= respointers[curres]):\r\n curres += 1\r\n\r\n # Add bonds\r\n for i in range(0, nbonh+nbona):\r\n phys.myPSF.addBond(i+1, final_bonds[i][0]/3+1, final_bonds[i][1]/3+1)\r\n phys.myPAR.addBond(i+1, atomnames[final_bonds[i][0]/3], atomnames[final_bonds[i][1]/3], forceconstants[0][final_bonds[i][2]/3], equilvals[0][final_bonds[i][2]/3])\r\n \r\n # Add angles\r\n for i in range(0, ntheth+ntheta):\r\n phys.myPSF.addAngle(i+1, final_angles[i][0]/3+1, final_angles[i][1]/3+1, final_angles[i][2]/3+1)\r\n phys.myPAR.addAngle(i+1, atomnames[final_angles[i][0]/3], atomnames[final_angles[i][1]/3], atomnames[final_angles[i][2]/3], forceconstants[1][final_angles[i][3]/3], equilvals[1][final_angles[i][3]/3])\r\n \r\n # Add dihedrals\r\n for i in range(0, len(final_dihedrals)):\r\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\r\n mult = mult_di[di_id][0]\r\n checked = mult_di[di_id][1]\r\n print di_id, \" \", mult\r\n if (not checked):\r\n if (mult == 1):\r\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\r\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], forceconstants[2][final_dihedrals[i][4]-1], int(equilvals[2][0][final_dihedrals[i][4]-1]), equilvals[2][1][final_dihedrals[i][4]-1])\r\n else:\r\n mult_di[di_id][1] = True\r\n # Add dihedral with the appropriate multiplicity\r\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\r\n fcvec = PARReader.VectorOfDouble()\r\n periodvec = PARReader.VectorOfInt()\r\n phasevec = PARReader.VectorOfDouble() \r\n for j in range(0, len(mult_di[di_id][2])):\r\n fcvec.push_back(mult_di[di_id][2][j])\r\n periodvec.push_back(mult_di[di_id][3][j])\r\n phasevec.push_back(mult_di[di_id][4][j])\r\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\r\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], mult, fcvec, periodvec, phasevec)\r\n \r\n\r\n\r\n\r\n for i in range(0, len(final_impropers)):\r\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\r\n mult = mult_im[im_id][0]\r\n checked = mult_im[im_id][1]\r\n print im_id, \" \", mult\r\n if (not checked):\r\n if (mult == 1):\r\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\r\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], forceconstants[2][final_impropers[i][4]-1], int(equilvals[2][0][final_impropers[i][4]-1]), equilvals[2][1][final_impropers[i][4]-1])\r\n else:\r\n mult_im[im_id][1] = True\r\n # Add dihedral with the appropriate multiplicity\r\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\r\n fcvec = PARReader.VectorOfDouble()\r\n periodvec = PARReader.VectorOfInt()\r\n phasevec = PARReader.VectorOfDouble() \r\n for j in range(0, len(mult_im[im_id][2])):\r\n fcvec.push_back(mult_im[im_id][2][j])\r\n periodvec.push_back(mult_im[im_id][3][j])\r\n phasevec.push_back(mult_im[im_id][4][j])\r\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\r\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], mult, fcvec, periodvec, phasevec)\r\n\r\n \r\n # Need to add garbage nonbonded stuff for now\r\n for i in range(0, natoms):\r\n phys.myPAR.addNonbonded(i, atomnames[i], 1, 1, 1, 1, 1, 1)\r\n\r\n # Add VDW parameters\r\n # AMBER has the Aij and Bij already in the parameter file\r\n # This actually makes life easier.\r\n # CHARMM does not, they simply have the original sigma and epsilon.\r\n # To compensate for this, for now we will leave the nonbondeds empty in phys.myPAR\r\n # We will then access the LennardJones parameter table in Topology directly\r\n k = 0\r\n phys.myTop.resizeLennardJonesParameters(ntypes)\r\n for i in range(0, ntypes):\r\n for j in range(i, ntypes):\r\n params = GenericTopology.LennardJonesParameters(lj_acoef[k], lj_bcoef[k])\r\n k += 1\r\n phys.myTop.setLennardJonesParameters(i, j, params)\r\n \r\n phys.myPAR.readFlag = 1\r\n phys.build()","def __init__(self, fileName):\n in_ = JInputStream.getInputStream(fileName)\n b = int()\n len = int()\n cs = int()\n addr = int()\n buf = [None]*255\n eof = False\n line = 0\n i = 0\n while len(ihxData):\n self.ihxData[i] = -1\n i += 1\n try:\n while not eof:\n while True:\n b = in_.read()\n if b < 0:\n raise IhxParseException(\"Inexpected end of file\")\n if not ((b != int(':'))):\n break\n line += 1\n len = self.readHexByte(in_)\n # length field \n cs = len\n b = self.readHexByte(in_)\n # address field \n cs += b\n addr = b << 8\n b = self.readHexByte(in_)\n cs += b\n addr |= b\n b = self.readHexByte(in_)\n # record type field\n cs += b\n while i < len:\n # data\n buf[i] = int(self.readHexByte(in_))\n cs += buf[i]\n i += 1\n cs += self.readHexByte(in_)\n # checksum\n if (cs & 0xff) != 0:\n raise IhxParseException(\"Checksum error\")\n if b == 0:\n # data record\n while i < len:\n if self.ihxData[addr + i] >= 0:\n System.err.println(\"Warning: Memory at position \" + Integer.toHexString(i) + \" overwritten\")\n self.ihxData[addr + i] = int((buf[i] & 255))\n i += 1\n elif b == 1:\n # eof record\n eof = True\n else:\n raise IhxParseException(\"Invalid record type: \" + b)\n except IhxParseException as e:\n raise IhxFileDamagedException(fileName, line, e.getLocalizedMessage())\n try:\n in_.close()\n except Exception as e:\n System.err.println(\"Warning: Error closing file \" + fileName + \": \" + e.getLocalizedMessage())","def load_data(filename):\n\n evidance = list()\n labels = list()\n row = list()\n\n with open(filename) as file:\n for j, item in enumerate(file):\n if(j == 0):\n continue\n row = []\n for i, el in enumerate(item.split(',')):\n # print(el)\n if(i in {0,2,4,11,12,13,14}):\n row.append(int(el))\n elif(i in {1,3,5,6,7,8,9}):\n row.append(float(el))\n elif(i==10):\n if(el == \"Jan\"):\n row.append(int(0))\n elif(el == \"Feb\"): \n row.append(int(1))\n elif(el == \"Mar\"): \n row.append(int(2))\n elif(el == \"Apr\"): \n row.append(int(3))\n elif(el == \"May\"): \n row.append(int(4))\n elif(el == \"June\"): \n row.append(int(5))\n elif(el == \"Jul\"): \n row.append(int(6))\n elif(el == \"Aug\"): \n row.append(int(7))\n elif(el == \"Sep\"): \n row.append(int(8))\n elif(el == \"Oct\"): \n row.append(int(9))\n elif(el == \"Nov\"): \n row.append(int(10))\n elif(el == \"Dec\"): \n row.append(int(11))\n else:\n print(\"wrong month Exit\")\n quit()\n elif(i==15):\n if(el == 'Returning_Visitor'):\n row.append(int(1))\n else:\n row.append(int(0))\n elif(i==16):\n if(el == 'TRUE'):\n row.append(int(1))\n else:\n row.append(int(0)) \n elif(i==17):\n if(el == 'TRUE\\n' or el == 'TRUE'):\n row.append(int(1))\n else:\n row.append(int(0))\n else:\n print(\"Incorrect value\")\n\n evidance.append(row)\n # print(item.split(',')[-1])\n if(item.split(',')[-1] == 'FALSE\\n'):\n labels.append(0)\n else:\n labels.append(1)\n\n # print(labels)\n return (evidance, labels)","def open(self):","def CSV_Load_File( self, infilename ):\n print( 'Loading \"{}\"'.format(infilename) )\n IN = open( infilename, 'r' )\n standname = None\n laststand = None\n for L in IN:\n if( L[0:9] == 'Site/Plot' ): continue\n col = L.split( ',' )\n standname = col[0]\n year = int(col[1])\n #if( re.search( '-', standname ) != None ):\n # loc = re.search( '-', standname )\n # year = int(standname[loc.start()+1:])\n # standname = standname[0:loc.start()]\n #print standname, year\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\n if( OPT['d'] ):\n if( dbh > 10.0 ): dbh *= 1.25\n if( dbh > 15.0 ): dbh *= 1.50\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\n ntree = len( self.Data.Stand[standname].Tree ) + 1\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\n laststand = standname\n IN.close()","def make_tag_data_raw_fast(mdp,filename):\n #\n fin = open(filename,'r')\n iter = 0\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"comb_path\":\n update_params(mdp,lsp)\n if not mdp.flag_out_open: ## -- try to open output file\n try:\n if mdp.flag_overwrite == \"True\": ## check string value!\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ## -- try open output file\n for file in glob.glob(mdp.input_path):\n # get sign which corrects for boundary condition\n tvals = file.split('/')[-1].split('_')[3].split('t')\n try:\n ## flip sign if requested\n bcsign = ((int(tvals[1])+int(tvals[2])) != (int(tvals[1])+int(tvals[2])) % mdp.corr_len)\n except IndexError:\n ## 2-point function\n bcsign = False\n try:\n # open correlator file\n mdp.corr_file = open(file,'r')\n except IOError:\n print \"Could not open file \",file\n continue\n ## -- get tag\n ## baryons:\n #mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_r'+file.split('/')[-1].split('_')[4][-1]\n ## with time source tag\n #mdp.tag = file.split('/')[-1].split('_')[3][:3]\\\n # +'_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\n # +file.split('/')[-1].split('_')[4][3:]\n ## no time source tag\n mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\n +file.split('/')[-1].split('_')[4][3:]\n #print file,',',mdp.tag\n iter+=1\n ##endif ! flag_out_open\n\n #save_data_fast(mdp)\n save_data_fast_bc(mdp,bcsign)\n mdp.corr_file.close()\n if iter%400 == 0:\n print \"file\",iter\n max_iter = None\n if not(max_iter is None) and iter==max_iter:\n print \"reached max file iterations, ending loop...\"\n break\n ## end comb_path\n pass\n\n elif lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data_fast(mdp)\n mdp.corr_file.close()\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def _load_data(self, save_temp=False):\n # directly read processed data and encode\n print ('Start tokenizing data...')\n self.data = json.loads(\n open(self.cfg.data_path+self.cfg.data_file, 'r', encoding='utf-8').read().lower())\n self.train, self.dev, self.test = [], [], []\n print ('Start encoding data...')\n p = progressbar.ProgressBar(len(self.data))\n p.start()\n p_idx = 0\n for fn, dial in self.data.items():\n p.update(p_idx)\n p_idx += 1\n if '.json' in fn:\n fn = fn.replace('.json', '')\n if 'all' in self.cfg.exp_domains or self.exp_files.get(fn):\n if self.dev_files.get(fn):\n self.dev.append(self._get_encoded_data(fn, dial))\n elif self.test_files.get(fn):\n self.test.append(self._get_encoded_data(fn, dial))\n else:\n if self.data_mode == 'train':\n self.train.append(self._get_encoded_data(fn, dial))\n elif self.data_mode == 'test':\n pass\n else:\n raise Exception('Wrong Reader Data Mode!!!')\n p.finish()","def load_datapair(self, ds):\n raise NotImplementedError(\"Define this in your derived checker class\")","def load_data(self):\n df= self.read_file()\n for row,col in df.iterrows():\n Employeeid = int(col['Empolyeeid'])\n Employee_Name = col['Employee_Name']\n Age = col['Age']\n Salary = col['Salary']\n self.table.put_item(\n Item={\n \"Employeeid\":Employeeid,\n \"Employee_Name\": Employee_Name,\n \"Age\": Age,\n \"Salary\": Salary\n }\n )\n return True","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def build(self, file_number, data):\n pass","def reading_data(fname,goal):\n \n #Reading of the EEG data\n data = pd.read_csv(fname)\n events_fname = fname.replace('_data','_events')\n labels= pd.read_csv(events_fname)\n\n if goal==\"training\":\n data=data.drop(['id' ], axis=1)#remove id\n labels=labels.drop(['id' ], axis=1)#remove id\n elif goal==\"testing\":\n labels=labels.drop(['id' ], axis=1)\n else:\n raise SystemExit(\"The goal variable is unknown for the function\")\n\n return data, labels","def load_data():\r\n #读取User数据\r\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\r\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\r\n users = users.filter(regex='UserID|Gender|Age|JobID')\r\n users_orig = users.values\r\n #改变User数据中性别和年龄\r\n gender_map = {'F':0, 'M':1}\r\n users['Gender'] = users['Gender'].map(gender_map)\r\n\r\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\r\n users['Age'] = users['Age'].map(age_map)\r\n\r\n #读取Movie数据集\r\n movies_title = ['MovieID', 'Title', 'Genres']\r\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\r\n movies_orig = movies.values\r\n #将Title中的年份去掉\r\n pattern = re.compile(r'^(.*)\\((\\d+)\\)$')\r\n\r\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #电影类型转数字字典\r\n genres_set = set()\r\n for val in movies['Genres'].str.split('|'):\r\n genres_set.update(val)\r\n\r\n genres_set.add('<PAD>')\r\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\r\n\r\n #将电影类型转成等长数字列表，长度是18\r\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\r\n\r\n for key in genres_map:\r\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\r\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\r\n \r\n movies['Genres'] = movies['Genres'].map(genres_map)\r\n\r\n #电影Title转数字字典\r\n title_set = set()\r\n for val in movies['Title'].str.split():\r\n title_set.update(val)\r\n \r\n title_set.add('<PAD>')\r\n title2int = {val:ii for ii, val in enumerate(title_set)}\r\n\r\n #将电影Title转成等长数字列表，长度是15\r\n title_count = 15\r\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\r\n \r\n for key in title_map:\r\n for cnt in range(title_count - len(title_map[key])):\r\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\r\n \r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #读取评分数据集\r\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\r\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\r\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\r\n\r\n #合并三个表\r\n data = pd.merge(pd.merge(ratings, users), movies)\r\n \r\n #将数据分成X和y两张表\r\n target_fields = ['ratings']\r\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\r\n \r\n features = features_pd.values\r\n targets_values = targets_pd.values\r\n \r\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig","def extract(self, data):","def prepare_dataset(fpath):\n raise NotImplementedError","def apply(self, opened_file):","def read_data():\n ADV_MAT = np.load('ADV.npy');\n ADJ_MAT = np.load('ADJ.npy');\n PR_MAT = np.load('PR.npy'); \n NN_MAT = np.load('NN.npy');\n for i in range(ADV_MAT.shape[0]):RUNNING_DATA['ADV___'+str(i)] = ADV_MAT[i];\n for i in range(ADJ_MAT.shape[0]):RUNNING_DATA['ADJ___'+str(i)] = ADJ_MAT[i];\n for i in range(PR_MAT.shape[0]):RUNNING_DATA['PR___'+str(i)] = PR_MAT[i];\n for i in range(NN_MAT.shape[0]):RUNNING_DATA['NN___'+str(i)] = NN_MAT[i];","def process_file(self, data, filename):\n\n for relation in data:\n if \"start_date\" in relation[1]:\n start = convert_to_date(relation[1][\"start_date\"])\n end = convert_to_date(relation[1][\"end_date\"])\n\n if start <= START_DATE and end >= START_DATE:\n first = relation[1][\"first\"]\n second = relation[1][\"second\"]\n if first not in self.relations:\n self.add_tag(first)\n if second not in self.relations:\n self.add_tag(second)\n\n if relation[0] == \"vassal\": \n self.relations[first][\"vassal\"].append(second)\n self.relations[second][\"overlord\"].append(first)\n elif relation[0] == \"alliance\":\n self.relations[first][\"alliance\"].append(second)\n self.relations[second][\"alliance\"].append(first)\n elif relation[0] == \"dependency\":\n self.relations[first][\"tributary\"].append(second)\n self.relations[second][\"hegemon\"].append(first)\n elif relation[0] == \"guarantee\":\n self.relations[first][\"guaranteeing\"].append(second)\n self.relations[second][\"guarantor\"].append(first)\n elif relation[0] == \"union\":\n self.relations[first][\"junior\"].append(second)\n self.relations[second][\"senior\"].append(first)\n elif relation[0] == \"royal_marriage\":\n self.relations[first][\"marriage\"].append(second)\n self.relations[second][\"marriage\"].append(first)","def read_data(self):\n data = np.genfromtxt(self.__file) # Planck SED\n self.__nu = 10.0**data[:,0]\n self.__nuF = 10.0**data[:,2]\n self.__err = 10.0**data[:,3]\n #self.__W = 10.0**data[:,4]\n self.__yerr = [ self.__nuF - self.__nuF / self.__err, \\\n self.__nuF * self.__err - self.__nuF ]\n self.__maxY = max( self.__nuF )\n self.__minY = min( self.__nuF )","def load_data(self):\n with open(self.config.drv_log, 'r') as f:\n add_data = [] # addtional data require angle smoothing\n ori_data = [] # given data\n ddir = os.path.dirname(self.config.drv_log)\n lines = list(csv.reader(f))\n np.random.shuffle(lines)\n for line in lines:\n if line[0] == 'center':\n continue\n ste_ang = float(line[3])\n if ste_ang == 0. and random.random() < 0.8:\n continue\n if '12_01' in line[0]:\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n else:\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n \n def angle_smoothing(data):\n \"\"\"helper function for smoothing steering angles.\n reference: \n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\n \"\"\"\n angles = np.asarray([itm[1] for itm in data])\n fwd = pd.ewma(angles, span=20)\n bwd = pd.ewma(angles[::-1], span=20)\n smooth = np.vstack((fwd, bwd[::-1]))\n smooth = np.mean(smooth, axis=0)\n angles = np.ndarray.tolist(smooth)\n smooth_data = []\n for i in range(len(data)):\n smooth_data.append([data[i][0], angles[i]])\n return smooth_data\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\n all_data = ori_data + add_data\n n1 = int(len(all_data) * 0.7)\n n2 = int(len(all_data) * 0.85)\n self.train = self.data_generator(all_data[:n1])\n self.valid = self.data_generator(all_data[n1:n2])\n self.test = self.data_generator(all_data[n2:])\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size","def import_data_helper(self): \n if len(self.components) == 1:\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\n else: \n global_id = []\n for c in self.components:\n global_id.append(hapi.ISO[c][0])\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)","def initialize_data(self , station = '', datasets = {} ):\n \n self.datasets = datasets\n self.datasets_keys = datasets.keys()\n self.station = station\n \n data = {} # container for the data of each dataset\n source_configuration = {} # container for the source_configuration of each dataset\n \n\n \n \"\"\" Looping over the datasets \"\"\"\n logging.info('*** Reading and Initializing the data from the netCDF files ')\n \n \n for k,v in datasets.items() :\n logging.info(' Initialising the dataset: *** %s ' , k )\n data[k] = {} \n data['cdm_tables'] = {} \n \n ### alternative with xarray \n #ds = xr.load_dataset(v) \n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n ### alternative with netCDF4\n #ds = nc.Dataset(v) \n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \n \n ###for h5py but cant extract date time units !!!\n ds = h5py.File(v , driver=\"core\" ) \n data[k]['df'] = ds # storing the entire file \n try: \n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\n except:\n data[k]['source_file'] = str(v) # temp fix \n \n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\n #data[k]['recordindex'] = ds['recordindex'].value \n #ds.close() \n logging.debug('Reading the file with h5py ')\n \n \n # add here appending datasets for the case of ncar_w and ncar_t \n \n \n self.data = data\n self.make_dataframe()\n ds.close()\n \n \"\"\" Reading the header_table, station_configuration, source_configuration \"\"\"\n for k,v in datasets.items() : \n \n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\n #data[k]['recordindex'] = d['recordindex'].values \n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \n data[k]['station_configuration'] = d.to_dataframe() \n #data[k]['station_configuration'] = d ### USELESS ? \n logging.debug('Done with %s station_configuration' , str(k) )\n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \n logging.debug('Loading the header_table') \n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['header_table'] = {}\n for var in d.variables:\n self.attributes['header_table'][var] = {}\n self.attributes['header_table'][var]['description'] = d[var].description\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \n data[k]['header_table'] = d.to_dataframe() \n logging.debug('Done with %s ' , k )\n \n logging.info(\"*** Loading the observations_table (might take time) %s\" , k ) \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['observations_table'] = {}\n for var in d.variables:\n self.attributes['observations_table'][var] = {}\n self.attributes['observations_table'][var]['description'] = d[var].description\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\n \n \n logging.info(\"*** Loading the source configuration %s\" , k ) \n try: \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\n d = d.isel(hdrlen=[0])\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \n logging.debug('Done with %s source_configuration' , k )\n except: \n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \n \n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \n data[k]['era5fb'] = d.to_dataframe() \n logging.debug('Done with %s era5 feedback ', k )\n \n \"\"\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \"\"\" \n if list(datasets.keys()).index(k) == 0 :\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\n data['cdm_tables'][t] = d \n \n d.close() \n ds.close()\n\n \"\"\" Reading the name of the original source file \"\"\"\n source_configuration[k] = {} \n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\n\n \n \"\"\" Storing the station configurations \"\"\" \n self.source_configuration = source_configuration \n \n \"\"\" Making all date_times \"\"\" \n self.make_all_datetime()\n \n \n \"\"\" feedback columns \"\"\"\n if 'era5_1' in list (self.data.keys() ):\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \n else:\n self.fb_columns = ['empty']","def read_inversion_info(file_dic):\n #print_file_test = open('file_test.txt','w')\n\n if not ( check_inversion_files(file_dic) ):\n print 'error(read_inversion_info): problem with lenstool file names'\n return 0\n \n file_generate_arcs = file_dic['file_generate_arcs']\n info_input_lens = fc.extract_second_identifiers( file_generate_arcs, \\\n 'potential' )\n#-------------------------------------------------------------------------------\n\n file_source = file_dic['file_source']\n info_src = np.loadtxt(file_source, unpack=False)\n if len(info_src) == 8 and np.isscalar(info_src[0]):\n #FIXME - check if the second condition is all we need\n info_src = [info_src]\n#-------------------------------------------------------------------------------\n\n file_make_inversion = file_dic['file_make_inversion']\n info_fited_param = fc.extract_second_identifiers( file_make_inversion, \\\n 'limit' )\n info_forme = fc.extract_parameter(file_make_inversion, 'forme')[0][0]\n\n#-------------------------------------------------------------------------------\n\n file_best_fit = file_dic['file_best_fit']\n info_best_fit = fc.extract_second_identifiers( file_best_fit, \\\n 'potentiel' )\n\n info_xi2 = fc.extract_parameter(file_best_fit, '#Chi2pos:')\n\n#-------------------------------------------------------------------------------\n file_chires = file_dic['file_chires']\n\n info_chires = extract_parameter(file_chires, '0')\n rmss_mean = [0.0, 0.0]\n rmsi_mean = [0.0, 0.0]\n for i in info_chires:\n if i[0] != 'A':\n rmss_mean[0] = rmss_mean[0] + float(i[7])\n rmss_mean[1] = rmss_mean[1] + 1.0\n \n rmsi_mean[0] = rmsi_mean[0] + float(i[8])\n rmsi_mean[1] = rmsi_mean[1] + 1.0\n\n rmss_mean = rmss_mean[0]/rmss_mean[1]\n rmsi_mean = rmsi_mean[0]/rmsi_mean[1]\n#-------------------------------------------------------------------------------\n out_dict = { 'xi2' : float(info_xi2[0][0]), \\\n 'best_fit_lens' : info_best_fit, \\\n 'rmsi_mean' : rmsi_mean, \\\n 'rmss_mean' : rmss_mean, \\\n 'fited_parameters' : info_fited_param[0].keys(), \\\n 'input_lens' : info_input_lens[len(info_input_lens) - 1], \\\n 'forme' : info_forme \\\n }\n #for i in out_dict.keys():\n # print i, out_dict[i]\n return out_dict"],"string":"[\n \"def load_data(self):\",\n \"def _read_data(self):\",\n \"def load_data(self) -> None:\",\n \"def read(self):\",\n \"def _load(self):\\n\\n # number of non-data header details at top of data file\\n header = 1\\n\\n # open file\\n weatherData = []\\n with open(self.wfile) as myfile:\\n if (self.lines > 0):\\n weatherData = [next(myfile) for x in xrange(self.lines + header)]\\n else:\\n weatherData = myfile.readlines()\\n\\n # get data stream from first line\\n streamHeader = weatherData.pop(0).rstrip()\\n if (streamHeader == 'FULL'):\\n self.dataStream = 0\\n elif (streamHeader == 'ADVANCED'):\\n self.dataStream = 1\\n elif (streamHeader == 'BASIC'):\\n self.dataStream = 2\\n else:\\n print \\\"Error: unecognised data stream from file %s\\\" % (self.wfile)\\n return -1\\n\\n # read data\\n inputData = []\\n for line in weatherData:\\n entries = line.split()\\n inputData.append(entries)\\n\\n # copy all into np array\\n self.data = np.array(inputData)\\n\\n return 0\",\n \"def get_file_data(filename):\",\n \"def load_data():\\n\\t\\t# load the data\\n\\t\\tDATPATH = \\\"../data/\\\"\\n\\t\\t#fnino = DATPATH + \\\"nino3.csv\\\" # 1871-2000\\n\\t\\tfnino = DATPATH + \\\"tas_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_nino3_tseries.csv\\\" # 1871-2016\\n\\t\\t#fnino = DATPATH + \\\"nino34.long.data\\\"\\n\\t\\t#nc_data_nino3 = netCDF4.Dataset(fnino)\\n\\t\\t#nino3_load = nc_data_nino3.variables['tas'][:]\\n\\t\\t#dnino = nino3_load.flatten()\\n\\n\\t\\tdnino = np.genfromtxt(fnino, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fismr = DATPATH + \\\"ismr.csv\\\" # 1871-2000\\n\\t\\t#fismr = DATPATH + \\\"psl_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_1_india_goswami_2002_tseries.csv\\\" # 1871-2016\\n\\t\\tfismr = DATPATH + \\\"pr_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_goswami_india_tseries.csv\\\" # 1871-2016\\n\\t\\tdismr = np.genfromtxt(fismr, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fvolc = DATPATH + \\\"robock.txt\\\" # 1871-2000\\n\\t\\tfvolc = DATPATH + \\\"sigl.txt\\\" # 1871-2016\\n\\t\\tdvolc = np.genfromtxt(fvolc, delimiter=\\\",\\\", dtype=float).flatten()\\n\\n\\t\\tfvolc_source = DATPATH + \\\"volc_source_850_1850.csv\\\" # 1871-2016\\n\\t\\tdvolc_source = np.genfromtxt(fvolc_source, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t# simple check for data consistency\\n\\t\\tassert dnino.shape == dismr.shape, \\\"Data sets are unequal!\\\"\\n\\t\\tassert int(dismr.shape[0]/12) == dvolc.shape[0], \\\"Data sets are unequal\\\"\\n\\t\\treturn dnino, dismr, dvolc, dvolc_source\",\n \"def parse_data(fp):\\n pass\",\n \"def prepare_data(self):\",\n \"def dataLoad():\\n try:\\n try: #Python3\\n f = open(__file__ + \\\".csv\\\",\\\"rt\\\")\\n except: #Python2\\n f = open(__file__ + \\\".csv\\\",\\\"rb\\\")\\n data = f.read().split(',')\\n entryCol.entry0.delete(0,END)\\n entryCol.entry0.insert(0,data[0])\\n entryCol.entry1.delete(0,END)\\n entryCol.entry1.insert(0,data[1])\\n entryCol.entry2.delete(0,END)\\n entryCol.entry2.insert(0,data[2])\\n entryCol.entry3.delete(0,END)\\n entryCol.entry3.insert(0,data[3])\\n botWind.writeN(\\\"DataLoad: File\\\")\\n except:\\n botWind.writeN(\\\"DataLoad: Default\\\")\",\n \"def write_data():\",\n \"def _read(self):\\n # initializng data dictionary\\n self.data={}\\n\\n f = FortranFile(self.filename)\\n # Default omnivor binary header\\n self.data['MK'] = f.readInts('i')\\n self.data['itime'] = f.readInts('i')\\n self.data['version'] = f.readString()\\n self.data['file_id'] = f.readInts('i')\\n self.data['sversion'] = f.readString()\\n # Velocity field\\n self.data['stype'] = f.readString()\\n self.data['is_grid'] = f.readInts('i')\\n nCPs = f.readInts('i')\\n self.data['nCPs'] = nCPs\\n if self.data['MK'] == 8:\\n real_char='d'\\n else:\\n real_char='f'\\n if self.data['is_grid']:\\n #print('File is a velocity grid file')\\n n1 = f.readInts('i')\\n n2 = f.readInts('i')\\n n3 = f.readInts('i')\\n self.data['n1'] = n1\\n self.data['n2'] = n2\\n self.data['n3'] = n3\\n self.data['is_straight'] = f.readInts('i')\\n self.data['v1'] = f.readReals(real_char)\\n self.data['v2'] = f.readReals(real_char)\\n self.data['v3'] = f.readReals(real_char)\\n\\n CPs_raw = f.readReals(real_char)\\n Utot_raw = f.readReals(real_char)\\n CPs = np.reshape(CPs_raw,(3,nCPs),order = 'F')\\n Utot = np.reshape(Utot_raw,(3,nCPs),order = 'F')\\n\\n acc=-1\\n CPsTab = np.zeros((3, n1,n2,n3))\\n UtotTab = np.zeros((3, n1,n2,n3))\\n # Reshaping the nasty way (this is natural order). \\n for i in range(0,n1):\\n for j in range(0,n2):\\n for k in range(0,n3):\\n acc=acc+1\\n CPsTab[0:3,i,j,k] = CPs[0:3,acc]\\n UtotTab[0:3,i,j,k] = Utot[0:3,acc]\\n\\n self.data['CPs'] = CPs\\n self.data['CPsTab'] = CPsTab\\n self.data['Utot'] = Utot\\n self.data['UtotTab'] = UtotTab\",\n \"def readOneData(self):\\n\\t\\tpass\",\n \"def readData(self,datafile = None):\\n self.datafile = datafile or self.datafile\\n self.data = []\\n for line in open(self.datafile):\\n userid,itemid,record,_ = line.split()\\n self.data.append((userid,itemid,int(record)))\",\n \"def load(self, input):\",\n \"def load(self):\\n #print self.fileInfo.name\\n progress = self.progress\\n filePath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\\n self.fileSize = os.path.getsize(filePath)\\n #--Localize\\n cells = self.cells\\n records = self.records\\n canSave = self.canSave\\n skipObjRecords = self.skipObjRecords\\n contTypes = set(['CREC','CNTC','NPCC'])\\n levTypes = set(('LEVC','LEVI'))\\n debrisIds = self.debrisIds\\n debrisTypes = set(debrisIds.keys())\\n #--Header\\n inPath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\\n ins = Tes3Reader(self.fileInfo.name,file(inPath,'rb'))\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n self.tes3 = Tes3(name,size,delFlag,recFlag,ins,True)\\n if not canSave: del self.tes3.others[:]\\n #--Progress info\\n progress = self.progress\\n progress(0.0,'Loading '+self.fileInfo.name)\\n #--Raw data read\\n while not ins.atEnd():\\n #--Get record info and handle it\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n #print \\\"%s [%d]\\\" % (name,size)\\n #--CELL?\\n if name == 'CELL':\\n record = Cell(name,size,delFlag,recFlag,ins,0,skipObjRecords)\\n cells.append(record)\\n if canSave: records.append(record)\\n #--Contents\\n elif canSave and name in contTypes:\\n if name == 'CREC':\\n record = Crec(name,size,delFlag,recFlag,ins,True)\\n elif name == 'CNTC':\\n record = Cntc(name,size,delFlag,recFlag,ins,True)\\n else:\\n record = Npcc(name,size,delFlag,recFlag,ins,True)\\n self.conts.append(record)\\n self.conts_id[record.getId()] = record\\n records.append(record)\\n #--File Map\\n elif name == 'FMAP':\\n record = Fmap(name,size,delFlag,recFlag,ins)\\n self.fmap = record\\n records.append(record)\\n #--Landscapes\\n elif name == 'LAND':\\n record = Land(name,size,delFlag,recFlag,ins)\\n self.lands[record.getId()] = record\\n records.append(record)\\n #--Scripts\\n elif canSave and name == 'SCPT':\\n record = Scpt(name,size,delFlag,recFlag,ins,True)\\n records.append(record)\\n if record.getRef():\\n self.refs_scpt[record] = record.getRef()\\n #--Save debris info?\\n elif name in debrisTypes:\\n record = Record(name,size,delFlag,recFlag,ins)\\n id = record.getId()\\n if id:\\n debrisIds[name].append(id.lower())\\n if canSave:\\n records.append(record)\\n #--Skip Non-cell?\\n elif not canSave:\\n ins.seek(size,1,name)\\n #--Keep non-cell?\\n else:\\n records.append(Record(name,size,delFlag,recFlag,ins))\\n #--Done Reading\\n ins.close()\\n #--Analyze Cells\\n cntCells = 0\\n progress.setMax(len(self.cells))\\n for cell in self.cells:\\n cell.load(None,1)\\n self.cells_id[cell.getId()] = cell\\n if not canSave:\\n cell.data = None #--Free some memory\\n #--Progress\\n cntCells += 1\\n progress(cntCells)\\n #--Scripts\\n if self.refs_scpt:\\n self.updateScptRefs()\",\n \"def data_for_file (self):\\n rowdata = np.array([self.Train_Time_min,self.Train_Time_max,self.Train_Time_avg,\\n self.Loss_Value_min,self.Loss_Value_max,self.Loss_Value_avg,\\n self.Iterations_min,self.Iterations_max,self.Iterations_avg,\\n self.precision_avg,self.recall_avg])\\n return rowdata\\n\\n\\n \\n\\n\\n #### FUNCTION DEFINTIIONS ####\",\n \"def load(self):\",\n \"def parse_region_data(self, region):\\n\\n #downloads newest data\\n self.download_data()\\n\\n TmpList = [[] for i in range(65)]\\n\\n #goes through all the files and saves their data to the variable\\n for zipName in self.ListOfZipFiles:\\n with zipfile.ZipFile(\\\"./\\\"+self.folder+\\\"/\\\"+zipName, 'r') as zf:\\n if self.getCsvFileByRegion(region) == None:\\n print(\\\"ERROR: neznamy nazev kraje: \\\" + region, file=sys.stderr)\\n return None\\n #opens zip file for reading\\n with zf.open(self.getCsvFileByRegion(region), \\\"r\\\") as f:\\n reader = f.read().decode(\\\"iso 8859-2\\\")\\n reader = StringIO(reader)\\n reader = csv.reader(reader, delimiter = \\\";\\\")\\n #saves every row of file\\n for row in reader:\\n TmpList[0].append(region)\\n i=1\\n #saves every column separated because of their types\\n for item in row:\\n if item != \\\"\\\":\\n if (i>0 and i<4) or (i>4 and i<6) or (i>6 and i<32) or (i>32 and i<35) or (i>35 and i<46) or i==61 or i==64:\\n if not re.search(\\\"^[-]{0,1}[0-9]*$\\\", item):\\n item = None\\n else:\\n item=int(item)\\n elif i>45 and i<52:\\n if not re.search(\\\"^[-]{0,1}[0-9]*[,.]{0,1}[0-9]+$\\\", item):\\n item = None\\n else:\\n item = item.replace(\\\",\\\", \\\".\\\")\\n if re.search(\\\"^[-]{1}.*$\\\", item):\\n item = item.replace(\\\"-\\\", \\\"\\\")\\n item = -(float(item))\\n else:\\n item = float(item)\\n elif i==6:\\n if not re.search(\\\"^[-]{0,1}[0-9]*$\\\", item):\\n item = None\\n else:\\n if (len(item) == 4):\\n #cas nijak neupravuji, jen rozdeluji minuty od hodin\\n #policie uz nastavila hodnoty podle nich pozname nevalidni hodnotu\\n #pro minuty tato hodnota je 60\\n #pro hodiny tato hodnota je 25\\n #dle meho nazoru mi prislo zbytecne na misto techto dvou hdonot davat nejakou jinou nevalidni hodnotu (napr. NaN, -1)\\n #prepsal bych nevalidni vystup na jiny nevalidni vystup\\n item = item[:2] + \\\":\\\" + item[2:]\\n else:\\n item = None\\n TmpList[i].append(item)\\n else:\\n TmpList[i].append(None)\\n i+=1\\n\\n ListNpArrays = [[] for i in range(65)]\\n\\n #transforms into numpy.ndarray\\n for x in range(0, 65):\\n ListNpArrays[x] = np.array(TmpList[x], dtype=object)\\n\\n return (self.headers, ListNpArrays)\",\n \"def data_input(self):\\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))\\n if not os.path.isfile('{0}/{1}.csv'.format(path, self.data_file)):\\n print 'Error: Dataset file is not exist.'\\n exit()\\n # Uplead Dataset.csv file.\\n f = open('{0}/{1}.csv'.format(path, self.data_file), 'r')\\n print 'Now uploading dataset File.....'\\n f = list(f)\\n # The Dataset contains heading, number of lines - heading\\n self.number_of_VOCs = sum(1 for row in f)-1\\n # Count number of columns, last column's value is empty, that is why -1.\\n self.number_of_columns = len(f[0].split(',')) -1\\n self.first_m_z = int(f[0].split(',')[3]) # find the first m/z value.\\n self.last_m_z = int(f[0].split(',')[-2]) # find the last m/z value.\\n print 'dataset includes ', self.number_of_VOCs, 'VOCs in all samples '\\n print ('dataset includes ', self.number_of_columns, ' Columns, ',\\n 'm/z values start from ', self.first_m_z,\\n 'and end ', self.last_m_z)\\n # Create a matrix with a shape of (number_of_VOCs X number_of_columns) filled with zeros.\\n self.dataset = np.zeros((self.number_of_VOCs,\\n self.number_of_columns))\\n for line in range(1, len(f)):\\n if int(float(f[line].strip().split(',')[0])) not in self.loaded_samples:\\n self.loaded_samples.append(int(float(f[line].strip().split(',')[0])))\\n for column in range(self.number_of_columns):\\n self.dataset[line-1][column] = int(float(f[line].strip().split(',')[column]))\",\n \"def read_data(self, loc):\\n pass\",\n \"def loadData(self,ins):\\n raise AbstractError\",\n \"def _read_data(self):\\n with self._open(self.filename, 'rb') as f:\\n try:\\n f.seek(self._offset_data, self._offset_whence)\\n except IOError:\\n print('Error: hedp.io.HamamatsuFile seeking outside of file limits.')\\n print(' Failed to parse file.')\\n print(\\\" Either the 'offset' or 'dtype' input arguments must be wrong!\\\")\\n raise\\n except:\\n raise\\n\\n data_len = np.prod(self.shape)*np.dtype(self._dtype).itemsize\\n data_str = f.read(data_len)\\n if data_len != len(data_str):\\n print(data_len, len(data_str))\\n raise ValueError('File ended before all data was read. Probably wrong offset or dtype!')\\n\\n\\n self.data = np.fromstring(data_str, dtype=self._dtype).reshape(self.shape[::-1])\\n self.data = np.ndarray.astype(self.data, 'float32')\\n\\n #self.data = np.fromfile(f, dtype=self._dtype,\\n # count=np.prod(self.shape)).reshape(self.shape[::-1])\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader('GLOB')\\n srData = ins.read(size,'GLOB.'+name)\\n bytesRead += 8+size\\n if name == 'NAME': self.id = cstrip(srData)\\n elif name == 'FNAM': self.type = srData\\n elif name == 'FLTV': self.value = struct.unpack('f',srData)\\n #--Deleted?\\n elif name == 'DELE': self.isDeleted = True\\n #--Bad record?\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\",\n \"def load_data(self):\\n raise NotImplementedError()\",\n \"def load(datastream):\",\n \"def loadData(self, file):\\n self.data = batchImport(file, self.ps)\",\n \"def load(self, filename):\\n # XXX Hay que comprobar los datos leidos y lanzar excepcion\\n f = open(filename)\\n prelaciones = []\\n asig = []\\n rec = []\\n l = f.readline()\\n while l:\\n # Activities and following activities\\n if l[0:21] == 'PRECEDENCE RELATIONS:':\\n f.readline()\\n l = f.readline()\\n while l[0] != '*':\\n data = l.split()\\n prel = (data[0], data[3:])\\n prelaciones.append(prel)\\n l = f.readline()\\n\\n # Activity duration and resource units needed\\n if l[0] == '-':\\n l = f.readline()\\n while l[0] != '*':\\n asig.append(l.split())\\n l = f.readline()\\n\\n # Name, type and unit of resources\\n if l[0:22] == 'RESOURCEAVAILABILITIES':\\n l = f.readline()\\n while l[0] != '*':\\n rec.append(l.split())\\n l = f.readline()\\n\\n l = f.readline()\\n \\n # Create data structure\\n cont = 1\\n activities = []\\n for prelacion in prelaciones:\\n activities.append([cont, prelacion[0], prelacion[1], '', '', '', '', '', ('Beta')])\\n cont += 1 \\n\\n # Update activities duration\\n for n in range(len(asig)): \\n activities[n][6] = float(asig[n][2])\\n\\n # Update resources\\n i = 1\\n m = 0\\n resources = []\\n if len(rec) < 2:\\n raise InvalidFileFormatException()\\n\\n for n in range(len(rec[1])):\\n # Renewable\\n if rec[0][m]=='R' or rec[0][m][0]=='R':\\n if rec[0][m]=='R':\\n row=[rec[0][m]+rec[0][i], 'Renewable', '', rec[1][n]] \\n m+=2\\n else:\\n row=[rec[0][m], 'Renewable', '', rec[1][n]] \\n m+=1 \\n # Non Renewable\\n elif rec[0][m]=='N' or rec[0][m][0]=='N':\\n if rec[0][m]=='N':\\n row=[rec[0][m]+rec[0][i], 'Non renewable', rec[1][n], '']\\n m+=2\\n else:\\n row=[rec[0][m], 'Non renewable', rec[1][n], ''] \\n m+=1\\n # Double constrained\\n elif rec[0][m]=='D' or rec[0][m][0]=='D':\\n if rec[0][m]=='D':\\n row=[rec[0][m]+rec[0][i], 'Double constrained', rec[1][n], rec[1][n]]\\n m+=2\\n else:\\n row=[rec[0][m], 'Double constrained', rec[1][n], rec[1][n]] \\n m+=1\\n \\n resources.append(row)\\n i += 2\\n # Note: Unlimited resources are not present on PSPLIB projects and so \\n # not taken into account here\\n\\n # Resources needed per activity\\n asignation = []\\n for n in range(len(asig)): \\n for m in range(3, 3+len(rec[1])): #len(self.rec[1]): number of resources \\n if asig[n][m] != '0': #unused resources are not shown\\n i = m-3\\n row = [asig[n][0], resources[i][0], asig[n][m]] \\n asignation.append(row)\\n \\n return (activities, [], resources, asignation)\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def __init__(self,site,startDate,endDate,path='verif_data/'):\\n self.site = site.upper()\\n self.startDateTime = datetime.strptime(startDate,'%Y%m%d')\\n self.endDateTime = datetime.strptime(endDate,'%Y%m%d')\\n years = range(self.startDateTime.year,self.endDateTime.year + 1)\\n data = []\\n for year in years:\\n self.filename=path + self.site + '_asos_' + str(year) + '.txt'\\n datafile = open(self.filename)\\n for line in datafile:\\n if line[0] != '#':\\n if 'station' in line:\\n self.header = [x.strip() for x in line[:-1].split(',')]\\n else:\\n dataline = line[:-2].split(',')\\n for i,val in enumerate(dataline[:-1]):\\n if val=='M':\\n dataline[i] = -999\\n dataline[1] = dataline[1].replace(' ','_')\\n dataline[1] = dataline[1].replace('-','')\\n currDateTime = datetime.strptime(dataline[1][:14],'%Y%m%d_%H:%M')\\n if currDateTime >= self.startDateTime and currDateTime <= self.endDateTime:\\n data.append(tuple(dataline))\\n datafile.close()\\n self.datatype = []\\n for item in self.header:\\n if item == 'station':\\n self.datatype.append((item,'S3'))\\n elif 'valid' in item:\\n self.datatype.append(('time','S14'))\\n elif 'skyc' in item:\\n self.datatype.append((item,'S3'))\\n elif item=='metar':\\n self.datatype.append((item,'S99'))\\n else:\\n self.datatype.append((item,float))\\n self.data = np.array(data,dtype=self.datatype)\",\n \"def data_preprocessing():\\n lineid_content = get_lineid_content()\\n print('Read movie_lines.txt file complete...')\\n convos = get_convos()\\n print('Read movie_conversations.txt file complete...')\\n print('Building dataset')\\n get_data(lineid_content, convos)\",\n \"def import_data(self):\\n\\n # Import ordered names of origins\\n origins_file = os.path.join(self.data_directory,'origins.txt')\\n self.origins = np.loadtxt(origins_file,dtype=str,ndmin=1)\\n\\n # Import ordered names of destinations\\n destinations_file = os.path.join(self.data_directory,'destinations.txt')\\n self.destinations = np.loadtxt(destinations_file,dtype=str,ndmin=1)\\n\\n # Import origin supply\\n originsupply_file = os.path.join(self.data_directory,'origin_supply.txt')\\n self.origin_supply = np.loadtxt(originsupply_file,ndmin=1).astype('float64')\\n\\n # In case origin supply is not a list\\n if not isinstance(self.origin_supply,(np.ndarray, np.generic)):\\n self.origin_supply = np.array([self.origin_supply])\\n\\n # Import destination demand\\n destinationdemand_file = os.path.join(self.data_directory,'destination_demand.txt')\\n self.destination_demand = np.loadtxt(destinationdemand_file,ndmin=1).astype('float64')\\n\\n # In case destination demand is not a list\\n if not isinstance(self.destination_demand,(np.ndarray, np.generic)):\\n self.destination_demand = np.array([self.destination_demand])\\n\\n # Import origin locations\\n originlocations_file = os.path.join(self.data_directory,'origin_locations.txt')\\n self.origin_locations = np.loadtxt(originlocations_file,ndmin=1)\\n\\n # Import destination locations\\n destinationlocations_file = os.path.join(self.data_directory,'destination_locations.txt')\\n self.destination_locations = np.loadtxt(destinationlocations_file,ndmin=1)\\n\\n # Import initial and final destination sizes\\n initialdestinationsizes_file = os.path.join(self.data_directory,'initial_destination_sizes.txt')\\n self.initial_destination_sizes = np.loadtxt(initialdestinationsizes_file,ndmin=1)\\n\\n # In case destination sizes are not a list\\n if not isinstance(self.initial_destination_sizes,(np.ndarray, np.generic)):\\n self.initial_destination_sizes = np.array([self.initial_destination_sizes])\\n\\n # Import N,M\\n self.N = self.origin_supply.shape[0]\\n self.M = self.initial_destination_sizes.shape[0]\\n\\n # Import cost matrix\\n costmatrix_file = os.path.join(self.data_directory,'cost_matrix.txt')\\n self.cost_matrix = np.loadtxt(costmatrix_file).astype('float64')\\n\\n # Reshape cost matrix if necessary\\n if self.N == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[:,np.newaxis],(self.N,self.M))\\n if self.M == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[np.newaxis,:],(self.N,self.M))\\n\\n # Compute total initial and final destination sizes\\n self.total_initial_sizes = np.sum(self.initial_destination_sizes)\\n\\n # Compute naive total cost\\n self.total_cost = 0\\n for i in range(self.N):\\n for j in range(self.M):\\n self.total_cost += self.cost_matrix[i,j]*(self.origin_supply[i]/self.N)\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n objectId = None\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader(self.name)\\n #print name,size\\n bytesRead += 8+size\\n subData = ins.read(size, self.name+'.'+name)\\n #--Id?\\n if name == 'NAME':\\n self.id = cstrip(subData)\\n #--Flags\\n elif name == 'DATA':\\n flags = struct.unpack('i',subData)[0]\\n if self.name == 'LEVC':\\n self.calcFromAllLevels = (flags & 1) == 1\\n else:\\n self.calcForEachItem = (flags & 1) == 1\\n self.calcFromAllLevels = (flags & 2) == 2\\n #--Chance None\\n elif name == 'NNAM':\\n self.chanceNone = struct.unpack('B',subData)[0]\\n #--Count\\n elif name == 'INDX':\\n self.count = struct.unpack('i',subData)[0]\\n #--Creature/Item Id?\\n elif name == 'CNAM' or name == 'INAM':\\n objectId = cstrip(subData)\\n #--PC Level\\n elif name == 'INTV':\\n pcLevel = struct.unpack('h',subData)[0]\\n self.entries.append((pcLevel,objectId))\\n objectId = None\\n #--Deleted?\\n elif name == 'DELE': \\n self.isDeleted = True\\n #--Else\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\\n #--No id?\\n if not self.id:\\n raise Tes3Error(self.inName,_('No id for %s record.') % (self.name,))\\n #--Bad count?\\n if self.count != len(self.entries):\\n self.count = len(self.entries)\\n self.setChanged()\",\n \"def __init__(self, raw_data_file,):\\n self.raw_data_file = raw_data_file\\n self.clean_data = self.cleanData()\\n self.microtrip_data = []\",\n \"def get_file_data(self):\\n self.switch_row(self.PATTERN_INFO_ROW)\\n\\n # get background image file path\\n if self.row[0] != 'No image':\\n # set path to background image, and get/set screen size\\n self.bg_img_path = self.row[self.BG_IMAGE_INDEX]\\n self.screen_width = int(self.row[self.WIDTH_INDEX])\\n self.screen_height = int(self.row[self.HEIGHT_INDEX])\\n self.SCREEN_SIZE = self.screen_width, self.screen_height\\n self.bg = True\\n \\n # get number of landmarks\\n self.num_lndmrks = int(self.row[self.LNDMRK_NUM_INDEX])\\n\\n # get starting row number for mpo path data\\n self.data_start_row = self.num_lndmrks + 1\\n self.tot_data_rows = self.tot_rows - self.data_start_row\\n self.data_row_num = self.data_start_row\",\n \"def extract_data(file_name):\\r\\n input_file_name = file_name\\r\\n \\r\\n def sort_data(input_file): \\r\\n \\\"\\\"\\\"This function extraxts the row data from the imput file\\\"\\\"\\\" \\r\\n with open(input_file,\\\"r\\\") as input_file: \\r\\n data = input_file.readlines() \\r\\n row_data = [] \\r\\n for line in data: \\r\\n row_data.append(line.split(', ')) \\r\\n return row_data \\r\\n \\r\\n data = sort_data(input_file_name)\\r\\n \\r\\n start_vertex = (float(data[0][0].split(' ')[0]),float(data[0][0].split(' ')[1]))\\r\\n end_vertex = (float(data[1][0].split(' ')[0]),float(data[1][0].split(' ')[1]))\\r\\n \\r\\n obstacles = {}\\r\\n for i in range(2, len(data)):\\r\\n lista = []\\r\\n for j in range(len(data[i])):\\r\\n lista.append( (float(data[i][j].split(' ')[0]),float(data[i][j].split(' ')[1])) )\\r\\n obstacles[i-1] = lista\\r\\n \\r\\n return start_vertex,end_vertex, obstacles\",\n \"def open_file(self):\\n file = open(self.file_name_dat, \\\"r\\\")\\n rows = []\\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\\n for line in file:\\n rows.append(line)\\n\\n # Getting information from the comments section\\n xdatatemp = []\\n ydatatemp = []\\n for row in rows:\\n if \\\"#\\\" in row: # Getting information from the comments\\n hashlinecount += 1\\n if hashlinecount == 7: # Getting the E_f value\\n temp = row.split()\\n if len(temp) == 4:\\n self.E_f = temp[3]\\n elif len(temp) == 7:\\n self.E_f = temp[6]\\n self.Efshift = True\\n continue\\n elif hashlinecount == 9: # Getting the min and max band energies\\n temp = row.split()\\n self.E_min = float(temp[4])\\n self.E_max = float(temp[5])\\n continue\\n elif hashlinecount == 11: # Gettiing the minimum and maximum band numbers to plot\\n temp = row.split()\\n self.band_number_min = int(temp[5])\\n self.band_number_max = int(temp[6])\\n continue\\n\\n elif row.strip() == \\\"\\\": # saving individual bands the first save will be a null band\\n self.band_data_x.append(xdatatemp) # Saving in a 2d list.\\n self.band_data_y.append(ydatatemp)\\n xdatatemp = []\\n ydatatemp = []\\n else:\\n t = row.split()\\n xdatatemp.append(float(t[0]))\\n ydatatemp.append(float(t[1]))\\n\\n # Getting rid of empty lists\\n self.band_data_x = [x for x in self.band_data_x if x]\\n self.band_data_y = [x for x in self.band_data_y if x]\\n\\n # opening the bands file to get the kpath\\n bands_file = open(self.file_name_bands, \\\"r\\\")\\n bands_file_rows = []\\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\\n for bands_file_line in bands_file:\\n bands_file_rows.append(bands_file_line)\\n\\n length_bands_file_rows = len(bands_file_rows)\\n for i in range(length_bands_file_rows-1, 0, -1):\\n temperary = bands_file_rows[i].split()\\n if len(temperary) == 2:\\n self.kpath_k.append(float(temperary[0].strip()))\\n self.kpath_symbol.append(str(temperary[1].strip(\\\"'\\\")))\\n elif len(temperary) is not 2:\\n break\\n self.kpath_symbol = latexify(self.kpath_symbol)\",\n \"def readdata(filename):\\n\\tdt = np.dtype([('date','int'),('val','<f8')])\\n\\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\\n\\treturn data\",\n \"def load_data(self, filename):\\r\\n #sqlcontext = SQLContext(self.sc)\\r\\n #df = sqlcontext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load(filename)\\r\\n #df = sc.textFile(r\\\"C:\\\\Users\\\\mohan\\\\Downloads\\\\patches.csv\\\").map(lambda line: line.split(\\\",\\\"))\\r\\n #print (df.count())\\r\\n df = self.sc.textFile(filename).map(lambda line: line.split(\\\",\\\"))\\r\\n l = df.map(lambda w: [int(float(c)) for c in w]).zipWithIndex()\\r\\n return l\\r\\n raise NotImplementedError\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = float(A[-1])\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = float(A[-1])\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = int(float(A[-1]))\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = int(float(A[-1]))\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\",\n \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the y component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[2])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"def read_data(self):\\r\\n IS_REMAPPED = 1\\r\\n if IS_REMAPPED:\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TRAINING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\\\\r\\n int(c)] for [f1, f2, c] in data_as_strings]\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TESTING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\\\\r\\n int(c)] for [f1, f2, c] in data_as_strings]\\r\\n else:\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TRAINING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TESTING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def predict(self, datafile):\",\n \"def rdspecdat(self):\\n # TODO : ugh. this is crude. Should have some checks for file format\\n # and probably better to use the astropy.io functions now.\\n try:\\n w, f, e = np.loadtxt(self.filename, unpack=True)\\n except:\\n w, f = np.loadtxt(self.filename, unpack=True)\\n e = []\",\n \"def readdata(self, filepaths):\\n pass\",\n \"def _load_data(self):\\n\\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\\\\n self.task_num\\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\\n\\n read_data_x = pd.read_csv(path_data_x)\\n read_data_y = pd.read_csv(path_data_y)\\n read_data_meta = pd.read_csv(path_meta)\\n\\n # Drop columns if it only contains 1 unique element\\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\\n\\n num_col = pd.read_csv(path_numerical_columns)\\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\\n col_idx = np.arange(read_data_x.shape[-1])\\n non_num_col = np.setdiff1d(col_idx, num_col)\\n\\n # new_data_x = np.array(read_data_x).astype(np.float32)\\n new_data_x = np.array(read_data_x)\\n new_data_y = np.array(read_data_y).astype(np.float32)\\n new_data_meta = np.array(read_data_meta).astype(np.float32)\\n\\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\\n\\n\\n # Winsorize dataset\\n len_feat = new_data_x.shape[-1]\\n idx_list = list(num_col)\\n for i in range(len_feat):\\n if i in idx_list:\\n cur_data = new_data_x[:, i]\\n cur_data = np.array(cur_data)\\n lower_p = np.percentile(cur_data, 5)\\n higher_p = np.percentile(cur_data, 95)\\n cur_data[cur_data < lower_p] = lower_p\\n cur_data[cur_data > higher_p] = higher_p\\n new_data_x[:, i] = cur_data\\n\\n # Make sure target data is one-hot encoded\\n if new_data_y.shape[-1] == 1:\\n num_class = len(np.unique(new_data_y))\\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\\n new_data_y = new_data_y.astype('float32')\\n self.orig_column_names = read_data_x.columns\\n self.data_x = new_data_x # N x F\\n self.data_y = new_data_y # N x C\\n self.numerical_idx = num_col # list of idx\\n self.non_num_idx = non_num_col # None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = new_data_meta.astype('float32') # N x 3\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def make_data_raw (mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname,',',mdp.tag\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n # write first header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n # write another header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def read_data(self):\\n if not self.header['data included']:\\n pass\\n elif self.header['file type'] in (21, 26):\\n self._isotope_data()\\n if os.path.exists(self.filename + '_txt'):\\n self._isotope_txt_data()\\n elif self.header['file type'] == 22:\\n # line scan types, no ImageHeader\\n warnings.warn('No data read for line scan, fix')\\n pass\\n elif self.header['file type'] in (31, 35):\\n self._beamstability_data()\\n else:\\n self._image_data()\",\n \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the z component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[1])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"def read_data(args):\\n\\n print(\\\"Start read_data\\\")\\n t_tot = 0 # sum of times for the all dataset\\n date_dirs = os.listdir(args.path_data_base)\\n for n_iter, date_dir in enumerate(date_dirs):\\n # get access to each sequence\\n path1 = os.path.join(args.path_data_base, date_dir)\\n if not os.path.isdir(path1):\\n continue\\n date_dirs2 = os.listdir(path1)\\n\\n for date_dir2 in date_dirs2:\\n path2 = os.path.join(path1, date_dir2)\\n if not os.path.isdir(path2):\\n continue\\n # read data\\n oxts_files = sorted(glob.glob(os.path.join(path2, 'oxts', 'data', '*.txt')))\\n oxts = KITTIDataset.load_oxts_packets_and_poses(oxts_files)\\n\\n \\\"\\\"\\\" Note on difference between ground truth and oxts solution:\\n - orientation is the same\\n - north and east axis are inverted\\n - position are closed to but different\\n => oxts solution is not loaded\\n \\\"\\\"\\\"\\n\\n print(\\\"\\\\n Sequence name : \\\" + date_dir2)\\n if len(oxts) < KITTIDataset.min_seq_dim: # sequence shorter than 30 s are rejected\\n cprint(\\\"Dataset is too short ({:.2f} s)\\\".format(len(oxts) / 100), 'yellow')\\n continue\\n lat_oxts = np.zeros(len(oxts))\\n lon_oxts = np.zeros(len(oxts))\\n alt_oxts = np.zeros(len(oxts))\\n roll_oxts = np.zeros(len(oxts))\\n pitch_oxts = np.zeros(len(oxts))\\n yaw_oxts = np.zeros(len(oxts))\\n roll_gt = np.zeros(len(oxts))\\n pitch_gt = np.zeros(len(oxts))\\n yaw_gt = np.zeros(len(oxts))\\n t = KITTIDataset.load_timestamps(path2)\\n acc = np.zeros((len(oxts), 3))\\n acc_bis = np.zeros((len(oxts), 3))\\n gyro = np.zeros((len(oxts), 3))\\n gyro_bis = np.zeros((len(oxts), 3))\\n p_gt = np.zeros((len(oxts), 3))\\n v_gt = np.zeros((len(oxts), 3))\\n v_rob_gt = np.zeros((len(oxts), 3))\\n\\n k_max = len(oxts)\\n for k in range(k_max):\\n oxts_k = oxts[k]\\n t[k] = 3600 * t[k].hour + 60 * t[k].minute + t[k].second + t[\\n k].microsecond / 1e6\\n lat_oxts[k] = oxts_k[0].lat\\n lon_oxts[k] = oxts_k[0].lon\\n alt_oxts[k] = oxts_k[0].alt\\n acc[k, 0] = oxts_k[0].af\\n acc[k, 1] = oxts_k[0].al\\n acc[k, 2] = oxts_k[0].au\\n acc_bis[k, 0] = oxts_k[0].ax\\n acc_bis[k, 1] = oxts_k[0].ay\\n acc_bis[k, 2] = oxts_k[0].az\\n gyro[k, 0] = oxts_k[0].wf\\n gyro[k, 1] = oxts_k[0].wl\\n gyro[k, 2] = oxts_k[0].wu\\n gyro_bis[k, 0] = oxts_k[0].wx\\n gyro_bis[k, 1] = oxts_k[0].wy\\n gyro_bis[k, 2] = oxts_k[0].wz\\n roll_oxts[k] = oxts_k[0].roll\\n pitch_oxts[k] = oxts_k[0].pitch\\n yaw_oxts[k] = oxts_k[0].yaw\\n v_gt[k, 0] = oxts_k[0].ve\\n v_gt[k, 1] = oxts_k[0].vn\\n v_gt[k, 2] = oxts_k[0].vu\\n v_rob_gt[k, 0] = oxts_k[0].vf\\n v_rob_gt[k, 1] = oxts_k[0].vl\\n v_rob_gt[k, 2] = oxts_k[0].vu\\n p_gt[k] = oxts_k[1][:3, 3]\\n Rot_gt_k = oxts_k[1][:3, :3]\\n roll_gt[k], pitch_gt[k], yaw_gt[k] = IEKF.to_rpy(Rot_gt_k)\\n\\n t0 = t[0]\\n t = np.array(t) - t[0]\\n # some data can have gps out\\n if np.max(t[:-1] - t[1:]) > 0.1:\\n cprint(date_dir2 + \\\" has time problem\\\", 'yellow')\\n ang_gt = np.zeros((roll_gt.shape[0], 3))\\n ang_gt[:, 0] = roll_gt\\n ang_gt[:, 1] = pitch_gt\\n ang_gt[:, 2] = yaw_gt\\n\\n p_oxts = lla2ned(lat_oxts, lon_oxts, alt_oxts, lat_oxts[0], lon_oxts[0],\\n alt_oxts[0], latlon_unit='deg', alt_unit='m', model='wgs84')\\n p_oxts[:, [0, 1]] = p_oxts[:, [1, 0]] # see note\\n\\n # take correct imu measurements\\n u = np.concatenate((gyro_bis, acc_bis), -1)\\n # convert from numpy\\n t = torch.from_numpy(t)\\n p_gt = torch.from_numpy(p_gt)\\n v_gt = torch.from_numpy(v_gt)\\n ang_gt = torch.from_numpy(ang_gt)\\n u = torch.from_numpy(u)\\n\\n # convert to float\\n t = t.float()\\n u = u.float()\\n p_gt = p_gt.float()\\n ang_gt = ang_gt.float()\\n v_gt = v_gt.float()\\n\\n mondict = {\\n 't': t, 'p_gt': p_gt, 'ang_gt': ang_gt, 'v_gt': v_gt,\\n 'u': u, 'name': date_dir2, 't0': t0\\n }\\n\\n t_tot += t[-1] - t[0]\\n KITTIDataset.dump(mondict, args.path_data_save, date_dir2)\\n print(\\\"\\\\n Total dataset duration : {:.2f} s\\\".format(t_tot))\",\n \"def read_data(self):\\n raise NotImplementedError\",\n \"def __init__(self, datafile):\\n self.FILE = open(datafile, 'w')\",\n \"def readdata(self, reflist , comment = '#' , regexp = None , substr = None, filename = True):\\n self.kpunten = []\\n datalist = []\\n prefixlist = []\\n if os.path.isfile(str(reflist)):\\n reflist = [reflist] #if we work with only one file this wraps it automatically in right format\\n for ref in reflist:\\n print('start with the collection of data from file %s' %ref)\\n plotf = open(ref, 'r')\\n if not filename:\\n prefixlist.append( os.path.dirname(ref) + '/')\\n else:\\n prefixlist.append(re.sub('\\\\.dat$' , '' , ref))\\n try:\\n if regexp != None:\\n raise ValueError\\n dataf = np.loadtxt(plotf,comments = comment)\\n print 'we readed data in with np.loadtxt'\\n except:\\n print('reading in data with numpy loadtxt failed or use reg exp to extract information')\\n dataf = np.array([])\\n kpuntenf = []\\n plotf.seek(0) #go back to beginning of file\\n for line in plotf:\\n if regexp is not None:\\n analyse = re.search(regexp,line)\\n if analyse:\\n kpuntenf.append((analyse.group(1), len(dataf)-1 ))\\n print 'we found the following matches: %s' % analyse.group(0)\\n if substr != None: \\n line = re.sub(substr, '' , line)\\n if line[0] != comment:\\n #print line\\n pline = np.array(map(float,line.split()))\\n if len(dataf) <= 1:\\n dataf = pline\\n else:\\n try:\\n dataf = np.vstack((dataf,pline))\\n except:\\n continue\\n self.kpunten.append(kpuntenf)\\n datalist.append(dataf)\\n\\n plotf.close()\\n self.datarg = datalist\\n self.prefix = prefixlist\\n self.reader = dr.ReaderOutput(reflist[0]) #Some plotting functions need a bit more information this info is extracted from the header of the files\\n self.reader.depvar['depvar'] += ' (a.u.)'\",\n \"def readFiles(opt, path, pathCopyData,minlat, maxlat, minlon, maxlon , variables, estaciones):\\n date = '\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d'\\n dirr = pathCopyData\\n patron2 = re.compile(date)\\n print(dirr + 'tfile.txt')\\n tempfile = df.read_csv(dirr + 'tfile.txt')\\n tempbase = df.read_csv(dirr + 'tbase.txt')\\n tfile = list(tempfile.values.flatten())\\n tbase = list(tempbase.values.flatten())\\n tfileCopy = list(tempfile.values.flatten())\\n tbaseCopy = list(tempbase.values.flatten())\\n l = len(tfile)\\n for i in range(l):\\n tfil = tfile[i]\\n tbas = tbase[i]\\n ls = tbas + '/' + tfil\\n f = patron2.findall(tfil)\\n cadena = clearString(tfil)\\n print(cadena)\\n try:\\n #net = open_netcdf(ls, tfil, cadena, pathCopyData)\\n net = Dataset(ls)\\n for xs in range(len(estaciones)):\\n minlat1 = minlat[xs]\\n maxlat1 = maxlat[xs]\\n minlon1 = minlon[xs]\\n maxlon1 = maxlon[xs]\\n estacion = estaciones[xs]\\n #checkFile(net, tfil, f[0], opt, path, minlat1, maxlat1, minlon1, maxlon1, variables, estacion)\\n var_cut = []\\n for i in variables:\\n var = net.variables[i][:,int(minlat1):int(maxlat1),int(minlon1):int(maxlon1)]\\n #print(LON)\\n #print(var)\\n #return\\n # celda.append(var)\\n # result = ne(var, LON, LAT, LONsize, LATsize, minlat, maxlat, minlon, maxlon)\\n var_cut.append(var)\\n\\n for ls in range(len(var_cut)):\\n saveData(var_cut[ls], variables[ls], f[0], opt, path, estacion)\\n tfileCopy.remove(tfil)\\n tbaseCopy.remove(tbas)\\n except (OSError, EOFError) as e:\\n print(e)\\n fdata = df.DataFrame(tfileCopy, columns=['nameFile'])\\n fbas = df.DataFrame(tbaseCopy, columns=['nameBase'])\\n fdata.to_csv(dirr + 'tfile.txt', encoding='utf-8', index=False)\\n fbas.to_csv(dirr + 'tbase.txt', encoding='utf-8', index=False)\\n if os.path.exists(pathCopyData + cadena):\\n os.remove(pathCopyData + cadena)\\n sys.exit()\\n # readFiles(1);\\n except tarfile.ReadError:\\n print('error2')\\n # fdata = df.DataFrame(tfile,columns=['nameFile']);\\n # fbas = df.DataFrame(tbase,columns=['nameBase']);\\n # fdata.to_csv(dirr+'tfile.txt',encoding='utf-8',index=False);\\n # fbas.to_csv(dirr+'tbase.txt',encoding='utf-8',index=False);\\n # readFiles(1);\\n except (KeyError, FileNotFoundError):\\n print('ERROR DE LECTURA')\",\n \"def test_get_infile(self):\\r\\n pass # not practically testable, but obvious file I/O\\r\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = float(A[-1])\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = float(A[-1])\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n L = int(L)\\n N = int(N)\\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\\n \\n return\",\n \"def _read_file(self):\\n\\n with open(self.file_name, 'rb') as f:\\n new_test = struct.unpack('<l', f.read(8)[4:])[0]\\n f.close()\\n\\n with open(self.file_name, 'rb') as f:\\n old_test = struct.unpack('<h', f.read(6)[4:])[0]\\n f.close()\\n\\n with open(self.file_name, 'rb') as f:\\n other_test = struct.unpack('<l', f.read(20)[16:])[0]\\n f.close()\\n\\n open_file = open(self.file_name, 'rb')\\n\\n if (other_test==202):\\n raw = open_file.read(1236)[11:]\\n self.model = '202'\\n elif ((not new_test==102) and old_test==102):\\n raw = open_file.read(1133)\\n self.model = '102old'\\n elif (new_test==102 and old_test==102):\\n raw = open_file.read(1224)\\n self.model = '102new'\\n\\n self.header = DpHeader(raw, self.model)\\n\\n self.data = DpData(open_file, \\n self.model, \\n self.header.interferogram_size, \\n self.header.number_of_coadds, \\n 2048*self.header.zero_fill,\\n self.header.laser_wavelength_microns, \\n self.header.dispersion_constant_xm,\\n self.header.dispersion_constant_xb)\\n\\n open_file.close()\",\n \"def _read(self, in_file):\\n #\\n # I know this function is long, but the FRD block is long as well...\\n # Splitting this into multiple functions would not help in my opinion.\\n # Therefore -> shut up pylint\\n # pylint: disable=too-many-branches\\n # pylint: disable=too-many-statements\\n #\\n self.setname = in_file.read(6).decode().strip()\\n self.value = float(in_file.read(12))\\n self.numnod = int(in_file.read(12))\\n self.text = in_file.read(20).decode().strip()\\n self.ictype = int(in_file.read(2))\\n self.numstep = int(in_file.read(5))\\n self.analys = in_file.read(10).decode().strip()\\n self.format = int(in_file.read(2))\\n in_file.read(1) # eol\\n\\n in_file.read(1) # pad byte\\n in_file.read(2) # key = -4\\n in_file.read(2) # pad bytes\\n self.name = in_file.read(8).decode().strip()\\n self.ncomps = int(in_file.read(5))\\n self.irtype = int(in_file.read(5))\\n if self.irtype != 1:\\n raise NotImplementedError()\\n in_file.read(1) # eol\\n\\n for i in range(self.ncomps):\\n entity = FRDEntity()\\n self.entities.append(entity)\\n\\n in_file.read(1) # pad byte\\n entity.key = int(in_file.read(2))\\n in_file.read(2) # pad bytes\\n entity.name = in_file.read(8).decode().strip()\\n entity.menu = int(in_file.read(5))\\n entity.ictype = int(in_file.read(5))\\n entity.icind1 = int(in_file.read(5))\\n if entity.ictype == 4:\\n entity.icind2 = int(in_file.read(5))\\n elif entity.ictype == 2 and i == 3:\\n entity.icind2 = int(in_file.read(5))\\n entity.iexist = int(in_file.read(5))\\n entity.icname = in_file.read(3).decode().strip()\\n self.ncomps -= 1\\n else:\\n entity.iexist = int(in_file.read(5))\\n in_file.read(1) # eol\\n\\n for i in range(self.numnod):\\n result = FRDNodeResult()\\n self.results.append(result)\\n if self.format < 2:\\n num_lines = int(self.ncomps/(6 + 1)) + 1\\n result.data = []\\n for j in range(num_lines):\\n in_file.read(3) # pad byte and key = -1 || -2\\n if result.node is None:\\n result.node = int(in_file.read(5*(self.format+1)))\\n else:\\n in_file.read(5*(self.format+1))\\n k_start = j*6\\n k_end = min(self.ncomps - k_start, (j+1)*6)\\n for _ in range(0, k_end):\\n result.data.append(float(in_file.read(12)))\\n in_file.read(1) # eol\\n else:\\n result.node = struct.unpack('i', in_file.read(4))[0]\\n result.data = struct.unpack(\\n 'f'*self.ncomps, in_file.read(self.ncomps*4))\\n\\n if self.format < 2:\\n in_file.readline() # last record for ascii only\",\n \"def _read(self, in_file):\\n in_file.read(18) # pad bytes\\n self.numnod = int(in_file.read(12))\\n in_file.read(37) # pad bytes\\n self.format = int(in_file.read(1))\\n in_file.read(1) # eol\\n self.nodes = []\\n\\n for _ in range(self.numnod):\\n node = FRDNode()\\n self.nodes.append(node)\\n if self.format < 2:\\n in_file.read(1)\\n node.key = int(in_file.read(2))\\n node.number = int(in_file.read(5*(self.format+1)))\\n node.pos = [float(in_file.read(12)) for j in range(3)]\\n in_file.read(1) # eol\\n else:\\n node.number = struct.unpack('i', in_file.read(4))[0]\\n if self.format == 2:\\n node.pos = struct.unpack('fff', in_file.read(12))\\n else:\\n node.pos = struct.unpack('ddd', in_file.read(24))\\n\\n if self.format < 2:\\n in_file.readline() # last record for ascii only\",\n \"def read_meta(self):\\n meta = cPickle.load(open('../sugar_analysis_data/META-CABALLO2.pkl'))\\n self.meta_sn_name_list = []\\n self.meta_zcmb = []\\n self.meta_x0 =[]\\n self.meta_x0_err = []\\n self.meta_x1 =[]\\n self.meta_x1_err = []\\n self.meta_c = []\\n self.meta_c_err = []\\n self.meta_mb = []\\n self.meta_mb_err = []\\n self.meta_cov_x0_x1 = [] \\n self.meta_cov_x0_c = []\\n self.meta_cov_x1_c = []\\n self.meta_cov_mb_x1 = []\\n self.meta_cov_mb_c = [] \\n self.meta_zhl = []\\n self.meta_zhl_err = []\\n self.meta_idr = []\\n for meta_sn_name in meta.keys(): \\n \\n if meta[meta_sn_name]['idr.subset'] != 'bad' and meta[meta_sn_name]['idr.subset'] != 'auxiliary':\\n \\n self.meta_sn_name_list.append(meta_sn_name)\\n self.meta_zhl_err.append(meta[meta_sn_name]['host.zhelio.err'])\\n self.meta_zhl.append(meta[meta_sn_name]['host.zhelio'])\\n self.meta_zcmb.append(meta[meta_sn_name]['host.zcmb'])\\n self.meta_x0.append(meta[meta_sn_name]['salt2.X0'])\\n self.meta_x0_err.append(meta[meta_sn_name]['salt2.X0.err'])\\n self.meta_x1.append(meta[meta_sn_name]['salt2.X1'])\\n self.meta_x1_err.append(meta[meta_sn_name]['salt2.X1.err'])\\n self.meta_c.append(meta[meta_sn_name]['salt2.Color'])\\n self.meta_c_err.append(meta[meta_sn_name]['salt2.Color.err'])\\n self.meta_mb.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B'])\\n self.meta_mb_err.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B.err'])\\n self.meta_cov_x0_x1.append(meta[meta_sn_name]['salt2.CovX0X1'])\\n self.meta_cov_x0_c.append(meta[meta_sn_name]['salt2.CovColorX0'])\\n self.meta_cov_x1_c.append(meta[meta_sn_name]['salt2.CovColorX1'])\\n self.meta_cov_mb_x1.append(meta[meta_sn_name]['salt2.CovRestFrameMag_0_BX1'])\\n self.meta_cov_mb_c.append(meta[meta_sn_name]['salt2.CovColorRestFrameMag_0_B'])\\n self.meta_idr.append(meta[meta_sn_name]['idr.subset'])\\n \\n self.meta_idr = np.array(self.meta_idr)\\n self.meta_zcmb = np.array(self.meta_zcmb)\\n self.meta_zhl = np.array(self.meta_zhl)\\n self.meta_zhl_err = np.array(self.meta_zhl_err)\\n self.meta_x0 = np.array(self.meta_x0)\\n self.meta_x0_err = np.array(self.meta_x0_err)\\n self.meta_x1 = np.array(self.meta_x1)\\n self.meta_x1_err = np.array(self.meta_x1_err) \\n self.meta_c = np.array(self.meta_c)\\n self.meta_c_err = np.array(self.meta_c_err)\\n self.meta_mb = np.array(self.meta_mb)\\n self.meta_mb_err = np.array(self.meta_mb_err)\\n self.meta_cov_x0_x1 = np.array(self.meta_cov_x0_x1)\\n self.meta_cov_x0_c = np.array(self.meta_cov_x0_c)\\n self.meta_cov_x1_c = np.array(self.meta_cov_x1_c)\\n self.meta_cov_mb_x1 = np.array(self.meta_cov_mb_x1)\\n self.meta_cov_mb_c = np.array(self.meta_cov_mb_c)\",\n \"def read_mb_file(self,idir='.',ifile=None, gmt=True, verbose=False):\\n \\n import numpy as np\\n import os\\n \\n if gmt==True:\\n gmt_file=idir+'/../maps/en_velo.gmt'\\n if isinstance(gmt,str):\\n gmt_file=gmt\\n \\n if gmt != False:\\n self.read_lon_lat(gmt_file,verbose=verbose)\\n \\n if ifile is None:\\n mb_file_basename= idir + '/mb_'+self.code+'_GPS.dat'\\n else:\\n mb_file_basename=ifile\\n \\n data_NEU = []\\n for i in range(1,4):\\n mb_file = mb_file_basename + str(i)\\n\\n # file\\n self.ifile=os.path.abspath(mb_file)\\n \\n data=np.genfromtxt(mb_file,skip_header=4)\\n \\n # reshape to ensure a 2D array\\n if len(data.shape)==1:\\n data=data.reshape((1,data.shape[0]))\\n \\n\\n\\n data_NEU.append(data)\\n\\n if data_NEU[0].shape == data_NEU[1].shape == data_NEU[2].shape:\\n self.data=np.zeros((data_NEU[0].shape[0],7))\\n self.data[:,0]=data_NEU[0][:,0]\\n self.data[:,1]=data_NEU[0][:,1]#*to_mm\\n self.data[:,2]=data_NEU[1][:,1]#*to_mm\\n self.data[:,3]=data_NEU[2][:,1]#*to_mm\\n\\n self.data[:,4]=data_NEU[0][:,2]#*to_mm\\n self.data[:,5]=data_NEU[1][:,2]#*to_mm\\n self.data[:,6]=data_NEU[2][:,2]#*to_mm\\n\\n else: \\n print(\\\"!!! Error reading \\\",mb_file_basename,\\\" :*dat1, *dat2, *dat3 do not have the same length\\\")\\n self.data = None\",\n \"def uadb_ascii_to_dataframe(file=''): \\n \\n if debug:\\n print(\\\"Running uadb_ascii_to_dataframe for: \\\", file) \\n \\n data = check_read_file(file=file, read=True) # TODO\\n \\n #source_file = [l for l in file.split('/') if '.txt' in l][0]\\n\\n nmiss = 0\\n search_h = False \\n read_data = []\\n \\n usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\\n\\n #usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd, iday, ident, numlev= 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\\n obs_id = 0\\n stations_id = [] \\n \\n for i, line in enumerate(data):\\n if line[0] == 'H':\\n try:\\n # Header\\n usi = int(line[2:14]) # unique station identifier\\n \\n ident = int(line[15:21].replace(' ',''))# WMO\\n if ident not in stations_id:\\n stations_id.append(ident)\\n \\n #if len(ident) == 4:\\n # ident = '0' + ident \\n #idflag = int(line[22:24]) # id flag\\n #d_src = int(line[25:28]) # source dataset\\n #version = float(line[29:34]) # version\\n #dateflag = int(line[35:37]) # date flag\\n year = line[38:42] # year\\n month = \\\"%02d\\\" % int(line[43:45])\\n day = \\\"%02d\\\" % int(line[46:48])\\n hour = line[49:53]\\n #locflag = int(line[54:56]) # Location Flag\\n lat = float(line[57:67])\\n lon = float(line[68:78])\\n #ele = float(line[79:85])\\n #stype = int(line[86:88])\\n numlev = int(line[89:93])\\n #pvers = line[94:102]\\n\\n if '99' in hour:\\n hour = hour.replace('99', '00')\\n \\n if '99' in day:\\n search_h = True\\n continue\\n \\n minutes = int(hour) % 100 \\n hour = \\\"%02d\\\" % (int(hour) // 100)\\n if minutes > 60 or minutes < 0:\\n minutes = 0\\n minutes = \\\"%02d\\\" % minutes\\n idate = datetime.strptime(year + month + day + hour + minutes, '%Y%m%d%H%M')\\n iday = int(year + month + day)\\n #pday = int(day)\\n search_h = False\\n\\n except Exception as e:\\n #print(\\\"Error: \\\", i, line, repr(e), \\\"Skipping Block:\\\")\\n search_h = True\\n #iprev = i\\n\\n elif search_h:\\n nmiss += 1\\n continue # Skipping block\\n\\n else:\\n # Data\\n #ltyp = int(line[0:4])\\n p = float(line[5:13])\\n \\n if p != -99999.0 and p != 9999.9: \\n press = float(line[5:13])*100 # converting to Pa, since P is given in mb (1 mb = 1 hPa) \\n else:\\n press = np.nan \\n \\n gph = float(line[14:22]) # gph [m]\\n \\n if gph == -999.0 or gph == -99999.00 or gph >= 99999.0:\\n gph = np.nan\\n \\n temp = float(line[23:29])\\n if temp == -999.0:\\n temp = np.nan \\n else:\\n temp = temp + 273.15\\n \\n rh = float(line[30:36]) # %\\n if rh == -999.0:\\n rh = np.nan\\n else:\\n rh = rh / 100. # convert to absolute ratio TODO\\n\\n wdir = float(line[37:43]) \\n if wdir == -999.0 or wdir == -999 :\\n wdir = np.nan\\n \\n wspd = float(line[44:50]) # [m/s], module of the velocity\\n if wspd <0 :\\n wspd = np.nan \\n \\n try:\\n \\n for value,var in zip([ gph, temp, wspd, wdir, rh], [ 'gph', 'temperature', 'wind_speed', 'wind_direction', 'relative_humidity'] ):\\n obs_id = obs_id +1\\n if not np.isnan(press): # when pressure is available, z_coord== pressure and z_type==1\\n z_type = 1 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n elif (np.isnan(press) and not np.isnan(gph) ) : # when pressure is not available, z_coord== gph and z_type==2 \\n z_type = 2 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, gph, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n else:\\n z_type = -2147483648 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n \\n except:\\n 0\\n \\n \\n \\n #column_names = ['source_file', 'product_code', 'report_id', 'observation_id', 'report_timestamp' , 'iday', 'station_id', 'lat@hdr', 'lon@hdr', 'vertco_reference_1@body', 'obsvalue@body', 'varno@body' , 'units', 'number_of_pressure_levels' ]\\n \\n df = pd.DataFrame(data= read_data, columns= column_names) \\n \\n df['observation_id'] = np.chararray.zfill( (df['observation_id'].astype(int)) .astype('S'+str(id_string_length ) ), id_string_length ) #converting to fixed length bite objects \\n df['report_id'] = np.chararray.zfill( (df['report_id'].astype(int)).astype ('S'+str(id_string_length ) ), id_string_length )\\n \\n df = df.replace([-999.9, -9999, -999, -999.0, -99999.0, -99999.9, 99999.0, -99999.00 ], np.nan)\\n \\n #df['observations_id'] =numpy.char.zfill(numpy.arange(ivar.shape[0]).astype('S10'), 10)\\n \\n df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n #df['report_id'] = numpy.int64 (df['report_id'] ) \\n #df['observation_id'] = numpy.int64 (df['observation_id'] ) \\n df = df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n \\n print('Done reading DF')\\n return df , stations_id\",\n \"def _process_data_file(self):\\n \\n with open(self.data_file, 'r') as f:\\n self.description = f.readline().strip()\\n data = np.loadtxt(self.data_file, skiprows=1)\\n\\n return data\",\n \"def make_data_raw_fast(mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data_fast(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def doImport(self, params):\\n #logging.info(params.filename)\\n raw_data = self.open_filename(params.filename)\\n #logging.info('file open ok')\\n data = self.split_to_blocks(raw_data)\\n #logging.info('splitting ok')\\n chromatograms = self.process_data_to_chromatograms(data)\\n #logging.info('extract ok')\\n \\n processed_data = []\\n \\n zero = params.field_results[\\\"zero\\\"]\\n MIC = params.field_results[\\\"MIC\\\"]\\n TIC = params.field_results[\\\"TIC\\\"]\\n round_MZ = params.field_results[\\\"round_MZ\\\"]\\n \\n for chrom in chromatograms:\\n \\n prefix = chrom['short_title']\\n if (\\\".\\\" in prefix) and round_MZ:\\n first,last = prefix.split('.')\\n prefix = first\\n \\n if (\\\"MIC\\\" in prefix) and (not MIC):\\n pass\\n elif (\\\"TIC\\\" in prefix) and (not TIC):\\n pass\\n else:\\n x = ImportDataset1D(name=prefix+'_time',data = chrom['time'])\\n if zero:\\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal_zero'])\\n else:\\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal'])\\n processed_data.append(x)\\n processed_data.append(y)\\n \\n return processed_data\",\n \"def readdata(self, fname):\\n\\t\\treturn self.__readtsv(fname)\",\n \"def loadDataFromFile(self, filePath):\\r\\n with open(filePath, \\\"r\\\") as signalFile:\\r\\n nameline = signalFile.readlines()[2:5]\\r\\n with open(filePath, \\\"r\\\") as signalFile:\\r\\n dataline = signalFile.readlines()[6:]\\r\\n #name1 = re.findall(r\\\"\\\\<StudentName graduate\\\\=\\\\\\\"([a-z]+)\\\\\\\"\\\\>([\\\\s\\\\w])\\\\<\\\\/StudentName\\\\>\\\", nameline[0], re.X | re.I)\\r\\n stugrad = re.findall(r\\\"\\\\\\\"([a-z]+)\\\\\\\"\\\", nameline[0], re.X | re.I)\\r\\n if str(stugrad[0]) == \\\"true\\\":\\r\\n self.chkGraduate.setChecked(True)\\r\\n elif str(stugrad[0]) == \\\"false\\\":\\r\\n self.chkGraduate.setChecked(False)\\r\\n stuname = re.findall(r\\\"\\\\>([\\\\w\\\\s]+)\\\\<\\\\/StudentName\\\\>\\\", nameline[0], re.X | re.I)\\r\\n self.txtStudentName.setText(str(stuname[0]))\\r\\n stuid = re.findall(r\\\"\\\\<StudentID\\\\>([-\\\\w]+)\\\\<\\\\/StudentID\\\\>\\\", nameline[1], re.X | re.I)\\r\\n self.txtStudentID.setText(str(stuid[0]))\\r\\n stucol = re.findall(r\\\"\\\\<College\\\\>([\\\\w\\\\s]+)\\\\<\\\\/College\\\\>\\\", nameline[2], re.X | re.I)\\r\\n index = self.cboCollege.findText(str(stucol[0]))\\r\\n self.cboCollege.setCurrentIndex(index)\\r\\n #stucol = name3.group(1)\\r\\n setvals = []\\r\\n dataname = [self.txtComponentName_1, self.txtComponentName_2, self.txtComponentName_3, self.txtComponentName_4,self.txtComponentName_5,self.txtComponentName_6,self.txtComponentName_7,self.txtComponentName_8,self.txtComponentName_9,self.txtComponentName_10,self.txtComponentName_11,self.txtComponentName_12,self.txtComponentName_13,self.txtComponentName_14,self.txtComponentName_15,self.txtComponentName_16,self.txtComponentName_17,self.txtComponentName_18,self.txtComponentName_19,self.txtComponentName_20]\\r\\n datanum = [self.txtComponentCount_1, self.txtComponentCount_2, self.txtComponentCount_3, self.txtComponentCount_4,self.txtComponentCount_5,self.txtComponentCount_6,self.txtComponentCount_7,self.txtComponentCount_8,self.txtComponentCount_9,self.txtComponentCount_10,self.txtComponentCount_11,self.txtComponentCount_12,self.txtComponentCount_13,self.txtComponentCount_14,self.txtComponentCount_15,self.txtComponentCount_16,self.txtComponentCount_17,self.txtComponentCount_18,self.txtComponentCount_19,self.txtComponentCount_20]\\r\\n counter = 0\\r\\n for data in dataline:\\r\\n stupro = re.findall(r\\\"name=\\\\\\\"([\\\\w\\\\s-]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n if counter < 20 and len(stupro) > 0:\\r\\n stupro = re.findall(r\\\"name=\\\\\\\"([\\\\w\\\\s-]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n dataname[counter].setText(str(stupro[0]))\\r\\n stunum = re.findall(r\\\"count=\\\\\\\"([0-9]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n datanum[counter].setText(str(stunum[0]))\\r\\n counter += 1\",\n \"def read_data(self):\\n self.data = reduce_spectrum(self.filename)\",\n \"def _get_file_contents(self, idx=None):\\n self._dataSet = netCDF4.Dataset(self.get_file_name())\\n return 1\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n curTest = None\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader('INFO')\\n srData = ins.read(size,'INFO.'+name)\\n bytesRead += 8+size\\n #--Ids\\n if name == 'INAM': self.id = cstrip(srData)\\n elif name == 'PNAM': self.prevId = cstrip(srData)\\n elif name == 'NNAM': self.nextId = cstrip(srData)\\n #--Text/Script\\n elif name == 'NAME': self.text = srData\\n elif name == 'BNAM': self.script = srData\\n elif name == 'SNAM': self.speak = srData\\n #--Quest flags\\n elif name == 'QSTN': self.qflag = 1\\n elif name == 'QSTF': self.qflag = 2\\n elif name == 'QSTR': self.qflag = 3\\n #--String/Value Tests\\n elif name == 'DATA': \\n (self.type, self.spDisp, self.spRank, self.spSex, self.pcRank, self.unk02\\n ) = struct.unpack('2i4B',srData)\\n elif name == 'ONAM': self.spId = cstrip(srData)\\n elif name == 'RNAM': self.spRace = cstrip(srData)\\n elif name == 'CNAM': self.spClass = cstrip(srData)\\n elif name == 'FNAM': self.spFaction = cstrip(srData)\\n elif name == 'ANAM': self.cell = cstrip(srData)\\n elif name == 'DNAM': self.pcFaction = cstrip(srData)\\n #--Function/Value Tests\\n elif name == 'SCVR': \\n (index,type,func,oper) = struct.unpack('BB2sB',srData[:5])\\n text = srData[5:]\\n curTest = Info_Test(type,func,oper,text)\\n self.tests[index-48] = curTest\\n elif name == 'INTV':\\n (curTest.value,) = struct.unpack('i',srData)\\n elif name == 'FLTV':\\n (curTest.value,) = struct.unpack('f',srData)\\n #--Deleted?\\n elif name == 'DELE': self.isDeleted = True\\n #--Bad record?\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\",\n \"def read_data(infile):\\n extension = os.path.splitext(infile)[1]\\n h = read_header(infile)\\n nx = int(h['num_x_pts'])\\n ny = int(h['num_y_pts'])\\n nt = int(h['num_t_pts'])\\n fid = open(infile, 'rb')\\n fid.seek(512) #skip header\\n if extension == '.aps' or extension == '.a3daps':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, ny, nt, order='F').copy() #make N-d image\\n elif extension == '.a3d':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, nt, ny, order='F').copy() #make N-d image\\n elif extension == '.ahi':\\n data = np.fromfile(fid, dtype = np.float32, count = 2* nx * ny * nt)\\n data = data.reshape(2, ny, nx, nt, order='F').copy()\\n real = data[0,:,:,:].copy()\\n imag = data[1,:,:,:].copy()\\n fid.close()\\n if extension != '.ahi':\\n return data\\n else:\\n return real, imag\",\n \"def readData(self):\\n f = open(self.filename)\\n self.time = []\\n self.data = []\\n for line in f:\\n if line.find('BAD FLAG') > 0:\\n self.badValue = float(line.split(':')[1].strip())\\n if line.find('LONGITUDE') > 0:\\n self.lon = line.split(':')[1].strip()\\n if line.find('LATITUDE') > 0:\\n self.lat = line.split(':')[1].strip()\\n if len(line) > 6 and line[2] == '-' and line[6] == '-':\\n parts = line.rsplit(None, 1)\\n # data line\\n timeStamp = datetime.datetime.strptime(parts[0], '%d-%b-%Y %H')\\n t = timeArray.datetimeToEpochTime(timeStamp)\\n self.time.append(t)\\n val = float(parts[1])\\n self.data.append(val)\\n\\n self.time = np.array(self.time)\\n self.data = np.array(self.data)\\n # remove bad values\\n if self.badValue:\\n goodIx = self.data != self.badValue\\n self.time = self.time[goodIx]\\n self.data = self.data[goodIx]\\n self.fileIsRead = True\",\n \"def readAMBERTop(self, phys, filename):\\r\\n\\r\\n def skipLine(data):\\r\\n nl = data.index('\\\\n')\\r\\n return data[nl+1:len(data)]\\r\\n\\r\\n def jumpTo(data, target):\\r\\n fp = data.index(target)\\r\\n return data[fp:len(data)]\\r\\n\\r\\n def readRemove(data, size):\\r\\n retval = data[0:size-1]\\r\\n return data[size:len(data)]\\r\\n\\r\\n def getInteger(data):\\r\\n pos = 0\\r\\n retval = \\\"\\\"\\r\\n while (not data[pos].isdigit()):\\r\\n pos = pos + 1\\r\\n while (data[pos].isdigit()):\\r\\n retval = retval + data[pos]\\r\\n pos = pos + 1\\r\\n data = data[pos:len(data)]\\r\\n return int(retval), data\\r\\n\\r\\n def parse(data, arr, str, count, dtype, tupsize=1):\\r\\n data = jumpTo(data, \\\"%FLAG \\\"+str)\\r\\n data = jumpTo(data, \\\"%FORMAT\\\")\\r\\n numPerLine, data = getInteger(data)\\r\\n fieldsize, data = getInteger(data)\\r\\n data = skipLine(data) \\r\\n \\r\\n arr2 = []\\r\\n numread = 0\\r\\n for j in range(0, (tupsize*count-1) / numPerLine + 1):\\r\\n for i in range(0, numPerLine):\\r\\n if (tupsize == 1):\\r\\n arr.append(dtype(data[0:fieldsize].strip()))\\r\\n else:\\r\\n arr2.append(dtype(data[0:fieldsize].strip()))\\r\\n if (len(arr2) == tupsize):\\r\\n arr.append(arr2)\\r\\n arr2 = []\\r\\n numread += 1\\r\\n data = data[fieldsize:len(data)]\\r\\n if (numread == tupsize*count):\\r\\n break\\r\\n data = skipLine(data) \\r\\n return data\\r\\n\\r\\n def scan(data, str):\\r\\n return (data.count(str) != 0)\\r\\n\\r\\n\\r\\n f = open(filename, 'r')\\r\\n data = f.read()\\r\\n\\r\\n # First Line: VERSION ...\\r\\n data = skipLine(data)\\r\\n\\r\\n # Go To: %FLAG POINTERS\\r\\n data = jumpTo(data, '%FLAG POINTERS')\\r\\n\\r\\n data = jumpTo(data, '%FORMAT')\\r\\n numPerLine, data = getInteger(data)\\r\\n fieldsize, data = getInteger(data)\\r\\n data = skipLine(data)\\r\\n \\r\\n temp = []\\r\\n numread = 0\\r\\n for j in range(0, 31 / numPerLine + 1):\\r\\n for i in range(0, numPerLine):\\r\\n temp.append(int(data[0:8]))\\r\\n data = data[8:len(data)]\\r\\n numread += 1\\r\\n if (numread == 31):\\r\\n break\\r\\n data = skipLine(data)\\r\\n \\r\\n [natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \\r\\n\\r\\n\\r\\n #################################################\\r\\n # Read AtomTypes\\r\\n atomnames = []\\r\\n charges = []\\r\\n masses = []\\r\\n atindex = []\\r\\n exclusions = []\\r\\n nparams = []\\r\\n reslabels = []\\r\\n respointers = []\\r\\n forceconstants = [[], [], []] # bond, angle, dihedral\\r\\n equilvals = [[], [], [[], []]] # bond, angle, dihedral\\r\\n scee_scales = []\\r\\n scnb_scales = []\\r\\n solty = []\\r\\n lj_acoef = []\\r\\n lj_bcoef = []\\r\\n\\r\\n data = parse(data, atomnames, \\\"ATOM_NAME\\\", natoms, str) \\r\\n data = parse(data, charges, \\\"CHARGE\\\", natoms, float)\\r\\n data = parse(data, masses, \\\"MASS\\\", natoms, float)\\r\\n data = parse(data, atindex, \\\"ATOM_TYPE_INDEX\\\", natoms, int)\\r\\n data = parse(data, exclusions, \\\"NUMBER_EXCLUDED_ATOMS\\\", natoms, int)\\r\\n data = parse(data, nparams, \\\"NONBONDED_PARM_INDEX\\\", ntypes*ntypes, int)\\r\\n data = parse(data, reslabels, \\\"RESIDUE_LABEL\\\", nres, str)\\r\\n data = parse(data, respointers, \\\"RESIDUE_POINTER\\\", nres, int)\\r\\n data = parse(data, forceconstants[0], \\\"BOND_FORCE_CONSTANT\\\", numbnd, float)\\r\\n data = parse(data, equilvals[0], \\\"BOND_EQUIL_VALUE\\\", numbnd, float)\\r\\n data = parse(data, forceconstants[1], \\\"ANGLE_FORCE_CONSTANT\\\", numang, float)\\r\\n data = parse(data, equilvals[1], \\\"ANGLE_EQUIL_VALUE\\\", numang, float)\\r\\n data = parse(data, forceconstants[2], \\\"DIHEDRAL_FORCE_CONSTANT\\\", nptra, float)\\r\\n data = parse(data, equilvals[2][0], \\\"DIHEDRAL_PERIODICITY\\\", nptra, float)\\r\\n data = parse(data, equilvals[2][1], \\\"DIHEDRAL_PHASE\\\", nptra, float)\\r\\n if (scan(data, \\\"SCEE_SCALE_FACTOR\\\")):\\r\\n data = parse(data, scee_scales, \\\"SCEE_SCALE_FACTORS\\\", nptra, float)\\r\\n else:\\r\\n for i in range(0, nptra):\\r\\n scee_scales.append(1.2) # Default \\r\\n if (scan(data, \\\"SCNB_SCALE_FACTOR\\\")):\\r\\n data = parse(data, scnb_scales, \\\"SCNB_SCALE_FACTORS\\\", nptra, float)\\r\\n else:\\r\\n for i in range(0, nptra):\\r\\n scnb_scales.append(2.0) # Default \\r\\n\\r\\n data = parse(data, solty, \\\"SOLTY\\\", natyp, float)\\r\\n data = parse(data, lj_acoef, \\\"LENNARD_JONES_ACOEF\\\", ntypes*(ntypes+1)/2, float)\\r\\n data = parse(data, lj_bcoef, \\\"LENNARD_JONES_BCOEF\\\", ntypes*(ntypes+1)/2, float)\\r\\n\\r\\n\\r\\n ##########################################################\\r\\n # STRUCTURE\\r\\n\\r\\n bonds = [[], []] # With H, Without H\\r\\n angles = [[], []] # With H, Without H\\r\\n dihedrals = [[], []] # With H, Without H\\r\\n impropers = [[], []] # With H, Without H\\r\\n excluded_atoms = [] \\r\\n hbond_acoef = []\\r\\n hbond_bcoef = []\\r\\n hbcut = []\\r\\n amber_atom_types = []\\r\\n tree_chain = []\\r\\n join_array = []\\r\\n irotat = []\\r\\n radii = []\\r\\n screen = []\\r\\n\\r\\n data = parse(data, bonds[0], \\\"BONDS_INC_HYDROGEN\\\", nbonh, int, 3)\\r\\n data = parse(data, bonds[1], \\\"BONDS_WITHOUT_HYDROGEN\\\", nbona, int, 3)\\r\\n data = parse(data, angles[0], \\\"ANGLES_INC_HYDROGEN\\\", ntheth, int, 4)\\r\\n data = parse(data, angles[1], \\\"ANGLES_WITHOUT_HYDROGEN\\\", ntheta, int, 4)\\r\\n data = parse(data, dihedrals[0], \\\"DIHEDRALS_INC_HYDROGEN\\\", nphih, int, 5)\\r\\n data = parse(data, dihedrals[1], \\\"DIHEDRALS_WITHOUT_HYDROGEN\\\", nphia, int, 5)\\r\\n \\r\\n # MERGE ARRAYS - PM HANDLES THE H+\\r\\n final_bonds = bonds[0] + bonds[1]\\r\\n final_angles = angles[0] + angles[1]\\r\\n final_dihedrals = dihedrals[0] + dihedrals[1]\\r\\n final_impropers = []\\r\\n \\r\\n # CLEAN UP THE TRASH\\r\\n del(bonds)\\r\\n del(angles)\\r\\n del(dihedrals)\\r\\n \\r\\n\\r\\n # Move impropers into their own array\\r\\n i = 0\\r\\n while (i < len(final_dihedrals)):\\r\\n if (final_dihedrals[i][2] < 0): # 1-4 exclusions are handled by our back end\\r\\n final_dihedrals[i][2] *= -1\\r\\n if (final_dihedrals[i][3] < 0):\\r\\n final_dihedrals[i][3] *= -1 # Make + again\\r\\n final_impropers.append(final_dihedrals[i])\\r\\n final_dihedrals.remove(final_dihedrals[i])\\r\\n i -= 1\\r\\n i += 1\\r\\n\\r\\n # Convert charge units\\r\\n for i in range(0, len(charges)):\\r\\n charges[i] /= 18.223\\r\\n\\r\\n\\r\\n data = parse(data, excluded_atoms, \\\"EXCLUDED_ATOMS_LIST\\\", nnb, int)\\r\\n data = parse(data, hbond_acoef, \\\"HBOND_ACOEF\\\", nphb, float)\\r\\n data = parse(data, hbond_bcoef, \\\"HBOND_BCOEF\\\", nphb, float)\\r\\n data = parse(data, hbcut, \\\"HBCUT\\\", nphb, float)\\r\\n data = parse(data, amber_atom_types, \\\"AMBER_ATOM_TYPE\\\", natoms, str)\\r\\n data = parse(data, tree_chain, \\\"TREE_CHAIN_CLASSIFICATION\\\", natoms, str)\\r\\n data = parse(data, join_array, \\\"JOIN_ARRAY\\\", natoms, int)\\r\\n data = parse(data, irotat, \\\"IROTAT\\\", natoms, int)\\r\\n data = parse(data, radii, \\\"RADII\\\", natoms, float)\\r\\n data = parse(data, screen, \\\"SCREEN\\\", natoms, float)\\r\\n\\r\\n # Further process dihedrals and impropers\\r\\n # Deal with multiplicity\\r\\n # A bit ugly, but the fastest for now\\r\\n # forceconstants[2][dihedrals[0][i][4]-1], int(equilvals[2][0][dihedrals[0][i][4]-1]), equilvals[2][1][dihedrals[0][i][4]-1]\\r\\n\\r\\n mult_di = dict()\\r\\n mult_im = dict()\\r\\n for i in range(0, len(final_dihedrals)):\\r\\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\\r\\n if (not mult_di.has_key(di_id)):\\r\\n mult_di[di_id] = [1, False, [forceconstants[2][final_dihedrals[i][4]-1]], [int(equilvals[2][0][final_dihedrals[i][4]-1])], [equilvals[2][1][final_dihedrals[i][4]-1]]]\\r\\n else:\\r\\n mult_di[di_id][0] += 1\\r\\n mult_di[di_id][2].append(forceconstants[2][final_dihedrals[i][4]-1])\\r\\n mult_di[di_id][3].append(int(equilvals[2][0][final_dihedrals[i][4]-1]))\\r\\n mult_di[di_id][4].append(equilvals[2][1][final_dihedrals[i][4]-1])\\r\\n \\r\\n for i in range(0, len(final_impropers)):\\r\\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\\r\\n if (not mult_im.has_key(di_id)):\\r\\n mult_im[im_id] = [1, False, [forceconstants[2][final_impropers[i][4]-1]], [int(equilvals[2][0][final_impropers[i][4]-1])], [equilvals[2][1][final_impropers[i][4]-1]]]\\r\\n else:\\r\\n mult_im[im_id][0] += 1\\r\\n mult_im[im_id][2].append(forceconstants[2][final_impropers[i][4]-1])\\r\\n mult_im[im_id][3].append(int(equilvals[2][0][final_impropers[i][4]-1]))\\r\\n mult_im[im_id][4].append(equilvals[2][1][final_impropers[i][4]-1])\\r\\n\\r\\n\\r\\n\\r\\n \\r\\n #[natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \\r\\n #phys.myPSF.createAll(natoms, nbonh+mbona, ntheth+mtheta,\\r\\n # len(dihedrals[0])+len(dihedrals[1]),\\r\\n # len(impropers[0])+len(impropers[1]),\\r\\n # 0, 0, 0, 0)\\r\\n \\r\\n # Add atoms\\r\\n curres = 1\\r\\n for i in range(0, natoms):\\r\\n phys.myPSF.addAtom(i, 'SIM', curres, reslabels[curres-1],\\r\\n atomnames[i], atomnames[i], charges[i],\\r\\n masses[i]) \\r\\n if (curres != nres and i >= respointers[curres]):\\r\\n curres += 1\\r\\n\\r\\n # Add bonds\\r\\n for i in range(0, nbonh+nbona):\\r\\n phys.myPSF.addBond(i+1, final_bonds[i][0]/3+1, final_bonds[i][1]/3+1)\\r\\n phys.myPAR.addBond(i+1, atomnames[final_bonds[i][0]/3], atomnames[final_bonds[i][1]/3], forceconstants[0][final_bonds[i][2]/3], equilvals[0][final_bonds[i][2]/3])\\r\\n \\r\\n # Add angles\\r\\n for i in range(0, ntheth+ntheta):\\r\\n phys.myPSF.addAngle(i+1, final_angles[i][0]/3+1, final_angles[i][1]/3+1, final_angles[i][2]/3+1)\\r\\n phys.myPAR.addAngle(i+1, atomnames[final_angles[i][0]/3], atomnames[final_angles[i][1]/3], atomnames[final_angles[i][2]/3], forceconstants[1][final_angles[i][3]/3], equilvals[1][final_angles[i][3]/3])\\r\\n \\r\\n # Add dihedrals\\r\\n for i in range(0, len(final_dihedrals)):\\r\\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\\r\\n mult = mult_di[di_id][0]\\r\\n checked = mult_di[di_id][1]\\r\\n print di_id, \\\" \\\", mult\\r\\n if (not checked):\\r\\n if (mult == 1):\\r\\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\\r\\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], forceconstants[2][final_dihedrals[i][4]-1], int(equilvals[2][0][final_dihedrals[i][4]-1]), equilvals[2][1][final_dihedrals[i][4]-1])\\r\\n else:\\r\\n mult_di[di_id][1] = True\\r\\n # Add dihedral with the appropriate multiplicity\\r\\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\\r\\n fcvec = PARReader.VectorOfDouble()\\r\\n periodvec = PARReader.VectorOfInt()\\r\\n phasevec = PARReader.VectorOfDouble() \\r\\n for j in range(0, len(mult_di[di_id][2])):\\r\\n fcvec.push_back(mult_di[di_id][2][j])\\r\\n periodvec.push_back(mult_di[di_id][3][j])\\r\\n phasevec.push_back(mult_di[di_id][4][j])\\r\\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\\r\\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], mult, fcvec, periodvec, phasevec)\\r\\n \\r\\n\\r\\n\\r\\n\\r\\n for i in range(0, len(final_impropers)):\\r\\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\\r\\n mult = mult_im[im_id][0]\\r\\n checked = mult_im[im_id][1]\\r\\n print im_id, \\\" \\\", mult\\r\\n if (not checked):\\r\\n if (mult == 1):\\r\\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\\r\\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], forceconstants[2][final_impropers[i][4]-1], int(equilvals[2][0][final_impropers[i][4]-1]), equilvals[2][1][final_impropers[i][4]-1])\\r\\n else:\\r\\n mult_im[im_id][1] = True\\r\\n # Add dihedral with the appropriate multiplicity\\r\\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\\r\\n fcvec = PARReader.VectorOfDouble()\\r\\n periodvec = PARReader.VectorOfInt()\\r\\n phasevec = PARReader.VectorOfDouble() \\r\\n for j in range(0, len(mult_im[im_id][2])):\\r\\n fcvec.push_back(mult_im[im_id][2][j])\\r\\n periodvec.push_back(mult_im[im_id][3][j])\\r\\n phasevec.push_back(mult_im[im_id][4][j])\\r\\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\\r\\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], mult, fcvec, periodvec, phasevec)\\r\\n\\r\\n \\r\\n # Need to add garbage nonbonded stuff for now\\r\\n for i in range(0, natoms):\\r\\n phys.myPAR.addNonbonded(i, atomnames[i], 1, 1, 1, 1, 1, 1)\\r\\n\\r\\n # Add VDW parameters\\r\\n # AMBER has the Aij and Bij already in the parameter file\\r\\n # This actually makes life easier.\\r\\n # CHARMM does not, they simply have the original sigma and epsilon.\\r\\n # To compensate for this, for now we will leave the nonbondeds empty in phys.myPAR\\r\\n # We will then access the LennardJones parameter table in Topology directly\\r\\n k = 0\\r\\n phys.myTop.resizeLennardJonesParameters(ntypes)\\r\\n for i in range(0, ntypes):\\r\\n for j in range(i, ntypes):\\r\\n params = GenericTopology.LennardJonesParameters(lj_acoef[k], lj_bcoef[k])\\r\\n k += 1\\r\\n phys.myTop.setLennardJonesParameters(i, j, params)\\r\\n \\r\\n phys.myPAR.readFlag = 1\\r\\n phys.build()\",\n \"def __init__(self, fileName):\\n in_ = JInputStream.getInputStream(fileName)\\n b = int()\\n len = int()\\n cs = int()\\n addr = int()\\n buf = [None]*255\\n eof = False\\n line = 0\\n i = 0\\n while len(ihxData):\\n self.ihxData[i] = -1\\n i += 1\\n try:\\n while not eof:\\n while True:\\n b = in_.read()\\n if b < 0:\\n raise IhxParseException(\\\"Inexpected end of file\\\")\\n if not ((b != int(':'))):\\n break\\n line += 1\\n len = self.readHexByte(in_)\\n # length field \\n cs = len\\n b = self.readHexByte(in_)\\n # address field \\n cs += b\\n addr = b << 8\\n b = self.readHexByte(in_)\\n cs += b\\n addr |= b\\n b = self.readHexByte(in_)\\n # record type field\\n cs += b\\n while i < len:\\n # data\\n buf[i] = int(self.readHexByte(in_))\\n cs += buf[i]\\n i += 1\\n cs += self.readHexByte(in_)\\n # checksum\\n if (cs & 0xff) != 0:\\n raise IhxParseException(\\\"Checksum error\\\")\\n if b == 0:\\n # data record\\n while i < len:\\n if self.ihxData[addr + i] >= 0:\\n System.err.println(\\\"Warning: Memory at position \\\" + Integer.toHexString(i) + \\\" overwritten\\\")\\n self.ihxData[addr + i] = int((buf[i] & 255))\\n i += 1\\n elif b == 1:\\n # eof record\\n eof = True\\n else:\\n raise IhxParseException(\\\"Invalid record type: \\\" + b)\\n except IhxParseException as e:\\n raise IhxFileDamagedException(fileName, line, e.getLocalizedMessage())\\n try:\\n in_.close()\\n except Exception as e:\\n System.err.println(\\\"Warning: Error closing file \\\" + fileName + \\\": \\\" + e.getLocalizedMessage())\",\n \"def load_data(filename):\\n\\n evidance = list()\\n labels = list()\\n row = list()\\n\\n with open(filename) as file:\\n for j, item in enumerate(file):\\n if(j == 0):\\n continue\\n row = []\\n for i, el in enumerate(item.split(',')):\\n # print(el)\\n if(i in {0,2,4,11,12,13,14}):\\n row.append(int(el))\\n elif(i in {1,3,5,6,7,8,9}):\\n row.append(float(el))\\n elif(i==10):\\n if(el == \\\"Jan\\\"):\\n row.append(int(0))\\n elif(el == \\\"Feb\\\"): \\n row.append(int(1))\\n elif(el == \\\"Mar\\\"): \\n row.append(int(2))\\n elif(el == \\\"Apr\\\"): \\n row.append(int(3))\\n elif(el == \\\"May\\\"): \\n row.append(int(4))\\n elif(el == \\\"June\\\"): \\n row.append(int(5))\\n elif(el == \\\"Jul\\\"): \\n row.append(int(6))\\n elif(el == \\\"Aug\\\"): \\n row.append(int(7))\\n elif(el == \\\"Sep\\\"): \\n row.append(int(8))\\n elif(el == \\\"Oct\\\"): \\n row.append(int(9))\\n elif(el == \\\"Nov\\\"): \\n row.append(int(10))\\n elif(el == \\\"Dec\\\"): \\n row.append(int(11))\\n else:\\n print(\\\"wrong month Exit\\\")\\n quit()\\n elif(i==15):\\n if(el == 'Returning_Visitor'):\\n row.append(int(1))\\n else:\\n row.append(int(0))\\n elif(i==16):\\n if(el == 'TRUE'):\\n row.append(int(1))\\n else:\\n row.append(int(0)) \\n elif(i==17):\\n if(el == 'TRUE\\\\n' or el == 'TRUE'):\\n row.append(int(1))\\n else:\\n row.append(int(0))\\n else:\\n print(\\\"Incorrect value\\\")\\n\\n evidance.append(row)\\n # print(item.split(',')[-1])\\n if(item.split(',')[-1] == 'FALSE\\\\n'):\\n labels.append(0)\\n else:\\n labels.append(1)\\n\\n # print(labels)\\n return (evidance, labels)\",\n \"def open(self):\",\n \"def CSV_Load_File( self, infilename ):\\n print( 'Loading \\\"{}\\\"'.format(infilename) )\\n IN = open( infilename, 'r' )\\n standname = None\\n laststand = None\\n for L in IN:\\n if( L[0:9] == 'Site/Plot' ): continue\\n col = L.split( ',' )\\n standname = col[0]\\n year = int(col[1])\\n #if( re.search( '-', standname ) != None ):\\n # loc = re.search( '-', standname )\\n # year = int(standname[loc.start()+1:])\\n # standname = standname[0:loc.start()]\\n #print standname, year\\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\\\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\\n if( OPT['d'] ):\\n if( dbh > 10.0 ): dbh *= 1.25\\n if( dbh > 15.0 ): dbh *= 1.50\\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\\n ntree = len( self.Data.Stand[standname].Tree ) + 1\\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\\n laststand = standname\\n IN.close()\",\n \"def make_tag_data_raw_fast(mdp,filename):\\n #\\n fin = open(filename,'r')\\n iter = 0\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"comb_path\\\":\\n update_params(mdp,lsp)\\n if not mdp.flag_out_open: ## -- try to open output file\\n try:\\n if mdp.flag_overwrite == \\\"True\\\": ## check string value!\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ## -- try open output file\\n for file in glob.glob(mdp.input_path):\\n # get sign which corrects for boundary condition\\n tvals = file.split('/')[-1].split('_')[3].split('t')\\n try:\\n ## flip sign if requested\\n bcsign = ((int(tvals[1])+int(tvals[2])) != (int(tvals[1])+int(tvals[2])) % mdp.corr_len)\\n except IndexError:\\n ## 2-point function\\n bcsign = False\\n try:\\n # open correlator file\\n mdp.corr_file = open(file,'r')\\n except IOError:\\n print \\\"Could not open file \\\",file\\n continue\\n ## -- get tag\\n ## baryons:\\n #mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_r'+file.split('/')[-1].split('_')[4][-1]\\n ## with time source tag\\n #mdp.tag = file.split('/')[-1].split('_')[3][:3]\\\\\\n # +'_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\\\\n # +file.split('/')[-1].split('_')[4][3:]\\n ## no time source tag\\n mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\\\\n +file.split('/')[-1].split('_')[4][3:]\\n #print file,',',mdp.tag\\n iter+=1\\n ##endif ! flag_out_open\\n\\n #save_data_fast(mdp)\\n save_data_fast_bc(mdp,bcsign)\\n mdp.corr_file.close()\\n if iter%400 == 0:\\n print \\\"file\\\",iter\\n max_iter = None\\n if not(max_iter is None) and iter==max_iter:\\n print \\\"reached max file iterations, ending loop...\\\"\\n break\\n ## end comb_path\\n pass\\n\\n elif lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data_fast(mdp)\\n mdp.corr_file.close()\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def _load_data(self, save_temp=False):\\n # directly read processed data and encode\\n print ('Start tokenizing data...')\\n self.data = json.loads(\\n open(self.cfg.data_path+self.cfg.data_file, 'r', encoding='utf-8').read().lower())\\n self.train, self.dev, self.test = [], [], []\\n print ('Start encoding data...')\\n p = progressbar.ProgressBar(len(self.data))\\n p.start()\\n p_idx = 0\\n for fn, dial in self.data.items():\\n p.update(p_idx)\\n p_idx += 1\\n if '.json' in fn:\\n fn = fn.replace('.json', '')\\n if 'all' in self.cfg.exp_domains or self.exp_files.get(fn):\\n if self.dev_files.get(fn):\\n self.dev.append(self._get_encoded_data(fn, dial))\\n elif self.test_files.get(fn):\\n self.test.append(self._get_encoded_data(fn, dial))\\n else:\\n if self.data_mode == 'train':\\n self.train.append(self._get_encoded_data(fn, dial))\\n elif self.data_mode == 'test':\\n pass\\n else:\\n raise Exception('Wrong Reader Data Mode!!!')\\n p.finish()\",\n \"def load_datapair(self, ds):\\n raise NotImplementedError(\\\"Define this in your derived checker class\\\")\",\n \"def load_data(self):\\n df= self.read_file()\\n for row,col in df.iterrows():\\n Employeeid = int(col['Empolyeeid'])\\n Employee_Name = col['Employee_Name']\\n Age = col['Age']\\n Salary = col['Salary']\\n self.table.put_item(\\n Item={\\n \\\"Employeeid\\\":Employeeid,\\n \\\"Employee_Name\\\": Employee_Name,\\n \\\"Age\\\": Age,\\n \\\"Salary\\\": Salary\\n }\\n )\\n return True\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def build(self, file_number, data):\\n pass\",\n \"def reading_data(fname,goal):\\n \\n #Reading of the EEG data\\n data = pd.read_csv(fname)\\n events_fname = fname.replace('_data','_events')\\n labels= pd.read_csv(events_fname)\\n\\n if goal==\\\"training\\\":\\n data=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n elif goal==\\\"testing\\\":\\n labels=labels.drop(['id' ], axis=1)\\n else:\\n raise SystemExit(\\\"The goal variable is unknown for the function\\\")\\n\\n return data, labels\",\n \"def load_data():\\r\\n #读取User数据\\r\\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\\r\\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\\r\\n users = users.filter(regex='UserID|Gender|Age|JobID')\\r\\n users_orig = users.values\\r\\n #改变User数据中性别和年龄\\r\\n gender_map = {'F':0, 'M':1}\\r\\n users['Gender'] = users['Gender'].map(gender_map)\\r\\n\\r\\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\\r\\n users['Age'] = users['Age'].map(age_map)\\r\\n\\r\\n #读取Movie数据集\\r\\n movies_title = ['MovieID', 'Title', 'Genres']\\r\\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\\r\\n movies_orig = movies.values\\r\\n #将Title中的年份去掉\\r\\n pattern = re.compile(r'^(.*)\\\\((\\\\d+)\\\\)$')\\r\\n\\r\\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #电影类型转数字字典\\r\\n genres_set = set()\\r\\n for val in movies['Genres'].str.split('|'):\\r\\n genres_set.update(val)\\r\\n\\r\\n genres_set.add('<PAD>')\\r\\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\\r\\n\\r\\n #将电影类型转成等长数字列表，长度是18\\r\\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\\r\\n\\r\\n for key in genres_map:\\r\\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\\r\\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\\r\\n \\r\\n movies['Genres'] = movies['Genres'].map(genres_map)\\r\\n\\r\\n #电影Title转数字字典\\r\\n title_set = set()\\r\\n for val in movies['Title'].str.split():\\r\\n title_set.update(val)\\r\\n \\r\\n title_set.add('<PAD>')\\r\\n title2int = {val:ii for ii, val in enumerate(title_set)}\\r\\n\\r\\n #将电影Title转成等长数字列表，长度是15\\r\\n title_count = 15\\r\\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\\r\\n \\r\\n for key in title_map:\\r\\n for cnt in range(title_count - len(title_map[key])):\\r\\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\\r\\n \\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #读取评分数据集\\r\\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\\r\\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\\r\\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\\r\\n\\r\\n #合并三个表\\r\\n data = pd.merge(pd.merge(ratings, users), movies)\\r\\n \\r\\n #将数据分成X和y两张表\\r\\n target_fields = ['ratings']\\r\\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\\r\\n \\r\\n features = features_pd.values\\r\\n targets_values = targets_pd.values\\r\\n \\r\\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig\",\n \"def extract(self, data):\",\n \"def prepare_dataset(fpath):\\n raise NotImplementedError\",\n \"def apply(self, opened_file):\",\n \"def read_data():\\n ADV_MAT = np.load('ADV.npy');\\n ADJ_MAT = np.load('ADJ.npy');\\n PR_MAT = np.load('PR.npy'); \\n NN_MAT = np.load('NN.npy');\\n for i in range(ADV_MAT.shape[0]):RUNNING_DATA['ADV___'+str(i)] = ADV_MAT[i];\\n for i in range(ADJ_MAT.shape[0]):RUNNING_DATA['ADJ___'+str(i)] = ADJ_MAT[i];\\n for i in range(PR_MAT.shape[0]):RUNNING_DATA['PR___'+str(i)] = PR_MAT[i];\\n for i in range(NN_MAT.shape[0]):RUNNING_DATA['NN___'+str(i)] = NN_MAT[i];\",\n \"def process_file(self, data, filename):\\n\\n for relation in data:\\n if \\\"start_date\\\" in relation[1]:\\n start = convert_to_date(relation[1][\\\"start_date\\\"])\\n end = convert_to_date(relation[1][\\\"end_date\\\"])\\n\\n if start <= START_DATE and end >= START_DATE:\\n first = relation[1][\\\"first\\\"]\\n second = relation[1][\\\"second\\\"]\\n if first not in self.relations:\\n self.add_tag(first)\\n if second not in self.relations:\\n self.add_tag(second)\\n\\n if relation[0] == \\\"vassal\\\": \\n self.relations[first][\\\"vassal\\\"].append(second)\\n self.relations[second][\\\"overlord\\\"].append(first)\\n elif relation[0] == \\\"alliance\\\":\\n self.relations[first][\\\"alliance\\\"].append(second)\\n self.relations[second][\\\"alliance\\\"].append(first)\\n elif relation[0] == \\\"dependency\\\":\\n self.relations[first][\\\"tributary\\\"].append(second)\\n self.relations[second][\\\"hegemon\\\"].append(first)\\n elif relation[0] == \\\"guarantee\\\":\\n self.relations[first][\\\"guaranteeing\\\"].append(second)\\n self.relations[second][\\\"guarantor\\\"].append(first)\\n elif relation[0] == \\\"union\\\":\\n self.relations[first][\\\"junior\\\"].append(second)\\n self.relations[second][\\\"senior\\\"].append(first)\\n elif relation[0] == \\\"royal_marriage\\\":\\n self.relations[first][\\\"marriage\\\"].append(second)\\n self.relations[second][\\\"marriage\\\"].append(first)\",\n \"def read_data(self):\\n data = np.genfromtxt(self.__file) # Planck SED\\n self.__nu = 10.0**data[:,0]\\n self.__nuF = 10.0**data[:,2]\\n self.__err = 10.0**data[:,3]\\n #self.__W = 10.0**data[:,4]\\n self.__yerr = [ self.__nuF - self.__nuF / self.__err, \\\\\\n self.__nuF * self.__err - self.__nuF ]\\n self.__maxY = max( self.__nuF )\\n self.__minY = min( self.__nuF )\",\n \"def load_data(self):\\n with open(self.config.drv_log, 'r') as f:\\n add_data = [] # addtional data require angle smoothing\\n ori_data = [] # given data\\n ddir = os.path.dirname(self.config.drv_log)\\n lines = list(csv.reader(f))\\n np.random.shuffle(lines)\\n for line in lines:\\n if line[0] == 'center':\\n continue\\n ste_ang = float(line[3])\\n if ste_ang == 0. and random.random() < 0.8:\\n continue\\n if '12_01' in line[0]:\\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n else:\\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n \\n def angle_smoothing(data):\\n \\\"\\\"\\\"helper function for smoothing steering angles.\\n reference: \\n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\\n \\\"\\\"\\\"\\n angles = np.asarray([itm[1] for itm in data])\\n fwd = pd.ewma(angles, span=20)\\n bwd = pd.ewma(angles[::-1], span=20)\\n smooth = np.vstack((fwd, bwd[::-1]))\\n smooth = np.mean(smooth, axis=0)\\n angles = np.ndarray.tolist(smooth)\\n smooth_data = []\\n for i in range(len(data)):\\n smooth_data.append([data[i][0], angles[i]])\\n return smooth_data\\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\\n all_data = ori_data + add_data\\n n1 = int(len(all_data) * 0.7)\\n n2 = int(len(all_data) * 0.85)\\n self.train = self.data_generator(all_data[:n1])\\n self.valid = self.data_generator(all_data[n1:n2])\\n self.test = self.data_generator(all_data[n2:])\\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size\",\n \"def import_data_helper(self): \\n if len(self.components) == 1:\\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\\n else: \\n global_id = []\\n for c in self.components:\\n global_id.append(hapi.ISO[c][0])\\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)\",\n \"def initialize_data(self , station = '', datasets = {} ):\\n \\n self.datasets = datasets\\n self.datasets_keys = datasets.keys()\\n self.station = station\\n \\n data = {} # container for the data of each dataset\\n source_configuration = {} # container for the source_configuration of each dataset\\n \\n\\n \\n \\\"\\\"\\\" Looping over the datasets \\\"\\\"\\\"\\n logging.info('*** Reading and Initializing the data from the netCDF files ')\\n \\n \\n for k,v in datasets.items() :\\n logging.info(' Initialising the dataset: *** %s ' , k )\\n data[k] = {} \\n data['cdm_tables'] = {} \\n \\n ### alternative with xarray \\n #ds = xr.load_dataset(v) \\n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n ### alternative with netCDF4\\n #ds = nc.Dataset(v) \\n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \\n \\n ###for h5py but cant extract date time units !!!\\n ds = h5py.File(v , driver=\\\"core\\\" ) \\n data[k]['df'] = ds # storing the entire file \\n try: \\n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\\n except:\\n data[k]['source_file'] = str(v) # temp fix \\n \\n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \\n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\\n #data[k]['recordindex'] = ds['recordindex'].value \\n #ds.close() \\n logging.debug('Reading the file with h5py ')\\n \\n \\n # add here appending datasets for the case of ncar_w and ncar_t \\n \\n \\n self.data = data\\n self.make_dataframe()\\n ds.close()\\n \\n \\\"\\\"\\\" Reading the header_table, station_configuration, source_configuration \\\"\\\"\\\"\\n for k,v in datasets.items() : \\n \\n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \\n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\\n #data[k]['recordindex'] = d['recordindex'].values \\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \\n data[k]['station_configuration'] = d.to_dataframe() \\n #data[k]['station_configuration'] = d ### USELESS ? \\n logging.debug('Done with %s station_configuration' , str(k) )\\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \\n logging.debug('Loading the header_table') \\n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['header_table'] = {}\\n for var in d.variables:\\n self.attributes['header_table'][var] = {}\\n self.attributes['header_table'][var]['description'] = d[var].description\\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \\n data[k]['header_table'] = d.to_dataframe() \\n logging.debug('Done with %s ' , k )\\n \\n logging.info(\\\"*** Loading the observations_table (might take time) %s\\\" , k ) \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['observations_table'] = {}\\n for var in d.variables:\\n self.attributes['observations_table'][var] = {}\\n self.attributes['observations_table'][var]['description'] = d[var].description\\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\\n \\n \\n logging.info(\\\"*** Loading the source configuration %s\\\" , k ) \\n try: \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\\n d = d.isel(hdrlen=[0])\\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \\n logging.debug('Done with %s source_configuration' , k )\\n except: \\n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \\n \\n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \\n data[k]['era5fb'] = d.to_dataframe() \\n logging.debug('Done with %s era5 feedback ', k )\\n \\n \\\"\\\"\\\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \\\"\\\"\\\" \\n if list(datasets.keys()).index(k) == 0 :\\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \\n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\\n data['cdm_tables'][t] = d \\n \\n d.close() \\n ds.close()\\n\\n \\\"\\\"\\\" Reading the name of the original source file \\\"\\\"\\\"\\n source_configuration[k] = {} \\n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\\n\\n \\n \\\"\\\"\\\" Storing the station configurations \\\"\\\"\\\" \\n self.source_configuration = source_configuration \\n \\n \\\"\\\"\\\" Making all date_times \\\"\\\"\\\" \\n self.make_all_datetime()\\n \\n \\n \\\"\\\"\\\" feedback columns \\\"\\\"\\\"\\n if 'era5_1' in list (self.data.keys() ):\\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \\n else:\\n self.fb_columns = ['empty']\",\n \"def read_inversion_info(file_dic):\\n #print_file_test = open('file_test.txt','w')\\n\\n if not ( check_inversion_files(file_dic) ):\\n print 'error(read_inversion_info): problem with lenstool file names'\\n return 0\\n \\n file_generate_arcs = file_dic['file_generate_arcs']\\n info_input_lens = fc.extract_second_identifiers( file_generate_arcs, \\\\\\n 'potential' )\\n#-------------------------------------------------------------------------------\\n\\n file_source = file_dic['file_source']\\n info_src = np.loadtxt(file_source, unpack=False)\\n if len(info_src) == 8 and np.isscalar(info_src[0]):\\n #FIXME - check if the second condition is all we need\\n info_src = [info_src]\\n#-------------------------------------------------------------------------------\\n\\n file_make_inversion = file_dic['file_make_inversion']\\n info_fited_param = fc.extract_second_identifiers( file_make_inversion, \\\\\\n 'limit' )\\n info_forme = fc.extract_parameter(file_make_inversion, 'forme')[0][0]\\n\\n#-------------------------------------------------------------------------------\\n\\n file_best_fit = file_dic['file_best_fit']\\n info_best_fit = fc.extract_second_identifiers( file_best_fit, \\\\\\n 'potentiel' )\\n\\n info_xi2 = fc.extract_parameter(file_best_fit, '#Chi2pos:')\\n\\n#-------------------------------------------------------------------------------\\n file_chires = file_dic['file_chires']\\n\\n info_chires = extract_parameter(file_chires, '0')\\n rmss_mean = [0.0, 0.0]\\n rmsi_mean = [0.0, 0.0]\\n for i in info_chires:\\n if i[0] != 'A':\\n rmss_mean[0] = rmss_mean[0] + float(i[7])\\n rmss_mean[1] = rmss_mean[1] + 1.0\\n \\n rmsi_mean[0] = rmsi_mean[0] + float(i[8])\\n rmsi_mean[1] = rmsi_mean[1] + 1.0\\n\\n rmss_mean = rmss_mean[0]/rmss_mean[1]\\n rmsi_mean = rmsi_mean[0]/rmsi_mean[1]\\n#-------------------------------------------------------------------------------\\n out_dict = { 'xi2' : float(info_xi2[0][0]), \\\\\\n 'best_fit_lens' : info_best_fit, \\\\\\n 'rmsi_mean' : rmsi_mean, \\\\\\n 'rmss_mean' : rmss_mean, \\\\\\n 'fited_parameters' : info_fited_param[0].keys(), \\\\\\n 'input_lens' : info_input_lens[len(info_input_lens) - 1], \\\\\\n 'forme' : info_forme \\\\\\n }\\n #for i in out_dict.keys():\\n # print i, out_dict[i]\\n return out_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7388499","0.7290581","0.71939653","0.65396297","0.6443839","0.6359168","0.6344289","0.622777","0.62194926","0.6214607","0.6213822","0.6159157","0.61423117","0.6136031","0.6111043","0.6104353","0.60962135","0.60925573","0.6089976","0.6075218","0.6048531","0.6029819","0.6029783","0.6022913","0.6008295","0.59835845","0.59808487","0.59717","0.5969598","0.5969598","0.5969598","0.5964615","0.5963177","0.5961191","0.59464836","0.5936595","0.5934951","0.5928706","0.59275407","0.5923761","0.59187186","0.5912831","0.59094757","0.590535","0.58943135","0.5888419","0.5888419","0.5888419","0.58872783","0.58845896","0.5881818","0.58792377","0.5870371","0.5864612","0.5862901","0.5860989","0.58590937","0.5857562","0.5851539","0.585117","0.58492124","0.58452886","0.58424413","0.5841246","0.5839079","0.58315724","0.58312756","0.58261436","0.5818827","0.58123136","0.5811935","0.5809991","0.5809069","0.5807695","0.5806935","0.58048975","0.5801177","0.58005667","0.57988936","0.57961535","0.5787455","0.5787121","0.5786493","0.5785911","0.5778448","0.5778394","0.57774955","0.5770257","0.57656175","0.57649046","0.5760979","0.57590723","0.5755032","0.57540435","0.5753703","0.57523173","0.57522035","0.5748234","0.57372934","0.57355225","0.57346874"],"string":"[\n \"0.7388499\",\n \"0.7290581\",\n \"0.71939653\",\n \"0.65396297\",\n \"0.6443839\",\n \"0.6359168\",\n \"0.6344289\",\n \"0.622777\",\n \"0.62194926\",\n \"0.6214607\",\n \"0.6213822\",\n \"0.6159157\",\n \"0.61423117\",\n \"0.6136031\",\n \"0.6111043\",\n \"0.6104353\",\n \"0.60962135\",\n \"0.60925573\",\n \"0.6089976\",\n \"0.6075218\",\n \"0.6048531\",\n \"0.6029819\",\n \"0.6029783\",\n \"0.6022913\",\n \"0.6008295\",\n \"0.59835845\",\n \"0.59808487\",\n \"0.59717\",\n \"0.5969598\",\n \"0.5969598\",\n \"0.5969598\",\n \"0.5964615\",\n \"0.5963177\",\n \"0.5961191\",\n \"0.59464836\",\n \"0.5936595\",\n \"0.5934951\",\n \"0.5928706\",\n \"0.59275407\",\n \"0.5923761\",\n \"0.59187186\",\n \"0.5912831\",\n \"0.59094757\",\n \"0.590535\",\n \"0.58943135\",\n \"0.5888419\",\n \"0.5888419\",\n \"0.5888419\",\n \"0.58872783\",\n \"0.58845896\",\n \"0.5881818\",\n \"0.58792377\",\n \"0.5870371\",\n \"0.5864612\",\n \"0.5862901\",\n \"0.5860989\",\n \"0.58590937\",\n \"0.5857562\",\n \"0.5851539\",\n \"0.585117\",\n \"0.58492124\",\n \"0.58452886\",\n \"0.58424413\",\n \"0.5841246\",\n \"0.5839079\",\n \"0.58315724\",\n \"0.58312756\",\n \"0.58261436\",\n \"0.5818827\",\n \"0.58123136\",\n \"0.5811935\",\n \"0.5809991\",\n \"0.5809069\",\n \"0.5807695\",\n \"0.5806935\",\n \"0.58048975\",\n \"0.5801177\",\n \"0.58005667\",\n \"0.57988936\",\n \"0.57961535\",\n \"0.5787455\",\n \"0.5787121\",\n \"0.5786493\",\n \"0.5785911\",\n \"0.5778448\",\n \"0.5778394\",\n \"0.57774955\",\n \"0.5770257\",\n \"0.57656175\",\n \"0.57649046\",\n \"0.5760979\",\n \"0.57590723\",\n \"0.5755032\",\n \"0.57540435\",\n \"0.5753703\",\n \"0.57523173\",\n \"0.57522035\",\n \"0.5748234\",\n \"0.57372934\",\n \"0.57355225\",\n \"0.57346874\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14297,"cells":{"query":{"kind":"string","value":"this func is from using insde the data file"},"document":{"kind":"string","value":"def simple_album_list():\r\n album_list = []\r\n data = dbase()\r\n for album in data.keys():\r\n album_list += [album]\r\n return album_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_data(self):","def _read_data(self):","def load_data(self) -> None:","def read(self):","def _load(self):\n\n # number of non-data header details at top of data file\n header = 1\n\n # open file\n weatherData = []\n with open(self.wfile) as myfile:\n if (self.lines > 0):\n weatherData = [next(myfile) for x in xrange(self.lines + header)]\n else:\n weatherData = myfile.readlines()\n\n # get data stream from first line\n streamHeader = weatherData.pop(0).rstrip()\n if (streamHeader == 'FULL'):\n self.dataStream = 0\n elif (streamHeader == 'ADVANCED'):\n self.dataStream = 1\n elif (streamHeader == 'BASIC'):\n self.dataStream = 2\n else:\n print \"Error: unecognised data stream from file %s\" % (self.wfile)\n return -1\n\n # read data\n inputData = []\n for line in weatherData:\n entries = line.split()\n inputData.append(entries)\n\n # copy all into np array\n self.data = np.array(inputData)\n\n return 0","def get_file_data(filename):","def load_data():\n\t\t# load the data\n\t\tDATPATH = \"../data/\"\n\t\t#fnino = DATPATH + \"nino3.csv\" # 1871-2000\n\t\tfnino = DATPATH + \"tas_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_nino3_tseries.csv\" # 1871-2016\n\t\t#fnino = DATPATH + \"nino34.long.data\"\n\t\t#nc_data_nino3 = netCDF4.Dataset(fnino)\n\t\t#nino3_load = nc_data_nino3.variables['tas'][:]\n\t\t#dnino = nino3_load.flatten()\n\n\t\tdnino = np.genfromtxt(fnino, delimiter=\",\", dtype=float).flatten()\n\t\t#fismr = DATPATH + \"ismr.csv\" # 1871-2000\n\t\t#fismr = DATPATH + \"psl_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_1_india_goswami_2002_tseries.csv\" # 1871-2016\n\t\tfismr = DATPATH + \"pr_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_goswami_india_tseries.csv\" # 1871-2016\n\t\tdismr = np.genfromtxt(fismr, delimiter=\",\", dtype=float).flatten()\n\t\t#fvolc = DATPATH + \"robock.txt\" # 1871-2000\n\t\tfvolc = DATPATH + \"sigl.txt\" # 1871-2016\n\t\tdvolc = np.genfromtxt(fvolc, delimiter=\",\", dtype=float).flatten()\n\n\t\tfvolc_source = DATPATH + \"volc_source_850_1850.csv\" # 1871-2016\n\t\tdvolc_source = np.genfromtxt(fvolc_source, delimiter=\",\", dtype=float).flatten()\n\t\t# simple check for data consistency\n\t\tassert dnino.shape == dismr.shape, \"Data sets are unequal!\"\n\t\tassert int(dismr.shape[0]/12) == dvolc.shape[0], \"Data sets are unequal\"\n\t\treturn dnino, dismr, dvolc, dvolc_source","def parse_data(fp):\n pass","def prepare_data(self):","def dataLoad():\n try:\n try: #Python3\n f = open(__file__ + \".csv\",\"rt\")\n except: #Python2\n f = open(__file__ + \".csv\",\"rb\")\n data = f.read().split(',')\n entryCol.entry0.delete(0,END)\n entryCol.entry0.insert(0,data[0])\n entryCol.entry1.delete(0,END)\n entryCol.entry1.insert(0,data[1])\n entryCol.entry2.delete(0,END)\n entryCol.entry2.insert(0,data[2])\n entryCol.entry3.delete(0,END)\n entryCol.entry3.insert(0,data[3])\n botWind.writeN(\"DataLoad: File\")\n except:\n botWind.writeN(\"DataLoad: Default\")","def write_data():","def _read(self):\n # initializng data dictionary\n self.data={}\n\n f = FortranFile(self.filename)\n # Default omnivor binary header\n self.data['MK'] = f.readInts('i')\n self.data['itime'] = f.readInts('i')\n self.data['version'] = f.readString()\n self.data['file_id'] = f.readInts('i')\n self.data['sversion'] = f.readString()\n # Velocity field\n self.data['stype'] = f.readString()\n self.data['is_grid'] = f.readInts('i')\n nCPs = f.readInts('i')\n self.data['nCPs'] = nCPs\n if self.data['MK'] == 8:\n real_char='d'\n else:\n real_char='f'\n if self.data['is_grid']:\n #print('File is a velocity grid file')\n n1 = f.readInts('i')\n n2 = f.readInts('i')\n n3 = f.readInts('i')\n self.data['n1'] = n1\n self.data['n2'] = n2\n self.data['n3'] = n3\n self.data['is_straight'] = f.readInts('i')\n self.data['v1'] = f.readReals(real_char)\n self.data['v2'] = f.readReals(real_char)\n self.data['v3'] = f.readReals(real_char)\n\n CPs_raw = f.readReals(real_char)\n Utot_raw = f.readReals(real_char)\n CPs = np.reshape(CPs_raw,(3,nCPs),order = 'F')\n Utot = np.reshape(Utot_raw,(3,nCPs),order = 'F')\n\n acc=-1\n CPsTab = np.zeros((3, n1,n2,n3))\n UtotTab = np.zeros((3, n1,n2,n3))\n # Reshaping the nasty way (this is natural order). \n for i in range(0,n1):\n for j in range(0,n2):\n for k in range(0,n3):\n acc=acc+1\n CPsTab[0:3,i,j,k] = CPs[0:3,acc]\n UtotTab[0:3,i,j,k] = Utot[0:3,acc]\n\n self.data['CPs'] = CPs\n self.data['CPsTab'] = CPsTab\n self.data['Utot'] = Utot\n self.data['UtotTab'] = UtotTab","def readOneData(self):\n\t\tpass","def readData(self,datafile = None):\n self.datafile = datafile or self.datafile\n self.data = []\n for line in open(self.datafile):\n userid,itemid,record,_ = line.split()\n self.data.append((userid,itemid,int(record)))","def load(self, input):","def load(self):\n #print self.fileInfo.name\n progress = self.progress\n filePath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\n self.fileSize = os.path.getsize(filePath)\n #--Localize\n cells = self.cells\n records = self.records\n canSave = self.canSave\n skipObjRecords = self.skipObjRecords\n contTypes = set(['CREC','CNTC','NPCC'])\n levTypes = set(('LEVC','LEVI'))\n debrisIds = self.debrisIds\n debrisTypes = set(debrisIds.keys())\n #--Header\n inPath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\n ins = Tes3Reader(self.fileInfo.name,file(inPath,'rb'))\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\n self.tes3 = Tes3(name,size,delFlag,recFlag,ins,True)\n if not canSave: del self.tes3.others[:]\n #--Progress info\n progress = self.progress\n progress(0.0,'Loading '+self.fileInfo.name)\n #--Raw data read\n while not ins.atEnd():\n #--Get record info and handle it\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\n #print \"%s [%d]\" % (name,size)\n #--CELL?\n if name == 'CELL':\n record = Cell(name,size,delFlag,recFlag,ins,0,skipObjRecords)\n cells.append(record)\n if canSave: records.append(record)\n #--Contents\n elif canSave and name in contTypes:\n if name == 'CREC':\n record = Crec(name,size,delFlag,recFlag,ins,True)\n elif name == 'CNTC':\n record = Cntc(name,size,delFlag,recFlag,ins,True)\n else:\n record = Npcc(name,size,delFlag,recFlag,ins,True)\n self.conts.append(record)\n self.conts_id[record.getId()] = record\n records.append(record)\n #--File Map\n elif name == 'FMAP':\n record = Fmap(name,size,delFlag,recFlag,ins)\n self.fmap = record\n records.append(record)\n #--Landscapes\n elif name == 'LAND':\n record = Land(name,size,delFlag,recFlag,ins)\n self.lands[record.getId()] = record\n records.append(record)\n #--Scripts\n elif canSave and name == 'SCPT':\n record = Scpt(name,size,delFlag,recFlag,ins,True)\n records.append(record)\n if record.getRef():\n self.refs_scpt[record] = record.getRef()\n #--Save debris info?\n elif name in debrisTypes:\n record = Record(name,size,delFlag,recFlag,ins)\n id = record.getId()\n if id:\n debrisIds[name].append(id.lower())\n if canSave:\n records.append(record)\n #--Skip Non-cell?\n elif not canSave:\n ins.seek(size,1,name)\n #--Keep non-cell?\n else:\n records.append(Record(name,size,delFlag,recFlag,ins))\n #--Done Reading\n ins.close()\n #--Analyze Cells\n cntCells = 0\n progress.setMax(len(self.cells))\n for cell in self.cells:\n cell.load(None,1)\n self.cells_id[cell.getId()] = cell\n if not canSave:\n cell.data = None #--Free some memory\n #--Progress\n cntCells += 1\n progress(cntCells)\n #--Scripts\n if self.refs_scpt:\n self.updateScptRefs()","def data_for_file (self):\n rowdata = np.array([self.Train_Time_min,self.Train_Time_max,self.Train_Time_avg,\n self.Loss_Value_min,self.Loss_Value_max,self.Loss_Value_avg,\n self.Iterations_min,self.Iterations_max,self.Iterations_avg,\n self.precision_avg,self.recall_avg])\n return rowdata\n\n\n \n\n\n #### FUNCTION DEFINTIIONS ####","def load(self):","def parse_region_data(self, region):\n\n #downloads newest data\n self.download_data()\n\n TmpList = [[] for i in range(65)]\n\n #goes through all the files and saves their data to the variable\n for zipName in self.ListOfZipFiles:\n with zipfile.ZipFile(\"./\"+self.folder+\"/\"+zipName, 'r') as zf:\n if self.getCsvFileByRegion(region) == None:\n print(\"ERROR: neznamy nazev kraje: \" + region, file=sys.stderr)\n return None\n #opens zip file for reading\n with zf.open(self.getCsvFileByRegion(region), \"r\") as f:\n reader = f.read().decode(\"iso 8859-2\")\n reader = StringIO(reader)\n reader = csv.reader(reader, delimiter = \";\")\n #saves every row of file\n for row in reader:\n TmpList[0].append(region)\n i=1\n #saves every column separated because of their types\n for item in row:\n if item != \"\":\n if (i>0 and i<4) or (i>4 and i<6) or (i>6 and i<32) or (i>32 and i<35) or (i>35 and i<46) or i==61 or i==64:\n if not re.search(\"^[-]{0,1}[0-9]*$\", item):\n item = None\n else:\n item=int(item)\n elif i>45 and i<52:\n if not re.search(\"^[-]{0,1}[0-9]*[,.]{0,1}[0-9]+$\", item):\n item = None\n else:\n item = item.replace(\",\", \".\")\n if re.search(\"^[-]{1}.*$\", item):\n item = item.replace(\"-\", \"\")\n item = -(float(item))\n else:\n item = float(item)\n elif i==6:\n if not re.search(\"^[-]{0,1}[0-9]*$\", item):\n item = None\n else:\n if (len(item) == 4):\n #cas nijak neupravuji, jen rozdeluji minuty od hodin\n #policie uz nastavila hodnoty podle nich pozname nevalidni hodnotu\n #pro minuty tato hodnota je 60\n #pro hodiny tato hodnota je 25\n #dle meho nazoru mi prislo zbytecne na misto techto dvou hdonot davat nejakou jinou nevalidni hodnotu (napr. NaN, -1)\n #prepsal bych nevalidni vystup na jiny nevalidni vystup\n item = item[:2] + \":\" + item[2:]\n else:\n item = None\n TmpList[i].append(item)\n else:\n TmpList[i].append(None)\n i+=1\n\n ListNpArrays = [[] for i in range(65)]\n\n #transforms into numpy.ndarray\n for x in range(0, 65):\n ListNpArrays[x] = np.array(TmpList[x], dtype=object)\n\n return (self.headers, ListNpArrays)","def data_input(self):\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))\n if not os.path.isfile('{0}/{1}.csv'.format(path, self.data_file)):\n print 'Error: Dataset file is not exist.'\n exit()\n # Uplead Dataset.csv file.\n f = open('{0}/{1}.csv'.format(path, self.data_file), 'r')\n print 'Now uploading dataset File.....'\n f = list(f)\n # The Dataset contains heading, number of lines - heading\n self.number_of_VOCs = sum(1 for row in f)-1\n # Count number of columns, last column's value is empty, that is why -1.\n self.number_of_columns = len(f[0].split(',')) -1\n self.first_m_z = int(f[0].split(',')[3]) # find the first m/z value.\n self.last_m_z = int(f[0].split(',')[-2]) # find the last m/z value.\n print 'dataset includes ', self.number_of_VOCs, 'VOCs in all samples '\n print ('dataset includes ', self.number_of_columns, ' Columns, ',\n 'm/z values start from ', self.first_m_z,\n 'and end ', self.last_m_z)\n # Create a matrix with a shape of (number_of_VOCs X number_of_columns) filled with zeros.\n self.dataset = np.zeros((self.number_of_VOCs,\n self.number_of_columns))\n for line in range(1, len(f)):\n if int(float(f[line].strip().split(',')[0])) not in self.loaded_samples:\n self.loaded_samples.append(int(float(f[line].strip().split(',')[0])))\n for column in range(self.number_of_columns):\n self.dataset[line-1][column] = int(float(f[line].strip().split(',')[column]))","def read_data(self, loc):\n pass","def loadData(self,ins):\n raise AbstractError","def _read_data(self):\n with self._open(self.filename, 'rb') as f:\n try:\n f.seek(self._offset_data, self._offset_whence)\n except IOError:\n print('Error: hedp.io.HamamatsuFile seeking outside of file limits.')\n print(' Failed to parse file.')\n print(\" Either the 'offset' or 'dtype' input arguments must be wrong!\")\n raise\n except:\n raise\n\n data_len = np.prod(self.shape)*np.dtype(self._dtype).itemsize\n data_str = f.read(data_len)\n if data_len != len(data_str):\n print(data_len, len(data_str))\n raise ValueError('File ended before all data was read. Probably wrong offset or dtype!')\n\n\n self.data = np.fromstring(data_str, dtype=self._dtype).reshape(self.shape[::-1])\n self.data = np.ndarray.astype(self.data, 'float32')\n\n #self.data = np.fromfile(f, dtype=self._dtype,\n # count=np.prod(self.shape)).reshape(self.shape[::-1])","def loadData(self,ins):\n #--Read subrecords\n bytesRead = 0\n while bytesRead < self.size:\n (name,size) = ins.unpackSubHeader('GLOB')\n srData = ins.read(size,'GLOB.'+name)\n bytesRead += 8+size\n if name == 'NAME': self.id = cstrip(srData)\n elif name == 'FNAM': self.type = srData\n elif name == 'FLTV': self.value = struct.unpack('f',srData)\n #--Deleted?\n elif name == 'DELE': self.isDeleted = True\n #--Bad record?\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)","def load_data(self):\n raise NotImplementedError()","def load(datastream):","def loadData(self, file):\n self.data = batchImport(file, self.ps)","def load(self, filename):\n # XXX Hay que comprobar los datos leidos y lanzar excepcion\n f = open(filename)\n prelaciones = []\n asig = []\n rec = []\n l = f.readline()\n while l:\n # Activities and following activities\n if l[0:21] == 'PRECEDENCE RELATIONS:':\n f.readline()\n l = f.readline()\n while l[0] != '*':\n data = l.split()\n prel = (data[0], data[3:])\n prelaciones.append(prel)\n l = f.readline()\n\n # Activity duration and resource units needed\n if l[0] == '-':\n l = f.readline()\n while l[0] != '*':\n asig.append(l.split())\n l = f.readline()\n\n # Name, type and unit of resources\n if l[0:22] == 'RESOURCEAVAILABILITIES':\n l = f.readline()\n while l[0] != '*':\n rec.append(l.split())\n l = f.readline()\n\n l = f.readline()\n \n # Create data structure\n cont = 1\n activities = []\n for prelacion in prelaciones:\n activities.append([cont, prelacion[0], prelacion[1], '', '', '', '', '', ('Beta')])\n cont += 1 \n\n # Update activities duration\n for n in range(len(asig)): \n activities[n][6] = float(asig[n][2])\n\n # Update resources\n i = 1\n m = 0\n resources = []\n if len(rec) < 2:\n raise InvalidFileFormatException()\n\n for n in range(len(rec[1])):\n # Renewable\n if rec[0][m]=='R' or rec[0][m][0]=='R':\n if rec[0][m]=='R':\n row=[rec[0][m]+rec[0][i], 'Renewable', '', rec[1][n]] \n m+=2\n else:\n row=[rec[0][m], 'Renewable', '', rec[1][n]] \n m+=1 \n # Non Renewable\n elif rec[0][m]=='N' or rec[0][m][0]=='N':\n if rec[0][m]=='N':\n row=[rec[0][m]+rec[0][i], 'Non renewable', rec[1][n], '']\n m+=2\n else:\n row=[rec[0][m], 'Non renewable', rec[1][n], ''] \n m+=1\n # Double constrained\n elif rec[0][m]=='D' or rec[0][m][0]=='D':\n if rec[0][m]=='D':\n row=[rec[0][m]+rec[0][i], 'Double constrained', rec[1][n], rec[1][n]]\n m+=2\n else:\n row=[rec[0][m], 'Double constrained', rec[1][n], rec[1][n]] \n m+=1\n \n resources.append(row)\n i += 2\n # Note: Unlimited resources are not present on PSPLIB projects and so \n # not taken into account here\n\n # Resources needed per activity\n asignation = []\n for n in range(len(asig)): \n for m in range(3, 3+len(rec[1])): #len(self.rec[1]): number of resources \n if asig[n][m] != '0': #unused resources are not shown\n i = m-3\n row = [asig[n][0], resources[i][0], asig[n][m]] \n asignation.append(row)\n \n return (activities, [], resources, asignation)","def read(self, sacc_data: sacc.Sacc) -> None:","def read(self, sacc_data: sacc.Sacc) -> None:","def read(self, sacc_data: sacc.Sacc) -> None:","def __init__(self,site,startDate,endDate,path='verif_data/'):\n self.site = site.upper()\n self.startDateTime = datetime.strptime(startDate,'%Y%m%d')\n self.endDateTime = datetime.strptime(endDate,'%Y%m%d')\n years = range(self.startDateTime.year,self.endDateTime.year + 1)\n data = []\n for year in years:\n self.filename=path + self.site + '_asos_' + str(year) + '.txt'\n datafile = open(self.filename)\n for line in datafile:\n if line[0] != '#':\n if 'station' in line:\n self.header = [x.strip() for x in line[:-1].split(',')]\n else:\n dataline = line[:-2].split(',')\n for i,val in enumerate(dataline[:-1]):\n if val=='M':\n dataline[i] = -999\n dataline[1] = dataline[1].replace(' ','_')\n dataline[1] = dataline[1].replace('-','')\n currDateTime = datetime.strptime(dataline[1][:14],'%Y%m%d_%H:%M')\n if currDateTime >= self.startDateTime and currDateTime <= self.endDateTime:\n data.append(tuple(dataline))\n datafile.close()\n self.datatype = []\n for item in self.header:\n if item == 'station':\n self.datatype.append((item,'S3'))\n elif 'valid' in item:\n self.datatype.append(('time','S14'))\n elif 'skyc' in item:\n self.datatype.append((item,'S3'))\n elif item=='metar':\n self.datatype.append((item,'S99'))\n else:\n self.datatype.append((item,float))\n self.data = np.array(data,dtype=self.datatype)","def data_preprocessing():\n lineid_content = get_lineid_content()\n print('Read movie_lines.txt file complete...')\n convos = get_convos()\n print('Read movie_conversations.txt file complete...')\n print('Building dataset')\n get_data(lineid_content, convos)","def import_data(self):\n\n # Import ordered names of origins\n origins_file = os.path.join(self.data_directory,'origins.txt')\n self.origins = np.loadtxt(origins_file,dtype=str,ndmin=1)\n\n # Import ordered names of destinations\n destinations_file = os.path.join(self.data_directory,'destinations.txt')\n self.destinations = np.loadtxt(destinations_file,dtype=str,ndmin=1)\n\n # Import origin supply\n originsupply_file = os.path.join(self.data_directory,'origin_supply.txt')\n self.origin_supply = np.loadtxt(originsupply_file,ndmin=1).astype('float64')\n\n # In case origin supply is not a list\n if not isinstance(self.origin_supply,(np.ndarray, np.generic)):\n self.origin_supply = np.array([self.origin_supply])\n\n # Import destination demand\n destinationdemand_file = os.path.join(self.data_directory,'destination_demand.txt')\n self.destination_demand = np.loadtxt(destinationdemand_file,ndmin=1).astype('float64')\n\n # In case destination demand is not a list\n if not isinstance(self.destination_demand,(np.ndarray, np.generic)):\n self.destination_demand = np.array([self.destination_demand])\n\n # Import origin locations\n originlocations_file = os.path.join(self.data_directory,'origin_locations.txt')\n self.origin_locations = np.loadtxt(originlocations_file,ndmin=1)\n\n # Import destination locations\n destinationlocations_file = os.path.join(self.data_directory,'destination_locations.txt')\n self.destination_locations = np.loadtxt(destinationlocations_file,ndmin=1)\n\n # Import initial and final destination sizes\n initialdestinationsizes_file = os.path.join(self.data_directory,'initial_destination_sizes.txt')\n self.initial_destination_sizes = np.loadtxt(initialdestinationsizes_file,ndmin=1)\n\n # In case destination sizes are not a list\n if not isinstance(self.initial_destination_sizes,(np.ndarray, np.generic)):\n self.initial_destination_sizes = np.array([self.initial_destination_sizes])\n\n # Import N,M\n self.N = self.origin_supply.shape[0]\n self.M = self.initial_destination_sizes.shape[0]\n\n # Import cost matrix\n costmatrix_file = os.path.join(self.data_directory,'cost_matrix.txt')\n self.cost_matrix = np.loadtxt(costmatrix_file).astype('float64')\n\n # Reshape cost matrix if necessary\n if self.N == 1:\n self.cost_matrix = np.reshape(self.cost_matrix[:,np.newaxis],(self.N,self.M))\n if self.M == 1:\n self.cost_matrix = np.reshape(self.cost_matrix[np.newaxis,:],(self.N,self.M))\n\n # Compute total initial and final destination sizes\n self.total_initial_sizes = np.sum(self.initial_destination_sizes)\n\n # Compute naive total cost\n self.total_cost = 0\n for i in range(self.N):\n for j in range(self.M):\n self.total_cost += self.cost_matrix[i,j]*(self.origin_supply[i]/self.N)","def loadData(self,ins):\n #--Read subrecords\n bytesRead = 0\n objectId = None\n while bytesRead < self.size:\n (name,size) = ins.unpackSubHeader(self.name)\n #print name,size\n bytesRead += 8+size\n subData = ins.read(size, self.name+'.'+name)\n #--Id?\n if name == 'NAME':\n self.id = cstrip(subData)\n #--Flags\n elif name == 'DATA':\n flags = struct.unpack('i',subData)[0]\n if self.name == 'LEVC':\n self.calcFromAllLevels = (flags & 1) == 1\n else:\n self.calcForEachItem = (flags & 1) == 1\n self.calcFromAllLevels = (flags & 2) == 2\n #--Chance None\n elif name == 'NNAM':\n self.chanceNone = struct.unpack('B',subData)[0]\n #--Count\n elif name == 'INDX':\n self.count = struct.unpack('i',subData)[0]\n #--Creature/Item Id?\n elif name == 'CNAM' or name == 'INAM':\n objectId = cstrip(subData)\n #--PC Level\n elif name == 'INTV':\n pcLevel = struct.unpack('h',subData)[0]\n self.entries.append((pcLevel,objectId))\n objectId = None\n #--Deleted?\n elif name == 'DELE': \n self.isDeleted = True\n #--Else\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\n #--No id?\n if not self.id:\n raise Tes3Error(self.inName,_('No id for %s record.') % (self.name,))\n #--Bad count?\n if self.count != len(self.entries):\n self.count = len(self.entries)\n self.setChanged()","def __init__(self, raw_data_file,):\n self.raw_data_file = raw_data_file\n self.clean_data = self.cleanData()\n self.microtrip_data = []","def get_file_data(self):\n self.switch_row(self.PATTERN_INFO_ROW)\n\n # get background image file path\n if self.row[0] != 'No image':\n # set path to background image, and get/set screen size\n self.bg_img_path = self.row[self.BG_IMAGE_INDEX]\n self.screen_width = int(self.row[self.WIDTH_INDEX])\n self.screen_height = int(self.row[self.HEIGHT_INDEX])\n self.SCREEN_SIZE = self.screen_width, self.screen_height\n self.bg = True\n \n # get number of landmarks\n self.num_lndmrks = int(self.row[self.LNDMRK_NUM_INDEX])\n\n # get starting row number for mpo path data\n self.data_start_row = self.num_lndmrks + 1\n self.tot_data_rows = self.tot_rows - self.data_start_row\n self.data_row_num = self.data_start_row","def extract_data(file_name):\r\n input_file_name = file_name\r\n \r\n def sort_data(input_file): \r\n \"\"\"This function extraxts the row data from the imput file\"\"\" \r\n with open(input_file,\"r\") as input_file: \r\n data = input_file.readlines() \r\n row_data = [] \r\n for line in data: \r\n row_data.append(line.split(', ')) \r\n return row_data \r\n \r\n data = sort_data(input_file_name)\r\n \r\n start_vertex = (float(data[0][0].split(' ')[0]),float(data[0][0].split(' ')[1]))\r\n end_vertex = (float(data[1][0].split(' ')[0]),float(data[1][0].split(' ')[1]))\r\n \r\n obstacles = {}\r\n for i in range(2, len(data)):\r\n lista = []\r\n for j in range(len(data[i])):\r\n lista.append( (float(data[i][j].split(' ')[0]),float(data[i][j].split(' ')[1])) )\r\n obstacles[i-1] = lista\r\n \r\n return start_vertex,end_vertex, obstacles","def open_file(self):\n file = open(self.file_name_dat, \"r\")\n rows = []\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\n for line in file:\n rows.append(line)\n\n # Getting information from the comments section\n xdatatemp = []\n ydatatemp = []\n for row in rows:\n if \"#\" in row: # Getting information from the comments\n hashlinecount += 1\n if hashlinecount == 7: # Getting the E_f value\n temp = row.split()\n if len(temp) == 4:\n self.E_f = temp[3]\n elif len(temp) == 7:\n self.E_f = temp[6]\n self.Efshift = True\n continue\n elif hashlinecount == 9: # Getting the min and max band energies\n temp = row.split()\n self.E_min = float(temp[4])\n self.E_max = float(temp[5])\n continue\n elif hashlinecount == 11: # Gettiing the minimum and maximum band numbers to plot\n temp = row.split()\n self.band_number_min = int(temp[5])\n self.band_number_max = int(temp[6])\n continue\n\n elif row.strip() == \"\": # saving individual bands the first save will be a null band\n self.band_data_x.append(xdatatemp) # Saving in a 2d list.\n self.band_data_y.append(ydatatemp)\n xdatatemp = []\n ydatatemp = []\n else:\n t = row.split()\n xdatatemp.append(float(t[0]))\n ydatatemp.append(float(t[1]))\n\n # Getting rid of empty lists\n self.band_data_x = [x for x in self.band_data_x if x]\n self.band_data_y = [x for x in self.band_data_y if x]\n\n # opening the bands file to get the kpath\n bands_file = open(self.file_name_bands, \"r\")\n bands_file_rows = []\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\n for bands_file_line in bands_file:\n bands_file_rows.append(bands_file_line)\n\n length_bands_file_rows = len(bands_file_rows)\n for i in range(length_bands_file_rows-1, 0, -1):\n temperary = bands_file_rows[i].split()\n if len(temperary) == 2:\n self.kpath_k.append(float(temperary[0].strip()))\n self.kpath_symbol.append(str(temperary[1].strip(\"'\")))\n elif len(temperary) is not 2:\n break\n self.kpath_symbol = latexify(self.kpath_symbol)","def readdata(filename):\n\tdt = np.dtype([('date','int'),('val','<f8')])\n\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\n\treturn data","def load_data(self, filename):\r\n #sqlcontext = SQLContext(self.sc)\r\n #df = sqlcontext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load(filename)\r\n #df = sc.textFile(r\"C:\\Users\\mohan\\Downloads\\patches.csv\").map(lambda line: line.split(\",\"))\r\n #print (df.count())\r\n df = self.sc.textFile(filename).map(lambda line: line.split(\",\"))\r\n l = df.map(lambda w: [int(float(c)) for c in w]).zipWithIndex()\r\n return l\r\n raise NotImplementedError","def loadSimData(datafile):\n \n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \n\n datafile = open(datafile,\"r\")\n for line in datafile:\n A = line.split()\n if A[0] == \"dt\": # Time interval between MD steps\n dt = float(A[-1])\n elif A[0] == \"ti\": # Beginning time for data acquisition\n ti = float(A[-1])\n elif A[0] == \"Lx\": # Box size in x\n Lx = float(A[-1]) \n elif A[0] == \"Ly\": # Box size in y\n Ly = float(A[-1])\n elif A[0] == \"totalStep\": # Total MD steps\n totalStep = float(A[-1])\n elif A[0] == \"nsamp\": # Data sampling frequency\n nsamp = float(A[-1])\n elif A[0] == \"nfil\": # Number of particles per polymer\n N = float(A[-1])\n elif A[0] == \"L\": # Number of particles\n L = float(A[-1])\n elif A[0] == \"B\": # Bond length between particles of a body\n B = float(A[-1])\n elif A[0] == \"kT\": # Boltzmann constant*Temperature\n kT = float(A[-1])\n elif A[0] == \"Fmc\": # Self propulsion force constant\n Fmc = float(A[-1]) \n elif A[0] == \"Kbend\": # Bending constant\n Kbend = float(A[-1])\n \n Lx /= B\n Ly /= B\n M = L/N\n dtSamp = dt*nsamp\n T = totalStep - ti\n nt = T/nsamp\n box_area = Lx*Ly\n body_length = B*(N-1)\n Pe = Fmc*body_length**2/kT\n persistence = Kbend/(kT*body_length)\n flexure = Pe/persistence","def loadSimData(datafile):\n \n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \n\n datafile = open(datafile,\"r\")\n for line in datafile:\n A = line.split()\n if A[0] == \"dt\": # Time interval between MD steps\n dt = float(A[-1])\n elif A[0] == \"ti\": # Beginning time for data acquisition\n ti = float(A[-1])\n elif A[0] == \"Lx\": # Box size in x\n Lx = float(A[-1]) \n elif A[0] == \"Ly\": # Box size in y\n Ly = float(A[-1])\n elif A[0] == \"totalStep\": # Total MD steps\n totalStep = float(A[-1])\n elif A[0] == \"nsamp\": # Data sampling frequency\n nsamp = float(A[-1])\n elif A[0] == \"nfil\": # Number of particles per polymer\n N = int(float(A[-1]))\n elif A[0] == \"L\": # Number of particles\n L = int(float(A[-1]))\n elif A[0] == \"B\": # Bond length between particles of a body\n B = float(A[-1])\n elif A[0] == \"kT\": # Boltzmann constant*Temperature\n kT = float(A[-1])\n elif A[0] == \"Fmc\": # Self propulsion force constant\n Fmc = float(A[-1]) \n elif A[0] == \"Kbend\": # Bending constant\n Kbend = float(A[-1])\n \n Lx /= B\n Ly /= B\n M = L/N\n dtSamp = dt*nsamp\n T = totalStep - ti\n nt = T/nsamp\n box_area = Lx*Ly\n body_length = B*(N-1)\n Pe = Fmc*body_length**2/kT\n persistence = Kbend/(kT*body_length)\n flexure = Pe/persistence","def dataArr(filename):\r\n #Open the file\r\n f=h5py.File(filename,'r')\r\n \r\n #Initialize the data arrays\r\n cdata=[]\r\n idxset=[]\r\n vertices=[]\r\n \r\n #Open groups in the file\r\n for group in f.keys():\r\n# print('Group- '+group)\r\n \r\n #Get the group\r\n currGroup=f[group]\r\n \r\n #Open keys in the group\r\n for key in currGroup.keys():\r\n# print('Key- '+key)\r\n \r\n #Append the data to the respective arrays\r\n if key=='cdata(Complex)':\r\n cdataGroup=currGroup[key]\r\n \r\n imag=[]\r\n real=[]\r\n #Open the keys in cdata\r\n for subkey in cdataGroup.keys():\r\n# print('Subkey- '+subkey)\r\n \r\n #Get the real and imaginary parts of the array\r\n if subkey=='Imag':\r\n imag=cdataGroup[subkey][()]\r\n elif subkey=='Real':\r\n real=cdataGroup[subkey][()]\r\n \r\n #Convert lists to numpy arrays\r\n imag=np.array(imag)\r\n real=np.array(real)\r\n #Get the cdata value\r\n cdata=real+1j*imag\r\n \r\n elif key=='idxset':\r\n idxset=currGroup[key][()]\r\n elif key=='vertices':\r\n vertices=currGroup[key][()]\r\n \r\n #Remove the y component from the vertices\r\n xVals=[]\r\n yVals=[]\r\n newVertices=[]\r\n for vertex in vertices:\r\n xVals.append(vertex[0])\r\n yVals.append(vertex[2])\r\n newVertices.append([vertex[0],vertex[1]])\r\n vertices=newVertices\r\n \r\n #Convert to numpy arrays\r\n cdata=np.array(cdata)\r\n xVals=np.array(xVals)\r\n yVals=np.array(yVals)\r\n \r\n #Close the file\r\n f.close()\r\n \r\n return cdata, xVals, yVals","def read_data(self):\r\n IS_REMAPPED = 1\r\n if IS_REMAPPED:\r\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\r\n self.TRAINING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\r\n int(c)] for [f1, f2, c] in data_as_strings]\r\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\r\n self.TESTING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\r\n int(c)] for [f1, f2, c] in data_as_strings]\r\n else:\r\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\r\n self.TRAINING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]\r\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\r\n self.TESTING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]","def prepare_data_train(fname):\n # Read data\n data = pd.read_csv(fname)\n # events file\n events_fname = fname.replace('_data','_events')\n # read event file\n labels= pd.read_csv(events_fname)\n clean=data.drop(['id' ], axis=1)#remove id\n labels=labels.drop(['id' ], axis=1)#remove id\n return clean,labels","def prepare_data_train(fname):\n # Read data\n data = pd.read_csv(fname)\n # events file\n events_fname = fname.replace('_data','_events')\n # read event file\n labels= pd.read_csv(events_fname)\n clean=data.drop(['id' ], axis=1)#remove id\n labels=labels.drop(['id' ], axis=1)#remove id\n return clean,labels","def prepare_data_train(fname):\n # Read data\n data = pd.read_csv(fname)\n # events file\n events_fname = fname.replace('_data','_events')\n # read event file\n labels= pd.read_csv(events_fname)\n clean=data.drop(['id' ], axis=1)#remove id\n labels=labels.drop(['id' ], axis=1)#remove id\n return clean,labels","def predict(self, datafile):","def rdspecdat(self):\n # TODO : ugh. this is crude. Should have some checks for file format\n # and probably better to use the astropy.io functions now.\n try:\n w, f, e = np.loadtxt(self.filename, unpack=True)\n except:\n w, f = np.loadtxt(self.filename, unpack=True)\n e = []","def readdata(self, filepaths):\n pass","def _load_data(self):\n\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\n self.task_num\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\n\n read_data_x = pd.read_csv(path_data_x)\n read_data_y = pd.read_csv(path_data_y)\n read_data_meta = pd.read_csv(path_meta)\n\n # Drop columns if it only contains 1 unique element\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\n\n num_col = pd.read_csv(path_numerical_columns)\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\n col_idx = np.arange(read_data_x.shape[-1])\n non_num_col = np.setdiff1d(col_idx, num_col)\n\n # new_data_x = np.array(read_data_x).astype(np.float32)\n new_data_x = np.array(read_data_x)\n new_data_y = np.array(read_data_y).astype(np.float32)\n new_data_meta = np.array(read_data_meta).astype(np.float32)\n\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\n\n\n # Winsorize dataset\n len_feat = new_data_x.shape[-1]\n idx_list = list(num_col)\n for i in range(len_feat):\n if i in idx_list:\n cur_data = new_data_x[:, i]\n cur_data = np.array(cur_data)\n lower_p = np.percentile(cur_data, 5)\n higher_p = np.percentile(cur_data, 95)\n cur_data[cur_data < lower_p] = lower_p\n cur_data[cur_data > higher_p] = higher_p\n new_data_x[:, i] = cur_data\n\n # Make sure target data is one-hot encoded\n if new_data_y.shape[-1] == 1:\n num_class = len(np.unique(new_data_y))\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\n new_data_y = new_data_y.astype('float32')\n self.orig_column_names = read_data_x.columns\n self.data_x = new_data_x # N x F\n self.data_y = new_data_y # N x C\n self.numerical_idx = num_col # list of idx\n self.non_num_idx = non_num_col # None\n\n # Calculate adjacency matrix\n self.meta_inf = new_data_meta.astype('float32') # N x 3\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def make_data_raw (mdp,do_makedata,filename):\n #\n fin = open(filename,'r')\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n ## -- do_makedata tells it to go ahead with generating a new data output file\n ## -- otherwise, just saves parameters to metadata\n if do_makedata:\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname,',',mdp.tag\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n # write first header\n #corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",\\\n # int(mdp.corr_num.strip(\"[]\").split(',')[0]))\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n #uf.write_section(mdp.save_file,mdp.key)\n for num,key in zip(mdp.corr_num,mdp.key):\n corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n uf.write_section(mdp.save_file,key)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n # write another header\n #corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",\\\n # int(mdp.corr_num.strip(\"[]\").split(',')[0]))\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n #uf.write_section(mdp.save_file,mdp.key)\n for num,key in zip(mdp.corr_num,mdp.key):\n corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n uf.write_section(mdp.save_file,key)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data(mdp)\n mdp.corr_file.close()\n ##endif do_makedata\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def read_data(self):\n if not self.header['data included']:\n pass\n elif self.header['file type'] in (21, 26):\n self._isotope_data()\n if os.path.exists(self.filename + '_txt'):\n self._isotope_txt_data()\n elif self.header['file type'] == 22:\n # line scan types, no ImageHeader\n warnings.warn('No data read for line scan, fix')\n pass\n elif self.header['file type'] in (31, 35):\n self._beamstability_data()\n else:\n self._image_data()","def dataArr(filename):\r\n #Open the file\r\n f=h5py.File(filename,'r')\r\n \r\n #Initialize the data arrays\r\n cdata=[]\r\n idxset=[]\r\n vertices=[]\r\n \r\n #Open groups in the file\r\n for group in f.keys():\r\n# print('Group- '+group)\r\n \r\n #Get the group\r\n currGroup=f[group]\r\n \r\n #Open keys in the group\r\n for key in currGroup.keys():\r\n# print('Key- '+key)\r\n \r\n #Append the data to the respective arrays\r\n if key=='cdata(Complex)':\r\n cdataGroup=currGroup[key]\r\n \r\n imag=[]\r\n real=[]\r\n #Open the keys in cdata\r\n for subkey in cdataGroup.keys():\r\n# print('Subkey- '+subkey)\r\n \r\n #Get the real and imaginary parts of the array\r\n if subkey=='Imag':\r\n imag=cdataGroup[subkey][()]\r\n elif subkey=='Real':\r\n real=cdataGroup[subkey][()]\r\n \r\n #Convert lists to numpy arrays\r\n imag=np.array(imag)\r\n real=np.array(real)\r\n #Get the cdata value\r\n cdata=real+1j*imag\r\n \r\n elif key=='idxset':\r\n idxset=currGroup[key][()]\r\n elif key=='vertices':\r\n vertices=currGroup[key][()]\r\n \r\n #Remove the z component from the vertices\r\n xVals=[]\r\n yVals=[]\r\n newVertices=[]\r\n for vertex in vertices:\r\n xVals.append(vertex[0])\r\n yVals.append(vertex[1])\r\n newVertices.append([vertex[0],vertex[1]])\r\n vertices=newVertices\r\n \r\n #Convert to numpy arrays\r\n cdata=np.array(cdata)\r\n xVals=np.array(xVals)\r\n yVals=np.array(yVals)\r\n \r\n #Close the file\r\n f.close()\r\n \r\n return cdata, xVals, yVals","def read_data(args):\n\n print(\"Start read_data\")\n t_tot = 0 # sum of times for the all dataset\n date_dirs = os.listdir(args.path_data_base)\n for n_iter, date_dir in enumerate(date_dirs):\n # get access to each sequence\n path1 = os.path.join(args.path_data_base, date_dir)\n if not os.path.isdir(path1):\n continue\n date_dirs2 = os.listdir(path1)\n\n for date_dir2 in date_dirs2:\n path2 = os.path.join(path1, date_dir2)\n if not os.path.isdir(path2):\n continue\n # read data\n oxts_files = sorted(glob.glob(os.path.join(path2, 'oxts', 'data', '*.txt')))\n oxts = KITTIDataset.load_oxts_packets_and_poses(oxts_files)\n\n \"\"\" Note on difference between ground truth and oxts solution:\n - orientation is the same\n - north and east axis are inverted\n - position are closed to but different\n => oxts solution is not loaded\n \"\"\"\n\n print(\"\\n Sequence name : \" + date_dir2)\n if len(oxts) < KITTIDataset.min_seq_dim: # sequence shorter than 30 s are rejected\n cprint(\"Dataset is too short ({:.2f} s)\".format(len(oxts) / 100), 'yellow')\n continue\n lat_oxts = np.zeros(len(oxts))\n lon_oxts = np.zeros(len(oxts))\n alt_oxts = np.zeros(len(oxts))\n roll_oxts = np.zeros(len(oxts))\n pitch_oxts = np.zeros(len(oxts))\n yaw_oxts = np.zeros(len(oxts))\n roll_gt = np.zeros(len(oxts))\n pitch_gt = np.zeros(len(oxts))\n yaw_gt = np.zeros(len(oxts))\n t = KITTIDataset.load_timestamps(path2)\n acc = np.zeros((len(oxts), 3))\n acc_bis = np.zeros((len(oxts), 3))\n gyro = np.zeros((len(oxts), 3))\n gyro_bis = np.zeros((len(oxts), 3))\n p_gt = np.zeros((len(oxts), 3))\n v_gt = np.zeros((len(oxts), 3))\n v_rob_gt = np.zeros((len(oxts), 3))\n\n k_max = len(oxts)\n for k in range(k_max):\n oxts_k = oxts[k]\n t[k] = 3600 * t[k].hour + 60 * t[k].minute + t[k].second + t[\n k].microsecond / 1e6\n lat_oxts[k] = oxts_k[0].lat\n lon_oxts[k] = oxts_k[0].lon\n alt_oxts[k] = oxts_k[0].alt\n acc[k, 0] = oxts_k[0].af\n acc[k, 1] = oxts_k[0].al\n acc[k, 2] = oxts_k[0].au\n acc_bis[k, 0] = oxts_k[0].ax\n acc_bis[k, 1] = oxts_k[0].ay\n acc_bis[k, 2] = oxts_k[0].az\n gyro[k, 0] = oxts_k[0].wf\n gyro[k, 1] = oxts_k[0].wl\n gyro[k, 2] = oxts_k[0].wu\n gyro_bis[k, 0] = oxts_k[0].wx\n gyro_bis[k, 1] = oxts_k[0].wy\n gyro_bis[k, 2] = oxts_k[0].wz\n roll_oxts[k] = oxts_k[0].roll\n pitch_oxts[k] = oxts_k[0].pitch\n yaw_oxts[k] = oxts_k[0].yaw\n v_gt[k, 0] = oxts_k[0].ve\n v_gt[k, 1] = oxts_k[0].vn\n v_gt[k, 2] = oxts_k[0].vu\n v_rob_gt[k, 0] = oxts_k[0].vf\n v_rob_gt[k, 1] = oxts_k[0].vl\n v_rob_gt[k, 2] = oxts_k[0].vu\n p_gt[k] = oxts_k[1][:3, 3]\n Rot_gt_k = oxts_k[1][:3, :3]\n roll_gt[k], pitch_gt[k], yaw_gt[k] = IEKF.to_rpy(Rot_gt_k)\n\n t0 = t[0]\n t = np.array(t) - t[0]\n # some data can have gps out\n if np.max(t[:-1] - t[1:]) > 0.1:\n cprint(date_dir2 + \" has time problem\", 'yellow')\n ang_gt = np.zeros((roll_gt.shape[0], 3))\n ang_gt[:, 0] = roll_gt\n ang_gt[:, 1] = pitch_gt\n ang_gt[:, 2] = yaw_gt\n\n p_oxts = lla2ned(lat_oxts, lon_oxts, alt_oxts, lat_oxts[0], lon_oxts[0],\n alt_oxts[0], latlon_unit='deg', alt_unit='m', model='wgs84')\n p_oxts[:, [0, 1]] = p_oxts[:, [1, 0]] # see note\n\n # take correct imu measurements\n u = np.concatenate((gyro_bis, acc_bis), -1)\n # convert from numpy\n t = torch.from_numpy(t)\n p_gt = torch.from_numpy(p_gt)\n v_gt = torch.from_numpy(v_gt)\n ang_gt = torch.from_numpy(ang_gt)\n u = torch.from_numpy(u)\n\n # convert to float\n t = t.float()\n u = u.float()\n p_gt = p_gt.float()\n ang_gt = ang_gt.float()\n v_gt = v_gt.float()\n\n mondict = {\n 't': t, 'p_gt': p_gt, 'ang_gt': ang_gt, 'v_gt': v_gt,\n 'u': u, 'name': date_dir2, 't0': t0\n }\n\n t_tot += t[-1] - t[0]\n KITTIDataset.dump(mondict, args.path_data_save, date_dir2)\n print(\"\\n Total dataset duration : {:.2f} s\".format(t_tot))","def read_data(self):\n raise NotImplementedError","def __init__(self, datafile):\n self.FILE = open(datafile, 'w')","def readdata(self, reflist , comment = '#' , regexp = None , substr = None, filename = True):\n self.kpunten = []\n datalist = []\n prefixlist = []\n if os.path.isfile(str(reflist)):\n reflist = [reflist] #if we work with only one file this wraps it automatically in right format\n for ref in reflist:\n print('start with the collection of data from file %s' %ref)\n plotf = open(ref, 'r')\n if not filename:\n prefixlist.append( os.path.dirname(ref) + '/')\n else:\n prefixlist.append(re.sub('\\.dat$' , '' , ref))\n try:\n if regexp != None:\n raise ValueError\n dataf = np.loadtxt(plotf,comments = comment)\n print 'we readed data in with np.loadtxt'\n except:\n print('reading in data with numpy loadtxt failed or use reg exp to extract information')\n dataf = np.array([])\n kpuntenf = []\n plotf.seek(0) #go back to beginning of file\n for line in plotf:\n if regexp is not None:\n analyse = re.search(regexp,line)\n if analyse:\n kpuntenf.append((analyse.group(1), len(dataf)-1 ))\n print 'we found the following matches: %s' % analyse.group(0)\n if substr != None: \n line = re.sub(substr, '' , line)\n if line[0] != comment:\n #print line\n pline = np.array(map(float,line.split()))\n if len(dataf) <= 1:\n dataf = pline\n else:\n try:\n dataf = np.vstack((dataf,pline))\n except:\n continue\n self.kpunten.append(kpuntenf)\n datalist.append(dataf)\n\n plotf.close()\n self.datarg = datalist\n self.prefix = prefixlist\n self.reader = dr.ReaderOutput(reflist[0]) #Some plotting functions need a bit more information this info is extracted from the header of the files\n self.reader.depvar['depvar'] += ' (a.u.)'","def readFiles(opt, path, pathCopyData,minlat, maxlat, minlon, maxlon , variables, estaciones):\n date = '\\d\\d\\d\\d-\\d\\d-\\d\\d'\n dirr = pathCopyData\n patron2 = re.compile(date)\n print(dirr + 'tfile.txt')\n tempfile = df.read_csv(dirr + 'tfile.txt')\n tempbase = df.read_csv(dirr + 'tbase.txt')\n tfile = list(tempfile.values.flatten())\n tbase = list(tempbase.values.flatten())\n tfileCopy = list(tempfile.values.flatten())\n tbaseCopy = list(tempbase.values.flatten())\n l = len(tfile)\n for i in range(l):\n tfil = tfile[i]\n tbas = tbase[i]\n ls = tbas + '/' + tfil\n f = patron2.findall(tfil)\n cadena = clearString(tfil)\n print(cadena)\n try:\n #net = open_netcdf(ls, tfil, cadena, pathCopyData)\n net = Dataset(ls)\n for xs in range(len(estaciones)):\n minlat1 = minlat[xs]\n maxlat1 = maxlat[xs]\n minlon1 = minlon[xs]\n maxlon1 = maxlon[xs]\n estacion = estaciones[xs]\n #checkFile(net, tfil, f[0], opt, path, minlat1, maxlat1, minlon1, maxlon1, variables, estacion)\n var_cut = []\n for i in variables:\n var = net.variables[i][:,int(minlat1):int(maxlat1),int(minlon1):int(maxlon1)]\n #print(LON)\n #print(var)\n #return\n # celda.append(var)\n # result = ne(var, LON, LAT, LONsize, LATsize, minlat, maxlat, minlon, maxlon)\n var_cut.append(var)\n\n for ls in range(len(var_cut)):\n saveData(var_cut[ls], variables[ls], f[0], opt, path, estacion)\n tfileCopy.remove(tfil)\n tbaseCopy.remove(tbas)\n except (OSError, EOFError) as e:\n print(e)\n fdata = df.DataFrame(tfileCopy, columns=['nameFile'])\n fbas = df.DataFrame(tbaseCopy, columns=['nameBase'])\n fdata.to_csv(dirr + 'tfile.txt', encoding='utf-8', index=False)\n fbas.to_csv(dirr + 'tbase.txt', encoding='utf-8', index=False)\n if os.path.exists(pathCopyData + cadena):\n os.remove(pathCopyData + cadena)\n sys.exit()\n # readFiles(1);\n except tarfile.ReadError:\n print('error2')\n # fdata = df.DataFrame(tfile,columns=['nameFile']);\n # fbas = df.DataFrame(tbase,columns=['nameBase']);\n # fdata.to_csv(dirr+'tfile.txt',encoding='utf-8',index=False);\n # fbas.to_csv(dirr+'tbase.txt',encoding='utf-8',index=False);\n # readFiles(1);\n except (KeyError, FileNotFoundError):\n print('ERROR DE LECTURA')","def test_get_infile(self):\r\n pass # not practically testable, but obvious file I/O\r","def loadSimData(datafile):\n \n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \n\n datafile = open(datafile,\"r\")\n for line in datafile:\n A = line.split()\n if A[0] == \"dt\": # Time interval between MD steps\n dt = float(A[-1])\n elif A[0] == \"ti\": # Beginning time for data acquisition\n ti = float(A[-1])\n elif A[0] == \"Lx\": # Box size in x\n Lx = float(A[-1]) \n elif A[0] == \"Ly\": # Box size in y\n Ly = float(A[-1])\n elif A[0] == \"totalStep\": # Total MD steps\n totalStep = float(A[-1])\n elif A[0] == \"nsamp\": # Data sampling frequency\n nsamp = float(A[-1])\n elif A[0] == \"nfil\": # Number of particles per polymer\n N = float(A[-1])\n elif A[0] == \"L\": # Number of particles\n L = float(A[-1])\n elif A[0] == \"B\": # Bond length between particles of a body\n B = float(A[-1])\n elif A[0] == \"kT\": # Boltzmann constant*Temperature\n kT = float(A[-1])\n elif A[0] == \"Fmc\": # Self propulsion force constant\n Fmc = float(A[-1]) \n elif A[0] == \"Kbend\": # Bending constant\n Kbend = float(A[-1])\n \n L = int(L)\n N = int(N)\n Lx /= B\n Ly /= B\n M = L/N\n dtSamp = dt*nsamp\n T = totalStep - ti\n nt = T/nsamp\n box_area = Lx*Ly\n body_length = B*(N-1)\n Pe = Fmc*body_length**2/kT\n persistence = Kbend/(kT*body_length)\n flexure = Pe/persistence\n \n return","def _read_file(self):\n\n with open(self.file_name, 'rb') as f:\n new_test = struct.unpack('<l', f.read(8)[4:])[0]\n f.close()\n\n with open(self.file_name, 'rb') as f:\n old_test = struct.unpack('<h', f.read(6)[4:])[0]\n f.close()\n\n with open(self.file_name, 'rb') as f:\n other_test = struct.unpack('<l', f.read(20)[16:])[0]\n f.close()\n\n open_file = open(self.file_name, 'rb')\n\n if (other_test==202):\n raw = open_file.read(1236)[11:]\n self.model = '202'\n elif ((not new_test==102) and old_test==102):\n raw = open_file.read(1133)\n self.model = '102old'\n elif (new_test==102 and old_test==102):\n raw = open_file.read(1224)\n self.model = '102new'\n\n self.header = DpHeader(raw, self.model)\n\n self.data = DpData(open_file, \n self.model, \n self.header.interferogram_size, \n self.header.number_of_coadds, \n 2048*self.header.zero_fill,\n self.header.laser_wavelength_microns, \n self.header.dispersion_constant_xm,\n self.header.dispersion_constant_xb)\n\n open_file.close()","def _read(self, in_file):\n #\n # I know this function is long, but the FRD block is long as well...\n # Splitting this into multiple functions would not help in my opinion.\n # Therefore -> shut up pylint\n # pylint: disable=too-many-branches\n # pylint: disable=too-many-statements\n #\n self.setname = in_file.read(6).decode().strip()\n self.value = float(in_file.read(12))\n self.numnod = int(in_file.read(12))\n self.text = in_file.read(20).decode().strip()\n self.ictype = int(in_file.read(2))\n self.numstep = int(in_file.read(5))\n self.analys = in_file.read(10).decode().strip()\n self.format = int(in_file.read(2))\n in_file.read(1) # eol\n\n in_file.read(1) # pad byte\n in_file.read(2) # key = -4\n in_file.read(2) # pad bytes\n self.name = in_file.read(8).decode().strip()\n self.ncomps = int(in_file.read(5))\n self.irtype = int(in_file.read(5))\n if self.irtype != 1:\n raise NotImplementedError()\n in_file.read(1) # eol\n\n for i in range(self.ncomps):\n entity = FRDEntity()\n self.entities.append(entity)\n\n in_file.read(1) # pad byte\n entity.key = int(in_file.read(2))\n in_file.read(2) # pad bytes\n entity.name = in_file.read(8).decode().strip()\n entity.menu = int(in_file.read(5))\n entity.ictype = int(in_file.read(5))\n entity.icind1 = int(in_file.read(5))\n if entity.ictype == 4:\n entity.icind2 = int(in_file.read(5))\n elif entity.ictype == 2 and i == 3:\n entity.icind2 = int(in_file.read(5))\n entity.iexist = int(in_file.read(5))\n entity.icname = in_file.read(3).decode().strip()\n self.ncomps -= 1\n else:\n entity.iexist = int(in_file.read(5))\n in_file.read(1) # eol\n\n for i in range(self.numnod):\n result = FRDNodeResult()\n self.results.append(result)\n if self.format < 2:\n num_lines = int(self.ncomps/(6 + 1)) + 1\n result.data = []\n for j in range(num_lines):\n in_file.read(3) # pad byte and key = -1 || -2\n if result.node is None:\n result.node = int(in_file.read(5*(self.format+1)))\n else:\n in_file.read(5*(self.format+1))\n k_start = j*6\n k_end = min(self.ncomps - k_start, (j+1)*6)\n for _ in range(0, k_end):\n result.data.append(float(in_file.read(12)))\n in_file.read(1) # eol\n else:\n result.node = struct.unpack('i', in_file.read(4))[0]\n result.data = struct.unpack(\n 'f'*self.ncomps, in_file.read(self.ncomps*4))\n\n if self.format < 2:\n in_file.readline() # last record for ascii only","def _read(self, in_file):\n in_file.read(18) # pad bytes\n self.numnod = int(in_file.read(12))\n in_file.read(37) # pad bytes\n self.format = int(in_file.read(1))\n in_file.read(1) # eol\n self.nodes = []\n\n for _ in range(self.numnod):\n node = FRDNode()\n self.nodes.append(node)\n if self.format < 2:\n in_file.read(1)\n node.key = int(in_file.read(2))\n node.number = int(in_file.read(5*(self.format+1)))\n node.pos = [float(in_file.read(12)) for j in range(3)]\n in_file.read(1) # eol\n else:\n node.number = struct.unpack('i', in_file.read(4))[0]\n if self.format == 2:\n node.pos = struct.unpack('fff', in_file.read(12))\n else:\n node.pos = struct.unpack('ddd', in_file.read(24))\n\n if self.format < 2:\n in_file.readline() # last record for ascii only","def read_meta(self):\n meta = cPickle.load(open('../sugar_analysis_data/META-CABALLO2.pkl'))\n self.meta_sn_name_list = []\n self.meta_zcmb = []\n self.meta_x0 =[]\n self.meta_x0_err = []\n self.meta_x1 =[]\n self.meta_x1_err = []\n self.meta_c = []\n self.meta_c_err = []\n self.meta_mb = []\n self.meta_mb_err = []\n self.meta_cov_x0_x1 = [] \n self.meta_cov_x0_c = []\n self.meta_cov_x1_c = []\n self.meta_cov_mb_x1 = []\n self.meta_cov_mb_c = [] \n self.meta_zhl = []\n self.meta_zhl_err = []\n self.meta_idr = []\n for meta_sn_name in meta.keys(): \n \n if meta[meta_sn_name]['idr.subset'] != 'bad' and meta[meta_sn_name]['idr.subset'] != 'auxiliary':\n \n self.meta_sn_name_list.append(meta_sn_name)\n self.meta_zhl_err.append(meta[meta_sn_name]['host.zhelio.err'])\n self.meta_zhl.append(meta[meta_sn_name]['host.zhelio'])\n self.meta_zcmb.append(meta[meta_sn_name]['host.zcmb'])\n self.meta_x0.append(meta[meta_sn_name]['salt2.X0'])\n self.meta_x0_err.append(meta[meta_sn_name]['salt2.X0.err'])\n self.meta_x1.append(meta[meta_sn_name]['salt2.X1'])\n self.meta_x1_err.append(meta[meta_sn_name]['salt2.X1.err'])\n self.meta_c.append(meta[meta_sn_name]['salt2.Color'])\n self.meta_c_err.append(meta[meta_sn_name]['salt2.Color.err'])\n self.meta_mb.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B'])\n self.meta_mb_err.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B.err'])\n self.meta_cov_x0_x1.append(meta[meta_sn_name]['salt2.CovX0X1'])\n self.meta_cov_x0_c.append(meta[meta_sn_name]['salt2.CovColorX0'])\n self.meta_cov_x1_c.append(meta[meta_sn_name]['salt2.CovColorX1'])\n self.meta_cov_mb_x1.append(meta[meta_sn_name]['salt2.CovRestFrameMag_0_BX1'])\n self.meta_cov_mb_c.append(meta[meta_sn_name]['salt2.CovColorRestFrameMag_0_B'])\n self.meta_idr.append(meta[meta_sn_name]['idr.subset'])\n \n self.meta_idr = np.array(self.meta_idr)\n self.meta_zcmb = np.array(self.meta_zcmb)\n self.meta_zhl = np.array(self.meta_zhl)\n self.meta_zhl_err = np.array(self.meta_zhl_err)\n self.meta_x0 = np.array(self.meta_x0)\n self.meta_x0_err = np.array(self.meta_x0_err)\n self.meta_x1 = np.array(self.meta_x1)\n self.meta_x1_err = np.array(self.meta_x1_err) \n self.meta_c = np.array(self.meta_c)\n self.meta_c_err = np.array(self.meta_c_err)\n self.meta_mb = np.array(self.meta_mb)\n self.meta_mb_err = np.array(self.meta_mb_err)\n self.meta_cov_x0_x1 = np.array(self.meta_cov_x0_x1)\n self.meta_cov_x0_c = np.array(self.meta_cov_x0_c)\n self.meta_cov_x1_c = np.array(self.meta_cov_x1_c)\n self.meta_cov_mb_x1 = np.array(self.meta_cov_mb_x1)\n self.meta_cov_mb_c = np.array(self.meta_cov_mb_c)","def read_mb_file(self,idir='.',ifile=None, gmt=True, verbose=False):\n \n import numpy as np\n import os\n \n if gmt==True:\n gmt_file=idir+'/../maps/en_velo.gmt'\n if isinstance(gmt,str):\n gmt_file=gmt\n \n if gmt != False:\n self.read_lon_lat(gmt_file,verbose=verbose)\n \n if ifile is None:\n mb_file_basename= idir + '/mb_'+self.code+'_GPS.dat'\n else:\n mb_file_basename=ifile\n \n data_NEU = []\n for i in range(1,4):\n mb_file = mb_file_basename + str(i)\n\n # file\n self.ifile=os.path.abspath(mb_file)\n \n data=np.genfromtxt(mb_file,skip_header=4)\n \n # reshape to ensure a 2D array\n if len(data.shape)==1:\n data=data.reshape((1,data.shape[0]))\n \n\n\n data_NEU.append(data)\n\n if data_NEU[0].shape == data_NEU[1].shape == data_NEU[2].shape:\n self.data=np.zeros((data_NEU[0].shape[0],7))\n self.data[:,0]=data_NEU[0][:,0]\n self.data[:,1]=data_NEU[0][:,1]#*to_mm\n self.data[:,2]=data_NEU[1][:,1]#*to_mm\n self.data[:,3]=data_NEU[2][:,1]#*to_mm\n\n self.data[:,4]=data_NEU[0][:,2]#*to_mm\n self.data[:,5]=data_NEU[1][:,2]#*to_mm\n self.data[:,6]=data_NEU[2][:,2]#*to_mm\n\n else: \n print(\"!!! Error reading \",mb_file_basename,\" :*dat1, *dat2, *dat3 do not have the same length\")\n self.data = None","def uadb_ascii_to_dataframe(file=''): \n \n if debug:\n print(\"Running uadb_ascii_to_dataframe for: \", file) \n \n data = check_read_file(file=file, read=True) # TODO\n \n #source_file = [l for l in file.split('/') if '.txt' in l][0]\n\n nmiss = 0\n search_h = False \n read_data = []\n \n usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\n\n #usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd, iday, ident, numlev= 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n obs_id = 0\n stations_id = [] \n \n for i, line in enumerate(data):\n if line[0] == 'H':\n try:\n # Header\n usi = int(line[2:14]) # unique station identifier\n \n ident = int(line[15:21].replace(' ',''))# WMO\n if ident not in stations_id:\n stations_id.append(ident)\n \n #if len(ident) == 4:\n # ident = '0' + ident \n #idflag = int(line[22:24]) # id flag\n #d_src = int(line[25:28]) # source dataset\n #version = float(line[29:34]) # version\n #dateflag = int(line[35:37]) # date flag\n year = line[38:42] # year\n month = \"%02d\" % int(line[43:45])\n day = \"%02d\" % int(line[46:48])\n hour = line[49:53]\n #locflag = int(line[54:56]) # Location Flag\n lat = float(line[57:67])\n lon = float(line[68:78])\n #ele = float(line[79:85])\n #stype = int(line[86:88])\n numlev = int(line[89:93])\n #pvers = line[94:102]\n\n if '99' in hour:\n hour = hour.replace('99', '00')\n \n if '99' in day:\n search_h = True\n continue\n \n minutes = int(hour) % 100 \n hour = \"%02d\" % (int(hour) // 100)\n if minutes > 60 or minutes < 0:\n minutes = 0\n minutes = \"%02d\" % minutes\n idate = datetime.strptime(year + month + day + hour + minutes, '%Y%m%d%H%M')\n iday = int(year + month + day)\n #pday = int(day)\n search_h = False\n\n except Exception as e:\n #print(\"Error: \", i, line, repr(e), \"Skipping Block:\")\n search_h = True\n #iprev = i\n\n elif search_h:\n nmiss += 1\n continue # Skipping block\n\n else:\n # Data\n #ltyp = int(line[0:4])\n p = float(line[5:13])\n \n if p != -99999.0 and p != 9999.9: \n press = float(line[5:13])*100 # converting to Pa, since P is given in mb (1 mb = 1 hPa) \n else:\n press = np.nan \n \n gph = float(line[14:22]) # gph [m]\n \n if gph == -999.0 or gph == -99999.00 or gph >= 99999.0:\n gph = np.nan\n \n temp = float(line[23:29])\n if temp == -999.0:\n temp = np.nan \n else:\n temp = temp + 273.15\n \n rh = float(line[30:36]) # %\n if rh == -999.0:\n rh = np.nan\n else:\n rh = rh / 100. # convert to absolute ratio TODO\n\n wdir = float(line[37:43]) \n if wdir == -999.0 or wdir == -999 :\n wdir = np.nan\n \n wspd = float(line[44:50]) # [m/s], module of the velocity\n if wspd <0 :\n wspd = np.nan \n \n try:\n \n for value,var in zip([ gph, temp, wspd, wdir, rh], [ 'gph', 'temperature', 'wind_speed', 'wind_direction', 'relative_humidity'] ):\n obs_id = obs_id +1\n if not np.isnan(press): # when pressure is available, z_coord== pressure and z_type==1\n z_type = 1 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n elif (np.isnan(press) and not np.isnan(gph) ) : # when pressure is not available, z_coord== gph and z_type==2 \n z_type = 2 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, gph, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n else:\n z_type = -2147483648 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n \n except:\n 0\n \n \n \n #column_names = ['source_file', 'product_code', 'report_id', 'observation_id', 'report_timestamp' , 'iday', 'station_id', 'lat@hdr', 'lon@hdr', 'vertco_reference_1@body', 'obsvalue@body', 'varno@body' , 'units', 'number_of_pressure_levels' ]\n \n df = pd.DataFrame(data= read_data, columns= column_names) \n \n df['observation_id'] = np.chararray.zfill( (df['observation_id'].astype(int)) .astype('S'+str(id_string_length ) ), id_string_length ) #converting to fixed length bite objects \n df['report_id'] = np.chararray.zfill( (df['report_id'].astype(int)).astype ('S'+str(id_string_length ) ), id_string_length )\n \n df = df.replace([-999.9, -9999, -999, -999.0, -99999.0, -99999.9, 99999.0, -99999.00 ], np.nan)\n \n #df['observations_id'] =numpy.char.zfill(numpy.arange(ivar.shape[0]).astype('S10'), 10)\n \n df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \n #df['report_id'] = numpy.int64 (df['report_id'] ) \n #df['observation_id'] = numpy.int64 (df['observation_id'] ) \n df = df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \n \n print('Done reading DF')\n return df , stations_id","def _process_data_file(self):\n \n with open(self.data_file, 'r') as f:\n self.description = f.readline().strip()\n data = np.loadtxt(self.data_file, skiprows=1)\n\n return data","def make_data_raw_fast(mdp,do_makedata,filename):\n #\n fin = open(filename,'r')\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n ## -- do_makedata tells it to go ahead with generating a new data output file\n ## -- otherwise, just saves parameters to metadata\n if do_makedata:\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data_fast(mdp)\n mdp.corr_file.close()\n ##endif do_makedata\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def doImport(self, params):\n #logging.info(params.filename)\n raw_data = self.open_filename(params.filename)\n #logging.info('file open ok')\n data = self.split_to_blocks(raw_data)\n #logging.info('splitting ok')\n chromatograms = self.process_data_to_chromatograms(data)\n #logging.info('extract ok')\n \n processed_data = []\n \n zero = params.field_results[\"zero\"]\n MIC = params.field_results[\"MIC\"]\n TIC = params.field_results[\"TIC\"]\n round_MZ = params.field_results[\"round_MZ\"]\n \n for chrom in chromatograms:\n \n prefix = chrom['short_title']\n if (\".\" in prefix) and round_MZ:\n first,last = prefix.split('.')\n prefix = first\n \n if (\"MIC\" in prefix) and (not MIC):\n pass\n elif (\"TIC\" in prefix) and (not TIC):\n pass\n else:\n x = ImportDataset1D(name=prefix+'_time',data = chrom['time'])\n if zero:\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal_zero'])\n else:\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal'])\n processed_data.append(x)\n processed_data.append(y)\n \n return processed_data","def readdata(self, fname):\n\t\treturn self.__readtsv(fname)","def loadDataFromFile(self, filePath):\r\n with open(filePath, \"r\") as signalFile:\r\n nameline = signalFile.readlines()[2:5]\r\n with open(filePath, \"r\") as signalFile:\r\n dataline = signalFile.readlines()[6:]\r\n #name1 = re.findall(r\"\\<StudentName graduate\\=\\\"([a-z]+)\\\"\\>([\\s\\w])\\<\\/StudentName\\>\", nameline[0], re.X | re.I)\r\n stugrad = re.findall(r\"\\\"([a-z]+)\\\"\", nameline[0], re.X | re.I)\r\n if str(stugrad[0]) == \"true\":\r\n self.chkGraduate.setChecked(True)\r\n elif str(stugrad[0]) == \"false\":\r\n self.chkGraduate.setChecked(False)\r\n stuname = re.findall(r\"\\>([\\w\\s]+)\\<\\/StudentName\\>\", nameline[0], re.X | re.I)\r\n self.txtStudentName.setText(str(stuname[0]))\r\n stuid = re.findall(r\"\\<StudentID\\>([-\\w]+)\\<\\/StudentID\\>\", nameline[1], re.X | re.I)\r\n self.txtStudentID.setText(str(stuid[0]))\r\n stucol = re.findall(r\"\\<College\\>([\\w\\s]+)\\<\\/College\\>\", nameline[2], re.X | re.I)\r\n index = self.cboCollege.findText(str(stucol[0]))\r\n self.cboCollege.setCurrentIndex(index)\r\n #stucol = name3.group(1)\r\n setvals = []\r\n dataname = [self.txtComponentName_1, self.txtComponentName_2, self.txtComponentName_3, self.txtComponentName_4,self.txtComponentName_5,self.txtComponentName_6,self.txtComponentName_7,self.txtComponentName_8,self.txtComponentName_9,self.txtComponentName_10,self.txtComponentName_11,self.txtComponentName_12,self.txtComponentName_13,self.txtComponentName_14,self.txtComponentName_15,self.txtComponentName_16,self.txtComponentName_17,self.txtComponentName_18,self.txtComponentName_19,self.txtComponentName_20]\r\n datanum = [self.txtComponentCount_1, self.txtComponentCount_2, self.txtComponentCount_3, self.txtComponentCount_4,self.txtComponentCount_5,self.txtComponentCount_6,self.txtComponentCount_7,self.txtComponentCount_8,self.txtComponentCount_9,self.txtComponentCount_10,self.txtComponentCount_11,self.txtComponentCount_12,self.txtComponentCount_13,self.txtComponentCount_14,self.txtComponentCount_15,self.txtComponentCount_16,self.txtComponentCount_17,self.txtComponentCount_18,self.txtComponentCount_19,self.txtComponentCount_20]\r\n counter = 0\r\n for data in dataline:\r\n stupro = re.findall(r\"name=\\\"([\\w\\s-]+)\\\"\", data, re.X | re.I)\r\n if counter < 20 and len(stupro) > 0:\r\n stupro = re.findall(r\"name=\\\"([\\w\\s-]+)\\\"\", data, re.X | re.I)\r\n dataname[counter].setText(str(stupro[0]))\r\n stunum = re.findall(r\"count=\\\"([0-9]+)\\\"\", data, re.X | re.I)\r\n datanum[counter].setText(str(stunum[0]))\r\n counter += 1","def read_data(self):\n self.data = reduce_spectrum(self.filename)","def _get_file_contents(self, idx=None):\n self._dataSet = netCDF4.Dataset(self.get_file_name())\n return 1","def loadData(self,ins):\n #--Read subrecords\n bytesRead = 0\n curTest = None\n while bytesRead < self.size:\n (name,size) = ins.unpackSubHeader('INFO')\n srData = ins.read(size,'INFO.'+name)\n bytesRead += 8+size\n #--Ids\n if name == 'INAM': self.id = cstrip(srData)\n elif name == 'PNAM': self.prevId = cstrip(srData)\n elif name == 'NNAM': self.nextId = cstrip(srData)\n #--Text/Script\n elif name == 'NAME': self.text = srData\n elif name == 'BNAM': self.script = srData\n elif name == 'SNAM': self.speak = srData\n #--Quest flags\n elif name == 'QSTN': self.qflag = 1\n elif name == 'QSTF': self.qflag = 2\n elif name == 'QSTR': self.qflag = 3\n #--String/Value Tests\n elif name == 'DATA': \n (self.type, self.spDisp, self.spRank, self.spSex, self.pcRank, self.unk02\n ) = struct.unpack('2i4B',srData)\n elif name == 'ONAM': self.spId = cstrip(srData)\n elif name == 'RNAM': self.spRace = cstrip(srData)\n elif name == 'CNAM': self.spClass = cstrip(srData)\n elif name == 'FNAM': self.spFaction = cstrip(srData)\n elif name == 'ANAM': self.cell = cstrip(srData)\n elif name == 'DNAM': self.pcFaction = cstrip(srData)\n #--Function/Value Tests\n elif name == 'SCVR': \n (index,type,func,oper) = struct.unpack('BB2sB',srData[:5])\n text = srData[5:]\n curTest = Info_Test(type,func,oper,text)\n self.tests[index-48] = curTest\n elif name == 'INTV':\n (curTest.value,) = struct.unpack('i',srData)\n elif name == 'FLTV':\n (curTest.value,) = struct.unpack('f',srData)\n #--Deleted?\n elif name == 'DELE': self.isDeleted = True\n #--Bad record?\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)","def read_data(infile):\n extension = os.path.splitext(infile)[1]\n h = read_header(infile)\n nx = int(h['num_x_pts'])\n ny = int(h['num_y_pts'])\n nt = int(h['num_t_pts'])\n fid = open(infile, 'rb')\n fid.seek(512) #skip header\n if extension == '.aps' or extension == '.a3daps':\n if(h['word_type']==7): #float32\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\n elif(h['word_type']==4): #uint16\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\n data = data * h['data_scale_factor'] #scaling factor\n data = data.reshape(nx, ny, nt, order='F').copy() #make N-d image\n elif extension == '.a3d':\n if(h['word_type']==7): #float32\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\n elif(h['word_type']==4): #uint16\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\n data = data * h['data_scale_factor'] #scaling factor\n data = data.reshape(nx, nt, ny, order='F').copy() #make N-d image\n elif extension == '.ahi':\n data = np.fromfile(fid, dtype = np.float32, count = 2* nx * ny * nt)\n data = data.reshape(2, ny, nx, nt, order='F').copy()\n real = data[0,:,:,:].copy()\n imag = data[1,:,:,:].copy()\n fid.close()\n if extension != '.ahi':\n return data\n else:\n return real, imag","def readData(self):\n f = open(self.filename)\n self.time = []\n self.data = []\n for line in f:\n if line.find('BAD FLAG') > 0:\n self.badValue = float(line.split(':')[1].strip())\n if line.find('LONGITUDE') > 0:\n self.lon = line.split(':')[1].strip()\n if line.find('LATITUDE') > 0:\n self.lat = line.split(':')[1].strip()\n if len(line) > 6 and line[2] == '-' and line[6] == '-':\n parts = line.rsplit(None, 1)\n # data line\n timeStamp = datetime.datetime.strptime(parts[0], '%d-%b-%Y %H')\n t = timeArray.datetimeToEpochTime(timeStamp)\n self.time.append(t)\n val = float(parts[1])\n self.data.append(val)\n\n self.time = np.array(self.time)\n self.data = np.array(self.data)\n # remove bad values\n if self.badValue:\n goodIx = self.data != self.badValue\n self.time = self.time[goodIx]\n self.data = self.data[goodIx]\n self.fileIsRead = True","def readAMBERTop(self, phys, filename):\r\n\r\n def skipLine(data):\r\n nl = data.index('\\n')\r\n return data[nl+1:len(data)]\r\n\r\n def jumpTo(data, target):\r\n fp = data.index(target)\r\n return data[fp:len(data)]\r\n\r\n def readRemove(data, size):\r\n retval = data[0:size-1]\r\n return data[size:len(data)]\r\n\r\n def getInteger(data):\r\n pos = 0\r\n retval = \"\"\r\n while (not data[pos].isdigit()):\r\n pos = pos + 1\r\n while (data[pos].isdigit()):\r\n retval = retval + data[pos]\r\n pos = pos + 1\r\n data = data[pos:len(data)]\r\n return int(retval), data\r\n\r\n def parse(data, arr, str, count, dtype, tupsize=1):\r\n data = jumpTo(data, \"%FLAG \"+str)\r\n data = jumpTo(data, \"%FORMAT\")\r\n numPerLine, data = getInteger(data)\r\n fieldsize, data = getInteger(data)\r\n data = skipLine(data) \r\n \r\n arr2 = []\r\n numread = 0\r\n for j in range(0, (tupsize*count-1) / numPerLine + 1):\r\n for i in range(0, numPerLine):\r\n if (tupsize == 1):\r\n arr.append(dtype(data[0:fieldsize].strip()))\r\n else:\r\n arr2.append(dtype(data[0:fieldsize].strip()))\r\n if (len(arr2) == tupsize):\r\n arr.append(arr2)\r\n arr2 = []\r\n numread += 1\r\n data = data[fieldsize:len(data)]\r\n if (numread == tupsize*count):\r\n break\r\n data = skipLine(data) \r\n return data\r\n\r\n def scan(data, str):\r\n return (data.count(str) != 0)\r\n\r\n\r\n f = open(filename, 'r')\r\n data = f.read()\r\n\r\n # First Line: VERSION ...\r\n data = skipLine(data)\r\n\r\n # Go To: %FLAG POINTERS\r\n data = jumpTo(data, '%FLAG POINTERS')\r\n\r\n data = jumpTo(data, '%FORMAT')\r\n numPerLine, data = getInteger(data)\r\n fieldsize, data = getInteger(data)\r\n data = skipLine(data)\r\n \r\n temp = []\r\n numread = 0\r\n for j in range(0, 31 / numPerLine + 1):\r\n for i in range(0, numPerLine):\r\n temp.append(int(data[0:8]))\r\n data = data[8:len(data)]\r\n numread += 1\r\n if (numread == 31):\r\n break\r\n data = skipLine(data)\r\n \r\n [natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \r\n\r\n\r\n #################################################\r\n # Read AtomTypes\r\n atomnames = []\r\n charges = []\r\n masses = []\r\n atindex = []\r\n exclusions = []\r\n nparams = []\r\n reslabels = []\r\n respointers = []\r\n forceconstants = [[], [], []] # bond, angle, dihedral\r\n equilvals = [[], [], [[], []]] # bond, angle, dihedral\r\n scee_scales = []\r\n scnb_scales = []\r\n solty = []\r\n lj_acoef = []\r\n lj_bcoef = []\r\n\r\n data = parse(data, atomnames, \"ATOM_NAME\", natoms, str) \r\n data = parse(data, charges, \"CHARGE\", natoms, float)\r\n data = parse(data, masses, \"MASS\", natoms, float)\r\n data = parse(data, atindex, \"ATOM_TYPE_INDEX\", natoms, int)\r\n data = parse(data, exclusions, \"NUMBER_EXCLUDED_ATOMS\", natoms, int)\r\n data = parse(data, nparams, \"NONBONDED_PARM_INDEX\", ntypes*ntypes, int)\r\n data = parse(data, reslabels, \"RESIDUE_LABEL\", nres, str)\r\n data = parse(data, respointers, \"RESIDUE_POINTER\", nres, int)\r\n data = parse(data, forceconstants[0], \"BOND_FORCE_CONSTANT\", numbnd, float)\r\n data = parse(data, equilvals[0], \"BOND_EQUIL_VALUE\", numbnd, float)\r\n data = parse(data, forceconstants[1], \"ANGLE_FORCE_CONSTANT\", numang, float)\r\n data = parse(data, equilvals[1], \"ANGLE_EQUIL_VALUE\", numang, float)\r\n data = parse(data, forceconstants[2], \"DIHEDRAL_FORCE_CONSTANT\", nptra, float)\r\n data = parse(data, equilvals[2][0], \"DIHEDRAL_PERIODICITY\", nptra, float)\r\n data = parse(data, equilvals[2][1], \"DIHEDRAL_PHASE\", nptra, float)\r\n if (scan(data, \"SCEE_SCALE_FACTOR\")):\r\n data = parse(data, scee_scales, \"SCEE_SCALE_FACTORS\", nptra, float)\r\n else:\r\n for i in range(0, nptra):\r\n scee_scales.append(1.2) # Default \r\n if (scan(data, \"SCNB_SCALE_FACTOR\")):\r\n data = parse(data, scnb_scales, \"SCNB_SCALE_FACTORS\", nptra, float)\r\n else:\r\n for i in range(0, nptra):\r\n scnb_scales.append(2.0) # Default \r\n\r\n data = parse(data, solty, \"SOLTY\", natyp, float)\r\n data = parse(data, lj_acoef, \"LENNARD_JONES_ACOEF\", ntypes*(ntypes+1)/2, float)\r\n data = parse(data, lj_bcoef, \"LENNARD_JONES_BCOEF\", ntypes*(ntypes+1)/2, float)\r\n\r\n\r\n ##########################################################\r\n # STRUCTURE\r\n\r\n bonds = [[], []] # With H, Without H\r\n angles = [[], []] # With H, Without H\r\n dihedrals = [[], []] # With H, Without H\r\n impropers = [[], []] # With H, Without H\r\n excluded_atoms = [] \r\n hbond_acoef = []\r\n hbond_bcoef = []\r\n hbcut = []\r\n amber_atom_types = []\r\n tree_chain = []\r\n join_array = []\r\n irotat = []\r\n radii = []\r\n screen = []\r\n\r\n data = parse(data, bonds[0], \"BONDS_INC_HYDROGEN\", nbonh, int, 3)\r\n data = parse(data, bonds[1], \"BONDS_WITHOUT_HYDROGEN\", nbona, int, 3)\r\n data = parse(data, angles[0], \"ANGLES_INC_HYDROGEN\", ntheth, int, 4)\r\n data = parse(data, angles[1], \"ANGLES_WITHOUT_HYDROGEN\", ntheta, int, 4)\r\n data = parse(data, dihedrals[0], \"DIHEDRALS_INC_HYDROGEN\", nphih, int, 5)\r\n data = parse(data, dihedrals[1], \"DIHEDRALS_WITHOUT_HYDROGEN\", nphia, int, 5)\r\n \r\n # MERGE ARRAYS - PM HANDLES THE H+\r\n final_bonds = bonds[0] + bonds[1]\r\n final_angles = angles[0] + angles[1]\r\n final_dihedrals = dihedrals[0] + dihedrals[1]\r\n final_impropers = []\r\n \r\n # CLEAN UP THE TRASH\r\n del(bonds)\r\n del(angles)\r\n del(dihedrals)\r\n \r\n\r\n # Move impropers into their own array\r\n i = 0\r\n while (i < len(final_dihedrals)):\r\n if (final_dihedrals[i][2] < 0): # 1-4 exclusions are handled by our back end\r\n final_dihedrals[i][2] *= -1\r\n if (final_dihedrals[i][3] < 0):\r\n final_dihedrals[i][3] *= -1 # Make + again\r\n final_impropers.append(final_dihedrals[i])\r\n final_dihedrals.remove(final_dihedrals[i])\r\n i -= 1\r\n i += 1\r\n\r\n # Convert charge units\r\n for i in range(0, len(charges)):\r\n charges[i] /= 18.223\r\n\r\n\r\n data = parse(data, excluded_atoms, \"EXCLUDED_ATOMS_LIST\", nnb, int)\r\n data = parse(data, hbond_acoef, \"HBOND_ACOEF\", nphb, float)\r\n data = parse(data, hbond_bcoef, \"HBOND_BCOEF\", nphb, float)\r\n data = parse(data, hbcut, \"HBCUT\", nphb, float)\r\n data = parse(data, amber_atom_types, \"AMBER_ATOM_TYPE\", natoms, str)\r\n data = parse(data, tree_chain, \"TREE_CHAIN_CLASSIFICATION\", natoms, str)\r\n data = parse(data, join_array, \"JOIN_ARRAY\", natoms, int)\r\n data = parse(data, irotat, \"IROTAT\", natoms, int)\r\n data = parse(data, radii, \"RADII\", natoms, float)\r\n data = parse(data, screen, \"SCREEN\", natoms, float)\r\n\r\n # Further process dihedrals and impropers\r\n # Deal with multiplicity\r\n # A bit ugly, but the fastest for now\r\n # forceconstants[2][dihedrals[0][i][4]-1], int(equilvals[2][0][dihedrals[0][i][4]-1]), equilvals[2][1][dihedrals[0][i][4]-1]\r\n\r\n mult_di = dict()\r\n mult_im = dict()\r\n for i in range(0, len(final_dihedrals)):\r\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\r\n if (not mult_di.has_key(di_id)):\r\n mult_di[di_id] = [1, False, [forceconstants[2][final_dihedrals[i][4]-1]], [int(equilvals[2][0][final_dihedrals[i][4]-1])], [equilvals[2][1][final_dihedrals[i][4]-1]]]\r\n else:\r\n mult_di[di_id][0] += 1\r\n mult_di[di_id][2].append(forceconstants[2][final_dihedrals[i][4]-1])\r\n mult_di[di_id][3].append(int(equilvals[2][0][final_dihedrals[i][4]-1]))\r\n mult_di[di_id][4].append(equilvals[2][1][final_dihedrals[i][4]-1])\r\n \r\n for i in range(0, len(final_impropers)):\r\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\r\n if (not mult_im.has_key(di_id)):\r\n mult_im[im_id] = [1, False, [forceconstants[2][final_impropers[i][4]-1]], [int(equilvals[2][0][final_impropers[i][4]-1])], [equilvals[2][1][final_impropers[i][4]-1]]]\r\n else:\r\n mult_im[im_id][0] += 1\r\n mult_im[im_id][2].append(forceconstants[2][final_impropers[i][4]-1])\r\n mult_im[im_id][3].append(int(equilvals[2][0][final_impropers[i][4]-1]))\r\n mult_im[im_id][4].append(equilvals[2][1][final_impropers[i][4]-1])\r\n\r\n\r\n\r\n \r\n #[natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \r\n #phys.myPSF.createAll(natoms, nbonh+mbona, ntheth+mtheta,\r\n # len(dihedrals[0])+len(dihedrals[1]),\r\n # len(impropers[0])+len(impropers[1]),\r\n # 0, 0, 0, 0)\r\n \r\n # Add atoms\r\n curres = 1\r\n for i in range(0, natoms):\r\n phys.myPSF.addAtom(i, 'SIM', curres, reslabels[curres-1],\r\n atomnames[i], atomnames[i], charges[i],\r\n masses[i]) \r\n if (curres != nres and i >= respointers[curres]):\r\n curres += 1\r\n\r\n # Add bonds\r\n for i in range(0, nbonh+nbona):\r\n phys.myPSF.addBond(i+1, final_bonds[i][0]/3+1, final_bonds[i][1]/3+1)\r\n phys.myPAR.addBond(i+1, atomnames[final_bonds[i][0]/3], atomnames[final_bonds[i][1]/3], forceconstants[0][final_bonds[i][2]/3], equilvals[0][final_bonds[i][2]/3])\r\n \r\n # Add angles\r\n for i in range(0, ntheth+ntheta):\r\n phys.myPSF.addAngle(i+1, final_angles[i][0]/3+1, final_angles[i][1]/3+1, final_angles[i][2]/3+1)\r\n phys.myPAR.addAngle(i+1, atomnames[final_angles[i][0]/3], atomnames[final_angles[i][1]/3], atomnames[final_angles[i][2]/3], forceconstants[1][final_angles[i][3]/3], equilvals[1][final_angles[i][3]/3])\r\n \r\n # Add dihedrals\r\n for i in range(0, len(final_dihedrals)):\r\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\r\n mult = mult_di[di_id][0]\r\n checked = mult_di[di_id][1]\r\n print di_id, \" \", mult\r\n if (not checked):\r\n if (mult == 1):\r\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\r\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], forceconstants[2][final_dihedrals[i][4]-1], int(equilvals[2][0][final_dihedrals[i][4]-1]), equilvals[2][1][final_dihedrals[i][4]-1])\r\n else:\r\n mult_di[di_id][1] = True\r\n # Add dihedral with the appropriate multiplicity\r\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\r\n fcvec = PARReader.VectorOfDouble()\r\n periodvec = PARReader.VectorOfInt()\r\n phasevec = PARReader.VectorOfDouble() \r\n for j in range(0, len(mult_di[di_id][2])):\r\n fcvec.push_back(mult_di[di_id][2][j])\r\n periodvec.push_back(mult_di[di_id][3][j])\r\n phasevec.push_back(mult_di[di_id][4][j])\r\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\r\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], mult, fcvec, periodvec, phasevec)\r\n \r\n\r\n\r\n\r\n for i in range(0, len(final_impropers)):\r\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\r\n mult = mult_im[im_id][0]\r\n checked = mult_im[im_id][1]\r\n print im_id, \" \", mult\r\n if (not checked):\r\n if (mult == 1):\r\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\r\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], forceconstants[2][final_impropers[i][4]-1], int(equilvals[2][0][final_impropers[i][4]-1]), equilvals[2][1][final_impropers[i][4]-1])\r\n else:\r\n mult_im[im_id][1] = True\r\n # Add dihedral with the appropriate multiplicity\r\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\r\n fcvec = PARReader.VectorOfDouble()\r\n periodvec = PARReader.VectorOfInt()\r\n phasevec = PARReader.VectorOfDouble() \r\n for j in range(0, len(mult_im[im_id][2])):\r\n fcvec.push_back(mult_im[im_id][2][j])\r\n periodvec.push_back(mult_im[im_id][3][j])\r\n phasevec.push_back(mult_im[im_id][4][j])\r\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\r\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], mult, fcvec, periodvec, phasevec)\r\n\r\n \r\n # Need to add garbage nonbonded stuff for now\r\n for i in range(0, natoms):\r\n phys.myPAR.addNonbonded(i, atomnames[i], 1, 1, 1, 1, 1, 1)\r\n\r\n # Add VDW parameters\r\n # AMBER has the Aij and Bij already in the parameter file\r\n # This actually makes life easier.\r\n # CHARMM does not, they simply have the original sigma and epsilon.\r\n # To compensate for this, for now we will leave the nonbondeds empty in phys.myPAR\r\n # We will then access the LennardJones parameter table in Topology directly\r\n k = 0\r\n phys.myTop.resizeLennardJonesParameters(ntypes)\r\n for i in range(0, ntypes):\r\n for j in range(i, ntypes):\r\n params = GenericTopology.LennardJonesParameters(lj_acoef[k], lj_bcoef[k])\r\n k += 1\r\n phys.myTop.setLennardJonesParameters(i, j, params)\r\n \r\n phys.myPAR.readFlag = 1\r\n phys.build()","def __init__(self, fileName):\n in_ = JInputStream.getInputStream(fileName)\n b = int()\n len = int()\n cs = int()\n addr = int()\n buf = [None]*255\n eof = False\n line = 0\n i = 0\n while len(ihxData):\n self.ihxData[i] = -1\n i += 1\n try:\n while not eof:\n while True:\n b = in_.read()\n if b < 0:\n raise IhxParseException(\"Inexpected end of file\")\n if not ((b != int(':'))):\n break\n line += 1\n len = self.readHexByte(in_)\n # length field \n cs = len\n b = self.readHexByte(in_)\n # address field \n cs += b\n addr = b << 8\n b = self.readHexByte(in_)\n cs += b\n addr |= b\n b = self.readHexByte(in_)\n # record type field\n cs += b\n while i < len:\n # data\n buf[i] = int(self.readHexByte(in_))\n cs += buf[i]\n i += 1\n cs += self.readHexByte(in_)\n # checksum\n if (cs & 0xff) != 0:\n raise IhxParseException(\"Checksum error\")\n if b == 0:\n # data record\n while i < len:\n if self.ihxData[addr + i] >= 0:\n System.err.println(\"Warning: Memory at position \" + Integer.toHexString(i) + \" overwritten\")\n self.ihxData[addr + i] = int((buf[i] & 255))\n i += 1\n elif b == 1:\n # eof record\n eof = True\n else:\n raise IhxParseException(\"Invalid record type: \" + b)\n except IhxParseException as e:\n raise IhxFileDamagedException(fileName, line, e.getLocalizedMessage())\n try:\n in_.close()\n except Exception as e:\n System.err.println(\"Warning: Error closing file \" + fileName + \": \" + e.getLocalizedMessage())","def load_data(filename):\n\n evidance = list()\n labels = list()\n row = list()\n\n with open(filename) as file:\n for j, item in enumerate(file):\n if(j == 0):\n continue\n row = []\n for i, el in enumerate(item.split(',')):\n # print(el)\n if(i in {0,2,4,11,12,13,14}):\n row.append(int(el))\n elif(i in {1,3,5,6,7,8,9}):\n row.append(float(el))\n elif(i==10):\n if(el == \"Jan\"):\n row.append(int(0))\n elif(el == \"Feb\"): \n row.append(int(1))\n elif(el == \"Mar\"): \n row.append(int(2))\n elif(el == \"Apr\"): \n row.append(int(3))\n elif(el == \"May\"): \n row.append(int(4))\n elif(el == \"June\"): \n row.append(int(5))\n elif(el == \"Jul\"): \n row.append(int(6))\n elif(el == \"Aug\"): \n row.append(int(7))\n elif(el == \"Sep\"): \n row.append(int(8))\n elif(el == \"Oct\"): \n row.append(int(9))\n elif(el == \"Nov\"): \n row.append(int(10))\n elif(el == \"Dec\"): \n row.append(int(11))\n else:\n print(\"wrong month Exit\")\n quit()\n elif(i==15):\n if(el == 'Returning_Visitor'):\n row.append(int(1))\n else:\n row.append(int(0))\n elif(i==16):\n if(el == 'TRUE'):\n row.append(int(1))\n else:\n row.append(int(0)) \n elif(i==17):\n if(el == 'TRUE\\n' or el == 'TRUE'):\n row.append(int(1))\n else:\n row.append(int(0))\n else:\n print(\"Incorrect value\")\n\n evidance.append(row)\n # print(item.split(',')[-1])\n if(item.split(',')[-1] == 'FALSE\\n'):\n labels.append(0)\n else:\n labels.append(1)\n\n # print(labels)\n return (evidance, labels)","def open(self):","def CSV_Load_File( self, infilename ):\n print( 'Loading \"{}\"'.format(infilename) )\n IN = open( infilename, 'r' )\n standname = None\n laststand = None\n for L in IN:\n if( L[0:9] == 'Site/Plot' ): continue\n col = L.split( ',' )\n standname = col[0]\n year = int(col[1])\n #if( re.search( '-', standname ) != None ):\n # loc = re.search( '-', standname )\n # year = int(standname[loc.start()+1:])\n # standname = standname[0:loc.start()]\n #print standname, year\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\n if( OPT['d'] ):\n if( dbh > 10.0 ): dbh *= 1.25\n if( dbh > 15.0 ): dbh *= 1.50\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\n ntree = len( self.Data.Stand[standname].Tree ) + 1\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\n laststand = standname\n IN.close()","def make_tag_data_raw_fast(mdp,filename):\n #\n fin = open(filename,'r')\n iter = 0\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"comb_path\":\n update_params(mdp,lsp)\n if not mdp.flag_out_open: ## -- try to open output file\n try:\n if mdp.flag_overwrite == \"True\": ## check string value!\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ## -- try open output file\n for file in glob.glob(mdp.input_path):\n # get sign which corrects for boundary condition\n tvals = file.split('/')[-1].split('_')[3].split('t')\n try:\n ## flip sign if requested\n bcsign = ((int(tvals[1])+int(tvals[2])) != (int(tvals[1])+int(tvals[2])) % mdp.corr_len)\n except IndexError:\n ## 2-point function\n bcsign = False\n try:\n # open correlator file\n mdp.corr_file = open(file,'r')\n except IOError:\n print \"Could not open file \",file\n continue\n ## -- get tag\n ## baryons:\n #mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_r'+file.split('/')[-1].split('_')[4][-1]\n ## with time source tag\n #mdp.tag = file.split('/')[-1].split('_')[3][:3]\\\n # +'_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\n # +file.split('/')[-1].split('_')[4][3:]\n ## no time source tag\n mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\n +file.split('/')[-1].split('_')[4][3:]\n #print file,',',mdp.tag\n iter+=1\n ##endif ! flag_out_open\n\n #save_data_fast(mdp)\n save_data_fast_bc(mdp,bcsign)\n mdp.corr_file.close()\n if iter%400 == 0:\n print \"file\",iter\n max_iter = None\n if not(max_iter is None) and iter==max_iter:\n print \"reached max file iterations, ending loop...\"\n break\n ## end comb_path\n pass\n\n elif lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data_fast(mdp)\n mdp.corr_file.close()\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def _load_data(self, save_temp=False):\n # directly read processed data and encode\n print ('Start tokenizing data...')\n self.data = json.loads(\n open(self.cfg.data_path+self.cfg.data_file, 'r', encoding='utf-8').read().lower())\n self.train, self.dev, self.test = [], [], []\n print ('Start encoding data...')\n p = progressbar.ProgressBar(len(self.data))\n p.start()\n p_idx = 0\n for fn, dial in self.data.items():\n p.update(p_idx)\n p_idx += 1\n if '.json' in fn:\n fn = fn.replace('.json', '')\n if 'all' in self.cfg.exp_domains or self.exp_files.get(fn):\n if self.dev_files.get(fn):\n self.dev.append(self._get_encoded_data(fn, dial))\n elif self.test_files.get(fn):\n self.test.append(self._get_encoded_data(fn, dial))\n else:\n if self.data_mode == 'train':\n self.train.append(self._get_encoded_data(fn, dial))\n elif self.data_mode == 'test':\n pass\n else:\n raise Exception('Wrong Reader Data Mode!!!')\n p.finish()","def load_datapair(self, ds):\n raise NotImplementedError(\"Define this in your derived checker class\")","def load_data(self):\n df= self.read_file()\n for row,col in df.iterrows():\n Employeeid = int(col['Empolyeeid'])\n Employee_Name = col['Employee_Name']\n Age = col['Age']\n Salary = col['Salary']\n self.table.put_item(\n Item={\n \"Employeeid\":Employeeid,\n \"Employee_Name\": Employee_Name,\n \"Age\": Age,\n \"Salary\": Salary\n }\n )\n return True","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def build(self, file_number, data):\n pass","def reading_data(fname,goal):\n \n #Reading of the EEG data\n data = pd.read_csv(fname)\n events_fname = fname.replace('_data','_events')\n labels= pd.read_csv(events_fname)\n\n if goal==\"training\":\n data=data.drop(['id' ], axis=1)#remove id\n labels=labels.drop(['id' ], axis=1)#remove id\n elif goal==\"testing\":\n labels=labels.drop(['id' ], axis=1)\n else:\n raise SystemExit(\"The goal variable is unknown for the function\")\n\n return data, labels","def load_data():\r\n #读取User数据\r\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\r\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\r\n users = users.filter(regex='UserID|Gender|Age|JobID')\r\n users_orig = users.values\r\n #改变User数据中性别和年龄\r\n gender_map = {'F':0, 'M':1}\r\n users['Gender'] = users['Gender'].map(gender_map)\r\n\r\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\r\n users['Age'] = users['Age'].map(age_map)\r\n\r\n #读取Movie数据集\r\n movies_title = ['MovieID', 'Title', 'Genres']\r\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\r\n movies_orig = movies.values\r\n #将Title中的年份去掉\r\n pattern = re.compile(r'^(.*)\\((\\d+)\\)$')\r\n\r\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #电影类型转数字字典\r\n genres_set = set()\r\n for val in movies['Genres'].str.split('|'):\r\n genres_set.update(val)\r\n\r\n genres_set.add('<PAD>')\r\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\r\n\r\n #将电影类型转成等长数字列表，长度是18\r\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\r\n\r\n for key in genres_map:\r\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\r\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\r\n \r\n movies['Genres'] = movies['Genres'].map(genres_map)\r\n\r\n #电影Title转数字字典\r\n title_set = set()\r\n for val in movies['Title'].str.split():\r\n title_set.update(val)\r\n \r\n title_set.add('<PAD>')\r\n title2int = {val:ii for ii, val in enumerate(title_set)}\r\n\r\n #将电影Title转成等长数字列表，长度是15\r\n title_count = 15\r\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\r\n \r\n for key in title_map:\r\n for cnt in range(title_count - len(title_map[key])):\r\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\r\n \r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #读取评分数据集\r\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\r\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\r\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\r\n\r\n #合并三个表\r\n data = pd.merge(pd.merge(ratings, users), movies)\r\n \r\n #将数据分成X和y两张表\r\n target_fields = ['ratings']\r\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\r\n \r\n features = features_pd.values\r\n targets_values = targets_pd.values\r\n \r\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig","def extract(self, data):","def prepare_dataset(fpath):\n raise NotImplementedError","def apply(self, opened_file):","def read_data():\n ADV_MAT = np.load('ADV.npy');\n ADJ_MAT = np.load('ADJ.npy');\n PR_MAT = np.load('PR.npy'); \n NN_MAT = np.load('NN.npy');\n for i in range(ADV_MAT.shape[0]):RUNNING_DATA['ADV___'+str(i)] = ADV_MAT[i];\n for i in range(ADJ_MAT.shape[0]):RUNNING_DATA['ADJ___'+str(i)] = ADJ_MAT[i];\n for i in range(PR_MAT.shape[0]):RUNNING_DATA['PR___'+str(i)] = PR_MAT[i];\n for i in range(NN_MAT.shape[0]):RUNNING_DATA['NN___'+str(i)] = NN_MAT[i];","def process_file(self, data, filename):\n\n for relation in data:\n if \"start_date\" in relation[1]:\n start = convert_to_date(relation[1][\"start_date\"])\n end = convert_to_date(relation[1][\"end_date\"])\n\n if start <= START_DATE and end >= START_DATE:\n first = relation[1][\"first\"]\n second = relation[1][\"second\"]\n if first not in self.relations:\n self.add_tag(first)\n if second not in self.relations:\n self.add_tag(second)\n\n if relation[0] == \"vassal\": \n self.relations[first][\"vassal\"].append(second)\n self.relations[second][\"overlord\"].append(first)\n elif relation[0] == \"alliance\":\n self.relations[first][\"alliance\"].append(second)\n self.relations[second][\"alliance\"].append(first)\n elif relation[0] == \"dependency\":\n self.relations[first][\"tributary\"].append(second)\n self.relations[second][\"hegemon\"].append(first)\n elif relation[0] == \"guarantee\":\n self.relations[first][\"guaranteeing\"].append(second)\n self.relations[second][\"guarantor\"].append(first)\n elif relation[0] == \"union\":\n self.relations[first][\"junior\"].append(second)\n self.relations[second][\"senior\"].append(first)\n elif relation[0] == \"royal_marriage\":\n self.relations[first][\"marriage\"].append(second)\n self.relations[second][\"marriage\"].append(first)","def read_data(self):\n data = np.genfromtxt(self.__file) # Planck SED\n self.__nu = 10.0**data[:,0]\n self.__nuF = 10.0**data[:,2]\n self.__err = 10.0**data[:,3]\n #self.__W = 10.0**data[:,4]\n self.__yerr = [ self.__nuF - self.__nuF / self.__err, \\\n self.__nuF * self.__err - self.__nuF ]\n self.__maxY = max( self.__nuF )\n self.__minY = min( self.__nuF )","def load_data(self):\n with open(self.config.drv_log, 'r') as f:\n add_data = [] # addtional data require angle smoothing\n ori_data = [] # given data\n ddir = os.path.dirname(self.config.drv_log)\n lines = list(csv.reader(f))\n np.random.shuffle(lines)\n for line in lines:\n if line[0] == 'center':\n continue\n ste_ang = float(line[3])\n if ste_ang == 0. and random.random() < 0.8:\n continue\n if '12_01' in line[0]:\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n else:\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n \n def angle_smoothing(data):\n \"\"\"helper function for smoothing steering angles.\n reference: \n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\n \"\"\"\n angles = np.asarray([itm[1] for itm in data])\n fwd = pd.ewma(angles, span=20)\n bwd = pd.ewma(angles[::-1], span=20)\n smooth = np.vstack((fwd, bwd[::-1]))\n smooth = np.mean(smooth, axis=0)\n angles = np.ndarray.tolist(smooth)\n smooth_data = []\n for i in range(len(data)):\n smooth_data.append([data[i][0], angles[i]])\n return smooth_data\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\n all_data = ori_data + add_data\n n1 = int(len(all_data) * 0.7)\n n2 = int(len(all_data) * 0.85)\n self.train = self.data_generator(all_data[:n1])\n self.valid = self.data_generator(all_data[n1:n2])\n self.test = self.data_generator(all_data[n2:])\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size","def import_data_helper(self): \n if len(self.components) == 1:\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\n else: \n global_id = []\n for c in self.components:\n global_id.append(hapi.ISO[c][0])\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)","def initialize_data(self , station = '', datasets = {} ):\n \n self.datasets = datasets\n self.datasets_keys = datasets.keys()\n self.station = station\n \n data = {} # container for the data of each dataset\n source_configuration = {} # container for the source_configuration of each dataset\n \n\n \n \"\"\" Looping over the datasets \"\"\"\n logging.info('*** Reading and Initializing the data from the netCDF files ')\n \n \n for k,v in datasets.items() :\n logging.info(' Initialising the dataset: *** %s ' , k )\n data[k] = {} \n data['cdm_tables'] = {} \n \n ### alternative with xarray \n #ds = xr.load_dataset(v) \n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n ### alternative with netCDF4\n #ds = nc.Dataset(v) \n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \n \n ###for h5py but cant extract date time units !!!\n ds = h5py.File(v , driver=\"core\" ) \n data[k]['df'] = ds # storing the entire file \n try: \n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\n except:\n data[k]['source_file'] = str(v) # temp fix \n \n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\n #data[k]['recordindex'] = ds['recordindex'].value \n #ds.close() \n logging.debug('Reading the file with h5py ')\n \n \n # add here appending datasets for the case of ncar_w and ncar_t \n \n \n self.data = data\n self.make_dataframe()\n ds.close()\n \n \"\"\" Reading the header_table, station_configuration, source_configuration \"\"\"\n for k,v in datasets.items() : \n \n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\n #data[k]['recordindex'] = d['recordindex'].values \n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \n data[k]['station_configuration'] = d.to_dataframe() \n #data[k]['station_configuration'] = d ### USELESS ? \n logging.debug('Done with %s station_configuration' , str(k) )\n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \n logging.debug('Loading the header_table') \n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['header_table'] = {}\n for var in d.variables:\n self.attributes['header_table'][var] = {}\n self.attributes['header_table'][var]['description'] = d[var].description\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \n data[k]['header_table'] = d.to_dataframe() \n logging.debug('Done with %s ' , k )\n \n logging.info(\"*** Loading the observations_table (might take time) %s\" , k ) \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['observations_table'] = {}\n for var in d.variables:\n self.attributes['observations_table'][var] = {}\n self.attributes['observations_table'][var]['description'] = d[var].description\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\n \n \n logging.info(\"*** Loading the source configuration %s\" , k ) \n try: \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\n d = d.isel(hdrlen=[0])\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \n logging.debug('Done with %s source_configuration' , k )\n except: \n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \n \n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \n data[k]['era5fb'] = d.to_dataframe() \n logging.debug('Done with %s era5 feedback ', k )\n \n \"\"\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \"\"\" \n if list(datasets.keys()).index(k) == 0 :\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\n data['cdm_tables'][t] = d \n \n d.close() \n ds.close()\n\n \"\"\" Reading the name of the original source file \"\"\"\n source_configuration[k] = {} \n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\n\n \n \"\"\" Storing the station configurations \"\"\" \n self.source_configuration = source_configuration \n \n \"\"\" Making all date_times \"\"\" \n self.make_all_datetime()\n \n \n \"\"\" feedback columns \"\"\"\n if 'era5_1' in list (self.data.keys() ):\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \n else:\n self.fb_columns = ['empty']","def read_inversion_info(file_dic):\n #print_file_test = open('file_test.txt','w')\n\n if not ( check_inversion_files(file_dic) ):\n print 'error(read_inversion_info): problem with lenstool file names'\n return 0\n \n file_generate_arcs = file_dic['file_generate_arcs']\n info_input_lens = fc.extract_second_identifiers( file_generate_arcs, \\\n 'potential' )\n#-------------------------------------------------------------------------------\n\n file_source = file_dic['file_source']\n info_src = np.loadtxt(file_source, unpack=False)\n if len(info_src) == 8 and np.isscalar(info_src[0]):\n #FIXME - check if the second condition is all we need\n info_src = [info_src]\n#-------------------------------------------------------------------------------\n\n file_make_inversion = file_dic['file_make_inversion']\n info_fited_param = fc.extract_second_identifiers( file_make_inversion, \\\n 'limit' )\n info_forme = fc.extract_parameter(file_make_inversion, 'forme')[0][0]\n\n#-------------------------------------------------------------------------------\n\n file_best_fit = file_dic['file_best_fit']\n info_best_fit = fc.extract_second_identifiers( file_best_fit, \\\n 'potentiel' )\n\n info_xi2 = fc.extract_parameter(file_best_fit, '#Chi2pos:')\n\n#-------------------------------------------------------------------------------\n file_chires = file_dic['file_chires']\n\n info_chires = extract_parameter(file_chires, '0')\n rmss_mean = [0.0, 0.0]\n rmsi_mean = [0.0, 0.0]\n for i in info_chires:\n if i[0] != 'A':\n rmss_mean[0] = rmss_mean[0] + float(i[7])\n rmss_mean[1] = rmss_mean[1] + 1.0\n \n rmsi_mean[0] = rmsi_mean[0] + float(i[8])\n rmsi_mean[1] = rmsi_mean[1] + 1.0\n\n rmss_mean = rmss_mean[0]/rmss_mean[1]\n rmsi_mean = rmsi_mean[0]/rmsi_mean[1]\n#-------------------------------------------------------------------------------\n out_dict = { 'xi2' : float(info_xi2[0][0]), \\\n 'best_fit_lens' : info_best_fit, \\\n 'rmsi_mean' : rmsi_mean, \\\n 'rmss_mean' : rmss_mean, \\\n 'fited_parameters' : info_fited_param[0].keys(), \\\n 'input_lens' : info_input_lens[len(info_input_lens) - 1], \\\n 'forme' : info_forme \\\n }\n #for i in out_dict.keys():\n # print i, out_dict[i]\n return out_dict"],"string":"[\n \"def load_data(self):\",\n \"def _read_data(self):\",\n \"def load_data(self) -> None:\",\n \"def read(self):\",\n \"def _load(self):\\n\\n # number of non-data header details at top of data file\\n header = 1\\n\\n # open file\\n weatherData = []\\n with open(self.wfile) as myfile:\\n if (self.lines > 0):\\n weatherData = [next(myfile) for x in xrange(self.lines + header)]\\n else:\\n weatherData = myfile.readlines()\\n\\n # get data stream from first line\\n streamHeader = weatherData.pop(0).rstrip()\\n if (streamHeader == 'FULL'):\\n self.dataStream = 0\\n elif (streamHeader == 'ADVANCED'):\\n self.dataStream = 1\\n elif (streamHeader == 'BASIC'):\\n self.dataStream = 2\\n else:\\n print \\\"Error: unecognised data stream from file %s\\\" % (self.wfile)\\n return -1\\n\\n # read data\\n inputData = []\\n for line in weatherData:\\n entries = line.split()\\n inputData.append(entries)\\n\\n # copy all into np array\\n self.data = np.array(inputData)\\n\\n return 0\",\n \"def get_file_data(filename):\",\n \"def load_data():\\n\\t\\t# load the data\\n\\t\\tDATPATH = \\\"../data/\\\"\\n\\t\\t#fnino = DATPATH + \\\"nino3.csv\\\" # 1871-2000\\n\\t\\tfnino = DATPATH + \\\"tas_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_nino3_tseries.csv\\\" # 1871-2016\\n\\t\\t#fnino = DATPATH + \\\"nino34.long.data\\\"\\n\\t\\t#nc_data_nino3 = netCDF4.Dataset(fnino)\\n\\t\\t#nino3_load = nc_data_nino3.variables['tas'][:]\\n\\t\\t#dnino = nino3_load.flatten()\\n\\n\\t\\tdnino = np.genfromtxt(fnino, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fismr = DATPATH + \\\"ismr.csv\\\" # 1871-2000\\n\\t\\t#fismr = DATPATH + \\\"psl_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_1_india_goswami_2002_tseries.csv\\\" # 1871-2016\\n\\t\\tfismr = DATPATH + \\\"pr_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_goswami_india_tseries.csv\\\" # 1871-2016\\n\\t\\tdismr = np.genfromtxt(fismr, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fvolc = DATPATH + \\\"robock.txt\\\" # 1871-2000\\n\\t\\tfvolc = DATPATH + \\\"sigl.txt\\\" # 1871-2016\\n\\t\\tdvolc = np.genfromtxt(fvolc, delimiter=\\\",\\\", dtype=float).flatten()\\n\\n\\t\\tfvolc_source = DATPATH + \\\"volc_source_850_1850.csv\\\" # 1871-2016\\n\\t\\tdvolc_source = np.genfromtxt(fvolc_source, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t# simple check for data consistency\\n\\t\\tassert dnino.shape == dismr.shape, \\\"Data sets are unequal!\\\"\\n\\t\\tassert int(dismr.shape[0]/12) == dvolc.shape[0], \\\"Data sets are unequal\\\"\\n\\t\\treturn dnino, dismr, dvolc, dvolc_source\",\n \"def parse_data(fp):\\n pass\",\n \"def prepare_data(self):\",\n \"def dataLoad():\\n try:\\n try: #Python3\\n f = open(__file__ + \\\".csv\\\",\\\"rt\\\")\\n except: #Python2\\n f = open(__file__ + \\\".csv\\\",\\\"rb\\\")\\n data = f.read().split(',')\\n entryCol.entry0.delete(0,END)\\n entryCol.entry0.insert(0,data[0])\\n entryCol.entry1.delete(0,END)\\n entryCol.entry1.insert(0,data[1])\\n entryCol.entry2.delete(0,END)\\n entryCol.entry2.insert(0,data[2])\\n entryCol.entry3.delete(0,END)\\n entryCol.entry3.insert(0,data[3])\\n botWind.writeN(\\\"DataLoad: File\\\")\\n except:\\n botWind.writeN(\\\"DataLoad: Default\\\")\",\n \"def write_data():\",\n \"def _read(self):\\n # initializng data dictionary\\n self.data={}\\n\\n f = FortranFile(self.filename)\\n # Default omnivor binary header\\n self.data['MK'] = f.readInts('i')\\n self.data['itime'] = f.readInts('i')\\n self.data['version'] = f.readString()\\n self.data['file_id'] = f.readInts('i')\\n self.data['sversion'] = f.readString()\\n # Velocity field\\n self.data['stype'] = f.readString()\\n self.data['is_grid'] = f.readInts('i')\\n nCPs = f.readInts('i')\\n self.data['nCPs'] = nCPs\\n if self.data['MK'] == 8:\\n real_char='d'\\n else:\\n real_char='f'\\n if self.data['is_grid']:\\n #print('File is a velocity grid file')\\n n1 = f.readInts('i')\\n n2 = f.readInts('i')\\n n3 = f.readInts('i')\\n self.data['n1'] = n1\\n self.data['n2'] = n2\\n self.data['n3'] = n3\\n self.data['is_straight'] = f.readInts('i')\\n self.data['v1'] = f.readReals(real_char)\\n self.data['v2'] = f.readReals(real_char)\\n self.data['v3'] = f.readReals(real_char)\\n\\n CPs_raw = f.readReals(real_char)\\n Utot_raw = f.readReals(real_char)\\n CPs = np.reshape(CPs_raw,(3,nCPs),order = 'F')\\n Utot = np.reshape(Utot_raw,(3,nCPs),order = 'F')\\n\\n acc=-1\\n CPsTab = np.zeros((3, n1,n2,n3))\\n UtotTab = np.zeros((3, n1,n2,n3))\\n # Reshaping the nasty way (this is natural order). \\n for i in range(0,n1):\\n for j in range(0,n2):\\n for k in range(0,n3):\\n acc=acc+1\\n CPsTab[0:3,i,j,k] = CPs[0:3,acc]\\n UtotTab[0:3,i,j,k] = Utot[0:3,acc]\\n\\n self.data['CPs'] = CPs\\n self.data['CPsTab'] = CPsTab\\n self.data['Utot'] = Utot\\n self.data['UtotTab'] = UtotTab\",\n \"def readOneData(self):\\n\\t\\tpass\",\n \"def readData(self,datafile = None):\\n self.datafile = datafile or self.datafile\\n self.data = []\\n for line in open(self.datafile):\\n userid,itemid,record,_ = line.split()\\n self.data.append((userid,itemid,int(record)))\",\n \"def load(self, input):\",\n \"def load(self):\\n #print self.fileInfo.name\\n progress = self.progress\\n filePath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\\n self.fileSize = os.path.getsize(filePath)\\n #--Localize\\n cells = self.cells\\n records = self.records\\n canSave = self.canSave\\n skipObjRecords = self.skipObjRecords\\n contTypes = set(['CREC','CNTC','NPCC'])\\n levTypes = set(('LEVC','LEVI'))\\n debrisIds = self.debrisIds\\n debrisTypes = set(debrisIds.keys())\\n #--Header\\n inPath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\\n ins = Tes3Reader(self.fileInfo.name,file(inPath,'rb'))\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n self.tes3 = Tes3(name,size,delFlag,recFlag,ins,True)\\n if not canSave: del self.tes3.others[:]\\n #--Progress info\\n progress = self.progress\\n progress(0.0,'Loading '+self.fileInfo.name)\\n #--Raw data read\\n while not ins.atEnd():\\n #--Get record info and handle it\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n #print \\\"%s [%d]\\\" % (name,size)\\n #--CELL?\\n if name == 'CELL':\\n record = Cell(name,size,delFlag,recFlag,ins,0,skipObjRecords)\\n cells.append(record)\\n if canSave: records.append(record)\\n #--Contents\\n elif canSave and name in contTypes:\\n if name == 'CREC':\\n record = Crec(name,size,delFlag,recFlag,ins,True)\\n elif name == 'CNTC':\\n record = Cntc(name,size,delFlag,recFlag,ins,True)\\n else:\\n record = Npcc(name,size,delFlag,recFlag,ins,True)\\n self.conts.append(record)\\n self.conts_id[record.getId()] = record\\n records.append(record)\\n #--File Map\\n elif name == 'FMAP':\\n record = Fmap(name,size,delFlag,recFlag,ins)\\n self.fmap = record\\n records.append(record)\\n #--Landscapes\\n elif name == 'LAND':\\n record = Land(name,size,delFlag,recFlag,ins)\\n self.lands[record.getId()] = record\\n records.append(record)\\n #--Scripts\\n elif canSave and name == 'SCPT':\\n record = Scpt(name,size,delFlag,recFlag,ins,True)\\n records.append(record)\\n if record.getRef():\\n self.refs_scpt[record] = record.getRef()\\n #--Save debris info?\\n elif name in debrisTypes:\\n record = Record(name,size,delFlag,recFlag,ins)\\n id = record.getId()\\n if id:\\n debrisIds[name].append(id.lower())\\n if canSave:\\n records.append(record)\\n #--Skip Non-cell?\\n elif not canSave:\\n ins.seek(size,1,name)\\n #--Keep non-cell?\\n else:\\n records.append(Record(name,size,delFlag,recFlag,ins))\\n #--Done Reading\\n ins.close()\\n #--Analyze Cells\\n cntCells = 0\\n progress.setMax(len(self.cells))\\n for cell in self.cells:\\n cell.load(None,1)\\n self.cells_id[cell.getId()] = cell\\n if not canSave:\\n cell.data = None #--Free some memory\\n #--Progress\\n cntCells += 1\\n progress(cntCells)\\n #--Scripts\\n if self.refs_scpt:\\n self.updateScptRefs()\",\n \"def data_for_file (self):\\n rowdata = np.array([self.Train_Time_min,self.Train_Time_max,self.Train_Time_avg,\\n self.Loss_Value_min,self.Loss_Value_max,self.Loss_Value_avg,\\n self.Iterations_min,self.Iterations_max,self.Iterations_avg,\\n self.precision_avg,self.recall_avg])\\n return rowdata\\n\\n\\n \\n\\n\\n #### FUNCTION DEFINTIIONS ####\",\n \"def load(self):\",\n \"def parse_region_data(self, region):\\n\\n #downloads newest data\\n self.download_data()\\n\\n TmpList = [[] for i in range(65)]\\n\\n #goes through all the files and saves their data to the variable\\n for zipName in self.ListOfZipFiles:\\n with zipfile.ZipFile(\\\"./\\\"+self.folder+\\\"/\\\"+zipName, 'r') as zf:\\n if self.getCsvFileByRegion(region) == None:\\n print(\\\"ERROR: neznamy nazev kraje: \\\" + region, file=sys.stderr)\\n return None\\n #opens zip file for reading\\n with zf.open(self.getCsvFileByRegion(region), \\\"r\\\") as f:\\n reader = f.read().decode(\\\"iso 8859-2\\\")\\n reader = StringIO(reader)\\n reader = csv.reader(reader, delimiter = \\\";\\\")\\n #saves every row of file\\n for row in reader:\\n TmpList[0].append(region)\\n i=1\\n #saves every column separated because of their types\\n for item in row:\\n if item != \\\"\\\":\\n if (i>0 and i<4) or (i>4 and i<6) or (i>6 and i<32) or (i>32 and i<35) or (i>35 and i<46) or i==61 or i==64:\\n if not re.search(\\\"^[-]{0,1}[0-9]*$\\\", item):\\n item = None\\n else:\\n item=int(item)\\n elif i>45 and i<52:\\n if not re.search(\\\"^[-]{0,1}[0-9]*[,.]{0,1}[0-9]+$\\\", item):\\n item = None\\n else:\\n item = item.replace(\\\",\\\", \\\".\\\")\\n if re.search(\\\"^[-]{1}.*$\\\", item):\\n item = item.replace(\\\"-\\\", \\\"\\\")\\n item = -(float(item))\\n else:\\n item = float(item)\\n elif i==6:\\n if not re.search(\\\"^[-]{0,1}[0-9]*$\\\", item):\\n item = None\\n else:\\n if (len(item) == 4):\\n #cas nijak neupravuji, jen rozdeluji minuty od hodin\\n #policie uz nastavila hodnoty podle nich pozname nevalidni hodnotu\\n #pro minuty tato hodnota je 60\\n #pro hodiny tato hodnota je 25\\n #dle meho nazoru mi prislo zbytecne na misto techto dvou hdonot davat nejakou jinou nevalidni hodnotu (napr. NaN, -1)\\n #prepsal bych nevalidni vystup na jiny nevalidni vystup\\n item = item[:2] + \\\":\\\" + item[2:]\\n else:\\n item = None\\n TmpList[i].append(item)\\n else:\\n TmpList[i].append(None)\\n i+=1\\n\\n ListNpArrays = [[] for i in range(65)]\\n\\n #transforms into numpy.ndarray\\n for x in range(0, 65):\\n ListNpArrays[x] = np.array(TmpList[x], dtype=object)\\n\\n return (self.headers, ListNpArrays)\",\n \"def data_input(self):\\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))\\n if not os.path.isfile('{0}/{1}.csv'.format(path, self.data_file)):\\n print 'Error: Dataset file is not exist.'\\n exit()\\n # Uplead Dataset.csv file.\\n f = open('{0}/{1}.csv'.format(path, self.data_file), 'r')\\n print 'Now uploading dataset File.....'\\n f = list(f)\\n # The Dataset contains heading, number of lines - heading\\n self.number_of_VOCs = sum(1 for row in f)-1\\n # Count number of columns, last column's value is empty, that is why -1.\\n self.number_of_columns = len(f[0].split(',')) -1\\n self.first_m_z = int(f[0].split(',')[3]) # find the first m/z value.\\n self.last_m_z = int(f[0].split(',')[-2]) # find the last m/z value.\\n print 'dataset includes ', self.number_of_VOCs, 'VOCs in all samples '\\n print ('dataset includes ', self.number_of_columns, ' Columns, ',\\n 'm/z values start from ', self.first_m_z,\\n 'and end ', self.last_m_z)\\n # Create a matrix with a shape of (number_of_VOCs X number_of_columns) filled with zeros.\\n self.dataset = np.zeros((self.number_of_VOCs,\\n self.number_of_columns))\\n for line in range(1, len(f)):\\n if int(float(f[line].strip().split(',')[0])) not in self.loaded_samples:\\n self.loaded_samples.append(int(float(f[line].strip().split(',')[0])))\\n for column in range(self.number_of_columns):\\n self.dataset[line-1][column] = int(float(f[line].strip().split(',')[column]))\",\n \"def read_data(self, loc):\\n pass\",\n \"def loadData(self,ins):\\n raise AbstractError\",\n \"def _read_data(self):\\n with self._open(self.filename, 'rb') as f:\\n try:\\n f.seek(self._offset_data, self._offset_whence)\\n except IOError:\\n print('Error: hedp.io.HamamatsuFile seeking outside of file limits.')\\n print(' Failed to parse file.')\\n print(\\\" Either the 'offset' or 'dtype' input arguments must be wrong!\\\")\\n raise\\n except:\\n raise\\n\\n data_len = np.prod(self.shape)*np.dtype(self._dtype).itemsize\\n data_str = f.read(data_len)\\n if data_len != len(data_str):\\n print(data_len, len(data_str))\\n raise ValueError('File ended before all data was read. Probably wrong offset or dtype!')\\n\\n\\n self.data = np.fromstring(data_str, dtype=self._dtype).reshape(self.shape[::-1])\\n self.data = np.ndarray.astype(self.data, 'float32')\\n\\n #self.data = np.fromfile(f, dtype=self._dtype,\\n # count=np.prod(self.shape)).reshape(self.shape[::-1])\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader('GLOB')\\n srData = ins.read(size,'GLOB.'+name)\\n bytesRead += 8+size\\n if name == 'NAME': self.id = cstrip(srData)\\n elif name == 'FNAM': self.type = srData\\n elif name == 'FLTV': self.value = struct.unpack('f',srData)\\n #--Deleted?\\n elif name == 'DELE': self.isDeleted = True\\n #--Bad record?\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\",\n \"def load_data(self):\\n raise NotImplementedError()\",\n \"def load(datastream):\",\n \"def loadData(self, file):\\n self.data = batchImport(file, self.ps)\",\n \"def load(self, filename):\\n # XXX Hay que comprobar los datos leidos y lanzar excepcion\\n f = open(filename)\\n prelaciones = []\\n asig = []\\n rec = []\\n l = f.readline()\\n while l:\\n # Activities and following activities\\n if l[0:21] == 'PRECEDENCE RELATIONS:':\\n f.readline()\\n l = f.readline()\\n while l[0] != '*':\\n data = l.split()\\n prel = (data[0], data[3:])\\n prelaciones.append(prel)\\n l = f.readline()\\n\\n # Activity duration and resource units needed\\n if l[0] == '-':\\n l = f.readline()\\n while l[0] != '*':\\n asig.append(l.split())\\n l = f.readline()\\n\\n # Name, type and unit of resources\\n if l[0:22] == 'RESOURCEAVAILABILITIES':\\n l = f.readline()\\n while l[0] != '*':\\n rec.append(l.split())\\n l = f.readline()\\n\\n l = f.readline()\\n \\n # Create data structure\\n cont = 1\\n activities = []\\n for prelacion in prelaciones:\\n activities.append([cont, prelacion[0], prelacion[1], '', '', '', '', '', ('Beta')])\\n cont += 1 \\n\\n # Update activities duration\\n for n in range(len(asig)): \\n activities[n][6] = float(asig[n][2])\\n\\n # Update resources\\n i = 1\\n m = 0\\n resources = []\\n if len(rec) < 2:\\n raise InvalidFileFormatException()\\n\\n for n in range(len(rec[1])):\\n # Renewable\\n if rec[0][m]=='R' or rec[0][m][0]=='R':\\n if rec[0][m]=='R':\\n row=[rec[0][m]+rec[0][i], 'Renewable', '', rec[1][n]] \\n m+=2\\n else:\\n row=[rec[0][m], 'Renewable', '', rec[1][n]] \\n m+=1 \\n # Non Renewable\\n elif rec[0][m]=='N' or rec[0][m][0]=='N':\\n if rec[0][m]=='N':\\n row=[rec[0][m]+rec[0][i], 'Non renewable', rec[1][n], '']\\n m+=2\\n else:\\n row=[rec[0][m], 'Non renewable', rec[1][n], ''] \\n m+=1\\n # Double constrained\\n elif rec[0][m]=='D' or rec[0][m][0]=='D':\\n if rec[0][m]=='D':\\n row=[rec[0][m]+rec[0][i], 'Double constrained', rec[1][n], rec[1][n]]\\n m+=2\\n else:\\n row=[rec[0][m], 'Double constrained', rec[1][n], rec[1][n]] \\n m+=1\\n \\n resources.append(row)\\n i += 2\\n # Note: Unlimited resources are not present on PSPLIB projects and so \\n # not taken into account here\\n\\n # Resources needed per activity\\n asignation = []\\n for n in range(len(asig)): \\n for m in range(3, 3+len(rec[1])): #len(self.rec[1]): number of resources \\n if asig[n][m] != '0': #unused resources are not shown\\n i = m-3\\n row = [asig[n][0], resources[i][0], asig[n][m]] \\n asignation.append(row)\\n \\n return (activities, [], resources, asignation)\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def read(self, sacc_data: sacc.Sacc) -> None:\",\n \"def __init__(self,site,startDate,endDate,path='verif_data/'):\\n self.site = site.upper()\\n self.startDateTime = datetime.strptime(startDate,'%Y%m%d')\\n self.endDateTime = datetime.strptime(endDate,'%Y%m%d')\\n years = range(self.startDateTime.year,self.endDateTime.year + 1)\\n data = []\\n for year in years:\\n self.filename=path + self.site + '_asos_' + str(year) + '.txt'\\n datafile = open(self.filename)\\n for line in datafile:\\n if line[0] != '#':\\n if 'station' in line:\\n self.header = [x.strip() for x in line[:-1].split(',')]\\n else:\\n dataline = line[:-2].split(',')\\n for i,val in enumerate(dataline[:-1]):\\n if val=='M':\\n dataline[i] = -999\\n dataline[1] = dataline[1].replace(' ','_')\\n dataline[1] = dataline[1].replace('-','')\\n currDateTime = datetime.strptime(dataline[1][:14],'%Y%m%d_%H:%M')\\n if currDateTime >= self.startDateTime and currDateTime <= self.endDateTime:\\n data.append(tuple(dataline))\\n datafile.close()\\n self.datatype = []\\n for item in self.header:\\n if item == 'station':\\n self.datatype.append((item,'S3'))\\n elif 'valid' in item:\\n self.datatype.append(('time','S14'))\\n elif 'skyc' in item:\\n self.datatype.append((item,'S3'))\\n elif item=='metar':\\n self.datatype.append((item,'S99'))\\n else:\\n self.datatype.append((item,float))\\n self.data = np.array(data,dtype=self.datatype)\",\n \"def data_preprocessing():\\n lineid_content = get_lineid_content()\\n print('Read movie_lines.txt file complete...')\\n convos = get_convos()\\n print('Read movie_conversations.txt file complete...')\\n print('Building dataset')\\n get_data(lineid_content, convos)\",\n \"def import_data(self):\\n\\n # Import ordered names of origins\\n origins_file = os.path.join(self.data_directory,'origins.txt')\\n self.origins = np.loadtxt(origins_file,dtype=str,ndmin=1)\\n\\n # Import ordered names of destinations\\n destinations_file = os.path.join(self.data_directory,'destinations.txt')\\n self.destinations = np.loadtxt(destinations_file,dtype=str,ndmin=1)\\n\\n # Import origin supply\\n originsupply_file = os.path.join(self.data_directory,'origin_supply.txt')\\n self.origin_supply = np.loadtxt(originsupply_file,ndmin=1).astype('float64')\\n\\n # In case origin supply is not a list\\n if not isinstance(self.origin_supply,(np.ndarray, np.generic)):\\n self.origin_supply = np.array([self.origin_supply])\\n\\n # Import destination demand\\n destinationdemand_file = os.path.join(self.data_directory,'destination_demand.txt')\\n self.destination_demand = np.loadtxt(destinationdemand_file,ndmin=1).astype('float64')\\n\\n # In case destination demand is not a list\\n if not isinstance(self.destination_demand,(np.ndarray, np.generic)):\\n self.destination_demand = np.array([self.destination_demand])\\n\\n # Import origin locations\\n originlocations_file = os.path.join(self.data_directory,'origin_locations.txt')\\n self.origin_locations = np.loadtxt(originlocations_file,ndmin=1)\\n\\n # Import destination locations\\n destinationlocations_file = os.path.join(self.data_directory,'destination_locations.txt')\\n self.destination_locations = np.loadtxt(destinationlocations_file,ndmin=1)\\n\\n # Import initial and final destination sizes\\n initialdestinationsizes_file = os.path.join(self.data_directory,'initial_destination_sizes.txt')\\n self.initial_destination_sizes = np.loadtxt(initialdestinationsizes_file,ndmin=1)\\n\\n # In case destination sizes are not a list\\n if not isinstance(self.initial_destination_sizes,(np.ndarray, np.generic)):\\n self.initial_destination_sizes = np.array([self.initial_destination_sizes])\\n\\n # Import N,M\\n self.N = self.origin_supply.shape[0]\\n self.M = self.initial_destination_sizes.shape[0]\\n\\n # Import cost matrix\\n costmatrix_file = os.path.join(self.data_directory,'cost_matrix.txt')\\n self.cost_matrix = np.loadtxt(costmatrix_file).astype('float64')\\n\\n # Reshape cost matrix if necessary\\n if self.N == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[:,np.newaxis],(self.N,self.M))\\n if self.M == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[np.newaxis,:],(self.N,self.M))\\n\\n # Compute total initial and final destination sizes\\n self.total_initial_sizes = np.sum(self.initial_destination_sizes)\\n\\n # Compute naive total cost\\n self.total_cost = 0\\n for i in range(self.N):\\n for j in range(self.M):\\n self.total_cost += self.cost_matrix[i,j]*(self.origin_supply[i]/self.N)\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n objectId = None\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader(self.name)\\n #print name,size\\n bytesRead += 8+size\\n subData = ins.read(size, self.name+'.'+name)\\n #--Id?\\n if name == 'NAME':\\n self.id = cstrip(subData)\\n #--Flags\\n elif name == 'DATA':\\n flags = struct.unpack('i',subData)[0]\\n if self.name == 'LEVC':\\n self.calcFromAllLevels = (flags & 1) == 1\\n else:\\n self.calcForEachItem = (flags & 1) == 1\\n self.calcFromAllLevels = (flags & 2) == 2\\n #--Chance None\\n elif name == 'NNAM':\\n self.chanceNone = struct.unpack('B',subData)[0]\\n #--Count\\n elif name == 'INDX':\\n self.count = struct.unpack('i',subData)[0]\\n #--Creature/Item Id?\\n elif name == 'CNAM' or name == 'INAM':\\n objectId = cstrip(subData)\\n #--PC Level\\n elif name == 'INTV':\\n pcLevel = struct.unpack('h',subData)[0]\\n self.entries.append((pcLevel,objectId))\\n objectId = None\\n #--Deleted?\\n elif name == 'DELE': \\n self.isDeleted = True\\n #--Else\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\\n #--No id?\\n if not self.id:\\n raise Tes3Error(self.inName,_('No id for %s record.') % (self.name,))\\n #--Bad count?\\n if self.count != len(self.entries):\\n self.count = len(self.entries)\\n self.setChanged()\",\n \"def __init__(self, raw_data_file,):\\n self.raw_data_file = raw_data_file\\n self.clean_data = self.cleanData()\\n self.microtrip_data = []\",\n \"def get_file_data(self):\\n self.switch_row(self.PATTERN_INFO_ROW)\\n\\n # get background image file path\\n if self.row[0] != 'No image':\\n # set path to background image, and get/set screen size\\n self.bg_img_path = self.row[self.BG_IMAGE_INDEX]\\n self.screen_width = int(self.row[self.WIDTH_INDEX])\\n self.screen_height = int(self.row[self.HEIGHT_INDEX])\\n self.SCREEN_SIZE = self.screen_width, self.screen_height\\n self.bg = True\\n \\n # get number of landmarks\\n self.num_lndmrks = int(self.row[self.LNDMRK_NUM_INDEX])\\n\\n # get starting row number for mpo path data\\n self.data_start_row = self.num_lndmrks + 1\\n self.tot_data_rows = self.tot_rows - self.data_start_row\\n self.data_row_num = self.data_start_row\",\n \"def extract_data(file_name):\\r\\n input_file_name = file_name\\r\\n \\r\\n def sort_data(input_file): \\r\\n \\\"\\\"\\\"This function extraxts the row data from the imput file\\\"\\\"\\\" \\r\\n with open(input_file,\\\"r\\\") as input_file: \\r\\n data = input_file.readlines() \\r\\n row_data = [] \\r\\n for line in data: \\r\\n row_data.append(line.split(', ')) \\r\\n return row_data \\r\\n \\r\\n data = sort_data(input_file_name)\\r\\n \\r\\n start_vertex = (float(data[0][0].split(' ')[0]),float(data[0][0].split(' ')[1]))\\r\\n end_vertex = (float(data[1][0].split(' ')[0]),float(data[1][0].split(' ')[1]))\\r\\n \\r\\n obstacles = {}\\r\\n for i in range(2, len(data)):\\r\\n lista = []\\r\\n for j in range(len(data[i])):\\r\\n lista.append( (float(data[i][j].split(' ')[0]),float(data[i][j].split(' ')[1])) )\\r\\n obstacles[i-1] = lista\\r\\n \\r\\n return start_vertex,end_vertex, obstacles\",\n \"def open_file(self):\\n file = open(self.file_name_dat, \\\"r\\\")\\n rows = []\\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\\n for line in file:\\n rows.append(line)\\n\\n # Getting information from the comments section\\n xdatatemp = []\\n ydatatemp = []\\n for row in rows:\\n if \\\"#\\\" in row: # Getting information from the comments\\n hashlinecount += 1\\n if hashlinecount == 7: # Getting the E_f value\\n temp = row.split()\\n if len(temp) == 4:\\n self.E_f = temp[3]\\n elif len(temp) == 7:\\n self.E_f = temp[6]\\n self.Efshift = True\\n continue\\n elif hashlinecount == 9: # Getting the min and max band energies\\n temp = row.split()\\n self.E_min = float(temp[4])\\n self.E_max = float(temp[5])\\n continue\\n elif hashlinecount == 11: # Gettiing the minimum and maximum band numbers to plot\\n temp = row.split()\\n self.band_number_min = int(temp[5])\\n self.band_number_max = int(temp[6])\\n continue\\n\\n elif row.strip() == \\\"\\\": # saving individual bands the first save will be a null band\\n self.band_data_x.append(xdatatemp) # Saving in a 2d list.\\n self.band_data_y.append(ydatatemp)\\n xdatatemp = []\\n ydatatemp = []\\n else:\\n t = row.split()\\n xdatatemp.append(float(t[0]))\\n ydatatemp.append(float(t[1]))\\n\\n # Getting rid of empty lists\\n self.band_data_x = [x for x in self.band_data_x if x]\\n self.band_data_y = [x for x in self.band_data_y if x]\\n\\n # opening the bands file to get the kpath\\n bands_file = open(self.file_name_bands, \\\"r\\\")\\n bands_file_rows = []\\n hashlinecount = 0 # looking at commented lines in order to extract fermi energy\\n for bands_file_line in bands_file:\\n bands_file_rows.append(bands_file_line)\\n\\n length_bands_file_rows = len(bands_file_rows)\\n for i in range(length_bands_file_rows-1, 0, -1):\\n temperary = bands_file_rows[i].split()\\n if len(temperary) == 2:\\n self.kpath_k.append(float(temperary[0].strip()))\\n self.kpath_symbol.append(str(temperary[1].strip(\\\"'\\\")))\\n elif len(temperary) is not 2:\\n break\\n self.kpath_symbol = latexify(self.kpath_symbol)\",\n \"def readdata(filename):\\n\\tdt = np.dtype([('date','int'),('val','<f8')])\\n\\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\\n\\treturn data\",\n \"def load_data(self, filename):\\r\\n #sqlcontext = SQLContext(self.sc)\\r\\n #df = sqlcontext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load(filename)\\r\\n #df = sc.textFile(r\\\"C:\\\\Users\\\\mohan\\\\Downloads\\\\patches.csv\\\").map(lambda line: line.split(\\\",\\\"))\\r\\n #print (df.count())\\r\\n df = self.sc.textFile(filename).map(lambda line: line.split(\\\",\\\"))\\r\\n l = df.map(lambda w: [int(float(c)) for c in w]).zipWithIndex()\\r\\n return l\\r\\n raise NotImplementedError\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = float(A[-1])\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = float(A[-1])\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = int(float(A[-1]))\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = int(float(A[-1]))\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\",\n \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the y component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[2])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"def read_data(self):\\r\\n IS_REMAPPED = 1\\r\\n if IS_REMAPPED:\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TRAINING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\\\\r\\n int(c)] for [f1, f2, c] in data_as_strings]\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TESTING_DATA = [[remap(float(f1),float(f2))[0],remap(float(f1),float(f2))[1],\\\\\\r\\n int(c)] for [f1, f2, c] in data_as_strings]\\r\\n else:\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TRAINING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]\\r\\n data_as_strings = list(csv.reader(open('ring-data.csv'), delimiter=','))\\r\\n self.TESTING_DATA = [[float(f1), float(f2), int(c)] for [f1, f2, c] in data_as_strings]\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def prepare_data_train(fname):\\n # Read data\\n data = pd.read_csv(fname)\\n # events file\\n events_fname = fname.replace('_data','_events')\\n # read event file\\n labels= pd.read_csv(events_fname)\\n clean=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n return clean,labels\",\n \"def predict(self, datafile):\",\n \"def rdspecdat(self):\\n # TODO : ugh. this is crude. Should have some checks for file format\\n # and probably better to use the astropy.io functions now.\\n try:\\n w, f, e = np.loadtxt(self.filename, unpack=True)\\n except:\\n w, f = np.loadtxt(self.filename, unpack=True)\\n e = []\",\n \"def readdata(self, filepaths):\\n pass\",\n \"def _load_data(self):\\n\\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\\\\n self.task_num\\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\\n\\n read_data_x = pd.read_csv(path_data_x)\\n read_data_y = pd.read_csv(path_data_y)\\n read_data_meta = pd.read_csv(path_meta)\\n\\n # Drop columns if it only contains 1 unique element\\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\\n\\n num_col = pd.read_csv(path_numerical_columns)\\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\\n col_idx = np.arange(read_data_x.shape[-1])\\n non_num_col = np.setdiff1d(col_idx, num_col)\\n\\n # new_data_x = np.array(read_data_x).astype(np.float32)\\n new_data_x = np.array(read_data_x)\\n new_data_y = np.array(read_data_y).astype(np.float32)\\n new_data_meta = np.array(read_data_meta).astype(np.float32)\\n\\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\\n\\n\\n # Winsorize dataset\\n len_feat = new_data_x.shape[-1]\\n idx_list = list(num_col)\\n for i in range(len_feat):\\n if i in idx_list:\\n cur_data = new_data_x[:, i]\\n cur_data = np.array(cur_data)\\n lower_p = np.percentile(cur_data, 5)\\n higher_p = np.percentile(cur_data, 95)\\n cur_data[cur_data < lower_p] = lower_p\\n cur_data[cur_data > higher_p] = higher_p\\n new_data_x[:, i] = cur_data\\n\\n # Make sure target data is one-hot encoded\\n if new_data_y.shape[-1] == 1:\\n num_class = len(np.unique(new_data_y))\\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\\n new_data_y = new_data_y.astype('float32')\\n self.orig_column_names = read_data_x.columns\\n self.data_x = new_data_x # N x F\\n self.data_y = new_data_y # N x C\\n self.numerical_idx = num_col # list of idx\\n self.non_num_idx = non_num_col # None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = new_data_meta.astype('float32') # N x 3\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def make_data_raw (mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname,',',mdp.tag\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n # write first header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n # write another header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def read_data(self):\\n if not self.header['data included']:\\n pass\\n elif self.header['file type'] in (21, 26):\\n self._isotope_data()\\n if os.path.exists(self.filename + '_txt'):\\n self._isotope_txt_data()\\n elif self.header['file type'] == 22:\\n # line scan types, no ImageHeader\\n warnings.warn('No data read for line scan, fix')\\n pass\\n elif self.header['file type'] in (31, 35):\\n self._beamstability_data()\\n else:\\n self._image_data()\",\n \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the z component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[1])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"def read_data(args):\\n\\n print(\\\"Start read_data\\\")\\n t_tot = 0 # sum of times for the all dataset\\n date_dirs = os.listdir(args.path_data_base)\\n for n_iter, date_dir in enumerate(date_dirs):\\n # get access to each sequence\\n path1 = os.path.join(args.path_data_base, date_dir)\\n if not os.path.isdir(path1):\\n continue\\n date_dirs2 = os.listdir(path1)\\n\\n for date_dir2 in date_dirs2:\\n path2 = os.path.join(path1, date_dir2)\\n if not os.path.isdir(path2):\\n continue\\n # read data\\n oxts_files = sorted(glob.glob(os.path.join(path2, 'oxts', 'data', '*.txt')))\\n oxts = KITTIDataset.load_oxts_packets_and_poses(oxts_files)\\n\\n \\\"\\\"\\\" Note on difference between ground truth and oxts solution:\\n - orientation is the same\\n - north and east axis are inverted\\n - position are closed to but different\\n => oxts solution is not loaded\\n \\\"\\\"\\\"\\n\\n print(\\\"\\\\n Sequence name : \\\" + date_dir2)\\n if len(oxts) < KITTIDataset.min_seq_dim: # sequence shorter than 30 s are rejected\\n cprint(\\\"Dataset is too short ({:.2f} s)\\\".format(len(oxts) / 100), 'yellow')\\n continue\\n lat_oxts = np.zeros(len(oxts))\\n lon_oxts = np.zeros(len(oxts))\\n alt_oxts = np.zeros(len(oxts))\\n roll_oxts = np.zeros(len(oxts))\\n pitch_oxts = np.zeros(len(oxts))\\n yaw_oxts = np.zeros(len(oxts))\\n roll_gt = np.zeros(len(oxts))\\n pitch_gt = np.zeros(len(oxts))\\n yaw_gt = np.zeros(len(oxts))\\n t = KITTIDataset.load_timestamps(path2)\\n acc = np.zeros((len(oxts), 3))\\n acc_bis = np.zeros((len(oxts), 3))\\n gyro = np.zeros((len(oxts), 3))\\n gyro_bis = np.zeros((len(oxts), 3))\\n p_gt = np.zeros((len(oxts), 3))\\n v_gt = np.zeros((len(oxts), 3))\\n v_rob_gt = np.zeros((len(oxts), 3))\\n\\n k_max = len(oxts)\\n for k in range(k_max):\\n oxts_k = oxts[k]\\n t[k] = 3600 * t[k].hour + 60 * t[k].minute + t[k].second + t[\\n k].microsecond / 1e6\\n lat_oxts[k] = oxts_k[0].lat\\n lon_oxts[k] = oxts_k[0].lon\\n alt_oxts[k] = oxts_k[0].alt\\n acc[k, 0] = oxts_k[0].af\\n acc[k, 1] = oxts_k[0].al\\n acc[k, 2] = oxts_k[0].au\\n acc_bis[k, 0] = oxts_k[0].ax\\n acc_bis[k, 1] = oxts_k[0].ay\\n acc_bis[k, 2] = oxts_k[0].az\\n gyro[k, 0] = oxts_k[0].wf\\n gyro[k, 1] = oxts_k[0].wl\\n gyro[k, 2] = oxts_k[0].wu\\n gyro_bis[k, 0] = oxts_k[0].wx\\n gyro_bis[k, 1] = oxts_k[0].wy\\n gyro_bis[k, 2] = oxts_k[0].wz\\n roll_oxts[k] = oxts_k[0].roll\\n pitch_oxts[k] = oxts_k[0].pitch\\n yaw_oxts[k] = oxts_k[0].yaw\\n v_gt[k, 0] = oxts_k[0].ve\\n v_gt[k, 1] = oxts_k[0].vn\\n v_gt[k, 2] = oxts_k[0].vu\\n v_rob_gt[k, 0] = oxts_k[0].vf\\n v_rob_gt[k, 1] = oxts_k[0].vl\\n v_rob_gt[k, 2] = oxts_k[0].vu\\n p_gt[k] = oxts_k[1][:3, 3]\\n Rot_gt_k = oxts_k[1][:3, :3]\\n roll_gt[k], pitch_gt[k], yaw_gt[k] = IEKF.to_rpy(Rot_gt_k)\\n\\n t0 = t[0]\\n t = np.array(t) - t[0]\\n # some data can have gps out\\n if np.max(t[:-1] - t[1:]) > 0.1:\\n cprint(date_dir2 + \\\" has time problem\\\", 'yellow')\\n ang_gt = np.zeros((roll_gt.shape[0], 3))\\n ang_gt[:, 0] = roll_gt\\n ang_gt[:, 1] = pitch_gt\\n ang_gt[:, 2] = yaw_gt\\n\\n p_oxts = lla2ned(lat_oxts, lon_oxts, alt_oxts, lat_oxts[0], lon_oxts[0],\\n alt_oxts[0], latlon_unit='deg', alt_unit='m', model='wgs84')\\n p_oxts[:, [0, 1]] = p_oxts[:, [1, 0]] # see note\\n\\n # take correct imu measurements\\n u = np.concatenate((gyro_bis, acc_bis), -1)\\n # convert from numpy\\n t = torch.from_numpy(t)\\n p_gt = torch.from_numpy(p_gt)\\n v_gt = torch.from_numpy(v_gt)\\n ang_gt = torch.from_numpy(ang_gt)\\n u = torch.from_numpy(u)\\n\\n # convert to float\\n t = t.float()\\n u = u.float()\\n p_gt = p_gt.float()\\n ang_gt = ang_gt.float()\\n v_gt = v_gt.float()\\n\\n mondict = {\\n 't': t, 'p_gt': p_gt, 'ang_gt': ang_gt, 'v_gt': v_gt,\\n 'u': u, 'name': date_dir2, 't0': t0\\n }\\n\\n t_tot += t[-1] - t[0]\\n KITTIDataset.dump(mondict, args.path_data_save, date_dir2)\\n print(\\\"\\\\n Total dataset duration : {:.2f} s\\\".format(t_tot))\",\n \"def read_data(self):\\n raise NotImplementedError\",\n \"def __init__(self, datafile):\\n self.FILE = open(datafile, 'w')\",\n \"def readdata(self, reflist , comment = '#' , regexp = None , substr = None, filename = True):\\n self.kpunten = []\\n datalist = []\\n prefixlist = []\\n if os.path.isfile(str(reflist)):\\n reflist = [reflist] #if we work with only one file this wraps it automatically in right format\\n for ref in reflist:\\n print('start with the collection of data from file %s' %ref)\\n plotf = open(ref, 'r')\\n if not filename:\\n prefixlist.append( os.path.dirname(ref) + '/')\\n else:\\n prefixlist.append(re.sub('\\\\.dat$' , '' , ref))\\n try:\\n if regexp != None:\\n raise ValueError\\n dataf = np.loadtxt(plotf,comments = comment)\\n print 'we readed data in with np.loadtxt'\\n except:\\n print('reading in data with numpy loadtxt failed or use reg exp to extract information')\\n dataf = np.array([])\\n kpuntenf = []\\n plotf.seek(0) #go back to beginning of file\\n for line in plotf:\\n if regexp is not None:\\n analyse = re.search(regexp,line)\\n if analyse:\\n kpuntenf.append((analyse.group(1), len(dataf)-1 ))\\n print 'we found the following matches: %s' % analyse.group(0)\\n if substr != None: \\n line = re.sub(substr, '' , line)\\n if line[0] != comment:\\n #print line\\n pline = np.array(map(float,line.split()))\\n if len(dataf) <= 1:\\n dataf = pline\\n else:\\n try:\\n dataf = np.vstack((dataf,pline))\\n except:\\n continue\\n self.kpunten.append(kpuntenf)\\n datalist.append(dataf)\\n\\n plotf.close()\\n self.datarg = datalist\\n self.prefix = prefixlist\\n self.reader = dr.ReaderOutput(reflist[0]) #Some plotting functions need a bit more information this info is extracted from the header of the files\\n self.reader.depvar['depvar'] += ' (a.u.)'\",\n \"def readFiles(opt, path, pathCopyData,minlat, maxlat, minlon, maxlon , variables, estaciones):\\n date = '\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d'\\n dirr = pathCopyData\\n patron2 = re.compile(date)\\n print(dirr + 'tfile.txt')\\n tempfile = df.read_csv(dirr + 'tfile.txt')\\n tempbase = df.read_csv(dirr + 'tbase.txt')\\n tfile = list(tempfile.values.flatten())\\n tbase = list(tempbase.values.flatten())\\n tfileCopy = list(tempfile.values.flatten())\\n tbaseCopy = list(tempbase.values.flatten())\\n l = len(tfile)\\n for i in range(l):\\n tfil = tfile[i]\\n tbas = tbase[i]\\n ls = tbas + '/' + tfil\\n f = patron2.findall(tfil)\\n cadena = clearString(tfil)\\n print(cadena)\\n try:\\n #net = open_netcdf(ls, tfil, cadena, pathCopyData)\\n net = Dataset(ls)\\n for xs in range(len(estaciones)):\\n minlat1 = minlat[xs]\\n maxlat1 = maxlat[xs]\\n minlon1 = minlon[xs]\\n maxlon1 = maxlon[xs]\\n estacion = estaciones[xs]\\n #checkFile(net, tfil, f[0], opt, path, minlat1, maxlat1, minlon1, maxlon1, variables, estacion)\\n var_cut = []\\n for i in variables:\\n var = net.variables[i][:,int(minlat1):int(maxlat1),int(minlon1):int(maxlon1)]\\n #print(LON)\\n #print(var)\\n #return\\n # celda.append(var)\\n # result = ne(var, LON, LAT, LONsize, LATsize, minlat, maxlat, minlon, maxlon)\\n var_cut.append(var)\\n\\n for ls in range(len(var_cut)):\\n saveData(var_cut[ls], variables[ls], f[0], opt, path, estacion)\\n tfileCopy.remove(tfil)\\n tbaseCopy.remove(tbas)\\n except (OSError, EOFError) as e:\\n print(e)\\n fdata = df.DataFrame(tfileCopy, columns=['nameFile'])\\n fbas = df.DataFrame(tbaseCopy, columns=['nameBase'])\\n fdata.to_csv(dirr + 'tfile.txt', encoding='utf-8', index=False)\\n fbas.to_csv(dirr + 'tbase.txt', encoding='utf-8', index=False)\\n if os.path.exists(pathCopyData + cadena):\\n os.remove(pathCopyData + cadena)\\n sys.exit()\\n # readFiles(1);\\n except tarfile.ReadError:\\n print('error2')\\n # fdata = df.DataFrame(tfile,columns=['nameFile']);\\n # fbas = df.DataFrame(tbase,columns=['nameBase']);\\n # fdata.to_csv(dirr+'tfile.txt',encoding='utf-8',index=False);\\n # fbas.to_csv(dirr+'tbase.txt',encoding='utf-8',index=False);\\n # readFiles(1);\\n except (KeyError, FileNotFoundError):\\n print('ERROR DE LECTURA')\",\n \"def test_get_infile(self):\\r\\n pass # not practically testable, but obvious file I/O\\r\",\n \"def loadSimData(datafile):\\n \\n global dt, ti, Lx, Ly, nsamp, N, M, L, B, totalStep, Fmc, Kbend, kT, \\\\\\n dtSamp, T, box_area, nt, body_length, Pe, persistence, flexure \\n\\n datafile = open(datafile,\\\"r\\\")\\n for line in datafile:\\n A = line.split()\\n if A[0] == \\\"dt\\\": # Time interval between MD steps\\n dt = float(A[-1])\\n elif A[0] == \\\"ti\\\": # Beginning time for data acquisition\\n ti = float(A[-1])\\n elif A[0] == \\\"Lx\\\": # Box size in x\\n Lx = float(A[-1]) \\n elif A[0] == \\\"Ly\\\": # Box size in y\\n Ly = float(A[-1])\\n elif A[0] == \\\"totalStep\\\": # Total MD steps\\n totalStep = float(A[-1])\\n elif A[0] == \\\"nsamp\\\": # Data sampling frequency\\n nsamp = float(A[-1])\\n elif A[0] == \\\"nfil\\\": # Number of particles per polymer\\n N = float(A[-1])\\n elif A[0] == \\\"L\\\": # Number of particles\\n L = float(A[-1])\\n elif A[0] == \\\"B\\\": # Bond length between particles of a body\\n B = float(A[-1])\\n elif A[0] == \\\"kT\\\": # Boltzmann constant*Temperature\\n kT = float(A[-1])\\n elif A[0] == \\\"Fmc\\\": # Self propulsion force constant\\n Fmc = float(A[-1]) \\n elif A[0] == \\\"Kbend\\\": # Bending constant\\n Kbend = float(A[-1])\\n \\n L = int(L)\\n N = int(N)\\n Lx /= B\\n Ly /= B\\n M = L/N\\n dtSamp = dt*nsamp\\n T = totalStep - ti\\n nt = T/nsamp\\n box_area = Lx*Ly\\n body_length = B*(N-1)\\n Pe = Fmc*body_length**2/kT\\n persistence = Kbend/(kT*body_length)\\n flexure = Pe/persistence\\n \\n return\",\n \"def _read_file(self):\\n\\n with open(self.file_name, 'rb') as f:\\n new_test = struct.unpack('<l', f.read(8)[4:])[0]\\n f.close()\\n\\n with open(self.file_name, 'rb') as f:\\n old_test = struct.unpack('<h', f.read(6)[4:])[0]\\n f.close()\\n\\n with open(self.file_name, 'rb') as f:\\n other_test = struct.unpack('<l', f.read(20)[16:])[0]\\n f.close()\\n\\n open_file = open(self.file_name, 'rb')\\n\\n if (other_test==202):\\n raw = open_file.read(1236)[11:]\\n self.model = '202'\\n elif ((not new_test==102) and old_test==102):\\n raw = open_file.read(1133)\\n self.model = '102old'\\n elif (new_test==102 and old_test==102):\\n raw = open_file.read(1224)\\n self.model = '102new'\\n\\n self.header = DpHeader(raw, self.model)\\n\\n self.data = DpData(open_file, \\n self.model, \\n self.header.interferogram_size, \\n self.header.number_of_coadds, \\n 2048*self.header.zero_fill,\\n self.header.laser_wavelength_microns, \\n self.header.dispersion_constant_xm,\\n self.header.dispersion_constant_xb)\\n\\n open_file.close()\",\n \"def _read(self, in_file):\\n #\\n # I know this function is long, but the FRD block is long as well...\\n # Splitting this into multiple functions would not help in my opinion.\\n # Therefore -> shut up pylint\\n # pylint: disable=too-many-branches\\n # pylint: disable=too-many-statements\\n #\\n self.setname = in_file.read(6).decode().strip()\\n self.value = float(in_file.read(12))\\n self.numnod = int(in_file.read(12))\\n self.text = in_file.read(20).decode().strip()\\n self.ictype = int(in_file.read(2))\\n self.numstep = int(in_file.read(5))\\n self.analys = in_file.read(10).decode().strip()\\n self.format = int(in_file.read(2))\\n in_file.read(1) # eol\\n\\n in_file.read(1) # pad byte\\n in_file.read(2) # key = -4\\n in_file.read(2) # pad bytes\\n self.name = in_file.read(8).decode().strip()\\n self.ncomps = int(in_file.read(5))\\n self.irtype = int(in_file.read(5))\\n if self.irtype != 1:\\n raise NotImplementedError()\\n in_file.read(1) # eol\\n\\n for i in range(self.ncomps):\\n entity = FRDEntity()\\n self.entities.append(entity)\\n\\n in_file.read(1) # pad byte\\n entity.key = int(in_file.read(2))\\n in_file.read(2) # pad bytes\\n entity.name = in_file.read(8).decode().strip()\\n entity.menu = int(in_file.read(5))\\n entity.ictype = int(in_file.read(5))\\n entity.icind1 = int(in_file.read(5))\\n if entity.ictype == 4:\\n entity.icind2 = int(in_file.read(5))\\n elif entity.ictype == 2 and i == 3:\\n entity.icind2 = int(in_file.read(5))\\n entity.iexist = int(in_file.read(5))\\n entity.icname = in_file.read(3).decode().strip()\\n self.ncomps -= 1\\n else:\\n entity.iexist = int(in_file.read(5))\\n in_file.read(1) # eol\\n\\n for i in range(self.numnod):\\n result = FRDNodeResult()\\n self.results.append(result)\\n if self.format < 2:\\n num_lines = int(self.ncomps/(6 + 1)) + 1\\n result.data = []\\n for j in range(num_lines):\\n in_file.read(3) # pad byte and key = -1 || -2\\n if result.node is None:\\n result.node = int(in_file.read(5*(self.format+1)))\\n else:\\n in_file.read(5*(self.format+1))\\n k_start = j*6\\n k_end = min(self.ncomps - k_start, (j+1)*6)\\n for _ in range(0, k_end):\\n result.data.append(float(in_file.read(12)))\\n in_file.read(1) # eol\\n else:\\n result.node = struct.unpack('i', in_file.read(4))[0]\\n result.data = struct.unpack(\\n 'f'*self.ncomps, in_file.read(self.ncomps*4))\\n\\n if self.format < 2:\\n in_file.readline() # last record for ascii only\",\n \"def _read(self, in_file):\\n in_file.read(18) # pad bytes\\n self.numnod = int(in_file.read(12))\\n in_file.read(37) # pad bytes\\n self.format = int(in_file.read(1))\\n in_file.read(1) # eol\\n self.nodes = []\\n\\n for _ in range(self.numnod):\\n node = FRDNode()\\n self.nodes.append(node)\\n if self.format < 2:\\n in_file.read(1)\\n node.key = int(in_file.read(2))\\n node.number = int(in_file.read(5*(self.format+1)))\\n node.pos = [float(in_file.read(12)) for j in range(3)]\\n in_file.read(1) # eol\\n else:\\n node.number = struct.unpack('i', in_file.read(4))[0]\\n if self.format == 2:\\n node.pos = struct.unpack('fff', in_file.read(12))\\n else:\\n node.pos = struct.unpack('ddd', in_file.read(24))\\n\\n if self.format < 2:\\n in_file.readline() # last record for ascii only\",\n \"def read_meta(self):\\n meta = cPickle.load(open('../sugar_analysis_data/META-CABALLO2.pkl'))\\n self.meta_sn_name_list = []\\n self.meta_zcmb = []\\n self.meta_x0 =[]\\n self.meta_x0_err = []\\n self.meta_x1 =[]\\n self.meta_x1_err = []\\n self.meta_c = []\\n self.meta_c_err = []\\n self.meta_mb = []\\n self.meta_mb_err = []\\n self.meta_cov_x0_x1 = [] \\n self.meta_cov_x0_c = []\\n self.meta_cov_x1_c = []\\n self.meta_cov_mb_x1 = []\\n self.meta_cov_mb_c = [] \\n self.meta_zhl = []\\n self.meta_zhl_err = []\\n self.meta_idr = []\\n for meta_sn_name in meta.keys(): \\n \\n if meta[meta_sn_name]['idr.subset'] != 'bad' and meta[meta_sn_name]['idr.subset'] != 'auxiliary':\\n \\n self.meta_sn_name_list.append(meta_sn_name)\\n self.meta_zhl_err.append(meta[meta_sn_name]['host.zhelio.err'])\\n self.meta_zhl.append(meta[meta_sn_name]['host.zhelio'])\\n self.meta_zcmb.append(meta[meta_sn_name]['host.zcmb'])\\n self.meta_x0.append(meta[meta_sn_name]['salt2.X0'])\\n self.meta_x0_err.append(meta[meta_sn_name]['salt2.X0.err'])\\n self.meta_x1.append(meta[meta_sn_name]['salt2.X1'])\\n self.meta_x1_err.append(meta[meta_sn_name]['salt2.X1.err'])\\n self.meta_c.append(meta[meta_sn_name]['salt2.Color'])\\n self.meta_c_err.append(meta[meta_sn_name]['salt2.Color.err'])\\n self.meta_mb.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B'])\\n self.meta_mb_err.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B.err'])\\n self.meta_cov_x0_x1.append(meta[meta_sn_name]['salt2.CovX0X1'])\\n self.meta_cov_x0_c.append(meta[meta_sn_name]['salt2.CovColorX0'])\\n self.meta_cov_x1_c.append(meta[meta_sn_name]['salt2.CovColorX1'])\\n self.meta_cov_mb_x1.append(meta[meta_sn_name]['salt2.CovRestFrameMag_0_BX1'])\\n self.meta_cov_mb_c.append(meta[meta_sn_name]['salt2.CovColorRestFrameMag_0_B'])\\n self.meta_idr.append(meta[meta_sn_name]['idr.subset'])\\n \\n self.meta_idr = np.array(self.meta_idr)\\n self.meta_zcmb = np.array(self.meta_zcmb)\\n self.meta_zhl = np.array(self.meta_zhl)\\n self.meta_zhl_err = np.array(self.meta_zhl_err)\\n self.meta_x0 = np.array(self.meta_x0)\\n self.meta_x0_err = np.array(self.meta_x0_err)\\n self.meta_x1 = np.array(self.meta_x1)\\n self.meta_x1_err = np.array(self.meta_x1_err) \\n self.meta_c = np.array(self.meta_c)\\n self.meta_c_err = np.array(self.meta_c_err)\\n self.meta_mb = np.array(self.meta_mb)\\n self.meta_mb_err = np.array(self.meta_mb_err)\\n self.meta_cov_x0_x1 = np.array(self.meta_cov_x0_x1)\\n self.meta_cov_x0_c = np.array(self.meta_cov_x0_c)\\n self.meta_cov_x1_c = np.array(self.meta_cov_x1_c)\\n self.meta_cov_mb_x1 = np.array(self.meta_cov_mb_x1)\\n self.meta_cov_mb_c = np.array(self.meta_cov_mb_c)\",\n \"def read_mb_file(self,idir='.',ifile=None, gmt=True, verbose=False):\\n \\n import numpy as np\\n import os\\n \\n if gmt==True:\\n gmt_file=idir+'/../maps/en_velo.gmt'\\n if isinstance(gmt,str):\\n gmt_file=gmt\\n \\n if gmt != False:\\n self.read_lon_lat(gmt_file,verbose=verbose)\\n \\n if ifile is None:\\n mb_file_basename= idir + '/mb_'+self.code+'_GPS.dat'\\n else:\\n mb_file_basename=ifile\\n \\n data_NEU = []\\n for i in range(1,4):\\n mb_file = mb_file_basename + str(i)\\n\\n # file\\n self.ifile=os.path.abspath(mb_file)\\n \\n data=np.genfromtxt(mb_file,skip_header=4)\\n \\n # reshape to ensure a 2D array\\n if len(data.shape)==1:\\n data=data.reshape((1,data.shape[0]))\\n \\n\\n\\n data_NEU.append(data)\\n\\n if data_NEU[0].shape == data_NEU[1].shape == data_NEU[2].shape:\\n self.data=np.zeros((data_NEU[0].shape[0],7))\\n self.data[:,0]=data_NEU[0][:,0]\\n self.data[:,1]=data_NEU[0][:,1]#*to_mm\\n self.data[:,2]=data_NEU[1][:,1]#*to_mm\\n self.data[:,3]=data_NEU[2][:,1]#*to_mm\\n\\n self.data[:,4]=data_NEU[0][:,2]#*to_mm\\n self.data[:,5]=data_NEU[1][:,2]#*to_mm\\n self.data[:,6]=data_NEU[2][:,2]#*to_mm\\n\\n else: \\n print(\\\"!!! Error reading \\\",mb_file_basename,\\\" :*dat1, *dat2, *dat3 do not have the same length\\\")\\n self.data = None\",\n \"def uadb_ascii_to_dataframe(file=''): \\n \\n if debug:\\n print(\\\"Running uadb_ascii_to_dataframe for: \\\", file) \\n \\n data = check_read_file(file=file, read=True) # TODO\\n \\n #source_file = [l for l in file.split('/') if '.txt' in l][0]\\n\\n nmiss = 0\\n search_h = False \\n read_data = []\\n \\n usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\\n\\n #usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd, iday, ident, numlev= 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\\n obs_id = 0\\n stations_id = [] \\n \\n for i, line in enumerate(data):\\n if line[0] == 'H':\\n try:\\n # Header\\n usi = int(line[2:14]) # unique station identifier\\n \\n ident = int(line[15:21].replace(' ',''))# WMO\\n if ident not in stations_id:\\n stations_id.append(ident)\\n \\n #if len(ident) == 4:\\n # ident = '0' + ident \\n #idflag = int(line[22:24]) # id flag\\n #d_src = int(line[25:28]) # source dataset\\n #version = float(line[29:34]) # version\\n #dateflag = int(line[35:37]) # date flag\\n year = line[38:42] # year\\n month = \\\"%02d\\\" % int(line[43:45])\\n day = \\\"%02d\\\" % int(line[46:48])\\n hour = line[49:53]\\n #locflag = int(line[54:56]) # Location Flag\\n lat = float(line[57:67])\\n lon = float(line[68:78])\\n #ele = float(line[79:85])\\n #stype = int(line[86:88])\\n numlev = int(line[89:93])\\n #pvers = line[94:102]\\n\\n if '99' in hour:\\n hour = hour.replace('99', '00')\\n \\n if '99' in day:\\n search_h = True\\n continue\\n \\n minutes = int(hour) % 100 \\n hour = \\\"%02d\\\" % (int(hour) // 100)\\n if minutes > 60 or minutes < 0:\\n minutes = 0\\n minutes = \\\"%02d\\\" % minutes\\n idate = datetime.strptime(year + month + day + hour + minutes, '%Y%m%d%H%M')\\n iday = int(year + month + day)\\n #pday = int(day)\\n search_h = False\\n\\n except Exception as e:\\n #print(\\\"Error: \\\", i, line, repr(e), \\\"Skipping Block:\\\")\\n search_h = True\\n #iprev = i\\n\\n elif search_h:\\n nmiss += 1\\n continue # Skipping block\\n\\n else:\\n # Data\\n #ltyp = int(line[0:4])\\n p = float(line[5:13])\\n \\n if p != -99999.0 and p != 9999.9: \\n press = float(line[5:13])*100 # converting to Pa, since P is given in mb (1 mb = 1 hPa) \\n else:\\n press = np.nan \\n \\n gph = float(line[14:22]) # gph [m]\\n \\n if gph == -999.0 or gph == -99999.00 or gph >= 99999.0:\\n gph = np.nan\\n \\n temp = float(line[23:29])\\n if temp == -999.0:\\n temp = np.nan \\n else:\\n temp = temp + 273.15\\n \\n rh = float(line[30:36]) # %\\n if rh == -999.0:\\n rh = np.nan\\n else:\\n rh = rh / 100. # convert to absolute ratio TODO\\n\\n wdir = float(line[37:43]) \\n if wdir == -999.0 or wdir == -999 :\\n wdir = np.nan\\n \\n wspd = float(line[44:50]) # [m/s], module of the velocity\\n if wspd <0 :\\n wspd = np.nan \\n \\n try:\\n \\n for value,var in zip([ gph, temp, wspd, wdir, rh], [ 'gph', 'temperature', 'wind_speed', 'wind_direction', 'relative_humidity'] ):\\n obs_id = obs_id +1\\n if not np.isnan(press): # when pressure is available, z_coord== pressure and z_type==1\\n z_type = 1 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n elif (np.isnan(press) and not np.isnan(gph) ) : # when pressure is not available, z_coord== gph and z_type==2 \\n z_type = 2 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, gph, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n else:\\n z_type = -2147483648 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n \\n except:\\n 0\\n \\n \\n \\n #column_names = ['source_file', 'product_code', 'report_id', 'observation_id', 'report_timestamp' , 'iday', 'station_id', 'lat@hdr', 'lon@hdr', 'vertco_reference_1@body', 'obsvalue@body', 'varno@body' , 'units', 'number_of_pressure_levels' ]\\n \\n df = pd.DataFrame(data= read_data, columns= column_names) \\n \\n df['observation_id'] = np.chararray.zfill( (df['observation_id'].astype(int)) .astype('S'+str(id_string_length ) ), id_string_length ) #converting to fixed length bite objects \\n df['report_id'] = np.chararray.zfill( (df['report_id'].astype(int)).astype ('S'+str(id_string_length ) ), id_string_length )\\n \\n df = df.replace([-999.9, -9999, -999, -999.0, -99999.0, -99999.9, 99999.0, -99999.00 ], np.nan)\\n \\n #df['observations_id'] =numpy.char.zfill(numpy.arange(ivar.shape[0]).astype('S10'), 10)\\n \\n df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n #df['report_id'] = numpy.int64 (df['report_id'] ) \\n #df['observation_id'] = numpy.int64 (df['observation_id'] ) \\n df = df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n \\n print('Done reading DF')\\n return df , stations_id\",\n \"def _process_data_file(self):\\n \\n with open(self.data_file, 'r') as f:\\n self.description = f.readline().strip()\\n data = np.loadtxt(self.data_file, skiprows=1)\\n\\n return data\",\n \"def make_data_raw_fast(mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data_fast(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def doImport(self, params):\\n #logging.info(params.filename)\\n raw_data = self.open_filename(params.filename)\\n #logging.info('file open ok')\\n data = self.split_to_blocks(raw_data)\\n #logging.info('splitting ok')\\n chromatograms = self.process_data_to_chromatograms(data)\\n #logging.info('extract ok')\\n \\n processed_data = []\\n \\n zero = params.field_results[\\\"zero\\\"]\\n MIC = params.field_results[\\\"MIC\\\"]\\n TIC = params.field_results[\\\"TIC\\\"]\\n round_MZ = params.field_results[\\\"round_MZ\\\"]\\n \\n for chrom in chromatograms:\\n \\n prefix = chrom['short_title']\\n if (\\\".\\\" in prefix) and round_MZ:\\n first,last = prefix.split('.')\\n prefix = first\\n \\n if (\\\"MIC\\\" in prefix) and (not MIC):\\n pass\\n elif (\\\"TIC\\\" in prefix) and (not TIC):\\n pass\\n else:\\n x = ImportDataset1D(name=prefix+'_time',data = chrom['time'])\\n if zero:\\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal_zero'])\\n else:\\n y = ImportDataset1D(name=prefix+'_signal',data = chrom['signal'])\\n processed_data.append(x)\\n processed_data.append(y)\\n \\n return processed_data\",\n \"def readdata(self, fname):\\n\\t\\treturn self.__readtsv(fname)\",\n \"def loadDataFromFile(self, filePath):\\r\\n with open(filePath, \\\"r\\\") as signalFile:\\r\\n nameline = signalFile.readlines()[2:5]\\r\\n with open(filePath, \\\"r\\\") as signalFile:\\r\\n dataline = signalFile.readlines()[6:]\\r\\n #name1 = re.findall(r\\\"\\\\<StudentName graduate\\\\=\\\\\\\"([a-z]+)\\\\\\\"\\\\>([\\\\s\\\\w])\\\\<\\\\/StudentName\\\\>\\\", nameline[0], re.X | re.I)\\r\\n stugrad = re.findall(r\\\"\\\\\\\"([a-z]+)\\\\\\\"\\\", nameline[0], re.X | re.I)\\r\\n if str(stugrad[0]) == \\\"true\\\":\\r\\n self.chkGraduate.setChecked(True)\\r\\n elif str(stugrad[0]) == \\\"false\\\":\\r\\n self.chkGraduate.setChecked(False)\\r\\n stuname = re.findall(r\\\"\\\\>([\\\\w\\\\s]+)\\\\<\\\\/StudentName\\\\>\\\", nameline[0], re.X | re.I)\\r\\n self.txtStudentName.setText(str(stuname[0]))\\r\\n stuid = re.findall(r\\\"\\\\<StudentID\\\\>([-\\\\w]+)\\\\<\\\\/StudentID\\\\>\\\", nameline[1], re.X | re.I)\\r\\n self.txtStudentID.setText(str(stuid[0]))\\r\\n stucol = re.findall(r\\\"\\\\<College\\\\>([\\\\w\\\\s]+)\\\\<\\\\/College\\\\>\\\", nameline[2], re.X | re.I)\\r\\n index = self.cboCollege.findText(str(stucol[0]))\\r\\n self.cboCollege.setCurrentIndex(index)\\r\\n #stucol = name3.group(1)\\r\\n setvals = []\\r\\n dataname = [self.txtComponentName_1, self.txtComponentName_2, self.txtComponentName_3, self.txtComponentName_4,self.txtComponentName_5,self.txtComponentName_6,self.txtComponentName_7,self.txtComponentName_8,self.txtComponentName_9,self.txtComponentName_10,self.txtComponentName_11,self.txtComponentName_12,self.txtComponentName_13,self.txtComponentName_14,self.txtComponentName_15,self.txtComponentName_16,self.txtComponentName_17,self.txtComponentName_18,self.txtComponentName_19,self.txtComponentName_20]\\r\\n datanum = [self.txtComponentCount_1, self.txtComponentCount_2, self.txtComponentCount_3, self.txtComponentCount_4,self.txtComponentCount_5,self.txtComponentCount_6,self.txtComponentCount_7,self.txtComponentCount_8,self.txtComponentCount_9,self.txtComponentCount_10,self.txtComponentCount_11,self.txtComponentCount_12,self.txtComponentCount_13,self.txtComponentCount_14,self.txtComponentCount_15,self.txtComponentCount_16,self.txtComponentCount_17,self.txtComponentCount_18,self.txtComponentCount_19,self.txtComponentCount_20]\\r\\n counter = 0\\r\\n for data in dataline:\\r\\n stupro = re.findall(r\\\"name=\\\\\\\"([\\\\w\\\\s-]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n if counter < 20 and len(stupro) > 0:\\r\\n stupro = re.findall(r\\\"name=\\\\\\\"([\\\\w\\\\s-]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n dataname[counter].setText(str(stupro[0]))\\r\\n stunum = re.findall(r\\\"count=\\\\\\\"([0-9]+)\\\\\\\"\\\", data, re.X | re.I)\\r\\n datanum[counter].setText(str(stunum[0]))\\r\\n counter += 1\",\n \"def read_data(self):\\n self.data = reduce_spectrum(self.filename)\",\n \"def _get_file_contents(self, idx=None):\\n self._dataSet = netCDF4.Dataset(self.get_file_name())\\n return 1\",\n \"def loadData(self,ins):\\n #--Read subrecords\\n bytesRead = 0\\n curTest = None\\n while bytesRead < self.size:\\n (name,size) = ins.unpackSubHeader('INFO')\\n srData = ins.read(size,'INFO.'+name)\\n bytesRead += 8+size\\n #--Ids\\n if name == 'INAM': self.id = cstrip(srData)\\n elif name == 'PNAM': self.prevId = cstrip(srData)\\n elif name == 'NNAM': self.nextId = cstrip(srData)\\n #--Text/Script\\n elif name == 'NAME': self.text = srData\\n elif name == 'BNAM': self.script = srData\\n elif name == 'SNAM': self.speak = srData\\n #--Quest flags\\n elif name == 'QSTN': self.qflag = 1\\n elif name == 'QSTF': self.qflag = 2\\n elif name == 'QSTR': self.qflag = 3\\n #--String/Value Tests\\n elif name == 'DATA': \\n (self.type, self.spDisp, self.spRank, self.spSex, self.pcRank, self.unk02\\n ) = struct.unpack('2i4B',srData)\\n elif name == 'ONAM': self.spId = cstrip(srData)\\n elif name == 'RNAM': self.spRace = cstrip(srData)\\n elif name == 'CNAM': self.spClass = cstrip(srData)\\n elif name == 'FNAM': self.spFaction = cstrip(srData)\\n elif name == 'ANAM': self.cell = cstrip(srData)\\n elif name == 'DNAM': self.pcFaction = cstrip(srData)\\n #--Function/Value Tests\\n elif name == 'SCVR': \\n (index,type,func,oper) = struct.unpack('BB2sB',srData[:5])\\n text = srData[5:]\\n curTest = Info_Test(type,func,oper,text)\\n self.tests[index-48] = curTest\\n elif name == 'INTV':\\n (curTest.value,) = struct.unpack('i',srData)\\n elif name == 'FLTV':\\n (curTest.value,) = struct.unpack('f',srData)\\n #--Deleted?\\n elif name == 'DELE': self.isDeleted = True\\n #--Bad record?\\n else: raise Tes3UnknownSubRecord(self.inName,name,self.name)\",\n \"def read_data(infile):\\n extension = os.path.splitext(infile)[1]\\n h = read_header(infile)\\n nx = int(h['num_x_pts'])\\n ny = int(h['num_y_pts'])\\n nt = int(h['num_t_pts'])\\n fid = open(infile, 'rb')\\n fid.seek(512) #skip header\\n if extension == '.aps' or extension == '.a3daps':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, ny, nt, order='F').copy() #make N-d image\\n elif extension == '.a3d':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, nt, ny, order='F').copy() #make N-d image\\n elif extension == '.ahi':\\n data = np.fromfile(fid, dtype = np.float32, count = 2* nx * ny * nt)\\n data = data.reshape(2, ny, nx, nt, order='F').copy()\\n real = data[0,:,:,:].copy()\\n imag = data[1,:,:,:].copy()\\n fid.close()\\n if extension != '.ahi':\\n return data\\n else:\\n return real, imag\",\n \"def readData(self):\\n f = open(self.filename)\\n self.time = []\\n self.data = []\\n for line in f:\\n if line.find('BAD FLAG') > 0:\\n self.badValue = float(line.split(':')[1].strip())\\n if line.find('LONGITUDE') > 0:\\n self.lon = line.split(':')[1].strip()\\n if line.find('LATITUDE') > 0:\\n self.lat = line.split(':')[1].strip()\\n if len(line) > 6 and line[2] == '-' and line[6] == '-':\\n parts = line.rsplit(None, 1)\\n # data line\\n timeStamp = datetime.datetime.strptime(parts[0], '%d-%b-%Y %H')\\n t = timeArray.datetimeToEpochTime(timeStamp)\\n self.time.append(t)\\n val = float(parts[1])\\n self.data.append(val)\\n\\n self.time = np.array(self.time)\\n self.data = np.array(self.data)\\n # remove bad values\\n if self.badValue:\\n goodIx = self.data != self.badValue\\n self.time = self.time[goodIx]\\n self.data = self.data[goodIx]\\n self.fileIsRead = True\",\n \"def readAMBERTop(self, phys, filename):\\r\\n\\r\\n def skipLine(data):\\r\\n nl = data.index('\\\\n')\\r\\n return data[nl+1:len(data)]\\r\\n\\r\\n def jumpTo(data, target):\\r\\n fp = data.index(target)\\r\\n return data[fp:len(data)]\\r\\n\\r\\n def readRemove(data, size):\\r\\n retval = data[0:size-1]\\r\\n return data[size:len(data)]\\r\\n\\r\\n def getInteger(data):\\r\\n pos = 0\\r\\n retval = \\\"\\\"\\r\\n while (not data[pos].isdigit()):\\r\\n pos = pos + 1\\r\\n while (data[pos].isdigit()):\\r\\n retval = retval + data[pos]\\r\\n pos = pos + 1\\r\\n data = data[pos:len(data)]\\r\\n return int(retval), data\\r\\n\\r\\n def parse(data, arr, str, count, dtype, tupsize=1):\\r\\n data = jumpTo(data, \\\"%FLAG \\\"+str)\\r\\n data = jumpTo(data, \\\"%FORMAT\\\")\\r\\n numPerLine, data = getInteger(data)\\r\\n fieldsize, data = getInteger(data)\\r\\n data = skipLine(data) \\r\\n \\r\\n arr2 = []\\r\\n numread = 0\\r\\n for j in range(0, (tupsize*count-1) / numPerLine + 1):\\r\\n for i in range(0, numPerLine):\\r\\n if (tupsize == 1):\\r\\n arr.append(dtype(data[0:fieldsize].strip()))\\r\\n else:\\r\\n arr2.append(dtype(data[0:fieldsize].strip()))\\r\\n if (len(arr2) == tupsize):\\r\\n arr.append(arr2)\\r\\n arr2 = []\\r\\n numread += 1\\r\\n data = data[fieldsize:len(data)]\\r\\n if (numread == tupsize*count):\\r\\n break\\r\\n data = skipLine(data) \\r\\n return data\\r\\n\\r\\n def scan(data, str):\\r\\n return (data.count(str) != 0)\\r\\n\\r\\n\\r\\n f = open(filename, 'r')\\r\\n data = f.read()\\r\\n\\r\\n # First Line: VERSION ...\\r\\n data = skipLine(data)\\r\\n\\r\\n # Go To: %FLAG POINTERS\\r\\n data = jumpTo(data, '%FLAG POINTERS')\\r\\n\\r\\n data = jumpTo(data, '%FORMAT')\\r\\n numPerLine, data = getInteger(data)\\r\\n fieldsize, data = getInteger(data)\\r\\n data = skipLine(data)\\r\\n \\r\\n temp = []\\r\\n numread = 0\\r\\n for j in range(0, 31 / numPerLine + 1):\\r\\n for i in range(0, numPerLine):\\r\\n temp.append(int(data[0:8]))\\r\\n data = data[8:len(data)]\\r\\n numread += 1\\r\\n if (numread == 31):\\r\\n break\\r\\n data = skipLine(data)\\r\\n \\r\\n [natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \\r\\n\\r\\n\\r\\n #################################################\\r\\n # Read AtomTypes\\r\\n atomnames = []\\r\\n charges = []\\r\\n masses = []\\r\\n atindex = []\\r\\n exclusions = []\\r\\n nparams = []\\r\\n reslabels = []\\r\\n respointers = []\\r\\n forceconstants = [[], [], []] # bond, angle, dihedral\\r\\n equilvals = [[], [], [[], []]] # bond, angle, dihedral\\r\\n scee_scales = []\\r\\n scnb_scales = []\\r\\n solty = []\\r\\n lj_acoef = []\\r\\n lj_bcoef = []\\r\\n\\r\\n data = parse(data, atomnames, \\\"ATOM_NAME\\\", natoms, str) \\r\\n data = parse(data, charges, \\\"CHARGE\\\", natoms, float)\\r\\n data = parse(data, masses, \\\"MASS\\\", natoms, float)\\r\\n data = parse(data, atindex, \\\"ATOM_TYPE_INDEX\\\", natoms, int)\\r\\n data = parse(data, exclusions, \\\"NUMBER_EXCLUDED_ATOMS\\\", natoms, int)\\r\\n data = parse(data, nparams, \\\"NONBONDED_PARM_INDEX\\\", ntypes*ntypes, int)\\r\\n data = parse(data, reslabels, \\\"RESIDUE_LABEL\\\", nres, str)\\r\\n data = parse(data, respointers, \\\"RESIDUE_POINTER\\\", nres, int)\\r\\n data = parse(data, forceconstants[0], \\\"BOND_FORCE_CONSTANT\\\", numbnd, float)\\r\\n data = parse(data, equilvals[0], \\\"BOND_EQUIL_VALUE\\\", numbnd, float)\\r\\n data = parse(data, forceconstants[1], \\\"ANGLE_FORCE_CONSTANT\\\", numang, float)\\r\\n data = parse(data, equilvals[1], \\\"ANGLE_EQUIL_VALUE\\\", numang, float)\\r\\n data = parse(data, forceconstants[2], \\\"DIHEDRAL_FORCE_CONSTANT\\\", nptra, float)\\r\\n data = parse(data, equilvals[2][0], \\\"DIHEDRAL_PERIODICITY\\\", nptra, float)\\r\\n data = parse(data, equilvals[2][1], \\\"DIHEDRAL_PHASE\\\", nptra, float)\\r\\n if (scan(data, \\\"SCEE_SCALE_FACTOR\\\")):\\r\\n data = parse(data, scee_scales, \\\"SCEE_SCALE_FACTORS\\\", nptra, float)\\r\\n else:\\r\\n for i in range(0, nptra):\\r\\n scee_scales.append(1.2) # Default \\r\\n if (scan(data, \\\"SCNB_SCALE_FACTOR\\\")):\\r\\n data = parse(data, scnb_scales, \\\"SCNB_SCALE_FACTORS\\\", nptra, float)\\r\\n else:\\r\\n for i in range(0, nptra):\\r\\n scnb_scales.append(2.0) # Default \\r\\n\\r\\n data = parse(data, solty, \\\"SOLTY\\\", natyp, float)\\r\\n data = parse(data, lj_acoef, \\\"LENNARD_JONES_ACOEF\\\", ntypes*(ntypes+1)/2, float)\\r\\n data = parse(data, lj_bcoef, \\\"LENNARD_JONES_BCOEF\\\", ntypes*(ntypes+1)/2, float)\\r\\n\\r\\n\\r\\n ##########################################################\\r\\n # STRUCTURE\\r\\n\\r\\n bonds = [[], []] # With H, Without H\\r\\n angles = [[], []] # With H, Without H\\r\\n dihedrals = [[], []] # With H, Without H\\r\\n impropers = [[], []] # With H, Without H\\r\\n excluded_atoms = [] \\r\\n hbond_acoef = []\\r\\n hbond_bcoef = []\\r\\n hbcut = []\\r\\n amber_atom_types = []\\r\\n tree_chain = []\\r\\n join_array = []\\r\\n irotat = []\\r\\n radii = []\\r\\n screen = []\\r\\n\\r\\n data = parse(data, bonds[0], \\\"BONDS_INC_HYDROGEN\\\", nbonh, int, 3)\\r\\n data = parse(data, bonds[1], \\\"BONDS_WITHOUT_HYDROGEN\\\", nbona, int, 3)\\r\\n data = parse(data, angles[0], \\\"ANGLES_INC_HYDROGEN\\\", ntheth, int, 4)\\r\\n data = parse(data, angles[1], \\\"ANGLES_WITHOUT_HYDROGEN\\\", ntheta, int, 4)\\r\\n data = parse(data, dihedrals[0], \\\"DIHEDRALS_INC_HYDROGEN\\\", nphih, int, 5)\\r\\n data = parse(data, dihedrals[1], \\\"DIHEDRALS_WITHOUT_HYDROGEN\\\", nphia, int, 5)\\r\\n \\r\\n # MERGE ARRAYS - PM HANDLES THE H+\\r\\n final_bonds = bonds[0] + bonds[1]\\r\\n final_angles = angles[0] + angles[1]\\r\\n final_dihedrals = dihedrals[0] + dihedrals[1]\\r\\n final_impropers = []\\r\\n \\r\\n # CLEAN UP THE TRASH\\r\\n del(bonds)\\r\\n del(angles)\\r\\n del(dihedrals)\\r\\n \\r\\n\\r\\n # Move impropers into their own array\\r\\n i = 0\\r\\n while (i < len(final_dihedrals)):\\r\\n if (final_dihedrals[i][2] < 0): # 1-4 exclusions are handled by our back end\\r\\n final_dihedrals[i][2] *= -1\\r\\n if (final_dihedrals[i][3] < 0):\\r\\n final_dihedrals[i][3] *= -1 # Make + again\\r\\n final_impropers.append(final_dihedrals[i])\\r\\n final_dihedrals.remove(final_dihedrals[i])\\r\\n i -= 1\\r\\n i += 1\\r\\n\\r\\n # Convert charge units\\r\\n for i in range(0, len(charges)):\\r\\n charges[i] /= 18.223\\r\\n\\r\\n\\r\\n data = parse(data, excluded_atoms, \\\"EXCLUDED_ATOMS_LIST\\\", nnb, int)\\r\\n data = parse(data, hbond_acoef, \\\"HBOND_ACOEF\\\", nphb, float)\\r\\n data = parse(data, hbond_bcoef, \\\"HBOND_BCOEF\\\", nphb, float)\\r\\n data = parse(data, hbcut, \\\"HBCUT\\\", nphb, float)\\r\\n data = parse(data, amber_atom_types, \\\"AMBER_ATOM_TYPE\\\", natoms, str)\\r\\n data = parse(data, tree_chain, \\\"TREE_CHAIN_CLASSIFICATION\\\", natoms, str)\\r\\n data = parse(data, join_array, \\\"JOIN_ARRAY\\\", natoms, int)\\r\\n data = parse(data, irotat, \\\"IROTAT\\\", natoms, int)\\r\\n data = parse(data, radii, \\\"RADII\\\", natoms, float)\\r\\n data = parse(data, screen, \\\"SCREEN\\\", natoms, float)\\r\\n\\r\\n # Further process dihedrals and impropers\\r\\n # Deal with multiplicity\\r\\n # A bit ugly, but the fastest for now\\r\\n # forceconstants[2][dihedrals[0][i][4]-1], int(equilvals[2][0][dihedrals[0][i][4]-1]), equilvals[2][1][dihedrals[0][i][4]-1]\\r\\n\\r\\n mult_di = dict()\\r\\n mult_im = dict()\\r\\n for i in range(0, len(final_dihedrals)):\\r\\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\\r\\n if (not mult_di.has_key(di_id)):\\r\\n mult_di[di_id] = [1, False, [forceconstants[2][final_dihedrals[i][4]-1]], [int(equilvals[2][0][final_dihedrals[i][4]-1])], [equilvals[2][1][final_dihedrals[i][4]-1]]]\\r\\n else:\\r\\n mult_di[di_id][0] += 1\\r\\n mult_di[di_id][2].append(forceconstants[2][final_dihedrals[i][4]-1])\\r\\n mult_di[di_id][3].append(int(equilvals[2][0][final_dihedrals[i][4]-1]))\\r\\n mult_di[di_id][4].append(equilvals[2][1][final_dihedrals[i][4]-1])\\r\\n \\r\\n for i in range(0, len(final_impropers)):\\r\\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\\r\\n if (not mult_im.has_key(di_id)):\\r\\n mult_im[im_id] = [1, False, [forceconstants[2][final_impropers[i][4]-1]], [int(equilvals[2][0][final_impropers[i][4]-1])], [equilvals[2][1][final_impropers[i][4]-1]]]\\r\\n else:\\r\\n mult_im[im_id][0] += 1\\r\\n mult_im[im_id][2].append(forceconstants[2][final_impropers[i][4]-1])\\r\\n mult_im[im_id][3].append(int(equilvals[2][0][final_impropers[i][4]-1]))\\r\\n mult_im[im_id][4].append(equilvals[2][1][final_impropers[i][4]-1])\\r\\n\\r\\n\\r\\n\\r\\n \\r\\n #[natoms, ntypes, nbonh, mbona, ntheth, mtheta, nphih, mphia, nhparm, nparm, nnb, nres, nbona, ntheta, nphia, numbnd, numang, nptra, natyp, nphb, ifpert, nbper, ngper, ndper, mbper, mgper, mdper, ifbox, nmxrs, ifcap, numextra] = temp \\r\\n #phys.myPSF.createAll(natoms, nbonh+mbona, ntheth+mtheta,\\r\\n # len(dihedrals[0])+len(dihedrals[1]),\\r\\n # len(impropers[0])+len(impropers[1]),\\r\\n # 0, 0, 0, 0)\\r\\n \\r\\n # Add atoms\\r\\n curres = 1\\r\\n for i in range(0, natoms):\\r\\n phys.myPSF.addAtom(i, 'SIM', curres, reslabels[curres-1],\\r\\n atomnames[i], atomnames[i], charges[i],\\r\\n masses[i]) \\r\\n if (curres != nres and i >= respointers[curres]):\\r\\n curres += 1\\r\\n\\r\\n # Add bonds\\r\\n for i in range(0, nbonh+nbona):\\r\\n phys.myPSF.addBond(i+1, final_bonds[i][0]/3+1, final_bonds[i][1]/3+1)\\r\\n phys.myPAR.addBond(i+1, atomnames[final_bonds[i][0]/3], atomnames[final_bonds[i][1]/3], forceconstants[0][final_bonds[i][2]/3], equilvals[0][final_bonds[i][2]/3])\\r\\n \\r\\n # Add angles\\r\\n for i in range(0, ntheth+ntheta):\\r\\n phys.myPSF.addAngle(i+1, final_angles[i][0]/3+1, final_angles[i][1]/3+1, final_angles[i][2]/3+1)\\r\\n phys.myPAR.addAngle(i+1, atomnames[final_angles[i][0]/3], atomnames[final_angles[i][1]/3], atomnames[final_angles[i][2]/3], forceconstants[1][final_angles[i][3]/3], equilvals[1][final_angles[i][3]/3])\\r\\n \\r\\n # Add dihedrals\\r\\n for i in range(0, len(final_dihedrals)):\\r\\n di_id = str(final_dihedrals[i][0])+' '+str(final_dihedrals[i][1])+' '+str(final_dihedrals[i][2])+' '+str(final_dihedrals[i][3])\\r\\n mult = mult_di[di_id][0]\\r\\n checked = mult_di[di_id][1]\\r\\n print di_id, \\\" \\\", mult\\r\\n if (not checked):\\r\\n if (mult == 1):\\r\\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\\r\\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], forceconstants[2][final_dihedrals[i][4]-1], int(equilvals[2][0][final_dihedrals[i][4]-1]), equilvals[2][1][final_dihedrals[i][4]-1])\\r\\n else:\\r\\n mult_di[di_id][1] = True\\r\\n # Add dihedral with the appropriate multiplicity\\r\\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\\r\\n fcvec = PARReader.VectorOfDouble()\\r\\n periodvec = PARReader.VectorOfInt()\\r\\n phasevec = PARReader.VectorOfDouble() \\r\\n for j in range(0, len(mult_di[di_id][2])):\\r\\n fcvec.push_back(mult_di[di_id][2][j])\\r\\n periodvec.push_back(mult_di[di_id][3][j])\\r\\n phasevec.push_back(mult_di[di_id][4][j])\\r\\n phys.myPSF.addDihedral(i+1, final_dihedrals[i][0]/3+1, final_dihedrals[i][1]/3+1, int(numpy.abs(final_dihedrals[i][2]))/3+1, final_dihedrals[i][3]/3+1)\\r\\n phys.myPAR.addDihedral(i+1, atomnames[final_dihedrals[i][0]/3], atomnames[final_dihedrals[i][1]/3], atomnames[int(numpy.abs(final_dihedrals[i][2]))/3], atomnames[final_dihedrals[i][3]/3], mult, fcvec, periodvec, phasevec)\\r\\n \\r\\n\\r\\n\\r\\n\\r\\n for i in range(0, len(final_impropers)):\\r\\n im_id = str(final_impropers[i][0])+' '+str(final_impropers[i][1])+' '+str(final_impropers[i][2])+' '+str(final_impropers[i][3])\\r\\n mult = mult_im[im_id][0]\\r\\n checked = mult_im[im_id][1]\\r\\n print im_id, \\\" \\\", mult\\r\\n if (not checked):\\r\\n if (mult == 1):\\r\\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\\r\\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], forceconstants[2][final_impropers[i][4]-1], int(equilvals[2][0][final_impropers[i][4]-1]), equilvals[2][1][final_impropers[i][4]-1])\\r\\n else:\\r\\n mult_im[im_id][1] = True\\r\\n # Add dihedral with the appropriate multiplicity\\r\\n # Force constants, periodicity and phase shifts are in [2], [3], and [4] respectively\\r\\n fcvec = PARReader.VectorOfDouble()\\r\\n periodvec = PARReader.VectorOfInt()\\r\\n phasevec = PARReader.VectorOfDouble() \\r\\n for j in range(0, len(mult_im[im_id][2])):\\r\\n fcvec.push_back(mult_im[im_id][2][j])\\r\\n periodvec.push_back(mult_im[im_id][3][j])\\r\\n phasevec.push_back(mult_im[im_id][4][j])\\r\\n phys.myPSF.addImproper(i+1, final_impropers[i][0]/3+1, final_impropers[i][1]/3+1, int(numpy.abs(final_impropers[i][2]))/3+1, final_impropers[i][3]/3+1)\\r\\n phys.myPAR.addImproper(i+1, atomnames[final_impropers[i][0]/3], atomnames[final_impropers[i][1]/3], atomnames[int(numpy.abs(final_impropers[i][2]))/3], atomnames[final_impropers[i][3]/3], mult, fcvec, periodvec, phasevec)\\r\\n\\r\\n \\r\\n # Need to add garbage nonbonded stuff for now\\r\\n for i in range(0, natoms):\\r\\n phys.myPAR.addNonbonded(i, atomnames[i], 1, 1, 1, 1, 1, 1)\\r\\n\\r\\n # Add VDW parameters\\r\\n # AMBER has the Aij and Bij already in the parameter file\\r\\n # This actually makes life easier.\\r\\n # CHARMM does not, they simply have the original sigma and epsilon.\\r\\n # To compensate for this, for now we will leave the nonbondeds empty in phys.myPAR\\r\\n # We will then access the LennardJones parameter table in Topology directly\\r\\n k = 0\\r\\n phys.myTop.resizeLennardJonesParameters(ntypes)\\r\\n for i in range(0, ntypes):\\r\\n for j in range(i, ntypes):\\r\\n params = GenericTopology.LennardJonesParameters(lj_acoef[k], lj_bcoef[k])\\r\\n k += 1\\r\\n phys.myTop.setLennardJonesParameters(i, j, params)\\r\\n \\r\\n phys.myPAR.readFlag = 1\\r\\n phys.build()\",\n \"def __init__(self, fileName):\\n in_ = JInputStream.getInputStream(fileName)\\n b = int()\\n len = int()\\n cs = int()\\n addr = int()\\n buf = [None]*255\\n eof = False\\n line = 0\\n i = 0\\n while len(ihxData):\\n self.ihxData[i] = -1\\n i += 1\\n try:\\n while not eof:\\n while True:\\n b = in_.read()\\n if b < 0:\\n raise IhxParseException(\\\"Inexpected end of file\\\")\\n if not ((b != int(':'))):\\n break\\n line += 1\\n len = self.readHexByte(in_)\\n # length field \\n cs = len\\n b = self.readHexByte(in_)\\n # address field \\n cs += b\\n addr = b << 8\\n b = self.readHexByte(in_)\\n cs += b\\n addr |= b\\n b = self.readHexByte(in_)\\n # record type field\\n cs += b\\n while i < len:\\n # data\\n buf[i] = int(self.readHexByte(in_))\\n cs += buf[i]\\n i += 1\\n cs += self.readHexByte(in_)\\n # checksum\\n if (cs & 0xff) != 0:\\n raise IhxParseException(\\\"Checksum error\\\")\\n if b == 0:\\n # data record\\n while i < len:\\n if self.ihxData[addr + i] >= 0:\\n System.err.println(\\\"Warning: Memory at position \\\" + Integer.toHexString(i) + \\\" overwritten\\\")\\n self.ihxData[addr + i] = int((buf[i] & 255))\\n i += 1\\n elif b == 1:\\n # eof record\\n eof = True\\n else:\\n raise IhxParseException(\\\"Invalid record type: \\\" + b)\\n except IhxParseException as e:\\n raise IhxFileDamagedException(fileName, line, e.getLocalizedMessage())\\n try:\\n in_.close()\\n except Exception as e:\\n System.err.println(\\\"Warning: Error closing file \\\" + fileName + \\\": \\\" + e.getLocalizedMessage())\",\n \"def load_data(filename):\\n\\n evidance = list()\\n labels = list()\\n row = list()\\n\\n with open(filename) as file:\\n for j, item in enumerate(file):\\n if(j == 0):\\n continue\\n row = []\\n for i, el in enumerate(item.split(',')):\\n # print(el)\\n if(i in {0,2,4,11,12,13,14}):\\n row.append(int(el))\\n elif(i in {1,3,5,6,7,8,9}):\\n row.append(float(el))\\n elif(i==10):\\n if(el == \\\"Jan\\\"):\\n row.append(int(0))\\n elif(el == \\\"Feb\\\"): \\n row.append(int(1))\\n elif(el == \\\"Mar\\\"): \\n row.append(int(2))\\n elif(el == \\\"Apr\\\"): \\n row.append(int(3))\\n elif(el == \\\"May\\\"): \\n row.append(int(4))\\n elif(el == \\\"June\\\"): \\n row.append(int(5))\\n elif(el == \\\"Jul\\\"): \\n row.append(int(6))\\n elif(el == \\\"Aug\\\"): \\n row.append(int(7))\\n elif(el == \\\"Sep\\\"): \\n row.append(int(8))\\n elif(el == \\\"Oct\\\"): \\n row.append(int(9))\\n elif(el == \\\"Nov\\\"): \\n row.append(int(10))\\n elif(el == \\\"Dec\\\"): \\n row.append(int(11))\\n else:\\n print(\\\"wrong month Exit\\\")\\n quit()\\n elif(i==15):\\n if(el == 'Returning_Visitor'):\\n row.append(int(1))\\n else:\\n row.append(int(0))\\n elif(i==16):\\n if(el == 'TRUE'):\\n row.append(int(1))\\n else:\\n row.append(int(0)) \\n elif(i==17):\\n if(el == 'TRUE\\\\n' or el == 'TRUE'):\\n row.append(int(1))\\n else:\\n row.append(int(0))\\n else:\\n print(\\\"Incorrect value\\\")\\n\\n evidance.append(row)\\n # print(item.split(',')[-1])\\n if(item.split(',')[-1] == 'FALSE\\\\n'):\\n labels.append(0)\\n else:\\n labels.append(1)\\n\\n # print(labels)\\n return (evidance, labels)\",\n \"def open(self):\",\n \"def CSV_Load_File( self, infilename ):\\n print( 'Loading \\\"{}\\\"'.format(infilename) )\\n IN = open( infilename, 'r' )\\n standname = None\\n laststand = None\\n for L in IN:\\n if( L[0:9] == 'Site/Plot' ): continue\\n col = L.split( ',' )\\n standname = col[0]\\n year = int(col[1])\\n #if( re.search( '-', standname ) != None ):\\n # loc = re.search( '-', standname )\\n # year = int(standname[loc.start()+1:])\\n # standname = standname[0:loc.start()]\\n #print standname, year\\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\\\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\\n if( OPT['d'] ):\\n if( dbh > 10.0 ): dbh *= 1.25\\n if( dbh > 15.0 ): dbh *= 1.50\\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\\n ntree = len( self.Data.Stand[standname].Tree ) + 1\\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\\n laststand = standname\\n IN.close()\",\n \"def make_tag_data_raw_fast(mdp,filename):\\n #\\n fin = open(filename,'r')\\n iter = 0\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"comb_path\\\":\\n update_params(mdp,lsp)\\n if not mdp.flag_out_open: ## -- try to open output file\\n try:\\n if mdp.flag_overwrite == \\\"True\\\": ## check string value!\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ## -- try open output file\\n for file in glob.glob(mdp.input_path):\\n # get sign which corrects for boundary condition\\n tvals = file.split('/')[-1].split('_')[3].split('t')\\n try:\\n ## flip sign if requested\\n bcsign = ((int(tvals[1])+int(tvals[2])) != (int(tvals[1])+int(tvals[2])) % mdp.corr_len)\\n except IndexError:\\n ## 2-point function\\n bcsign = False\\n try:\\n # open correlator file\\n mdp.corr_file = open(file,'r')\\n except IOError:\\n print \\\"Could not open file \\\",file\\n continue\\n ## -- get tag\\n ## baryons:\\n #mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_r'+file.split('/')[-1].split('_')[4][-1]\\n ## with time source tag\\n #mdp.tag = file.split('/')[-1].split('_')[3][:3]\\\\\\n # +'_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\\\\n # +file.split('/')[-1].split('_')[4][3:]\\n ## no time source tag\\n mdp.tag = '_'+file.split('/')[-1].split('_')[1][1:]+'_'+file.split('/')[-1].split('_')[4][0]\\\\\\n +file.split('/')[-1].split('_')[4][3:]\\n #print file,',',mdp.tag\\n iter+=1\\n ##endif ! flag_out_open\\n\\n #save_data_fast(mdp)\\n save_data_fast_bc(mdp,bcsign)\\n mdp.corr_file.close()\\n if iter%400 == 0:\\n print \\\"file\\\",iter\\n max_iter = None\\n if not(max_iter is None) and iter==max_iter:\\n print \\\"reached max file iterations, ending loop...\\\"\\n break\\n ## end comb_path\\n pass\\n\\n elif lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data_fast(mdp)\\n mdp.corr_file.close()\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def _load_data(self, save_temp=False):\\n # directly read processed data and encode\\n print ('Start tokenizing data...')\\n self.data = json.loads(\\n open(self.cfg.data_path+self.cfg.data_file, 'r', encoding='utf-8').read().lower())\\n self.train, self.dev, self.test = [], [], []\\n print ('Start encoding data...')\\n p = progressbar.ProgressBar(len(self.data))\\n p.start()\\n p_idx = 0\\n for fn, dial in self.data.items():\\n p.update(p_idx)\\n p_idx += 1\\n if '.json' in fn:\\n fn = fn.replace('.json', '')\\n if 'all' in self.cfg.exp_domains or self.exp_files.get(fn):\\n if self.dev_files.get(fn):\\n self.dev.append(self._get_encoded_data(fn, dial))\\n elif self.test_files.get(fn):\\n self.test.append(self._get_encoded_data(fn, dial))\\n else:\\n if self.data_mode == 'train':\\n self.train.append(self._get_encoded_data(fn, dial))\\n elif self.data_mode == 'test':\\n pass\\n else:\\n raise Exception('Wrong Reader Data Mode!!!')\\n p.finish()\",\n \"def load_datapair(self, ds):\\n raise NotImplementedError(\\\"Define this in your derived checker class\\\")\",\n \"def load_data(self):\\n df= self.read_file()\\n for row,col in df.iterrows():\\n Employeeid = int(col['Empolyeeid'])\\n Employee_Name = col['Employee_Name']\\n Age = col['Age']\\n Salary = col['Salary']\\n self.table.put_item(\\n Item={\\n \\\"Employeeid\\\":Employeeid,\\n \\\"Employee_Name\\\": Employee_Name,\\n \\\"Age\\\": Age,\\n \\\"Salary\\\": Salary\\n }\\n )\\n return True\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def build(self, file_number, data):\\n pass\",\n \"def reading_data(fname,goal):\\n \\n #Reading of the EEG data\\n data = pd.read_csv(fname)\\n events_fname = fname.replace('_data','_events')\\n labels= pd.read_csv(events_fname)\\n\\n if goal==\\\"training\\\":\\n data=data.drop(['id' ], axis=1)#remove id\\n labels=labels.drop(['id' ], axis=1)#remove id\\n elif goal==\\\"testing\\\":\\n labels=labels.drop(['id' ], axis=1)\\n else:\\n raise SystemExit(\\\"The goal variable is unknown for the function\\\")\\n\\n return data, labels\",\n \"def load_data():\\r\\n #读取User数据\\r\\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\\r\\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\\r\\n users = users.filter(regex='UserID|Gender|Age|JobID')\\r\\n users_orig = users.values\\r\\n #改变User数据中性别和年龄\\r\\n gender_map = {'F':0, 'M':1}\\r\\n users['Gender'] = users['Gender'].map(gender_map)\\r\\n\\r\\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\\r\\n users['Age'] = users['Age'].map(age_map)\\r\\n\\r\\n #读取Movie数据集\\r\\n movies_title = ['MovieID', 'Title', 'Genres']\\r\\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\\r\\n movies_orig = movies.values\\r\\n #将Title中的年份去掉\\r\\n pattern = re.compile(r'^(.*)\\\\((\\\\d+)\\\\)$')\\r\\n\\r\\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #电影类型转数字字典\\r\\n genres_set = set()\\r\\n for val in movies['Genres'].str.split('|'):\\r\\n genres_set.update(val)\\r\\n\\r\\n genres_set.add('<PAD>')\\r\\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\\r\\n\\r\\n #将电影类型转成等长数字列表，长度是18\\r\\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\\r\\n\\r\\n for key in genres_map:\\r\\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\\r\\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\\r\\n \\r\\n movies['Genres'] = movies['Genres'].map(genres_map)\\r\\n\\r\\n #电影Title转数字字典\\r\\n title_set = set()\\r\\n for val in movies['Title'].str.split():\\r\\n title_set.update(val)\\r\\n \\r\\n title_set.add('<PAD>')\\r\\n title2int = {val:ii for ii, val in enumerate(title_set)}\\r\\n\\r\\n #将电影Title转成等长数字列表，长度是15\\r\\n title_count = 15\\r\\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\\r\\n \\r\\n for key in title_map:\\r\\n for cnt in range(title_count - len(title_map[key])):\\r\\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\\r\\n \\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #读取评分数据集\\r\\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\\r\\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\\r\\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\\r\\n\\r\\n #合并三个表\\r\\n data = pd.merge(pd.merge(ratings, users), movies)\\r\\n \\r\\n #将数据分成X和y两张表\\r\\n target_fields = ['ratings']\\r\\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\\r\\n \\r\\n features = features_pd.values\\r\\n targets_values = targets_pd.values\\r\\n \\r\\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig\",\n \"def extract(self, data):\",\n \"def prepare_dataset(fpath):\\n raise NotImplementedError\",\n \"def apply(self, opened_file):\",\n \"def read_data():\\n ADV_MAT = np.load('ADV.npy');\\n ADJ_MAT = np.load('ADJ.npy');\\n PR_MAT = np.load('PR.npy'); \\n NN_MAT = np.load('NN.npy');\\n for i in range(ADV_MAT.shape[0]):RUNNING_DATA['ADV___'+str(i)] = ADV_MAT[i];\\n for i in range(ADJ_MAT.shape[0]):RUNNING_DATA['ADJ___'+str(i)] = ADJ_MAT[i];\\n for i in range(PR_MAT.shape[0]):RUNNING_DATA['PR___'+str(i)] = PR_MAT[i];\\n for i in range(NN_MAT.shape[0]):RUNNING_DATA['NN___'+str(i)] = NN_MAT[i];\",\n \"def process_file(self, data, filename):\\n\\n for relation in data:\\n if \\\"start_date\\\" in relation[1]:\\n start = convert_to_date(relation[1][\\\"start_date\\\"])\\n end = convert_to_date(relation[1][\\\"end_date\\\"])\\n\\n if start <= START_DATE and end >= START_DATE:\\n first = relation[1][\\\"first\\\"]\\n second = relation[1][\\\"second\\\"]\\n if first not in self.relations:\\n self.add_tag(first)\\n if second not in self.relations:\\n self.add_tag(second)\\n\\n if relation[0] == \\\"vassal\\\": \\n self.relations[first][\\\"vassal\\\"].append(second)\\n self.relations[second][\\\"overlord\\\"].append(first)\\n elif relation[0] == \\\"alliance\\\":\\n self.relations[first][\\\"alliance\\\"].append(second)\\n self.relations[second][\\\"alliance\\\"].append(first)\\n elif relation[0] == \\\"dependency\\\":\\n self.relations[first][\\\"tributary\\\"].append(second)\\n self.relations[second][\\\"hegemon\\\"].append(first)\\n elif relation[0] == \\\"guarantee\\\":\\n self.relations[first][\\\"guaranteeing\\\"].append(second)\\n self.relations[second][\\\"guarantor\\\"].append(first)\\n elif relation[0] == \\\"union\\\":\\n self.relations[first][\\\"junior\\\"].append(second)\\n self.relations[second][\\\"senior\\\"].append(first)\\n elif relation[0] == \\\"royal_marriage\\\":\\n self.relations[first][\\\"marriage\\\"].append(second)\\n self.relations[second][\\\"marriage\\\"].append(first)\",\n \"def read_data(self):\\n data = np.genfromtxt(self.__file) # Planck SED\\n self.__nu = 10.0**data[:,0]\\n self.__nuF = 10.0**data[:,2]\\n self.__err = 10.0**data[:,3]\\n #self.__W = 10.0**data[:,4]\\n self.__yerr = [ self.__nuF - self.__nuF / self.__err, \\\\\\n self.__nuF * self.__err - self.__nuF ]\\n self.__maxY = max( self.__nuF )\\n self.__minY = min( self.__nuF )\",\n \"def load_data(self):\\n with open(self.config.drv_log, 'r') as f:\\n add_data = [] # addtional data require angle smoothing\\n ori_data = [] # given data\\n ddir = os.path.dirname(self.config.drv_log)\\n lines = list(csv.reader(f))\\n np.random.shuffle(lines)\\n for line in lines:\\n if line[0] == 'center':\\n continue\\n ste_ang = float(line[3])\\n if ste_ang == 0. and random.random() < 0.8:\\n continue\\n if '12_01' in line[0]:\\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n else:\\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n \\n def angle_smoothing(data):\\n \\\"\\\"\\\"helper function for smoothing steering angles.\\n reference: \\n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\\n \\\"\\\"\\\"\\n angles = np.asarray([itm[1] for itm in data])\\n fwd = pd.ewma(angles, span=20)\\n bwd = pd.ewma(angles[::-1], span=20)\\n smooth = np.vstack((fwd, bwd[::-1]))\\n smooth = np.mean(smooth, axis=0)\\n angles = np.ndarray.tolist(smooth)\\n smooth_data = []\\n for i in range(len(data)):\\n smooth_data.append([data[i][0], angles[i]])\\n return smooth_data\\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\\n all_data = ori_data + add_data\\n n1 = int(len(all_data) * 0.7)\\n n2 = int(len(all_data) * 0.85)\\n self.train = self.data_generator(all_data[:n1])\\n self.valid = self.data_generator(all_data[n1:n2])\\n self.test = self.data_generator(all_data[n2:])\\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size\",\n \"def import_data_helper(self): \\n if len(self.components) == 1:\\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\\n else: \\n global_id = []\\n for c in self.components:\\n global_id.append(hapi.ISO[c][0])\\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)\",\n \"def initialize_data(self , station = '', datasets = {} ):\\n \\n self.datasets = datasets\\n self.datasets_keys = datasets.keys()\\n self.station = station\\n \\n data = {} # container for the data of each dataset\\n source_configuration = {} # container for the source_configuration of each dataset\\n \\n\\n \\n \\\"\\\"\\\" Looping over the datasets \\\"\\\"\\\"\\n logging.info('*** Reading and Initializing the data from the netCDF files ')\\n \\n \\n for k,v in datasets.items() :\\n logging.info(' Initialising the dataset: *** %s ' , k )\\n data[k] = {} \\n data['cdm_tables'] = {} \\n \\n ### alternative with xarray \\n #ds = xr.load_dataset(v) \\n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n ### alternative with netCDF4\\n #ds = nc.Dataset(v) \\n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \\n \\n ###for h5py but cant extract date time units !!!\\n ds = h5py.File(v , driver=\\\"core\\\" ) \\n data[k]['df'] = ds # storing the entire file \\n try: \\n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\\n except:\\n data[k]['source_file'] = str(v) # temp fix \\n \\n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \\n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\\n #data[k]['recordindex'] = ds['recordindex'].value \\n #ds.close() \\n logging.debug('Reading the file with h5py ')\\n \\n \\n # add here appending datasets for the case of ncar_w and ncar_t \\n \\n \\n self.data = data\\n self.make_dataframe()\\n ds.close()\\n \\n \\\"\\\"\\\" Reading the header_table, station_configuration, source_configuration \\\"\\\"\\\"\\n for k,v in datasets.items() : \\n \\n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \\n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\\n #data[k]['recordindex'] = d['recordindex'].values \\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \\n data[k]['station_configuration'] = d.to_dataframe() \\n #data[k]['station_configuration'] = d ### USELESS ? \\n logging.debug('Done with %s station_configuration' , str(k) )\\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \\n logging.debug('Loading the header_table') \\n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['header_table'] = {}\\n for var in d.variables:\\n self.attributes['header_table'][var] = {}\\n self.attributes['header_table'][var]['description'] = d[var].description\\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \\n data[k]['header_table'] = d.to_dataframe() \\n logging.debug('Done with %s ' , k )\\n \\n logging.info(\\\"*** Loading the observations_table (might take time) %s\\\" , k ) \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['observations_table'] = {}\\n for var in d.variables:\\n self.attributes['observations_table'][var] = {}\\n self.attributes['observations_table'][var]['description'] = d[var].description\\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\\n \\n \\n logging.info(\\\"*** Loading the source configuration %s\\\" , k ) \\n try: \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\\n d = d.isel(hdrlen=[0])\\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \\n logging.debug('Done with %s source_configuration' , k )\\n except: \\n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \\n \\n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \\n data[k]['era5fb'] = d.to_dataframe() \\n logging.debug('Done with %s era5 feedback ', k )\\n \\n \\\"\\\"\\\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \\\"\\\"\\\" \\n if list(datasets.keys()).index(k) == 0 :\\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \\n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\\n data['cdm_tables'][t] = d \\n \\n d.close() \\n ds.close()\\n\\n \\\"\\\"\\\" Reading the name of the original source file \\\"\\\"\\\"\\n source_configuration[k] = {} \\n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\\n\\n \\n \\\"\\\"\\\" Storing the station configurations \\\"\\\"\\\" \\n self.source_configuration = source_configuration \\n \\n \\\"\\\"\\\" Making all date_times \\\"\\\"\\\" \\n self.make_all_datetime()\\n \\n \\n \\\"\\\"\\\" feedback columns \\\"\\\"\\\"\\n if 'era5_1' in list (self.data.keys() ):\\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \\n else:\\n self.fb_columns = ['empty']\",\n \"def read_inversion_info(file_dic):\\n #print_file_test = open('file_test.txt','w')\\n\\n if not ( check_inversion_files(file_dic) ):\\n print 'error(read_inversion_info): problem with lenstool file names'\\n return 0\\n \\n file_generate_arcs = file_dic['file_generate_arcs']\\n info_input_lens = fc.extract_second_identifiers( file_generate_arcs, \\\\\\n 'potential' )\\n#-------------------------------------------------------------------------------\\n\\n file_source = file_dic['file_source']\\n info_src = np.loadtxt(file_source, unpack=False)\\n if len(info_src) == 8 and np.isscalar(info_src[0]):\\n #FIXME - check if the second condition is all we need\\n info_src = [info_src]\\n#-------------------------------------------------------------------------------\\n\\n file_make_inversion = file_dic['file_make_inversion']\\n info_fited_param = fc.extract_second_identifiers( file_make_inversion, \\\\\\n 'limit' )\\n info_forme = fc.extract_parameter(file_make_inversion, 'forme')[0][0]\\n\\n#-------------------------------------------------------------------------------\\n\\n file_best_fit = file_dic['file_best_fit']\\n info_best_fit = fc.extract_second_identifiers( file_best_fit, \\\\\\n 'potentiel' )\\n\\n info_xi2 = fc.extract_parameter(file_best_fit, '#Chi2pos:')\\n\\n#-------------------------------------------------------------------------------\\n file_chires = file_dic['file_chires']\\n\\n info_chires = extract_parameter(file_chires, '0')\\n rmss_mean = [0.0, 0.0]\\n rmsi_mean = [0.0, 0.0]\\n for i in info_chires:\\n if i[0] != 'A':\\n rmss_mean[0] = rmss_mean[0] + float(i[7])\\n rmss_mean[1] = rmss_mean[1] + 1.0\\n \\n rmsi_mean[0] = rmsi_mean[0] + float(i[8])\\n rmsi_mean[1] = rmsi_mean[1] + 1.0\\n\\n rmss_mean = rmss_mean[0]/rmss_mean[1]\\n rmsi_mean = rmsi_mean[0]/rmsi_mean[1]\\n#-------------------------------------------------------------------------------\\n out_dict = { 'xi2' : float(info_xi2[0][0]), \\\\\\n 'best_fit_lens' : info_best_fit, \\\\\\n 'rmsi_mean' : rmsi_mean, \\\\\\n 'rmss_mean' : rmss_mean, \\\\\\n 'fited_parameters' : info_fited_param[0].keys(), \\\\\\n 'input_lens' : info_input_lens[len(info_input_lens) - 1], \\\\\\n 'forme' : info_forme \\\\\\n }\\n #for i in out_dict.keys():\\n # print i, out_dict[i]\\n return out_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7388499","0.7290581","0.71939653","0.65396297","0.6443839","0.6359168","0.6344289","0.622777","0.62194926","0.6214607","0.6213822","0.6159157","0.61423117","0.6136031","0.6111043","0.6104353","0.60962135","0.60925573","0.6089976","0.6075218","0.6048531","0.6029819","0.6029783","0.6022913","0.6008295","0.59835845","0.59808487","0.59717","0.5969598","0.5969598","0.5969598","0.5964615","0.5963177","0.5961191","0.59464836","0.5936595","0.5934951","0.5928706","0.59275407","0.5923761","0.59187186","0.5912831","0.59094757","0.590535","0.58943135","0.5888419","0.5888419","0.5888419","0.58872783","0.58845896","0.5881818","0.58792377","0.5870371","0.5864612","0.5862901","0.5860989","0.58590937","0.5857562","0.5851539","0.585117","0.58492124","0.58452886","0.58424413","0.5841246","0.5839079","0.58315724","0.58312756","0.58261436","0.5818827","0.58123136","0.5811935","0.5809991","0.5809069","0.5807695","0.5806935","0.58048975","0.5801177","0.58005667","0.57988936","0.57961535","0.5787455","0.5787121","0.5786493","0.5785911","0.5778448","0.5778394","0.57774955","0.5770257","0.57656175","0.57649046","0.5760979","0.57590723","0.5755032","0.57540435","0.5753703","0.57523173","0.57522035","0.5748234","0.57372934","0.57355225","0.57346874"],"string":"[\n \"0.7388499\",\n \"0.7290581\",\n \"0.71939653\",\n \"0.65396297\",\n \"0.6443839\",\n \"0.6359168\",\n \"0.6344289\",\n \"0.622777\",\n \"0.62194926\",\n \"0.6214607\",\n \"0.6213822\",\n \"0.6159157\",\n \"0.61423117\",\n \"0.6136031\",\n \"0.6111043\",\n \"0.6104353\",\n \"0.60962135\",\n \"0.60925573\",\n \"0.6089976\",\n \"0.6075218\",\n \"0.6048531\",\n \"0.6029819\",\n \"0.6029783\",\n \"0.6022913\",\n \"0.6008295\",\n \"0.59835845\",\n \"0.59808487\",\n \"0.59717\",\n \"0.5969598\",\n \"0.5969598\",\n \"0.5969598\",\n \"0.5964615\",\n \"0.5963177\",\n \"0.5961191\",\n \"0.59464836\",\n \"0.5936595\",\n \"0.5934951\",\n \"0.5928706\",\n \"0.59275407\",\n \"0.5923761\",\n \"0.59187186\",\n \"0.5912831\",\n \"0.59094757\",\n \"0.590535\",\n \"0.58943135\",\n \"0.5888419\",\n \"0.5888419\",\n \"0.5888419\",\n \"0.58872783\",\n \"0.58845896\",\n \"0.5881818\",\n \"0.58792377\",\n \"0.5870371\",\n \"0.5864612\",\n \"0.5862901\",\n \"0.5860989\",\n \"0.58590937\",\n \"0.5857562\",\n \"0.5851539\",\n \"0.585117\",\n \"0.58492124\",\n \"0.58452886\",\n \"0.58424413\",\n \"0.5841246\",\n \"0.5839079\",\n \"0.58315724\",\n \"0.58312756\",\n \"0.58261436\",\n \"0.5818827\",\n \"0.58123136\",\n \"0.5811935\",\n \"0.5809991\",\n \"0.5809069\",\n \"0.5807695\",\n \"0.5806935\",\n \"0.58048975\",\n \"0.5801177\",\n \"0.58005667\",\n \"0.57988936\",\n \"0.57961535\",\n \"0.5787455\",\n \"0.5787121\",\n \"0.5786493\",\n \"0.5785911\",\n \"0.5778448\",\n \"0.5778394\",\n \"0.57774955\",\n \"0.5770257\",\n \"0.57656175\",\n \"0.57649046\",\n \"0.5760979\",\n \"0.57590723\",\n \"0.5755032\",\n \"0.57540435\",\n \"0.5753703\",\n \"0.57523173\",\n \"0.57522035\",\n \"0.5748234\",\n \"0.57372934\",\n \"0.57355225\",\n \"0.57346874\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":14298,"cells":{"query":{"kind":"string","value":"this func makes a list of all the album"},"document":{"kind":"string","value":"def album_list_for_user():\r\n answer = \"\"\r\n data = dbase()\r\n for album in data.keys():\r\n answer += album + \", \"\r\n return answer[:-2]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def album_list(self):\n\n artist_id = self.addon_args[\"artist_id\"][0]\n\n xbmcplugin.setContent(self.addon_handle, \"albums\")\n\n for album in self.connection.walk_artist(artist_id):\n self.add_album(album)\n\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_ALBUM)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_ARTIST)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_VIDEO_YEAR)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def simple_album_list():\r\n album_list = []\r\n data = dbase()\r\n for album in data.keys():\r\n album_list += [album]\r\n return album_list","def get_albums(self):\n self.artist = self.artists_list.currentText()\n self.c_albums = [x['album'] for x in dmlc.list_albums(self.artist)\n if [x['album'] in self.albums_map[self.artist]]]\n self.albums_list.clear()\n self.albums_list.addItems(self.c_albums)\n self.update_navigation_buttons()","def get_album_list():\n\n # TODO: Paginate this, etc\n entities = PhotoAlbum.query().order(-PhotoAlbum.title).fetch(1000)\n\n return entities","def get_albums():\n return query_multiple(request.args, album_search, \\\n album_filter, Album, albums_schema)","def get_albums(self):\n artist = self.get_request_arg(\"artist\")\n if artist:\n lib = self.ctrl.library\n lst = sorted(self.ctrl.library.get_albums(artist))\n albums = [{\"artist\": artist,\n \"album\": album,\n \"path\": lib.get_path(artist, album)} for album in lst]\n if lst:\n return self.resp_from_data(albums)\n return self.resp_from_data(\n {\"message\": f\"No album found for artist={artist}\"}, 400)","def simple_songs_list(name_of_album):\r\n songs = []\r\n data1 = dbase()\r\n data1 = data1[name_of_album][0]\r\n for song in data1.keys():\r\n songs += [song]\r\n return songs","def albums_by_genre_list(self):\n\n genre = self.addon_args[\"foldername\"][0].decode(\"utf-8\")\n\n xbmcplugin.setContent(self.addon_handle, \"albums\")\n\n for album in self.connection.walk_album_list_genre(genre):\n self.add_album(album, show_artist=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def get_albums(entity_url: str) -> list:\n entity_url = entity_url.rstrip(\"/\")\n response = requests.get(entity_url)\n soup = BeautifulSoup(response.text, \"html.parser\")\n albums = []\n for link in soup.find_all('a'):\n url = link.get('href')\n if url is not None and \"/album/\" in url:\n if url.startswith(\"http\"):\n albums.append(url)\n else:\n albums.append(f\"{entity_url}{url}\")\n return albums","def tracked_albums():\n print('Your Google Photos Albums ([X] = tracked):')\n albums = get_albums(service)\n for i, a in enumerate(albums):\n check = 'X' if a.id in library.get_album_ids() else ' '\n print('[{}] {}. {}'.format(check, i+1, a.title))\n return albums","def songs_list(name_of_album):\r\n songs = \"\"\r\n data = dbase()\r\n data = data[name_of_album][0]\r\n for song in data.keys():\r\n songs += song\r\n songs += \", \"\r\n return songs[:-2]","def genre_list(self):\n\n for genre in self.connection.walk_genres():\n url = self.build_url({\n \"mode\": \"albums_by_genre_list\",\n \"foldername\": genre[\"value\"].encode(\"utf-8\")})\n\n li = xbmcgui.ListItem(genre[\"value\"])\n xbmcplugin.addDirectoryItem(\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def read_artist_albums(id, name):\n list_a = [(x.name, str(x.release_year), str(x.id))\n for x in dmla.list_albums_by_artist('', id, 'Jaar')]\n list_c = [(x['album'], x['year']) for x in dmlc.list_albums(name)]\n return list_a, list_c","def get_albums_alpha(session_):\n artists = session_.query(Album).order_by(Album.title.asc()).all()\n return artists","def all_titles(our_data):\n return [album['album'] for album in our_data]","def search_for_album(album_name):\n\n print(f'Searching for album: {album_name}')\n\n search_result = spotifyObject.search(q=f'\"{album_name}\"', limit=20, type='album')\n\n items = search_result['albums']['items']\n\n results = []\n\n for item in items:\n if len(item['artists']) > 1:\n artists = tuple(art['name'] for art in item['artists'])\n else:\n artists = item['artists'][0]['name']\n\n results.append((artists, item['name'], item['id']))\n\n return results","def get_albums(playlist_name):\n\n playlist_id = find_playlist(playlist_name)\n \n items = get_playlist_tracks(playlist_id=playlist_id)\n \n track_values = []\n \n for item in items:\n track = item['track']\n album = track['album']\n artists = tuple(artist['name'] for artist in album['artists'])\n \n track_values.append((album['name'], artists[0]))\n \n album_details = namedtuple('AlbumDetails', 'album artist')\n \n for tup in dict.fromkeys(track_values):\n yield album_details(*tup)","def get_albums(self):\n if self.artist_list.count() == 0: # this happens when the panel is reshown\n return # after another panel was shown\n self.c_artist = self.artist_list.currentText()\n ## self.last_handled = self.artist_list.currentIndex()\n # remember first handled item for currency communication over panels\n self._parent.current_data = self.c_artist\n self.a_artist = self.artist_map[self.c_artist]\n a_albums, c_albums = read_artist_albums(self.a_artist, self.c_artist)\n for name, year, id, *rest in self.albums_to_save[self.c_artist]:\n a_albums.append((name, year, str(id)))\n self.clementine_albums.clear()\n for item, year in c_albums:\n new = qtw.QTreeWidgetItem([item])\n new.setData(0, core.Qt.UserRole, year)\n try:\n new.setText(1, str(self.albums_map[self.c_artist][item][1]))\n except KeyError:\n pass\n self.clementine_albums.addTopLevelItem(new)\n self.albums_albums.clear()\n self.lookup = collections.defaultdict(list)\n for item in a_albums:\n new = qtw.QTreeWidgetItem([x.replace('None', '') for x in item])\n self.albums_albums.addTopLevelItem(new)\n self.lookup[item[0]].append(item[2])\n self.tracks = collections.defaultdict(list)","def get_albums(username):\n cur = mysql.connection.cursor()\n cur.execute(\"SELECT * FROM album WHERE username = '{0}'\".format(username))\n return cur.fetchall()","def get_albums(self, limit=None):\n url = (\"https://api.imgur.com/3/account/{0}/albums/{1}\".format(self.name,\n '{}'))\n resp = self._imgur._send_request(url, limit=limit)\n return [Album(alb, self._imgur, False) for alb in resp]","def get_albums(self, offset=None):\n return self.__get('albums')","def get_albums(self, ctx, page, templ_vars):\n if 'type' in page.meta and page.meta['type'] == 'index':\n album_pages = sorted(\n templ_vars['site']['categories']['gallery'],\n key=lambda album: album['datetime'],\n )\n albums = {}\n for album_page in album_pages:\n image_list = []\n images = map(\n lambda i: i['thumb_src'],\n self.albums[album_page['slug']]\n )\n image_list += images[:PREVIEW_IMGS_NUM]\n albums[album_page['slug']] = image_list\n templ_vars['site']['albums'] = albums","def albums(self, albums, **kwargs):\n album_list = map(self._get_album_id, albums)\n return self._get(API.ALBUMS.value, ids=\",\".join(album_list), **kwargs)","def get_albums_by_artist(albumtype, search_for, sort_on):\n return list(dmla.list_albums_by_artist(albumtype, search_for, sort_on))","def getAlbums():\n\n r = requests.get(ALBUMS_URL, headers=HEADER, timeout=5)\n\n if r.status_code == 200:\n \n try:\n albums = [] \n soup = BeautifulSoup(r.text, \"html.parser\")\n album = soup.find_all(\"div\", class_=\"duv\")\n for i,al in enumerate(album): \n temp = {}\n temp['link'] = al.find_all(\"a\")[0]['href']\n temp['album'] = al.find_all(\"span\", class_=\"title\")[0].text\n albums.append(temp)\n\n if len(albums) > 0:\n return albums\n else:\n print(\"No albums found on site2!\")\n sys.exit(0)\n \n except Exception as e:\n print(\"Failed to get albums from site2\\n\", e)\n sys.exit(0)\n\n else:\n print(\"Albums Url fetch failed! Status code: {}\".format(r.status_code))\n sys.exit(0)","def fetchAlbumIds(artist_id):\n url = 'https://api.spotify.com/v1/artists/' + artist_id + '/albums?market=US&album_type=album'\n req = requests.get(url)\n\n data = req.json()\n\n #checking for bad return value\n if not req.ok:\n print \"error : \" + data['error']['message']\n return \"error : \" + data['error']['message']\n\n albums = []\n for item in data['items']:\n \talbums.append(item['id'])\n\n return albums","def GetAlbums(self, start=0, end=0, sortmethod='label', sortorder='ascending', filter='', artistid=None):\n self.logger.debug(\"Loading all albums for ARTISTID \" + str(artistid))\n try:\n xbmc = Server(self.url('/jsonrpc', True))\n sort = {'order': sortorder, 'method': sortmethod, 'ignorearticle': True}\n properties=['artist', 'title', 'year', 'description', 'thumbnail']\n limits = {'start': int(start), 'end': int(end)}\n if artistid is not None:\n filter = {'artistid': int(artistid)}\n else:\n filter = {'or': [\n {'field': 'album', 'operator': 'contains', 'value': filter},\n {'field': 'artist', 'operator': 'contains', 'value': filter}\n ]}\n return xbmc.AudioLibrary.GetAlbums(properties=properties, limits=limits, sort=sort, filter=filter)\n except ValueError:\n return","def get_albums(self):\n return AlbumView.get_by_artist(self.name)","def read_album_tracks(id, artist_name, album_name):\n list_a = [x.name for x in dmla.list_tracks(id)]\n list_c = [x['title'] for x in dmlc.list_tracks_for_album(artist_name, album_name)\n if x['track'] != -1]\n return list_a, list_c","def sort_album(self):\n self.sort('album')","def print_album(albums):\n print(\"\\nPrinting album data:\")\n for album in albums:\n print(f\"Artist Name: {album['name'].title()}\")\n print(f\"\\tAlbum Name: {album['album'].title()}\")\n if (album['num_songs']):\n print(f\"\\tNumber Songs: {album['num_songs']}\")\n\n print(\"\")","def all_artists(our_data):\n return [album['artist'] for album in our_data]","def cmd_account_albums(client, args):\n account_albums = client.get_account_albums(args.username, args.page)\n data = [item.__dict__ for item in account_albums]\n generate_output({'account_albums': data}, args.output_file)","def album_parser(data):\n album_ids = []\n for item in data['data']:\n album_ids.append(item['id'])\n return album_ids","def song_album(ans):\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n if ans == song:\r\n return album","def get_album_tracks(self):\n track_list = self.soup.findAll('div', class_='chart_row')\n number_of_tracks = 0\n titles = []\n urls = []\n track_numbers = []\n \n for track in track_list:\n track_title = re.sub(' Lyrics', '', \" \".join(track.h3.text.split()))\n lyrics_url = track.a['href']\n track_number = track.span.span.text.strip()\n \n if track_number == '':\n # Sometimes there are additional urls that are not a song's lyrics. Skip these.\n continue\n else:\n track_number = int(track_number)\n \n number_of_tracks += 1\n titles.append(track_title)\n urls.append(lyrics_url)\n track_numbers.append(track_number)\n \n if self.song_order:\n # Check that order values are okay.\n for number in self.song_order:\n if number > number_of_tracks:\n raise SongOrderValueError(f'Track number given ({number}) exceeds number of tracks ({number_of_tracks})')\n \n for title, url, number in zip(titles, urls, track_numbers):\n if self.song_order:\n if number not in self.song_order:\n print(f'Skipping song: {number:02d} {title}')\n continue\n \n lyrics = self.get_single_lyrics(url)\n self.album.add_song(Song(title=title, track_number=number, lyrics=lyrics))\n\n self.album.number_of_tracks = number_of_tracks","def get_albums_by_text(albumtype, search_type, search_for, sort_on):\n if albumtype == 'studio':\n search_on = {0: '*', 2: 'name', 3: 'produced_by', 4: 'credits', 5: 'bezetting'}[search_type]\n elif albumtype == 'live':\n search_on = {0: '*', 2: 'name', 3: 'name', 4: 'produced_by', 5: 'bezetting'}[search_type]\n return list(dmla.list_albums_by_search(albumtype, search_on, search_for, sort_on))","def getSongsFromAlbum(albumLink):\n albumLink = str(albumLink)\n try:\n html = urllib.request.urlopen(albumLink).read()\n soup = bs4.BeautifulSoup(html, 'html.parser')\n table = soup.findAll(\"a\")[5:]\n songLinks = []\n for entry in table:\n text = str(re.findall(\"\\\".*\\\"\", str(entry)))\n text = re.sub(\"[\\]\\['\\\"]\", \"\", text)\n link = albumLink + str(text)\n songLinks.append(link)\n except:\n return []\n return songLinks","def get_by_artist(cls, artist):\n results = cls.query().filter(FileRecord.artist == artist).order_by(\n FileRecord.year).all()\n albums = []\n if results and len(results) > 0:\n for result in results:\n albums.append(cls(*result))\n\n return albums\n\n return albums","def GetRecentAlbums(self, limit=5):\n self.logger.debug(\"Fetching recently added Music\")\n try:\n xbmc = Server(self.url('/jsonrpc', True))\n properties = ['artist', 'albumlabel', 'year', 'description', 'thumbnail']\n limits = {'start': 0, 'end': int(limit)}\n return xbmc.AudioLibrary.GetRecentlyAddedAlbums(properties=properties, limits=limits)\n except:\n self.logger.error(\"Unable to fetch recently added Music!\")\n return","def test_top_albums(self):\n \n rss = AppleRSS()\n objs = rss.get_top_albums(limit=10)\n \n self.__test_artists('top_albums', objs)","def albums(date, album_type, genre):\n urlhandle = f\"{amazon_charts_url}/albums\"\n params = {\n \"type\": album_type,\n \"date\": date,\n \"genre\": genre,\n }\n\n data = utilities.RequestData(urlhandle, params)\n return utilities.RequestGet(data)[\"data\"]","def getAlbumsFromArtist(artistLink):\n artistLink = str(artistLink)\n url = \"http://www.ohhla.com/\"+artistLink\n if artistLink[0:4]==\"http:\":\n url = artistLink\n try:\n html = urllib.request.urlopen(url).read()\n soup = bs4.BeautifulSoup(html, 'html.parser')\n table = soup.findAll(\"a\")[5:]\n albumLinks = []\n for entry in table:\n text = str(re.findall(\"\\\".*\\\"\", str(entry)))\n text = re.sub(\"[\\]\\['\\\"]\", \"\", text)\n link = url + str(text)\n if len(re.findall(\"(?:http)\",link)) == 1:\n albumLinks.append(link)\n except:\n return []\n return albumLinks","def cmd_album_images(client, args):\n album_images = client.get_album_images(args.album_id)\n data = [item.__dict__ for item in album_images]\n generate_output({'album_images': data}, args.output_file)","def pic (self, list) : \n result = []\n for pmod in list :\n result.append (pmod.photo_uri)\n return result","def get_songs(self, song_list):\n self.songs = [[s.name, s.movie_name] for s in song_list\n if s.raga == self.name]","def get_tracks_from_albums(sp, album_uri_list):\n\n track_list = [[\"track_name\", \"track_uri\", \"track_release_date\"]]\n\n print(\"Log: Pulling data from Spotify. This can take a while...\")\n\n for album_uri in album_uri_list:\n album_tracks = sp.album_tracks(album_uri, limit=50, offset=0)[\"items\"]\n count_tracks_in_album = len(album_tracks)\n album_release_date = sp.album(album_uri)[\"release_date\"]\n\n # This part is probably very slow and should be improved by accessing the API less often\n for track_number in range(count_tracks_in_album):\n track_name = album_tracks[track_number][\"name\"]\n track_uri = album_tracks[track_number][\"uri\"]\n \n track_list.append([track_name, track_uri, album_release_date])\n\n # Create df from list of tracks for all albums\n track_df = pd.DataFrame(data=track_list[1:], columns=track_list[0])\n \n print(\"Log: Finished pulling all tracks from albums.\")\n return track_df","def test_get_all_need_transform_two_albums(self):\n album = Album(artist='Artist', album='Album',\n totaltracks=1, totalseconds=120)\n pk = album.insert(self.app.db, self.app.curs)\n album = Album(artist='Artist', album='Album 2',\n totaltracks=1, totalseconds=120)\n pk = album.insert(self.app.db, self.app.curs)\n self.assertEqual(self.get_album_count(), 2)\n\n albums = Album.get_all_need_transform(self.app.curs, 1)\n self.assertEqual(len(albums), 2)","def get_albums(self, only_published=True, only_active=True, list_only=False,\n only_smart=False, exclude_smart=False, tags=None, tags_exclusive=True):\n if not tags:\n tags = []\n\n if not hasattr(tags, '__iter__'):\n tags = [tags]\n\n options = {\n 'only_published': int(bool(only_published)),\n 'only_active': int(bool(only_active)),\n 'list_only': int(bool(list_only)),\n 'only_smart': int(bool(only_smart)),\n 'exclude_smart': int(bool(exclude_smart)),\n }\n\n if tags:\n options.update({'tags': ','.join(str(tag) for tag in tags),\n 'tags_exclusive': int(bool(tags_exclusive))})\n\n data = self._get('get_album_list', options)\n return data['albums']","def parseAlbumData(self, filename):\n doc = parse(filename)\n stack = []\n last_top_key = None\n if self.use_album:\n album_list_key = \"List of Albums\"\n else:\n album_list_key = \"List of Rolls\"\n for event, node in doc:\n if event == START_ELEMENT:\n stack.append(node)\n level = len(stack)\n if level == 3:\n if node.nodeName == 'key':\n doc.expandNode(node)\n last_top_key = self.getText(node)\n stack.pop()\n elif last_top_key == 'List of Keywords':\n doc.expandNode(node)\n self.keywords = self.dePlist(node)\n stack.pop()\n elif last_top_key == 'List of Faces':\n doc.expandNode(node)\n self.faces = dict([\n (k, v['name']) for k,v in\n self.dePlist(node, ['name']).items()\n ])\n stack.pop()\n elif last_top_key == 'Major Version':\n doc.expandNode(node)\n major_version = self.dePlist(node)\n stack.pop()\n if major_version != self.major_version:\n raise iPhotoLibraryError, \\\n \"Sorry, I can't understand version %i iPhoto Libraries.\" % major_version\n elif last_top_key == 'Minor Version':\n doc.expandNode(node)\n minor_version = self.dePlist(node)\n stack.pop()\n if minor_version > self.minor_version:\n self.status(\n \"\\nI don't recognise iPhoto libraries when the minor version is %i, but let's try anyway.\\n\" % minor_version,\n force=True\n )\n\n elif level == 4:\n # process large items individually so we don't\n # load them all into memory.\n if last_top_key == album_list_key:\n doc.expandNode(node)\n self.albums.append(self.dePlist(node))\n stack.pop()\n elif last_top_key == 'Master Image List':\n doc.expandNode(node)\n if node.nodeName == 'key':\n last_image_key = self.getText(node)\n else:\n self.images[last_image_key] = self.dePlist(\n node, self.interesting_image_keys\n )\n stack.pop()\n elif event == END_ELEMENT:\n stack.pop()","def multiple_years(our_data, start, end):\n count = start\n album_list = []\n while count <= end:\n album_list.append(find_by_year(our_data,count))\n count += 1","def cmd_account_album_ids(client, args):\n account_album_ids = client.get_account_album_ids(args.username, args.page)\n generate_output({'account_album_ids': account_album_ids})","def get_top_albums(\n self, period: Period, limit: int = 50, page: int = 1\n ) -> ListModel[Album]:\n assert isinstance(period, Period)\n\n return self.retrieve(\n bind=Album,\n flatten=\"album\",\n params=dict(\n method=\"user.getTopAlbums\",\n user=self.name,\n limit=limit,\n page=page,\n period=period.value,\n ),\n )","def get_all_songs():\r\n return [Song.song_json(song) for song in Song.query.all()]","def prepare_batch(self, iterator):\n elements = []\n\n for label, album_ids in iterator:\n for album_id in album_ids:\n image_path = os.path.join(self.image_folder, album_id)\n # If path doesn't exist, continue\n if not os.path.exists(image_path):\n continue\n images = [os.path.join(image_path, img_name)\n for img_name in sorted(os.listdir(image_path))]\n # If no photo available, continue\n if len(images) == 0:\n continue\n\n elements.append((label, images))\n\n random.shuffle(elements)\n\n return sorted(elements, key=lambda p: len(p[1]), reverse=True)","def get_albums_from(artist_uri):\n album_uris = []\n results = spotify.artist_albums(artist_uri, album_type='album')\n albums = results['items']\n # get URIs for each album\n for album in albums:\n album_uris.append(album['uri'])\n\n return album_uris","def findsimilaralbums(index, similarartists): # {{{1\n albums = []\n for [similarity, artistname] in similarartists:\n for albumname in index.findalbumsbyartist(artistname):\n albums.append([similarity, [artistname, albumname]])\n return albums","def get_all_musicians(self):\n self.cursor.execute(\"select * from musicians\")\n self.connection.commit()\n return self.cursor.fetchall()","def selectSongs():\n\tsql =\"select songs.title, artist.name, album.name from songs, album, \" \\\n\t+ \"artist join songs_album on songs.id=songs_album.songs_id \" \\\n\t+ \"join songs_artist on songs.id=songs_artist.songs_id \" \\\n\t+ \"where album.id=songs_album.album_id \" \\\n\t+ \"and artist.id=songs_artist.artist_id\"\n\tc, conn = connect()\n\tretr = c.execute(sql)\n\tsongs = []\n\tfor entry in retr:\n\t\tsongs.append(music.song(title=entry[0], artist=entry[1], album=entry[2]))\n\treturn songs","def find_album_playlist(data):\n\n return data['album'].lower() + '.m3u'","def get(request, slug):\n calbums = [x.json for x in get_album_contributors(get_album_by_slug(slug))]\n \n response = render_to_response(\n \"data/list.json\",\n {\"data\": calbums},\n content_type=\"application/json\",\n )\n response['Cache-Control'] = 'no-cache'\n return response","def lists():\n name = []\n author = []\n year = []\n condition = []\n for row in sorted(inform, key=itemgetter(1, 0)):\n # sort the list by author then by title\n # traverse all thing in list and append to gain classified information\n name.append(row[0])\n author.append(row[1])\n year.append(row[2])\n condition.append(row[3])\n long = len(name)\n require = 0\n for i in range(1, long + 1, 1):\n if condition[i - 1] == \"r\":\n print(\"*\", end=\"\")\n print(\"{}.{:<30} by {:<20}({})\".format(i, name[i - 1], author[i - 1], year[i - 1]))\n require = require + 1\n else:\n print(\" {}.{:<30} by {:<20}({})\".format(i, name[i - 1], author[i - 1], year[i - 1]))\n if \"r\" in condition:\n print(\"You need to listen to {} albums\".format(require))\n else:\n print(\"No albums left to listen to. Why not add a new album?\")","def get_tracks_from(album_uri):\n track_uris = []\n album = spotify.album_tracks(album_id=album_uri)\n\n for track in album['items']:\n track_uris.append(track['uri'])\n\n return track_uris","def get_album_songs(self, album_id):\n url = get_album_url(album_id)\n result = self.get_request(url)\n\n return result['album']['songs']","def get_songs(self):\n search_object = {\"size\":25000,\n 'query': {'term': {FIELD_FINGERPRINTED: True}}, \"fields\": [FIELD_SONGNAME, FIELD_FILE_SHA1,\n FIELD_TOTAL_HASHES]}\n response = self.cursor.search(index = SONGS_INDEXNAME, body=search_object)\n #print(\"get_songs response: \",response)\n arr = []\n for hit in response[\"hits\"][\"hits\"]:\n dct = {\"song_name\":hit['_source'][FIELD_SONGNAME],\"total_hashes\":hit['_source'][FIELD_TOTAL_HASHES],\n \"file_sha1\":hit['_source'][FIELD_FILE_SHA1]}\n arr.append(dct)\n return arr","def get_songs(library):\n songs = []\n for song in library:\n title, artist, album = song['title'], song['artist'], song['album']\n seconds = int(song['durationMillis']) // 1000\n songs.append({'artist': artist, 'title': title, 'album': album, 'seconds': seconds})\n return songs","def album(self, q, page=None):\r\n return self.get('album', q, page)","def getPictures (self, list) :\n\n result = []\n for event in list :\n eventEntry = {}\n eventEntry ['id'] = link = event.answer.id\n eventEntry ['time'] = event.timeOf\n eventEntry ['comments'] = event.answer.comments\n eventEntry ['location'] = self.where (event.answer)\n eventEntry ['problem'] = event.answer.survey ['problem_type']\n eventEntry ['pictures'] = self.pic (Picture.objects.filter (answer__id = link))\n result.append (eventEntry)\n \n return result","def candidates(self, items, artist, album, va_likely):\n candidate_list = []\n candidate_list.append(WikiAlbum(artist, album))\n\n return candidate_list","def current_user_saved_albums(self, limit=20, offset=0, **kwargs):\n return self._get(API.MY_ALBUMS.value, limit=limit, offset=offset, **kwargs)","def get_songs_by_album(self, album_id):\n return self.__get('song', album_id)","def appendAlbum(song):\n\tsql = []\n\tsql.append(\"INSERT INTO ALBUM ('name') VALUES ('\" \n\t+ '/'.join(song.album) + \"');\")\n\t\n\tsql.append(\"INSERT INTO songs_album ('songs_id', 'album_id')\"\n\t+ \" VALUES ((select id from songs where hash = '\" + str(song.hash) + \"'), \"\n\t+ \"(select id from album where name = '\" + '/'.join(song.album) + \"'));\")\n\tsql.append(\"INSERT INTO artist_album ('artist_id', 'album_id')\"\n\t+ \" VALUES ((select id from songs where hash = '\" + str(song.hash) + \"'), \"\n\t+ \"(select id from album where name = '\" + '/'.join(song.album) + \"'));\")\n\t\n\treturn sql","def get_random_album(self):\n lib = self.ctrl.library\n artist, album = lib.get_random_album()\n return self.resp_from_data({\n \"artist\": artist,\n \"album\": album,\n \"path\": lib.get_path(artist, album)\n })","def get_images(self, ctx, page):\n is_imgur = 'source' in page.meta and page.meta['source'] == 'imgur'\n if 'type' in page.meta and page.meta['type'] == 'album':\n album = page.meta\n images = []\n if is_imgur:\n pp.pprint(page.meta)\n # bind to template via json\n images = self.get_imgur_album_images(page)\n self.albums[album['slug']] = images\n else:\n # get paths of all of the images in the album\n srcs = []\n # get absolute paths of images in album for each file type\n for file_type in FILE_TYPES:\n imgs = glob.glob(\n GALLERY_DIR + album['slug'] + '/*.' + file_type\n )\n\n for img in imgs:\n img_rel_path = (\n REL_GALLERY_DIR +\n album['slug'] + '/' + img.split('/')[-1]\n )\n srcs.append(img_rel_path)\n\n # split full srcs and thumb srcs from srcs into two lists\n images = []\n thumb_srcs = filter(\n lambda src: src.split('/')[-1].startswith(THUMB_PREFIX),\n srcs\n )\n for thumb_src in thumb_srcs:\n src = thumb_src.replace(THUMB_PREFIX, '')\n thumb_width, thumb_height = self.calc_img_hw(thumb_src)\n width, height = self.calc_img_hw(src)\n images.append({\n 'thumb_src': thumb_src,\n 'thumb_width': thumb_width,\n 'thumb_height': thumb_height,\n\n 'src': src,\n 'width': width,\n 'height': height,\n })\n self.albums[album['slug']] = images","def get_album(self):\n return self._album","def extract_all_albums(lines):\n album_dict = {}\n for index, line in enumerate(lines):\n if line.startswith(\"<a\"):\n line_index = int(index)\n line = line.strip()\n albumid = int((lines[index + 1]).strip())\n album_classed = extract_album(lines, line_index)\n album_dict[albumid] = album_classed #assigns the key(id) to the values\n return album_dict","def get_artist_list():\n return list(dmla.list_artists())","def get_songs(path):\r\n song_list = []\r\n genre_paths = glob.glob(path + '/*')\r\n for genre_path in genre_paths:\r\n artist_paths = glob.glob(genre_path + '/*')\r\n for artist_path in artist_paths:\r\n album_paths = glob.glob(artist_path + '/*')\r\n for album_path in album_paths:\r\n lyrics_paths = glob.glob(album_path + '/*.txt')\r\n for lyrics_path in lyrics_paths:\r\n song = {}\r\n song[\"genre\"] = genre_path.replace(path + '/', '')\r\n song[\"artist\"] = artist_path.replace(genre_path + '/', '')\r\n song[\"album\"] = album_path.replace(artist_path + '/', '')\r\n song[\"lyrics\"] = open(lyrics_path).read()\r\n song[\"name\"] = lyrics_path[:-4].replace(album_path + '/', '')\r\n song[\"x\"] = 0\r\n song[\"y\"] = 0\r\n song_list.append(song)\r\n return song_list","def test_get_all_need_transform_no_albums(self):\n self.assertEqual(Album.get_all_need_transform(self.app.curs, 1), [])","def dbase():\r\n albums_data = {}\r\n song_dict = {}\r\n songs_list = []\r\n with open(PATH, 'r') as f:\r\n data = f.read()\r\n temp = data.split(\"#\")\r\n for album in temp[1:]:\r\n index = album.find(\"::\")\r\n albums_data[album[:index]] = \"\"\r\n for album in temp[1:]:\r\n album = album.split(\"*\")\r\n album_name = album[0][:-7]\r\n release_Date = album[0][-5:]\r\n del album[0]\r\n for song in album:\r\n info = song.split(\"::\")\r\n song_name = info[0]\r\n del info[0]\r\n songs_list = info\r\n song_dict[song_name] = songs_list\r\n albums_data[album_name] = (song_dict.copy(), release_Date)\r\n song_dict.clear()\r\n return albums_data","def get_best_new_albums(param1, var1):\n global session\n headers = {\n 'authority': 'www.pitchfork.com',\n 'method': 'GET',\n 'scheme': 'https',\n 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36',\n }\n html = requests.get(param1, headers=headers).text\n soup = bs(html, 'html.parser')\n wraps = soup(\"a\", {\"class\": \"link-block\"})\n\n for wrap in wraps:\n var1.append(wrap)","def test_get_all_need_transform_one_album(self):\n orig_album = Album(artist='Artist', album='Album',\n totaltracks=1, totalseconds=120)\n pk = orig_album.insert(self.app.db, self.app.curs)\n self.assertEqual(self.get_album_count(), 1)\n\n albums = Album.get_all_need_transform(self.app.curs, 1)\n self.assertEqual(len(albums), 1)\n self.assertEqual(albums[0].pk, pk)","def fetchAlbumInfo(album_id):\n url = 'https://api.spotify.com/v1/albums/' + album_id\n req = requests.get(url)\n\n data = req.json() \n\n if not req.ok:\n print \"error : \" + data['error']['message']\n return {}\n\n\n #create a new dictionary\n album_info_dict = {}\n #keys for the dictionary\n album_info_dict['artist_id'] = data['artists'][0]['id']\n album_info_dict['album_id'] = album_id\n album_info_dict['name'] = data['name']\n album_info_dict['year'] = data['release_date'][0:4]\n album_info_dict['popularity'] = int(data['popularity']) #Spotify's popularity-meter, an integer\n\n return album_info_dict","def get_album(album_id, albumtype):\n result = {'titel': '',\n 'artist': '',\n # 'artistid': '',\n 'artist_name': '',\n 'details': [('Label/jaar:', ''),\n ('Produced by:', ''),\n ('Credits:', ''),\n ('Bezetting:', ''),\n ('Tevens met:', '')],\n 'tracks': {},\n 'opnames': []}\n if album_id:\n album = dmla.list_album_details(album_id)\n result['titel'] = album.name\n result['artist'] = album.artist\n # result['artistid'] = album.artist.id\n result['artist_name'] = album.artist.get_name()\n text = album.label\n if album.release_year:\n if text:\n text += ', '\n text += str(album.release_year)\n result['details'] = [('Label/jaar:', text),\n ('Produced by:', album.produced_by),\n ('Credits:', album.credits),\n ('Bezetting:', album.bezetting),\n ('Tevens met:', album.additional)]\n if album:\n result['tracks'] = {x.volgnr: (x.name, x.written_by, x.credits)\n for x in dmla.list_tracks(album_id)}\n result['opnames'] = [(x.type, x.oms) for x in\n dmla.list_recordings(album_id)]\n if albumtype == 'live':\n result['details'].pop(0)\n return result","def multiple_ranks(our_data,start, end):\n count = start\n album_list = []\n while count <= end:\n album_list.append(find_by_rank(count))\n count += 1","def display_all():\n results = artwork_db.get_all_artwork()\n for artist in results:\n print(artist)","def get_albums_from_artists(sp, artist_uri_list):\n\n # Create header for output df\n albums_list = [[\"name\", \"album_uri\", \"album_release_date\", \"artist_uri\"]]\n\n print(\"Log: Pulling data from Spotify. This can take a while...\")\n\n # Loop through list of artist uris\n for artist_uri in artist_uri_list:\n # Get album from artist\n albums = sp.artist_albums(artist_uri)\n \n # Append each album to list\n for album in albums[\"items\"]:\n album_name = album[\"name\"]\n album_uri = album[\"uri\"]\n album_release_date = album[\"release_date\"]\n albums_list.append([album_name, album_uri, album_release_date, artist_uri])\n\n # Create df from list of albums for all artist\n albums_df = pd.DataFrame(data=albums_list[1:], columns=albums_list[0])\n\n print(\"Log: Finished pulling all albums from artist.\")\n return albums_df","def test_get_albums_no_params(self, id_name_elms, service_config, request):\n service_config.album_store.get_all.return_value = id_name_elms\n params = avalon.web.request.Parameters(request)\n\n service = avalon.web.services.AvalonMetadataService(service_config)\n results = service.get_albums(params)\n\n assert results == id_name_elms, 'Expected all albums returned'","def search_albums(self, needle):\n return self._album_search.search(searchable(needle))","def album():\n\tif not request.vars.id:\n\t\tredirect(URL('index'))\n\tid = request.vars.id\n\treleasegroupname = db.executesql(\"select m1.name, m2.id from release_name as m1, release_group as m2 where m1.id = m2.name and m2.id = \"+id+\";\")\n\treleasegroup = db.executesql(\"select distinct(m2.id),m3.name,m5.name,m7.track_count,m2.date_year,m2.date_month,m2.date_day,m6.name,m2.barcode from release_group m1,release m2,release_name m3,medium m4,medium_format m5, country m6, tracklist m7 where m5.id = m4.format and m4.release = m2.id and m2.name = m3.id and m2.release_group = m1.id and m2.country = m6.id and m4.tracklist = m7.id and m1.id = \"+id+\";\")\n\turls = db.executesql(\"select m2.url from l_release_group_url m1, url m2 where m1.entity1 = m2.id and m1.entity0 = \"+id+\";\")\n\tmbid = db.executesql(\"select m1.gid from release_group m1 where m1.id = \"+id+\";\")\n\trating = db.executesql(\"select rating, count from release_group_rating_global where id = \"+id+\";\")\n\tavg_rating = float(rating[0][0])/float(rating[0][1])\n\tif auth.user:\n\t\turating = db.executesql(\"select rating from release_group_rating_local where id = \"+str(id)+\" and user_id = \"+str(auth.user.id)+\";\")\n\t\tif urating:\n\t\t\tuser_rating = urating[0][0]\n\t\telse:\n\t\t\tuser_rating = 0\n\telse:\n\t\tuser_rating = 0\n\ttry:\n\t\tproxy = urllib2.ProxyHandler({'http': 'http://cs5090240:phone01202767129@10.10.78.62:3128'})\n\t\tauthentication = urllib2.HTTPBasicAuthHandler()\n\t\topener = urllib2.build_opener(proxy, authentication, urllib2.HTTPHandler)\n\t\turllib2.install_opener(opener)\n\t\tdestination = 'http://www.coverartarchive.org/release-group/'+mbid[0][0]\n\t\t#dbg.set_trace()\n\t\treq = urllib2.Request(url=destination,data=\"\")\n\t\tf = urllib2.urlopen(req)\n\t\t#dbg.set_trace()\n\t\tdata =json.load(f)\n\t\t#dbg.set_trace()\n\t\tcoverarturl = data['images'][0]['image']\n\texcept:\n\t\tcoverarturl = URL('static','images/no_image_found.jpg')\n\tlinks = []\n\twikiurl = \"\"\n\tfor url in urls:\n\t\tif \"wikipedia\" in url[0]:\n\t\t\twikiurl = url[0]\n\t\telse:\n\t\t\tlinks.append(url[0])\n\tif wikiurl:\n\t\t#wiki = get_wiki_desc(str(wikiurl)+\"?printable=yes\")\n\t\twiki = \"Description available at \"+str(wikiurl)\n\telse:\n\t\twiki = \"No wiki description\"\n\treturn dict(releasegroupname=releasegroupname, releasegroup=releasegroup, links=links, wiki=wiki, wikiurl=wikiurl, coverarturl=coverarturl, avg_rating=avg_rating, user_rating=user_rating)","def get_album(album_id):\n return query_single(album_id, Album, album_schema)","def add_songs(self, name, year, title):\n\n album_found = find_object(name, self.album)\n if album_found is None:\n print(\"Not Found \" + name)\n album_found = Album(name, year, self.name)\n self.add_album(album_found)\n else:\n print(\"Found album \"+name)\n\n album_found.add_songs(title)","def get_song_list(self):\n return self.song_list","def random_by_genre_list(self):\n\n for genre in self.connection.walk_genres():\n url = self.build_url({\n \"mode\": \"random_by_genre_track_list\",\n \"foldername\": genre[\"value\"].encode(\"utf-8\")})\n\n li = xbmcgui.ListItem(genre[\"value\"])\n xbmcplugin.addDirectoryItem(\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def test_cm_score_albums(dates):\n test = pycmc.charts.cm_score.albums(\n cm_album_id=\"6943281\",\n chart_type=\"spotify-top\",\n start_date=dates[\"start\"],\n )\n assert isinstance(test, list)\n assert len(test)","def get_albums_recent_played(session_):\n artists = session_.query(Album).order_by(Album.played_at.desc()).all()\n return artists","def get(self):\n return PhotoGalleryService().get_all(), 200","def get_tracks(self):\n self.c_album = self.albums_list.currentText()\n self.clementine_tracks.clear()\n self.albums_tracks.clear()\n if self.artists_list.count() == 0: # this happens when the panel is reshown\n return # after another panel was shown\n if self.albums_list.count() == 0: # this happens during screen buildup\n return # when only the first combobox is filled\n if not self.albums_map[self.artist]:\n qtw.QMessageBox.information(self, self._parent.title,\n \"No (matched) albums for this artist\")\n return\n try:\n self.a_album = self.albums_map[self.artist][self.c_album][1]\n except KeyError:\n qtw.QMessageBox.information(self, self._parent.title, \"This album \"\n \"has not been matched yet\")\n return\n a_tracks, c_tracks = read_album_tracks(self.a_album,\n self.artist, self.c_album)\n for item in c_tracks:\n new = qtw.QTreeWidgetItem([item])\n self.clementine_tracks.addTopLevelItem(new)\n for item in a_tracks:\n new = qtw.QTreeWidgetItem([item])\n self.albums_tracks.addTopLevelItem(new)\n reimport_possible = False\n if len(c_tracks) != len(a_tracks):\n reimport_possible = True\n else:\n for ix, item in enumerate(a_tracks):\n try:\n if not (item.startswith(c_tracks[ix][0])\n or c_tracks[ix][0].startswith(item)):\n reimport_possible = True\n except IndexError:\n reimport_possible = True\n self.b_copy.setEnabled(reimport_possible)","def get_all_sets(user_id):\n def helper():\n page_num = 1\n while True:\n obj = jsonp_to_obj(flickr.photosets_getList(user_id=user_id, page=page_num))\n for x in obj['photosets']['photoset']:\n yield (x['id'], x['title']['_content']) \n if page_num == obj['photosets']['pages']:\n break\n page_num+=1\n\n return list(helper())","def test_get_songs_by_album(self, track_elms, service_config, request):\n album_id = uuid.UUID(avalon.compat.to_uuid_input('f83fdec7-510f-44a5-87dc-61832669a582'))\n service_config.track_store.get_by_album.return_value = track_elms\n service_config.id_cache.get_album_id.return_value = album_id\n request.args['album'] = 'Album'\n params = avalon.web.request.Parameters(request)\n\n service = avalon.web.services.AvalonMetadataService(service_config)\n results = service.get_songs(params)\n\n assert results == track_elms, 'Expected matching tracks returned'\n service_config.track_store.get_by_album.assert_called_with(album_id)"],"string":"[\n \"def album_list(self):\\n\\n artist_id = self.addon_args[\\\"artist_id\\\"][0]\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"albums\\\")\\n\\n for album in self.connection.walk_artist(artist_id):\\n self.add_album(album)\\n\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_ALBUM)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_ARTIST)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_VIDEO_YEAR)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def simple_album_list():\\r\\n album_list = []\\r\\n data = dbase()\\r\\n for album in data.keys():\\r\\n album_list += [album]\\r\\n return album_list\",\n \"def get_albums(self):\\n self.artist = self.artists_list.currentText()\\n self.c_albums = [x['album'] for x in dmlc.list_albums(self.artist)\\n if [x['album'] in self.albums_map[self.artist]]]\\n self.albums_list.clear()\\n self.albums_list.addItems(self.c_albums)\\n self.update_navigation_buttons()\",\n \"def get_album_list():\\n\\n # TODO: Paginate this, etc\\n entities = PhotoAlbum.query().order(-PhotoAlbum.title).fetch(1000)\\n\\n return entities\",\n \"def get_albums():\\n return query_multiple(request.args, album_search, \\\\\\n album_filter, Album, albums_schema)\",\n \"def get_albums(self):\\n artist = self.get_request_arg(\\\"artist\\\")\\n if artist:\\n lib = self.ctrl.library\\n lst = sorted(self.ctrl.library.get_albums(artist))\\n albums = [{\\\"artist\\\": artist,\\n \\\"album\\\": album,\\n \\\"path\\\": lib.get_path(artist, album)} for album in lst]\\n if lst:\\n return self.resp_from_data(albums)\\n return self.resp_from_data(\\n {\\\"message\\\": f\\\"No album found for artist={artist}\\\"}, 400)\",\n \"def simple_songs_list(name_of_album):\\r\\n songs = []\\r\\n data1 = dbase()\\r\\n data1 = data1[name_of_album][0]\\r\\n for song in data1.keys():\\r\\n songs += [song]\\r\\n return songs\",\n \"def albums_by_genre_list(self):\\n\\n genre = self.addon_args[\\\"foldername\\\"][0].decode(\\\"utf-8\\\")\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"albums\\\")\\n\\n for album in self.connection.walk_album_list_genre(genre):\\n self.add_album(album, show_artist=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def get_albums(entity_url: str) -> list:\\n entity_url = entity_url.rstrip(\\\"/\\\")\\n response = requests.get(entity_url)\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n albums = []\\n for link in soup.find_all('a'):\\n url = link.get('href')\\n if url is not None and \\\"/album/\\\" in url:\\n if url.startswith(\\\"http\\\"):\\n albums.append(url)\\n else:\\n albums.append(f\\\"{entity_url}{url}\\\")\\n return albums\",\n \"def tracked_albums():\\n print('Your Google Photos Albums ([X] = tracked):')\\n albums = get_albums(service)\\n for i, a in enumerate(albums):\\n check = 'X' if a.id in library.get_album_ids() else ' '\\n print('[{}] {}. {}'.format(check, i+1, a.title))\\n return albums\",\n \"def songs_list(name_of_album):\\r\\n songs = \\\"\\\"\\r\\n data = dbase()\\r\\n data = data[name_of_album][0]\\r\\n for song in data.keys():\\r\\n songs += song\\r\\n songs += \\\", \\\"\\r\\n return songs[:-2]\",\n \"def genre_list(self):\\n\\n for genre in self.connection.walk_genres():\\n url = self.build_url({\\n \\\"mode\\\": \\\"albums_by_genre_list\\\",\\n \\\"foldername\\\": genre[\\\"value\\\"].encode(\\\"utf-8\\\")})\\n\\n li = xbmcgui.ListItem(genre[\\\"value\\\"])\\n xbmcplugin.addDirectoryItem(\\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def read_artist_albums(id, name):\\n list_a = [(x.name, str(x.release_year), str(x.id))\\n for x in dmla.list_albums_by_artist('', id, 'Jaar')]\\n list_c = [(x['album'], x['year']) for x in dmlc.list_albums(name)]\\n return list_a, list_c\",\n \"def get_albums_alpha(session_):\\n artists = session_.query(Album).order_by(Album.title.asc()).all()\\n return artists\",\n \"def all_titles(our_data):\\n return [album['album'] for album in our_data]\",\n \"def search_for_album(album_name):\\n\\n print(f'Searching for album: {album_name}')\\n\\n search_result = spotifyObject.search(q=f'\\\"{album_name}\\\"', limit=20, type='album')\\n\\n items = search_result['albums']['items']\\n\\n results = []\\n\\n for item in items:\\n if len(item['artists']) > 1:\\n artists = tuple(art['name'] for art in item['artists'])\\n else:\\n artists = item['artists'][0]['name']\\n\\n results.append((artists, item['name'], item['id']))\\n\\n return results\",\n \"def get_albums(playlist_name):\\n\\n playlist_id = find_playlist(playlist_name)\\n \\n items = get_playlist_tracks(playlist_id=playlist_id)\\n \\n track_values = []\\n \\n for item in items:\\n track = item['track']\\n album = track['album']\\n artists = tuple(artist['name'] for artist in album['artists'])\\n \\n track_values.append((album['name'], artists[0]))\\n \\n album_details = namedtuple('AlbumDetails', 'album artist')\\n \\n for tup in dict.fromkeys(track_values):\\n yield album_details(*tup)\",\n \"def get_albums(self):\\n if self.artist_list.count() == 0: # this happens when the panel is reshown\\n return # after another panel was shown\\n self.c_artist = self.artist_list.currentText()\\n ## self.last_handled = self.artist_list.currentIndex()\\n # remember first handled item for currency communication over panels\\n self._parent.current_data = self.c_artist\\n self.a_artist = self.artist_map[self.c_artist]\\n a_albums, c_albums = read_artist_albums(self.a_artist, self.c_artist)\\n for name, year, id, *rest in self.albums_to_save[self.c_artist]:\\n a_albums.append((name, year, str(id)))\\n self.clementine_albums.clear()\\n for item, year in c_albums:\\n new = qtw.QTreeWidgetItem([item])\\n new.setData(0, core.Qt.UserRole, year)\\n try:\\n new.setText(1, str(self.albums_map[self.c_artist][item][1]))\\n except KeyError:\\n pass\\n self.clementine_albums.addTopLevelItem(new)\\n self.albums_albums.clear()\\n self.lookup = collections.defaultdict(list)\\n for item in a_albums:\\n new = qtw.QTreeWidgetItem([x.replace('None', '') for x in item])\\n self.albums_albums.addTopLevelItem(new)\\n self.lookup[item[0]].append(item[2])\\n self.tracks = collections.defaultdict(list)\",\n \"def get_albums(username):\\n cur = mysql.connection.cursor()\\n cur.execute(\\\"SELECT * FROM album WHERE username = '{0}'\\\".format(username))\\n return cur.fetchall()\",\n \"def get_albums(self, limit=None):\\n url = (\\\"https://api.imgur.com/3/account/{0}/albums/{1}\\\".format(self.name,\\n '{}'))\\n resp = self._imgur._send_request(url, limit=limit)\\n return [Album(alb, self._imgur, False) for alb in resp]\",\n \"def get_albums(self, offset=None):\\n return self.__get('albums')\",\n \"def get_albums(self, ctx, page, templ_vars):\\n if 'type' in page.meta and page.meta['type'] == 'index':\\n album_pages = sorted(\\n templ_vars['site']['categories']['gallery'],\\n key=lambda album: album['datetime'],\\n )\\n albums = {}\\n for album_page in album_pages:\\n image_list = []\\n images = map(\\n lambda i: i['thumb_src'],\\n self.albums[album_page['slug']]\\n )\\n image_list += images[:PREVIEW_IMGS_NUM]\\n albums[album_page['slug']] = image_list\\n templ_vars['site']['albums'] = albums\",\n \"def albums(self, albums, **kwargs):\\n album_list = map(self._get_album_id, albums)\\n return self._get(API.ALBUMS.value, ids=\\\",\\\".join(album_list), **kwargs)\",\n \"def get_albums_by_artist(albumtype, search_for, sort_on):\\n return list(dmla.list_albums_by_artist(albumtype, search_for, sort_on))\",\n \"def getAlbums():\\n\\n r = requests.get(ALBUMS_URL, headers=HEADER, timeout=5)\\n\\n if r.status_code == 200:\\n \\n try:\\n albums = [] \\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n album = soup.find_all(\\\"div\\\", class_=\\\"duv\\\")\\n for i,al in enumerate(album): \\n temp = {}\\n temp['link'] = al.find_all(\\\"a\\\")[0]['href']\\n temp['album'] = al.find_all(\\\"span\\\", class_=\\\"title\\\")[0].text\\n albums.append(temp)\\n\\n if len(albums) > 0:\\n return albums\\n else:\\n print(\\\"No albums found on site2!\\\")\\n sys.exit(0)\\n \\n except Exception as e:\\n print(\\\"Failed to get albums from site2\\\\n\\\", e)\\n sys.exit(0)\\n\\n else:\\n print(\\\"Albums Url fetch failed! Status code: {}\\\".format(r.status_code))\\n sys.exit(0)\",\n \"def fetchAlbumIds(artist_id):\\n url = 'https://api.spotify.com/v1/artists/' + artist_id + '/albums?market=US&album_type=album'\\n req = requests.get(url)\\n\\n data = req.json()\\n\\n #checking for bad return value\\n if not req.ok:\\n print \\\"error : \\\" + data['error']['message']\\n return \\\"error : \\\" + data['error']['message']\\n\\n albums = []\\n for item in data['items']:\\n \\talbums.append(item['id'])\\n\\n return albums\",\n \"def GetAlbums(self, start=0, end=0, sortmethod='label', sortorder='ascending', filter='', artistid=None):\\n self.logger.debug(\\\"Loading all albums for ARTISTID \\\" + str(artistid))\\n try:\\n xbmc = Server(self.url('/jsonrpc', True))\\n sort = {'order': sortorder, 'method': sortmethod, 'ignorearticle': True}\\n properties=['artist', 'title', 'year', 'description', 'thumbnail']\\n limits = {'start': int(start), 'end': int(end)}\\n if artistid is not None:\\n filter = {'artistid': int(artistid)}\\n else:\\n filter = {'or': [\\n {'field': 'album', 'operator': 'contains', 'value': filter},\\n {'field': 'artist', 'operator': 'contains', 'value': filter}\\n ]}\\n return xbmc.AudioLibrary.GetAlbums(properties=properties, limits=limits, sort=sort, filter=filter)\\n except ValueError:\\n return\",\n \"def get_albums(self):\\n return AlbumView.get_by_artist(self.name)\",\n \"def read_album_tracks(id, artist_name, album_name):\\n list_a = [x.name for x in dmla.list_tracks(id)]\\n list_c = [x['title'] for x in dmlc.list_tracks_for_album(artist_name, album_name)\\n if x['track'] != -1]\\n return list_a, list_c\",\n \"def sort_album(self):\\n self.sort('album')\",\n \"def print_album(albums):\\n print(\\\"\\\\nPrinting album data:\\\")\\n for album in albums:\\n print(f\\\"Artist Name: {album['name'].title()}\\\")\\n print(f\\\"\\\\tAlbum Name: {album['album'].title()}\\\")\\n if (album['num_songs']):\\n print(f\\\"\\\\tNumber Songs: {album['num_songs']}\\\")\\n\\n print(\\\"\\\")\",\n \"def all_artists(our_data):\\n return [album['artist'] for album in our_data]\",\n \"def cmd_account_albums(client, args):\\n account_albums = client.get_account_albums(args.username, args.page)\\n data = [item.__dict__ for item in account_albums]\\n generate_output({'account_albums': data}, args.output_file)\",\n \"def album_parser(data):\\n album_ids = []\\n for item in data['data']:\\n album_ids.append(item['id'])\\n return album_ids\",\n \"def song_album(ans):\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n if ans == song:\\r\\n return album\",\n \"def get_album_tracks(self):\\n track_list = self.soup.findAll('div', class_='chart_row')\\n number_of_tracks = 0\\n titles = []\\n urls = []\\n track_numbers = []\\n \\n for track in track_list:\\n track_title = re.sub(' Lyrics', '', \\\" \\\".join(track.h3.text.split()))\\n lyrics_url = track.a['href']\\n track_number = track.span.span.text.strip()\\n \\n if track_number == '':\\n # Sometimes there are additional urls that are not a song's lyrics. Skip these.\\n continue\\n else:\\n track_number = int(track_number)\\n \\n number_of_tracks += 1\\n titles.append(track_title)\\n urls.append(lyrics_url)\\n track_numbers.append(track_number)\\n \\n if self.song_order:\\n # Check that order values are okay.\\n for number in self.song_order:\\n if number > number_of_tracks:\\n raise SongOrderValueError(f'Track number given ({number}) exceeds number of tracks ({number_of_tracks})')\\n \\n for title, url, number in zip(titles, urls, track_numbers):\\n if self.song_order:\\n if number not in self.song_order:\\n print(f'Skipping song: {number:02d} {title}')\\n continue\\n \\n lyrics = self.get_single_lyrics(url)\\n self.album.add_song(Song(title=title, track_number=number, lyrics=lyrics))\\n\\n self.album.number_of_tracks = number_of_tracks\",\n \"def get_albums_by_text(albumtype, search_type, search_for, sort_on):\\n if albumtype == 'studio':\\n search_on = {0: '*', 2: 'name', 3: 'produced_by', 4: 'credits', 5: 'bezetting'}[search_type]\\n elif albumtype == 'live':\\n search_on = {0: '*', 2: 'name', 3: 'name', 4: 'produced_by', 5: 'bezetting'}[search_type]\\n return list(dmla.list_albums_by_search(albumtype, search_on, search_for, sort_on))\",\n \"def getSongsFromAlbum(albumLink):\\n albumLink = str(albumLink)\\n try:\\n html = urllib.request.urlopen(albumLink).read()\\n soup = bs4.BeautifulSoup(html, 'html.parser')\\n table = soup.findAll(\\\"a\\\")[5:]\\n songLinks = []\\n for entry in table:\\n text = str(re.findall(\\\"\\\\\\\".*\\\\\\\"\\\", str(entry)))\\n text = re.sub(\\\"[\\\\]\\\\['\\\\\\\"]\\\", \\\"\\\", text)\\n link = albumLink + str(text)\\n songLinks.append(link)\\n except:\\n return []\\n return songLinks\",\n \"def get_by_artist(cls, artist):\\n results = cls.query().filter(FileRecord.artist == artist).order_by(\\n FileRecord.year).all()\\n albums = []\\n if results and len(results) > 0:\\n for result in results:\\n albums.append(cls(*result))\\n\\n return albums\\n\\n return albums\",\n \"def GetRecentAlbums(self, limit=5):\\n self.logger.debug(\\\"Fetching recently added Music\\\")\\n try:\\n xbmc = Server(self.url('/jsonrpc', True))\\n properties = ['artist', 'albumlabel', 'year', 'description', 'thumbnail']\\n limits = {'start': 0, 'end': int(limit)}\\n return xbmc.AudioLibrary.GetRecentlyAddedAlbums(properties=properties, limits=limits)\\n except:\\n self.logger.error(\\\"Unable to fetch recently added Music!\\\")\\n return\",\n \"def test_top_albums(self):\\n \\n rss = AppleRSS()\\n objs = rss.get_top_albums(limit=10)\\n \\n self.__test_artists('top_albums', objs)\",\n \"def albums(date, album_type, genre):\\n urlhandle = f\\\"{amazon_charts_url}/albums\\\"\\n params = {\\n \\\"type\\\": album_type,\\n \\\"date\\\": date,\\n \\\"genre\\\": genre,\\n }\\n\\n data = utilities.RequestData(urlhandle, params)\\n return utilities.RequestGet(data)[\\\"data\\\"]\",\n \"def getAlbumsFromArtist(artistLink):\\n artistLink = str(artistLink)\\n url = \\\"http://www.ohhla.com/\\\"+artistLink\\n if artistLink[0:4]==\\\"http:\\\":\\n url = artistLink\\n try:\\n html = urllib.request.urlopen(url).read()\\n soup = bs4.BeautifulSoup(html, 'html.parser')\\n table = soup.findAll(\\\"a\\\")[5:]\\n albumLinks = []\\n for entry in table:\\n text = str(re.findall(\\\"\\\\\\\".*\\\\\\\"\\\", str(entry)))\\n text = re.sub(\\\"[\\\\]\\\\['\\\\\\\"]\\\", \\\"\\\", text)\\n link = url + str(text)\\n if len(re.findall(\\\"(?:http)\\\",link)) == 1:\\n albumLinks.append(link)\\n except:\\n return []\\n return albumLinks\",\n \"def cmd_album_images(client, args):\\n album_images = client.get_album_images(args.album_id)\\n data = [item.__dict__ for item in album_images]\\n generate_output({'album_images': data}, args.output_file)\",\n \"def pic (self, list) : \\n result = []\\n for pmod in list :\\n result.append (pmod.photo_uri)\\n return result\",\n \"def get_songs(self, song_list):\\n self.songs = [[s.name, s.movie_name] for s in song_list\\n if s.raga == self.name]\",\n \"def get_tracks_from_albums(sp, album_uri_list):\\n\\n track_list = [[\\\"track_name\\\", \\\"track_uri\\\", \\\"track_release_date\\\"]]\\n\\n print(\\\"Log: Pulling data from Spotify. This can take a while...\\\")\\n\\n for album_uri in album_uri_list:\\n album_tracks = sp.album_tracks(album_uri, limit=50, offset=0)[\\\"items\\\"]\\n count_tracks_in_album = len(album_tracks)\\n album_release_date = sp.album(album_uri)[\\\"release_date\\\"]\\n\\n # This part is probably very slow and should be improved by accessing the API less often\\n for track_number in range(count_tracks_in_album):\\n track_name = album_tracks[track_number][\\\"name\\\"]\\n track_uri = album_tracks[track_number][\\\"uri\\\"]\\n \\n track_list.append([track_name, track_uri, album_release_date])\\n\\n # Create df from list of tracks for all albums\\n track_df = pd.DataFrame(data=track_list[1:], columns=track_list[0])\\n \\n print(\\\"Log: Finished pulling all tracks from albums.\\\")\\n return track_df\",\n \"def test_get_all_need_transform_two_albums(self):\\n album = Album(artist='Artist', album='Album',\\n totaltracks=1, totalseconds=120)\\n pk = album.insert(self.app.db, self.app.curs)\\n album = Album(artist='Artist', album='Album 2',\\n totaltracks=1, totalseconds=120)\\n pk = album.insert(self.app.db, self.app.curs)\\n self.assertEqual(self.get_album_count(), 2)\\n\\n albums = Album.get_all_need_transform(self.app.curs, 1)\\n self.assertEqual(len(albums), 2)\",\n \"def get_albums(self, only_published=True, only_active=True, list_only=False,\\n only_smart=False, exclude_smart=False, tags=None, tags_exclusive=True):\\n if not tags:\\n tags = []\\n\\n if not hasattr(tags, '__iter__'):\\n tags = [tags]\\n\\n options = {\\n 'only_published': int(bool(only_published)),\\n 'only_active': int(bool(only_active)),\\n 'list_only': int(bool(list_only)),\\n 'only_smart': int(bool(only_smart)),\\n 'exclude_smart': int(bool(exclude_smart)),\\n }\\n\\n if tags:\\n options.update({'tags': ','.join(str(tag) for tag in tags),\\n 'tags_exclusive': int(bool(tags_exclusive))})\\n\\n data = self._get('get_album_list', options)\\n return data['albums']\",\n \"def parseAlbumData(self, filename):\\n doc = parse(filename)\\n stack = []\\n last_top_key = None\\n if self.use_album:\\n album_list_key = \\\"List of Albums\\\"\\n else:\\n album_list_key = \\\"List of Rolls\\\"\\n for event, node in doc:\\n if event == START_ELEMENT:\\n stack.append(node)\\n level = len(stack)\\n if level == 3:\\n if node.nodeName == 'key':\\n doc.expandNode(node)\\n last_top_key = self.getText(node)\\n stack.pop()\\n elif last_top_key == 'List of Keywords':\\n doc.expandNode(node)\\n self.keywords = self.dePlist(node)\\n stack.pop()\\n elif last_top_key == 'List of Faces':\\n doc.expandNode(node)\\n self.faces = dict([\\n (k, v['name']) for k,v in\\n self.dePlist(node, ['name']).items()\\n ])\\n stack.pop()\\n elif last_top_key == 'Major Version':\\n doc.expandNode(node)\\n major_version = self.dePlist(node)\\n stack.pop()\\n if major_version != self.major_version:\\n raise iPhotoLibraryError, \\\\\\n \\\"Sorry, I can't understand version %i iPhoto Libraries.\\\" % major_version\\n elif last_top_key == 'Minor Version':\\n doc.expandNode(node)\\n minor_version = self.dePlist(node)\\n stack.pop()\\n if minor_version > self.minor_version:\\n self.status(\\n \\\"\\\\nI don't recognise iPhoto libraries when the minor version is %i, but let's try anyway.\\\\n\\\" % minor_version,\\n force=True\\n )\\n\\n elif level == 4:\\n # process large items individually so we don't\\n # load them all into memory.\\n if last_top_key == album_list_key:\\n doc.expandNode(node)\\n self.albums.append(self.dePlist(node))\\n stack.pop()\\n elif last_top_key == 'Master Image List':\\n doc.expandNode(node)\\n if node.nodeName == 'key':\\n last_image_key = self.getText(node)\\n else:\\n self.images[last_image_key] = self.dePlist(\\n node, self.interesting_image_keys\\n )\\n stack.pop()\\n elif event == END_ELEMENT:\\n stack.pop()\",\n \"def multiple_years(our_data, start, end):\\n count = start\\n album_list = []\\n while count <= end:\\n album_list.append(find_by_year(our_data,count))\\n count += 1\",\n \"def cmd_account_album_ids(client, args):\\n account_album_ids = client.get_account_album_ids(args.username, args.page)\\n generate_output({'account_album_ids': account_album_ids})\",\n \"def get_top_albums(\\n self, period: Period, limit: int = 50, page: int = 1\\n ) -> ListModel[Album]:\\n assert isinstance(period, Period)\\n\\n return self.retrieve(\\n bind=Album,\\n flatten=\\\"album\\\",\\n params=dict(\\n method=\\\"user.getTopAlbums\\\",\\n user=self.name,\\n limit=limit,\\n page=page,\\n period=period.value,\\n ),\\n )\",\n \"def get_all_songs():\\r\\n return [Song.song_json(song) for song in Song.query.all()]\",\n \"def prepare_batch(self, iterator):\\n elements = []\\n\\n for label, album_ids in iterator:\\n for album_id in album_ids:\\n image_path = os.path.join(self.image_folder, album_id)\\n # If path doesn't exist, continue\\n if not os.path.exists(image_path):\\n continue\\n images = [os.path.join(image_path, img_name)\\n for img_name in sorted(os.listdir(image_path))]\\n # If no photo available, continue\\n if len(images) == 0:\\n continue\\n\\n elements.append((label, images))\\n\\n random.shuffle(elements)\\n\\n return sorted(elements, key=lambda p: len(p[1]), reverse=True)\",\n \"def get_albums_from(artist_uri):\\n album_uris = []\\n results = spotify.artist_albums(artist_uri, album_type='album')\\n albums = results['items']\\n # get URIs for each album\\n for album in albums:\\n album_uris.append(album['uri'])\\n\\n return album_uris\",\n \"def findsimilaralbums(index, similarartists): # {{{1\\n albums = []\\n for [similarity, artistname] in similarartists:\\n for albumname in index.findalbumsbyartist(artistname):\\n albums.append([similarity, [artistname, albumname]])\\n return albums\",\n \"def get_all_musicians(self):\\n self.cursor.execute(\\\"select * from musicians\\\")\\n self.connection.commit()\\n return self.cursor.fetchall()\",\n \"def selectSongs():\\n\\tsql =\\\"select songs.title, artist.name, album.name from songs, album, \\\" \\\\\\n\\t+ \\\"artist join songs_album on songs.id=songs_album.songs_id \\\" \\\\\\n\\t+ \\\"join songs_artist on songs.id=songs_artist.songs_id \\\" \\\\\\n\\t+ \\\"where album.id=songs_album.album_id \\\" \\\\\\n\\t+ \\\"and artist.id=songs_artist.artist_id\\\"\\n\\tc, conn = connect()\\n\\tretr = c.execute(sql)\\n\\tsongs = []\\n\\tfor entry in retr:\\n\\t\\tsongs.append(music.song(title=entry[0], artist=entry[1], album=entry[2]))\\n\\treturn songs\",\n \"def find_album_playlist(data):\\n\\n return data['album'].lower() + '.m3u'\",\n \"def get(request, slug):\\n calbums = [x.json for x in get_album_contributors(get_album_by_slug(slug))]\\n \\n response = render_to_response(\\n \\\"data/list.json\\\",\\n {\\\"data\\\": calbums},\\n content_type=\\\"application/json\\\",\\n )\\n response['Cache-Control'] = 'no-cache'\\n return response\",\n \"def lists():\\n name = []\\n author = []\\n year = []\\n condition = []\\n for row in sorted(inform, key=itemgetter(1, 0)):\\n # sort the list by author then by title\\n # traverse all thing in list and append to gain classified information\\n name.append(row[0])\\n author.append(row[1])\\n year.append(row[2])\\n condition.append(row[3])\\n long = len(name)\\n require = 0\\n for i in range(1, long + 1, 1):\\n if condition[i - 1] == \\\"r\\\":\\n print(\\\"*\\\", end=\\\"\\\")\\n print(\\\"{}.{:<30} by {:<20}({})\\\".format(i, name[i - 1], author[i - 1], year[i - 1]))\\n require = require + 1\\n else:\\n print(\\\" {}.{:<30} by {:<20}({})\\\".format(i, name[i - 1], author[i - 1], year[i - 1]))\\n if \\\"r\\\" in condition:\\n print(\\\"You need to listen to {} albums\\\".format(require))\\n else:\\n print(\\\"No albums left to listen to. Why not add a new album?\\\")\",\n \"def get_tracks_from(album_uri):\\n track_uris = []\\n album = spotify.album_tracks(album_id=album_uri)\\n\\n for track in album['items']:\\n track_uris.append(track['uri'])\\n\\n return track_uris\",\n \"def get_album_songs(self, album_id):\\n url = get_album_url(album_id)\\n result = self.get_request(url)\\n\\n return result['album']['songs']\",\n \"def get_songs(self):\\n search_object = {\\\"size\\\":25000,\\n 'query': {'term': {FIELD_FINGERPRINTED: True}}, \\\"fields\\\": [FIELD_SONGNAME, FIELD_FILE_SHA1,\\n FIELD_TOTAL_HASHES]}\\n response = self.cursor.search(index = SONGS_INDEXNAME, body=search_object)\\n #print(\\\"get_songs response: \\\",response)\\n arr = []\\n for hit in response[\\\"hits\\\"][\\\"hits\\\"]:\\n dct = {\\\"song_name\\\":hit['_source'][FIELD_SONGNAME],\\\"total_hashes\\\":hit['_source'][FIELD_TOTAL_HASHES],\\n \\\"file_sha1\\\":hit['_source'][FIELD_FILE_SHA1]}\\n arr.append(dct)\\n return arr\",\n \"def get_songs(library):\\n songs = []\\n for song in library:\\n title, artist, album = song['title'], song['artist'], song['album']\\n seconds = int(song['durationMillis']) // 1000\\n songs.append({'artist': artist, 'title': title, 'album': album, 'seconds': seconds})\\n return songs\",\n \"def album(self, q, page=None):\\r\\n return self.get('album', q, page)\",\n \"def getPictures (self, list) :\\n\\n result = []\\n for event in list :\\n eventEntry = {}\\n eventEntry ['id'] = link = event.answer.id\\n eventEntry ['time'] = event.timeOf\\n eventEntry ['comments'] = event.answer.comments\\n eventEntry ['location'] = self.where (event.answer)\\n eventEntry ['problem'] = event.answer.survey ['problem_type']\\n eventEntry ['pictures'] = self.pic (Picture.objects.filter (answer__id = link))\\n result.append (eventEntry)\\n \\n return result\",\n \"def candidates(self, items, artist, album, va_likely):\\n candidate_list = []\\n candidate_list.append(WikiAlbum(artist, album))\\n\\n return candidate_list\",\n \"def current_user_saved_albums(self, limit=20, offset=0, **kwargs):\\n return self._get(API.MY_ALBUMS.value, limit=limit, offset=offset, **kwargs)\",\n \"def get_songs_by_album(self, album_id):\\n return self.__get('song', album_id)\",\n \"def appendAlbum(song):\\n\\tsql = []\\n\\tsql.append(\\\"INSERT INTO ALBUM ('name') VALUES ('\\\" \\n\\t+ '/'.join(song.album) + \\\"');\\\")\\n\\t\\n\\tsql.append(\\\"INSERT INTO songs_album ('songs_id', 'album_id')\\\"\\n\\t+ \\\" VALUES ((select id from songs where hash = '\\\" + str(song.hash) + \\\"'), \\\"\\n\\t+ \\\"(select id from album where name = '\\\" + '/'.join(song.album) + \\\"'));\\\")\\n\\tsql.append(\\\"INSERT INTO artist_album ('artist_id', 'album_id')\\\"\\n\\t+ \\\" VALUES ((select id from songs where hash = '\\\" + str(song.hash) + \\\"'), \\\"\\n\\t+ \\\"(select id from album where name = '\\\" + '/'.join(song.album) + \\\"'));\\\")\\n\\t\\n\\treturn sql\",\n \"def get_random_album(self):\\n lib = self.ctrl.library\\n artist, album = lib.get_random_album()\\n return self.resp_from_data({\\n \\\"artist\\\": artist,\\n \\\"album\\\": album,\\n \\\"path\\\": lib.get_path(artist, album)\\n })\",\n \"def get_images(self, ctx, page):\\n is_imgur = 'source' in page.meta and page.meta['source'] == 'imgur'\\n if 'type' in page.meta and page.meta['type'] == 'album':\\n album = page.meta\\n images = []\\n if is_imgur:\\n pp.pprint(page.meta)\\n # bind to template via json\\n images = self.get_imgur_album_images(page)\\n self.albums[album['slug']] = images\\n else:\\n # get paths of all of the images in the album\\n srcs = []\\n # get absolute paths of images in album for each file type\\n for file_type in FILE_TYPES:\\n imgs = glob.glob(\\n GALLERY_DIR + album['slug'] + '/*.' + file_type\\n )\\n\\n for img in imgs:\\n img_rel_path = (\\n REL_GALLERY_DIR +\\n album['slug'] + '/' + img.split('/')[-1]\\n )\\n srcs.append(img_rel_path)\\n\\n # split full srcs and thumb srcs from srcs into two lists\\n images = []\\n thumb_srcs = filter(\\n lambda src: src.split('/')[-1].startswith(THUMB_PREFIX),\\n srcs\\n )\\n for thumb_src in thumb_srcs:\\n src = thumb_src.replace(THUMB_PREFIX, '')\\n thumb_width, thumb_height = self.calc_img_hw(thumb_src)\\n width, height = self.calc_img_hw(src)\\n images.append({\\n 'thumb_src': thumb_src,\\n 'thumb_width': thumb_width,\\n 'thumb_height': thumb_height,\\n\\n 'src': src,\\n 'width': width,\\n 'height': height,\\n })\\n self.albums[album['slug']] = images\",\n \"def get_album(self):\\n return self._album\",\n \"def extract_all_albums(lines):\\n album_dict = {}\\n for index, line in enumerate(lines):\\n if line.startswith(\\\"<a\\\"):\\n line_index = int(index)\\n line = line.strip()\\n albumid = int((lines[index + 1]).strip())\\n album_classed = extract_album(lines, line_index)\\n album_dict[albumid] = album_classed #assigns the key(id) to the values\\n return album_dict\",\n \"def get_artist_list():\\n return list(dmla.list_artists())\",\n \"def get_songs(path):\\r\\n song_list = []\\r\\n genre_paths = glob.glob(path + '/*')\\r\\n for genre_path in genre_paths:\\r\\n artist_paths = glob.glob(genre_path + '/*')\\r\\n for artist_path in artist_paths:\\r\\n album_paths = glob.glob(artist_path + '/*')\\r\\n for album_path in album_paths:\\r\\n lyrics_paths = glob.glob(album_path + '/*.txt')\\r\\n for lyrics_path in lyrics_paths:\\r\\n song = {}\\r\\n song[\\\"genre\\\"] = genre_path.replace(path + '/', '')\\r\\n song[\\\"artist\\\"] = artist_path.replace(genre_path + '/', '')\\r\\n song[\\\"album\\\"] = album_path.replace(artist_path + '/', '')\\r\\n song[\\\"lyrics\\\"] = open(lyrics_path).read()\\r\\n song[\\\"name\\\"] = lyrics_path[:-4].replace(album_path + '/', '')\\r\\n song[\\\"x\\\"] = 0\\r\\n song[\\\"y\\\"] = 0\\r\\n song_list.append(song)\\r\\n return song_list\",\n \"def test_get_all_need_transform_no_albums(self):\\n self.assertEqual(Album.get_all_need_transform(self.app.curs, 1), [])\",\n \"def dbase():\\r\\n albums_data = {}\\r\\n song_dict = {}\\r\\n songs_list = []\\r\\n with open(PATH, 'r') as f:\\r\\n data = f.read()\\r\\n temp = data.split(\\\"#\\\")\\r\\n for album in temp[1:]:\\r\\n index = album.find(\\\"::\\\")\\r\\n albums_data[album[:index]] = \\\"\\\"\\r\\n for album in temp[1:]:\\r\\n album = album.split(\\\"*\\\")\\r\\n album_name = album[0][:-7]\\r\\n release_Date = album[0][-5:]\\r\\n del album[0]\\r\\n for song in album:\\r\\n info = song.split(\\\"::\\\")\\r\\n song_name = info[0]\\r\\n del info[0]\\r\\n songs_list = info\\r\\n song_dict[song_name] = songs_list\\r\\n albums_data[album_name] = (song_dict.copy(), release_Date)\\r\\n song_dict.clear()\\r\\n return albums_data\",\n \"def get_best_new_albums(param1, var1):\\n global session\\n headers = {\\n 'authority': 'www.pitchfork.com',\\n 'method': 'GET',\\n 'scheme': 'https',\\n 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36',\\n }\\n html = requests.get(param1, headers=headers).text\\n soup = bs(html, 'html.parser')\\n wraps = soup(\\\"a\\\", {\\\"class\\\": \\\"link-block\\\"})\\n\\n for wrap in wraps:\\n var1.append(wrap)\",\n \"def test_get_all_need_transform_one_album(self):\\n orig_album = Album(artist='Artist', album='Album',\\n totaltracks=1, totalseconds=120)\\n pk = orig_album.insert(self.app.db, self.app.curs)\\n self.assertEqual(self.get_album_count(), 1)\\n\\n albums = Album.get_all_need_transform(self.app.curs, 1)\\n self.assertEqual(len(albums), 1)\\n self.assertEqual(albums[0].pk, pk)\",\n \"def fetchAlbumInfo(album_id):\\n url = 'https://api.spotify.com/v1/albums/' + album_id\\n req = requests.get(url)\\n\\n data = req.json() \\n\\n if not req.ok:\\n print \\\"error : \\\" + data['error']['message']\\n return {}\\n\\n\\n #create a new dictionary\\n album_info_dict = {}\\n #keys for the dictionary\\n album_info_dict['artist_id'] = data['artists'][0]['id']\\n album_info_dict['album_id'] = album_id\\n album_info_dict['name'] = data['name']\\n album_info_dict['year'] = data['release_date'][0:4]\\n album_info_dict['popularity'] = int(data['popularity']) #Spotify's popularity-meter, an integer\\n\\n return album_info_dict\",\n \"def get_album(album_id, albumtype):\\n result = {'titel': '',\\n 'artist': '',\\n # 'artistid': '',\\n 'artist_name': '',\\n 'details': [('Label/jaar:', ''),\\n ('Produced by:', ''),\\n ('Credits:', ''),\\n ('Bezetting:', ''),\\n ('Tevens met:', '')],\\n 'tracks': {},\\n 'opnames': []}\\n if album_id:\\n album = dmla.list_album_details(album_id)\\n result['titel'] = album.name\\n result['artist'] = album.artist\\n # result['artistid'] = album.artist.id\\n result['artist_name'] = album.artist.get_name()\\n text = album.label\\n if album.release_year:\\n if text:\\n text += ', '\\n text += str(album.release_year)\\n result['details'] = [('Label/jaar:', text),\\n ('Produced by:', album.produced_by),\\n ('Credits:', album.credits),\\n ('Bezetting:', album.bezetting),\\n ('Tevens met:', album.additional)]\\n if album:\\n result['tracks'] = {x.volgnr: (x.name, x.written_by, x.credits)\\n for x in dmla.list_tracks(album_id)}\\n result['opnames'] = [(x.type, x.oms) for x in\\n dmla.list_recordings(album_id)]\\n if albumtype == 'live':\\n result['details'].pop(0)\\n return result\",\n \"def multiple_ranks(our_data,start, end):\\n count = start\\n album_list = []\\n while count <= end:\\n album_list.append(find_by_rank(count))\\n count += 1\",\n \"def display_all():\\n results = artwork_db.get_all_artwork()\\n for artist in results:\\n print(artist)\",\n \"def get_albums_from_artists(sp, artist_uri_list):\\n\\n # Create header for output df\\n albums_list = [[\\\"name\\\", \\\"album_uri\\\", \\\"album_release_date\\\", \\\"artist_uri\\\"]]\\n\\n print(\\\"Log: Pulling data from Spotify. This can take a while...\\\")\\n\\n # Loop through list of artist uris\\n for artist_uri in artist_uri_list:\\n # Get album from artist\\n albums = sp.artist_albums(artist_uri)\\n \\n # Append each album to list\\n for album in albums[\\\"items\\\"]:\\n album_name = album[\\\"name\\\"]\\n album_uri = album[\\\"uri\\\"]\\n album_release_date = album[\\\"release_date\\\"]\\n albums_list.append([album_name, album_uri, album_release_date, artist_uri])\\n\\n # Create df from list of albums for all artist\\n albums_df = pd.DataFrame(data=albums_list[1:], columns=albums_list[0])\\n\\n print(\\\"Log: Finished pulling all albums from artist.\\\")\\n return albums_df\",\n \"def test_get_albums_no_params(self, id_name_elms, service_config, request):\\n service_config.album_store.get_all.return_value = id_name_elms\\n params = avalon.web.request.Parameters(request)\\n\\n service = avalon.web.services.AvalonMetadataService(service_config)\\n results = service.get_albums(params)\\n\\n assert results == id_name_elms, 'Expected all albums returned'\",\n \"def search_albums(self, needle):\\n return self._album_search.search(searchable(needle))\",\n \"def album():\\n\\tif not request.vars.id:\\n\\t\\tredirect(URL('index'))\\n\\tid = request.vars.id\\n\\treleasegroupname = db.executesql(\\\"select m1.name, m2.id from release_name as m1, release_group as m2 where m1.id = m2.name and m2.id = \\\"+id+\\\";\\\")\\n\\treleasegroup = db.executesql(\\\"select distinct(m2.id),m3.name,m5.name,m7.track_count,m2.date_year,m2.date_month,m2.date_day,m6.name,m2.barcode from release_group m1,release m2,release_name m3,medium m4,medium_format m5, country m6, tracklist m7 where m5.id = m4.format and m4.release = m2.id and m2.name = m3.id and m2.release_group = m1.id and m2.country = m6.id and m4.tracklist = m7.id and m1.id = \\\"+id+\\\";\\\")\\n\\turls = db.executesql(\\\"select m2.url from l_release_group_url m1, url m2 where m1.entity1 = m2.id and m1.entity0 = \\\"+id+\\\";\\\")\\n\\tmbid = db.executesql(\\\"select m1.gid from release_group m1 where m1.id = \\\"+id+\\\";\\\")\\n\\trating = db.executesql(\\\"select rating, count from release_group_rating_global where id = \\\"+id+\\\";\\\")\\n\\tavg_rating = float(rating[0][0])/float(rating[0][1])\\n\\tif auth.user:\\n\\t\\turating = db.executesql(\\\"select rating from release_group_rating_local where id = \\\"+str(id)+\\\" and user_id = \\\"+str(auth.user.id)+\\\";\\\")\\n\\t\\tif urating:\\n\\t\\t\\tuser_rating = urating[0][0]\\n\\t\\telse:\\n\\t\\t\\tuser_rating = 0\\n\\telse:\\n\\t\\tuser_rating = 0\\n\\ttry:\\n\\t\\tproxy = urllib2.ProxyHandler({'http': 'http://cs5090240:phone01202767129@10.10.78.62:3128'})\\n\\t\\tauthentication = urllib2.HTTPBasicAuthHandler()\\n\\t\\topener = urllib2.build_opener(proxy, authentication, urllib2.HTTPHandler)\\n\\t\\turllib2.install_opener(opener)\\n\\t\\tdestination = 'http://www.coverartarchive.org/release-group/'+mbid[0][0]\\n\\t\\t#dbg.set_trace()\\n\\t\\treq = urllib2.Request(url=destination,data=\\\"\\\")\\n\\t\\tf = urllib2.urlopen(req)\\n\\t\\t#dbg.set_trace()\\n\\t\\tdata =json.load(f)\\n\\t\\t#dbg.set_trace()\\n\\t\\tcoverarturl = data['images'][0]['image']\\n\\texcept:\\n\\t\\tcoverarturl = URL('static','images/no_image_found.jpg')\\n\\tlinks = []\\n\\twikiurl = \\\"\\\"\\n\\tfor url in urls:\\n\\t\\tif \\\"wikipedia\\\" in url[0]:\\n\\t\\t\\twikiurl = url[0]\\n\\t\\telse:\\n\\t\\t\\tlinks.append(url[0])\\n\\tif wikiurl:\\n\\t\\t#wiki = get_wiki_desc(str(wikiurl)+\\\"?printable=yes\\\")\\n\\t\\twiki = \\\"Description available at \\\"+str(wikiurl)\\n\\telse:\\n\\t\\twiki = \\\"No wiki description\\\"\\n\\treturn dict(releasegroupname=releasegroupname, releasegroup=releasegroup, links=links, wiki=wiki, wikiurl=wikiurl, coverarturl=coverarturl, avg_rating=avg_rating, user_rating=user_rating)\",\n \"def get_album(album_id):\\n return query_single(album_id, Album, album_schema)\",\n \"def add_songs(self, name, year, title):\\n\\n album_found = find_object(name, self.album)\\n if album_found is None:\\n print(\\\"Not Found \\\" + name)\\n album_found = Album(name, year, self.name)\\n self.add_album(album_found)\\n else:\\n print(\\\"Found album \\\"+name)\\n\\n album_found.add_songs(title)\",\n \"def get_song_list(self):\\n return self.song_list\",\n \"def random_by_genre_list(self):\\n\\n for genre in self.connection.walk_genres():\\n url = self.build_url({\\n \\\"mode\\\": \\\"random_by_genre_track_list\\\",\\n \\\"foldername\\\": genre[\\\"value\\\"].encode(\\\"utf-8\\\")})\\n\\n li = xbmcgui.ListItem(genre[\\\"value\\\"])\\n xbmcplugin.addDirectoryItem(\\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def test_cm_score_albums(dates):\\n test = pycmc.charts.cm_score.albums(\\n cm_album_id=\\\"6943281\\\",\\n chart_type=\\\"spotify-top\\\",\\n start_date=dates[\\\"start\\\"],\\n )\\n assert isinstance(test, list)\\n assert len(test)\",\n \"def get_albums_recent_played(session_):\\n artists = session_.query(Album).order_by(Album.played_at.desc()).all()\\n return artists\",\n \"def get(self):\\n return PhotoGalleryService().get_all(), 200\",\n \"def get_tracks(self):\\n self.c_album = self.albums_list.currentText()\\n self.clementine_tracks.clear()\\n self.albums_tracks.clear()\\n if self.artists_list.count() == 0: # this happens when the panel is reshown\\n return # after another panel was shown\\n if self.albums_list.count() == 0: # this happens during screen buildup\\n return # when only the first combobox is filled\\n if not self.albums_map[self.artist]:\\n qtw.QMessageBox.information(self, self._parent.title,\\n \\\"No (matched) albums for this artist\\\")\\n return\\n try:\\n self.a_album = self.albums_map[self.artist][self.c_album][1]\\n except KeyError:\\n qtw.QMessageBox.information(self, self._parent.title, \\\"This album \\\"\\n \\\"has not been matched yet\\\")\\n return\\n a_tracks, c_tracks = read_album_tracks(self.a_album,\\n self.artist, self.c_album)\\n for item in c_tracks:\\n new = qtw.QTreeWidgetItem([item])\\n self.clementine_tracks.addTopLevelItem(new)\\n for item in a_tracks:\\n new = qtw.QTreeWidgetItem([item])\\n self.albums_tracks.addTopLevelItem(new)\\n reimport_possible = False\\n if len(c_tracks) != len(a_tracks):\\n reimport_possible = True\\n else:\\n for ix, item in enumerate(a_tracks):\\n try:\\n if not (item.startswith(c_tracks[ix][0])\\n or c_tracks[ix][0].startswith(item)):\\n reimport_possible = True\\n except IndexError:\\n reimport_possible = True\\n self.b_copy.setEnabled(reimport_possible)\",\n \"def get_all_sets(user_id):\\n def helper():\\n page_num = 1\\n while True:\\n obj = jsonp_to_obj(flickr.photosets_getList(user_id=user_id, page=page_num))\\n for x in obj['photosets']['photoset']:\\n yield (x['id'], x['title']['_content']) \\n if page_num == obj['photosets']['pages']:\\n break\\n page_num+=1\\n\\n return list(helper())\",\n \"def test_get_songs_by_album(self, track_elms, service_config, request):\\n album_id = uuid.UUID(avalon.compat.to_uuid_input('f83fdec7-510f-44a5-87dc-61832669a582'))\\n service_config.track_store.get_by_album.return_value = track_elms\\n service_config.id_cache.get_album_id.return_value = album_id\\n request.args['album'] = 'Album'\\n params = avalon.web.request.Parameters(request)\\n\\n service = avalon.web.services.AvalonMetadataService(service_config)\\n results = service.get_songs(params)\\n\\n assert results == track_elms, 'Expected matching tracks returned'\\n service_config.track_store.get_by_album.assert_called_with(album_id)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8068434","0.80611455","0.80573","0.80511075","0.76079464","0.7442075","0.73546255","0.73167896","0.7289843","0.724343","0.7202005","0.7099588","0.70723593","0.7056376","0.705161","0.7005844","0.6928635","0.68897617","0.6879395","0.6865144","0.68555135","0.6843721","0.680122","0.67724466","0.6764758","0.6756737","0.6717459","0.67088366","0.6621029","0.65870374","0.6563595","0.6560031","0.65583175","0.6506385","0.6446733","0.6442215","0.6383495","0.63778186","0.63539344","0.6344917","0.63078314","0.6299733","0.6298833","0.62682164","0.6258863","0.6257029","0.6207951","0.6181731","0.6168285","0.6164265","0.6156701","0.6156303","0.6144837","0.614455","0.6129432","0.6047854","0.60320514","0.6014323","0.6001557","0.59991235","0.59971917","0.59933156","0.59916264","0.59883225","0.5950941","0.5946742","0.59451467","0.59438485","0.59433174","0.593553","0.5932554","0.5929146","0.59265244","0.5916983","0.5915789","0.59136385","0.5901936","0.5898792","0.5886816","0.586285","0.58620995","0.5857038","0.58546627","0.58368725","0.5834608","0.5826653","0.58234143","0.58137167","0.58097595","0.58095336","0.5808842","0.57943976","0.5785617","0.5778275","0.5778198","0.5773909","0.5768541","0.57679576","0.5751068","0.57406396"],"string":"[\n \"0.8068434\",\n \"0.80611455\",\n \"0.80573\",\n \"0.80511075\",\n \"0.76079464\",\n \"0.7442075\",\n \"0.73546255\",\n \"0.73167896\",\n \"0.7289843\",\n \"0.724343\",\n \"0.7202005\",\n \"0.7099588\",\n \"0.70723593\",\n \"0.7056376\",\n \"0.705161\",\n \"0.7005844\",\n \"0.6928635\",\n \"0.68897617\",\n \"0.6879395\",\n \"0.6865144\",\n \"0.68555135\",\n \"0.6843721\",\n \"0.680122\",\n \"0.67724466\",\n \"0.6764758\",\n \"0.6756737\",\n \"0.6717459\",\n \"0.67088366\",\n \"0.6621029\",\n \"0.65870374\",\n \"0.6563595\",\n \"0.6560031\",\n \"0.65583175\",\n \"0.6506385\",\n \"0.6446733\",\n \"0.6442215\",\n \"0.6383495\",\n \"0.63778186\",\n \"0.63539344\",\n \"0.6344917\",\n \"0.63078314\",\n \"0.6299733\",\n \"0.6298833\",\n \"0.62682164\",\n \"0.6258863\",\n \"0.6257029\",\n \"0.6207951\",\n \"0.6181731\",\n \"0.6168285\",\n \"0.6164265\",\n \"0.6156701\",\n \"0.6156303\",\n \"0.6144837\",\n \"0.614455\",\n \"0.6129432\",\n \"0.6047854\",\n \"0.60320514\",\n \"0.6014323\",\n \"0.6001557\",\n \"0.59991235\",\n \"0.59971917\",\n \"0.59933156\",\n \"0.59916264\",\n \"0.59883225\",\n \"0.5950941\",\n \"0.5946742\",\n \"0.59451467\",\n \"0.59438485\",\n \"0.59433174\",\n \"0.593553\",\n \"0.5932554\",\n \"0.5929146\",\n \"0.59265244\",\n \"0.5916983\",\n \"0.5915789\",\n \"0.59136385\",\n \"0.5901936\",\n \"0.5898792\",\n \"0.5886816\",\n \"0.586285\",\n \"0.58620995\",\n \"0.5857038\",\n \"0.58546627\",\n \"0.58368725\",\n \"0.5834608\",\n \"0.5826653\",\n \"0.58234143\",\n \"0.58137167\",\n \"0.58097595\",\n \"0.58095336\",\n \"0.5808842\",\n \"0.57943976\",\n \"0.5785617\",\n \"0.5778275\",\n \"0.5778198\",\n \"0.5773909\",\n \"0.5768541\",\n \"0.57679576\",\n \"0.5751068\",\n \"0.57406396\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7306566"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":14299,"cells":{"query":{"kind":"string","value":"This function makes a list of all the songs in album"},"document":{"kind":"string","value":"def songs_list(name_of_album):\r\n songs = \"\"\r\n data = dbase()\r\n data = data[name_of_album][0]\r\n for song in data.keys():\r\n songs += song\r\n songs += \", \"\r\n return songs[:-2]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def simple_songs_list(name_of_album):\r\n songs = []\r\n data1 = dbase()\r\n data1 = data1[name_of_album][0]\r\n for song in data1.keys():\r\n songs += [song]\r\n return songs","def album_list(self):\n\n artist_id = self.addon_args[\"artist_id\"][0]\n\n xbmcplugin.setContent(self.addon_handle, \"albums\")\n\n for album in self.connection.walk_artist(artist_id):\n self.add_album(album)\n\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_ALBUM)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_ARTIST)\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmcplugin.SORT_METHOD_VIDEO_YEAR)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def get_album_list():\n\n # TODO: Paginate this, etc\n entities = PhotoAlbum.query().order(-PhotoAlbum.title).fetch(1000)\n\n return entities","def simple_album_list():\r\n album_list = []\r\n data = dbase()\r\n for album in data.keys():\r\n album_list += [album]\r\n return album_list","def get_albums(self):\n self.artist = self.artists_list.currentText()\n self.c_albums = [x['album'] for x in dmlc.list_albums(self.artist)\n if [x['album'] in self.albums_map[self.artist]]]\n self.albums_list.clear()\n self.albums_list.addItems(self.c_albums)\n self.update_navigation_buttons()","def get_songs_by_album(self, album_id):\n return self.__get('song', album_id)","def get_album_songs(self, album_id):\n url = get_album_url(album_id)\n result = self.get_request(url)\n\n return result['album']['songs']","def get_albums():\n return query_multiple(request.args, album_search, \\\n album_filter, Album, albums_schema)","def search_for_album(album_name):\n\n print(f'Searching for album: {album_name}')\n\n search_result = spotifyObject.search(q=f'\"{album_name}\"', limit=20, type='album')\n\n items = search_result['albums']['items']\n\n results = []\n\n for item in items:\n if len(item['artists']) > 1:\n artists = tuple(art['name'] for art in item['artists'])\n else:\n artists = item['artists'][0]['name']\n\n results.append((artists, item['name'], item['id']))\n\n return results","def get_all_songs():\r\n return [Song.song_json(song) for song in Song.query.all()]","def read_album_tracks(id, artist_name, album_name):\n list_a = [x.name for x in dmla.list_tracks(id)]\n list_c = [x['title'] for x in dmlc.list_tracks_for_album(artist_name, album_name)\n if x['track'] != -1]\n return list_a, list_c","def get_albums(playlist_name):\n\n playlist_id = find_playlist(playlist_name)\n \n items = get_playlist_tracks(playlist_id=playlist_id)\n \n track_values = []\n \n for item in items:\n track = item['track']\n album = track['album']\n artists = tuple(artist['name'] for artist in album['artists'])\n \n track_values.append((album['name'], artists[0]))\n \n album_details = namedtuple('AlbumDetails', 'album artist')\n \n for tup in dict.fromkeys(track_values):\n yield album_details(*tup)","def getSongsFromAlbum(albumLink):\n albumLink = str(albumLink)\n try:\n html = urllib.request.urlopen(albumLink).read()\n soup = bs4.BeautifulSoup(html, 'html.parser')\n table = soup.findAll(\"a\")[5:]\n songLinks = []\n for entry in table:\n text = str(re.findall(\"\\\".*\\\"\", str(entry)))\n text = re.sub(\"[\\]\\['\\\"]\", \"\", text)\n link = albumLink + str(text)\n songLinks.append(link)\n except:\n return []\n return songLinks","def get_albums(self):\n artist = self.get_request_arg(\"artist\")\n if artist:\n lib = self.ctrl.library\n lst = sorted(self.ctrl.library.get_albums(artist))\n albums = [{\"artist\": artist,\n \"album\": album,\n \"path\": lib.get_path(artist, album)} for album in lst]\n if lst:\n return self.resp_from_data(albums)\n return self.resp_from_data(\n {\"message\": f\"No album found for artist={artist}\"}, 400)","def get_albums_alpha(session_):\n artists = session_.query(Album).order_by(Album.title.asc()).all()\n return artists","def get_album_tracks(self):\n track_list = self.soup.findAll('div', class_='chart_row')\n number_of_tracks = 0\n titles = []\n urls = []\n track_numbers = []\n \n for track in track_list:\n track_title = re.sub(' Lyrics', '', \" \".join(track.h3.text.split()))\n lyrics_url = track.a['href']\n track_number = track.span.span.text.strip()\n \n if track_number == '':\n # Sometimes there are additional urls that are not a song's lyrics. Skip these.\n continue\n else:\n track_number = int(track_number)\n \n number_of_tracks += 1\n titles.append(track_title)\n urls.append(lyrics_url)\n track_numbers.append(track_number)\n \n if self.song_order:\n # Check that order values are okay.\n for number in self.song_order:\n if number > number_of_tracks:\n raise SongOrderValueError(f'Track number given ({number}) exceeds number of tracks ({number_of_tracks})')\n \n for title, url, number in zip(titles, urls, track_numbers):\n if self.song_order:\n if number not in self.song_order:\n print(f'Skipping song: {number:02d} {title}')\n continue\n \n lyrics = self.get_single_lyrics(url)\n self.album.add_song(Song(title=title, track_number=number, lyrics=lyrics))\n\n self.album.number_of_tracks = number_of_tracks","def selectSongs():\n\tsql =\"select songs.title, artist.name, album.name from songs, album, \" \\\n\t+ \"artist join songs_album on songs.id=songs_album.songs_id \" \\\n\t+ \"join songs_artist on songs.id=songs_artist.songs_id \" \\\n\t+ \"where album.id=songs_album.album_id \" \\\n\t+ \"and artist.id=songs_artist.artist_id\"\n\tc, conn = connect()\n\tretr = c.execute(sql)\n\tsongs = []\n\tfor entry in retr:\n\t\tsongs.append(music.song(title=entry[0], artist=entry[1], album=entry[2]))\n\treturn songs","def get_song_list(self):\n return self.song_list","def get_albums_by_artist(albumtype, search_for, sort_on):\n return list(dmla.list_albums_by_artist(albumtype, search_for, sort_on))","def song_album(ans):\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n if ans == song:\r\n return album","def tracked_albums():\n print('Your Google Photos Albums ([X] = tracked):')\n albums = get_albums(service)\n for i, a in enumerate(albums):\n check = 'X' if a.id in library.get_album_ids() else ' '\n print('[{}] {}. {}'.format(check, i+1, a.title))\n return albums","def album_list_for_user():\r\n answer = \"\"\r\n data = dbase()\r\n for album in data.keys():\r\n answer += album + \", \"\r\n return answer[:-2]","def get_songs(library):\n songs = []\n for song in library:\n title, artist, album = song['title'], song['artist'], song['album']\n seconds = int(song['durationMillis']) // 1000\n songs.append({'artist': artist, 'title': title, 'album': album, 'seconds': seconds})\n return songs","def get_songs(self, song_list):\n self.songs = [[s.name, s.movie_name] for s in song_list\n if s.raga == self.name]","def read_artist_albums(id, name):\n list_a = [(x.name, str(x.release_year), str(x.id))\n for x in dmla.list_albums_by_artist('', id, 'Jaar')]\n list_c = [(x['album'], x['year']) for x in dmlc.list_albums(name)]\n return list_a, list_c","def getAllSongs(self):\n return self.__songDictionary","def get_songs(self):\n search_object = {\"size\":25000,\n 'query': {'term': {FIELD_FINGERPRINTED: True}}, \"fields\": [FIELD_SONGNAME, FIELD_FILE_SHA1,\n FIELD_TOTAL_HASHES]}\n response = self.cursor.search(index = SONGS_INDEXNAME, body=search_object)\n #print(\"get_songs response: \",response)\n arr = []\n for hit in response[\"hits\"][\"hits\"]:\n dct = {\"song_name\":hit['_source'][FIELD_SONGNAME],\"total_hashes\":hit['_source'][FIELD_TOTAL_HASHES],\n \"file_sha1\":hit['_source'][FIELD_FILE_SHA1]}\n arr.append(dct)\n return arr","def get_albums(entity_url: str) -> list:\n entity_url = entity_url.rstrip(\"/\")\n response = requests.get(entity_url)\n soup = BeautifulSoup(response.text, \"html.parser\")\n albums = []\n for link in soup.find_all('a'):\n url = link.get('href')\n if url is not None and \"/album/\" in url:\n if url.startswith(\"http\"):\n albums.append(url)\n else:\n albums.append(f\"{entity_url}{url}\")\n return albums","def albums_by_genre_list(self):\n\n genre = self.addon_args[\"foldername\"][0].decode(\"utf-8\")\n\n xbmcplugin.setContent(self.addon_handle, \"albums\")\n\n for album in self.connection.walk_album_list_genre(genre):\n self.add_album(album, show_artist=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def all_titles(our_data):\n return [album['album'] for album in our_data]","def get_songs(path):\r\n song_list = []\r\n genre_paths = glob.glob(path + '/*')\r\n for genre_path in genre_paths:\r\n artist_paths = glob.glob(genre_path + '/*')\r\n for artist_path in artist_paths:\r\n album_paths = glob.glob(artist_path + '/*')\r\n for album_path in album_paths:\r\n lyrics_paths = glob.glob(album_path + '/*.txt')\r\n for lyrics_path in lyrics_paths:\r\n song = {}\r\n song[\"genre\"] = genre_path.replace(path + '/', '')\r\n song[\"artist\"] = artist_path.replace(genre_path + '/', '')\r\n song[\"album\"] = album_path.replace(artist_path + '/', '')\r\n song[\"lyrics\"] = open(lyrics_path).read()\r\n song[\"name\"] = lyrics_path[:-4].replace(album_path + '/', '')\r\n song[\"x\"] = 0\r\n song[\"y\"] = 0\r\n song_list.append(song)\r\n return song_list","def songs(self):\n return Html.find_song_names(self.content)","def albums(self, albums, **kwargs):\n album_list = map(self._get_album_id, albums)\n return self._get(API.ALBUMS.value, ids=\",\".join(album_list), **kwargs)","def GetAlbums(self, start=0, end=0, sortmethod='label', sortorder='ascending', filter='', artistid=None):\n self.logger.debug(\"Loading all albums for ARTISTID \" + str(artistid))\n try:\n xbmc = Server(self.url('/jsonrpc', True))\n sort = {'order': sortorder, 'method': sortmethod, 'ignorearticle': True}\n properties=['artist', 'title', 'year', 'description', 'thumbnail']\n limits = {'start': int(start), 'end': int(end)}\n if artistid is not None:\n filter = {'artistid': int(artistid)}\n else:\n filter = {'or': [\n {'field': 'album', 'operator': 'contains', 'value': filter},\n {'field': 'artist', 'operator': 'contains', 'value': filter}\n ]}\n return xbmc.AudioLibrary.GetAlbums(properties=properties, limits=limits, sort=sort, filter=filter)\n except ValueError:\n return","def test_get_songs_by_album(self, track_elms, service_config, request):\n album_id = uuid.UUID(avalon.compat.to_uuid_input('f83fdec7-510f-44a5-87dc-61832669a582'))\n service_config.track_store.get_by_album.return_value = track_elms\n service_config.id_cache.get_album_id.return_value = album_id\n request.args['album'] = 'Album'\n params = avalon.web.request.Parameters(request)\n\n service = avalon.web.services.AvalonMetadataService(service_config)\n results = service.get_songs(params)\n\n assert results == track_elms, 'Expected matching tracks returned'\n service_config.track_store.get_by_album.assert_called_with(album_id)","def get_albums(username):\n cur = mysql.connection.cursor()\n cur.execute(\"SELECT * FROM album WHERE username = '{0}'\".format(username))\n return cur.fetchall()","def get_song_list(self, artist: str) -> List[str]:\n artist = self.just_replace_strings_with_nothing(artist)\n\n url = self.list_url + artist + '/songs'\n\n resp = requests.get(url)\n\n content = bs4.BeautifulSoup(resp.content)\n\n song_list = content.text[content.text.index(\n 'MP3s') + 5:content.text.index('About Song List')]\n\n song_list = re.sub('\\n', ',', song_list)\n song_list = re.sub(',+', ',', song_list)\n song_list = re.sub(', ,', ', ', song_list)\n\n song_list = re.split(',', song_list)\n for i in range(len(song_list)):\n song_list[i] = song_list[i].lstrip(' ')\n song_list[i] = re.sub(\"[.,']\", '', song_list[i])\n song_list[i] = re.sub(\"&\", 'and', song_list[i])\n song_list[i] = re.sub('\\s+', ' ', song_list[i])\n\n song_list = [i for i in song_list if i != '']\n\n return song_list","def fetchAlbumIds(artist_id):\n url = 'https://api.spotify.com/v1/artists/' + artist_id + '/albums?market=US&album_type=album'\n req = requests.get(url)\n\n data = req.json()\n\n #checking for bad return value\n if not req.ok:\n print \"error : \" + data['error']['message']\n return \"error : \" + data['error']['message']\n\n albums = []\n for item in data['items']:\n \talbums.append(item['id'])\n\n return albums","def get_all_songs() -> Generator[dict, None, None]:\n\n logging.debug(\"Fetching from server\")\n\n api = _get_api()\n\n for song_page in api.get_all_songs(incremental=True):\n for song in song_page:\n yield song","def get_tracks_from_albums(sp, album_uri_list):\n\n track_list = [[\"track_name\", \"track_uri\", \"track_release_date\"]]\n\n print(\"Log: Pulling data from Spotify. This can take a while...\")\n\n for album_uri in album_uri_list:\n album_tracks = sp.album_tracks(album_uri, limit=50, offset=0)[\"items\"]\n count_tracks_in_album = len(album_tracks)\n album_release_date = sp.album(album_uri)[\"release_date\"]\n\n # This part is probably very slow and should be improved by accessing the API less often\n for track_number in range(count_tracks_in_album):\n track_name = album_tracks[track_number][\"name\"]\n track_uri = album_tracks[track_number][\"uri\"]\n \n track_list.append([track_name, track_uri, album_release_date])\n\n # Create df from list of tracks for all albums\n track_df = pd.DataFrame(data=track_list[1:], columns=track_list[0])\n \n print(\"Log: Finished pulling all tracks from albums.\")\n return track_df","def get_all_musicians(self):\n self.cursor.execute(\"select * from musicians\")\n self.connection.commit()\n return self.cursor.fetchall()","def genre_list(self):\n\n for genre in self.connection.walk_genres():\n url = self.build_url({\n \"mode\": \"albums_by_genre_list\",\n \"foldername\": genre[\"value\"].encode(\"utf-8\")})\n\n li = xbmcgui.ListItem(genre[\"value\"])\n xbmcplugin.addDirectoryItem(\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def print_album(albums):\n print(\"\\nPrinting album data:\")\n for album in albums:\n print(f\"Artist Name: {album['name'].title()}\")\n print(f\"\\tAlbum Name: {album['album'].title()}\")\n if (album['num_songs']):\n print(f\"\\tNumber Songs: {album['num_songs']}\")\n\n print(\"\")","def get_songs(self, offset=None):\n return self.__get('songs')","def GetRecentAlbums(self, limit=5):\n self.logger.debug(\"Fetching recently added Music\")\n try:\n xbmc = Server(self.url('/jsonrpc', True))\n properties = ['artist', 'albumlabel', 'year', 'description', 'thumbnail']\n limits = {'start': 0, 'end': int(limit)}\n return xbmc.AudioLibrary.GetRecentlyAddedAlbums(properties=properties, limits=limits)\n except:\n self.logger.error(\"Unable to fetch recently added Music!\")\n return","def all_artists(our_data):\n return [album['artist'] for album in our_data]","def get_tracks_from(album_uri):\n track_uris = []\n album = spotify.album_tracks(album_id=album_uri)\n\n for track in album['items']:\n track_uris.append(track['uri'])\n\n return track_uris","def get_albums(self):\n if self.artist_list.count() == 0: # this happens when the panel is reshown\n return # after another panel was shown\n self.c_artist = self.artist_list.currentText()\n ## self.last_handled = self.artist_list.currentIndex()\n # remember first handled item for currency communication over panels\n self._parent.current_data = self.c_artist\n self.a_artist = self.artist_map[self.c_artist]\n a_albums, c_albums = read_artist_albums(self.a_artist, self.c_artist)\n for name, year, id, *rest in self.albums_to_save[self.c_artist]:\n a_albums.append((name, year, str(id)))\n self.clementine_albums.clear()\n for item, year in c_albums:\n new = qtw.QTreeWidgetItem([item])\n new.setData(0, core.Qt.UserRole, year)\n try:\n new.setText(1, str(self.albums_map[self.c_artist][item][1]))\n except KeyError:\n pass\n self.clementine_albums.addTopLevelItem(new)\n self.albums_albums.clear()\n self.lookup = collections.defaultdict(list)\n for item in a_albums:\n new = qtw.QTreeWidgetItem([x.replace('None', '') for x in item])\n self.albums_albums.addTopLevelItem(new)\n self.lookup[item[0]].append(item[2])\n self.tracks = collections.defaultdict(list)","def scrape_all_songs():\n print('Scraping all songs from {}'.format(URL))\n\n soup = scrapekit.handle_url(URL)\n song_elements = []\n tables = soup.findAll('table')\n\n for t in tables:\n field_index = scrapekit.get_col_index(t, field_name=\"Song\")\n\n if field_index:\n song_elements.extend(scrapekit.scrape_table_col(t, field_index))\n\n links = []\n for element in song_elements:\n l = element.find('a')\n if l:\n links.append(PREFIX + l.attrs.get('href', ''))\n return links","def get_albums_by_text(albumtype, search_type, search_for, sort_on):\n if albumtype == 'studio':\n search_on = {0: '*', 2: 'name', 3: 'produced_by', 4: 'credits', 5: 'bezetting'}[search_type]\n elif albumtype == 'live':\n search_on = {0: '*', 2: 'name', 3: 'name', 4: 'produced_by', 5: 'bezetting'}[search_type]\n return list(dmla.list_albums_by_search(albumtype, search_on, search_for, sort_on))","def test_get_songs_by_album_id(self, track_elms, service_config, request):\n album_id = uuid.UUID(avalon.compat.to_uuid_input('37cac253-2bca-4a3a-be9f-2ac655e04ad8'))\n service_config.track_store.get_by_album.return_value = track_elms\n request.args['album_id'] = six.text_type(album_id)\n params = avalon.web.request.Parameters(request)\n\n service = avalon.web.services.AvalonMetadataService(service_config)\n results = service.get_songs(params)\n\n assert results == track_elms, 'Expected matching tracks returned'\n service_config.track_store.get_by_album.assert_called_with(album_id)","def find_album_playlist(data):\n\n return data['album'].lower() + '.m3u'","def getAlbumsFromArtist(artistLink):\n artistLink = str(artistLink)\n url = \"http://www.ohhla.com/\"+artistLink\n if artistLink[0:4]==\"http:\":\n url = artistLink\n try:\n html = urllib.request.urlopen(url).read()\n soup = bs4.BeautifulSoup(html, 'html.parser')\n table = soup.findAll(\"a\")[5:]\n albumLinks = []\n for entry in table:\n text = str(re.findall(\"\\\".*\\\"\", str(entry)))\n text = re.sub(\"[\\]\\['\\\"]\", \"\", text)\n link = url + str(text)\n if len(re.findall(\"(?:http)\",link)) == 1:\n albumLinks.append(link)\n except:\n return []\n return albumLinks","def appendAlbum(song):\n\tsql = []\n\tsql.append(\"INSERT INTO ALBUM ('name') VALUES ('\" \n\t+ '/'.join(song.album) + \"');\")\n\t\n\tsql.append(\"INSERT INTO songs_album ('songs_id', 'album_id')\"\n\t+ \" VALUES ((select id from songs where hash = '\" + str(song.hash) + \"'), \"\n\t+ \"(select id from album where name = '\" + '/'.join(song.album) + \"'));\")\n\tsql.append(\"INSERT INTO artist_album ('artist_id', 'album_id')\"\n\t+ \" VALUES ((select id from songs where hash = '\" + str(song.hash) + \"'), \"\n\t+ \"(select id from album where name = '\" + '/'.join(song.album) + \"'));\")\n\t\n\treturn sql","def song_lyrics(ans):\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n if ans == song:\r\n words = dbase()[album][0][song]\r\n words = words[2]\r\n return words","def get_by_artist(cls, artist):\n results = cls.query().filter(FileRecord.artist == artist).order_by(\n FileRecord.year).all()\n albums = []\n if results and len(results) > 0:\n for result in results:\n albums.append(cls(*result))\n\n return albums\n\n return albums","def get_songs_of_all_artists(self):\n\t\tartists = self.db.artists.find()\n\t\tall_songs = []\n\t\tfor artist in artists:\n\t\t\tall_songs.extend(artist['songs'])\n\t\tall_songs = list(set(all_songs))\n\t\treturn all_songs","def getAlbums():\n\n r = requests.get(ALBUMS_URL, headers=HEADER, timeout=5)\n\n if r.status_code == 200:\n \n try:\n albums = [] \n soup = BeautifulSoup(r.text, \"html.parser\")\n album = soup.find_all(\"div\", class_=\"duv\")\n for i,al in enumerate(album): \n temp = {}\n temp['link'] = al.find_all(\"a\")[0]['href']\n temp['album'] = al.find_all(\"span\", class_=\"title\")[0].text\n albums.append(temp)\n\n if len(albums) > 0:\n return albums\n else:\n print(\"No albums found on site2!\")\n sys.exit(0)\n \n except Exception as e:\n print(\"Failed to get albums from site2\\n\", e)\n sys.exit(0)\n\n else:\n print(\"Albums Url fetch failed! Status code: {}\".format(r.status_code))\n sys.exit(0)","def search_song(self, name):\n self.logger.debug('Searched for Song: {}'.format(name))\n results = self.sp.search(q='track:' + name, type='track')\n songs = [song for song in results['tracks']['items']]\n i = 1\n songs_ls = []\n table_ls = []\n for song in songs:\n table_ls.append([i,\n song['name'][0:20].strip(),\n song['album']['name'][0:20].strip(),\n \"%0.2f\" % (song['duration_ms'] / 60000),\n song['popularity']])\n songs_ls.append(song['uri'])\n i = i + 1\n return songs_ls, table_ls","def test_album_tracks_lastfm(monkeypatch, bot):\n song = Song('Sabaton', '1 6 4 8')\n with monkeypatch.context() as mkp:\n # An empty list should be returned if we can't find the album's name\n mkp.setattr(song, 'fetch_album_name', lambda: None)\n assert bot.get_album_tracks_lastfm(song) == []\n\n tracks = bot.get_album_tracks_lastfm(song)\n tracks = '\\n'.join(tracks)\n assert 'carolus rex' in tracks\n assert 'en livstid i krig' in tracks","def get_albums(self):\n return AlbumView.get_by_artist(self.name)","def get_tracks(self):\n artist = self.get_request_arg(\"artist\")\n album = self.get_request_arg(\"album\")\n if not (album and artist):\n return self.resp_from_data(\n {\"message\": \"Please specify a valid artist and album\"}, 403)\n else:\n tracks = self.ctrl.library.get_tracks(artist, album)\n return self.resp_from_data(tracks)","def list(ctx):\n\tfor songName in songs:\n\t\tyield from bot.send_message(ctx.message.author,songName)","def search_for_tracks(album_id):\n \n track_results = spotifyObject.album_tracks(album_id)\n track_results = track_results['items']\n ids = [track['id'] for track in track_results]\n\n return ids","def get_all_songs(self):\n to_send = self.db.get_all_songs()\n to_send = '$'.join(to_send)\n self.send_message(to_send)","def add_songs(self, name, year, title):\n\n album_found = find_object(name, self.album)\n if album_found is None:\n print(\"Not Found \" + name)\n album_found = Album(name, year, self.name)\n self.add_album(album_found)\n else:\n print(\"Found album \"+name)\n\n album_found.add_songs(title)","def get_songs_of_artist(self, artist_id: int):\n\t\tartist = self.db.artists.find_one({'id': artist_id})\n\t\treturn artist['songs']","def get_albums(self, offset=None):\n return self.__get('albums')","def get_artist_list():\n return list(dmla.list_artists())","def view_songs_push(self):\n #clear all data first\n self.model.removeRows(0, self.model.rowCount())\n songs = glob.glob(\"Fixed/*/*/*\")\n for song in songs:\n data = mutagen.File(song, easy=True)\n track = get_track(data['title'][0], data['artist'][0])\n self.add_track_to_box(track)","def search_song(self, name, album=None, artist=None):\n\n endpoint = \"/search\"\n query = f\"track:{self._strip_punctuation(name)}\"\n if artist:\n query += f\" artist:{self._strip_punctuation(artist)}\"\n if album:\n query += f\" album:{self._strip_punctuation(album)}\"\n response = self._send(endpoint, \"GET\", params={\"q\": query, \"type\": \"track\"})\n tracks = response.json()[\"tracks\"]\n if tracks[\"total\"] == 0:\n raise SongNotFoundError(\n f\"song name={name} artist={artist} album={album} could not be found\"\n )\n return tracks[\"items\"]","def get_albums(self, limit=None):\n url = (\"https://api.imgur.com/3/account/{0}/albums/{1}\".format(self.name,\n '{}'))\n resp = self._imgur._send_request(url, limit=limit)\n return [Album(alb, self._imgur, False) for alb in resp]","def get_albums_recent_played(session_):\n artists = session_.query(Album).order_by(Album.played_at.desc()).all()\n return artists","def sort_album(self):\n self.sort('album')","def findsimilaralbums(index, similarartists): # {{{1\n albums = []\n for [similarity, artistname] in similarartists:\n for albumname in index.findalbumsbyartist(artistname):\n albums.append([similarity, [artistname, albumname]])\n return albums","def get_all_song_names(self):\n try:\n # Auto-close\n with closing(self.connection) as con:\n # Auto-commit\n with con:\n # Auto-close\n with closing(con.cursor()) as cursor:\n cursor.execute(\"\"\"\n SELECT name\n FROM nodes\n WHERE type = \"song\";\n \"\"\")\n return [x[0] for x in cursor.fetchall()]\n except sqlite3.OperationalError as e:\n print(\"ERROR: Could not retrieve songs: {}\".format(str(e)))\n return []","def album_tracks(self, album_id, limit=50, offset=0, **kwargs):\n _id = self._get_album_id(album_id)\n # pylint: disable=no-member\n return self._get(\n API.ALBUM_TRACKS.value.format(id=_id), limit=limit, offset=offset, **kwargs\n )","async def search_song(album_name):\n async with aiohttp.ClientSession() as session:\n async with session.get('https://bandcamp.com/api/fuzzysearch/1/autocomplete?q=' + album_name) as resp:\n response = await resp.json()\n\n results = response.get('auto', {}).get('results', [])\n results = [res for res in results if res.get('type') == 't']\n if not results:\n raise NotFound\n result = results[0]\n async with session.get(result.get('url', 'https://bandcamp.com/')) as resp:\n response = await resp.text()\n try:\n result['release_date'] = response.split('album_release_date: \"')[-1].split('\",')[0].split(':')[0]\n except:\n result['release_date'] = '01 Jan 1970 00'\n result['TrackAlbum'] = bs4.BeautifulSoup(response, 'html.parser').find('span', itemprop='inAlbum').text.strip()\n\n return BandcampSong(result)","def playlist(self):\n def iconv(s):\n encoding = self.options[\"id3_encoding\"]\n try:\n if encoding:\n return s.encode('latin1').decode(encoding).encode('utf-8')\n else:\n return s.encode('latin1')\n except UnicodeEncodeError:\n return \"\"\n\n lst = []\n r = self.x.playlist_list_entries()\n r.wait()\n for id in r.get_list():\n r = self.x.medialib_get_info(id)\n r.wait()\n if r.iserror():\n print r.get_error()\n lst.append(' ')\n continue\n song = r.get_propdict()\n try:\n artist = iconv(song[('plugin/id3v2', 'artist')])\n except KeyError:\n try:\n artist = iconv(song[('plugin/mad', 'artist')])\n except KeyError:\n artist = ''\n try:\n title = iconv(song[('plugin/id3v2', 'title')])\n except KeyError:\n try:\n title = iconv(song[('plugin/mad', 'title')])\n except KeyError:\n title = ''\n if artist == \"\" and title == \"\":\n name = os.path.split(song[('server', 'url')])[1]\n name = os.path.splitext(name)[0]\n name = urllib.unquote(name.decode('utf-8').encode('latin1'))\n name = name.replace(\"+\", \" \")\n lst.append(' ' + name)\n else:\n lst.append(' %s - %s' % (artist.ljust(6), title))\n\n return lst","async def search_album(album_name):\n async with aiohttp.ClientSession() as session:\n async with session.get('https://bandcamp.com/api/fuzzysearch/1/autocomplete?q=' + album_name) as resp:\n response = await resp.json()\n\n results = response.get('auto', {}).get('results', [])\n results = [res for res in results if res.get('type') == 'a']\n if not results:\n raise NotFound\n result = results[0]\n async with session.get(result.get('url', 'https://bandcamp.com/')) as resp:\n response = await resp.text()\n try:\n result['release_date'] = response.split('album_release_date: \"')[-1].split('\",')[0].split(':')[0]\n except:\n result['release_date'] = '01 Jan 1970 00'\n result['track_list'] = [getattr(aa.find('span'), 'text', '') for aa in bs4.BeautifulSoup(response, 'html.parser').find('table', {'class':'track_list'}).find_all('tr')]\n\n return BandcampAlbum(result)","def findMP3s(path=None):\n \n if not path:\n \n lib_path = r'.\\songs'\n else:\n lib_path = path\n \n \n all_songs = []\n \n #folder from os.walk is: root, dirnames, filenames\n for rt, dirs, files in os.walk(lib_path):\n \n for fp in files:\n if fnmatch.fnmatch(fp, '*.mp3'):\n fullpath = r'{}\\{}'.format(rt, fp)\n all_songs.append(fullpath)\n \n #pp2(all_songs)\n print 'found {} songs'.format(len(all_songs))\n \n return all_songs","def _get_mb_tracks(albumid):\n ns = {'mb': 'http://musicbrainz.org/ns/mmd-2.0#'}\n url = \"http://musicbrainz.org/ws/2/release/\" + albumid\n query = {\"inc\": \"recordings\"}\n wdata = _do_query(url, query, err='album search error')\n\n if not wdata:\n return None\n\n root = ET.fromstring(wdata)\n tlist = root.find(\"./mb:release/mb:medium-list/mb:medium/mb:track-list\",\n namespaces=ns)\n mb_songs = tlist.findall(\"mb:track\", namespaces=ns)\n tracks = []\n path = \"./mb:recording/mb:\"\n\n for track in mb_songs:\n\n try:\n title, length, rawlength = \"unknown\", 0, 0\n title = track.find(path + \"title\", namespaces=ns).text\n rawlength = track.find(path + \"length\", namespaces=ns).text\n length = int(round(float(rawlength) / 1000))\n\n except (ValueError, AttributeError):\n xprint(\"not found\")\n\n tracks.append(dict(title=title, length=length, rawlength=rawlength))\n\n return tracks","def get_playlist_songs(self, playlist_id):\n values = {'action' : 'playlist_songs',\n 'filter' : playlist_id,\n }\n root = self.__call_api(values)\n songs = root.getElementsByTagName('song')\n if not songs:\n return None\n l= []\n try:\n for song in songs:\n song_id = int(song.getAttribute('id'))\n song_title = song.getElementsByTagName('title')[0].childNodes[0].data\n artist_id = int(song.getElementsByTagName('artist')[0].getAttribute('id'))\n artist_name = song.getElementsByTagName('artist')[0].childNodes[0].data\n album_id = int(song.getElementsByTagName('album')[0].getAttribute('id'))\n album_name = song.getElementsByTagName('album')[0].childNodes[0].data\n\n song_track = int(song.getElementsByTagName('track')[0].childNodes[0].data)\n song_time = int(song.getElementsByTagName('time')[0].childNodes[0].data)\n song_size = int(song.getElementsByTagName('size')[0].childNodes[0].data)\n\n try: # New Ampache puts nothing here...\n precise_rating = int(song.getElementsByTagName('preciserating')[0].childNodes[0].data)\n except:\n precise_rating = 0\n try:\n rating = float(song.getElementsByTagName('rating')[0].childNodes[0].data)\n except:\n rating = 0\n art = song.getElementsByTagName('art')[0].childNodes[0].data\n url = song.getElementsByTagName('url')[0].childNodes[0].data\n song_dict = {\n 'song_id' : song_id,\n 'song_title' : song_title,\n 'artist_id' : artist_id,\n 'artist_name' : artist_name,\n 'album_id' : album_id,\n 'album_name' : album_name,\n 'song_track' : song_track,\n 'song_time' : song_time,\n 'song_size' : song_size,\n 'precise_rating' : precise_rating,\n 'rating' : rating,\n 'art' : art,\n 'url' : url,\n }\n l.append(song_dict)\n except:\n print(\"This playlist failed\", playlist_id)\n traceback.print_exc()\n return None\n return l","def _query_songs_by_artist(self, entities: List[str]):\n artists = []\n for e in entities:\n artists += self.kb_api.get_songs_by_artist(e)\n\n return artists","def add_songs(self):\n settings = dict(initialdir=pathlib.Path().absolute(), title=\"Choose songs\", filetypes=(\n (\"flac files\", \"*.flac\"),\n (\"mp3 files\", \"*.mp3\"),\n (\"all files\", \"*\")))\n\n songs = filedialog.askopenfilenames(**settings)\n\n for song in songs:\n self.update_playlist(song)\n\n self.listbox.insert(\"end\", *[song['name'] for song in self.song_list])\n with open('last_list.pkl', 'wb') as f:\n pickle.dump(self.song_list, f)","def cmd_account_albums(client, args):\n account_albums = client.get_account_albums(args.username, args.page)\n data = [item.__dict__ for item in account_albums]\n generate_output({'account_albums': data}, args.output_file)","def get_albums_from(artist_uri):\n album_uris = []\n results = spotify.artist_albums(artist_uri, album_type='album')\n albums = results['items']\n # get URIs for each album\n for album in albums:\n album_uris.append(album['uri'])\n\n return album_uris","def _query_artist_by_song(self, entities: List[str]):\n artists = []\n for e in entities:\n artists += [\n song.get('artist_name')\n for song\n in self.kb_api.get_song_data(e)\n ]\n\n return artists","def listSongNameAndArtists(self, resultList, initIndex=0):\n stringToReturn = '' \n for r in range(len(resultList)):\n stringToReturn += f\"{r + initIndex + 1}) {resultList[r]['name']} by {self.listAllArtistsInResult(resultList[r])}\\n\"\n\n return stringToReturn","def get_albums_most_played(session_):\n artists = session_.query(Album).order_by(Album.plays.desc()).all()\n return artists","def get_songs(self, per_page=10, sort=\"title\") -> \"Iterable[Song]\":\n assert sort in [\"title\", \"popularity\"]\n page = 1\n while page != None:\n response = self._api_session.request(\n \"GET\",\n f\"artists/{self.id}/songs\",\n params={\"page\": page, \"per_page\": per_page, \"sort\": sort},\n )\n data = response.json()[\"response\"]\n page = data[\"next_page\"]\n for song in data[\"songs\"]:\n yield Song(song, self._api_session, self._web_session)","def random_by_genre_track_list(self):\n\n genre = self.addon_args[\"foldername\"][0].decode(\"utf-8\")\n\n xbmcplugin.setContent(self.addon_handle, \"songs\")\n\n for track in self.connection.walk_random_songs(\n size=self.random_count, genre=genre):\n self.add_track(track, show_artist=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def test_top_albums(self):\n \n rss = AppleRSS()\n objs = rss.get_top_albums(limit=10)\n \n self.__test_artists('top_albums', objs)","def get_songs(url, daily):\n\ttry:\n\t\tpage = requests.get(url)\n\texcept (requests.exceptions.ConnectionError, requests.exceptions.ConnectTimeout):\n\t\treturn []\n\n\thtml = BeautifulSoup(page.text, 'html.parser')\n\tresults = []\n\tif daily:\n\t\t# Get just the latest day's group of listings\n\t\tlisting_divs = [html.find('div', class_='episode-music')]\n\telse:\n\t\t# Get all days' listings\n\t\tlisting_divs = html.find_all('div', class_='episode-music')\n\tfor div in listing_divs:\n\t\t# Parse into songs\n\t\tsong_groups = div.find_all('div', class_='episode-music-group')\n\t\t# Divs with additional class \"last\" are the links to amazon; we don't want those\n\t\tlast_divs = div.find_all('div', class_='last')\n\t\tsong_listings = [song for song in song_groups if song not in last_divs]\n\n\n\t\tfor song in song_listings:\n\t\t\ttitle = song.find('a', class_='episode-music-title').text.encode('utf8')\n\t\t\tartist = song.find('div', class_='episode-music-artist').text.encode('utf8')\n\t\t\tresults.append({'title': title, 'artist': artist})\n\t\t\tlogging.debug('get_songs: found song {0} by {1}'.format(title, artist))\n\treturn results","def getSongsSpotify(song_name,access_token):\n song_name = song_name.strip()\n query = \"https://api.spotify.com/v1/search?q={}&type=track&limit=20&offset=0\".format(song_name)\n response = requests.get(\n query,\n headers={\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer {}\".format(access_token)\n }\n )\n response_json = response.json()\n # \n \n songs_no = response_json[\"tracks\"][\"total\"]\n if songs_no == 0 :\n return {\"songs_no\" : songs_no}\n songs = response_json[\"tracks\"][\"items\"]\n if(len(songs)<5):\n uri = [songs[0][\"uri\"]]\n names = [songs[0][\"name\"]]\n artists = [songs[0][\"artists\"][0][\"name\"]]\n imageUrl = [songs[0][\"album\"][\"images\"][-1][\"url\"]]\n response_obj = {\n \"songs_no\" : songs_no,\n \"uri\" : uri,\n \"names\" : names,\n \"artists\" : artists,\n \"images\" : imageUrl\n }\n else:\n uri = [ songs[i][\"uri\"] for i in range(0,5)]\n names = [songs[i][\"name\"] for i in range(0,5)]\n artists = [songs[i][\"artists\"][0][\"name\"] for i in range(0,5)]\n imageUrl = [songs[i][\"album\"][\"images\"][-1][\"url\"] for i in range(0,5)]\n response_obj = {\n \"songs_no\" : songs_no,\n \"uri\" : uri,\n \"names\" : names,\n \"artists\" : artists,\n \"images\" : imageUrl\n }\n return response_obj","def print_songs(self):\n\t\tfor i,s in enumerate(self._songs):\n\t\t\tprint('{0}. {1}'.format(i, s.print_info()))","def get_all_songs_in_pl(self, playlist):\n to_send = self.db.get_songs(playlist)\n to_send = DOLLAR.join(to_send)\n self.send_message(to_send)","def song_by_word(ans):\r\n songs_list = \"\"\r\n ans = ans.lower()\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n song = str(song)\r\n if ans in song.lower():\r\n songs_list += song + \", \"\r\n return songs_list[:-2]","def get_tracks(self):\n self.c_album = self.albums_list.currentText()\n self.clementine_tracks.clear()\n self.albums_tracks.clear()\n if self.artists_list.count() == 0: # this happens when the panel is reshown\n return # after another panel was shown\n if self.albums_list.count() == 0: # this happens during screen buildup\n return # when only the first combobox is filled\n if not self.albums_map[self.artist]:\n qtw.QMessageBox.information(self, self._parent.title,\n \"No (matched) albums for this artist\")\n return\n try:\n self.a_album = self.albums_map[self.artist][self.c_album][1]\n except KeyError:\n qtw.QMessageBox.information(self, self._parent.title, \"This album \"\n \"has not been matched yet\")\n return\n a_tracks, c_tracks = read_album_tracks(self.a_album,\n self.artist, self.c_album)\n for item in c_tracks:\n new = qtw.QTreeWidgetItem([item])\n self.clementine_tracks.addTopLevelItem(new)\n for item in a_tracks:\n new = qtw.QTreeWidgetItem([item])\n self.albums_tracks.addTopLevelItem(new)\n reimport_possible = False\n if len(c_tracks) != len(a_tracks):\n reimport_possible = True\n else:\n for ix, item in enumerate(a_tracks):\n try:\n if not (item.startswith(c_tracks[ix][0])\n or c_tracks[ix][0].startswith(item)):\n reimport_possible = True\n except IndexError:\n reimport_possible = True\n self.b_copy.setEnabled(reimport_possible)","def search_album_art(artist, title, select_index=0, return_all=False):\r\n # TODO: add soundcloud search as well if spotify comes up with no results.\r\n # Soundcloud has it disabled\r\n artist, title = parse.quote(artist), parse.quote(title)\r\n header = {'Authorization': 'Bearer ' + get_spotify_access_token()}\r\n # TODO: search through playlists too\r\n links = []\r\n links_set = set()\r\n for code in COUNTRY_CODES:\r\n url = f'https://api.spotify.com/v1/search?q={title}+artist:{artist}&type=track&market={code}'\r\n r = requests.get(url, headers=header).json()\r\n if 'tracks' in r:\r\n links_from_country = [item['album']['images'][0]['url'] for item in r['tracks']['items']]\r\n for link in links_from_country:\r\n if link not in links_set:\r\n links.append(link)\r\n links_set.add(link)\r\n if return_all: return links\r\n return links[0]"],"string":"[\n \"def simple_songs_list(name_of_album):\\r\\n songs = []\\r\\n data1 = dbase()\\r\\n data1 = data1[name_of_album][0]\\r\\n for song in data1.keys():\\r\\n songs += [song]\\r\\n return songs\",\n \"def album_list(self):\\n\\n artist_id = self.addon_args[\\\"artist_id\\\"][0]\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"albums\\\")\\n\\n for album in self.connection.walk_artist(artist_id):\\n self.add_album(album)\\n\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_ALBUM)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_ARTIST)\\n xbmcplugin.addSortMethod(\\n self.addon_handle, xbmcplugin.SORT_METHOD_VIDEO_YEAR)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def get_album_list():\\n\\n # TODO: Paginate this, etc\\n entities = PhotoAlbum.query().order(-PhotoAlbum.title).fetch(1000)\\n\\n return entities\",\n \"def simple_album_list():\\r\\n album_list = []\\r\\n data = dbase()\\r\\n for album in data.keys():\\r\\n album_list += [album]\\r\\n return album_list\",\n \"def get_albums(self):\\n self.artist = self.artists_list.currentText()\\n self.c_albums = [x['album'] for x in dmlc.list_albums(self.artist)\\n if [x['album'] in self.albums_map[self.artist]]]\\n self.albums_list.clear()\\n self.albums_list.addItems(self.c_albums)\\n self.update_navigation_buttons()\",\n \"def get_songs_by_album(self, album_id):\\n return self.__get('song', album_id)\",\n \"def get_album_songs(self, album_id):\\n url = get_album_url(album_id)\\n result = self.get_request(url)\\n\\n return result['album']['songs']\",\n \"def get_albums():\\n return query_multiple(request.args, album_search, \\\\\\n album_filter, Album, albums_schema)\",\n \"def search_for_album(album_name):\\n\\n print(f'Searching for album: {album_name}')\\n\\n search_result = spotifyObject.search(q=f'\\\"{album_name}\\\"', limit=20, type='album')\\n\\n items = search_result['albums']['items']\\n\\n results = []\\n\\n for item in items:\\n if len(item['artists']) > 1:\\n artists = tuple(art['name'] for art in item['artists'])\\n else:\\n artists = item['artists'][0]['name']\\n\\n results.append((artists, item['name'], item['id']))\\n\\n return results\",\n \"def get_all_songs():\\r\\n return [Song.song_json(song) for song in Song.query.all()]\",\n \"def read_album_tracks(id, artist_name, album_name):\\n list_a = [x.name for x in dmla.list_tracks(id)]\\n list_c = [x['title'] for x in dmlc.list_tracks_for_album(artist_name, album_name)\\n if x['track'] != -1]\\n return list_a, list_c\",\n \"def get_albums(playlist_name):\\n\\n playlist_id = find_playlist(playlist_name)\\n \\n items = get_playlist_tracks(playlist_id=playlist_id)\\n \\n track_values = []\\n \\n for item in items:\\n track = item['track']\\n album = track['album']\\n artists = tuple(artist['name'] for artist in album['artists'])\\n \\n track_values.append((album['name'], artists[0]))\\n \\n album_details = namedtuple('AlbumDetails', 'album artist')\\n \\n for tup in dict.fromkeys(track_values):\\n yield album_details(*tup)\",\n \"def getSongsFromAlbum(albumLink):\\n albumLink = str(albumLink)\\n try:\\n html = urllib.request.urlopen(albumLink).read()\\n soup = bs4.BeautifulSoup(html, 'html.parser')\\n table = soup.findAll(\\\"a\\\")[5:]\\n songLinks = []\\n for entry in table:\\n text = str(re.findall(\\\"\\\\\\\".*\\\\\\\"\\\", str(entry)))\\n text = re.sub(\\\"[\\\\]\\\\['\\\\\\\"]\\\", \\\"\\\", text)\\n link = albumLink + str(text)\\n songLinks.append(link)\\n except:\\n return []\\n return songLinks\",\n \"def get_albums(self):\\n artist = self.get_request_arg(\\\"artist\\\")\\n if artist:\\n lib = self.ctrl.library\\n lst = sorted(self.ctrl.library.get_albums(artist))\\n albums = [{\\\"artist\\\": artist,\\n \\\"album\\\": album,\\n \\\"path\\\": lib.get_path(artist, album)} for album in lst]\\n if lst:\\n return self.resp_from_data(albums)\\n return self.resp_from_data(\\n {\\\"message\\\": f\\\"No album found for artist={artist}\\\"}, 400)\",\n \"def get_albums_alpha(session_):\\n artists = session_.query(Album).order_by(Album.title.asc()).all()\\n return artists\",\n \"def get_album_tracks(self):\\n track_list = self.soup.findAll('div', class_='chart_row')\\n number_of_tracks = 0\\n titles = []\\n urls = []\\n track_numbers = []\\n \\n for track in track_list:\\n track_title = re.sub(' Lyrics', '', \\\" \\\".join(track.h3.text.split()))\\n lyrics_url = track.a['href']\\n track_number = track.span.span.text.strip()\\n \\n if track_number == '':\\n # Sometimes there are additional urls that are not a song's lyrics. Skip these.\\n continue\\n else:\\n track_number = int(track_number)\\n \\n number_of_tracks += 1\\n titles.append(track_title)\\n urls.append(lyrics_url)\\n track_numbers.append(track_number)\\n \\n if self.song_order:\\n # Check that order values are okay.\\n for number in self.song_order:\\n if number > number_of_tracks:\\n raise SongOrderValueError(f'Track number given ({number}) exceeds number of tracks ({number_of_tracks})')\\n \\n for title, url, number in zip(titles, urls, track_numbers):\\n if self.song_order:\\n if number not in self.song_order:\\n print(f'Skipping song: {number:02d} {title}')\\n continue\\n \\n lyrics = self.get_single_lyrics(url)\\n self.album.add_song(Song(title=title, track_number=number, lyrics=lyrics))\\n\\n self.album.number_of_tracks = number_of_tracks\",\n \"def selectSongs():\\n\\tsql =\\\"select songs.title, artist.name, album.name from songs, album, \\\" \\\\\\n\\t+ \\\"artist join songs_album on songs.id=songs_album.songs_id \\\" \\\\\\n\\t+ \\\"join songs_artist on songs.id=songs_artist.songs_id \\\" \\\\\\n\\t+ \\\"where album.id=songs_album.album_id \\\" \\\\\\n\\t+ \\\"and artist.id=songs_artist.artist_id\\\"\\n\\tc, conn = connect()\\n\\tretr = c.execute(sql)\\n\\tsongs = []\\n\\tfor entry in retr:\\n\\t\\tsongs.append(music.song(title=entry[0], artist=entry[1], album=entry[2]))\\n\\treturn songs\",\n \"def get_song_list(self):\\n return self.song_list\",\n \"def get_albums_by_artist(albumtype, search_for, sort_on):\\n return list(dmla.list_albums_by_artist(albumtype, search_for, sort_on))\",\n \"def song_album(ans):\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n if ans == song:\\r\\n return album\",\n \"def tracked_albums():\\n print('Your Google Photos Albums ([X] = tracked):')\\n albums = get_albums(service)\\n for i, a in enumerate(albums):\\n check = 'X' if a.id in library.get_album_ids() else ' '\\n print('[{}] {}. {}'.format(check, i+1, a.title))\\n return albums\",\n \"def album_list_for_user():\\r\\n answer = \\\"\\\"\\r\\n data = dbase()\\r\\n for album in data.keys():\\r\\n answer += album + \\\", \\\"\\r\\n return answer[:-2]\",\n \"def get_songs(library):\\n songs = []\\n for song in library:\\n title, artist, album = song['title'], song['artist'], song['album']\\n seconds = int(song['durationMillis']) // 1000\\n songs.append({'artist': artist, 'title': title, 'album': album, 'seconds': seconds})\\n return songs\",\n \"def get_songs(self, song_list):\\n self.songs = [[s.name, s.movie_name] for s in song_list\\n if s.raga == self.name]\",\n \"def read_artist_albums(id, name):\\n list_a = [(x.name, str(x.release_year), str(x.id))\\n for x in dmla.list_albums_by_artist('', id, 'Jaar')]\\n list_c = [(x['album'], x['year']) for x in dmlc.list_albums(name)]\\n return list_a, list_c\",\n \"def getAllSongs(self):\\n return self.__songDictionary\",\n \"def get_songs(self):\\n search_object = {\\\"size\\\":25000,\\n 'query': {'term': {FIELD_FINGERPRINTED: True}}, \\\"fields\\\": [FIELD_SONGNAME, FIELD_FILE_SHA1,\\n FIELD_TOTAL_HASHES]}\\n response = self.cursor.search(index = SONGS_INDEXNAME, body=search_object)\\n #print(\\\"get_songs response: \\\",response)\\n arr = []\\n for hit in response[\\\"hits\\\"][\\\"hits\\\"]:\\n dct = {\\\"song_name\\\":hit['_source'][FIELD_SONGNAME],\\\"total_hashes\\\":hit['_source'][FIELD_TOTAL_HASHES],\\n \\\"file_sha1\\\":hit['_source'][FIELD_FILE_SHA1]}\\n arr.append(dct)\\n return arr\",\n \"def get_albums(entity_url: str) -> list:\\n entity_url = entity_url.rstrip(\\\"/\\\")\\n response = requests.get(entity_url)\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n albums = []\\n for link in soup.find_all('a'):\\n url = link.get('href')\\n if url is not None and \\\"/album/\\\" in url:\\n if url.startswith(\\\"http\\\"):\\n albums.append(url)\\n else:\\n albums.append(f\\\"{entity_url}{url}\\\")\\n return albums\",\n \"def albums_by_genre_list(self):\\n\\n genre = self.addon_args[\\\"foldername\\\"][0].decode(\\\"utf-8\\\")\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"albums\\\")\\n\\n for album in self.connection.walk_album_list_genre(genre):\\n self.add_album(album, show_artist=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def all_titles(our_data):\\n return [album['album'] for album in our_data]\",\n \"def get_songs(path):\\r\\n song_list = []\\r\\n genre_paths = glob.glob(path + '/*')\\r\\n for genre_path in genre_paths:\\r\\n artist_paths = glob.glob(genre_path + '/*')\\r\\n for artist_path in artist_paths:\\r\\n album_paths = glob.glob(artist_path + '/*')\\r\\n for album_path in album_paths:\\r\\n lyrics_paths = glob.glob(album_path + '/*.txt')\\r\\n for lyrics_path in lyrics_paths:\\r\\n song = {}\\r\\n song[\\\"genre\\\"] = genre_path.replace(path + '/', '')\\r\\n song[\\\"artist\\\"] = artist_path.replace(genre_path + '/', '')\\r\\n song[\\\"album\\\"] = album_path.replace(artist_path + '/', '')\\r\\n song[\\\"lyrics\\\"] = open(lyrics_path).read()\\r\\n song[\\\"name\\\"] = lyrics_path[:-4].replace(album_path + '/', '')\\r\\n song[\\\"x\\\"] = 0\\r\\n song[\\\"y\\\"] = 0\\r\\n song_list.append(song)\\r\\n return song_list\",\n \"def songs(self):\\n return Html.find_song_names(self.content)\",\n \"def albums(self, albums, **kwargs):\\n album_list = map(self._get_album_id, albums)\\n return self._get(API.ALBUMS.value, ids=\\\",\\\".join(album_list), **kwargs)\",\n \"def GetAlbums(self, start=0, end=0, sortmethod='label', sortorder='ascending', filter='', artistid=None):\\n self.logger.debug(\\\"Loading all albums for ARTISTID \\\" + str(artistid))\\n try:\\n xbmc = Server(self.url('/jsonrpc', True))\\n sort = {'order': sortorder, 'method': sortmethod, 'ignorearticle': True}\\n properties=['artist', 'title', 'year', 'description', 'thumbnail']\\n limits = {'start': int(start), 'end': int(end)}\\n if artistid is not None:\\n filter = {'artistid': int(artistid)}\\n else:\\n filter = {'or': [\\n {'field': 'album', 'operator': 'contains', 'value': filter},\\n {'field': 'artist', 'operator': 'contains', 'value': filter}\\n ]}\\n return xbmc.AudioLibrary.GetAlbums(properties=properties, limits=limits, sort=sort, filter=filter)\\n except ValueError:\\n return\",\n \"def test_get_songs_by_album(self, track_elms, service_config, request):\\n album_id = uuid.UUID(avalon.compat.to_uuid_input('f83fdec7-510f-44a5-87dc-61832669a582'))\\n service_config.track_store.get_by_album.return_value = track_elms\\n service_config.id_cache.get_album_id.return_value = album_id\\n request.args['album'] = 'Album'\\n params = avalon.web.request.Parameters(request)\\n\\n service = avalon.web.services.AvalonMetadataService(service_config)\\n results = service.get_songs(params)\\n\\n assert results == track_elms, 'Expected matching tracks returned'\\n service_config.track_store.get_by_album.assert_called_with(album_id)\",\n \"def get_albums(username):\\n cur = mysql.connection.cursor()\\n cur.execute(\\\"SELECT * FROM album WHERE username = '{0}'\\\".format(username))\\n return cur.fetchall()\",\n \"def get_song_list(self, artist: str) -> List[str]:\\n artist = self.just_replace_strings_with_nothing(artist)\\n\\n url = self.list_url + artist + '/songs'\\n\\n resp = requests.get(url)\\n\\n content = bs4.BeautifulSoup(resp.content)\\n\\n song_list = content.text[content.text.index(\\n 'MP3s') + 5:content.text.index('About Song List')]\\n\\n song_list = re.sub('\\\\n', ',', song_list)\\n song_list = re.sub(',+', ',', song_list)\\n song_list = re.sub(', ,', ', ', song_list)\\n\\n song_list = re.split(',', song_list)\\n for i in range(len(song_list)):\\n song_list[i] = song_list[i].lstrip(' ')\\n song_list[i] = re.sub(\\\"[.,']\\\", '', song_list[i])\\n song_list[i] = re.sub(\\\"&\\\", 'and', song_list[i])\\n song_list[i] = re.sub('\\\\s+', ' ', song_list[i])\\n\\n song_list = [i for i in song_list if i != '']\\n\\n return song_list\",\n \"def fetchAlbumIds(artist_id):\\n url = 'https://api.spotify.com/v1/artists/' + artist_id + '/albums?market=US&album_type=album'\\n req = requests.get(url)\\n\\n data = req.json()\\n\\n #checking for bad return value\\n if not req.ok:\\n print \\\"error : \\\" + data['error']['message']\\n return \\\"error : \\\" + data['error']['message']\\n\\n albums = []\\n for item in data['items']:\\n \\talbums.append(item['id'])\\n\\n return albums\",\n \"def get_all_songs() -> Generator[dict, None, None]:\\n\\n logging.debug(\\\"Fetching from server\\\")\\n\\n api = _get_api()\\n\\n for song_page in api.get_all_songs(incremental=True):\\n for song in song_page:\\n yield song\",\n \"def get_tracks_from_albums(sp, album_uri_list):\\n\\n track_list = [[\\\"track_name\\\", \\\"track_uri\\\", \\\"track_release_date\\\"]]\\n\\n print(\\\"Log: Pulling data from Spotify. This can take a while...\\\")\\n\\n for album_uri in album_uri_list:\\n album_tracks = sp.album_tracks(album_uri, limit=50, offset=0)[\\\"items\\\"]\\n count_tracks_in_album = len(album_tracks)\\n album_release_date = sp.album(album_uri)[\\\"release_date\\\"]\\n\\n # This part is probably very slow and should be improved by accessing the API less often\\n for track_number in range(count_tracks_in_album):\\n track_name = album_tracks[track_number][\\\"name\\\"]\\n track_uri = album_tracks[track_number][\\\"uri\\\"]\\n \\n track_list.append([track_name, track_uri, album_release_date])\\n\\n # Create df from list of tracks for all albums\\n track_df = pd.DataFrame(data=track_list[1:], columns=track_list[0])\\n \\n print(\\\"Log: Finished pulling all tracks from albums.\\\")\\n return track_df\",\n \"def get_all_musicians(self):\\n self.cursor.execute(\\\"select * from musicians\\\")\\n self.connection.commit()\\n return self.cursor.fetchall()\",\n \"def genre_list(self):\\n\\n for genre in self.connection.walk_genres():\\n url = self.build_url({\\n \\\"mode\\\": \\\"albums_by_genre_list\\\",\\n \\\"foldername\\\": genre[\\\"value\\\"].encode(\\\"utf-8\\\")})\\n\\n li = xbmcgui.ListItem(genre[\\\"value\\\"])\\n xbmcplugin.addDirectoryItem(\\n handle=self.addon_handle, url=url, listitem=li, isFolder=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def print_album(albums):\\n print(\\\"\\\\nPrinting album data:\\\")\\n for album in albums:\\n print(f\\\"Artist Name: {album['name'].title()}\\\")\\n print(f\\\"\\\\tAlbum Name: {album['album'].title()}\\\")\\n if (album['num_songs']):\\n print(f\\\"\\\\tNumber Songs: {album['num_songs']}\\\")\\n\\n print(\\\"\\\")\",\n \"def get_songs(self, offset=None):\\n return self.__get('songs')\",\n \"def GetRecentAlbums(self, limit=5):\\n self.logger.debug(\\\"Fetching recently added Music\\\")\\n try:\\n xbmc = Server(self.url('/jsonrpc', True))\\n properties = ['artist', 'albumlabel', 'year', 'description', 'thumbnail']\\n limits = {'start': 0, 'end': int(limit)}\\n return xbmc.AudioLibrary.GetRecentlyAddedAlbums(properties=properties, limits=limits)\\n except:\\n self.logger.error(\\\"Unable to fetch recently added Music!\\\")\\n return\",\n \"def all_artists(our_data):\\n return [album['artist'] for album in our_data]\",\n \"def get_tracks_from(album_uri):\\n track_uris = []\\n album = spotify.album_tracks(album_id=album_uri)\\n\\n for track in album['items']:\\n track_uris.append(track['uri'])\\n\\n return track_uris\",\n \"def get_albums(self):\\n if self.artist_list.count() == 0: # this happens when the panel is reshown\\n return # after another panel was shown\\n self.c_artist = self.artist_list.currentText()\\n ## self.last_handled = self.artist_list.currentIndex()\\n # remember first handled item for currency communication over panels\\n self._parent.current_data = self.c_artist\\n self.a_artist = self.artist_map[self.c_artist]\\n a_albums, c_albums = read_artist_albums(self.a_artist, self.c_artist)\\n for name, year, id, *rest in self.albums_to_save[self.c_artist]:\\n a_albums.append((name, year, str(id)))\\n self.clementine_albums.clear()\\n for item, year in c_albums:\\n new = qtw.QTreeWidgetItem([item])\\n new.setData(0, core.Qt.UserRole, year)\\n try:\\n new.setText(1, str(self.albums_map[self.c_artist][item][1]))\\n except KeyError:\\n pass\\n self.clementine_albums.addTopLevelItem(new)\\n self.albums_albums.clear()\\n self.lookup = collections.defaultdict(list)\\n for item in a_albums:\\n new = qtw.QTreeWidgetItem([x.replace('None', '') for x in item])\\n self.albums_albums.addTopLevelItem(new)\\n self.lookup[item[0]].append(item[2])\\n self.tracks = collections.defaultdict(list)\",\n \"def scrape_all_songs():\\n print('Scraping all songs from {}'.format(URL))\\n\\n soup = scrapekit.handle_url(URL)\\n song_elements = []\\n tables = soup.findAll('table')\\n\\n for t in tables:\\n field_index = scrapekit.get_col_index(t, field_name=\\\"Song\\\")\\n\\n if field_index:\\n song_elements.extend(scrapekit.scrape_table_col(t, field_index))\\n\\n links = []\\n for element in song_elements:\\n l = element.find('a')\\n if l:\\n links.append(PREFIX + l.attrs.get('href', ''))\\n return links\",\n \"def get_albums_by_text(albumtype, search_type, search_for, sort_on):\\n if albumtype == 'studio':\\n search_on = {0: '*', 2: 'name', 3: 'produced_by', 4: 'credits', 5: 'bezetting'}[search_type]\\n elif albumtype == 'live':\\n search_on = {0: '*', 2: 'name', 3: 'name', 4: 'produced_by', 5: 'bezetting'}[search_type]\\n return list(dmla.list_albums_by_search(albumtype, search_on, search_for, sort_on))\",\n \"def test_get_songs_by_album_id(self, track_elms, service_config, request):\\n album_id = uuid.UUID(avalon.compat.to_uuid_input('37cac253-2bca-4a3a-be9f-2ac655e04ad8'))\\n service_config.track_store.get_by_album.return_value = track_elms\\n request.args['album_id'] = six.text_type(album_id)\\n params = avalon.web.request.Parameters(request)\\n\\n service = avalon.web.services.AvalonMetadataService(service_config)\\n results = service.get_songs(params)\\n\\n assert results == track_elms, 'Expected matching tracks returned'\\n service_config.track_store.get_by_album.assert_called_with(album_id)\",\n \"def find_album_playlist(data):\\n\\n return data['album'].lower() + '.m3u'\",\n \"def getAlbumsFromArtist(artistLink):\\n artistLink = str(artistLink)\\n url = \\\"http://www.ohhla.com/\\\"+artistLink\\n if artistLink[0:4]==\\\"http:\\\":\\n url = artistLink\\n try:\\n html = urllib.request.urlopen(url).read()\\n soup = bs4.BeautifulSoup(html, 'html.parser')\\n table = soup.findAll(\\\"a\\\")[5:]\\n albumLinks = []\\n for entry in table:\\n text = str(re.findall(\\\"\\\\\\\".*\\\\\\\"\\\", str(entry)))\\n text = re.sub(\\\"[\\\\]\\\\['\\\\\\\"]\\\", \\\"\\\", text)\\n link = url + str(text)\\n if len(re.findall(\\\"(?:http)\\\",link)) == 1:\\n albumLinks.append(link)\\n except:\\n return []\\n return albumLinks\",\n \"def appendAlbum(song):\\n\\tsql = []\\n\\tsql.append(\\\"INSERT INTO ALBUM ('name') VALUES ('\\\" \\n\\t+ '/'.join(song.album) + \\\"');\\\")\\n\\t\\n\\tsql.append(\\\"INSERT INTO songs_album ('songs_id', 'album_id')\\\"\\n\\t+ \\\" VALUES ((select id from songs where hash = '\\\" + str(song.hash) + \\\"'), \\\"\\n\\t+ \\\"(select id from album where name = '\\\" + '/'.join(song.album) + \\\"'));\\\")\\n\\tsql.append(\\\"INSERT INTO artist_album ('artist_id', 'album_id')\\\"\\n\\t+ \\\" VALUES ((select id from songs where hash = '\\\" + str(song.hash) + \\\"'), \\\"\\n\\t+ \\\"(select id from album where name = '\\\" + '/'.join(song.album) + \\\"'));\\\")\\n\\t\\n\\treturn sql\",\n \"def song_lyrics(ans):\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n if ans == song:\\r\\n words = dbase()[album][0][song]\\r\\n words = words[2]\\r\\n return words\",\n \"def get_by_artist(cls, artist):\\n results = cls.query().filter(FileRecord.artist == artist).order_by(\\n FileRecord.year).all()\\n albums = []\\n if results and len(results) > 0:\\n for result in results:\\n albums.append(cls(*result))\\n\\n return albums\\n\\n return albums\",\n \"def get_songs_of_all_artists(self):\\n\\t\\tartists = self.db.artists.find()\\n\\t\\tall_songs = []\\n\\t\\tfor artist in artists:\\n\\t\\t\\tall_songs.extend(artist['songs'])\\n\\t\\tall_songs = list(set(all_songs))\\n\\t\\treturn all_songs\",\n \"def getAlbums():\\n\\n r = requests.get(ALBUMS_URL, headers=HEADER, timeout=5)\\n\\n if r.status_code == 200:\\n \\n try:\\n albums = [] \\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n album = soup.find_all(\\\"div\\\", class_=\\\"duv\\\")\\n for i,al in enumerate(album): \\n temp = {}\\n temp['link'] = al.find_all(\\\"a\\\")[0]['href']\\n temp['album'] = al.find_all(\\\"span\\\", class_=\\\"title\\\")[0].text\\n albums.append(temp)\\n\\n if len(albums) > 0:\\n return albums\\n else:\\n print(\\\"No albums found on site2!\\\")\\n sys.exit(0)\\n \\n except Exception as e:\\n print(\\\"Failed to get albums from site2\\\\n\\\", e)\\n sys.exit(0)\\n\\n else:\\n print(\\\"Albums Url fetch failed! Status code: {}\\\".format(r.status_code))\\n sys.exit(0)\",\n \"def search_song(self, name):\\n self.logger.debug('Searched for Song: {}'.format(name))\\n results = self.sp.search(q='track:' + name, type='track')\\n songs = [song for song in results['tracks']['items']]\\n i = 1\\n songs_ls = []\\n table_ls = []\\n for song in songs:\\n table_ls.append([i,\\n song['name'][0:20].strip(),\\n song['album']['name'][0:20].strip(),\\n \\\"%0.2f\\\" % (song['duration_ms'] / 60000),\\n song['popularity']])\\n songs_ls.append(song['uri'])\\n i = i + 1\\n return songs_ls, table_ls\",\n \"def test_album_tracks_lastfm(monkeypatch, bot):\\n song = Song('Sabaton', '1 6 4 8')\\n with monkeypatch.context() as mkp:\\n # An empty list should be returned if we can't find the album's name\\n mkp.setattr(song, 'fetch_album_name', lambda: None)\\n assert bot.get_album_tracks_lastfm(song) == []\\n\\n tracks = bot.get_album_tracks_lastfm(song)\\n tracks = '\\\\n'.join(tracks)\\n assert 'carolus rex' in tracks\\n assert 'en livstid i krig' in tracks\",\n \"def get_albums(self):\\n return AlbumView.get_by_artist(self.name)\",\n \"def get_tracks(self):\\n artist = self.get_request_arg(\\\"artist\\\")\\n album = self.get_request_arg(\\\"album\\\")\\n if not (album and artist):\\n return self.resp_from_data(\\n {\\\"message\\\": \\\"Please specify a valid artist and album\\\"}, 403)\\n else:\\n tracks = self.ctrl.library.get_tracks(artist, album)\\n return self.resp_from_data(tracks)\",\n \"def list(ctx):\\n\\tfor songName in songs:\\n\\t\\tyield from bot.send_message(ctx.message.author,songName)\",\n \"def search_for_tracks(album_id):\\n \\n track_results = spotifyObject.album_tracks(album_id)\\n track_results = track_results['items']\\n ids = [track['id'] for track in track_results]\\n\\n return ids\",\n \"def get_all_songs(self):\\n to_send = self.db.get_all_songs()\\n to_send = '$'.join(to_send)\\n self.send_message(to_send)\",\n \"def add_songs(self, name, year, title):\\n\\n album_found = find_object(name, self.album)\\n if album_found is None:\\n print(\\\"Not Found \\\" + name)\\n album_found = Album(name, year, self.name)\\n self.add_album(album_found)\\n else:\\n print(\\\"Found album \\\"+name)\\n\\n album_found.add_songs(title)\",\n \"def get_songs_of_artist(self, artist_id: int):\\n\\t\\tartist = self.db.artists.find_one({'id': artist_id})\\n\\t\\treturn artist['songs']\",\n \"def get_albums(self, offset=None):\\n return self.__get('albums')\",\n \"def get_artist_list():\\n return list(dmla.list_artists())\",\n \"def view_songs_push(self):\\n #clear all data first\\n self.model.removeRows(0, self.model.rowCount())\\n songs = glob.glob(\\\"Fixed/*/*/*\\\")\\n for song in songs:\\n data = mutagen.File(song, easy=True)\\n track = get_track(data['title'][0], data['artist'][0])\\n self.add_track_to_box(track)\",\n \"def search_song(self, name, album=None, artist=None):\\n\\n endpoint = \\\"/search\\\"\\n query = f\\\"track:{self._strip_punctuation(name)}\\\"\\n if artist:\\n query += f\\\" artist:{self._strip_punctuation(artist)}\\\"\\n if album:\\n query += f\\\" album:{self._strip_punctuation(album)}\\\"\\n response = self._send(endpoint, \\\"GET\\\", params={\\\"q\\\": query, \\\"type\\\": \\\"track\\\"})\\n tracks = response.json()[\\\"tracks\\\"]\\n if tracks[\\\"total\\\"] == 0:\\n raise SongNotFoundError(\\n f\\\"song name={name} artist={artist} album={album} could not be found\\\"\\n )\\n return tracks[\\\"items\\\"]\",\n \"def get_albums(self, limit=None):\\n url = (\\\"https://api.imgur.com/3/account/{0}/albums/{1}\\\".format(self.name,\\n '{}'))\\n resp = self._imgur._send_request(url, limit=limit)\\n return [Album(alb, self._imgur, False) for alb in resp]\",\n \"def get_albums_recent_played(session_):\\n artists = session_.query(Album).order_by(Album.played_at.desc()).all()\\n return artists\",\n \"def sort_album(self):\\n self.sort('album')\",\n \"def findsimilaralbums(index, similarartists): # {{{1\\n albums = []\\n for [similarity, artistname] in similarartists:\\n for albumname in index.findalbumsbyartist(artistname):\\n albums.append([similarity, [artistname, albumname]])\\n return albums\",\n \"def get_all_song_names(self):\\n try:\\n # Auto-close\\n with closing(self.connection) as con:\\n # Auto-commit\\n with con:\\n # Auto-close\\n with closing(con.cursor()) as cursor:\\n cursor.execute(\\\"\\\"\\\"\\n SELECT name\\n FROM nodes\\n WHERE type = \\\"song\\\";\\n \\\"\\\"\\\")\\n return [x[0] for x in cursor.fetchall()]\\n except sqlite3.OperationalError as e:\\n print(\\\"ERROR: Could not retrieve songs: {}\\\".format(str(e)))\\n return []\",\n \"def album_tracks(self, album_id, limit=50, offset=0, **kwargs):\\n _id = self._get_album_id(album_id)\\n # pylint: disable=no-member\\n return self._get(\\n API.ALBUM_TRACKS.value.format(id=_id), limit=limit, offset=offset, **kwargs\\n )\",\n \"async def search_song(album_name):\\n async with aiohttp.ClientSession() as session:\\n async with session.get('https://bandcamp.com/api/fuzzysearch/1/autocomplete?q=' + album_name) as resp:\\n response = await resp.json()\\n\\n results = response.get('auto', {}).get('results', [])\\n results = [res for res in results if res.get('type') == 't']\\n if not results:\\n raise NotFound\\n result = results[0]\\n async with session.get(result.get('url', 'https://bandcamp.com/')) as resp:\\n response = await resp.text()\\n try:\\n result['release_date'] = response.split('album_release_date: \\\"')[-1].split('\\\",')[0].split(':')[0]\\n except:\\n result['release_date'] = '01 Jan 1970 00'\\n result['TrackAlbum'] = bs4.BeautifulSoup(response, 'html.parser').find('span', itemprop='inAlbum').text.strip()\\n\\n return BandcampSong(result)\",\n \"def playlist(self):\\n def iconv(s):\\n encoding = self.options[\\\"id3_encoding\\\"]\\n try:\\n if encoding:\\n return s.encode('latin1').decode(encoding).encode('utf-8')\\n else:\\n return s.encode('latin1')\\n except UnicodeEncodeError:\\n return \\\"\\\"\\n\\n lst = []\\n r = self.x.playlist_list_entries()\\n r.wait()\\n for id in r.get_list():\\n r = self.x.medialib_get_info(id)\\n r.wait()\\n if r.iserror():\\n print r.get_error()\\n lst.append(' ')\\n continue\\n song = r.get_propdict()\\n try:\\n artist = iconv(song[('plugin/id3v2', 'artist')])\\n except KeyError:\\n try:\\n artist = iconv(song[('plugin/mad', 'artist')])\\n except KeyError:\\n artist = ''\\n try:\\n title = iconv(song[('plugin/id3v2', 'title')])\\n except KeyError:\\n try:\\n title = iconv(song[('plugin/mad', 'title')])\\n except KeyError:\\n title = ''\\n if artist == \\\"\\\" and title == \\\"\\\":\\n name = os.path.split(song[('server', 'url')])[1]\\n name = os.path.splitext(name)[0]\\n name = urllib.unquote(name.decode('utf-8').encode('latin1'))\\n name = name.replace(\\\"+\\\", \\\" \\\")\\n lst.append(' ' + name)\\n else:\\n lst.append(' %s - %s' % (artist.ljust(6), title))\\n\\n return lst\",\n \"async def search_album(album_name):\\n async with aiohttp.ClientSession() as session:\\n async with session.get('https://bandcamp.com/api/fuzzysearch/1/autocomplete?q=' + album_name) as resp:\\n response = await resp.json()\\n\\n results = response.get('auto', {}).get('results', [])\\n results = [res for res in results if res.get('type') == 'a']\\n if not results:\\n raise NotFound\\n result = results[0]\\n async with session.get(result.get('url', 'https://bandcamp.com/')) as resp:\\n response = await resp.text()\\n try:\\n result['release_date'] = response.split('album_release_date: \\\"')[-1].split('\\\",')[0].split(':')[0]\\n except:\\n result['release_date'] = '01 Jan 1970 00'\\n result['track_list'] = [getattr(aa.find('span'), 'text', '') for aa in bs4.BeautifulSoup(response, 'html.parser').find('table', {'class':'track_list'}).find_all('tr')]\\n\\n return BandcampAlbum(result)\",\n \"def findMP3s(path=None):\\n \\n if not path:\\n \\n lib_path = r'.\\\\songs'\\n else:\\n lib_path = path\\n \\n \\n all_songs = []\\n \\n #folder from os.walk is: root, dirnames, filenames\\n for rt, dirs, files in os.walk(lib_path):\\n \\n for fp in files:\\n if fnmatch.fnmatch(fp, '*.mp3'):\\n fullpath = r'{}\\\\{}'.format(rt, fp)\\n all_songs.append(fullpath)\\n \\n #pp2(all_songs)\\n print 'found {} songs'.format(len(all_songs))\\n \\n return all_songs\",\n \"def _get_mb_tracks(albumid):\\n ns = {'mb': 'http://musicbrainz.org/ns/mmd-2.0#'}\\n url = \\\"http://musicbrainz.org/ws/2/release/\\\" + albumid\\n query = {\\\"inc\\\": \\\"recordings\\\"}\\n wdata = _do_query(url, query, err='album search error')\\n\\n if not wdata:\\n return None\\n\\n root = ET.fromstring(wdata)\\n tlist = root.find(\\\"./mb:release/mb:medium-list/mb:medium/mb:track-list\\\",\\n namespaces=ns)\\n mb_songs = tlist.findall(\\\"mb:track\\\", namespaces=ns)\\n tracks = []\\n path = \\\"./mb:recording/mb:\\\"\\n\\n for track in mb_songs:\\n\\n try:\\n title, length, rawlength = \\\"unknown\\\", 0, 0\\n title = track.find(path + \\\"title\\\", namespaces=ns).text\\n rawlength = track.find(path + \\\"length\\\", namespaces=ns).text\\n length = int(round(float(rawlength) / 1000))\\n\\n except (ValueError, AttributeError):\\n xprint(\\\"not found\\\")\\n\\n tracks.append(dict(title=title, length=length, rawlength=rawlength))\\n\\n return tracks\",\n \"def get_playlist_songs(self, playlist_id):\\n values = {'action' : 'playlist_songs',\\n 'filter' : playlist_id,\\n }\\n root = self.__call_api(values)\\n songs = root.getElementsByTagName('song')\\n if not songs:\\n return None\\n l= []\\n try:\\n for song in songs:\\n song_id = int(song.getAttribute('id'))\\n song_title = song.getElementsByTagName('title')[0].childNodes[0].data\\n artist_id = int(song.getElementsByTagName('artist')[0].getAttribute('id'))\\n artist_name = song.getElementsByTagName('artist')[0].childNodes[0].data\\n album_id = int(song.getElementsByTagName('album')[0].getAttribute('id'))\\n album_name = song.getElementsByTagName('album')[0].childNodes[0].data\\n\\n song_track = int(song.getElementsByTagName('track')[0].childNodes[0].data)\\n song_time = int(song.getElementsByTagName('time')[0].childNodes[0].data)\\n song_size = int(song.getElementsByTagName('size')[0].childNodes[0].data)\\n\\n try: # New Ampache puts nothing here...\\n precise_rating = int(song.getElementsByTagName('preciserating')[0].childNodes[0].data)\\n except:\\n precise_rating = 0\\n try:\\n rating = float(song.getElementsByTagName('rating')[0].childNodes[0].data)\\n except:\\n rating = 0\\n art = song.getElementsByTagName('art')[0].childNodes[0].data\\n url = song.getElementsByTagName('url')[0].childNodes[0].data\\n song_dict = {\\n 'song_id' : song_id,\\n 'song_title' : song_title,\\n 'artist_id' : artist_id,\\n 'artist_name' : artist_name,\\n 'album_id' : album_id,\\n 'album_name' : album_name,\\n 'song_track' : song_track,\\n 'song_time' : song_time,\\n 'song_size' : song_size,\\n 'precise_rating' : precise_rating,\\n 'rating' : rating,\\n 'art' : art,\\n 'url' : url,\\n }\\n l.append(song_dict)\\n except:\\n print(\\\"This playlist failed\\\", playlist_id)\\n traceback.print_exc()\\n return None\\n return l\",\n \"def _query_songs_by_artist(self, entities: List[str]):\\n artists = []\\n for e in entities:\\n artists += self.kb_api.get_songs_by_artist(e)\\n\\n return artists\",\n \"def add_songs(self):\\n settings = dict(initialdir=pathlib.Path().absolute(), title=\\\"Choose songs\\\", filetypes=(\\n (\\\"flac files\\\", \\\"*.flac\\\"),\\n (\\\"mp3 files\\\", \\\"*.mp3\\\"),\\n (\\\"all files\\\", \\\"*\\\")))\\n\\n songs = filedialog.askopenfilenames(**settings)\\n\\n for song in songs:\\n self.update_playlist(song)\\n\\n self.listbox.insert(\\\"end\\\", *[song['name'] for song in self.song_list])\\n with open('last_list.pkl', 'wb') as f:\\n pickle.dump(self.song_list, f)\",\n \"def cmd_account_albums(client, args):\\n account_albums = client.get_account_albums(args.username, args.page)\\n data = [item.__dict__ for item in account_albums]\\n generate_output({'account_albums': data}, args.output_file)\",\n \"def get_albums_from(artist_uri):\\n album_uris = []\\n results = spotify.artist_albums(artist_uri, album_type='album')\\n albums = results['items']\\n # get URIs for each album\\n for album in albums:\\n album_uris.append(album['uri'])\\n\\n return album_uris\",\n \"def _query_artist_by_song(self, entities: List[str]):\\n artists = []\\n for e in entities:\\n artists += [\\n song.get('artist_name')\\n for song\\n in self.kb_api.get_song_data(e)\\n ]\\n\\n return artists\",\n \"def listSongNameAndArtists(self, resultList, initIndex=0):\\n stringToReturn = '' \\n for r in range(len(resultList)):\\n stringToReturn += f\\\"{r + initIndex + 1}) {resultList[r]['name']} by {self.listAllArtistsInResult(resultList[r])}\\\\n\\\"\\n\\n return stringToReturn\",\n \"def get_albums_most_played(session_):\\n artists = session_.query(Album).order_by(Album.plays.desc()).all()\\n return artists\",\n \"def get_songs(self, per_page=10, sort=\\\"title\\\") -> \\\"Iterable[Song]\\\":\\n assert sort in [\\\"title\\\", \\\"popularity\\\"]\\n page = 1\\n while page != None:\\n response = self._api_session.request(\\n \\\"GET\\\",\\n f\\\"artists/{self.id}/songs\\\",\\n params={\\\"page\\\": page, \\\"per_page\\\": per_page, \\\"sort\\\": sort},\\n )\\n data = response.json()[\\\"response\\\"]\\n page = data[\\\"next_page\\\"]\\n for song in data[\\\"songs\\\"]:\\n yield Song(song, self._api_session, self._web_session)\",\n \"def random_by_genre_track_list(self):\\n\\n genre = self.addon_args[\\\"foldername\\\"][0].decode(\\\"utf-8\\\")\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"songs\\\")\\n\\n for track in self.connection.walk_random_songs(\\n size=self.random_count, genre=genre):\\n self.add_track(track, show_artist=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def test_top_albums(self):\\n \\n rss = AppleRSS()\\n objs = rss.get_top_albums(limit=10)\\n \\n self.__test_artists('top_albums', objs)\",\n \"def get_songs(url, daily):\\n\\ttry:\\n\\t\\tpage = requests.get(url)\\n\\texcept (requests.exceptions.ConnectionError, requests.exceptions.ConnectTimeout):\\n\\t\\treturn []\\n\\n\\thtml = BeautifulSoup(page.text, 'html.parser')\\n\\tresults = []\\n\\tif daily:\\n\\t\\t# Get just the latest day's group of listings\\n\\t\\tlisting_divs = [html.find('div', class_='episode-music')]\\n\\telse:\\n\\t\\t# Get all days' listings\\n\\t\\tlisting_divs = html.find_all('div', class_='episode-music')\\n\\tfor div in listing_divs:\\n\\t\\t# Parse into songs\\n\\t\\tsong_groups = div.find_all('div', class_='episode-music-group')\\n\\t\\t# Divs with additional class \\\"last\\\" are the links to amazon; we don't want those\\n\\t\\tlast_divs = div.find_all('div', class_='last')\\n\\t\\tsong_listings = [song for song in song_groups if song not in last_divs]\\n\\n\\n\\t\\tfor song in song_listings:\\n\\t\\t\\ttitle = song.find('a', class_='episode-music-title').text.encode('utf8')\\n\\t\\t\\tartist = song.find('div', class_='episode-music-artist').text.encode('utf8')\\n\\t\\t\\tresults.append({'title': title, 'artist': artist})\\n\\t\\t\\tlogging.debug('get_songs: found song {0} by {1}'.format(title, artist))\\n\\treturn results\",\n \"def getSongsSpotify(song_name,access_token):\\n song_name = song_name.strip()\\n query = \\\"https://api.spotify.com/v1/search?q={}&type=track&limit=20&offset=0\\\".format(song_name)\\n response = requests.get(\\n query,\\n headers={\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"Bearer {}\\\".format(access_token)\\n }\\n )\\n response_json = response.json()\\n # \\n \\n songs_no = response_json[\\\"tracks\\\"][\\\"total\\\"]\\n if songs_no == 0 :\\n return {\\\"songs_no\\\" : songs_no}\\n songs = response_json[\\\"tracks\\\"][\\\"items\\\"]\\n if(len(songs)<5):\\n uri = [songs[0][\\\"uri\\\"]]\\n names = [songs[0][\\\"name\\\"]]\\n artists = [songs[0][\\\"artists\\\"][0][\\\"name\\\"]]\\n imageUrl = [songs[0][\\\"album\\\"][\\\"images\\\"][-1][\\\"url\\\"]]\\n response_obj = {\\n \\\"songs_no\\\" : songs_no,\\n \\\"uri\\\" : uri,\\n \\\"names\\\" : names,\\n \\\"artists\\\" : artists,\\n \\\"images\\\" : imageUrl\\n }\\n else:\\n uri = [ songs[i][\\\"uri\\\"] for i in range(0,5)]\\n names = [songs[i][\\\"name\\\"] for i in range(0,5)]\\n artists = [songs[i][\\\"artists\\\"][0][\\\"name\\\"] for i in range(0,5)]\\n imageUrl = [songs[i][\\\"album\\\"][\\\"images\\\"][-1][\\\"url\\\"] for i in range(0,5)]\\n response_obj = {\\n \\\"songs_no\\\" : songs_no,\\n \\\"uri\\\" : uri,\\n \\\"names\\\" : names,\\n \\\"artists\\\" : artists,\\n \\\"images\\\" : imageUrl\\n }\\n return response_obj\",\n \"def print_songs(self):\\n\\t\\tfor i,s in enumerate(self._songs):\\n\\t\\t\\tprint('{0}. {1}'.format(i, s.print_info()))\",\n \"def get_all_songs_in_pl(self, playlist):\\n to_send = self.db.get_songs(playlist)\\n to_send = DOLLAR.join(to_send)\\n self.send_message(to_send)\",\n \"def song_by_word(ans):\\r\\n songs_list = \\\"\\\"\\r\\n ans = ans.lower()\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n song = str(song)\\r\\n if ans in song.lower():\\r\\n songs_list += song + \\\", \\\"\\r\\n return songs_list[:-2]\",\n \"def get_tracks(self):\\n self.c_album = self.albums_list.currentText()\\n self.clementine_tracks.clear()\\n self.albums_tracks.clear()\\n if self.artists_list.count() == 0: # this happens when the panel is reshown\\n return # after another panel was shown\\n if self.albums_list.count() == 0: # this happens during screen buildup\\n return # when only the first combobox is filled\\n if not self.albums_map[self.artist]:\\n qtw.QMessageBox.information(self, self._parent.title,\\n \\\"No (matched) albums for this artist\\\")\\n return\\n try:\\n self.a_album = self.albums_map[self.artist][self.c_album][1]\\n except KeyError:\\n qtw.QMessageBox.information(self, self._parent.title, \\\"This album \\\"\\n \\\"has not been matched yet\\\")\\n return\\n a_tracks, c_tracks = read_album_tracks(self.a_album,\\n self.artist, self.c_album)\\n for item in c_tracks:\\n new = qtw.QTreeWidgetItem([item])\\n self.clementine_tracks.addTopLevelItem(new)\\n for item in a_tracks:\\n new = qtw.QTreeWidgetItem([item])\\n self.albums_tracks.addTopLevelItem(new)\\n reimport_possible = False\\n if len(c_tracks) != len(a_tracks):\\n reimport_possible = True\\n else:\\n for ix, item in enumerate(a_tracks):\\n try:\\n if not (item.startswith(c_tracks[ix][0])\\n or c_tracks[ix][0].startswith(item)):\\n reimport_possible = True\\n except IndexError:\\n reimport_possible = True\\n self.b_copy.setEnabled(reimport_possible)\",\n \"def search_album_art(artist, title, select_index=0, return_all=False):\\r\\n # TODO: add soundcloud search as well if spotify comes up with no results.\\r\\n # Soundcloud has it disabled\\r\\n artist, title = parse.quote(artist), parse.quote(title)\\r\\n header = {'Authorization': 'Bearer ' + get_spotify_access_token()}\\r\\n # TODO: search through playlists too\\r\\n links = []\\r\\n links_set = set()\\r\\n for code in COUNTRY_CODES:\\r\\n url = f'https://api.spotify.com/v1/search?q={title}+artist:{artist}&type=track&market={code}'\\r\\n r = requests.get(url, headers=header).json()\\r\\n if 'tracks' in r:\\r\\n links_from_country = [item['album']['images'][0]['url'] for item in r['tracks']['items']]\\r\\n for link in links_from_country:\\r\\n if link not in links_set:\\r\\n links.append(link)\\r\\n links_set.add(link)\\r\\n if return_all: return links\\r\\n return links[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8295144","0.78646994","0.78634316","0.7836907","0.7691762","0.76080465","0.75683284","0.7421076","0.738293","0.7337441","0.7323385","0.7289032","0.7279224","0.72707593","0.7217044","0.7155472","0.71446615","0.7133181","0.71268225","0.7097782","0.70707077","0.70656383","0.70617455","0.70434916","0.70361507","0.6992326","0.69890827","0.69252425","0.6912125","0.68963075","0.6878331","0.6825593","0.6815053","0.68074495","0.68057364","0.6790909","0.67903185","0.67658985","0.6671013","0.6659479","0.6638428","0.66160357","0.66109335","0.6599925","0.6573182","0.65725243","0.6561456","0.6547879","0.6543005","0.6535035","0.6492453","0.6479548","0.6475999","0.64629245","0.6455498","0.6450149","0.6423243","0.6419144","0.64162236","0.6408944","0.64070356","0.64034146","0.639529","0.6386081","0.63742155","0.6321782","0.6282911","0.62814873","0.62599075","0.6259312","0.6253671","0.6250098","0.6246692","0.6207227","0.6195458","0.6182567","0.6174371","0.6172554","0.61724085","0.61584425","0.61563784","0.6113453","0.6106706","0.6094564","0.6094169","0.60834885","0.60778785","0.6077449","0.6072426","0.607215","0.60709834","0.60672987","0.60420054","0.6039187","0.6035967","0.6028417","0.6026423","0.60185075","0.6018329","0.60007304"],"string":"[\n \"0.8295144\",\n \"0.78646994\",\n \"0.78634316\",\n \"0.7836907\",\n \"0.7691762\",\n \"0.76080465\",\n \"0.75683284\",\n \"0.7421076\",\n \"0.738293\",\n \"0.7337441\",\n \"0.7323385\",\n \"0.7289032\",\n \"0.7279224\",\n \"0.72707593\",\n \"0.7217044\",\n \"0.7155472\",\n \"0.71446615\",\n \"0.7133181\",\n \"0.71268225\",\n \"0.7097782\",\n \"0.70707077\",\n \"0.70656383\",\n \"0.70617455\",\n \"0.70434916\",\n \"0.70361507\",\n \"0.6992326\",\n \"0.69890827\",\n \"0.69252425\",\n \"0.6912125\",\n \"0.68963075\",\n \"0.6878331\",\n \"0.6825593\",\n \"0.6815053\",\n \"0.68074495\",\n \"0.68057364\",\n \"0.6790909\",\n \"0.67903185\",\n \"0.67658985\",\n \"0.6671013\",\n \"0.6659479\",\n \"0.6638428\",\n \"0.66160357\",\n \"0.66109335\",\n \"0.6599925\",\n \"0.6573182\",\n \"0.65725243\",\n \"0.6561456\",\n \"0.6547879\",\n \"0.6543005\",\n \"0.6535035\",\n \"0.6492453\",\n \"0.6479548\",\n \"0.6475999\",\n \"0.64629245\",\n \"0.6455498\",\n \"0.6450149\",\n \"0.6423243\",\n \"0.6419144\",\n \"0.64162236\",\n \"0.6408944\",\n \"0.64070356\",\n \"0.64034146\",\n \"0.639529\",\n \"0.6386081\",\n \"0.63742155\",\n \"0.6321782\",\n \"0.6282911\",\n \"0.62814873\",\n \"0.62599075\",\n \"0.6259312\",\n \"0.6253671\",\n \"0.6250098\",\n \"0.6246692\",\n \"0.6207227\",\n \"0.6195458\",\n \"0.6182567\",\n \"0.6174371\",\n \"0.6172554\",\n \"0.61724085\",\n \"0.61584425\",\n \"0.61563784\",\n \"0.6113453\",\n \"0.6106706\",\n \"0.6094564\",\n \"0.6094169\",\n \"0.60834885\",\n \"0.60778785\",\n \"0.6077449\",\n \"0.6072426\",\n \"0.607215\",\n \"0.60709834\",\n \"0.60672987\",\n \"0.60420054\",\n \"0.6039187\",\n \"0.6035967\",\n \"0.6028417\",\n \"0.6026423\",\n \"0.60185075\",\n \"0.6018329\",\n \"0.60007304\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81815284"},"document_rank":{"kind":"string","value":"1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":142,"numItemsPerPage":100,"numTotalItems":95772,"offset":14200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjA1MjUwNiwianRpIjoiMjA1NjU1ODQtYTg0Mi00OTcyLTlkMDUtZjEyMzBhOTZiYjYxIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwNTYxMDYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.iRyc2s9V0AnBdSAyDDRY66GYxLyMyf__ltRKEtLJ6g23OURkz0I7TI2ZMVR9EWWNyYyd009RmJV9abVNVeh9DQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Check all files against the defined precommit hooks. | def lint(session):
session.install("pre-commit")
session.run("pre-commit", "run", "--all-files") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def precommit(exit=True):\n tmpdir = tempfile.mkdtemp()\n\n try:\n copy_index(tmpdir)\n\n modified = check_output(['git', 'diff', '--cached', '--name-only',\n '--diff-filter=ACMRT'])\n modified = [name.strip() for name in modified.splitlines()]\n pa... | [
"0.6876963",
"0.68466324",
"0.67995423",
"0.6654954",
"0.6572371",
"0.6481038",
"0.64283687",
"0.63959146",
"0.6318683",
"0.63177663",
"0.6232557",
"0.6230077",
"0.6152871",
"0.6043679",
"0.60293245",
"0.6020104",
"0.6003532",
"0.59991527",
"0.5969695",
"0.59456915",
"0.58171... | 0.70069766 | 0 |
Run integration tests, unit tests, and doctests sequentially | def run_quick(session):
run_tests(session)
run_doctests(session) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runalltests():\n doctest.testmod()",
"def _run_ci_integration_test():\n _run_install(False)\n _run_integration_tests_on_github(False)",
"def main():\n run_test_all()",
"def runTests(self):\n \n pass",
"def run_doctests(session):\n session.run_always(\"pip\", \"install\", \"-e\"... | [
"0.74199253",
"0.714732",
"0.71423995",
"0.71035886",
"0.705361",
"0.6876069",
"0.67916864",
"0.67511946",
"0.6729915",
"0.6713778",
"0.6660046",
"0.6654544",
"0.66172045",
"0.6584335",
"0.65719473",
"0.6557498",
"0.6537281",
"0.6525023",
"0.6501568",
"0.6495328",
"0.64940125... | 0.7066262 | 4 |
Load the EEG data, which is a particular split of the first trial of subject 337 in the large training data. | def load(extended=False):
_fetch_large()
if extended:
return _load(cache_experiment_extended, _parse_experiment)
else:
return _load(cache_experiment, _parse_experiment) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readEEGepoch(eegfilename, mainDir):\n # subject = 'ES9007' \n datapath = os.path.join(mainDir)\n os.chdir(datapath)\n \n folders = os.listdir(datapath)\n \n for dir in folders:\n \n os.chdir(os.path.join(datapath, dir))\n file =... | [
"0.6603588",
"0.6308554",
"0.62607586",
"0.62329745",
"0.6205543",
"0.60054445",
"0.60021067",
"0.5892239",
"0.58778375",
"0.58696485",
"0.5865206",
"0.58328277",
"0.58099234",
"0.57833",
"0.5776546",
"0.57653904",
"0.5742434",
"0.5740489",
"0.57217824",
"0.5721316",
"0.57048... | 0.0 | -1 |
Load the full EEG data. | def load_full():
_fetch_full()
return _load(cache_full, _parse_full) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self) -> None:",
"def load_data(self):",
"def load(self) -> None:\n self._load_data()\n self._load_poses()\n self._load_timestamps()",
"def _read_eeg(eeg_file):\r\n pass",
"def load_data(self):\n raise NotImplementedError()",
"def load(self):\n pass",
... | [
"0.7056107",
"0.6855266",
"0.64944243",
"0.6453749",
"0.6389463",
"0.6356763",
"0.6356763",
"0.6356763",
"0.6356763",
"0.6346757",
"0.63448316",
"0.628291",
"0.62805426",
"0.6273152",
"0.6271349",
"0.6236898",
"0.6213318",
"0.61869174",
"0.6132568",
"0.6101102",
"0.60924596",... | 0.0 | -1 |
Fetch the large training and test data set. | def _fetch_large():
# Large training data:
resource(
target=data_path("eeg", "SMNI_CMI_TRAIN.tar.gz"),
url="https://kdd.ics.uci.edu/databases/eeg/SMNI_CMI_TRAIN.tar.gz",
)
dependency(
target=data_path("eeg", "train"),
source=data_path("eeg", "SMNI_CMI_TRAIN.tar.gz"),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_training_and_test_sets(normalize):\n class_labels = []\n test_labels = []\n norm = None\n if normalize == True:\n norm = loading.get_normalize_vector()\n\n for i in range(0, 10):\n [training, test] = loading.load_number_set(i, 0.7, norm_vector=norm)\n labels = [str(i)]... | [
"0.6598382",
"0.6573951",
"0.6541831",
"0.6520022",
"0.64941293",
"0.6416177",
"0.6404021",
"0.64026076",
"0.63473725",
"0.6340111",
"0.6321149",
"0.6319699",
"0.63178426",
"0.6309703",
"0.6307903",
"0.6301534",
"0.6286118",
"0.62821674",
"0.6274003",
"0.6271432",
"0.62667745... | 0.7176988 | 0 |
Fetch the full data set. | def _fetch_full():
resource(
target=data_path("eeg", "eeg_full.tar"),
url="https://kdd.ics.uci.edu/databases/eeg/eeg_full.tar",
)
dependency(
target=data_path("eeg", "full"),
source=data_path("eeg", "eeg_full.tar"),
commands=[
"mkdir full",
"ta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetch_data(self):\n pass",
"def fetch_data(self):",
"def fetch_data():\n data.fetch_data()\n data.start_updating()",
"def fetch(self):\n # type: () -> List[List[Any]]\n if self._request.next_uri is None:\n self._finished = True\n return []\n respon... | [
"0.7292134",
"0.7166098",
"0.69632185",
"0.68717474",
"0.68225473",
"0.6602098",
"0.6591393",
"0.6559346",
"0.6547175",
"0.6547175",
"0.6523688",
"0.64388525",
"0.6358625",
"0.6345627",
"0.6340102",
"0.63101643",
"0.6285294",
"0.6239476",
"0.62394583",
"0.6239034",
"0.618789"... | 0.0 | -1 |
Trim string to fit on terminal (assuming 80column display) | def trim(s):
return s if len(s) <= 80 else s[:77] + "..." | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def truncate(string):",
"def trim(string, left=True, right=True):\n lspaces=0;\n rspaces=0;\n if (left):\n for i in range (len(string)):\n if not (string[i] == ' ' or string[i] == '\\t' or string[i] == '\\r' or string[i] == '\\n'):\n break;\n ... | [
"0.70734745",
"0.6830786",
"0.6794939",
"0.6749982",
"0.6673864",
"0.6670376",
"0.6625764",
"0.65689117",
"0.6558081",
"0.652869",
"0.64897746",
"0.64516795",
"0.6439728",
"0.6387328",
"0.6361083",
"0.62939423",
"0.62538517",
"0.62494695",
"0.6247001",
"0.62291694",
"0.622815... | 0.74525046 | 3 |
This method is used to get appropriate error messages from the exception. | def _get_error_message_from_exception(self, e):
error_code = AWSSECURITYHUB_ERR_CODE_UNAVAILABLE
error_msg = AWSSECURITYHUB_ERR_MSG_UNAVAILABLE
try:
if e.args:
if len(e.args) > 1:
error_code = e.args[0]
error_msg = e.args[1]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error(self, e):\n return \"{}: {} ({})\".format(e.__class__.__name__, e.__doc__, e.message)",
"def get_error_message(self):\n return self.error_message.get_error_message()",
"def exception(self) -> exceptions.ErrorMessageException:\n\n return ErrorMessage.ERROR_CODES_TO_EXCEPTIONS.get(... | [
"0.7292581",
"0.7146587",
"0.71316063",
"0.70570153",
"0.7033765",
"0.7033765",
"0.7033765",
"0.70254177",
"0.7002314",
"0.69653606",
"0.6963725",
"0.6946914",
"0.694056",
"0.69257855",
"0.68925625",
"0.6857166",
"0.6846722",
"0.68250126",
"0.6807385",
"0.67811245",
"0.678112... | 0.6984752 | 9 |
This method is to check if the provided input parameter value is a nonzero positive integer and returns the integer value of the parameter itself. | def _validate_integer(self, action_result, parameter, key, allow_zero=False):
if parameter is not None:
try:
if not float(parameter).is_integer():
return action_result.set_status(phantom.APP_ERROR, AWSSECURITYHUB_VALID_INT_MSG.format(param=key)), None
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def positive_int(arg):\n try:\n n = int(arg)\n assert n > 0\n return n\n except (ValueError, AssertionError):\n raise argparse.ArgumentTypeError('%s is not a positive integer' % arg)",
"def posint_zero_p(value):\n # check if the value has the expected ... | [
"0.74226147",
"0.74018186",
"0.7358888",
"0.7341269",
"0.72722924",
"0.7248723",
"0.72295064",
"0.7205128",
"0.71804637",
"0.7158422",
"0.7157822",
"0.7041932",
"0.70318663",
"0.6935737",
"0.69280696",
"0.6923878",
"0.68814045",
"0.6756427",
"0.6744842",
"0.6740722",
"0.67209... | 0.65633446 | 28 |
This function is used to create the container in Phantom using finding data. | def _create_container(self, finding):
container_dict = {}
container_dict['name'] = finding['Title']
container_dict['source_data_identifier'] = finding['Id']
container_dict['description'] = finding['Description']
container_creation_status, container_creation_msg, container_id = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_container(self):\n pass",
"def createContainer(tag, data={}): #@NoSelf",
"def container_factory(self, name):",
"def container_factory(self, name):",
"def container_factory(self, name):",
"def container_factory(self, name):",
"def container_factory(self, name):",
"def __create_... | [
"0.5875233",
"0.58578813",
"0.5711849",
"0.5711849",
"0.5711849",
"0.5711849",
"0.5711849",
"0.5676269",
"0.5545769",
"0.5408113",
"0.53612626",
"0.5329194",
"0.5291496",
"0.5263945",
"0.5263945",
"0.51851237",
"0.5161798",
"0.5128938",
"0.5122123",
"0.51019007",
"0.50853336"... | 0.65444756 | 0 |
This function is used to create artifacts in given container using finding data. | def _create_artifacts(self, finding, container_id):
artifacts = []
for resource in finding.pop('Resources'):
resource_artifact = {}
resource_artifact['name'] = '{} Resource Artifact'.format(resource['Type'])
resource_artifact['container_id'] = container_id
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_container(self, finding):\n\n container_dict = {}\n container_dict['name'] = finding['Title']\n container_dict['source_data_identifier'] = finding['Id']\n container_dict['description'] = finding['Description']\n\n container_creation_status, container_creation_msg, con... | [
"0.62061214",
"0.57416934",
"0.573482",
"0.573116",
"0.56448776",
"0.5568765",
"0.55421925",
"0.55335236",
"0.54728556",
"0.5449445",
"0.54188776",
"0.53918684",
"0.53792435",
"0.5363252",
"0.5348222",
"0.5341792",
"0.52852637",
"0.52717286",
"0.5253826",
"0.52310616",
"0.523... | 0.73780525 | 0 |
This function is used to handle on_poll. | def _handle_on_poll(self, param):
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
action_result = self.add_action_result(ActionResult(dict(param)))
config = self.get_config()
container_count = int(param.get(phantom.APP_JSON_CONTAINER_COUNT))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def poll(self, poll_input):",
"def poll(self):\n self.poll_function(self.connection)",
"def poll(self):\n raise NotImplementedError()",
"def setup_poll(self):\n while True:\n try:\n self.do_polling()\n time.sleep(0.01)\n except Keyboard... | [
"0.76818335",
"0.7355514",
"0.7202711",
"0.7128685",
"0.71189326",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.7010415",
"0.69312245",
... | 0.0 | -1 |
Return collected metadata of a dataproduct. | def dataproduct(self, identity, dataproduct_id):
metadata = {}
permissions = self.permission.dataproduct_permissions(
dataproduct_id, identity
) or {}
session = self.config_models.session()
# find Group or Data layer object
OWSLayer = self.config_models.mod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_metadata(self):\n return self.client._perform_json(\n \"GET\", \"/projects/%s/recipes/%s/metadata\" % (self.project_key, self.recipe_name))",
"def summarize_metadata(self):\n meta_dict = {}\n for comp in self.dataset.data_vars:\n for mkey, mvalue in self.dat... | [
"0.6686516",
"0.6594631",
"0.65593004",
"0.6541971",
"0.6431339",
"0.6426287",
"0.6409416",
"0.63947934",
"0.638867",
"0.6377498",
"0.63504374",
"0.6335236",
"0.63153744",
"0.6300704",
"0.627984",
"0.6277133",
"0.62743515",
"0.62743515",
"0.62743515",
"0.62743515",
"0.6274351... | 0.75031686 | 0 |
Recursively collect metadata of a dataproduct. | def dataproduct_metadata(self, ows_layer, permissions, session):
metadata = {}
# type
sublayers = None
data_set_view = None
searchterms = []
if ows_layer.type == 'group':
if ows_layer.name not in permissions.get('group_layers', []):
# group la... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataproduct(self, identity, dataproduct_id):\n metadata = {}\n\n permissions = self.permission.dataproduct_permissions(\n dataproduct_id, identity\n ) or {}\n\n session = self.config_models.session()\n\n # find Group or Data layer object\n OWSLayer = self.co... | [
"0.62223345",
"0.6028894",
"0.5872434",
"0.5834364",
"0.5798437",
"0.575176",
"0.5705367",
"0.55762815",
"0.5575156",
"0.5539546",
"0.5454352",
"0.54196703",
"0.54158056",
"0.5383527",
"0.5330761",
"0.5273302",
"0.5259924",
"0.5257529",
"0.5253101",
"0.52238184",
"0.521929",
... | 0.52869976 | 15 |
Collect metadata of a basic DataSet dataproduct. | def basic_dataset_metadata(self, data_set_view, session):
metadata = {}
contacts = self.basic_dataset_contacts(data_set_view, session)
metadata = {
'identifier': data_set_view.name,
'display': data_set_view.data_set.data_set_name,
'type': 'datasetview',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataproduct(self, identity, dataproduct_id):\n metadata = {}\n\n permissions = self.permission.dataproduct_permissions(\n dataproduct_id, identity\n ) or {}\n\n session = self.config_models.session()\n\n # find Group or Data layer object\n OWSLayer = self.co... | [
"0.67701876",
"0.6577738",
"0.6516718",
"0.6471045",
"0.62507623",
"0.62314963",
"0.62190706",
"0.6218088",
"0.62123156",
"0.62123156",
"0.61886233",
"0.6174235",
"0.6168924",
"0.6141858",
"0.612278",
"0.6120039",
"0.60999846",
"0.60504276",
"0.6031817",
"0.59935963",
"0.5977... | 0.7206746 | 0 |
Return contacts metadata for a dataproduct. | def dataproduct_contacts(self, ows_layer, session):
# collect contacts for layer and related GDI resources
gdi_oids = [ows_layer.gdi_oid]
if ows_layer.type == 'data':
# include data source
gdi_oids.append(
ows_layer.data_set_view.data_set.gdi_oid_data_sour... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataproduct(self, identity, dataproduct_id):\n metadata = {}\n\n permissions = self.permission.dataproduct_permissions(\n dataproduct_id, identity\n ) or {}\n\n session = self.config_models.session()\n\n # find Group or Data layer object\n OWSLayer = self.co... | [
"0.63011616",
"0.6096609",
"0.59576094",
"0.5945618",
"0.59220904",
"0.5815128",
"0.57454026",
"0.56265146",
"0.56265146",
"0.56265146",
"0.5608655",
"0.5568946",
"0.54889625",
"0.5485552",
"0.5429544",
"0.5420704",
"0.53999895",
"0.53758997",
"0.53610945",
"0.5360896",
"0.53... | 0.593956 | 4 |
Return contacts metadata for a basic DataSet dataproduct. | def basic_dataset_contacts(self, data_set_view, session):
# collect contacts for basic DataSet and related GDI resources
gdi_oids = [
data_set_view.gdi_oid, data_set_view.data_set.gdi_oid_data_source
]
return self.contacts(gdi_oids, session) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def basic_dataset_metadata(self, data_set_view, session):\n metadata = {}\n\n contacts = self.basic_dataset_contacts(data_set_view, session)\n\n metadata = {\n 'identifier': data_set_view.name,\n 'display': data_set_view.data_set.data_set_name,\n 'type': 'datas... | [
"0.70957017",
"0.6029492",
"0.60244423",
"0.5971579",
"0.5823789",
"0.5810419",
"0.5684084",
"0.5576861",
"0.55693126",
"0.5546394",
"0.554111",
"0.5482603",
"0.54492116",
"0.54473823",
"0.5446493",
"0.5446493",
"0.5446493",
"0.54323864",
"0.5393266",
"0.538147",
"0.5356055",... | 0.72183543 | 0 |
Return contacts metadata for a list of resource IDs. | def contacts(self, gdi_oids, session):
contacts = []
ResourceContact = self.config_models.model('resource_contact')
Contact = self.config_models.model('contact')
query = session.query(ResourceContact) \
.filter(ResourceContact.gdi_oid_resource.in_(gdi_oids)) \
.o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getcontacts():\n contacts = {}\n\n try:\n #get list of contact ids\n contactids = r.smembers(\"contacts\")\n\n #for each contact id get data\n for contactid in contactids:\n contacts.update(_getcontact(str(contactid)))\n return contacts\n except:\n ... | [
"0.6895001",
"0.6349526",
"0.62968177",
"0.6285123",
"0.61312366",
"0.60384965",
"0.6011176",
"0.5951977",
"0.5888752",
"0.5837271",
"0.5823244",
"0.5820674",
"0.576577",
"0.57647824",
"0.57629967",
"0.57116294",
"0.5709567",
"0.5701766",
"0.5696976",
"0.5671242",
"0.56564695... | 0.58337533 | 10 |
Return datasource metadata for a dataproduct. | def dataproduct_datasource(self, ows_layer, session):
metadata = {}
if ows_layer.type == 'group':
# group layer
return metadata
data_set = ows_layer.data_set_view.data_set
data_source = data_set.data_source
if data_source.connection_type == 'database':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataproduct(self, identity, dataproduct_id):\n metadata = {}\n\n permissions = self.permission.dataproduct_permissions(\n dataproduct_id, identity\n ) or {}\n\n session = self.config_models.session()\n\n # find Group or Data layer object\n OWSLayer = self.co... | [
"0.74106634",
"0.6391926",
"0.60912377",
"0.60485494",
"0.57594264",
"0.5758313",
"0.5735054",
"0.56168944",
"0.55876154",
"0.55693054",
"0.5500405",
"0.54809636",
"0.5465075",
"0.54416764",
"0.5426445",
"0.53999317",
"0.5371702",
"0.53709596",
"0.5331371",
"0.53281933",
"0.5... | 0.61659366 | 2 |
Return table metadata for a data_set. | def dataset_info(self, data_source_id, table_name):
# NOTE: form field returns 'None' as string if not set
if not table_name or table_name == 'None':
# empty table name
return None
# parse schema and table name
parts = table_name.split('.')
if len(parts) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def basic_dataset_metadata(self, data_set_view, session):\n metadata = {}\n\n contacts = self.basic_dataset_contacts(data_set_view, session)\n\n metadata = {\n 'identifier': data_set_view.name,\n 'display': data_set_view.data_set.data_set_name,\n 'type': 'datas... | [
"0.7403433",
"0.6452928",
"0.6423298",
"0.6297942",
"0.62787217",
"0.6275273",
"0.62073505",
"0.62066114",
"0.6149771",
"0.605354",
"0.60337037",
"0.5911093",
"0.59013206",
"0.58804536",
"0.587467",
"0.586729",
"0.5864545",
"0.5843912",
"0.58337164",
"0.57818174",
"0.577952",... | 0.6074714 | 9 |
Return primary key, geometry columns, types and srids from a PostGIS table. | def postgis_metadata(self, data_source_id, schema, table_name):
metadata = {}
try:
engine = self.engine_for_data_source(data_source_id)
if engine is None:
return {
'error': "FEHLER: DataSource nicht gefunden"
}
# c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_table_info(self):\n epsg = None\n meta = MetaData()\n table_obj = Table(self._table, meta,\n autoload=True, autoload_with=self._engine)\n if not self._columns:\n self._columns = table_obj.columns.keys()\n geo_cols = [(col.name, col.type... | [
"0.67692256",
"0.675476",
"0.62519324",
"0.60473233",
"0.5883852",
"0.58504605",
"0.5701166",
"0.5491535",
"0.5480299",
"0.5475951",
"0.5452421",
"0.53603435",
"0.5338191",
"0.5314427",
"0.52559847",
"0.52456456",
"0.5243877",
"0.5240255",
"0.52377796",
"0.5215144",
"0.521342... | 0.696644 | 0 |
Return SQLAlchemy engine for a data_source. | def engine_for_data_source(self, data_source_id):
engine = None
# find data_source
DataSource = self.config_models.model('data_source')
session = self.config_models.session()
query = session.query(DataSource) \
.filter_by(gdi_oid=data_source_id)
data_source =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_database_engine() -> Engine:\n return engine",
"def get_db_engine():\n # get database connection url\n connection_url = get_db_connection_url()\n\n # Create engine from connection url\n engine = create_engine(connection_url)\n\n return engine",
"def get_engine(self, db_name):\n ... | [
"0.7026651",
"0.6842614",
"0.67209196",
"0.6679287",
"0.6633262",
"0.6460196",
"0.6328281",
"0.6285054",
"0.6285054",
"0.62150884",
"0.61740327",
"0.61366487",
"0.61350334",
"0.6116982",
"0.60951006",
"0.60835916",
"0.6050043",
"0.5965481",
"0.5956415",
"0.59335893",
"0.58849... | 0.87228316 | 0 |
Return any WMS datasource for a dataproduct. | def dataproduct_wms(self, ows_layer, session):
wms_datasource = None
# get WMS root layer
root_layer = None
WmsWfs = self.config_models.model('wms_wfs')
query = session.query(WmsWfs).filter_by(ows_type='WMS')
# eager load relation
query = query.options(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_datasource_of():\n global datasource_of\n\n if not datasource_of:\n datasource_of = stixhelpers.datasource_of()\n \n return datasource_of",
"def get_datasource(self):\n return None",
"def get_datasource_list():\n global datasource_list\n\n if not datasource_list:\n ... | [
"0.71008384",
"0.6631266",
"0.598743",
"0.59302306",
"0.59302306",
"0.59197843",
"0.58158386",
"0.577935",
"0.5589404",
"0.55361176",
"0.55361176",
"0.55361176",
"0.55361176",
"0.5373815",
"0.5368272",
"0.52805233",
"0.52325875",
"0.52294",
"0.52106684",
"0.5157559",
"0.51001... | 0.6252136 | 2 |
Recursively check if layer is a WMS layer. | def layer_in_ows(self, ows_layer, root_layer):
if root_layer is None:
# no WMS root layer
return False
in_wms = False
# get parent groups
parents = [p.group for p in ows_layer.parents]
for parent in parents:
if parent.gdi_oid == root_layer.gdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_layer(obj):\n # TODO(b/110718070): Replace with isinstance(obj, base_layer.Layer).\n return hasattr(obj, \"_is_layer\") and not isinstance(obj, type)",
"def is_feature_layer(layer):\n return getattr(layer, '_is_feature_layer', False)",
"def IsLayer(self, *args):\n return _XCAFDoc.XCAFDoc_Layer... | [
"0.69694364",
"0.6235193",
"0.6218354",
"0.593918",
"0.5923161",
"0.58760947",
"0.5860823",
"0.5807114",
"0.5681459",
"0.55938524",
"0.5574623",
"0.5512457",
"0.5449315",
"0.54430646",
"0.53812546",
"0.53716415",
"0.53391325",
"0.5313924",
"0.5287635",
"0.52501976",
"0.524820... | 0.76703054 | 0 |
Return ows_metadata for a layer. | def ows_metadata(self, layer):
ows_metadata = {}
if layer.ows_metadata:
try:
# load JSON from ows_metadata
ows_metadata = json.loads(layer.ows_metadata)
except ValueError as e:
self.logger.warning(
"Invalid JSON... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataproduct_metadata(self, ows_layer, permissions, session):\n metadata = {}\n\n # type\n sublayers = None\n data_set_view = None\n searchterms = []\n if ows_layer.type == 'group':\n if ows_layer.name not in permissions.get('group_layers', []):\n ... | [
"0.6738899",
"0.62456894",
"0.59765214",
"0.56073684",
"0.5586877",
"0.5581771",
"0.5532094",
"0.5492781",
"0.54767776",
"0.54743665",
"0.5456338",
"0.54506356",
"0.54184407",
"0.53791165",
"0.5372126",
"0.53449667",
"0.53406215",
"0.5337875",
"0.5326182",
"0.5314207",
"0.530... | 0.81569564 | 0 |
Split comma separated values into list. | def split_values(self, value):
if value:
return [s.strip() for s in value.split(',')]
else:
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_by_comma(s):\n return s.strip().split(\",\")",
"def separate_comma(s):\n return s.split(',')",
"def to_python(self, value):\n # Return an empty list if no input was given.\n if not value:\n return []\n return value.split(',')",
"def pure_list(comma_list):\n ... | [
"0.78025526",
"0.76226556",
"0.7475591",
"0.72795135",
"0.7144818",
"0.69944984",
"0.69677824",
"0.6933669",
"0.6908274",
"0.68442553",
"0.6830242",
"0.6824286",
"0.67987007",
"0.67562747",
"0.6737418",
"0.6710399",
"0.67035455",
"0.66600406",
"0.6642529",
"0.6638843",
"0.660... | 0.8507184 | 0 |
Update QML with embedded symbols. | def update_qml(self, qml):
if qml is None:
return qml
try:
# parse XML
root = ElementTree.fromstring(qml)
# embed symbols
self.embed_qml_symbols(root, 'SvgMarker', 'name')
self.embed_qml_symbols(root, 'SVGFill', 'svgFile')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def embed_qml_symbols(self, root, layer_class, prop_key):\n for svgprop in root.findall(\".//layer[@class='%s']/prop[@k='%s']\" %\n (layer_class, prop_key)):\n symbol_path = svgprop.get('v')\n path = os.path.abspath(\n os.path.join(QGS_... | [
"0.67538667",
"0.5128117",
"0.509659",
"0.5053252",
"0.4964151",
"0.49451157",
"0.4761072",
"0.4732717",
"0.47295678",
"0.46906155",
"0.4686383",
"0.46371529",
"0.46117783",
"0.4515449",
"0.4510255",
"0.4473232",
"0.44729185",
"0.44690013",
"0.4440391",
"0.4439548",
"0.443494... | 0.76550114 | 0 |
Embed symbol resources as base64 in QML. | def embed_qml_symbols(self, root, layer_class, prop_key):
for svgprop in root.findall(".//layer[@class='%s']/prop[@k='%s']" %
(layer_class, prop_key)):
symbol_path = svgprop.get('v')
path = os.path.abspath(
os.path.join(QGS_RESOURCES_DI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_qml(self, qml):\n if qml is None:\n return qml\n\n try:\n # parse XML\n root = ElementTree.fromstring(qml)\n\n # embed symbols\n self.embed_qml_symbols(root, 'SvgMarker', 'name')\n self.embed_qml_symbols(root, 'SVGFill', 'sv... | [
"0.55629766",
"0.54794717",
"0.5280593",
"0.49432886",
"0.49401248",
"0.48893714",
"0.48839408",
"0.48512277",
"0.48236877",
"0.48004314",
"0.47988746",
"0.47885874",
"0.47874668",
"0.4764687",
"0.47481993",
"0.47099677",
"0.46938792",
"0.46663794",
"0.4606227",
"0.45966935",
... | 0.66117233 | 0 |
Change the time unit from epoch time to hours since 1800 | def change_time_units(var):
century18 = dt.datetime(1800,1,1,0)
#for i,j in enumerate(var[:]):
# date = dt.datetime.utcfromtimestamp(j)
# seconds = (date - century18).total_seconds()
# hours = int( seconds / 60 / 60 )
# var[i] = hours
def change_unit(date):
date = dt.date... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _unit_hr(self):\n return self.time_base * 60.0",
"def unit_hr(self):\n return self.time_base * 60.0",
"def itow2utc(itow: int) -> datetime.time:\r\n\r\n utc = datetime(1980, 1, 6) + timedelta(seconds=(itow / 1000) - (35 - 19))\r\n return utc.time()",
"def epoch_time(when):\n if not... | [
"0.6538163",
"0.6491768",
"0.6192531",
"0.5787748",
"0.572735",
"0.572301",
"0.571299",
"0.57039964",
"0.5693369",
"0.5687686",
"0.5628768",
"0.56278694",
"0.5623892",
"0.56043375",
"0.5601073",
"0.55941534",
"0.5590749",
"0.55859935",
"0.55822754",
"0.55742246",
"0.555413",
... | 0.6157713 | 3 |
Adds human readable date variable. Assumes date is in seconds since epoch. time_var is netCDF.Variable object. | def add_utc_date(nc, time_var):
# Create Variable
utc = nc.createVariable('utc_time', int, ('time'))
setattr(utc, 'standard_name', "time")
setattr(utc, 'long_name', "UTC date yyyy-mm-dd hh:00:00 as yyyymmddhh")
setattr(utc, "units","Gregorian_year month day hour")
toUTC = lambda d: int(dt.datet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def date_info_day(date_str, infile):\n #date_str = str(sys.argv[1])\n #infile = './' + date_str + '.nc'\n\n # prepare date\n year,mon,day = date_str.split('-')\n year_num = int(float(year))\n mon_num = int(float(mon))\n day_num = int(float(day))\n\n\n datesec_calc = []\n val_pr_day = 4\n... | [
"0.53371304",
"0.52994335",
"0.5053854",
"0.5022046",
"0.5021961",
"0.5021961",
"0.50010127",
"0.4921834",
"0.49154162",
"0.48844352",
"0.48474166",
"0.48312527",
"0.4828444",
"0.48283142",
"0.47912452",
"0.47758126",
"0.4751266",
"0.47495",
"0.47279778",
"0.46959502",
"0.465... | 0.63723165 | 0 |
Return True if variable has missing start and end | def check_if_reduce_needed(vars_to_modify):
for var in vars_to_modify:
if len(var.dimensions) > 2 and var[0,0,:].mask.all() and \
var[-1,1,:,:].mask.all():
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isUndefinedRange(program: ghidra.program.model.listing.Program, startAddress: ghidra.program.model.address.Address, endAddress: ghidra.program.model.address.Address) -> bool:\n ...",
"def isEmpty(self):\n return self.start == -1 and self.end == -1",
"def isValid(self, start, end):\n fo... | [
"0.68972343",
"0.6854934",
"0.6404567",
"0.63445413",
"0.62717694",
"0.62168694",
"0.62168694",
"0.6185749",
"0.6160765",
"0.6113537",
"0.6075577",
"0.60534394",
"0.60275",
"0.60223347",
"0.5987251",
"0.59226704",
"0.58350503",
"0.5816686",
"0.5812118",
"0.58019555",
"0.57760... | 0.0 | -1 |
Adds a time bounds variable to variable. Assumes time dimension is called 'time' | def add_time_bounds(nc, varname):
THREE_HOURS = 60*60*3 # in seconds
bnds_name = 'time_bnds'
bounds_dim = 'nv'
# Create bounds dimension
nc.createDimension(bounds_dim, 2)
# Get variable matching varname
time_var = nc.variables['time']
time_var.setncattr('bounds', bnds_name)
time_d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __define_variable_time(self, initial_guess, minimum, maximum):\n i = 0\n for nlp in self.nlp:\n if isinstance(nlp[\"tf\"], self.CX):\n time_bounds = Bounds(minimum[i], maximum[i], interpolation=InterpolationType.CONSTANT)\n time_init = InitialConditions(in... | [
"0.6587515",
"0.6190563",
"0.5912615",
"0.587993",
"0.58253014",
"0.56894726",
"0.56684715",
"0.56504494",
"0.5623413",
"0.5619893",
"0.55306077",
"0.55090916",
"0.5483374",
"0.5463484",
"0.5454657",
"0.5432568",
"0.5394258",
"0.53902286",
"0.53509086",
"0.5346871",
"0.534403... | 0.7952806 | 0 |
Changes fill value for all variables in file | def change_fill_value(nc, fill_value):
outfile = 'tmp' + str(random.randint(1,100000)) + '.nc'
out_nc = copync.copy_dimensions(nc, outfile)
copync.copy_variables(nc, out_nc, new_fill_value=fill_value)
out_nc.close()
return outfile | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setupVariables(self, file, variables, wordsize):\n file.write(self.getStringForVariables(variables, wordsize) + '\\n')\n return",
"def fill(self, file = None):\n\t\tif file == None:\n\t\t\tself.prefill = {}\n\t\telse:\n\t\t\tf = open('sudoku.txt')\n\t\t\tself.prefill = f.read()\n\t\t\tf.close()... | [
"0.63592917",
"0.6181873",
"0.61443293",
"0.6081401",
"0.602255",
"0.5872441",
"0.572699",
"0.5706657",
"0.5658155",
"0.5626804",
"0.56069094",
"0.55748194",
"0.5568049",
"0.54713637",
"0.5433601",
"0.542916",
"0.5417756",
"0.5386758",
"0.5309297",
"0.530289",
"0.52563107",
... | 0.60068417 | 5 |
Creates a maze path from current cursor to the finish. The returns true if a path is found, false if not the. NOTE unlike rest of the game, this solution is recusive, adn highlights why NOT to use recursion, as depth errors are encountered easily | def create_solution(self, cursor):
self._visited = [cursor.get_current_cell()]
try:
solved = self._recursive_solve(cursor)
except RecursionError as re:
logging.warning("No solution found due to recusion limit - this is why we use iterative !!!")
solved = Fal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maze_solver_rec(maze, start, end):\r\n def find_path(maze, pos, end):\r\n mark(maze, pos)\r\n if pos == end:\r\n print(pos, end=' ')\r\n return True\r\n for i in range(4):\r\n nextp = pos[0]+dirs[i][0], pos[1]+dirs[i][1]\r\n if passable(maze, ... | [
"0.7348416",
"0.69578207",
"0.68458307",
"0.6739615",
"0.6575507",
"0.6509359",
"0.64783865",
"0.64704174",
"0.63894373",
"0.6322013",
"0.631913",
"0.6312608",
"0.62871957",
"0.6240864",
"0.6233506",
"0.6221758",
"0.61917",
"0.61521137",
"0.6146944",
"0.61003155",
"0.6091624"... | 0.0 | -1 |
Use inspect_links to get a list of all the external links in the site | def cleanup_links(path, inspect_links=False):
with open(path) as f:
text = f.read()
# if 'BokehJS does not appear to have successfully loaded' in text:
# for k, v in BOKEH_REPLACEMENTS.items():
# text = text.replace(k, v)
text = component_links(text, path)
soup = BeautifulSoup... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getlinks(url):\n page = Linkfetcher(url)\n page.linkfetch()\n for i, url in enumerate(page):\n print(\"%d ==> %s\" % (i, url))",
"def getLinks(self):\n\t\threfs = []\n\t\tfor link in self.bsource.find_all('a'):\n\t\t\threfs.append(link.get('href'))\n\t\treturn hrefs",
"def __url_list(self, ... | [
"0.7155601",
"0.6977096",
"0.6939031",
"0.68616354",
"0.68225676",
"0.6807519",
"0.67517316",
"0.6705572",
"0.6699865",
"0.669449",
"0.669449",
"0.669449",
"0.669449",
"0.669449",
"0.6660218",
"0.66458374",
"0.6635376",
"0.6626044",
"0.66240597",
"0.6589426",
"0.6579488",
"... | 0.0 | -1 |
Calculate the probability of of creating an offspring with a dominant allele | def calculate_probability(k: int, m: int, n: int) -> float:
population = ["AA" for _ in range(k)] + ["Aa" for _ in range(m)] + ["aa" for _ in range(n)]
pairings = it.combinations(population, 2)
probabilities = [PROBABILITIES[pairing] for pairing in pairings]
output = sum(probabilities) / len(probabiliti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pr_dominant_offpring(offspring_zygosity):\n\n homozygous_dominant, heterozygous, homozygous_recessive = offspring_zygosity\n\n total = homozygous_dominant + heterozygous + homozygous_recessive\n dominant = homozygous_dominant + heterozygous\n\n pr_dominant = dominant / total\n\n return pr_domina... | [
"0.71508455",
"0.7009012",
"0.68244606",
"0.64912534",
"0.63507146",
"0.6219815",
"0.6198249",
"0.61633426",
"0.60935193",
"0.60492826",
"0.60419685",
"0.6011867",
"0.59874773",
"0.59808606",
"0.5976723",
"0.5940034",
"0.59356415",
"0.59274435",
"0.59157497",
"0.589901",
"0.5... | 0.0 | -1 |
Initialize some callbacks inline Use this constructor to provide credentials and certificate callbacks inline, instead of defining your own class for these ones. You can e.g. also pass in one of the credential objects as 'credentials' instead of creating a function which returns a hardcoded object. | def __init__(self, credentials=None, certificate=None):
if credentials is not None:
self.credentials = credentials
if certificate is not None:
self.certificate = certificate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, req, credentials_fn):\n self.req = req\n self.credentials_fn = credentials_fn",
"def __init__(self, cred=None, max_tries=5, callback=newcred):\n self.set_cred(cred)\n self.try_count = 1\n self.max_tries = max_tries\n self.callback = callback",
"def _... | [
"0.65539837",
"0.64415884",
"0.6256035",
"0.6152882",
"0.61327577",
"0.6122212",
"0.60726905",
"0.5963776",
"0.5948654",
"0.5945171",
"0.5938793",
"0.59084725",
"0.59006447",
"0.58869493",
"0.5884443",
"0.5865886",
"0.58515286",
"0.5847019",
"0.57956296",
"0.5794808",
"0.5778... | 0.7311035 | 0 |
Progress output callback Override this function with your own progress reporting function | def sideband_progress(self, string): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def OnProgress(bytes_read, total_bytes, percent):\n sys.stdout.write(\"progress: %.2f%% \\r\" % (percent))\n sys.stdout.flush()",
"def reportProgress(self):\n \n pass",
"def doProgress(self,progress,message):\n pass",
"def progress(self, arg, num_done, info=''):\n pass",
"def ge... | [
"0.7794072",
"0.7742129",
"0.7723824",
"0.7527916",
"0.727533",
"0.7107738",
"0.70911974",
"0.7045239",
"0.70367855",
"0.7021728",
"0.6993632",
"0.69873875",
"0.6929215",
"0.6918921",
"0.6918631",
"0.68901145",
"0.687302",
"0.6857004",
"0.6809645",
"0.67812383",
"0.6764005",
... | 0.6427917 | 57 |
Credentials callback If the remote server requires authentication, this function will be called and its return value used for authentication. Override it if you want to be able to perform authentication. | def credentials(self, url, username_from_url, allowed_types):
raise Passthrough | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote_auth(self, auth_info: Optional[Dict[str, str]] = None) -> Dict[str, str]:\n if not self._connected:\n return None\n if auth_info is not None:\n self._remote_auth = auth_info\n return self._remote_auth",
"def authenticate_credentials(self, **credentials):\n ... | [
"0.65524274",
"0.6089398",
"0.58988243",
"0.5894323",
"0.5894309",
"0.5875045",
"0.5870578",
"0.58598083",
"0.58362657",
"0.57793766",
"0.577223",
"0.56979597",
"0.56545985",
"0.56085044",
"0.5590495",
"0.5579785",
"0.5576196",
"0.5566388",
"0.55437064",
"0.5538214",
"0.54772... | 0.53605944 | 27 |
Certificate callback Override with your own function to determine whether the accept the server's certificate. | def certificate_check(self, certificate, valid, host):
raise Passthrough | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_server_certificate(self):\n return self._verify_server_certificate",
"def verify_server_certificate(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"verify_server_certificate\")",
"def verify_server_certificate(self) -> bool:\n return pulumi.get(self, \"verify_... | [
"0.69057816",
"0.6904875",
"0.6790211",
"0.646486",
"0.63657093",
"0.61513364",
"0.6096049",
"0.60317755",
"0.5979905",
"0.5940069",
"0.5871149",
"0.58208966",
"0.57788336",
"0.5753447",
"0.57468694",
"0.574129",
"0.570776",
"0.56400156",
"0.5629674",
"0.56151485",
"0.5603868... | 0.7030046 | 0 |
Transfer progress callback Override with your own function to report transfer progress. | def transfer_progress(self, stats): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onTransferUpdate(self, api, transfer):\n logging.info('Transfer update ({} {});'\n ' Progress: {} KB of {} KB, {} KB/s'\n .format(transfer,\n transfer.getFileName(),\n transfer.getTransferredBytes() / 1024,\n... | [
"0.7220874",
"0.7112329",
"0.69391227",
"0.69087076",
"0.68912673",
"0.6877024",
"0.6770549",
"0.67233896",
"0.67167825",
"0.6671872",
"0.66329044",
"0.6622333",
"0.66127753",
"0.65370864",
"0.65070117",
"0.64977056",
"0.6437404",
"0.6410456",
"0.63769513",
"0.6359364",
"0.63... | 0.7890586 | 0 |
Update tips callabck Override with your own function to report reference updates | def update_tips(self, refname, old, new): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __editShowCallTips(self):\n self.activeWindow().callTip()",
"def update_tips(self, usageText):\r\n try:\r\n self.widgetList[13].config(text=usageText, justify=LEFT)\r\n except TclError:\r\n kT.debug_log(\"Changed menu\", sys.exc_info()[2])\r\n return",
"def... | [
"0.6555432",
"0.64051604",
"0.5982053",
"0.58614695",
"0.5771405",
"0.570853",
"0.5652499",
"0.5582469",
"0.5555294",
"0.5554527",
"0.55478805",
"0.5545224",
"0.5434774",
"0.5425506",
"0.5390354",
"0.5354941",
"0.53439796",
"0.53439796",
"0.53439796",
"0.5318367",
"0.5292375"... | 0.8590424 | 0 |
Push update reference callback Override with your own function to report the remote's acceptace or rejection of reference updates. | def push_update_reference(self, refname, message): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ref_updated(self, event):\n pass",
"def _notify_update(self, cuds_object):",
"def notify(self, ref_output=None, moves_made=None):\n pass",
"def update_callback(app):\n print(\"Update callback invoked for %s\" % app.name)\n\n # TODO: Add integration tests here\n return True",
"def... | [
"0.700554",
"0.586919",
"0.58252454",
"0.55110234",
"0.5484074",
"0.5471983",
"0.5470453",
"0.5423666",
"0.5410064",
"0.53476197",
"0.53475934",
"0.5346236",
"0.5307683",
"0.528483",
"0.5284799",
"0.52797055",
"0.5253185",
"0.52331346",
"0.5220044",
"0.5199602",
"0.5196633",
... | 0.7560829 | 0 |
The constructor is for internal use only | def __init__(self, repo, ptr):
self._repo = repo
self._remote = ptr
self._stored_exception = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self) -> None:\n # TODO: Provide the complete construct... | [
"0.83402354",
"0.83402354",
"0.83402354",
"0.83402354",
"0.82908845",
"0.8187017",
"0.8024304",
"0.79844433",
"0.7911797",
"0.7875458",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
"0.78700167",
... | 0.0 | -1 |
Name of the remote | def name(self):
return maybe_string(C.git_remote_name(self._remote)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote_hostname(self):\n return pn_connection_remote_hostname(self._impl)",
"def get_remote_name(self, file_path: str, remote_name: str):\n if remote_name is None:\n remote_name = os.path.basename(file_path)\n return remote_name",
"def getRemoteHost():",
"def server_name(s... | [
"0.7341725",
"0.7134672",
"0.7032486",
"0.696617",
"0.693461",
"0.6897008",
"0.68189836",
"0.67615247",
"0.6744413",
"0.67394644",
"0.67394644",
"0.6712816",
"0.67052776",
"0.6688957",
"0.66647696",
"0.65945596",
"0.65823656",
"0.6563257",
"0.65593636",
"0.6494698",
"0.648166... | 0.7960541 | 0 |
Url of the remote | def url(self):
return maybe_string(C.git_remote_url(self._remote)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote_url(self) -> str:\n return f\"https://api.figma.com/v1/files/{self.file_id}\"",
"def url(self):\n url = self.url\n return url",
"def getRemoteUrl(self):\n # try getting the remote object by unique id\n remote_url = self._getRemoteUrlTheOldWay()\n remote_obj ... | [
"0.7809164",
"0.7752618",
"0.7744077",
"0.76482004",
"0.76482004",
"0.76167583",
"0.7611277",
"0.7611277",
"0.76024985",
"0.7592173",
"0.7590239",
"0.7567472",
"0.75656253",
"0.75656253",
"0.7537656",
"0.7537656",
"0.7537656",
"0.7537656",
"0.7537656",
"0.7537656",
"0.7537656... | 0.7905048 | 0 |
Push url of the remote | def push_url(self):
return maybe_string(C.git_remote_pushurl(self._remote)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_push_url(self, name, url):\n err = C.git_remote_set_pushurl(self._repo._repo, to_bytes(name), to_bytes(url))\n check_error(err)",
"def remote_push(self, pNamespace):",
"def push(self):\n origin = self.git_repo.remotes.origin\n origin.push()",
"def push(args):\n if args.... | [
"0.7094837",
"0.6979935",
"0.68169296",
"0.67966676",
"0.66819257",
"0.6680206",
"0.66366327",
"0.6635456",
"0.6486271",
"0.6448664",
"0.64341855",
"0.62836725",
"0.625129",
"0.62458855",
"0.62370825",
"0.62095743",
"0.6193338",
"0.61739475",
"0.6118299",
"0.60442275",
"0.602... | 0.7172953 | 0 |
Save a remote to its repository's configuration. | def save(self):
err = C.git_remote_save(self._remote)
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote_save(self, file_path=None, delete_local=False, remote_target=None):\n if not file_path:\n file_path = self.fname\n\n f = File()\n if not remote_target:\n remote_target = self.remote.get('target', None)\n LOG.info(\"Saving picture %s to %s\" % (file_path,... | [
"0.6913942",
"0.6310363",
"0.6138643",
"0.6070944",
"0.5944239",
"0.59436154",
"0.5880474",
"0.58746165",
"0.5848079",
"0.5820438",
"0.57785755",
"0.57339436",
"0.56392133",
"0.56184226",
"0.5592695",
"0.55855554",
"0.5575766",
"0.556263",
"0.5523025",
"0.55052197",
"0.548237... | 0.78605765 | 0 |
Perform a fetch against this remote. Returns a object. | def fetch(self, refspecs=None, message=None, callbacks=None):
fetch_opts = ffi.new('git_fetch_options *')
err = C.git_fetch_init_options(fetch_opts, C.GIT_FETCH_OPTIONS_VERSION)
if callbacks is None:
callbacks = RemoteCallbacks()
callbacks._fill_fetch_options(fetch_opts)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch(self, remote, *args):\n return self.cmd('fetch', remote, *args)",
"def fetch(self) -> Fetch:\n return self._fetch",
"def do_fetch(self):\n pass",
"def Fetch(self, request, global_params=None):\n config = self.GetMethodConfig('Fetch')\n return self._RunMethod(\n ... | [
"0.80714464",
"0.75665337",
"0.7185627",
"0.7021557",
"0.6945869",
"0.6904508",
"0.6821936",
"0.6758298",
"0.67004114",
"0.67004114",
"0.658733",
"0.6578733",
"0.6369617",
"0.6337702",
"0.63221866",
"0.63072085",
"0.62113595",
"0.6193085",
"0.6188289",
"0.61807245",
"0.615491... | 0.0 | -1 |
Total number of refspecs in this remote | def refspec_count(self):
return C.git_remote_refspec_count(self._remote) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_refs(self):\n return self._n_refs",
"def get_total_rehashes(self):\n return self.count_rehashes",
"def __len__(self):\n total_objs = 0\n\n if self._shelve is not None:\n total_objs += len(self._shelve)\n\n if self._dict is not None:\n total_objs +=... | [
"0.6764848",
"0.644436",
"0.64315546",
"0.6388287",
"0.63136035",
"0.63098884",
"0.62839144",
"0.62362033",
"0.6197885",
"0.61837137",
"0.6138996",
"0.6132474",
"0.6095574",
"0.6055916",
"0.60469747",
"0.60452753",
"0.6015536",
"0.5992",
"0.59615195",
"0.5940339",
"0.5924425"... | 0.85212857 | 0 |
Return the object at the given position. | def get_refspec(self, n):
spec = C.git_remote_get_refspec(self._remote, n)
return Refspec(self, spec) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getObjectFromPosition(position):\n for object in self.objects:\n if (int(self.position[0]) == int(object.position[0])) and (int(self.position[1]) == int(object.position[1])):\n return object\n\n return None",
"def get_object_... | [
"0.80690587",
"0.7702966",
"0.75877064",
"0.7253439",
"0.7253439",
"0.711118",
"0.70315605",
"0.69795954",
"0.6899463",
"0.6829157",
"0.6797154",
"0.6665862",
"0.66282725",
"0.6590538",
"0.6577159",
"0.65110797",
"0.64988065",
"0.64636296",
"0.64549494",
"0.6383208",
"0.63799... | 0.0 | -1 |
Refspecs that will be used for fetching | def fetch_refspecs(self):
specs = ffi.new('git_strarray *')
err = C.git_remote_get_fetch_refspecs(specs, self._remote)
check_error(err)
return strarray_to_strings(specs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resolve_references(self):\n self.specs = self._resolve_partial(self.parsed_url, self.specs, ())",
"def list_refs(self):\n pass",
"def list_defect_refs(self):\n print('-----------\\nDEFECT_REFs\\n-----------')\n self._print_dict(self.defect_refs)",
"def refs(self):\n return ... | [
"0.7066698",
"0.68261486",
"0.6297913",
"0.6244789",
"0.6160808",
"0.60727483",
"0.6071554",
"0.60701585",
"0.60671026",
"0.60483557",
"0.5974551",
"0.59565634",
"0.5944758",
"0.5904378",
"0.58879405",
"0.5882945",
"0.58410007",
"0.58331317",
"0.5822716",
"0.5815069",
"0.5786... | 0.64266026 | 2 |
Refspecs that will be used for pushing | def push_refspecs(self):
specs = ffi.new('git_strarray *')
err = C.git_remote_get_push_refspecs(specs, self._remote)
check_error(err)
return strarray_to_strings(specs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_refs(self):\n pass",
"def push_update_reference(self, refname, message):",
"def refs(self):\n return self._refs",
"def updateScptRefs(self):\n for scpt in self.refs_scpt.keys():\n self.refs_scpt[scpt] = scpt.getRef()\n self.scptRefs = set(self.refs_scpt.values(... | [
"0.6074251",
"0.6062355",
"0.5899434",
"0.5841198",
"0.57566303",
"0.5702181",
"0.56438404",
"0.5604831",
"0.55328727",
"0.5516635",
"0.55016184",
"0.54588157",
"0.54560244",
"0.5452985",
"0.5420332",
"0.53919894",
"0.53701526",
"0.53461057",
"0.5345899",
"0.53423005",
"0.534... | 0.63625705 | 0 |
Push the given refspec to the remote. Raises ``GitError`` on protocol error or unpack failure. | def push(self, specs, callbacks=None):
push_opts = ffi.new('git_push_options *')
err = C.git_push_init_options(push_opts, C.GIT_PUSH_OPTIONS_VERSION)
if callbacks is None:
callbacks = RemoteCallbacks()
callbacks._fill_push_options(push_opts)
# Build custom callback ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_push(self, name, refspec):\n\n err = C.git_remote_add_push(self._repo._repo, to_bytes(name), to_bytes(refspec))\n check_error(err)",
"def _push(project: Project):\n repo = project.repo\n remote_name = project.config.get('github.remote')\n remote = repo.remote(remote_name)\n resu... | [
"0.75390047",
"0.66317374",
"0.65131384",
"0.6271647",
"0.6256484",
"0.61509675",
"0.60761815",
"0.60623205",
"0.59913486",
"0.5926646",
"0.59092313",
"0.59082264",
"0.5887252",
"0.5856967",
"0.5835155",
"0.58231914",
"0.57830864",
"0.5763566",
"0.57582784",
"0.573426",
"0.57... | 0.6599917 | 2 |
Call fn and return the credentials object | def get_credentials(fn, url, username, allowed):
url_str = maybe_string(url)
username_str = maybe_string(username)
creds = fn(url_str, username_str, allowed)
credential_type = getattr(creds, 'credential_type', None)
credential_tuple = getattr(creds, 'credential_tuple', None)
if not credential... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_creds():\n\tcredentials = None\n\tif os.path.exists('token.pickle'):\n\t\twith open('token.pickle', 'rb') as token:\n\t\t\tcredentials = pickle.load(token)\n\t# If there are no (valid) credentials available, let the user log in.\n\tif not credentials or not credentials.valid:\n\t\tif credentials and creden... | [
"0.60266757",
"0.59413564",
"0.5846529",
"0.581955",
"0.58150387",
"0.58117324",
"0.58065265",
"0.5805673",
"0.57518536",
"0.57470065",
"0.57216364",
"0.5717514",
"0.5700105",
"0.56918997",
"0.56840074",
"0.56808466",
"0.5680676",
"0.5675405",
"0.56722444",
"0.5664514",
"0.56... | 0.6949153 | 0 |
Create a new remote with the given name and url. Returns a object. If 'fetch' is provided, this fetch refspec will be used instead of the default | def create(self, name, url, fetch=None):
cremote = ffi.new('git_remote **')
if fetch:
err = C.git_remote_create_with_fetchspec(cremote, self._repo._repo, to_bytes(name), to_bytes(url), to_bytes(fetch))
else:
err = C.git_remote_create(cremote, self._repo._repo, to_bytes(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_remote(self, name: str, url: str, **kwargs: Any) -> Remote:\n return Remote.create(self, name, url, **kwargs)",
"def fetch(self, remote, *args):\n return self.cmd('fetch', remote, *args)",
"def add_fetch(self, name, refspec):\n\n err = C.git_remote_add_fetch(self._repo._repo, to... | [
"0.75323445",
"0.65989864",
"0.6469159",
"0.63439584",
"0.6275085",
"0.5893828",
"0.57484573",
"0.5714986",
"0.5672253",
"0.55887216",
"0.5522805",
"0.54660064",
"0.5437272",
"0.5430468",
"0.5422879",
"0.5369303",
"0.5340879",
"0.5239376",
"0.5218165",
"0.52128315",
"0.520344... | 0.8370093 | 0 |
Rename a remote in the configuration. The refspecs in standard format will be renamed. Returns a list of fetch refspecs (list of strings) which were not in the standard format and thus could not be remapped. | def rename(self, name, new_name):
if not new_name:
raise ValueError("Current remote name must be a non-empty string")
if not new_name:
raise ValueError("New remote name must be a non-empty string")
problems = ffi.new('git_strarray *')
err = C.git_remote_rename(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __gitRenameRemote(self):\n remotes = self.vcs.gitGetRemotesList(self.project.getProjectPath())\n remote, ok = QInputDialog.getItem(\n None,\n self.tr(\"Rename\"),\n self.tr(\"Select a remote repository:\"),\n remotes,\n 0, False)\n if ... | [
"0.63354367",
"0.59142274",
"0.5644527",
"0.5351181",
"0.52948046",
"0.5238612",
"0.5197955",
"0.5156204",
"0.51546645",
"0.51463294",
"0.5122313",
"0.50963366",
"0.509136",
"0.50567704",
"0.5055112",
"0.5046217",
"0.5033727",
"0.5026965",
"0.50152934",
"0.50002825",
"0.49920... | 0.6283107 | 1 |
Remove a remote from the configuration All remotetracking branches and configuration settings for the remote will be removed. | def delete(self, name):
err = C.git_remote_delete(self._repo._repo, to_bytes(name))
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __gitRemoveRemote(self):\n remotes = self.vcs.gitGetRemotesList(self.project.getProjectPath())\n remote, ok = QInputDialog.getItem(\n None,\n self.tr(\"Remove\"),\n self.tr(\"Select a remote repository:\"),\n remotes,\n 0, False)\n if ... | [
"0.70583093",
"0.6800985",
"0.66090995",
"0.6535535",
"0.6374104",
"0.61728114",
"0.6059473",
"0.59848565",
"0.59562194",
"0.5875511",
"0.58684534",
"0.58503515",
"0.57999897",
"0.57549345",
"0.5706319",
"0.56782377",
"0.5565795",
"0.55190265",
"0.55089784",
"0.54649514",
"0.... | 0.53543514 | 24 |
Set the URL for a remote | def set_url(self, name, url):
err = C.git_remote_set_url(self._repo._repo, to_bytes(name), to_bytes(url))
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setRemoteUrl(self, value, **kwargs):\n if value:\n value = urlparse.urlunparse(urlparse.urlparse(value))\n self.getField('remoteUrl').set(self, value, **kwargs)",
"def _set_url(self): \n self.url = self.geturl()",
"def set_url(self, url):\n self.url = url",
"def ... | [
"0.84798807",
"0.7785831",
"0.75199544",
"0.7435115",
"0.74106765",
"0.73305047",
"0.72442555",
"0.6981874",
"0.6786525",
"0.67801994",
"0.67682236",
"0.67159164",
"0.67159164",
"0.67159164",
"0.67159164",
"0.67159164",
"0.67159164",
"0.67159164",
"0.6587842",
"0.65743047",
"... | 0.75991076 | 2 |
Set the pushURL for a remote | def set_push_url(self, name, url):
err = C.git_remote_set_pushurl(self._repo._repo, to_bytes(name), to_bytes(url))
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def git_remote_url(self, git_remote_url):\n self._git_remote_url = git_remote_url",
"def push_url(self):\n\n return maybe_string(C.git_remote_pushurl(self._remote))",
"def setRemoteUrl(self, value, **kwargs):\n if value:\n value = urlparse.urlunparse(urlparse.urlparse(value))\n ... | [
"0.72324055",
"0.71841437",
"0.69710207",
"0.66419417",
"0.6640427",
"0.6405677",
"0.61682636",
"0.6008587",
"0.5950349",
"0.5879518",
"0.5878566",
"0.5850352",
"0.5836166",
"0.5828629",
"0.58109",
"0.5789106",
"0.57801384",
"0.5705603",
"0.5705531",
"0.5667261",
"0.56259835"... | 0.77281153 | 0 |
Add a fetch refspec (str) to the remote | def add_fetch(self, name, refspec):
err = C.git_remote_add_fetch(self._repo._repo, to_bytes(name), to_bytes(refspec))
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_push(self, name, refspec):\n\n err = C.git_remote_add_push(self._repo._repo, to_bytes(name), to_bytes(refspec))\n check_error(err)",
"def fetch_pull(ref):\n origin.fetch(tags=True)\n repo.git.checkout(\"{}\".format(ref))\n repo.git.pull(\"origin\", \"{}\".fo... | [
"0.65405554",
"0.63312775",
"0.5966933",
"0.57998365",
"0.5740081",
"0.5735921",
"0.57280624",
"0.57224613",
"0.5703283",
"0.56584454",
"0.56575096",
"0.5571788",
"0.55627555",
"0.55626905",
"0.5540516",
"0.548836",
"0.5485281",
"0.5485032",
"0.5458031",
"0.54532164",
"0.5430... | 0.8219534 | 0 |
Add a push refspec (str) to the remote | def add_push(self, name, refspec):
err = C.git_remote_add_push(self._repo._repo, to_bytes(name), to_bytes(refspec))
check_error(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_fetch(self, name, refspec):\n\n err = C.git_remote_add_fetch(self._repo._repo, to_bytes(name), to_bytes(refspec))\n check_error(err)",
"def cmd_push_review(remote):\n return ['git', 'push', remote]",
"def _git_add(repo, path, contents='example!\\n'):\n path.write_text(contents)\n ... | [
"0.68458635",
"0.6618803",
"0.6397485",
"0.627628",
"0.61030674",
"0.6059851",
"0.6020045",
"0.60113615",
"0.59857327",
"0.59671175",
"0.5963221",
"0.59560585",
"0.59545165",
"0.59238607",
"0.5921502",
"0.5911289",
"0.58821833",
"0.5830431",
"0.5815642",
"0.5809405",
"0.58039... | 0.7897243 | 0 |
Downloads the 6B GloVe embeddings (approx. 1GB) | def download_glove ():
# Get the URL ...
print("Downloading https://nlp.stanford.edu/data/glove.6B.zip ...")
res = requests.get("https://nlp.stanford.edu/data/glove.6B.zip", stream=True)
if res.status_code != 200:
print("Could not download the 6B GloVe Dataset! The server responded with code " + res.status_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_glove_embedding():\n embedding = {}\n N = 400_000\n print(\"Reading glove embedding...\")\n with open(GLOVE_EMBD_PATH, \"rb\") as f:\n for line in tqdm(f, total=N):\n line = line.decode().split()\n word = line[0].lower()\n vector = np.array(line[1:]).asty... | [
"0.7076395",
"0.64558613",
"0.6354679",
"0.61358154",
"0.61284745",
"0.60184133",
"0.6010079",
"0.59805787",
"0.5972328",
"0.59562606",
"0.5899516",
"0.5830953",
"0.57806957",
"0.5730951",
"0.5724267",
"0.57074106",
"0.5684446",
"0.5642139",
"0.5635222",
"0.5618081",
"0.56131... | 0.7243812 | 0 |
Runs the Stanza module | def run_stanza (arguments):
if arguments.download:
# Download the full Stanza dataset
result = input("ATTENTION! This will download the full English Stanza corpus (approx. 400 MB). Do you wish to continue (y/n)? ")
if result == "y" or result == "yes":
# For a list of processors, see https://stanford... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n main()",
"def run(self, args):\n pass",
"def main():\n\ttoken = os.getenv(\"BOT_TOKEN\")\n\tapplication = Application.builder().token(token).read_timeout(30).write_timeout(30).build()\n\tload_interactions(application)\n\tprint(\"Simple Media Converter instance started!\")\n\tapplication.... | [
"0.6028765",
"0.59991264",
"0.5996253",
"0.5902331",
"0.5847812",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
"0.58472365",
... | 0.6173642 | 0 |
Runs the GloVe module | def run_glove (arguments):
if arguments.download:
# Download the 6B GloVe dataset
result = input("ATTENTION! This will download approximately 1GB of data. Do you wish to continue (y/n)? ")
if result == "y" or result == "yes":
download_glove()
sys.exit(0) # Afterwards exit normally | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n obj = VplexStorageview()\n obj.perform_module_operation()",
"def main():\n\timport docopt\n\targs = docopt.docopt(main.__doc__)\n\tv = Ventilator(args[\"--port\"], int(args[\"--speed\"]))\n\tif args[\"--debug\"]:\n\t\tlogging.basicConfig(level=logging.DEBUG)\n\telse:\n\t\tlogging.basicConfig(... | [
"0.6183873",
"0.61158395",
"0.6082892",
"0.60333556",
"0.5949687",
"0.59383035",
"0.5913861",
"0.58946764",
"0.5856102",
"0.58391666",
"0.58317477",
"0.5813667",
"0.5773393",
"0.5761071",
"0.57519263",
"0.5735813",
"0.5703431",
"0.56931114",
"0.569222",
"0.569222",
"0.5691295... | 0.6541627 | 0 |
Runs the Data module | def run_data (arguments):
if arguments.define_labels:
data.define_labels()
elif arguments.preprocess:
# Preprocess from data_raw --> data_preprocessed
data.preprocess()
elif arguments.annotate:
# Annotate from data_preprocessed --> data_annotated
reverse = False # DEBUG
annotator.annotate(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n\t\tself.print_header_information()\n\n\t\t#self.get_number_of_instances_from_user()\n\n\t\t#self.compile_dataframe(self.number_of_instances)\n\n\t\tprint \"\\n{}\".format(self.data)\n\n\t\t# Uncomment these lines for debugging\n\t\tself.compile_dataframe_default()\n\t\t# print \"\\n{}\".format(sel... | [
"0.7152246",
"0.6843876",
"0.6765248",
"0.66976744",
"0.66915",
"0.6601861",
"0.6464472",
"0.64639926",
"0.6440317",
"0.6437759",
"0.64203024",
"0.6409332",
"0.6399841",
"0.63861287",
"0.63166547",
"0.6303447",
"0.6303447",
"0.6303447",
"0.6303447",
"0.6303447",
"0.6303447",
... | 0.6965196 | 1 |
Runs the Model module | def run_model (arguments):
if arguments.train is not None:
# Train a new model, optionally with a certain number of epochs
predictor = None
if len(arguments.train) > 0:
predictor = train(n_epochs=arguments.train[0])
else:
predictor = train()
# Afterwards save it
now = datetime.now(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RunModel(self):\n raise UnimplementedMethodException()",
"def _doRun(self, model: Model):\n raise Exception(\"Not implemented\")",
"def run(self) -> None:\n self.model = self.trainer.train_model(self.model, self.data)",
"def run():\n print('*-----------------------------------*')... | [
"0.74780583",
"0.74485666",
"0.7332655",
"0.6933836",
"0.6836743",
"0.6831378",
"0.68108004",
"0.67838705",
"0.67409885",
"0.6730487",
"0.6706073",
"0.6684699",
"0.6684699",
"0.6684699",
"0.6684699",
"0.6684699",
"0.6632394",
"0.66102797",
"0.6589398",
"0.6587255",
"0.6561436... | 0.6601213 | 18 |
Starts up a console to interact with a predictor. | def tinker (predictor):
col_size = predictor.max_label_length()
while True:
try:
sentence = input('\nEnter a sentence to classify ("exit" to quit): ')
if sentence == 'exit':
print("Goodbye!")
sys.exit(0)
print("")
results = predictor.predict(sentence)
print(f"Index ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def console():\n start_console()",
"def start_console(self):\n return",
"def main():\n print_title()\n run_terminal()",
"def console():\r\n while True:\r\n interpret_command(input(\"POM> \"))",
"def interactive_shell(model):\n model.logger.info(\"\"\"\nThis is an interactive mo... | [
"0.7140405",
"0.67129403",
"0.66399276",
"0.66183984",
"0.6476843",
"0.6476411",
"0.64149994",
"0.6340279",
"0.6307932",
"0.61199385",
"0.609409",
"0.6075318",
"0.60666025",
"0.59807616",
"0.5940575",
"0.5931328",
"0.5929386",
"0.5908151",
"0.5894459",
"0.58894145",
"0.588659... | 0.0 | -1 |
x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature | def __init__(self,*args):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, *args, **kwargs): # real signature unknown; restored from __doc__\n pass",
"def __init__(self, *args, **kwargs): # real signature unknown\n pass",
"def __init__(self, *args, **kwargs): # real signature unknown\n pass",
"def __init__(self, *args, **kwargs): # real signa... | [
"0.7274706",
"0.72482175",
"0.72482175",
"0.72482175",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"0.711465",
"... | 0.0 | -1 |
tests where symbols are looked up properly | def test_symbol_lookup(self):
def check_lookup(symbol, expected):
op = BaseWhereOperator.get_operator(symbol)
self.assertEqual(op, expected)
check_lookup('EQ', EqualsOperator)
check_lookup('IN', InOperator)
check_lookup('GT', GreaterThanOperator)
check_l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_symbol_lookup(self):\r\n\r\n def check_lookup(symbol, expected):\r\n op = BaseWhereOperator.get_operator(symbol)\r\n self.assertEqual(op, expected)\r\n\r\n check_lookup('EQ', EqualsOperator)\r\n check_lookup('IN', InOperator)\r\n check_lookup('GT', Greater... | [
"0.7615396",
"0.68382215",
"0.6708252",
"0.659964",
"0.64436066",
"0.6381638",
"0.63209176",
"0.6301587",
"0.6294034",
"0.62852746",
"0.62724996",
"0.62620026",
"0.61632264",
"0.61437625",
"0.6124064",
"0.61073846",
"0.6088397",
"0.6071878",
"0.60535747",
"0.60304224",
"0.602... | 0.7542443 | 1 |
tests symbols are rendered properly | def test_operator_rendering(self):
self.assertEqual("=", six.text_type(EqualsOperator()))
self.assertEqual("IN", six.text_type(InOperator()))
self.assertEqual(">", six.text_type(GreaterThanOperator()))
self.assertEqual(">=", six.text_type(GreaterThanOrEqualOperator()))
self.asser... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_greek(self):\r\n self.assertEquals(preview.latex_preview('pi'), r'\\pi')",
"def test_assembleNormal(self):\n self.assertEqual(irc.assembleFormattedText(A.normal[\"hello\"]), \"\\x0fhello\")",
"def test_metrics_symmetry(self):\n font = robofab.world.OpenFont(self.operator.path)\n\n... | [
"0.6552125",
"0.6234604",
"0.623234",
"0.62025",
"0.61964214",
"0.61918604",
"0.6165361",
"0.61635154",
"0.61174566",
"0.6103421",
"0.6103421",
"0.6051215",
"0.6034262",
"0.6032363",
"0.60313624",
"0.60218436",
"0.6020844",
"0.5986874",
"0.59701574",
"0.59653497",
"0.59608334... | 0.54867905 | 92 |
matrix_set_diag operator implemented in numpy. Returns a numpy array with the diagonals of input array replaced with the provided diagonal values. | def matrix_set_diag(input_np, diagonal, k=0, align="RIGHT_LEFT"):
out = np.array(input_np, copy=True)
cols = input_np.shape[-1]
rows = input_np.shape[-2]
onlyOneDiagonal = True
if isinstance(k, (tuple, list)):
if len(k) < 2 or k[0] == k[1]:
k = k[0]
else:
on... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_diag(x, new_diag):\n arr_shape = x.shape\n off_diag = (1 - _torch.eye(arr_shape[-1])) * x\n diag = _torch.einsum(\"ij,...i->...ij\", _torch.eye(new_diag.shape[-1]), new_diag)\n return diag + off_diag",
"def set_mat_diag(mat, diag=0, val=0):\n m = mat.shape[0]\n step = m + 1\n start =... | [
"0.74522567",
"0.7357435",
"0.71183485",
"0.67566574",
"0.6728996",
"0.6719767",
"0.65984404",
"0.65970296",
"0.6580404",
"0.65264386",
"0.65135664",
"0.64551324",
"0.64260054",
"0.6417673",
"0.63621813",
"0.6301156",
"0.62709117",
"0.62459886",
"0.6244929",
"0.6234028",
"0.6... | 0.7570219 | 0 |
Calculates the position that the robot should go to in order to grab the can | def make_goal(self):
# TODO: prevent calculating positions too close to a wall
# TODO: visualize this process better
# TODO: publish goal in map frame to prevent errors if the robot moves in time
global ms
rospy.loginfo('Calculating navigation goal')
if self.objective =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getRobotPosition(self):\n return self.position",
"def getRobotPosition(self):\n return self.position",
"def getMovement(self):\n # store the robot's current location and set the directional movement to 0,0 so that the robot won't move by default\n currentLocation = (self.me['x']... | [
"0.69911605",
"0.69911605",
"0.6852636",
"0.67995936",
"0.67995936",
"0.67509824",
"0.67409366",
"0.6737135",
"0.6691059",
"0.6644769",
"0.64602256",
"0.6424652",
"0.6421678",
"0.6356969",
"0.6341534",
"0.6329218",
"0.63217485",
"0.62897986",
"0.62783897",
"0.62641644",
"0.62... | 0.0 | -1 |
A python generator function to create a list of points to try visiting. | def generate_points(self, userdata):
# TODO: generate along multiple axes
x, y, _ = userdata.initial_point
x, y = 0, 0
i = 0
for _ in range(self.num_points):
point = PointStamped()
point.header.frame_id = "map"
point.point.x = x + i * self.spa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def points_generator(self):\n rows, cols = self.game.board.board_size\n points = [Point(i, j) for i, j in product(range(rows), range(cols))]\n for point in points:\n yield point",
"def __iter__(self):\n for point in self.points:\n yield point",
"def iter_points... | [
"0.76917976",
"0.73582846",
"0.7342994",
"0.69955987",
"0.6941514",
"0.67175186",
"0.67048067",
"0.65479225",
"0.6507981",
"0.6483514",
"0.6348818",
"0.63456964",
"0.6335949",
"0.6255297",
"0.62488467",
"0.62213814",
"0.6162217",
"0.6159257",
"0.61049867",
"0.61001927",
"0.60... | 0.65718514 | 7 |
Handles the update of a standard pre computed function. | def standard_handler(args_dict: dict):
color_sequence = args_dict['color_sequence']
step = args_dict['step']
# Updating step for the next iteration.
args_dict['step'] = (step + 1) % args_dict['sequence_len']
return color_sequence[step] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def precalculate():\n pass",
"def precalculate():\n pass",
"def update_fn(self, x, t):\n pass",
"def update_normalization_function(self):\n self._cache[\"input\"][\"function\"] = self.function.currentText()\n\n indices = range(5, 10)\n\n # If the function is a spline, then i... | [
"0.70787203",
"0.70787203",
"0.6705146",
"0.65600014",
"0.6420105",
"0.6341481",
"0.6067209",
"0.59551567",
"0.5861058",
"0.5829065",
"0.5824679",
"0.5819529",
"0.5812678",
"0.5810944",
"0.5807107",
"0.5793102",
"0.57898974",
"0.57892376",
"0.57845485",
"0.5782907",
"0.574194... | 0.0 | -1 |
The stream effect has the colors of the LEDs move like a stream, where the color of a LEDs is given to its neighbor in the next update step. | def stream_handler(args_dict: dict):
color_sequence = args_dict['color_sequence']
color_seq_len = args_dict['color_seq_len']
color_itr = args_dict['color_itr']
n_leds = args_dict['n_leds']
step_sequence = [color_sequence[c % color_seq_len] for c in range(color_itr, n_leds + color_itr)]
# Upda... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show(self):\n\t\tself.processQueue()\n\t\tself.flattenLayers()\n\t\tcount = 0\n\t\tfor v in self.ledsColorBuffer:\n\t\t\tself.strip.setPixelColor(count, v)\n\t\t\tcount += 1\n\t\tself.strip.show()",
"def demo(self):\n self.clear()\n\n white = neo.Color(255, 255, 255)\n black = neo.Color(... | [
"0.6599297",
"0.65627533",
"0.6331775",
"0.62021",
"0.6179241",
"0.61761546",
"0.6153618",
"0.59917074",
"0.59736294",
"0.59636015",
"0.5950049",
"0.59427375",
"0.5905459",
"0.5854121",
"0.58495724",
"0.5800509",
"0.57976097",
"0.5783879",
"0.577515",
"0.57635885",
"0.5758384... | 0.67854285 | 0 |
Handles the update of a wave function. | def wave_handler(args_dict: dict):
color_sequence = args_dict['color_sequence']
wave = args_dict['wave']
color_delay = args_dict['color_delay']
n_leds = args_dict['n_leds']
delay_itr = args_dict['delay_itr']
color_itr = args_dict['color_itr']
step_sequence = []
color_seq_len = args_di... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n\n if self._old:\n # delete wave from last update cycle\n self._pi.wave_delete(self._old)\n self._old = None\n\n # show power state\n self._pi.write(pins.Q4, self._state.powered)\n\n # update wave\n if self._state.ready:\n ... | [
"0.6819183",
"0.6701062",
"0.63038915",
"0.62662625",
"0.62470883",
"0.6141726",
"0.61200964",
"0.59765255",
"0.5942967",
"0.58980507",
"0.5840194",
"0.58309966",
"0.579967",
"0.579967",
"0.57926077",
"0.57561326",
"0.5737451",
"0.5725876",
"0.57178116",
"0.5711229",
"0.56968... | 0.5560634 | 28 |
The breathe effect is comprised of all LEDs changing intensity in a sinusoidal manner, imitating an inflating balloon. | def breathe_handler(args_dict: dict):
color_sequence = args_dict['color_sequence']
wave = args_dict['wave']
color_delay = args_dict['color_delay']
n_leds = args_dict['n_leds']
delay_itr = args_dict['delay_itr']
color_itr = args_dict['color_itr']
color_seq_len = args_dict['color_seq_len']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def breathe(self, params):\n intensity = params.get('intensity', 255)\n wait_ms = params.get('wait_ms', 2)\n try:\n while True:\n await self.lights.breathe(intensity, wait_ms=wait_ms)\n except KeyboardInterrupt:\n pass",
"def high_bri(self):\... | [
"0.6964535",
"0.6239078",
"0.6162239",
"0.60917026",
"0.60406834",
"0.59964126",
"0.5932551",
"0.58437055",
"0.58312666",
"0.57445794",
"0.5697897",
"0.5670556",
"0.5610512",
"0.55797124",
"0.5572094",
"0.5562731",
"0.55571395",
"0.55501294",
"0.5548731",
"0.55295014",
"0.551... | 0.6069812 | 4 |
Setup step for build classes, in which they compute any of the values that are constant across simulation timesteps. | def pre_build(cls, ops, signals, rng):
logger.debug("===================")
logger.debug("PRE BUILD %s", ops)
logger.debug("sets %s", [op.sets for op in ops])
logger.debug("incs %s", [op.incs for op in ops])
logger.debug("reads %s", [op.reads for op in ops])
logger.debug(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_step(self):\n\n pass",
"def setUpClass(self):\n self.tmin, self.tmax, self.dt = (0, 10.0, 0.1)\n self.start = { 'x' : 100.0, 'y' : 100.0, 'z' : 100.0,\n 'psi' : 30.0, 'theta' : 0.0, 'phi' : 0.0,\n 'v': 1.0, 'weight' : 100.0, 'fuel' : 100.... | [
"0.614566",
"0.61346555",
"0.6083438",
"0.6083438",
"0.6077321",
"0.60715204",
"0.59854054",
"0.5961216",
"0.5942173",
"0.5899589",
"0.5819842",
"0.580994",
"0.5805824",
"0.5779355",
"0.5774248",
"0.5766029",
"0.5758788",
"0.5754554",
"0.57363355",
"0.5700088",
"0.5699738",
... | 0.0 | -1 |
Build the computations implementing a single simulator timestep. | def build(cls, ops, signals):
logger.debug("===================")
logger.debug("BUILD %s", ops)
if ops not in cls.op_builds:
raise BuildError("Operators build has not been initialized "
"(missed pre-build step)")
output = cls.op_builds[ops].bui... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(self):\n #print('ntimes=%s nelements=%s ntotal=%s' % (self.ntimes, self.nelements, self.ntotal))\n #print('self.IDs', self.data)\n self.itime = 0\n self.ielement = 0\n self.itotal = 0\n\n assert self.ntimes > 0, 'ntimes=%s' % self.ntimes\n assert self.nele... | [
"0.6338849",
"0.6307557",
"0.6180494",
"0.61792386",
"0.6127942",
"0.6053869",
"0.6036666",
"0.5997529",
"0.5973617",
"0.5908682",
"0.59040534",
"0.5851188",
"0.5851188",
"0.5825474",
"0.5798762",
"0.57727605",
"0.57400477",
"0.5735636",
"0.5731924",
"0.57221514",
"0.57075906... | 0.0 | -1 |
A decorator for adding a class to the build function registry. | def register(cls, nengo_op):
def register_builder(build_class):
if not issubclass(build_class, OpBuilder):
warnings.warn("Build classes should inherit from OpBuilder")
if nengo_op in cls.builders:
warnings.warn("Operator '%s' already has a builder. "
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extension(klass):\n registry.register(klass)\n return klass",
"def register(name):\n def func(cls):\n \"\"\"\n See register\n \"\"\"\n REGISTRY[name] = cls()\n return cls\n return func",
"def setup_callable_class(builder: IRBuilder) -> None:\n # Check to se... | [
"0.6521618",
"0.6413174",
"0.6319414",
"0.62337",
"0.62155426",
"0.60622054",
"0.60152584",
"0.59121466",
"0.59074944",
"0.5901764",
"0.5901582",
"0.58695376",
"0.5853394",
"0.5813251",
"0.5687428",
"0.56729025",
"0.5658591",
"0.5650654",
"0.56480503",
"0.56203526",
"0.562035... | 0.5497395 | 29 |
This function builds whatever computations need to be executed in each simulation timestep. | def build_step(self, signals):
raise BuildError("OpBuilders must implement a `build_step` function") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_simulations(self):\n pass",
"def step(self, actions):\n\n representative_data = []\n original_data = []\n\n actions = np.array(actions).reshape(3, -1)\n\n for np_data, df_data, calculator, som, action in zip(self.np_data_list, self.df_data_list, self.calculators, self.... | [
"0.64679617",
"0.62343687",
"0.61193407",
"0.60880333",
"0.6070247",
"0.6005587",
"0.59664106",
"0.5957791",
"0.59144515",
"0.5898824",
"0.58904433",
"0.5888304",
"0.587554",
"0.5850894",
"0.5832759",
"0.5809431",
"0.57444245",
"0.5740893",
"0.5739802",
"0.57186806",
"0.57119... | 0.0 | -1 |
Init the python user log | def init_log():
log_op = LogOp()
log_op.config_log(logging.DEBUG) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_init(self):\n pass",
"def startLogFiles():\n #global techLog\n global userl\n _generic_startLogFiles(True)",
"def init_log():\n os.system('rm -rf /target/testdriver.log || true')\n os.system('touch /target/testdriver.log')\n os.system(f\"chown {uid_gid_output} /target/testdriv... | [
"0.70920324",
"0.6949306",
"0.6900478",
"0.6739623",
"0.6732682",
"0.66928965",
"0.6689308",
"0.66476536",
"0.6639534",
"0.6600425",
"0.6592752",
"0.64675075",
"0.64674103",
"0.64523363",
"0.64106053",
"0.63840264",
"0.6368207",
"0.63438475",
"0.63296264",
"0.63265544",
"0.63... | 0.63439995 | 17 |
Retrieve al cursos from graph | def get_cursos(request):
if request.method == 'GET':
cursos = Curso.nodes.all()
cursos_list = []
for i in range(0, len(cursos)):
cursos_list.append(cursos[i].__dict__["nombre"])
return JsonResponse({"cursos": cursos_list}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_full_graph(self):",
"def graph(self):\n ...",
"def download_chicago_graph():\n\n\tG = ox.graph_from_place(\"Chicago,IL, United States\", network_type='drive')\n\treturn G",
"def getData(graph, request):\r\n results = list(graph.query(request))\r\n return results",
"def get_graph(self)... | [
"0.60265046",
"0.5684899",
"0.5633646",
"0.56242675",
"0.5566034",
"0.5564065",
"0.55356544",
"0.55326086",
"0.5355961",
"0.53407145",
"0.5306177",
"0.5233318",
"0.52299714",
"0.51916367",
"0.51910627",
"0.5189231",
"0.51728517",
"0.5166251",
"0.5166251",
"0.51599175",
"0.515... | 0.57523143 | 1 |
Retrieve al cursos from graph | def get_curso_fecha_inicio(request):
if request.method == 'GET':
serializer = CursoSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
if "curso" in serializer.validated_data:
try:
curso = Curso.nodes.get(nombre__icont... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_full_graph(self):",
"def get_cursos(request):\n if request.method == 'GET':\n cursos = Curso.nodes.all()\n cursos_list = []\n for i in range(0, len(cursos)):\n cursos_list.append(cursos[i].__dict__[\"nombre\"])\n return JsonResponse({\"cursos\": cursos_list})",
... | [
"0.60265046",
"0.57523143",
"0.5684899",
"0.5633646",
"0.56242675",
"0.5566034",
"0.5564065",
"0.55356544",
"0.55326086",
"0.5355961",
"0.53407145",
"0.5306177",
"0.5233318",
"0.52299714",
"0.51916367",
"0.51910627",
"0.5189231",
"0.51728517",
"0.5166251",
"0.5166251",
"0.515... | 0.0 | -1 |
Retrieve al cursos from graph | def get_curso_inscripcion(request):
if request.method == 'GET':
serializer = CursoSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
if "curso" in serializer.validated_data:
try:
curso = Curso.nodes.get(nombre__iconta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_full_graph(self):",
"def get_cursos(request):\n if request.method == 'GET':\n cursos = Curso.nodes.all()\n cursos_list = []\n for i in range(0, len(cursos)):\n cursos_list.append(cursos[i].__dict__[\"nombre\"])\n return JsonResponse({\"cursos\": cursos_list})",
... | [
"0.60265046",
"0.57523143",
"0.5684899",
"0.5633646",
"0.56242675",
"0.5566034",
"0.5564065",
"0.55356544",
"0.55326086",
"0.5355961",
"0.53407145",
"0.5306177",
"0.5233318",
"0.52299714",
"0.51916367",
"0.51910627",
"0.5189231",
"0.51728517",
"0.5166251",
"0.5166251",
"0.515... | 0.0 | -1 |
Retrieve al cursos from graph | def get_curso_prerequisitos(request):
if request.method == 'GET':
serializer = CursoSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
if "curso" in serializer.validated_data:
try:
curso = Curso.nodes.get(nombre__icon... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_full_graph(self):",
"def get_cursos(request):\n if request.method == 'GET':\n cursos = Curso.nodes.all()\n cursos_list = []\n for i in range(0, len(cursos)):\n cursos_list.append(cursos[i].__dict__[\"nombre\"])\n return JsonResponse({\"cursos\": cursos_list})",
... | [
"0.60265046",
"0.57523143",
"0.5684899",
"0.5633646",
"0.56242675",
"0.5566034",
"0.5564065",
"0.55356544",
"0.55326086",
"0.5355961",
"0.53407145",
"0.5306177",
"0.5233318",
"0.52299714",
"0.51916367",
"0.51910627",
"0.5189231",
"0.51728517",
"0.5166251",
"0.5166251",
"0.515... | 0.0 | -1 |
This method returns database object. | def get_db(self):
self.logger.info('in get_db()')
try:
return self.client[self.db_name]
except Exception as e:
self.logger.error(f'Error occurred while getting client {e}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db(self) -> Database:\n return self.impl.db",
"def db(self):\n return DbManager(self)",
"def _get_db(self):\n return DB(\n ClientStorage.ClientStorage((self.server, self.port))\n )",
"def get_db(self):\n return self._db",
"def get_db():\n # this is a bit... | [
"0.86168927",
"0.8025105",
"0.79949725",
"0.7977423",
"0.79444736",
"0.7923698",
"0.77936614",
"0.77742386",
"0.7715764",
"0.77024984",
"0.7693181",
"0.76827276",
"0.76591754",
"0.76430446",
"0.7642511",
"0.7626138",
"0.7614707",
"0.7599347",
"0.7566285",
"0.7554123",
"0.7538... | 0.70857936 | 78 |
This method saves single record. | def save_single_record(self, record, collection_name):
try:
self.logger.info('in save_single_record()')
collection = self.get_db()[collection_name]
record_id = collection.insert_one(record)
self.logger.info('out save_single_record()')
return record_id
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, record):\n self.collection.insert(record)\n self.record = record\n\n return self",
"def save_record(record):\n record. save_details()",
"def save(self, record):\n pass",
"def save(self):\n if self.id:\n self.update()\n else:\n self... | [
"0.76350296",
"0.75677395",
"0.7516946",
"0.74222285",
"0.7256106",
"0.7197828",
"0.7089604",
"0.70852476",
"0.70795155",
"0.7063954",
"0.70558286",
"0.7047323",
"0.704174",
"0.7036781",
"0.70347714",
"0.7025638",
"0.7003997",
"0.7003997",
"0.6996204",
"0.6996204",
"0.6996204... | 0.71013534 | 6 |
This method saves multiple record. | def save_multiple_records(self, records, collection_name):
try:
self.logger.info('in save_multiple_records()')
collection = self.get_db()[collection_name]
record_ids = collection.insert_many(records)
self.logger.info('out save_multiple_records()')
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_all(self, obj_list):\n\n for obj in obj_list:\n self.save(obj)",
"def save_all(self):\r\n for index in range(self.count()):\r\n self.save(index)",
"def save(self, values):",
"def save_many(self, reports: List[Report]):\n for report in reports:\n ... | [
"0.7074574",
"0.6988772",
"0.69310373",
"0.66361165",
"0.6571776",
"0.6490498",
"0.64761966",
"0.64698094",
"0.63603204",
"0.6359975",
"0.6303776",
"0.628135",
"0.6271079",
"0.6242702",
"0.62330705",
"0.6218874",
"0.6204224",
"0.6177481",
"0.6177481",
"0.6177481",
"0.6158188"... | 0.6909438 | 3 |
This method returns single record. | def get_record(self, collection_name, filter):
try:
self.logger.info('in get_record()')
collection = self.get_db()[collection_name]
record = collection.find_one(filter)
self.logger.info('in get_record()')
return record
except Exception as e:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def single(self):\n records = list(self)\n num_records = len(records)\n if num_records == 0:\n raise ResultError(\"Cannot retrieve a single record, because this result is empty.\")\n elif num_records != 1:\n raise ResultError(\"Expected a result with a single recor... | [
"0.77948505",
"0.74141467",
"0.71552324",
"0.7137933",
"0.7098952",
"0.70341307",
"0.6938491",
"0.6912008",
"0.6770571",
"0.67072624",
"0.66781634",
"0.664886",
"0.6641641",
"0.66205245",
"0.6617445",
"0.66103",
"0.6580788",
"0.6577504",
"0.65559554",
"0.6552827",
"0.6549993"... | 0.73423696 | 2 |
This method updates record. | def update_record(self, collection_name, update_record, update_condition):
try:
self.logger.info('in update_record()')
collection = self.get_db()[collection_name]
collection.update_one(update_condition, {"$set": update_record})
self.logger.info('out update_record(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n self.getDbRecord().update()",
"def test_update_record(self):\n pass",
"def update(self, record):\n record = dict_for_mongo(record)\n id_dict = {'_id': self.record['_id']}\n self.collection.update(id_dict, {'$set': record})\n\n # Set record to the la... | [
"0.7799289",
"0.72846174",
"0.7275487",
"0.7256945",
"0.7204704",
"0.7160709",
"0.7111528",
"0.7063098",
"0.70586115",
"0.7034917",
"0.6997285",
"0.69445753",
"0.68872887",
"0.6810321",
"0.67267716",
"0.67267716",
"0.6694292",
"0.668465",
"0.66819084",
"0.66540956",
"0.665283... | 0.7225001 | 4 |
This method deletes record. | def delete_record(self, collection_name, delete_condition):
try:
self.logger.info('in delete_record()')
collection = self.get_db()[collection_name]
collection.delete_one(delete_condition)
self.logger.info('out delete_record()')
except Exception as e:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_record(self, record_id):\r\n self.record.deleteObject(id=record_id)",
"def delete_record(records):\n delete_record()",
"def delete_record(self):\r\n try:\r\n db.session.delete(self)\r\n db.session.commit()\r\n return {\"error\": False}\r\n exc... | [
"0.8355644",
"0.7927568",
"0.7882613",
"0.78776604",
"0.78410935",
"0.7828673",
"0.77133566",
"0.74574685",
"0.7367979",
"0.73655236",
"0.73345995",
"0.72475743",
"0.71694946",
"0.7153106",
"0.70947415",
"0.70702696",
"0.70161307",
"0.70099473",
"0.7000162",
"0.69995856",
"0.... | 0.72149444 | 12 |
This method return multiple record. | def get_records(self, collection_name,num_records):
try:
self.logger.info('in get_records()')
collection = self.get_db()[collection_name]
records = collection.find().limit(num_records)
self.logger.info('out get_records()')
return records
excep... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def records(self):\r\n return Records(self)",
"def get_records(self) -> List[DBModelInstance]:\n return self._get_all_records()",
"async def get_entries(self, *args,convert = True, listed=False, as_dict=False):\r\n consts = args\r\n condition = condition = \" AND \".join(consts)\r\n... | [
"0.66132104",
"0.620525",
"0.6192388",
"0.6140267",
"0.61181104",
"0.61163604",
"0.60370296",
"0.60240805",
"0.597363",
"0.5935963",
"0.5874714",
"0.586277",
"0.58496785",
"0.5838122",
"0.5794863",
"0.57405573",
"0.5740384",
"0.5723769",
"0.57133895",
"0.5712744",
"0.56913215... | 0.5455201 | 44 |
Predict the BDEs of each bond in a list of molecules. | def predict(smiles_list, drop_duplicates=True, batch_size=1, verbose=False):
molecule_list = [Molecule(smiles=smiles) for smiles in smiles_list]
smiles_list = [mol.smiles for mol in molecule_list]
pred_df = pd.concat(
(
get_fragments(mol, drop_duplicates=drop_duplicates)
fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(smiles_list, drop_duplicates=True, verbose=True):\n\n is_valid = pd.Series({smiles: not check_input(smiles)[0] for smiles in smiles_list}, name='is_valid')\n pred_df = pd.concat([predict_bdes(smiles, draw=False) for smiles in smiles_list])\n pred_df = pred_df.merge(is_valid, left_on='molecule'... | [
"0.6377305",
"0.57994026",
"0.56501853",
"0.52041197",
"0.5169816",
"0.5126491",
"0.51183426",
"0.51093304",
"0.50972986",
"0.50279194",
"0.5017863",
"0.49950963",
"0.49750766",
"0.4968196",
"0.49513084",
"0.4942393",
"0.49264514",
"0.49236074",
"0.4922351",
"0.4922289",
"0.4... | 0.58677953 | 1 |
This function organise the data base to a difficult build | def dbase():
albums_data = {}
song_dict = {}
songs_list = []
with open(PATH, 'r') as f:
data = f.read()
temp = data.split("#")
for album in temp[1:]:
index = album.find("::")
albums_data[album[:index]] = ""
for album in temp[1:]:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _do_build ():\n if os.path.exists(\"./database\"):\n data_path = \"./database/\"\n elif os.path.exists(\"../database\"):\n data_path = \"../database/\"\n elif os.path.exists(\"../../database\"):\n data_path = \"../../database/\"\n else:\n data_path = \".\"\n\n dir_spe... | [
"0.6698447",
"0.65494096",
"0.6505635",
"0.6406873",
"0.63481224",
"0.62501156",
"0.62164736",
"0.612963",
"0.6124272",
"0.6096875",
"0.6047516",
"0.60284543",
"0.6016084",
"0.5973622",
"0.5927254",
"0.59236556",
"0.5918624",
"0.59123254",
"0.59106165",
"0.59070426",
"0.58868... | 0.0 | -1 |
this func is from using insde the data file | def simple_songs_list(name_of_album):
songs = []
data1 = dbase()
data1 = data1[name_of_album][0]
for song in data1.keys():
songs += [song]
return songs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self):",
"def _read_data(self):",
"def load_data(self) -> None:",
"def read(self):",
"def _load(self):\n\n # number of non-data header details at top of data file\n header = 1\n\n # open file\n weatherData = []\n with open(self.wfile) as myfile:\n ... | [
"0.7388499",
"0.7290581",
"0.71939653",
"0.65396297",
"0.6443839",
"0.6359168",
"0.6344289",
"0.622777",
"0.62194926",
"0.6214607",
"0.6213822",
"0.6159157",
"0.61423117",
"0.6136031",
"0.6111043",
"0.6104353",
"0.60962135",
"0.60925573",
"0.6089976",
"0.6075218",
"0.6048531"... | 0.0 | -1 |
this func is from using insde the data file | def simple_album_list():
album_list = []
data = dbase()
for album in data.keys():
album_list += [album]
return album_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self):",
"def _read_data(self):",
"def load_data(self) -> None:",
"def read(self):",
"def _load(self):\n\n # number of non-data header details at top of data file\n header = 1\n\n # open file\n weatherData = []\n with open(self.wfile) as myfile:\n ... | [
"0.7388499",
"0.7290581",
"0.71939653",
"0.65396297",
"0.6443839",
"0.6359168",
"0.6344289",
"0.622777",
"0.62194926",
"0.6214607",
"0.6213822",
"0.6159157",
"0.61423117",
"0.6136031",
"0.6111043",
"0.6104353",
"0.60962135",
"0.60925573",
"0.6089976",
"0.6075218",
"0.6048531"... | 0.0 | -1 |
this func makes a list of all the album | def album_list_for_user():
answer = ""
data = dbase()
for album in data.keys():
answer += album + ", "
return answer[:-2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def album_list(self):\n\n artist_id = self.addon_args[\"artist_id\"][0]\n\n xbmcplugin.setContent(self.addon_handle, \"albums\")\n\n for album in self.connection.walk_artist(artist_id):\n self.add_album(album)\n\n xbmcplugin.addSortMethod(\n self.addon_handle, xbmc... | [
"0.8068434",
"0.80611455",
"0.80573",
"0.80511075",
"0.76079464",
"0.7442075",
"0.73546255",
"0.73167896",
"0.7289843",
"0.724343",
"0.7202005",
"0.7099588",
"0.70723593",
"0.7056376",
"0.705161",
"0.7005844",
"0.6928635",
"0.68897617",
"0.6879395",
"0.6865144",
"0.68555135",... | 0.7306566 | 8 |
This function makes a list of all the songs in album | def songs_list(name_of_album):
songs = ""
data = dbase()
data = data[name_of_album][0]
for song in data.keys():
songs += song
songs += ", "
return songs[:-2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def simple_songs_list(name_of_album):\r\n songs = []\r\n data1 = dbase()\r\n data1 = data1[name_of_album][0]\r\n for song in data1.keys():\r\n songs += [song]\r\n return songs",
"def album_list(self):\n\n artist_id = self.addon_args[\"artist_id\"][0]\n\n xbmcplugin.setContent(... | [
"0.8295144",
"0.78646994",
"0.78634316",
"0.7836907",
"0.7691762",
"0.76080465",
"0.75683284",
"0.7421076",
"0.738293",
"0.7337441",
"0.7323385",
"0.7289032",
"0.7279224",
"0.72707593",
"0.7217044",
"0.7155472",
"0.71446615",
"0.7133181",
"0.71268225",
"0.7097782",
"0.7070707... | 0.81815284 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.