | Hyperbolic Tangent Nonlinear Activation Function | [
{
"docid": "8a8d72a1dcac6398226531aa510de7b4",
"score": "0.709707",
"text": "def tangent_hyperbolic(z):\n return np.tanh(z)",
"title": ""
}
] | [
{
"docid": "0768039b3e11f67b65f2e77de62fb04c",
"score": "0.8281349",
"text": "def HiperbolicTangensActivationFunction( x ):\n return tanh( x )",
"title": ""
},
{
"docid": "3f12af731c1234fe21563b14525f2d64",
"score": "0.8131882",
"text": "def HiperbolicTangensDerivActivationFunctio... |
677e5819b96ac20217667fc0c75fed70 | Print a menu recursively. Raises HandinError if a menu type is unhandled. Raises KeyError if a necessary menu value did not exist. This includes 'type' for all menus, 'choices' for type=menu, and 'id' for type=selection. | [
{
"docid": "08922252a5f03f19c356b91b826bce7e",
"score": "0.766687",
"text": "def _print_menu_choices(menu, key, choices, level=0):\n indent = \" \" * level\n if menu['type'] == 'selection':\n print \"%s [%s] %s\" % (\n indent,\n choices[menu['id']],\n menu.get('display_name', key)... | [
{
"docid": "f529799e4495690bc0e73382ebad7e33",
"score": "0.6648107",
"text": "def print_menu():\n # your code goes here!\n print(\"Our menu:\")\n print(\"*******************\")\n for k, v in menu.items():\n print(k, \": \", v)\n print(\"*******************\\n\")",
"title": ""
... |
66d4561fba67f4ed4ecf2a6249b8e5e1 | Initializes an ITS Spot module | [
{
"docid": "b1bae47fe8f1a01d318d74babf874e1f",
"score": "0.0",
"text": "def __init__(self, RandomGen_seed, CF, SPOT_tree, msg_limit = 800000):\n self.random_generator = Random_generator(RandomGen_seed)\n self.CF = CF\n\n self.Travelmsgs = []\n self.BehaviorMsgs = []\n ... | [
{
"docid": "8cb44e717080eb9ee738ac54804b6f6a",
"score": "0.608191",
"text": "def __init__(self):\n _libBornAgainCore.SimulationFactory_swiginit(self, _libBornAgainCore.new_SimulationFactory())",
"title": ""
},
{
"docid": "248cebe817b7b1ed68dac8656112abaa",
"score": "0.6079417",
... |
9bb5cf61509db898f75f7d1b7d42d038 | Retrieves the body of the email from S3. This requires that you set up a previous action in your SES rules to store the message in S3. | [
{
"docid": "a06ad3cd3402b630d16da19a4fc388f9",
"score": "0.81179136",
"text": "def body(self):\n\n if self._body is None:\n log.debug(\"Downloading message body from s3://{}/{}\".format(\n self.s3_bucket, self.message_id))\n s3 = boto3.client('s3')\n ... | [
{
"docid": "a7e98b7bf32ee40c89ed274de3a937a9",
"score": "0.6889813",
"text": "def read(self):\n return self._s3_key.get_contents_as_string()",
"title": ""
},
{
"docid": "dfccaf4e4df65eefb2badef46de88a72",
"score": "0.6478215",
"text": "def file_contents(bucket, key):\n s3 =... |
8953dd3455a97a0169841fcd630a9cb3 | Getter for the job id. | [
{
"docid": "f8ffee80f80151da03d30823017e8ff6",
"score": "0.8907434",
"text": "def get_id(self) -> str:\n return self._job.reference.job_id",
"title": ""
}
] | [
{
"docid": "7e139d093137b627e99f2305b14524a3",
"score": "0.90870863",
"text": "def get_id( self ):\n return self.job_id",
"title": ""
},
{
"docid": "76f0ccaa1b770534f3f4898c3fc7ab37",
"score": "0.90781033",
"text": "def job_id(self) -> str:\n return pulumi.get(self, \"j... |
168c50cc3ee7c2f854f947842ef62d03 | Transcribes the data received via bluetooth to numpy arrays. | [
{
"docid": "a6df46c8d9d50b9ee7a572a18f274bbd",
"score": "0.55152607",
"text": "def bluetoothDataToNPArrays(dataFromBT, numDataPoints, numFeatures):\n\n designMatrix = np.zeros((numDataPoints * 2, numFeatures))\n targetMatrix = np.zeros((numDataPoints * 2, 1))\n\n for i in range(numDataPoints):\... | [
{
"docid": "5e8c673c8e23761577125b46a7aa4813",
"score": "0.5778601",
"text": "def __raw_data_to_array(sequence, sampling_rate):\n return sequence['wave']",
"title": ""
},
{
"docid": "6cdd4c901322160893c3d8e9de33a45d",
"score": "0.576799",
"text": "def to_array(self):",
"ti... |
fb22f1b9401be2b7df1b042d77eca87f | Handles receiving a line | [
{
"docid": "368c708b261ca98ca1f3b807dfd56602",
"score": "0.5919577",
"text": "def recv_line(data):\n global board_elements\n global canvas\n if len(data) == 4:\n top_left = (int(data[1].split(\" \")[0]), int(data[1].split(\" \")[1]))\n bottom_right = (int(data[2].split(\" \")[0]),... | [
{
"docid": "86af36f9f2c5f587adb43d6c498dd5a3",
"score": "0.84185743",
"text": "def line_received(self, line):\n return",
"title": ""
},
{
"docid": "438969ca43b81399f41b78da3ee8d66f",
"score": "0.82127386",
"text": "def lineReceived(self, line):\n\n self.on_line(line)",
... |
147bd0f59ffe20aebac804e6ad054b7d | Get for all complete groups an array of shrinkages | [
{
"docid": "2375913ef054d497d82a1003805966db",
"score": "0.5447506",
"text": "def __get_shrinkage_factor(self, X_group):\n group_colnames = X_group.columns.to_list()\n counts = X_group.groupby(group_colnames).size()\n\n # Groups that are split on all\n most_granular_groups = ... | [
{
"docid": "71185b4db892fbe483f9ea0dd369d699",
"score": "0.6408654",
"text": "def optimal_solution(self, groupSizes: List[int]) -> List[List[int]]:\n buckets = defaultdict(list)\n for i, size in enumerate(groupSizes):\n buckets[size].append(i)\n result = []\n for size, bucket in buckets... |
c223d889be4179ae4643b672a0439505 | Return a DataFrame with the new engineered features. | [
{
"docid": "dfa0852d1e350742153d1b8458dc02d6",
"score": "0.0",
"text": "def extract_features(df, mode, update_map):\n # Day is a circular variable, hence we represent it using 2 new features :\n new_df = df.copy()\n new_df['x_day'] = df['day'].apply(lambda x: math.cos(2*math.pi*x/30))\n new_... | [
{
"docid": "fe2b04d95a0dda89cf405385e4663a4d",
"score": "0.7165676",
"text": "def feature_engineering(train_df, test_df):\n \n #Not used as is part of the data transformation function\n\n return train_df.copy(), test_df.copy()",
"title": ""
},
{
"docid": "c25f7058fbe27ea494d58cbe678... |
2f4f315d0039d47a4074d3dc266cb091 | Create the extension point. | [
{
"docid": "f82827c97b51b736dbfc3b699976159b",
"score": "0.0",
"text": "def __init__(self, interface):\n property.__init__(self, self.extensions)\n self.interface = interface\n self.__doc__ = 'List of components that implement `%s`' % \\\n self.interface.__name... | [
{
"docid": "c9bf0407a546c47d63f27fc8ba660f5b",
"score": "0.65477675",
"text": "def __init__(self, **config):\n\t\t\n\t\tsuper(Extension, self).__init__()",
"title": ""
},
{
"docid": "96fb74a85a7c9e79d2047613ec8afa98",
"score": "0.65290415",
"text": "def test_make_extension(self):\n ... |
cba97e2777e69a0e8feafccd1b304c2b | This method exports conf files to other members. | [
{
"docid": "ca49f261cf2744bbfc49d6263aa2dec2",
"score": "0.7163174",
"text": "def _export_conf_to_members(self, members):\n try:\n for member in members:\n COPY_CMD = (system.SEND_CONF_TO_SERVER % (system.VERTICA_CONF,\n ... | [
{
"docid": "53e11f1817e4cc0e5a994a7e9f2d97f4",
"score": "0.64293945",
"text": "def export_config(self, f_name=None):\n self.update_history()\n if f_name is not None:\n self.config.config_export(f_name)\n else:\n self.config.config_export(self.config.confname)",... |
ee1cf8ae640d9d6335f1a2b91185bac0 | calculate min error for all learners | [
{
"docid": "47cfa6338e4dcfde0b322f4f814868f3",
"score": "0.7466648",
"text": "def getMinError(self):\n return np.array([learner.getMinError()()\n for learner in self._learners])",
"title": ""
}
] | [
{
"docid": "317a5e9d0284abde4cc9dd6f16d84f2a",
"score": "0.64385617",
"text": "def batch_training(self, num_minibatches):\n\n epoch_tr_err = 0 # initialize the training error for each epoch to zero\n\n if self.with_metrics:\n\n for l in range(num_minibatches): # minibatch loop... |
b12f72f1722d41320902c9e5d681fc56 | performs costeffectiveness and costbenefit analyses | [
{
"docid": "7289a2b99a2ee2c83553060f08c9e11c",
"score": "0.0",
"text": "def report_CEA_CBA(multi_cohort_outcomes_SOC, multi_cohort_outcomes_NSB):\n\n # define two strategies\n SOC_diagnostic_strategy = Econ.Strategy(\n name='SOC Diagnostic',\n cost_obs=multi_cohort_outcomes_SOC.meanC... | [
{
"docid": "15992c3ac7d433e6ebac76decbf55a3c",
"score": "0.7366483",
"text": "def compute_costs_benefits(self):\n\n # unit costs\n if self.verbose:\n print(\"Preparing unit values...\")\n self._create_unit_cost_matrix()\n\n # costs\n if self.verbose:\n ... |
462594201d5d2bf56be495970fb8cac8 | This just calls drop_series on the characters specified. | [
{
"docid": "b8ba7aeb9c2e2bbbeefc466fdee26036",
"score": "0.7531891",
"text": "def drop_series_list(item: pd.Series, chars_to_drop=None) -> pd.Series:\n if chars_to_drop is None:\n chars_to_drop = [\"-\", None, \"/\", \"\"]\n for char_to_drop in chars_to_drop:\n item = drop_series(ite... | [
{
"docid": "f83bca0f7ea21874cf2e6713ce456691",
"score": "0.7848441",
"text": "def drop_series(item: pd.Series, char_to_drop) -> pd.Series:\n return item[item != char_to_drop]",
"title": ""
},
{
"docid": "2d396762a72c513b02da28942e414a14",
"score": "0.6176747",
"text": "def remove_... |
1a9b779989c37015734fc8703de0b700 | Return to the user their total earnings so far. | [
{
"docid": "6b4c43b9b62b044df327c9b90569703c",
"score": "0.7550638",
"text": "def calculate_total_earnings():\n all_earnings = worksheet_update.col_values(7)\n del all_earnings[0]\n # Used method 3 to turn a list of strings into a list of integers\n # https://www.geeksforgeeks.org/python-con... | [
{
"docid": "c65615b462a8b663f0d3f469f92bd2ec",
"score": "0.78068686",
"text": "def total_earnings(self):\n return self.total_balance - self.net_invested",
"title": ""
},
{
"docid": "a7832efbb787ce74a2c76cc6a4c42a3e",
"score": "0.66952634",
"text": "def update_total(self):\n\n ... |
1e8ad7e43807b35e02ff996c8e179da4 | get user object from json | [
{
"docid": "8860bb34b741f37fe997ccdaf086faa1",
"score": "0.0",
"text": "def from_json(cls, json_data):\r\n user = User(json_data['username'])\r\n tasks = []\r\n\r\n for task_data in json_data['tasks']:\r\n tasks.append(Task.from_json(task_data))\r\n user.tasks = ta... | [
{
"docid": "f302e7e62f562844057606b1fa43a3bf",
"score": "0.7866385",
"text": "def get_tweet_user(json):\n return get_user(json)",
"title": ""
},
{
"docid": "5c448dd5475ebe6a26e1a8f723653225",
"score": "0.7602358",
"text": "def getuser(id):\n user = singleuser(id)\n user_json... |