0 else 0)\\n test['GarageYrBlt'] = test['GarageYrBlt'].fillna(0)\\n test['GarageCond'] = test['GarageCond'].fillna('None')\\n test['GarageFinish'] = test['GarageFinish'].fillna('None')\\n test['GarageQual'] = test['GarageQual'].fillna('None')\\n test['GarageType'] = test['GarageType'].fillna('None')\\n #new feature 'GaragePresent' is created. There are houses without garage\\n test['GaragePresent'] = test['GarageArea'].apply(lambda x: 1 if x > 0 else 0)\\n \\n #Here we can create one extra feature GaragePresent\\n# train['GaragePresent'] = train['GarageArea'].apply(lambda x: 0 if x is 'None' else 1)\\n# test['GaragePresent'] = test['GarageArea'].apply(lambda x: 0 if x is 'None' else 1)\\n ##No improvement on model\\n \\n #impute 'None' for missing values\\n train['BsmtFinType1'] = train['BsmtFinType1'].fillna('None')\\n train['BsmtFinType2'] = train['BsmtFinType2'].fillna('None')\\n train['BsmtQual'] = train['BsmtQual'].fillna('None')\\n train['BsmtExposure'] = train['BsmtExposure'].fillna('None')\\n train['BsmtCond'] = train['BsmtCond'].fillna('None')\\n #new feature 'BsmtPresent' is created. There are houses without basement\\n train['BsmtPresent'] = train['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\\n test['BsmtFinType1'] = test['BsmtFinType1'].fillna('None')\\n test['BsmtFinType2'] = test['BsmtFinType2'].fillna('None')\\n test['BsmtQual'] = test['BsmtQual'].fillna('None')\\n test['BsmtExposure'] = test['BsmtExposure'].fillna('None')\\n test['BsmtCond'] = test['BsmtCond'].fillna('None')\\n #new feature 'BsmtPresent' is created. There are houses without basement.\\n test['BsmtPresent'] = test['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\\n \\n #MasVnrArea, MasVnrType\\n #impute zero and 'None'\\n train['MasVnrArea'] = train['MasVnrArea'].fillna(0)\\n train['MasVnrType'] = train['MasVnrType'].fillna('None')\\n test['MasVnrArea'] = test['MasVnrArea'].fillna(0)\\n test['MasVnrType'] = test['MasVnrType'].fillna('None')\\n \\n #Imputing missing 'MSZoning' values based on 'MSSubClass'.\\n #Range of 'MSZoning' values depends on 'MSSubClass'\\n test['MSZoning'] = test.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))\\n\\n #Creating new features 'BuildingAge and 'BuildingGarageAge'\\n train['BuildingAge'] = train['YearBuilt'].apply(lambda x:2019-x) \\n test['BuildingAge'] = test['YearBuilt'].apply(lambda x:2019-x)\\n \\n train['BuildingGarageAge'] = train['GarageYrBlt'].apply(lambda x: (2019-x) if x>0 else x)\\n test['BuildingGarageAge'] = test['GarageYrBlt'].apply(lambda x: (2019-x) if x>0 else x)\\n\\n\\n# ##No improvement on model bu using below new features.\\n \\n# train['BuildingRemodAddAge'] = train['YearRemodAdd'].apply(lambda x:2019-x)\\n# test['BuildingRemodAddAge'] = test['YearRemodAdd'].apply(lambda x:2019-x) \\n#\\n# train['BuildingSoldAge'] = train['YrSold'].apply(lambda x:2019-x)\\n# test['BuildingSoldAge'] = test['YrSold'].apply(lambda x:2019-x)\\n\\n# train.drop(['YrSold', 'YearRemodAdd', 'GarageYrBlt', 'YearBuilt'], axis=1, inplace=True)\\n# test.drop(['YrSold', 'YearRemodAdd', 'GarageYrBlt', 'YearBuilt'], axis=1, inplace=True) \\n \\n #Electrical\\n #train['Electrical'].mode() is 'SBrkr'\\n train['Electrical'] = train['Electrical'].fillna('SBrkr')\\n \\n #Updating predictor list\\n self.update_predictor_list(train) \\n \\n #impute values for remaining missing data\\n for i in self.catVariable:\\n train[i] = train[i].fillna('None')\\n test[i] = test[i].fillna('None')\\n for i in self.numVariable:\\n train[i] = train[i].fillna(0)\\n test[i] = test[i].fillna(0)\\n \\n \\n return train, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3e6284d3440944f5ebe316f52fec2\",\n \"score\": \"0.49980274\",\n \"text\": \"def main():\\n p = 0.3\\n## dnn = SimGNNTrainer(100, 0, 5, 10000)\\n## result = []\\n## for r in range(10):\\n## dnn.train(p, r / 10)\\n## ac = 1 - dnn.test(p, r/10)\\n## result.append(ac)\\n## print(r, ac)\\n## print(result)\\n## plt.plot(result, color=\\\"red\\\")\\n \\n dnn = GNNTrainer(100, 5, 1000)\\n result = []\\n for r in range(10):\\n dnn.train(p, r / 10)\\n ac = 1 - dnn.test(p, r/10)\\n result.append(ac)\\n print(r, ac)\\n print(result)\\n plt.plot(np.linspace(0, 0.9, 10), result, color=\\\"green\\\", label=\\\"GCN with 5 features\\\")\\n dnn = GNNTrainer(100, 10, 1000)\\n result = []\\n for r in range(10):\\n dnn.train(p, r / 10)\\n ac = 1 - dnn.test(p, r/10)\\n result.append(ac)\\n print(r, ac)\\n print(result)\\n plt.plot(np.linspace(0, 0.9, 10), result, color=\\\"red\\\", label=\\\"GCN with 10 features\\\")\\n dnn = GNNTrainer(100, 50, 1000)\\n result = []\\n for r in range(10):\\n dnn.train(p, r / 10)\\n ac = 1 - dnn.test(p, r/10)\\n result.append(ac)\\n print(r, ac)\\n print(result)\\n plt.plot(np.linspace(0, 0.9, 10), result, color=\\\"blue\\\", label=\\\"GCN with 50 features\\\")\\n## dnn = SimGNNTrainer(100, 5, 0, 1000)\\n## result = []\\n## for r in range(10):\\n## dnn.train(p, r / 10)\\n## ac = 1 - dnn.test(p, r/10)\\n## result.append(ac)\\n## print(r, ac)\\n## print(result)\\n## plt.plot(result, color=\\\"blue\\\")\\n plt.plot(np.linspace(0, 0.9, 10), subgraphPrecision(100, 0.3, 10, 1000, 5), color=\\\"black\\\", label=\\\"Subgraph counts with 5 features\\\")\\n plt.legend()\\n plt.xlabel(\\\"noise\\\")\\n plt.ylabel(\\\"accuracy\\\")\\n plt.savefig(\\\"gcn.pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad9a3c196f4061fba8327370d4d337a6\",\n \"score\": \"0.49940363\",\n \"text\": \"def getGeometricFactor(self):\\r\\n k_list = []\\r\\n num_rdg = len(self.readings)\\r\\n for k in range(num_rdg):\\r\\n num_dipole = len(self.readings[k].Vdp)\\r\\n for j in range(num_dipole):\\r\\n if (self.readings[k].Vdp[j].flagRho == \\\"Accept\\\"):\\r\\n # k_a = self.readings[k].Vdp[j].K\\r\\n k_a = self.readings[k].Vdp[j].calcGeoFactor(self.readings[k].Idp)\\r\\n k_list.append(1 / k_a)\\r\\n return np.asarray(k_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"774d911951cd4db5f1476de1230e2cbd\",\n \"score\": \"0.49842182\",\n \"text\": \"def gf0(self, hartree=False) -> xr.DataArray:\\n e_onsite = self.e_onsite - (0 if hartree is False else hartree*self.U)\\n gf_0 = 1./(e_onsite + self.z - self.hybrid_fct)\\n return gf_0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf333e886ebfa86632c1a3ecbdbd2a57\",\n \"score\": \"0.49828964\",\n \"text\": \"def get_Hom_Het(self, vcf):\\r\\n\\r\\n\\t\\tHet_num = 0\\r\\n\\t\\tHom_num = 0\\t\\r\\n\\t\\tfor line in vcf:\\r\\n\\t\\t\\thehe = line.split('\\\\t')[-2].split(':')\\r\\n\\t\\t\\txixi = line.split('\\\\t')[-1].split(':')\\r\\n\\t\\t\\tformat = dict(zip(hehe,xixi))\\r\\n\\t\\t\\tif not format.has_key('GT'):\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\tif format['GT'] == '1/1' or format['GT'] == '0/0':\\r\\n\\t\\t\\t\\tHom_num += 1\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tHet_num += 1\\r\\n\\t\\treturn Hom_num, Het_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4977e636ecbe1a0f5ffe3b7d4b4e7a0a\",\n \"score\": \"0.49827123\",\n \"text\": \"def get_greynoise_data(self, ip_address):\\n # Malicious sample\\n # {\\n # \\\"ip\\\": \\\"222.187.238.136\\\",\\n # \\\"noise\\\": true,\\n # \\\"riot\\\": false,\\n # \\\"classification\\\": \\\"malicious\\\",\\n # \\\"name\\\": \\\"unknown\\\",\\n # \\\"link\\\": \\\"https://viz.greynoise.io/ip/222.187.238.136\\\",\\n # \\\"last_seen\\\": \\\"2021-06-23\\\",\\n # \\\"message\\\": \\\"Success\\\"\\n # }\\n #\\n # Benign sample\\n # {\\n # \\\"ip\\\": \\\"8.8.8.8\\\",\\n # \\\"noise\\\": false,\\n # \\\"riot\\\": true,\\n # \\\"classification\\\": \\\"benign\\\",\\n # \\\"name\\\": \\\"Google Public DNS\\\",\\n # \\\"link\\\": \\\"https://viz.greynoise.io/riot/8.8.8.8\\\",\\n # \\\"last_seen\\\": \\\"2021-06-23\\\",\\n # \\\"message\\\": \\\"Success\\\"\\n # }\\n #\\n # Unknown sample\\n # {\\n # \\\"ip\\\": \\\"123.123.115.117\\\",\\n # \\\"noise\\\": false,\\n # \\\"riot\\\": false,\\n # \\\"message\\\": \\\"IP not observed scanning the internet or contained in RIOT data set.\\\"\\n # }\\n try:\\n gn_headers = {\\n \\\"key\\\": self.api_key,\\n \\\"User-Agent\\\": \\\"greynoise-redelk-enrichment\\\",\\n }\\n gn_data = requests.get(\\n f\\\"{self.greynoise_url}{ip_address}\\\", headers=gn_headers\\n )\\n json_result = gn_data.json()\\n result = {\\n \\\"ip\\\": ip_address,\\n \\\"noise\\\": get_value(\\\"noise\\\", json_result, False),\\n \\\"riot\\\": get_value(\\\"riot\\\", json_result, False),\\n \\\"classification\\\": get_value(\\\"classification\\\", json_result, \\\"unknown\\\"),\\n \\\"name\\\": get_value(\\\"name\\\", json_result, \\\"unknown\\\"),\\n \\\"link\\\": get_value(\\\"link\\\", json_result, \\\"unknown\\\"),\\n \\\"last_seen\\\": get_value(\\\"last_seen\\\", json_result, None),\\n \\\"message\\\": get_value(\\\"message\\\", json_result, \\\"unknown\\\"),\\n \\\"query_timestamp\\\": int(time()),\\n }\\n return result\\n # pylint: disable=broad-except\\n except Exception as error:\\n self.logger.error(\\\"Error getting greynoise IP %s\\\", ip_address)\\n self.logger.exception(error)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34787e172acbccfa66bc1fcac8cdcd4a\",\n \"score\": \"0.4981036\",\n \"text\": \"def get_aged_g2_from_g12q(g12q, age_edge, age_center=None, timeperframe=1, time_sampling=\\\"log\\\", num_bufs=8):\\n\\n arr = rotate_g12q_to_rectangle(g12q)\\n m, n = arr.shape # m should be 2*n-1\\n # age_edge, age_center = get_qedge( qstart=slice_start,qend= slice_end,\\n # qwidth = slice_width, noqs =slice_num )\\n # print(arr.shape)\\n age_edge = np.int_(age_edge)\\n if age_center is None:\\n age_center = (age_edge[:, 0] + age_edge[:, 1]) // 2\\n\\n age_edge_ = age_edge * 2\\n age_center_ = age_center * timeperframe\\n g2_aged = {}\\n lag_dict = {}\\n # print( age_edge, age_center)\\n for i, age in enumerate(age_center_):\\n age_edges_0, age_edges_1 = age_edge_[i][0], age_edge_[i][1]\\n # print(i, age, age_edges_0, age_edges_1)\\n g2i = arr[age_edges_0:age_edges_1].mean(axis=0)\\n # print('here')\\n g2i_ = np.array(g2i)\\n g2_aged[age] = g2i_[np.nonzero(g2i_)[0]]\\n N = len(g2_aged[age])\\n lag_dict[age] = np.arange(N) * 1.0\\n if time_sampling == \\\"log\\\":\\n num_levels = int(np.log(N / (num_bufs - 1)) / np.log(2) + 1) + 1\\n tot_channels, lag_steps, dict_lag = multi_tau_lags(num_levels, num_bufs)\\n # max_taus= lag_steps[age].max()\\n lag_steps_ = lag_steps[lag_steps < N]\\n # print(i, age, lag_steps, N, lag_steps_, len(g2_aged[age]))\\n g2_aged[age] = g2_aged[age][lag_steps_]\\n lag_dict[age] = lag_steps_ * 1.0\\n # print( lag_dict[age] )\\n lag_dict[age] *= timeperframe\\n\\n return lag_dict, g2_aged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41697f0b12e3af9d6b5fd38e811132f9\",\n \"score\": \"0.4979514\",\n \"text\": \"def mel_gff_list():\\n\\tmod_gff3 = sys.argv[3]\\n\\twith open(mod_gff3, 'r') as f:\\n\\t\\tgff = [line.strip().split('\\\\t') for line in f]\\n\\t\\tf.close()\\n\\treturn gff\\n\\t#gff_list ex/:\\n\\t#['2L', 'FlyBase', 'gene', '7529', '9484', '.', '+', '.', 'ID=FBgn0031208;Name=CG11023;Ontology_term=SO:0000010,SO:0000087,GO:0016929,GO:0016926;Dbxref=FlyBase:FBan0011023,FlyBase_Annotation_IDs:CG11023,GB_protein:ACZ94128,GB_protein:AAO41164,GB:AI944728,GB:AJ564667,GB_protein:CAD92822,GB:BF495604,UniProt/TrEMBL:Q86BM6,INTERPRO:IPR003653,GB_protein:AGB92323,UniProt/TrEMBL:M9PAY1,OrthoDB7_Drosophila:EOG796K1P,OrthoDB7_Diptera:EOG7X1604,EntrezGene:33155,UniProt/TrEMBL:E1JHP8,UniProt/TrEMBL:Q6KEV3,OrthoDB7_Insecta:EOG7Q8QM7,OrthoDB7_Arthropoda:EOG7R5K68,OrthoDB7_Metazoa:EOG7D59MP,InterologFinder:33155,BIOGRID:59420,FlyAtlas:CG11023-RA,GenomeRNAi:33155;gbunit=AE014134;derived_computed_cyto=21A5-21A5'], ['2L', 'FlyBase', 'gene', '9839', '21376', '.', '-', '.', 'ID=FBgn0002121;Name=l(2)gl;fullname=lethal (2) giant larvae;Alias=Lgl,lgl,lethal giant larvae,lethal giant larve,lethal giant larva,lethal(2)giant larvae,Complementation group 2.1,Lethal Giant Larvae,dlgl,p127l(2)gl,LGL,l(2) giant larva,CG2671,L(2)GL,p127,l(2)giant larvae,D-LGL,l(2),gl,l[[2]]gl,l-gl,lethal-giant-larvae,Lethal giant larvae,Lethal (2) giant larvae,L(2)gl,Lethal (2) giant larva,Lethal-giant-larvae,MENE (2L)-B,lethal(2) giant larvae,p127[l(2)gl],lethal(2)-giant larvae,lethal-2-giant larvae,l(2) giant larvae,lethal- giant-larvae,Lethal(2)giant larvae,Lethal-2-giant larvae;Ontology_term=SO:0000010,SO:0000087,GO:0005578,GO:0005886,GO:0007269,GO:0016082,GO:0008021,GO:0008283,GO:0016334,GO:0016336,GO:0016333,GO:0016335,GO:0016327,GO:0005829,GO:0045175,GO:0016332,GO:0045184,GO:0007399,GO:0005938,GO:0005737,GO:0007179,GO:0045197,GO:0045196,GO:0002009,GO:0005918,GO:0008105,GO:0045167,GO:0008104,GO:0045746,GO:0007423,GO:0008285,GO:0001738,GO:0016323,GO:0007391,GO:0005856,GO:0030154,GO:0042127,GO:0005614,GO:0045159,GO:0035072,GO:0007559,GO:0045200,GO:0008360,GO:0019991,GO:0007406,GO:0051726,GO:0051668,GO:0007314,GO:0016325,GO:0030036,GO:0030863,GO:0035070,GO:0055059,GO:0035212,GO:0035293,GO:0090163,GO:0048730,GO:0000132,GO:0098725,GO:0060429,GO:0007293,GO:0045176,GO:0072697,GO:0000149,SO:0000548,GO:0005920,GO:0017022,GO:0004860,GO:0006469;Dbxref=FlyBase:FBan0002671,FlyBase_Annotation_IDs:CG2671,INTERPRO:IPR015943,GB_protein:AAN10503,GB_protein:AAG22256,GB_protein:AAN10502,GB_protein:AAN10501,GB_protein:AAF51570,GB_protein:AAG22255,INTERPRO:IPR017986,GB:AA246243,GB:AW942062,GB:AY051654,GB_protein:AAK93078,GB:BH809482,GB:CZ471313,GB:CZ482024,GB:CZ484691,GB:M17022,GB_protein:AAA28671,GB_protein:AAA28672,GB:X05426,GB_protein:CAA29007,UniProt/Swiss-Prot:P08111,INTERPRO:IPR000664,INTERPRO:IPR001680,INTERPRO:IPR013577,GB_protein:AGB92324,UniProt/TrEMBL:M9NCX1,UniProt/TrEMBL:M9PBJ2,OrthoDB7_Drosophila:EOG7CW2GT,OrthoDB7_Diptera:EOG7DRVK2,GB_protein:AFH03479,GB_protein:AFH03478,GB_protein:AFH03481,GB_protein:AFH03480,EntrezGene:33156,INTERPRO:IPR013905,BDGP_clone:PC00404,OrthoDB7_Insecta:EOG7SRGKH,OrthoDB7_Arthropoda:EOG7ZDD82,OrthoDB7_Metazoa:EOG79W94C,InterologFinder:33156,FlyAtlas:CG2671-RB,BIOGRID:59421,Fly-FISH:CG2671,GenomeRNAi:33156,INTERACTIVEFLY:/cytoskel/lethl2g1.htm;gbunit=AE014134;derived_computed_cyto=21A5-21A5'],\\n\\t# ['2L', 'FlyBase', 'ncRNA', '286383', '288292', '.', '+', '.', 'ID=FBtr0347595;Name=CR46263-RA;Parent=FBgn0267996;Dbxref=FlyBase_Annotation_IDs:CR46263-RA;score_text=Weakly Supported;score=0'], ['2L', 'FlyBase', 'gene', '287252', '289144', '.', '-', '.', 'ID=FBgn0025686;Name=Amnionless;fullname=Amnionless ortholog;Alias=FBgn0031246,CG11592,CK02467,BEST:CK02467,dAMN,Amnionless;Ontology_term=SO:0000010,SO:0000087,GO:0046331,GO:0097206,GO:0016021,GO:0097017;Dbxref=FlyBase:FBan0011592,FlyBase_Annotation_IDs:CG11592,GB_protein:AAF51514,GB:AA141784,GB:CZ468687,UniProt/TrEMBL:Q9VPN2,GB_protein:AGB92350,OrthoDB7_Drosophila:EOG7CGKJK,EntrezGene:33199,BDGP_clone:IP03221,OrthoDB7_Diptera:EOG774804,INTERPRO:IPR026112,OrthoDB7_Insecta:EOG7G266G,OrthoDB7_Arthropoda:EOG7P65FW,OrthoDB7_Metazoa:EOG7ZGX2W,InterologFinder:33199,FlyAtlas:CG11592-RA,GenomeRNAi:33199;gbunit=AE014134;derived_computed_cyto=21B7-21B7'], ['2L', 'FlyBase', 'gene', '292419', '293222', '.', '+', '.', 'ID=FBgn0031247;Name=CG11562;Alias=FBgn0063011,BcDNA:RE44650;Ontology_term=SO:0000010,SO:0000087,GO:0005739,GO:0003674,GO:0008150;Dbxref=FlyBase:FBan0011562,FlyBase_Annotation_IDs:CG11562,GB_protein:AAF51513,GB:AI520524,GB:AI945841,GB:AY119645,GB_protein:AAM50299,GB:BE662187,GB:BI358003,UniProt/TrEMBL:Q9VPN3,OrthoDB7_Drosophila:EOG7HTW3H,OrthoDB7_Diptera:EOG7200K9,EntrezGene:33200,BDGP_clone:RE44650,OrthoDB7_Insecta:EOG7B9454,OrthoDB7_Arthropoda:EOG7RK278,OrthoDB7_Metazoa:EOG78H3X3,FlyAtlas:CG11562-RA,INTERPRO:IPR031568,Fly-FISH:CG11562,GenomeRNAi:33200;gbunit=AE014134;derived_computed_cyto=21B7-21B7'], ['2L', 'FlyBase', 'gene', '292959', '294681', '.', '-', '.', 'ID=FBgn0017457;Name=U2af38;fullname=U2 small nuclear riboprotein auxiliary factor 38;Alias=FBgn0010626,U2AF38,U2AF,dU2AF38,DU2AF38,CG3582,dU2AF[38],l(2)06751,u2af38,U2AF 38;Ontology_term=GO:0089701,SO:0000010,SO:0000087,GO:0000398,GO:0008187,GO:0005681,GO:0005686,GO:0000381,GO:0005634,GO:0003729,GO:0007052,GO:0071011,GO:0008380,GO:0000166,GO:0046872;Dbxref=FlyBase:FBan0003582,FlyBase_Annotation_IDs:CG3582,GB_protein:AAF51512,GB:AA264081,GB:AA820431,GB:AC004115,GB:AC008371,GB:AI061776,GB:AI455418,GB:AI944553,GB:AQ026079,GB:AY058537,GB_protein:AAL13766,GB:U67066,GB_protein:AAB17271,UniProt/Swiss-Prot:Q94535,INTERPRO:IPR000504,INTERPRO:IPR000571,INTERPRO:IPR009145,INTERPRO:IPR012677,GB_protein:AGB92351,UniProt/TrEMBL:M9PBM1,OrthoDB7_Drosophila:EOG7FRM2M,OrthoDB7_Diptera:EOG700KS6,EntrezGene:33201,BDGP_clone:LD24048,OrthoDB7_Insecta:EOG76QSHP,OrthoDB7_Arthropoda:EOG7KMJ7T,OrthoDB7_Metazoa:EOG70089G,apodroso:10448-U2af38[k14504],InterologFinder:33201,FlyAtlas:CG3582-RA,BIOGRID:59457,Fly-FISH:CG3582,GenomeRNAi:33201;gbunit=AE014134;derived_computed_cyto=21B7-21B8']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c64ab36c50faefc4502fb6a7c2c3989\",\n \"score\": \"0.4975732\",\n \"text\": \"def extract_DJS_data(self):\\n super().complete_data()\\n if self.exclude_names is not None:\\n self.exclude_features()\\n self.angles_ankle = self.angles_df.loc[self.features[0]]\\n self.GRF_vertical = self.GRF_df.loc[self.features[1]]\\n self.moment_ankle = self.moments_df.loc[self.features[2]]\\n self.power_ankle = self.power_df.loc[self.features[3]]\\n self.all_dfs_ankle = pd.concat([self.angles_ankle, self.GRF_vertical, \\n self.moment_ankle, self.power_ankle], axis=0)\\n #Changing the features and adding units\\n for i in range(4):\\n self.features[i] += ' '+self.units[i] #Ankle angle\\n self.index_ankle = pd.MultiIndex.from_product([self.features,\\n self.angles_ankle.index], \\n names=['Feature', 'Gait cycle %'])\\n self.all_dfs_ankle.index = self.index_ankle\\n \\n return self.all_dfs_ankle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50c0ac8413fde68a6d9e35906f5194b\",\n \"score\": \"0.4969445\",\n \"text\": \"def get_gini_index(data):\\n # Skip first target class column\\n col_data = data.iloc[:, 1:]\\n best_mix_gini = math.inf\\n best_threshold = math.inf\\n best_attribute = \\\"\\\"\\n # Iterate over all attributes\\n for attr in col_data.columns:\\n threshold_unique = []\\n # Get unique threshold values\\n for val in data[attr]:\\n if val not in threshold_unique:\\n threshold_unique.append(float(val))\\n\\n # Iterate over all thresholds\\n for threshold in threshold_unique:\\n list_lowerthreshold = []\\n list_higherthreshold = []\\n target_lowerthreshold = []\\n target_higherthreshold = []\\n\\n for idx in range(0, len(data[attr])):\\n val = data[attr].tolist()[idx]\\n if val < threshold:\\n # Append lower than threshold values\\n list_lowerthreshold.append(float(val))\\n # Append target class values for less than threshold\\n target_lowerthreshold.append(data[\\\"Type\\\"].tolist()[idx])\\n elif val >= threshold:\\n # Append higher than threshold values\\n list_higherthreshold.append(float(val))\\n # Append target class values higher than threshold\\n target_higherthreshold.append(data[\\\"Type\\\"].tolist()[idx])\\n\\n\\n # Calculate gini for lower values than threshold\\n if(len(list_lowerthreshold) !=0):\\n try:\\n\\n list_zero_lower = [list_lowerthreshold[idx] for idx in range(0,len(list_lowerthreshold)) if target_lowerthreshold[idx] == \\\"Cupcake\\\"]\\n prob_zero_lower = len(list_zero_lower) / len(list_lowerthreshold)\\n\\n list_one_lower = [list_lowerthreshold[idx] for idx in range(0,len(list_lowerthreshold)) if target_lowerthreshold[idx] == \\\"Muffin\\\"]\\n prob_one_lower = len(list_one_lower) / len(list_lowerthreshold)\\n\\n ginival_lower = 1 - prob_zero_lower ** 2 - prob_one_lower ** 2\\n except:\\n ginival_lower = 0\\n else:\\n ginival_lower = 0\\n\\n # Calculate gini for higher values than threshold\\n if (len(list_higherthreshold) != 0):\\n\\n try:\\n list_zero_higher = [list_higherthreshold[idx] for idx in range(0,len(list_higherthreshold)) if target_higherthreshold[idx] == \\\"Cupcake\\\"]\\n\\n prob_zero_higher = len(list_zero_higher) / len(list_higherthreshold)\\n\\n list_one_higher = [list_higherthreshold[idx] for idx in range(0,len(list_higherthreshold)) if target_higherthreshold[idx] == \\\"Muffin\\\"]\\n\\n prob_one_higher = len(list_one_higher) / len(list_higherthreshold)\\n ginival_higher = 1 - prob_zero_higher ** 2 - prob_one_higher ** 2\\n except:\\n ginival_higher = 0\\n else:\\n ginival_higher = 0\\n\\n\\n\\n\\n\\n total_val = len(list_lowerthreshold)+len(list_higherthreshold)\\n\\n\\n if total_val!=0:\\n # Calclulate weighted gini\\n mix_gini_low = len(list_lowerthreshold) * ginival_lower\\n mix_gini_high = len(list_higherthreshold) * ginival_higher\\n mix_gini = (mix_gini_low + mix_gini_high)/total_val\\n\\n if mix_gini < best_mix_gini:\\n # Set best gini, threshold and attribute\\n best_mix_gini = mix_gini\\n best_threshold = threshold\\n best_attribute = attr\\n\\n return best_mix_gini, best_threshold, best_attribute\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d9957f62c5c3d177669d8906df5321\",\n \"score\": \"0.49669987\",\n \"text\": \"def train_HMM(train_file_name):\\r\\n\\r\\n pos_data = read_data_train(train_file_name+'.txt')\\r\\n sent_inds = read_data_ind(train_file_name+'.ind')\\r\\n\\r\\n ####################\\r\\n # STUDENT CODE HERE\\r\\n sent_data = []\\r\\n for start, end in zip(sent_inds, sent_inds[1:]):\\r\\n sent_data.append(pos_data[start:end])\\r\\n sent_data.append(pos_data[sent_inds[-1]:]) # don't forget the last sentence!\\r\\n\\r\\n prior = np.zeros(N_tags)\\r\\n transition = np.zeros((N_tags, N_tags))\\r\\n emission = dict()\\r\\n\\r\\n for sent in sent_data:\\r\\n prior[UNIVERSAL_TAGS.index(sent[0][1])] += 1\\r\\n prior = np.log(prior / prior.sum())\\r\\n\\r\\n for sent in sent_data:\\r\\n for first, second in zip(sent, sent[1:]):\\r\\n transition[UNIVERSAL_TAGS.index(first[1])][UNIVERSAL_TAGS.index(second[1])] += 1\\r\\n for i, row in enumerate(transition):\\r\\n transition[i] = [np.log(float(i)/sum(row)) for i in row]\\r\\n transition = np.asarray(transition)\\r\\n\\r\\n tag_count = {t: 0 for t in UNIVERSAL_TAGS}\\r\\n for pos in pos_data:\\r\\n emission.setdefault((pos[1], pos[0]), 0)\\r\\n emission[(pos[1], pos[0])] += 1\\r\\n tag_count[pos[1]] += 1\\r\\n for key in emission:\\r\\n emission[key] = np.log(float(emission[key])/tag_count[key[0]])\\r\\n ####################\\r\\n\\r\\n return prior, transition, emission\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fac4d87c0a7f6314900e45192fb285\",\n \"score\": \"0.49575096\",\n \"text\": \"def __init__(self,x_cords,y_cords,data,distance_type = 'euclidean'):\\r\\n self.all_points = self.np.array([[x,y] for x,y in zip(x_cords,y_cords)])\\r\\n self.data = self.np.array(data).reshape((len(data),1))\\r\\n if distance_type == 'euclidean':\\r\\n self.distance_function = self.euclideanDistance\\r\\n elif distance_type == 'haversine':\\r\\n self.distance_function = self.haversineDistance\\r\\n\\r\\n\\r\\n\\r\\n # calculate all distances, and the individual semivarance\\r\\n self.X_distance_data,self.Y_semivariance_data = self.calcPointPairs()\\r\\n self.num_pairs = self.X_distance_data.shape[0]\\r\\n\\r\\n self.distance_from_all_points = self.calculated_distance_pairs()\\r\\n\\r\\n # we will hold universall values for the variogram, but will reset them\\r\\n # in function calls.\\r\\n self.X_mean = []\\r\\n self.Y_mean = []\\r\\n\\r\\n # This is the h (distance) vector that will be used to fit a model.\\r\\n self.h = self.np.linspace(min(self.X_distance_data),max(self.X_distance_data),100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2977bf73ccdd430be7c337a2584944\",\n \"score\": \"0.49549648\",\n \"text\": \"def _read_ged(self) -> None:\\n id = \\\"\\\"\\n fam_id = \\\"\\\"\\n indi_flag = False\\n\\n for tag, arg in self._validate_gedcom():\\n if tag == \\\"INDI\\\" and not indi_flag:\\n id = arg\\n if arg not in self._individual.keys():\\n self._individual[arg] = Individual(arg)\\n elif tag == \\\"NAME\\\" and not indi_flag:\\n self._individual[id].set_name(arg)\\n elif tag == \\\"SEX\\\" and not indi_flag:\\n self._individual[id].set_gender(arg)\\n elif tag == \\\"DATE\\\" and not indi_flag:\\n self._individual[id].set_birth_death_date(arg)\\n elif tag == \\\"FAMC\\\" and not indi_flag:\\n self._individual[id].set_child(arg)\\n elif tag == \\\"FAMS\\\" and not indi_flag:\\n self._individual[id].set_spouse(arg)\\n elif tag == \\\"FAM\\\":\\n indi_flag = True\\n fam_id = arg\\n if arg not in self._family.keys():\\n self._family[fam_id] = Family(fam_id)\\n elif tag == \\\"HUSB\\\":\\n self._family[fam_id].set_husband_id(arg)\\n self._family[fam_id].set_husband_name(self._individual[arg].get_name())\\n elif tag == \\\"WIFE\\\":\\n self._family[fam_id].set_wife_id(arg)\\n self._family[fam_id].set_wife_name(self._individual[arg].get_name())\\n elif tag == \\\"CHIL\\\":\\n self._family[fam_id].set_child(arg)\\n elif tag == \\\"DATE\\\":\\n self._family[fam_id].set_marriage_divorce_date(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e13ef2bc000182dd57dff9d9390ec7d\",\n \"score\": \"0.49530223\",\n \"text\": \"def gather_EEG(self, flags):\\n machine_id = 0\\n machines = {}\\n edf_file = []\\n table = []\\n for file in glob.glob(os.path.join(flags.data_path, 'physionet.org/files/capslpdb/1.0.0/*.edf')):\\n\\n # Fetch all data from file\\n edf_file.append(file)\\n try:\\n data = pyedflib.EdfReader(file)\\n except OSError:\\n print(\\\"Crashed\\\")\\n continue\\n\\n ch_freq = data.getSampleFrequencies()\\n data = mne.io.read_raw_edf(file)\\n ch = [c.lower() for c in data.ch_names]\\n\\n # Create state Dict (ID)\\n state_dict = {}\\n for n, f in zip(ch, ch_freq):\\n state_dict[n] = f\\n state_set = set(state_dict.items())\\n\\n # Create or assign ID\\n if state_set not in table:\\n id = copy.deepcopy(machine_id)\\n machine_id +=1\\n table.append(state_set)\\n else:\\n id = table.index(state_set)\\n\\n # Add of update the dictionnary\\n if id not in machines.keys():\\n machines[id] = {}\\n machines[id]['state'] = state_set\\n machines[id]['amount'] = 1\\n machines[id]['dates'] = [data.info['meas_date']]\\n machines[id]['names'] = [file]\\n else:\\n machines[id]['amount'] += 1 \\n machines[id]['dates'].append(data.info['meas_date'])\\n machines[id]['names'].append(file)\\n \\n _table = []\\n for id, machine in machines.items():\\n if machine['amount'] > 4:\\n ch = [c[0] for c in machine['state']]\\n freq = [c[1] for c in machine['state']]\\n\\n _table.append(set(ch))\\n print(\\\"___________________________________________________\\\")\\n print(\\\"Machine ID: \\\", id)\\n print(\\\"Recording amount: \\\", machine['amount'])\\n print(\\\"Channels: \\\", ch)\\n print('Freqs: ', freq)\\n print(\\\"Dates:\\\")\\n for d in machine['dates']:\\n print(d)\\n print(\\\"Files:\\\")\\n for f in machine['names']:\\n print(f)\\n\\n return list(set.intersection(*_table))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea5172218a6bc2c3ea2e2dd6fc92435c\",\n \"score\": \"0.4952294\",\n \"text\": \"def gru_cell(self, Xt, h_t_minus_1,variable_scope):\\n with tf.variable_scope(variable_scope):\\n # 1.update gate: decides how much past information is kept and how much new information is added.\\n print(Xt)\\n print(self.W_z)\\n print(h_t_minus_1)\\n print(self.U_z)\\n z_t = tf.nn.sigmoid(tf.matmul(Xt, self.W_z) + tf.matmul(h_t_minus_1,self.U_z) + self.b_z) # z_t:[batch_size,self.hidden_size]\\n # 2.reset gate: controls how much the past state contributes to the candidate state.\\n r_t = tf.nn.sigmoid(tf.matmul(Xt, self.W_r) + tf.matmul(h_t_minus_1,self.U_r) + self.b_r) # r_t:[batch_size,self.hidden_size]\\n # 3.compute candiate state h_t~\\n h_t_candiate = tf.nn.tanh(tf.matmul(Xt, self.W_h) +r_t * (tf.matmul(h_t_minus_1, self.U_h)) + self.b_h) # h_t_candiate:[batch_size,self.hidden_size]\\n # 4.compute new state: a linear combine of pervious hidden state and the current new state h_t~\\n h_t = (1 - z_t) * h_t_minus_1 + z_t * h_t_candiate # h_t:[batch_size,hidden_size]\\n return h_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c64d1f41864c7bbdc568dbe3bbe200\",\n \"score\": \"0.49498013\",\n \"text\": \"def load_distiller_mgf(self):\\n data = {}\\n\\n alm = [i for i in gen_allowed_mass_diff_with_sign(n=4, z=1)]\\n\\n with mgf.read(self.path) as spectra:\\n for spectrum in spectra:\\n mass = spectrum['params']['pepmass'][0]\\n precursor_chrg = int(spectrum['params']['charge'][0])\\n mass = calculatePrecursor(mass, precursor_chrg)\\n\\n scanid = int(parse_scan_id(spectrum['params']['title']))\\n\\n if scanid in self.ids_to_be_referenced:\\n if self.ids_to_be_referenced[scanid] in data:\\n mass1 = data[self.ids_to_be_referenced[scanid]]['params']['pepmass'][0]\\n precursor_chrg1 = int(data[self.ids_to_be_referenced[scanid]]['params']['charge'][0])\\n mass1 = calculatePrecursor(mass1, precursor_chrg1)\\n diff = abs(mass1 - mass)\\n diff2 = [abs(diff - abs(i)) for i in alm]\\n pos = diff2.index(min(diff2))\\n p = \\\"mass1:\\\\t {0}\\\\n\\\"\\n p += \\\"mass:\\\\t {1}\\\\n\\\"\\n p += \\\"scanid:\\\\t {2}\\\\n\\\"\\n p += \\\"charge:\\\\t {3}\\\\n\\\"\\n p += \\\"charge2:\\\\t {4}\\\\n\\\"\\n p += \\\"scanid2:\\\\t {5}\\\\n\\\"\\n if diff > 21: # distiller changes precursor charge therefore precurosr mass calculation is wrong\\n print(p.format(mass1, mass, scanid, precursor_chrg1, spectrum['params']['charge'][0], self.ids_to_be_referenced[scanid]))\\n print(diff)\\n print(diff2)\\n print(\\\"----------------\\\")\\n else:\\n self.references.add(Reference(ppm=self.ppm,\\n id_2=scanid,\\n id_1=self.ids_to_be_referenced[scanid], # also scanid\\n peak_list_2=spectrum['m/z array'],\\n peak_list_1=data[self.ids_to_be_referenced[scanid]]['m/z array'],\\n mass_2=mass,\\n mass_1=mass1,\\n charge=spectrum['params']['charge'][0],\\n extra_mass=alm[pos],\\n int_list_2=spectrum['intensity array'],\\n int_list_1=data[self.ids_to_be_referenced[scanid]]['intensity array'],\\n params2=spectrum['params'],\\n params1=data[self.ids_to_be_referenced[scanid]]['params']))\\n del(data[self.ids_to_be_referenced[scanid]])\\n del(self.ids_to_be_referenced[scanid])\\n else:\\n data[scanid] = spectrum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f2c8b47ed3ece6e9794559fa2de105\",\n \"score\": \"0.49489737\",\n \"text\": \"def eval_h(self):\\n h_indices = {}\\n for node in self.G:\\n forward_cites = [self.G.nodes[child]['forward_cites'] for child in self.G.successors(node)]\\n h_indices[node] = CitationNetwork.h_index(forward_cites)\\n nx.set_node_attributes(self.G, h_indices, 'h_index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc4af6e5a9fc96108802bc2894324ee\",\n \"score\": \"0.49463764\",\n \"text\": \"def Glafic_source(filename, dataformat):\\n data = open(filename, 'r')\\n lines = data.readlines()\\n iid = []\\n AE = []\\n ME = []\\n for k in range(len(lines))[1:]:\\n [ids, einstein_angle, mass_in_RE] = lines[k].split()\\n iid.append(int(ids))\\n AE.append(float(einstein_angle))\\n ME.append(float(mass_in_RE)) # [Msun/h]\\n iid = np.asarray(iid)\\n AE = np.asarray(AE)\\n ME = np.asarray(ME)\\n if dataformat == 'dictionary':\\n Glafic = {'ID' : iid,\\n 'AE' : AE,\\n 'ME' : ME}\\n return Glafic\\n else:\\n return iid, AE, ME\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994471,"cells":{"query_id":{"kind":"string","value":"e00d4854127bcc72d1cd6d58082bb8aa"},"query":{"kind":"string","value":"Reads a YAML file and return a dictionary of its contents."},"positive_passages":{"kind":"list like","value":[{"docid":"befca0db1c00c8ff4b3347f5daa50d39","score":"0.6859964","text":"def read_yaml_config(path):\n with open(path) as input_file:\n data = input_file.read()\n try:\n return yaml.load(data)\n except yaml.YAMLError, e:\n raise ValueError('Error parsing YAML file %s: %s' % (path, e))","title":""}],"string":"[\n {\n \"docid\": \"befca0db1c00c8ff4b3347f5daa50d39\",\n \"score\": \"0.6859964\",\n \"text\": \"def read_yaml_config(path):\\n with open(path) as input_file:\\n data = input_file.read()\\n try:\\n return yaml.load(data)\\n except yaml.YAMLError, e:\\n raise ValueError('Error parsing YAML file %s: %s' % (path, e))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8790fe5b5c4502b493d535daebb90b16","score":"0.82360923","text":"def read_yaml() -> dict:\n with open(CONFIG_FILE) as config_file:\n return yaml.load(config_file, Loader=yaml.FullLoader)","title":""},{"docid":"f2b97221e12faf0a13d9506dc59f0d55","score":"0.81965977","text":"def read_yaml(path: Path) -> t.Dict[str, t.Any]: # type: ignore[misc]\n\n with open(path) as f:\n return dict(yaml.load(f, Loader=yaml.FullLoader))","title":""},{"docid":"9d184fcae1a63cec81f609f7f4190d57","score":"0.81817836","text":"def read_yaml(path: str) -> dict:\n with open(path, mode='r', encoding='utf-8') as file:\n content = yaml.load(file, Loader=yaml.SafeLoader)\n return content","title":""},{"docid":"4eb2b5cf720b4915cec034c392501a9a","score":"0.79615206","text":"def read_yaml(yaml_file):\n with open(yaml_file, 'r', encoding='utf-8') as yaml_fh:\n ss_dict = yaml.load(yaml_fh)\n return ss_dict","title":""},{"docid":"aa684a96cfc2a65a7f8f48f4881c9309","score":"0.7874171","text":"def read_yaml_file(path: Path) -> dict:\n with open(path) as stream:\n try:\n return yaml.safe_load(stream)\n except yaml.YAMLError as exc:\n raise exc","title":""},{"docid":"546cf121741a26acdb1b39ec7adeb272","score":"0.7868081","text":"def _read_file(yaml_file_path):\n obj = {}\n with open(yaml_file_path, \"r\") as stream:\n try:\n obj = yaml.load(stream, Loader=yaml.FullLoader)\n except yaml.YAMLError as ex:\n print(ex)\n return obj","title":""},{"docid":"41d967324933ed1016fc5d719eb78707","score":"0.78496516","text":"def read_yaml_file(filename: str) -> dict:\n with open(filename, 'r') as stream:\n LOGGER.info('Chargement du fichier de configuration %s', filename)\n return ConfigLoader.read_yaml_stream(stream)","title":""},{"docid":"1f0c8e84f462a7e9b459311db81d900c","score":"0.7770052","text":"def yaml_to_dict(file_name):\n file_path = os.path.abspath(file_name)\n with open(file_path, \"r\") as conf_:\n content = yaml.safe_load(conf_)\n\n return content","title":""},{"docid":"5e49645affd343157f6fc9a343cd2d3d","score":"0.7649594","text":"def _read_file(path):\n try:\n with salt.utils.files.fopen(path, \"rb\") as contents:\n return salt.utils.yaml.safe_load(contents)\n except OSError:\n return {}","title":""},{"docid":"5a6b195724f394ff4876dc45ee278ce2","score":"0.75858617","text":"def get_yaml_dict(yaml_file):\n\ttry:\n\t\twith open(yaml_file, \"r\") as file_:\n\t\t\tyaml_dict = yaml.safe_load(file_.read()) or {}\n\t\treturn yaml_dict\n\texcept FileNotFoundError:\n\t\treturn {}","title":""},{"docid":"d90c9c4d07932f64582049aa1e11244d","score":"0.75544655","text":"def load_from_file(file_path: str) -> dict:\n return ConfigLoader.read_yaml_file(file_path)","title":""},{"docid":"d244ca7629b0aca6f3b479f17ee63d8d","score":"0.7523064","text":"def load_yaml_as_dict(filepath):\n with open(filepath) as open_file:\n yaml_dict = yaml.safe_load(open_file)\n if yaml_dict:\n return yaml_dict\n return {}","title":""},{"docid":"ddc2bfac497b5809ed22279adb440c64","score":"0.75091386","text":"def load_yaml(yaml_path: Path) -> dict:\n with yaml_path.open(\"r\") as file_open:\n yaml_dict = load_string(file_open)\n return yaml_dict","title":""},{"docid":"da293a24b72636bcc89588e97c08b3c4","score":"0.74767953","text":"def read_config(config_path: str) -> Dict:\n fin = open(config_path, encoding=\"utf-8\")\n data = yaml.load(fin, Loader=yaml.FullLoader)\n return data","title":""},{"docid":"0d9cfc5d9691ff8cb27f59ee6bd550de","score":"0.7476096","text":"def read_yaml(filepath):\n with open(filepath, encoding=\"utf-8\") as stream:\n data = yaml.safe_load(stream)\n return data","title":""},{"docid":"4f9012f524e8d976051cc1ae6e401702","score":"0.7447727","text":"def get_dict_from_yml(self, filename):\n\n fid = open(os.path.join(RESOURCE_PATH, filename), 'r')\n result = yaml.load(fid)\n fid.close()\n\n if result is None:\n raise SampleException('dict is None')\n\n return result","title":""},{"docid":"10ff90d4e22722a8dc0ad6d38b3156ba","score":"0.7424501","text":"def read_yaml(self, filename):\n with open(filename, 'r') as f:\n self.__dict.update(yaml.load(f, Loader=yaml.SafeLoader))","title":""},{"docid":"8489a232c50ac8573bf7977bddc788e9","score":"0.7371884","text":"def _load_yaml_file(yaml_file: Text) -> Dict:\n with open(yaml_file, mode='rb') as stream:\n try:\n yaml_content = yaml.load(stream)\n except yaml.YAMLError as ex:\n err_msg = f\"YAMLError:\\nfile:{yaml_file}\\nerror:{ex}\"\n logger.error(err_msg)\n raise FileNotFoundError\n\n return yaml_content","title":""},{"docid":"28a2694ce54b98561b94c2485500060d","score":"0.7356315","text":"def read(self):\n import yaml\n from os.path import exists\n if exists(self.filename):\n with lock:\n with open(self.filename) as handle:\n try:\n data = yaml.safe_load(handle.read()) # (2)\n return data\n except yaml.YAMLError:\n return {} # (3)\n else:\n return {}","title":""},{"docid":"0e90d619ce45de415664844151580d34","score":"0.7290625","text":"def read_config_file(path: str) -> dict:\n if not os.path.isfile(path):\n raise FileNotFoundError('The config file is missing.')\n\n with open(path, 'r') as config_file:\n return yaml.safe_load(config_file)","title":""},{"docid":"790760830fd21d58d1c8608f2e0b429c","score":"0.7272207","text":"def yaml2dict(yaml_path: str):\n assert yaml_path.endswith('.yml') or yaml_path.endswith('.yaml')\n\n with open(yaml_path) as f:\n doc = yaml.full_load(f)\n return doc","title":""},{"docid":"602cea88b2a2a8ea12f0a60c69981925","score":"0.72694516","text":"def read_yaml(yaml_file):\n try:\n # Read the package yaml file\n yaml = YAML()\n yaml.explicit_start = True\n yaml.indent(mapping=3)\n yaml.preserve_quotes = True # not necessary for your current input\n\n with open(yaml_file) as fp:\n data = yaml.load(fp)\n yaml.dump(data, sys.stdout)\n fp.close()\n except (IOError, ValueError):\n # Log error and raise exception if package yaml can't be read.\n error_msg = '{1}\\nError loading {0}\\n'.format(yaml_file, traceback.format_exc())\n LOG.error(\"Reading the vars.yml file failed with \" + error_msg)\n raise ValueError(error_msg)\n\n return data","title":""},{"docid":"4dc5bb9b77032e9b6517e1ec671d4b2c","score":"0.7246916","text":"def read_configs(infile):\n cfg = {}\n with open(infile) as hdl:\n cfg = yaml.safe_load(hdl)\n return cfg","title":""},{"docid":"dfb628323b2fd268185eac64e8c49254","score":"0.71985924","text":"def yaml_to_dict(file_path):\r\n loaded_file = {}\r\n try:\r\n with open(file_path, encoding=\"utf-8\") as file:\r\n loaded_file = yaml.safe_load(file.read())\r\n except Exception as error:\r\n loaded_file = {'erreur': error}\r\n\r\n return loaded_file","title":""},{"docid":"13b0d01cdc3c125eb9b9bf1dcee9a648","score":"0.7184562","text":"def load():\n\n path = pathlib.Path(__file__).parent.absolute()\n file = f\"{path}/config.yml\"\n\n config = open(file)\n\n data = yaml.load(config, Loader=yaml.FullLoader)\n\n return data","title":""},{"docid":"70376f3d523515e59415ce81a1321969","score":"0.71795964","text":"def load_yaml_file(filename):\n with open(filename) as f:\n contents = yaml.load(f, Loader=yaml.FullLoader)\n return contents","title":""},{"docid":"332087c78a3ca5842f472f20be81c50a","score":"0.7174574","text":"def yml_to_dict(yml_file_path):\n yml_path = Path(yml_file_path).resolve()\n\n if not yml_path.is_file():\n return None\n\n yml_dict = None\n with open(yml_path, encoding='utf-8') as file:\n yml_dict = yaml.load(file, Loader=yaml.BaseLoader)\n\n return yml_dict","title":""},{"docid":"c3b9b5c844f40921f195f94f1d278401","score":"0.71663755","text":"def load_yaml_file(self, yaml_file):\r\n self.called_tools.append('load_yaml_file')\r\n\r\n try:\r\n with open(yaml_file, 'r') as file:\r\n dict_result = yaml.safe_load(file.read())\r\n except FileNotFoundError as error:\r\n raise RbkcliException.ToolsError(error)\r\n\r\n return dict_result","title":""},{"docid":"ebe27a2e3edec8b10295bea54ef0a149","score":"0.7148037","text":"def load_yaml(filename):\n try:\n with open(filename, 'r') as stream:\n data = yaml.load(stream)\n except:\n print(\"ERROR: Supplied configuration file {} is not a properly-formatted YAML file.\".format(filename))\n sys.exit(1)\n\n return data","title":""},{"docid":"4b32003d2fa390b8636f3bf1dfefba9c","score":"0.7131626","text":"def load_yaml(file_name: str = None, resource: str = None, package: str = None) -> dict:\n #\n if file_name is not None:\n with open(file_name, 'r', encoding='utf-8') as f:\n yaml_string = f.read()\n else:\n with importlib.resources.open_text(package, resource) as r:\n yaml_string = r.read()\n\n the_map = yaml.load(yaml_string)\n\n if not isinstance(the_map, dict):\n raise YcError('Yaml source should map to a dictionary {}'.format(the_map))\n\n return the_map","title":""},{"docid":"8fcdf6416ddd6fa3b6673c7db21c1009","score":"0.7130792","text":"def read_config():\n\n with open(\"config.yml\", 'r') as ymlfile:\n return yaml.load(ymlfile)","title":""},{"docid":"3f420f1f42421f860e2269cba4cb47eb","score":"0.7118985","text":"def load_yaml(file_path):\n with open(file_path) as fin:\n content = yaml.load(fin, Loader=yaml.FullLoader)\n return content","title":""},{"docid":"20168970028563355ad391b7947f981c","score":"0.70954406","text":"def read_yaml_file(file_name, validate) -> Dict:\n print(cyan(\"Reading\"),\n cyan(file_name, bold=True))\n\n with open(file_name, 'r', encoding='utf-8') as f:\n content = f.read()\n\n if not validate:\n return yaml.safe_load(content)\n\n if \"REDACTED\" in content or \"DATA+OMITTED\" in content:\n print(red(\"REDACTED\", bold=True),\n red(\"or\"),\n red(\"DATA+OMITTED\", bold=True),\n red(\"found in\"),\n red(file_name, bold=True))\n print(red(\"Ensure the context was dumped with\"),\n red(\"--raw\", bold=True))\n print(red(\"i.e.\"),\n red(\"kubectl config view --raw\", bold=True))\n sys.exit(3)\n\n return yaml.safe_load(content)","title":""},{"docid":"4f783468dc43536058f6abc776bcbc61","score":"0.70877725","text":"def read_yaml(file_path):\n\n with open(os.path.join(DATA_DIRECTORY, file_path), 'r') as yaml_file:\n try:\n return yaml.safe_load(yaml_file)\n except yaml.YAMLError as err:\n print(err)","title":""},{"docid":"008677bef962fc7204fbfd587509f2fa","score":"0.7087709","text":"def read_config():\n with open('config.yaml') as file:\n config = yaml.load(file, Loader=yaml.FullLoader)\n return config","title":""},{"docid":"f29fd9318abaacd45e347387ef722e61","score":"0.7084344","text":"def read_yaml_file(filepath):\n data = None\n if os.path.exists(filepath):\n with open(filepath, 'r') as f:\n data = yaml.safe_load(f)\n return data","title":""},{"docid":"a395000be05284e9e246b423c37dd1df","score":"0.7077685","text":"def load_yaml(yaml_file):\n f = open(yaml_file)\n y = f.read()\n return yaml.load(y)","title":""},{"docid":"71520d6baa40d99825c26fc4bd4fd1ee","score":"0.707119","text":"def load_yaml_vars(infile):\n with open(infile, 'r') as f:\n var_dict = yaml.safe_load(f)\n\n return(var_dict)","title":""},{"docid":"dba063d815957a627e73de5c5f2abed6","score":"0.7046259","text":"def load_yaml(file_path):\n content = None\n with open(file_path, 'r') as f:\n content = yaml.safe_load(f)\n return content","title":""},{"docid":"a4503e32485f48f52b481caceb38932a","score":"0.7043907","text":"def deserialize(self, file):\n\t\treturn yaml.load(file)","title":""},{"docid":"89efa095ddf3aa6a2aa7b6439b1fdb19","score":"0.70262045","text":"def load_yaml(filepath):\n\n with open(filepath) as f:\n data = yaml.load(f, Loader=Loader)\n\n return data","title":""},{"docid":"9b8cb5e9c2c36f4344a274829d7fc9c6","score":"0.7002157","text":"def parse_config(config_file):\n if config_file is None:\n return {}\n return yaml.load(open(config_file, 'r').read())","title":""},{"docid":"639c73f9caf25350e6628de452e105fe","score":"0.70004666","text":"def yaml_to_dict(inp_yaml):\n with open(inp_yaml, 'r') as stream:\n try:\n out = yaml.load(stream)\n except yaml.YAMLError as exc:\n print(exc)\n return out","title":""},{"docid":"81dae1b4002d25dcf7af7b9b614a1962","score":"0.6971694","text":"def loads_yaml(file_content):\n return load_yaml(file_content)","title":""},{"docid":"dca90614ada944cc98130b9625d66842","score":"0.6969649","text":"def load_yaml(filepath):\n with open(filepath) as fp:\n r = yaml.load(fp)\n return r","title":""},{"docid":"2a9aaa315a5bf203d4b50a8c83f49cb9","score":"0.69647294","text":"def _read_logging_config() -> dict:\n with open(constants.FILE_YAML, 'rt') as f:\n return yaml.safe_load(f.read())","title":""},{"docid":"8f082bbc1d2f47367bd58412c68ec132","score":"0.69617224","text":"def read_from_configuration(file_name):\n\n with open(file_name, 'r') as yaml_fobj:\n yaml_content = yaml.load(yaml_fobj)\n\n return yaml_content","title":""},{"docid":"d060f9a090f6cad754bbf1db160e5616","score":"0.69449025","text":"def read_yaml_file(self, file_name):\n with open(os.path.join(self.__path(), os.path.basename(file_name)), 'rt') as yamlfile:\n return yaml.load(yamlfile)","title":""},{"docid":"d58db7f999e21813cdc074493029eefd","score":"0.69419944","text":"def read_file(filename):\n try:\n with open(filename, 'r') as stream:\n data_loaded = yaml.load(stream)\n\n return data_loaded\n except IOError:\n print(\"Could not read file:\", filename)\n except TypeError:\n print('something went wrong')","title":""},{"docid":"462a4884fe26e838ab1ab35048376fe3","score":"0.69201773","text":"def load_yaml(filename):\n with open(filename) as file:\n return yaml.load(file, Loader=yaml.FullLoader)","title":""},{"docid":"6fd659a560ec55f78f86a4fd7d73ab3a","score":"0.6917695","text":"def LoadYaml(filepath):\n with open(filepath, 'r') as f:\n doc = yaml.load(f)\n logger.debug(doc)\n return doc","title":""},{"docid":"73153175692f5e710eb384683634c388","score":"0.69048584","text":"def _read_yaml_file(path_yaml: Path) -> Any:\n # TODO: modify so that mkdocs.yml can be read, but Python won't be executed...\n\n # Based on: https://github.com/yaml/pyyaml/issues/86#issuecomment-380252434\n yaml.add_multi_constructor('', lambda _loader, _suffix, _node: None)\n yaml.add_multi_constructor('!', lambda _loader, _suffix, _node: None)\n yaml.add_multi_constructor('!!', lambda _loader, _suffix, _node: None)\n try:\n return yaml.unsafe_load(path_yaml.read_text())\n except (FileNotFoundError, KeyError) as err: # pragma: no cover\n logger.warning(f'Unexpected error reading the {path_yaml.name} file ({path_yaml}): {err}')\n return {}\n except yaml.constructor.ConstructorError:\n logger.exception('Warning: burying poorly handled yaml error')\n return {}","title":""},{"docid":"26218e3ed4270379570ce64b9c93c2de","score":"0.68852204","text":"def _convert_yaml_to_dict(self, inputfilename):\n yamldict = {}\n\n try:\n with open(inputfilename, \"r\", encoding=\"utf-8\") as yaml_file:\n yamldict = yaml.load(yaml_file)\n except yaml.YAMLError as exc:\n if hasattr(exc, 'problem_mark'):\n mark = exc.problem_mark\n print(\"Error position: (%s:%s)\" % (mark.line+1, mark.column+1))\n else:\n errmsg = \"Unexpected error while parsing the YAML:\", sys.exc_info()[1]\n logging.error(errmsg)\n raise Exception(errmsg)\n\n return yamldict","title":""},{"docid":"4d1c93a280b3a8a9e7bb7bf4678cf8dd","score":"0.68692064","text":"def read_config(file_name):\n with open(file_name) as fin:\n config = yaml.safe_load(fin.read())\n return config","title":""},{"docid":"4d1c93a280b3a8a9e7bb7bf4678cf8dd","score":"0.68692064","text":"def read_config(file_name):\n with open(file_name) as fin:\n config = yaml.safe_load(fin.read())\n return config","title":""},{"docid":"574e7ebce2cb9c06587fbf0d373cafbf","score":"0.6854796","text":"def parse(self):\n with open(self.file_path, \"r\") as f:\n self.content = yaml.safe_load(f)\n return self.content","title":""},{"docid":"fe9db23695d46d3b035ab00c45f23bad","score":"0.6844314","text":"def load_config(config_path: str) -> dict:\n with open(config_path, 'r') as f:\n config = yaml.load(f, Loader=yaml.FullLoader)\n return config","title":""},{"docid":"2c85e70119dfcb0a8873619009951afc","score":"0.6834853","text":"def parse_config(config_file):\n return yaml.load(open(config_file), Loader=yaml.FullLoader)","title":""},{"docid":"39fdd5c3e73bdb01524d08f4bd6c817f","score":"0.6834625","text":"def load_yaml(self, name):\n y = open(name, 'r')\n yml = yaml.safe_load(y)\n y.close()\n return yml","title":""},{"docid":"6772370f77b55905d765893698ef7963","score":"0.6832744","text":"def read_yaml_file(pfilename: str) -> dict:\n # read the yaml file \"profiles.yaml\" for the list of profiles using pyyaml\n with open(pfilename, 'r') as stream:\n # try to load the yaml file\n try:\n profiles = yaml.safe_load(stream)\n # deal with a yaml error (for now just raise the error)\n except yaml.YAMLError as exc:\n raise exc\n # return the profiles\n return profiles","title":""},{"docid":"dab0120f5a73ccb19df4c8b1387206e4","score":"0.678199","text":"def file_loads_yaml(file_path):\n file = None\n try:\n file = open(file_path)\n return loads_yaml(file)\n finally:\n if file:\n file.close()","title":""},{"docid":"96e132a48c6060c76bc9aeba20b44c1f","score":"0.6781286","text":"def load_yaml(file_path):\n try:\n with open(file_path, 'r') as F:\n return yaml.load(F)\n except ImportError:\n print(\"import yaml is required\")\n exit(1)","title":""},{"docid":"cf751a783676e439579be6e6addda0ab","score":"0.67756873","text":"def read(self):\n with open(self.file_name) as f:\n d = yaml.load(f)\n if d:\n self.update(d)\n f.close()","title":""},{"docid":"c3f9b6600fdda61f8fe47d47a1ca1770","score":"0.6756012","text":"def load_yaml(path):\n stream = load_file(path)\n return yaml.load(stream)","title":""},{"docid":"71d7df142abd8c46cfc36f595d346428","score":"0.6753374","text":"def __load_data(self):\n try:\n fh = open(self.ifile)\n data = yaml.load(fh)\n fh.close()\n return data\n except Exception, e:\n LOGGER.debug(str(e))\n LOGGER.error(_(\"You need to specify an existing file\"))\n sys.exit(1)","title":""},{"docid":"aef4e3d6673014bbc9a3b60ded604dd5","score":"0.6726021","text":"def read_config(filename):\n \n with open(filename, 'r') as stream:\n try:\n config = yaml.load(stream)\n return config\n except yaml.YAMLError as exc:\n print(exc)","title":""},{"docid":"7042dbf0f7c1c4c75f0975a0cfb561a2","score":"0.6705862","text":"def read_config_file(self):\n\n with open(self.file_path) as stream:\n LOG.info(\"Parsing pod file: %s\", self.file_path)\n cfg = yaml.load(stream)\n return cfg","title":""},{"docid":"4740d435f78765f04a09030303fa7493","score":"0.6697818","text":"def parse_settings():\n\n with open(\"settings.yaml\", \"r\") as data:\n settings = yaml.safe_load(data)\n return settings","title":""},{"docid":"24fd0ede51d674cad7853f1bdcf7a634","score":"0.6696523","text":"def config(self) -> Dict[str, Any]:\n try:\n with open(self.directory.joinpath(\"wordgoal.yml\"), \"r\") as stream:\n return cast(Dict[str, Any], safe_load(stream))\n except FileNotFoundError:\n return {}","title":""},{"docid":"35eca6d438cdfaf4e2d09aea7f04913e","score":"0.66908723","text":"def load_config_data() -> map:\n\n cfg_file = get_config_file()\n\n with open(cfg_file, 'r') as cfg:\n data = yaml.safe_load(cfg)\n\n return data","title":""},{"docid":"31d2fe01c6db0321e4f11df232b52967","score":"0.6690024","text":"def read_from_yaml(yml_file):\n logging.info(\"reading yaml file: %s\" % yml_file)\n sg_dict = {}\n yaml_stream = open(yml_file, \"r\")\n security_groups = yaml.load_all(yaml_stream)\n for security_group in security_groups:\n sg_dict = {sg_name: sg_attributes for (sg_name, sg_attributes) in security_group.items()}\n yaml_stream.close()\n logging.debug(\"sg_dict: %s\" % sg_dict)\n return sg_dict","title":""},{"docid":"2aabb8906047d24d35e80eea24831fd7","score":"0.6687082","text":"def _read_yaml(path):\n with open(path, \"r\") as f:\n try:\n hist = yaml.safe_load(f)\n except yaml.YAMLError as exc:\n print(exc)\n return hist","title":""},{"docid":"3ccc026172ef9e45953c715dab5b4f4c","score":"0.66453356","text":"def load_yaml_file(file_path: str):\n with codecs.open(file_path, \"r\") as f:\n return yaml.safe_load(f)","title":""},{"docid":"3595b2f0fc1fd5cd01d4ebe90d31ed84","score":"0.6639965","text":"def parse_config_file(self, filename):\n self.logger.info(f'Loading config file: {filename}')\n with open(filename, 'r') as f:\n return yaml.load(f, Loader=yaml.FullLoader)","title":""},{"docid":"56e06741d1c1425c53bbb9df46dd1d64","score":"0.6638672","text":"def get_config_yaml():\n return yaml.load(open('test/test_config.yaml', 'r')) or {}","title":""},{"docid":"c17bb0ed03c314efc6cda793b66976f9","score":"0.6632735","text":"def get_config() -> Dict[str, str]:\n with open(\"default_conf.yaml\") as file:\n config = yaml.safe_load(file)\n return config","title":""},{"docid":"eed8fb9d0b5ea423d2e66c861c3677e6","score":"0.66229504","text":"def read_config_file(\n filename: Union[Path, Text], reader_type: Union[Text, List[Text]] = \"safe\"\n) -> Dict[Text, Any]:\n return read_validated_yaml(filename, CONFIG_SCHEMA_FILE, reader_type)","title":""},{"docid":"cb6c2a851cdb4851dc0770d4b962abdd","score":"0.66061336","text":"def parse_config(self, path: str) -> dict:\n try:\n with Path(path).open(\"r\") as file:\n return yaml.safe_load(file)\n except (FileNotFoundError, ParserError) as error:\n raise argparse.ArgumentError(self, error)","title":""},{"docid":"495888e4ded497bed181229b7a4d1daf","score":"0.66040313","text":"def read_config(config_path):\n with open(config_path, 'r') as ymlfile:\n return yaml.load(ymlfile)","title":""},{"docid":"375864ae5e8a3c862bb1bde33d3fb72d","score":"0.65909624","text":"def _load_file(self, file: TextIO) -> Dict[str, Any]:\n import toml\n\n config_dict = toml.load(file)\n return cast(Dict[str, Any], config_dict)","title":""},{"docid":"5421484bebffc1d256fd8192b65cd4aa","score":"0.6590941","text":"def load(cls, filename):\n f = open(filename)\n result = cls._from_yaml_dict(yaml.load(f))\n f.close()\n return result","title":""},{"docid":"f4b3f7918824d693e8199d799b9586d1","score":"0.6587401","text":"def get_config(conf_file):\n from easydict import EasyDict as edict\n\n with open(conf_file, \"r\") as file_descriptor:\n data = yaml.load(file_descriptor)\n\n # convert the data into an easyDictionary\n return edict(data)","title":""},{"docid":"cc35c89e53387117a975f2555fb84275","score":"0.6587145","text":"def read_yaml_args_file(args_file_path: Path) -> Dict[str, Union[str, int, bool, List, Dict, datetime]]:\r\n with open(args_file_path, 'r') as f:\r\n return remove_empty_values(yaml.safe_load(f))","title":""},{"docid":"6ea3fa74329ace5701188428b286b852","score":"0.65742385","text":"def load(self, path):\n context = {}\n with codecs.open(self.source + path, 'r', encoding='utf-8') as f:\n content = f.read()\n result = re.search(r'^(---\\s*\\n.*?\\n?)^(---\\s*$\\n?)', content, re.DOTALL|re.MULTILINE)\n if result:\n front_matter = result.group(1)\n context = yaml.load(front_matter)\n content = content[result.end(0):len(content)]\n return content, context","title":""},{"docid":"a7fb17e84bc1aa081e48b145f3352070","score":"0.65531045","text":"def load_config(config_path: str) -> dict:\n with fsspec.open(config_path, mode=\"r\") as f:\n config = yaml.safe_load(f)\n return config","title":""},{"docid":"02423169ca9a96fafd9e3223897bafbc","score":"0.65272075","text":"def load(name):\n if not os.path.isfile(name):\n raise ValueError(f\"File {name} does not exists.\")\n return yaml.load(open(name), Loader=yaml.Loader)","title":""},{"docid":"ccf763e99b8b88c99e5f38c6382cd189","score":"0.6517302","text":"def load_config(filepath: str) -> Dict[str, Any]:\n with open(filepath, mode=\"r\", encoding=\"utf-8\") as file:\n return parse(file.read())","title":""},{"docid":"8316c3da86f0e10483bb2849fceeccf2","score":"0.6498666","text":"def load_yaml(fname):\n with open(fname, 'r') as outfile:\n yaml_file = yaml.load(outfile) # Loader=yaml.FullLoader\n return yaml_file","title":""},{"docid":"7202b02674f170a804d644131cc81801","score":"0.6497122","text":"def load_yaml(file_path):\n with open(file_path, 'r') as usernames_yaml:\n try:\n data = yaml.safe_load(usernames_yaml)\n except yaml.YAMLError as exc:\n print(exc)\n return data","title":""},{"docid":"676d4183402646b22e9dfadaab3616bd","score":"0.6489465","text":"def load_yaml(path):\n with open(path, 'r', encoding='utf-8') as stream:\n try:\n res = yaml.safe_load(stream)\n except yaml.YAMLError as exc:\n print(exc)\n return res","title":""},{"docid":"5805cda0e77846a17d904332f60f18f6","score":"0.64880127","text":"def read_dict_file(file):\n with open(file) as data:\n data = yaml.load(data, Loader=yaml.FullLoader)\n email_list = []\n server_list = []\n subject_list = []\n for email in data['emails']:\n email_list.append(email)\n for server in data['servers']:\n server_list.append(server)\n for subject in data['subjects']:\n subject_list.append(subject)\n return email_list, server_list, subject_list","title":""},{"docid":"ae0404260be401cf93a6ae26e36d48a8","score":"0.6484009","text":"def get_config():\n with open('config.yaml', 'r') as file:\n config = yaml.load(file, Loader=yaml.FullLoader)\n return config","title":""},{"docid":"86a4ec82c0b22b0a1a19ce4d6b0c9e39","score":"0.64752114","text":"def load_yaml_config(config_file):\n if type(config_file) is file:\n CONFIG.update(yaml.load(config_file) or {})\n return CONFIG\n else:\n try:\n with open(config_file, 'r') as f:\n content = yaml.load(f)\n CONFIG.update(content)\n return content\n except IOError as e:\n e.message = \"Could not open configuration file \\\"{}\\\".\".format(config_file)\n raise e","title":""},{"docid":"2c110977a05353c45aeaa6c4ffaf0838","score":"0.64426583","text":"def read_yaml_file(\n filename: Union[Text, Path], reader_type: Union[Text, List[Text]] = \"safe\"\n) -> Union[List[Any], Dict[Text, Any]]:\n try:\n return read_yaml(read_file(filename, DEFAULT_ENCODING), reader_type)\n except (YAMLError, DuplicateKeyError) as e:\n raise YamlSyntaxException(filename, e)","title":""},{"docid":"e8bc7e7ecaca290f80b302ed99cc16cc","score":"0.6427357","text":"def _parse(filename):\n with open(filename, \"r\") as fp:\n config_str = jinja2.Template(fp.read()).render()\n config = yaml.load(config_str, Loader=yaml.Loader)\n\n return config","title":""},{"docid":"e9c3c89cf36bd2cb45f22fc6e2efb06b","score":"0.64152056","text":"def read_model_configuration(filename: Union[Path, Text]) -> Dict[Text, Any]:\n return read_validated_yaml(filename, MODEL_CONFIG_SCHEMA_FILE)","title":""},{"docid":"ee2ca8dbb8bff7743c63d0fe6b161bd6","score":"0.6381088","text":"def readYML():\n with open(ROAinfoFile, 'r') as infile:\n ROAinfo = yaml.load(infile)\n return ROAinfo","title":""},{"docid":"c4750262a580fb7555a91999cf8126cf","score":"0.63790244","text":"def load_config(path):\r\n return yaml.load(open('config.yaml', 'r'), Loader=yaml.SafeLoader)","title":""},{"docid":"8ae2b4043978441b19ed6a43cb5833c7","score":"0.6373652","text":"def load(filename):\n try:\n with open(filename, 'r') as f:\n return yaml.safe_load(f)\n except yaml.scanner.ScannerError as e:\t# For errors parsing schema.yaml\n logging.error(\"YAML errror: %s \", e)\n raise","title":""},{"docid":"7cb56df60fa1252478b6cdfb33bd6191","score":"0.63606393","text":"def _load_file(path):\n with open(path) as f:\n return yaml.safe_load(f)","title":""}],"string":"[\n {\n \"docid\": \"8790fe5b5c4502b493d535daebb90b16\",\n \"score\": \"0.82360923\",\n \"text\": \"def read_yaml() -> dict:\\n with open(CONFIG_FILE) as config_file:\\n return yaml.load(config_file, Loader=yaml.FullLoader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b97221e12faf0a13d9506dc59f0d55\",\n \"score\": \"0.81965977\",\n \"text\": \"def read_yaml(path: Path) -> t.Dict[str, t.Any]: # type: ignore[misc]\\n\\n with open(path) as f:\\n return dict(yaml.load(f, Loader=yaml.FullLoader))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d184fcae1a63cec81f609f7f4190d57\",\n \"score\": \"0.81817836\",\n \"text\": \"def read_yaml(path: str) -> dict:\\n with open(path, mode='r', encoding='utf-8') as file:\\n content = yaml.load(file, Loader=yaml.SafeLoader)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb2b5cf720b4915cec034c392501a9a\",\n \"score\": \"0.79615206\",\n \"text\": \"def read_yaml(yaml_file):\\n with open(yaml_file, 'r', encoding='utf-8') as yaml_fh:\\n ss_dict = yaml.load(yaml_fh)\\n return ss_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa684a96cfc2a65a7f8f48f4881c9309\",\n \"score\": \"0.7874171\",\n \"text\": \"def read_yaml_file(path: Path) -> dict:\\n with open(path) as stream:\\n try:\\n return yaml.safe_load(stream)\\n except yaml.YAMLError as exc:\\n raise exc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546cf121741a26acdb1b39ec7adeb272\",\n \"score\": \"0.7868081\",\n \"text\": \"def _read_file(yaml_file_path):\\n obj = {}\\n with open(yaml_file_path, \\\"r\\\") as stream:\\n try:\\n obj = yaml.load(stream, Loader=yaml.FullLoader)\\n except yaml.YAMLError as ex:\\n print(ex)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41d967324933ed1016fc5d719eb78707\",\n \"score\": \"0.78496516\",\n \"text\": \"def read_yaml_file(filename: str) -> dict:\\n with open(filename, 'r') as stream:\\n LOGGER.info('Chargement du fichier de configuration %s', filename)\\n return ConfigLoader.read_yaml_stream(stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0c8e84f462a7e9b459311db81d900c\",\n \"score\": \"0.7770052\",\n \"text\": \"def yaml_to_dict(file_name):\\n file_path = os.path.abspath(file_name)\\n with open(file_path, \\\"r\\\") as conf_:\\n content = yaml.safe_load(conf_)\\n\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e49645affd343157f6fc9a343cd2d3d\",\n \"score\": \"0.7649594\",\n \"text\": \"def _read_file(path):\\n try:\\n with salt.utils.files.fopen(path, \\\"rb\\\") as contents:\\n return salt.utils.yaml.safe_load(contents)\\n except OSError:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a6b195724f394ff4876dc45ee278ce2\",\n \"score\": \"0.75858617\",\n \"text\": \"def get_yaml_dict(yaml_file):\\n\\ttry:\\n\\t\\twith open(yaml_file, \\\"r\\\") as file_:\\n\\t\\t\\tyaml_dict = yaml.safe_load(file_.read()) or {}\\n\\t\\treturn yaml_dict\\n\\texcept FileNotFoundError:\\n\\t\\treturn {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90c9c4d07932f64582049aa1e11244d\",\n \"score\": \"0.75544655\",\n \"text\": \"def load_from_file(file_path: str) -> dict:\\n return ConfigLoader.read_yaml_file(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d244ca7629b0aca6f3b479f17ee63d8d\",\n \"score\": \"0.7523064\",\n \"text\": \"def load_yaml_as_dict(filepath):\\n with open(filepath) as open_file:\\n yaml_dict = yaml.safe_load(open_file)\\n if yaml_dict:\\n return yaml_dict\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc2bfac497b5809ed22279adb440c64\",\n \"score\": \"0.75091386\",\n \"text\": \"def load_yaml(yaml_path: Path) -> dict:\\n with yaml_path.open(\\\"r\\\") as file_open:\\n yaml_dict = load_string(file_open)\\n return yaml_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da293a24b72636bcc89588e97c08b3c4\",\n \"score\": \"0.74767953\",\n \"text\": \"def read_config(config_path: str) -> Dict:\\n fin = open(config_path, encoding=\\\"utf-8\\\")\\n data = yaml.load(fin, Loader=yaml.FullLoader)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9cfc5d9691ff8cb27f59ee6bd550de\",\n \"score\": \"0.7476096\",\n \"text\": \"def read_yaml(filepath):\\n with open(filepath, encoding=\\\"utf-8\\\") as stream:\\n data = yaml.safe_load(stream)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9012f524e8d976051cc1ae6e401702\",\n \"score\": \"0.7447727\",\n \"text\": \"def get_dict_from_yml(self, filename):\\n\\n fid = open(os.path.join(RESOURCE_PATH, filename), 'r')\\n result = yaml.load(fid)\\n fid.close()\\n\\n if result is None:\\n raise SampleException('dict is None')\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ff90d4e22722a8dc0ad6d38b3156ba\",\n \"score\": \"0.7424501\",\n \"text\": \"def read_yaml(self, filename):\\n with open(filename, 'r') as f:\\n self.__dict.update(yaml.load(f, Loader=yaml.SafeLoader))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8489a232c50ac8573bf7977bddc788e9\",\n \"score\": \"0.7371884\",\n \"text\": \"def _load_yaml_file(yaml_file: Text) -> Dict:\\n with open(yaml_file, mode='rb') as stream:\\n try:\\n yaml_content = yaml.load(stream)\\n except yaml.YAMLError as ex:\\n err_msg = f\\\"YAMLError:\\\\nfile:{yaml_file}\\\\nerror:{ex}\\\"\\n logger.error(err_msg)\\n raise FileNotFoundError\\n\\n return yaml_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a2694ce54b98561b94c2485500060d\",\n \"score\": \"0.7356315\",\n \"text\": \"def read(self):\\n import yaml\\n from os.path import exists\\n if exists(self.filename):\\n with lock:\\n with open(self.filename) as handle:\\n try:\\n data = yaml.safe_load(handle.read()) # (2)\\n return data\\n except yaml.YAMLError:\\n return {} # (3)\\n else:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e90d619ce45de415664844151580d34\",\n \"score\": \"0.7290625\",\n \"text\": \"def read_config_file(path: str) -> dict:\\n if not os.path.isfile(path):\\n raise FileNotFoundError('The config file is missing.')\\n\\n with open(path, 'r') as config_file:\\n return yaml.safe_load(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790760830fd21d58d1c8608f2e0b429c\",\n \"score\": \"0.7272207\",\n \"text\": \"def yaml2dict(yaml_path: str):\\n assert yaml_path.endswith('.yml') or yaml_path.endswith('.yaml')\\n\\n with open(yaml_path) as f:\\n doc = yaml.full_load(f)\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602cea88b2a2a8ea12f0a60c69981925\",\n \"score\": \"0.72694516\",\n \"text\": \"def read_yaml(yaml_file):\\n try:\\n # Read the package yaml file\\n yaml = YAML()\\n yaml.explicit_start = True\\n yaml.indent(mapping=3)\\n yaml.preserve_quotes = True # not necessary for your current input\\n\\n with open(yaml_file) as fp:\\n data = yaml.load(fp)\\n yaml.dump(data, sys.stdout)\\n fp.close()\\n except (IOError, ValueError):\\n # Log error and raise exception if package yaml can't be read.\\n error_msg = '{1}\\\\nError loading {0}\\\\n'.format(yaml_file, traceback.format_exc())\\n LOG.error(\\\"Reading the vars.yml file failed with \\\" + error_msg)\\n raise ValueError(error_msg)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc5bb9b77032e9b6517e1ec671d4b2c\",\n \"score\": \"0.7246916\",\n \"text\": \"def read_configs(infile):\\n cfg = {}\\n with open(infile) as hdl:\\n cfg = yaml.safe_load(hdl)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb628323b2fd268185eac64e8c49254\",\n \"score\": \"0.71985924\",\n \"text\": \"def yaml_to_dict(file_path):\\r\\n loaded_file = {}\\r\\n try:\\r\\n with open(file_path, encoding=\\\"utf-8\\\") as file:\\r\\n loaded_file = yaml.safe_load(file.read())\\r\\n except Exception as error:\\r\\n loaded_file = {'erreur': error}\\r\\n\\r\\n return loaded_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b0d01cdc3c125eb9b9bf1dcee9a648\",\n \"score\": \"0.7184562\",\n \"text\": \"def load():\\n\\n path = pathlib.Path(__file__).parent.absolute()\\n file = f\\\"{path}/config.yml\\\"\\n\\n config = open(file)\\n\\n data = yaml.load(config, Loader=yaml.FullLoader)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70376f3d523515e59415ce81a1321969\",\n \"score\": \"0.71795964\",\n \"text\": \"def load_yaml_file(filename):\\n with open(filename) as f:\\n contents = yaml.load(f, Loader=yaml.FullLoader)\\n return contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332087c78a3ca5842f472f20be81c50a\",\n \"score\": \"0.7174574\",\n \"text\": \"def yml_to_dict(yml_file_path):\\n yml_path = Path(yml_file_path).resolve()\\n\\n if not yml_path.is_file():\\n return None\\n\\n yml_dict = None\\n with open(yml_path, encoding='utf-8') as file:\\n yml_dict = yaml.load(file, Loader=yaml.BaseLoader)\\n\\n return yml_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b9b5c844f40921f195f94f1d278401\",\n \"score\": \"0.71663755\",\n \"text\": \"def load_yaml_file(self, yaml_file):\\r\\n self.called_tools.append('load_yaml_file')\\r\\n\\r\\n try:\\r\\n with open(yaml_file, 'r') as file:\\r\\n dict_result = yaml.safe_load(file.read())\\r\\n except FileNotFoundError as error:\\r\\n raise RbkcliException.ToolsError(error)\\r\\n\\r\\n return dict_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebe27a2e3edec8b10295bea54ef0a149\",\n \"score\": \"0.7148037\",\n \"text\": \"def load_yaml(filename):\\n try:\\n with open(filename, 'r') as stream:\\n data = yaml.load(stream)\\n except:\\n print(\\\"ERROR: Supplied configuration file {} is not a properly-formatted YAML file.\\\".format(filename))\\n sys.exit(1)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b32003d2fa390b8636f3bf1dfefba9c\",\n \"score\": \"0.7131626\",\n \"text\": \"def load_yaml(file_name: str = None, resource: str = None, package: str = None) -> dict:\\n #\\n if file_name is not None:\\n with open(file_name, 'r', encoding='utf-8') as f:\\n yaml_string = f.read()\\n else:\\n with importlib.resources.open_text(package, resource) as r:\\n yaml_string = r.read()\\n\\n the_map = yaml.load(yaml_string)\\n\\n if not isinstance(the_map, dict):\\n raise YcError('Yaml source should map to a dictionary {}'.format(the_map))\\n\\n return the_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fcdf6416ddd6fa3b6673c7db21c1009\",\n \"score\": \"0.7130792\",\n \"text\": \"def read_config():\\n\\n with open(\\\"config.yml\\\", 'r') as ymlfile:\\n return yaml.load(ymlfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f420f1f42421f860e2269cba4cb47eb\",\n \"score\": \"0.7118985\",\n \"text\": \"def load_yaml(file_path):\\n with open(file_path) as fin:\\n content = yaml.load(fin, Loader=yaml.FullLoader)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20168970028563355ad391b7947f981c\",\n \"score\": \"0.70954406\",\n \"text\": \"def read_yaml_file(file_name, validate) -> Dict:\\n print(cyan(\\\"Reading\\\"),\\n cyan(file_name, bold=True))\\n\\n with open(file_name, 'r', encoding='utf-8') as f:\\n content = f.read()\\n\\n if not validate:\\n return yaml.safe_load(content)\\n\\n if \\\"REDACTED\\\" in content or \\\"DATA+OMITTED\\\" in content:\\n print(red(\\\"REDACTED\\\", bold=True),\\n red(\\\"or\\\"),\\n red(\\\"DATA+OMITTED\\\", bold=True),\\n red(\\\"found in\\\"),\\n red(file_name, bold=True))\\n print(red(\\\"Ensure the context was dumped with\\\"),\\n red(\\\"--raw\\\", bold=True))\\n print(red(\\\"i.e.\\\"),\\n red(\\\"kubectl config view --raw\\\", bold=True))\\n sys.exit(3)\\n\\n return yaml.safe_load(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f783468dc43536058f6abc776bcbc61\",\n \"score\": \"0.70877725\",\n \"text\": \"def read_yaml(file_path):\\n\\n with open(os.path.join(DATA_DIRECTORY, file_path), 'r') as yaml_file:\\n try:\\n return yaml.safe_load(yaml_file)\\n except yaml.YAMLError as err:\\n print(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008677bef962fc7204fbfd587509f2fa\",\n \"score\": \"0.7087709\",\n \"text\": \"def read_config():\\n with open('config.yaml') as file:\\n config = yaml.load(file, Loader=yaml.FullLoader)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f29fd9318abaacd45e347387ef722e61\",\n \"score\": \"0.7084344\",\n \"text\": \"def read_yaml_file(filepath):\\n data = None\\n if os.path.exists(filepath):\\n with open(filepath, 'r') as f:\\n data = yaml.safe_load(f)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a395000be05284e9e246b423c37dd1df\",\n \"score\": \"0.7077685\",\n \"text\": \"def load_yaml(yaml_file):\\n f = open(yaml_file)\\n y = f.read()\\n return yaml.load(y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71520d6baa40d99825c26fc4bd4fd1ee\",\n \"score\": \"0.707119\",\n \"text\": \"def load_yaml_vars(infile):\\n with open(infile, 'r') as f:\\n var_dict = yaml.safe_load(f)\\n\\n return(var_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba063d815957a627e73de5c5f2abed6\",\n \"score\": \"0.7046259\",\n \"text\": \"def load_yaml(file_path):\\n content = None\\n with open(file_path, 'r') as f:\\n content = yaml.safe_load(f)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4503e32485f48f52b481caceb38932a\",\n \"score\": \"0.7043907\",\n \"text\": \"def deserialize(self, file):\\n\\t\\treturn yaml.load(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89efa095ddf3aa6a2aa7b6439b1fdb19\",\n \"score\": \"0.70262045\",\n \"text\": \"def load_yaml(filepath):\\n\\n with open(filepath) as f:\\n data = yaml.load(f, Loader=Loader)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8cb5e9c2c36f4344a274829d7fc9c6\",\n \"score\": \"0.7002157\",\n \"text\": \"def parse_config(config_file):\\n if config_file is None:\\n return {}\\n return yaml.load(open(config_file, 'r').read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639c73f9caf25350e6628de452e105fe\",\n \"score\": \"0.70004666\",\n \"text\": \"def yaml_to_dict(inp_yaml):\\n with open(inp_yaml, 'r') as stream:\\n try:\\n out = yaml.load(stream)\\n except yaml.YAMLError as exc:\\n print(exc)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81dae1b4002d25dcf7af7b9b614a1962\",\n \"score\": \"0.6971694\",\n \"text\": \"def loads_yaml(file_content):\\n return load_yaml(file_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca90614ada944cc98130b9625d66842\",\n \"score\": \"0.6969649\",\n \"text\": \"def load_yaml(filepath):\\n with open(filepath) as fp:\\n r = yaml.load(fp)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9aaa315a5bf203d4b50a8c83f49cb9\",\n \"score\": \"0.69647294\",\n \"text\": \"def _read_logging_config() -> dict:\\n with open(constants.FILE_YAML, 'rt') as f:\\n return yaml.safe_load(f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f082bbc1d2f47367bd58412c68ec132\",\n \"score\": \"0.69617224\",\n \"text\": \"def read_from_configuration(file_name):\\n\\n with open(file_name, 'r') as yaml_fobj:\\n yaml_content = yaml.load(yaml_fobj)\\n\\n return yaml_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d060f9a090f6cad754bbf1db160e5616\",\n \"score\": \"0.69449025\",\n \"text\": \"def read_yaml_file(self, file_name):\\n with open(os.path.join(self.__path(), os.path.basename(file_name)), 'rt') as yamlfile:\\n return yaml.load(yamlfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58db7f999e21813cdc074493029eefd\",\n \"score\": \"0.69419944\",\n \"text\": \"def read_file(filename):\\n try:\\n with open(filename, 'r') as stream:\\n data_loaded = yaml.load(stream)\\n\\n return data_loaded\\n except IOError:\\n print(\\\"Could not read file:\\\", filename)\\n except TypeError:\\n print('something went wrong')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462a4884fe26e838ab1ab35048376fe3\",\n \"score\": \"0.69201773\",\n \"text\": \"def load_yaml(filename):\\n with open(filename) as file:\\n return yaml.load(file, Loader=yaml.FullLoader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd659a560ec55f78f86a4fd7d73ab3a\",\n \"score\": \"0.6917695\",\n \"text\": \"def LoadYaml(filepath):\\n with open(filepath, 'r') as f:\\n doc = yaml.load(f)\\n logger.debug(doc)\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73153175692f5e710eb384683634c388\",\n \"score\": \"0.69048584\",\n \"text\": \"def _read_yaml_file(path_yaml: Path) -> Any:\\n # TODO: modify so that mkdocs.yml can be read, but Python won't be executed...\\n\\n # Based on: https://github.com/yaml/pyyaml/issues/86#issuecomment-380252434\\n yaml.add_multi_constructor('', lambda _loader, _suffix, _node: None)\\n yaml.add_multi_constructor('!', lambda _loader, _suffix, _node: None)\\n yaml.add_multi_constructor('!!', lambda _loader, _suffix, _node: None)\\n try:\\n return yaml.unsafe_load(path_yaml.read_text())\\n except (FileNotFoundError, KeyError) as err: # pragma: no cover\\n logger.warning(f'Unexpected error reading the {path_yaml.name} file ({path_yaml}): {err}')\\n return {}\\n except yaml.constructor.ConstructorError:\\n logger.exception('Warning: burying poorly handled yaml error')\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26218e3ed4270379570ce64b9c93c2de\",\n \"score\": \"0.68852204\",\n \"text\": \"def _convert_yaml_to_dict(self, inputfilename):\\n yamldict = {}\\n\\n try:\\n with open(inputfilename, \\\"r\\\", encoding=\\\"utf-8\\\") as yaml_file:\\n yamldict = yaml.load(yaml_file)\\n except yaml.YAMLError as exc:\\n if hasattr(exc, 'problem_mark'):\\n mark = exc.problem_mark\\n print(\\\"Error position: (%s:%s)\\\" % (mark.line+1, mark.column+1))\\n else:\\n errmsg = \\\"Unexpected error while parsing the YAML:\\\", sys.exc_info()[1]\\n logging.error(errmsg)\\n raise Exception(errmsg)\\n\\n return yamldict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d1c93a280b3a8a9e7bb7bf4678cf8dd\",\n \"score\": \"0.68692064\",\n \"text\": \"def read_config(file_name):\\n with open(file_name) as fin:\\n config = yaml.safe_load(fin.read())\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d1c93a280b3a8a9e7bb7bf4678cf8dd\",\n \"score\": \"0.68692064\",\n \"text\": \"def read_config(file_name):\\n with open(file_name) as fin:\\n config = yaml.safe_load(fin.read())\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574e7ebce2cb9c06587fbf0d373cafbf\",\n \"score\": \"0.6854796\",\n \"text\": \"def parse(self):\\n with open(self.file_path, \\\"r\\\") as f:\\n self.content = yaml.safe_load(f)\\n return self.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe9db23695d46d3b035ab00c45f23bad\",\n \"score\": \"0.6844314\",\n \"text\": \"def load_config(config_path: str) -> dict:\\n with open(config_path, 'r') as f:\\n config = yaml.load(f, Loader=yaml.FullLoader)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c85e70119dfcb0a8873619009951afc\",\n \"score\": \"0.6834853\",\n \"text\": \"def parse_config(config_file):\\n return yaml.load(open(config_file), Loader=yaml.FullLoader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fdd5c3e73bdb01524d08f4bd6c817f\",\n \"score\": \"0.6834625\",\n \"text\": \"def load_yaml(self, name):\\n y = open(name, 'r')\\n yml = yaml.safe_load(y)\\n y.close()\\n return yml\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6772370f77b55905d765893698ef7963\",\n \"score\": \"0.6832744\",\n \"text\": \"def read_yaml_file(pfilename: str) -> dict:\\n # read the yaml file \\\"profiles.yaml\\\" for the list of profiles using pyyaml\\n with open(pfilename, 'r') as stream:\\n # try to load the yaml file\\n try:\\n profiles = yaml.safe_load(stream)\\n # deal with a yaml error (for now just raise the error)\\n except yaml.YAMLError as exc:\\n raise exc\\n # return the profiles\\n return profiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab0120f5a73ccb19df4c8b1387206e4\",\n \"score\": \"0.678199\",\n \"text\": \"def file_loads_yaml(file_path):\\n file = None\\n try:\\n file = open(file_path)\\n return loads_yaml(file)\\n finally:\\n if file:\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e132a48c6060c76bc9aeba20b44c1f\",\n \"score\": \"0.6781286\",\n \"text\": \"def load_yaml(file_path):\\n try:\\n with open(file_path, 'r') as F:\\n return yaml.load(F)\\n except ImportError:\\n print(\\\"import yaml is required\\\")\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf751a783676e439579be6e6addda0ab\",\n \"score\": \"0.67756873\",\n \"text\": \"def read(self):\\n with open(self.file_name) as f:\\n d = yaml.load(f)\\n if d:\\n self.update(d)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f9b6600fdda61f8fe47d47a1ca1770\",\n \"score\": \"0.6756012\",\n \"text\": \"def load_yaml(path):\\n stream = load_file(path)\\n return yaml.load(stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d7df142abd8c46cfc36f595d346428\",\n \"score\": \"0.6753374\",\n \"text\": \"def __load_data(self):\\n try:\\n fh = open(self.ifile)\\n data = yaml.load(fh)\\n fh.close()\\n return data\\n except Exception, e:\\n LOGGER.debug(str(e))\\n LOGGER.error(_(\\\"You need to specify an existing file\\\"))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef4e3d6673014bbc9a3b60ded604dd5\",\n \"score\": \"0.6726021\",\n \"text\": \"def read_config(filename):\\n \\n with open(filename, 'r') as stream:\\n try:\\n config = yaml.load(stream)\\n return config\\n except yaml.YAMLError as exc:\\n print(exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7042dbf0f7c1c4c75f0975a0cfb561a2\",\n \"score\": \"0.6705862\",\n \"text\": \"def read_config_file(self):\\n\\n with open(self.file_path) as stream:\\n LOG.info(\\\"Parsing pod file: %s\\\", self.file_path)\\n cfg = yaml.load(stream)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4740d435f78765f04a09030303fa7493\",\n \"score\": \"0.6697818\",\n \"text\": \"def parse_settings():\\n\\n with open(\\\"settings.yaml\\\", \\\"r\\\") as data:\\n settings = yaml.safe_load(data)\\n return settings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24fd0ede51d674cad7853f1bdcf7a634\",\n \"score\": \"0.6696523\",\n \"text\": \"def config(self) -> Dict[str, Any]:\\n try:\\n with open(self.directory.joinpath(\\\"wordgoal.yml\\\"), \\\"r\\\") as stream:\\n return cast(Dict[str, Any], safe_load(stream))\\n except FileNotFoundError:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35eca6d438cdfaf4e2d09aea7f04913e\",\n \"score\": \"0.66908723\",\n \"text\": \"def load_config_data() -> map:\\n\\n cfg_file = get_config_file()\\n\\n with open(cfg_file, 'r') as cfg:\\n data = yaml.safe_load(cfg)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d2fe01c6db0321e4f11df232b52967\",\n \"score\": \"0.6690024\",\n \"text\": \"def read_from_yaml(yml_file):\\n logging.info(\\\"reading yaml file: %s\\\" % yml_file)\\n sg_dict = {}\\n yaml_stream = open(yml_file, \\\"r\\\")\\n security_groups = yaml.load_all(yaml_stream)\\n for security_group in security_groups:\\n sg_dict = {sg_name: sg_attributes for (sg_name, sg_attributes) in security_group.items()}\\n yaml_stream.close()\\n logging.debug(\\\"sg_dict: %s\\\" % sg_dict)\\n return sg_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aabb8906047d24d35e80eea24831fd7\",\n \"score\": \"0.6687082\",\n \"text\": \"def _read_yaml(path):\\n with open(path, \\\"r\\\") as f:\\n try:\\n hist = yaml.safe_load(f)\\n except yaml.YAMLError as exc:\\n print(exc)\\n return hist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ccc026172ef9e45953c715dab5b4f4c\",\n \"score\": \"0.66453356\",\n \"text\": \"def load_yaml_file(file_path: str):\\n with codecs.open(file_path, \\\"r\\\") as f:\\n return yaml.safe_load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3595b2f0fc1fd5cd01d4ebe90d31ed84\",\n \"score\": \"0.6639965\",\n \"text\": \"def parse_config_file(self, filename):\\n self.logger.info(f'Loading config file: {filename}')\\n with open(filename, 'r') as f:\\n return yaml.load(f, Loader=yaml.FullLoader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e06741d1c1425c53bbb9df46dd1d64\",\n \"score\": \"0.6638672\",\n \"text\": \"def get_config_yaml():\\n return yaml.load(open('test/test_config.yaml', 'r')) or {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17bb0ed03c314efc6cda793b66976f9\",\n \"score\": \"0.6632735\",\n \"text\": \"def get_config() -> Dict[str, str]:\\n with open(\\\"default_conf.yaml\\\") as file:\\n config = yaml.safe_load(file)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed8fb9d0b5ea423d2e66c861c3677e6\",\n \"score\": \"0.66229504\",\n \"text\": \"def read_config_file(\\n filename: Union[Path, Text], reader_type: Union[Text, List[Text]] = \\\"safe\\\"\\n) -> Dict[Text, Any]:\\n return read_validated_yaml(filename, CONFIG_SCHEMA_FILE, reader_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb6c2a851cdb4851dc0770d4b962abdd\",\n \"score\": \"0.66061336\",\n \"text\": \"def parse_config(self, path: str) -> dict:\\n try:\\n with Path(path).open(\\\"r\\\") as file:\\n return yaml.safe_load(file)\\n except (FileNotFoundError, ParserError) as error:\\n raise argparse.ArgumentError(self, error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495888e4ded497bed181229b7a4d1daf\",\n \"score\": \"0.66040313\",\n \"text\": \"def read_config(config_path):\\n with open(config_path, 'r') as ymlfile:\\n return yaml.load(ymlfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375864ae5e8a3c862bb1bde33d3fb72d\",\n \"score\": \"0.65909624\",\n \"text\": \"def _load_file(self, file: TextIO) -> Dict[str, Any]:\\n import toml\\n\\n config_dict = toml.load(file)\\n return cast(Dict[str, Any], config_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5421484bebffc1d256fd8192b65cd4aa\",\n \"score\": \"0.6590941\",\n \"text\": \"def load(cls, filename):\\n f = open(filename)\\n result = cls._from_yaml_dict(yaml.load(f))\\n f.close()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b3f7918824d693e8199d799b9586d1\",\n \"score\": \"0.6587401\",\n \"text\": \"def get_config(conf_file):\\n from easydict import EasyDict as edict\\n\\n with open(conf_file, \\\"r\\\") as file_descriptor:\\n data = yaml.load(file_descriptor)\\n\\n # convert the data into an easyDictionary\\n return edict(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc35c89e53387117a975f2555fb84275\",\n \"score\": \"0.6587145\",\n \"text\": \"def read_yaml_args_file(args_file_path: Path) -> Dict[str, Union[str, int, bool, List, Dict, datetime]]:\\r\\n with open(args_file_path, 'r') as f:\\r\\n return remove_empty_values(yaml.safe_load(f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea3fa74329ace5701188428b286b852\",\n \"score\": \"0.65742385\",\n \"text\": \"def load(self, path):\\n context = {}\\n with codecs.open(self.source + path, 'r', encoding='utf-8') as f:\\n content = f.read()\\n result = re.search(r'^(---\\\\s*\\\\n.*?\\\\n?)^(---\\\\s*$\\\\n?)', content, re.DOTALL|re.MULTILINE)\\n if result:\\n front_matter = result.group(1)\\n context = yaml.load(front_matter)\\n content = content[result.end(0):len(content)]\\n return content, context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7fb17e84bc1aa081e48b145f3352070\",\n \"score\": \"0.65531045\",\n \"text\": \"def load_config(config_path: str) -> dict:\\n with fsspec.open(config_path, mode=\\\"r\\\") as f:\\n config = yaml.safe_load(f)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02423169ca9a96fafd9e3223897bafbc\",\n \"score\": \"0.65272075\",\n \"text\": \"def load(name):\\n if not os.path.isfile(name):\\n raise ValueError(f\\\"File {name} does not exists.\\\")\\n return yaml.load(open(name), Loader=yaml.Loader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf763e99b8b88c99e5f38c6382cd189\",\n \"score\": \"0.6517302\",\n \"text\": \"def load_config(filepath: str) -> Dict[str, Any]:\\n with open(filepath, mode=\\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n return parse(file.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8316c3da86f0e10483bb2849fceeccf2\",\n \"score\": \"0.6498666\",\n \"text\": \"def load_yaml(fname):\\n with open(fname, 'r') as outfile:\\n yaml_file = yaml.load(outfile) # Loader=yaml.FullLoader\\n return yaml_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7202b02674f170a804d644131cc81801\",\n \"score\": \"0.6497122\",\n \"text\": \"def load_yaml(file_path):\\n with open(file_path, 'r') as usernames_yaml:\\n try:\\n data = yaml.safe_load(usernames_yaml)\\n except yaml.YAMLError as exc:\\n print(exc)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"676d4183402646b22e9dfadaab3616bd\",\n \"score\": \"0.6489465\",\n \"text\": \"def load_yaml(path):\\n with open(path, 'r', encoding='utf-8') as stream:\\n try:\\n res = yaml.safe_load(stream)\\n except yaml.YAMLError as exc:\\n print(exc)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5805cda0e77846a17d904332f60f18f6\",\n \"score\": \"0.64880127\",\n \"text\": \"def read_dict_file(file):\\n with open(file) as data:\\n data = yaml.load(data, Loader=yaml.FullLoader)\\n email_list = []\\n server_list = []\\n subject_list = []\\n for email in data['emails']:\\n email_list.append(email)\\n for server in data['servers']:\\n server_list.append(server)\\n for subject in data['subjects']:\\n subject_list.append(subject)\\n return email_list, server_list, subject_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0404260be401cf93a6ae26e36d48a8\",\n \"score\": \"0.6484009\",\n \"text\": \"def get_config():\\n with open('config.yaml', 'r') as file:\\n config = yaml.load(file, Loader=yaml.FullLoader)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a4ec82c0b22b0a1a19ce4d6b0c9e39\",\n \"score\": \"0.64752114\",\n \"text\": \"def load_yaml_config(config_file):\\n if type(config_file) is file:\\n CONFIG.update(yaml.load(config_file) or {})\\n return CONFIG\\n else:\\n try:\\n with open(config_file, 'r') as f:\\n content = yaml.load(f)\\n CONFIG.update(content)\\n return content\\n except IOError as e:\\n e.message = \\\"Could not open configuration file \\\\\\\"{}\\\\\\\".\\\".format(config_file)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c110977a05353c45aeaa6c4ffaf0838\",\n \"score\": \"0.64426583\",\n \"text\": \"def read_yaml_file(\\n filename: Union[Text, Path], reader_type: Union[Text, List[Text]] = \\\"safe\\\"\\n) -> Union[List[Any], Dict[Text, Any]]:\\n try:\\n return read_yaml(read_file(filename, DEFAULT_ENCODING), reader_type)\\n except (YAMLError, DuplicateKeyError) as e:\\n raise YamlSyntaxException(filename, e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8bc7e7ecaca290f80b302ed99cc16cc\",\n \"score\": \"0.6427357\",\n \"text\": \"def _parse(filename):\\n with open(filename, \\\"r\\\") as fp:\\n config_str = jinja2.Template(fp.read()).render()\\n config = yaml.load(config_str, Loader=yaml.Loader)\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c3c89cf36bd2cb45f22fc6e2efb06b\",\n \"score\": \"0.64152056\",\n \"text\": \"def read_model_configuration(filename: Union[Path, Text]) -> Dict[Text, Any]:\\n return read_validated_yaml(filename, MODEL_CONFIG_SCHEMA_FILE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2ca8dbb8bff7743c63d0fe6b161bd6\",\n \"score\": \"0.6381088\",\n \"text\": \"def readYML():\\n with open(ROAinfoFile, 'r') as infile:\\n ROAinfo = yaml.load(infile)\\n return ROAinfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4750262a580fb7555a91999cf8126cf\",\n \"score\": \"0.63790244\",\n \"text\": \"def load_config(path):\\r\\n return yaml.load(open('config.yaml', 'r'), Loader=yaml.SafeLoader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae2b4043978441b19ed6a43cb5833c7\",\n \"score\": \"0.6373652\",\n \"text\": \"def load(filename):\\n try:\\n with open(filename, 'r') as f:\\n return yaml.safe_load(f)\\n except yaml.scanner.ScannerError as e:\\t# For errors parsing schema.yaml\\n logging.error(\\\"YAML errror: %s \\\", e)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb56df60fa1252478b6cdfb33bd6191\",\n \"score\": \"0.63606393\",\n \"text\": \"def _load_file(path):\\n with open(path) as f:\\n return yaml.safe_load(f)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994472,"cells":{"query_id":{"kind":"string","value":"2c0be3bdb13395e5faec089c68c32cc0"},"query":{"kind":"string","value":"Loop for receiving messages from the server and calling the given handler."},"positive_passages":{"kind":"list like","value":[{"docid":"3457228a51d88c8edcc897239a313816","score":"0.64994836","text":"async def handle(self):\n\n # wait until the socket object is created\n while self.__socket is None:\n await asyncio.sleep(0)\n\n # wait until the socket is ready\n await self.__socket.ensure_open()\n\n # loop while socket is open\n while not self.__socket.closed:\n try:\n # wait to receive data from the socket\n data = await self.__socket.recv()\n\n # if data is None exit loop\n if data is None:\n break\n\n # decode packet into event and message\n event, message = self.__encoder.decode(data)\n\n # check for parsing error\n if event is not None:\n try:\n # run the event's handler and get the event and message for the response\n event, response = await self.__call_event_handler(event, message)\n\n # if the response is not none send it\n if response is not None:\n await self.send(event, message)\n except Exception as e:\n self.logger.error(\"Exception when executing the handler for the '\" + event\n + \"' event. Error: \" + str(e))\n except websockets.ConnectionClosedError as e:\n self.logger.error(\"Connection closed: \" + str(e))\n self.logger.info(\"Disconnected.\")","title":""}],"string":"[\n {\n \"docid\": \"3457228a51d88c8edcc897239a313816\",\n \"score\": \"0.64994836\",\n \"text\": \"async def handle(self):\\n\\n # wait until the socket object is created\\n while self.__socket is None:\\n await asyncio.sleep(0)\\n\\n # wait until the socket is ready\\n await self.__socket.ensure_open()\\n\\n # loop while socket is open\\n while not self.__socket.closed:\\n try:\\n # wait to receive data from the socket\\n data = await self.__socket.recv()\\n\\n # if data is None exit loop\\n if data is None:\\n break\\n\\n # decode packet into event and message\\n event, message = self.__encoder.decode(data)\\n\\n # check for parsing error\\n if event is not None:\\n try:\\n # run the event's handler and get the event and message for the response\\n event, response = await self.__call_event_handler(event, message)\\n\\n # if the response is not none send it\\n if response is not None:\\n await self.send(event, message)\\n except Exception as e:\\n self.logger.error(\\\"Exception when executing the handler for the '\\\" + event\\n + \\\"' event. Error: \\\" + str(e))\\n except websockets.ConnectionClosedError as e:\\n self.logger.error(\\\"Connection closed: \\\" + str(e))\\n self.logger.info(\\\"Disconnected.\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1f6eb6286e8f5b57133d53eb6f7141b0","score":"0.73473716","text":"def handle(self):\n sock = self.request\n response = \"\"\n\n while True:\n try:\n objects = _read_objects(sock)\n response = server._callback(objects)\n except ConnectionClosed:\n return\n except ConnectionResetError:\n sock.close()\n except Exception:\n sock.close()\n\n _write_objects(sock, response)","title":""},{"docid":"095dffc9db44d375b4a219112a0950f3","score":"0.7273748","text":"def run(self):\n\n while(True):\n message = Replyer.receive()\n method, params = Marshaller.unmarshall(message)\n response = getattr(self.server, method)(*params)\n Replyer.send(Marshaller.marshall(response))","title":""},{"docid":"b6619dd6e3df7946362a387d72331bfa","score":"0.7205196","text":"def run_loop(self):\n\n while self.pending_calls:\n args = self.pending_calls.dequeue()\n\n method = args.get('method')\n peer_id = args.get('peer_id')\n raw_cmd = args.get('raw_cmd')\n fn = args.get('fn')\n\n if method == 'send':\n self._send_message(peer_id, raw_cmd, fn)\n elif method in ['response', 'call']:\n fn()\n else:\n raise ValueError('method %s undefined' % (method))","title":""},{"docid":"e18f2e8211d16ba1b9f8204e458c701f","score":"0.72048277","text":"def receive_forever(self):\n\n while True:\n body, addr = self.receive()\n if body == None and addr == None:\n # There was an error with the received message\n continue\n\n if body['type'] not in self.handlers:\n # Provided type is not a registered handler\n logging.debug('Invalid message type', body)\n self.send(Error.json(Error.BAD_REQ, 'invalid message type'), addr)\n continue\n\n try:\n self.handlers[body['type']](self, body, addr)\n except CloseServer:\n self.close()\n return\n except Exception as e:\n self.send(Error.json(Error.SERVER_ERR, str(e)), addr)","title":""},{"docid":"be4be43c27814c8c9e804ad8362c3dcf","score":"0.71238244","text":"def run(self):\n self.connect()\n self.action_helo()\n i = 200\n while True:\n try:\n message = self.receive_message()\n except socket.timeout:\n # no data from server\n log.debug(\"timeout waiting for message\")\n if self.__terminate:\n return\n else:\n continue\n self.handle_message(message)\n i = i - 1\n if i < 0:\n break\n self.action_bye()","title":""},{"docid":"5ca1b4f646aad3a2d2ff3da422d50635","score":"0.70701617","text":"def run_loop(self):\n\n while(1):\n try:\n payload = self._recv()\n except UnicodeError as e:\n logging.warning(f\"Failed to decode : {e.reason}\")\n continue\n except KeyboardInterrupt:\n if self._closing:\n logging.warning(\"Forced shutting down now.\")\n os._exit(-1)\n else:\n logging.info(\"Received KeyboardInterrupt. Ctrl+C again to force shutting down.\")\n self._closing = True\n continue\n\n send_id, recv_id, msg = self._parse_msg(payload)\n if msg is None:\n continue\n else:\n result, cont = self._execute_cmd(msg.cmd, msg.data)\n if not cont:\n logging.info(\"Shutting down.\")\n break\n\n # Currently not expecting to invoke any callback on ModelServer\n # side, so second parameter 0\n self._send_msg(0, send_id, result)","title":""},{"docid":"7a5823264048390dfb119e082e5e01a8","score":"0.7060128","text":"def run(self):\n channels = self.channel_backend.registry.all_channel_names()\n while True:\n channel, message = self.channel_backend.receive_many_blocking(channels)\n # Handle the message\n consumer = self.channel_backend.registry.consumer_for_channel(channel)\n if self.callback:\n self.callback(channel, message)\n consumer(channel=channel, **message)","title":""},{"docid":"ad809a85e7fe5fdf9cc756ed66e03a2b","score":"0.7050459","text":"async def read(ws: websockets.WebSocketClientProtocol, handler: Callable[[AnyStr], Coroutine]):\n async for message in ws:\n await handler(message)","title":""},{"docid":"87c83765aef7e6278fc86a7d6545460b","score":"0.7024942","text":"def run(self):\n for data in self.__iter_data():\n self.on_message(data)","title":""},{"docid":"01e31593ed2d9f13a55251cbf141d500","score":"0.6971264","text":"def _process_loop(self):\n for msg in self._event_queue:\n self._process_incoming_message(msg)","title":""},{"docid":"6f96f5742b2606027a53eab8f1987ba5","score":"0.6967414","text":"def __callbackHandler(self):\n while True:\n callback, msg_obj = SessionManager.callback_queue.get()\n callback(msg_obj)","title":""},{"docid":"2b0678be59c9ec58a97fe2f4b6b4a03c","score":"0.6963102","text":"def run(self):\r\n while True:\r\n soc, address = self.queue.get()\r\n self.handle_client(soc, address)","title":""},{"docid":"6e8520162fe27e75d7d25d3a012960bd","score":"0.6959939","text":"def __message_loop(self):\n\n # loop until we're told to stop\n while self.__parsing:\n # receive message data from the server and pass it along to the\n # world model as-is. the world model parses it and stores it within\n # itself for perusal at our leisure.\n raw_msg = self.__sock.recv()\n\n msg_type = self.msg_handler.handle_message(raw_msg)\n if msg_type is not None:\n # we send commands all at once every cycle, ie. whenever a\n # 'sense_body' command is received\n if msg_type == handler.ActionHandler.CommandType.SENSE_BODY:\n self.__send_commands = True","title":""},{"docid":"34813cd5814096560b6b405420741d07","score":"0.69063914","text":"def read_commands_loop(self):\n while True:\n msg = self.autopilot.recv_msg()\n if msg:\n if self.debug: print(f\"Receive: {msg}\")\n if msg.id in self.handlers:\n handle_thread = threading.Thread(\n target=self.handlers[msg.id],\n args=(msg,)\n )\n handle_thread.start()\n time.sleep(0.1)","title":""},{"docid":"ef9902643a343dfa8108fa417f34828a","score":"0.69027877","text":"def _run(self):\n self.soc.settimeout(1)\n while self.connected and not self.shutdown:\n try:\n data = self.soc.recv(2**16)\n data_list = self._check_for_multiple(data)\n for item in data_list:\n try:\n jdata = json.loads(item.decode('utf-8'))\n func = self.handler[jdata['topic']]\n handler_thread = Thread(target=func, args=[jdata])\n handler_thread.start()\n except json.JSONDecodeError as e:\n print('\\n\\n', e)\n print('Caused by:', item)\n except socket.timeout:\n pass\n except ConnectionResetError:\n print(self, ':: Master has shut down. Stopping subscriber')\n self.shutdown = True\n self.soc.close()\n self.connected = False","title":""},{"docid":"31320fa0916e3afe50f3c0701962d3ed","score":"0.67917025","text":"def main_loop(self):\n self.log.info(\"Beginning to scan for new inbox messages...\")\n while True:\n messages = self.r.get_unread()\n for message in messages:\n if message.subject == \"username mention\":\n self.respond_to_username_mention(message)\n elif message.subject == \"edit request\":\n self.respond_to_edit_request(message)\n elif message.subject == \"delete request\":\n self.respond_to_delete_request(message)\n elif message.subject == \"user translation default request\":\n self.respond_to_user_translation_request(message)\n elif message.subject == \"subreddit translation default request\":\n self.respond_to_subreddit_translation_request(message)\n message.mark_as_read()\n sleep(30)","title":""},{"docid":"cf4f07e74bb70cb1fb46a81ed9d76c1f","score":"0.6763541","text":"def _get_received_messages(self, response_handler):\n for recvmsg in _utils.getsome(self._channel.poll_received_message):\n _TRACE(\"ECHO_RECV_MSG msgtype: %s msgbody: %s\",\n recvmsg.msgtype, recvmsg.message)\n if response_handler:\n response_handler(recvmsg.msgtype, recvmsg.message)","title":""},{"docid":"449e4b9a3ead12396edcf84b20f414f2","score":"0.67121315","text":"def run(self):\n\n from twisted.internet import reactor\n\n while True:\n item = self.queue.get()\n if item.get('json', None) is not None:\n for json in item['json']:\n if json['cmd'] == 'refresh':\n continue\n client = self.session.get(item['uid'])\n if client is None:\n continue\n if json['cmd'] == 'keepalive':\n client.updateLastAction()\n continue\n if item.get('type', None) is not None:\n client.type = item['type']\n client.addResponse(self.protocol.parse(client, json))\n if callable(item.get('callback', None)):\n reactor.callFromThread(item['callback'],\n client.getResponse())\n self.queue.task_done()","title":""},{"docid":"14942e32f6d81c388ad8dd588906da53","score":"0.6702382","text":"def run(self):\n self.setup_connection()\n while True:\n self.send_command()\n if self.socket_got_data():\n self.get_next_message()\n print('received', msg_parser.get_command_name(self.buffer))\n self.process_message()\n\n # This method for debug purpose only\n # it stop executing when all known blocks processed\n self.finish()","title":""},{"docid":"5a9b09c3ecf9efa7dda2f230ce4e63f1","score":"0.6667263","text":"def incoming_message_handler(self, running):\n while running:\n try:\n self.unicast_receive()\n except (socket.timeout, BlockingIOError):\n pass","title":""},{"docid":"4bb9672c224b43a28260ea1adb976ab2","score":"0.663664","text":"async def handle_messages(self, /) -> NoReturn:\n while True:\n message = await self.read_queue.get()\n try:\n await asyncio.to_thread(self.buffer.update, message)\n async for message in self._emit_responses(message):\n await self.write_queue.put(message)\n except (DeviceError, MessageError) as exc:\n await self.logger.error('Message handling error', exc_info=exc)","title":""},{"docid":"c83e840862c5f55eccfa0eed9019fa88","score":"0.66135174","text":"async def _receiver(self, websocket):\n async for msg in websocket:\n LOGGER.debug(F\"Received message: {msg}\")\n self.handle_message(msg)","title":""},{"docid":"cc9d143c418d7785434439c36290a3c2","score":"0.660933","text":"def run(self):\n try:\n while True:\n self.eventHandler()\n self.poller()\n except Exception as e:\n print('Error: %s' % e)\n finally:\n sys.exit(0)","title":""},{"docid":"4309b71dac67ebb1411d1fdbb9228c06","score":"0.6609241","text":"def run(self):\r\n self.serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n self.serverSocket.bind((self.hostname, self.server_port))\r\n self.serverSocket.listen(1)\r\n while not self.done:\r\n conn_socket, addr = self.serverSocket.accept()\r\n handler = ConnectionHandler(conn_socket, addr, self.timeout)\r\n handler.run()","title":""},{"docid":"de9d4db5ac2136b1e3e46e2a60fee168","score":"0.6603911","text":"def __recv_worker(self):\n while True:\n msg = self.atomic_b.wait_for_msg(None)\n self.msg_handler(msg)","title":""},{"docid":"298497e9d6b1e3fc5ad3707f984ad8f9","score":"0.660326","text":"def handler_thread(self):\n while not self.done:\n try:\n message = self.message_queue.get(timeout=5)\n self.handle_message(message)\n except Queue.Empty:\n logger.debug(\"timeout getting message\")","title":""},{"docid":"7383fe923d38aa11f8b9032277e7096d","score":"0.6590043","text":"def run(self):\n\n inputSocketList = []\n inputSocketList.append(self._clientSocket)\n\n while not self.shutdownEvent.is_set():\n readyToRead, readyToWrite, inputError = select.select(inputSocketList, [], [], self._selectTimeout)\n\n for sock in readyToRead:\n # Read a message off of the socket\n msgData = MessageHandler.recvMsg(sock)\n\n # Process the message\n if msgData is not None:\n self.__processMsg(msgData)\n else:\n break\n\n # Cleanup\n self.__shutdown()","title":""},{"docid":"b3334f97149eb2abfa7b7a794850272f","score":"0.65867937","text":"def handle(self):\n self.ip = self.client_address[0]\n self.port = self.client_address[1]\n self.connection = self.request\n\n self.user = User()\n\n # Loop that listens for messages from the client\n while True:\n\n received_string = self.connection.recv(4096)\n \n message = json.loads(received_string)\n\n request = message['request']\n content = message['content']\n\n print 'The request is: ' + request\n\n if request == 'login':\n self.handle_login(content)\n elif request == 'logout':\n self.handle_logout()\n elif request == 'msg':\n self.handle_msg(content)\n elif request == 'names':\n self.handle_names()\n elif request == 'help':\n self.handle_help()","title":""},{"docid":"75ad583343fcfaa2137aba8336b8a5b9","score":"0.6577251","text":"def _receiver(self):\n\n while self._run_receive:\n try:\n if self._something_to_receive():\n header = self._receive_header()\n if Response.is_msg_response(header):\n body = self._receive_data(header[Response.DLEN])\n self._handle_msg_response(body, header)\n\n elif AsyncMsg.is_async_msg(header):\n header = AsyncMsg.convert_to_async_header(header)\n body = self._receive_data(header[AsyncMsg.DLEN])\n self._handle_async_msg(body, header)\n else:\n raise SpheroError(\"Unknown data received from sphero. Header: {}\".format(header))\n except SpheroError:\n # TODO release all blocked threads waiting to receive data\n print \"RECEIVER CRASHED\"\n self._receiver_crashed = True\n #time.sleep(0.01)","title":""},{"docid":"0ac361cd89300750dc1cc55a1216d3c7","score":"0.6574794","text":"def listen(self):\n try:\n while 1:\n print \"Loop...\"\n # Process incoming XMPP messages.\n self.cl.Process(5)\n\n # Process shoutbox messages.\n self.process_shoutbox_messages()\n\n # Sleep before next loop iteration.\n #time.sleep(1)\n\n # Reconnect to XMPP if necessary.\n if not self.cl.isConnected():\n self.cl.reconnectAndReauth()\n\n except KeyboardInterrupt:\n print \"Exiting...\"","title":""},{"docid":"cb355fe0a9dc0d205dd475e307982807","score":"0.65730107","text":"def main_loop(irc: ssl.SSLSocket):\n while True:\n data_ch = irc.recv(1024)\n raw_message = data_ch.decode('UTF-8')\n\n for line in raw_message.splitlines():\n if line.startswith('PING :tmi.twitch.tv'):\n send_pong(irc)\n else:\n components = line.split()\n command = components[1]\n\n if command == 'PRIVMSG':\n parse_chat(irc, line)\n elif command == 'NOTICE':\n print(\"NOTICE: {}\".format(line[line.find(\":\", 1, -1):]))","title":""},{"docid":"9144766313286dc144f85b816c626ea4","score":"0.6531432","text":"def recv_loop(self):\n while True:\n recv_msg = self.s.recv(SEND_BUFFER_SIZE).decode(\"ISO-8859-1\")\n if recv_msg:\n message = self.process_received_message(recv_msg)\n sys.stdout.write(\"\\t\" + message + \"\\n\")\n sys.stdout.flush()\n else:\n os._exit(0)","title":""},{"docid":"ee538df655a759f839d5e644bbfc248c","score":"0.6527932","text":"def handle(ws):\n if ws.path == '/echo':\n sem = Semaphore()\n while True:\n logger.debug(\"loop\")\n data = ws.wait()\n logger.info(\"data {}\".format(data))\n pool.apply_async(process, args=(data, ws,sem), callback=log_result)\n\n elif ws.path == '/data':\n for i in xrange(10000):\n ws.send(\"0 %s %s\\n\" % (i, random.random()))\n gevent.sleep(0.1)\n while True:\n logger.info (\"loop\")\n data = ws.recvmsg()\n pool.apply_async(process, args=(data, ws,sem), callback=log_result)","title":""},{"docid":"87ef517931eafa99aa4220ad0ce40d23","score":"0.6526114","text":"def serverloop():\n # Create a regular internet socket (TCP/IP):\n server_socket = socket.socket()\n # Bind the socket to listen on a specific port on our computer:\n server_socket.bind((IP, PORT))\n # Begin listening on the socket, with a particular queue size:\n server_socket.listen(MAXIMUM_QUEUE_SIZE)\n\n # Do this forever (until server process is killed):\n while True:\n # Accept a connection from next client:\n # for each connection we get the socket and connection details\n (client_socket, client_ip_and_port) = server_socket.accept()\n # Process the client's request:\n respond(client_socket, client_ip_and_port)\n # Close the client connection:\n client_socket.close()","title":""},{"docid":"f5f2c9870f4ab471c1739a2915f7431a","score":"0.65247244","text":"def process_receivedMessages():\n process_receivedMessagesAlertDb=alertsdb(\"process_receivedMessages\")\n #print(\"Processing the input received from client\")\n #return \"received message from clinent:{} \\n\".format(input_str)\n while True:\n input=ClientsToServerMessages.get(block=True)\n try:\n inputJson = json.loads(input)\n functionname=inputJson[\"method\"]\n args=inputJson[\"params\"]\n FunctionsDictionary[functionname](process_receivedMessagesAlertDb,**args)\n except Exception as ex:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n log.error(\"Exception in process_receivedMessages:{} {} {} {}\".format(exc_type, fname, exc_tb.tb_lineno,ex))\n time.sleep(2)\n continue","title":""},{"docid":"7c1014430b5ed764c332922c7edb315e","score":"0.652419","text":"def run(self):\n\n while True:\n self.wait_on_message()","title":""},{"docid":"37898ff6e9bf47450f963867ad4ec8d4","score":"0.65201706","text":"def listen(self):\n\n while True:\n self.client, address = self.socket.accept()\n print(('CONNECTED: @ ' + str(address)))\n\n try:\n ciphertext = self.request_handler(\n self.client.recv(4096).decode('utf-8'))\n self.client.sendall(ciphertext[0])\n self.client.sendall(ciphertext[1])\n print('file uploaded, awaiting next client')\n except ValueError:\n pass\n print(('DISCONNECTED: @ ' + str(address)))\n self.socket.close()","title":""},{"docid":"c395d6d04edc0d2b19fe840916c55b16","score":"0.6504693","text":"def processMessages(conn, addr):\n while True:\n try:\n # --- receive request --- #\n data = conn.recv(CHUNK_SIZE) # Receive data\n if not data:\n conn.close()\n msg = data.decode(FORMAT) # Decode incoming data\n print(\"Received: \", msg)\n # ----------------------- #\n\n # --- send response --- #\n time.sleep(SLEEP_INTERVAL) # Intervals \n print(\"sending response\")\n conn.sendall(bytes('Server here.', FORMAT)) # Send data\n # --------------------- #\n\n except:\n conn.close() # Close connection\n print(\"Connection closed by\", addr)\n sys.exit() # Quit the thread.","title":""},{"docid":"50a082e5b3af211aebc78cc686f36737","score":"0.6502024","text":"def run(self):\n while True:\n data, addr = self.sock.recvfrom(1024)\n self.lock.acquire()\n try:\n self.client.server_message.append(data)\n finally:\n self.lock.release()","title":""},{"docid":"40b55b76e483a8d749792712b9c50208","score":"0.64911175","text":"async def dispatch_queue(self):\n\n while True:\n try:\n await self.process_one()\n except Exception:\n self.log.exception(\"Error in message handler\")","title":""},{"docid":"7349b5acf1678d84d94d2fb418125a40","score":"0.6489239","text":"async def event_loop(self, *args: EventHandler):\n while True:\n await self.get_event(*args)","title":""},{"docid":"9430a1a896924cd29bc32b4783f269d0","score":"0.64866155","text":"def listen(self, message_consumer):\n while not self._rfile.closed:\n try:\n request_str = self._read_message()\n except ValueError:\n if self._rfile.closed:\n return\n else:\n log.exception(\"Failed to read from rfile\")\n\n if request_str is None:\n break\n\n try:\n message_consumer(json.loads(request_str.decode('utf-8')))\n except ValueError:\n log.exception(\"Failed to parse JSON message %s\", request_str)\n continue","title":""},{"docid":"f7a4513afc2b0c70d2f7b4bcacde296c","score":"0.6466923","text":"def run(self):\n while self.running:\n for msg in self.sub.listen():\n if msg['type'] == 'message':\n message = msg['data'].decode(\"utf-8\")\n event = message.split(';')[0]\n value = message.split(';')[1]\n uid = message.split(';')[2]\n\n if event == \"SonarLeftDetected\" or event == \"SonarRightDetected\":\n dir = \"left\" if event == \"SonarLeftDetected\" else \"right\"\n self.obstacle_detected(eval(value), dir)\n if event == \"SonarLeftNothingDetected\" or event == \"SonarRightNothingDetected\":\n self.obstacle_detected(0, None)\n if event == \"robotIsFalling\" or event == \"robotHasFallen\":\n self.robot_fallen()","title":""},{"docid":"885e51046069dd35403b1f2e4a03c726","score":"0.64516777","text":"def run(self):\n self.poller = select.epoll()\n self.pollmask = select.EPOLLIN | select.EPOLLHUP | select.EPOLLERR\n self.poller.register(self.server,self.pollmask)\n while True:\n # poll sockets\n try:\n fds = self.poller.poll(timeout=1)\n except:\n return\n for (fd,event) in fds:\n # handle errors\n if event & (select.POLLHUP | select.POLLERR):\n self.handleError(fd)\n continue\n # handle the server socket\n if fd == self.server.fileno():\n self.handleServer()\n continue\n # handle client socket\n self.setTime(fd)\n self.handleClient(fd)\n self.markSweep()","title":""},{"docid":"5de3dc91b368b6ca6812d12fa5e749ea","score":"0.64459133","text":"def run(self):\n\n try:\n while True:\n connection, address = self.serverSock.accept()\n serverRunThread = threading.Thread(target=self.handler, args=(connection, address))\n serverRunThread.daemon = True\n serverRunThread.start()\n\n self.users.append((connection, address))\n print_safe(' [*] {0}:{1} connected'.format(address[0], str(address[1])))\n\n except KeyboardInterrupt:\n self.serverSock.close()\n sys.exit(0)","title":""},{"docid":"d8cf2ce2c67feda3c3da88e8692947ce","score":"0.64447606","text":"def execute(self):\n self.running = True\n\n self.log_info(\"Starting...\")\n while self.running:\n # self.log(\"Running...\")\n try:\n message = self.inbox.get_nowait()\n self.handle(message)\n\n # yield to event loop after processing the message\n self.tick()\n\n except Empty:\n # Empty signifies that the queue is empty, so yield to another node\n self.tick()\n # pass\n\n # self.tick()\n # self.stop()\n self.log_info(\"Exiting...\")","title":""},{"docid":"a7687aefa0f91d13a48afd3b09a36519","score":"0.6433072","text":"def message_loop(self):\r\n\r\n\t\treturn","title":""},{"docid":"b3785f961604497f7f0684ba96150a91","score":"0.6401567","text":"def execute(self):\n sock = self.sock\n # Look for responses from all recipients\n while True:\n print(sys.stderr, \"waiting to receive\")\n try:\n data, server = sock.recvfrom(16)\n self.on_receive(data, server)\n except socket.timeout:\n print(sys.stderr, \"timed out, no more responses\")\n break\n else:\n print(sys.stderr, 'received \"%s\" from %s' % (data, server))","title":""},{"docid":"f62a599bc9b65d7df0e06aaf861f7241","score":"0.6397384","text":"def __callback(self):\n while self.is_connected:\n data = self._connection.next()\n json_data = json.loads(data)\n\n event = json_data['ev']\n\n if event == 'update':\n self.on_update(json_data['data'])\n\n else:\n print ('Unknown event: %s' % event)","title":""},{"docid":"08779d35b580179262609f3925eebd2a","score":"0.63903886","text":"async def _run_inbox_handler(self, inbox, handler_name):\n # Run the handler in a threadpool, so that it cannot block other handlers (from a different task),\n # or the main client thread. The number of worker threads forms an upper bound on how many instances\n # of the same handler can be running simultaneously.\n tp = ThreadPoolExecutor(max_workers=4)\n while True:\n handler_arg = await inbox.get()\n # NOTE: we MUST use getattr here using the handler name, as opposed to directly passing\n # the handler in order for the handler to be able to be updated without cancelling\n # the running task created for this coroutine\n handler = getattr(self, handler_name)\n if inspect.iscoroutinefunction(handler):\n # Wrap the coroutine in a function so it can be run in ThreadPool\n def coro_wrapper(coro, arg):\n asyncio_compat.run(coro(arg))\n\n # TODO: get exceptions to propogate\n tp.submit(coro_wrapper, handler, handler_arg)\n else:\n # Run function directly in ThreadPool\n # TODO: get exceptions to propogate\n tp.submit(handler, handler_arg)","title":""},{"docid":"a658766653444a4f21f3c3ac6053762f","score":"0.63893706","text":"def runner(self):\n try:\n print(\"\\nWhen a thread is not connected, press Ctrl C \" +\n \"with no arguments to shutdown the server\\n\")\n while True:\n print(\"Waiting for connection...\")\n client, address = self.server.accept()\n handler = ClientHandler(client, self.server_name)\n handler.start()\n print(\"\\n... connected from:\", address)\n print(\"On IP, Port:\", client.getsockname())\n print(\"\")\n except KeyboardInterrupt:\n self.server.close()\n sys.exit(0)\n except OSError:\n print(\"Attempted to get message, however, either a keyboard interrupt caused a stop,\\n\" +\n \"or arguments given caused an error, server shutting down. Original Error: OSError\")\n self.server.close()\n except BrokenPipeError:\n print(\"Interrupts caused failed execution, server shutting down. Original Error: BrokenPipeError\")\n self.server.close()","title":""},{"docid":"081cc5dd00998705f785e23d67b30373","score":"0.6371375","text":"def loop_forever(self):\r\n while True:\r\n self.client.wait_msg()","title":""},{"docid":"9c7c1d7fc9378a402a684e2ed66b9cd9","score":"0.63677037","text":"def run(self):\n\n while not self._stop.is_set():\n sleep(INTERVAL)\n try:\n messages = self.socket.recv(BUFSIZE).split('\\n')\n except socket.error as error_:\n if error_[0] == errno.EWOULDBLOCK:\n continue\n elif error_[0] == errno.WSAECONNRESET:\n print('I tried to recv something after connection closed, like the dumb computer I am.')\n break\n else:\n raise error_\n for message in messages:\n if message == '':\n continue \n# print message\n message_dict = loads(message)\n self.command(message_dict)","title":""},{"docid":"71f107fa73d5f89984d8ce7c9b2a2654","score":"0.63586986","text":"def run(self):\n try:\n while True:\n self.log('Loop start', level=logging.DEBUG)\n \n connection = None\n channel = None\n \n try:\n connection = self.fresh_connection()\n if connection.connection_open:\n self.log('Got connection', level=logging.DEBUG)\n channel = connection.channel()\n \n channel.queue_declare(\n queue=self.__queueName,\n durable=True,\n exclusive=False,\n auto_delete=False)\n \n channel.basic_consume(\n consumer=self.__handle_delivery,\n queue=self.__queueName)\n \n pika.asyncore_loop()\n \n self.log('Finished with connection', level=logging.DEBUG)\n else:\n self.log('No connection', level=logging.DEBUG)\n finally:\n if not channel is None:\n self.log('Closing channel', level=logging.DEBUG)\n channel.close()\n channel = None\n \n if not connection is None:\n self.log('Closing connection', level=logging.DEBUG)\n connection.close()\n connection = None\n \n time.sleep(self.__loopTimeout)\n except QuitApplication as e:\n self.log('Quitting application ({0})'.format(e.signalName))\n finally:\n self.log('Log Server stopped')","title":""},{"docid":"159c6bfe518dcb2fc6cd713ea7916e36","score":"0.6350875","text":"def on_loop(self):\n data = self.last_read_buffer + self.read()\n if data:\n packets = data.split(MSG_END_HEADER)\n self.last_read_buffer = packets[-1]\n packets.pop()\n for packet in packets:\n self.handle_packet(packet)","title":""},{"docid":"f0bad153c7451bb88dba7b94183ac2d0","score":"0.63482934","text":"def run(self):\n while True:\n self.handle_ready()\n p = self.polling_set()\n # Poll.\n events = p.poll()\n\n self.handle_events(events)\n self.remove_finished()","title":""},{"docid":"84fb38004a13c51a6f205c8450e34df4","score":"0.6304422","text":"def loop(server):\n\n # create logger\n rootLogger = create_logger(server.logfolder)\n if rootLogger is not None:\n server.logging_enabled = True\n\n try:\n # This is the main loop\n while not server.break_loop:\n try:\n server.poll()\n if server.serving:\n if not len(server):\n # The server is serving, but there are no\n # connections so put in a sleep\n time.sleep(0.1)\n else:\n # if the server is not serving, put a sleep in the loop\n time.sleep(0.25)\n except NoService, e:\n server.log_exception(e)\n # Unable to bind\n # GUI will handle this, so loop continues\n except Exception, e:\n # log the exception and exit the main loop\n server.log_exception(e)\n return 1\n except KeyboardInterrupt:\n return 0\n finally:\n # shutdown the server\n server.shutdown()\n return 0","title":""},{"docid":"b50fdf448e9ab6994d3e4ea34410f13c","score":"0.6301584","text":"async def process_events(self):\n try:\n while True:\n j = await self.recv()\n await self.process_packet(j)\n except websockets.ConnectionClosed as err:\n log.exception('Connection failed')\n await self.reconnect(err)","title":""},{"docid":"a2ff189b215ddc2d3ee84e559a78a6fd","score":"0.62858033","text":"def handle(self):\n port = self.request.getpeername()[1]\n self.session = self.server.mainserver.create_session(session_id=port)\n self.session.connect(self)\n\n while True:\n try:\n messages = self.receive_message()\n if messages:\n for message in messages:\n self.handle_message(message)\n except EOFError:\n break\n except IOError:\n logger.exception(\"IOError in CoreAuxRequestHandler\")\n break","title":""},{"docid":"2af06f5a9882abc2646f200a80bfaf53","score":"0.625085","text":"def loop(self, filename, handler):\n tailer = FileTailer(filename)\n while True:\n entry, comment = tailer.poll()\n if not entry:\n time.sleep(1.0)\n continue\n kos, not_kos, error = self.koscheck_logentry(entry)\n handler(comment, kos, not_kos, error)","title":""},{"docid":"fc4359c2aee0074b225c6e123d386eae","score":"0.6238515","text":"def _listen(self):\n # start server\n self._socket.bind('tcp://{}:{}'.format(self.zmqconfig.get('host', ZMQ_DEFAULT_HOST), self.zmqconfig.get('port', ZMQ_DEFAULT_PORT)))\n while True:\n query = self._socket.recv_pyobj()\n self._socket.send_pyobj('QUERY_RECEIVED')\n callback_string = self._socket.recv_pyobj()\n callback = pickle.loads(callback_string)\n self._socket.send_pyobj('CALLBACK_RECEIVED')\n self._execute(query, callback)","title":""},{"docid":"9509b008ee461576c678a379a44dce23","score":"0.6231346","text":"def _receive_loop(self, ws_url):\n\n if ws_url not in self._conns:\n self._logr.warning(\"<{}> is not an active connection\".format(ws_url))\n return\n\n if ws_url not in self._messages:\n self._messages[ws_url] = {}\n\n while not self._receive_stop_events[ws_url].is_set():\n try:\n raw_res = yield self._conns[ws_url].read_message()\n\n self._logr.debug(\"Read message: {}\".format(raw_res))\n\n if raw_res is None:\n self._logr.debug(\"Cannot read message: Closed WS connection\")\n yield tornado.gen.sleep(self.SLEEP_AFTER_ERR_SECS)\n continue\n\n msg_res = self._parse_msg_response(raw_res)\n\n if msg_res:\n self._messages[ws_url][msg_res.id] = msg_res\n conditions = self._msg_conditions.get(ws_url, None)\n\n if conditions and msg_res.id in conditions:\n self._logr.debug(\"Notifying: {}\".format(msg_res.id))\n conditions[msg_res.id].notify_all()\n except Exception as ex:\n self._logr.warning(\"Error in read loop: {}\".format(ex), exc_info=True)\n yield tornado.gen.sleep(self.SLEEP_AFTER_ERR_SECS)\n\n self._receive_stop_events[ws_url].clear()","title":""},{"docid":"50aefc1f5b8e70679c98785f6f90b75f","score":"0.6230563","text":"def incoming(self):\n with ThreadPoolExecutor(max_workers=self.worker_limit) as executor:\n with socket(AF_INET, SOCK_STREAM) as sock:\n sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n sock.bind((\"0.0.0.0\", self.socket_port))\n sock.listen(10)\n self.logger.info(\"Server listening on local TCP port %s\",\n self.socket_port)\n while True:\n conn, addr = sock.accept()\n executor.submit(self.handler, conn, addr)\n self.logger.info(\"Server shutting down\")","title":""},{"docid":"bdddb1a1c5739a3e99331cace2e216ba","score":"0.623004","text":"def loop(self):\r\n self.client.check_msg()\r\n return;","title":""},{"docid":"8564f11c70394715d85e581628f00514","score":"0.62184477","text":"def run_main_loop(self):\n result = self.parse_input()\n while result:\n result = self.parse_input()\n if result == 2:\n self.respond(\"? unknown command\\n\\n\")","title":""},{"docid":"439177dc2306ff1e4df6810bf2c22722","score":"0.62142485","text":"def run(self):\r\n self.handle_connection()","title":""},{"docid":"62daf70752f3e8a10360fecc09b51b78","score":"0.6211222","text":"def loop(self):\n host, port = self._server_init()\n self._logger.info(\"Listener on: {}:{}\".format(host, port))\n downstream_addr = (\n self._config.get(\"Downstream\", \"Host\"),\n int(self._config.get(\"Downstream\", \"Port\")),\n )\n self._logger.info(\n \"Connected to downstream: {}:{}\".format(\n downstream_addr[0], downstream_addr[1]\n )\n )\n downstream_cli = socket.create_connection(downstream_addr)\n self._logger.info(\"Converter started.\")\n upstream_conn, client_address = self._server.accept()\n self._logger.info(\"Client connected: {}\".format(client_address))\n self._loop(upstream_conn, downstream_cli, downstream_addr)","title":""},{"docid":"d75fe90ea52757d96af39dd1725be59d","score":"0.62013793","text":"def handler ( self ) :\r\n\r\n\r\n # counter\r\n \r\n self.count = self.count + 1\r\n\r\n # updates number of events to send\r\n \r\n if self.number > 0 : self.number = self.number - 1\r\n \r\n # stops and deletes timer\r\n \r\n self.stop()\r\n \r\n## if not self.timer is None : del( self.timer )\r\n\r\n # next event\r\n \r\n if self.nextTimeMs is None : self.nextTimeMs = self.clockMs()\r\n \r\n self.nextTimeMs = self.nextTimeMs + self.periodMs\r\n \r\n # triggers one shot timer for next event\r\n \r\n self.trigger()\r\n \r\n # external call back\r\n \r\n if not self.command is None :\r\n \r\n # already busy executing command\r\n \r\n if self.busy : return\r\n \r\n self.busy = True\r\n \r\n self.command()\r\n \r\n self.busy = False","title":""},{"docid":"cd4fe24cdb425449c42e39abd239be6e","score":"0.619992","text":"def _dispatcher(self):\n while 1:\n try:\n response = self._incoming_messages.get(block=True)\n self._format_incoming(response)\n now = time.time()\n # match the event to the best command\n for command in self.commands[:]:\n if command.deadline is not False and command.deadline < now:\n self.unregister_command(command) # silently remove him\n continue\n match = command.matches(self, response)\n if match:\n command(_SlackBotWrapper(self, response), response, *match.groups())\n if command.activations is not False and command.activations <= 0:\n self.unregister_command(command)\n except Exception:\n self.debug(u'\\n'.join([traceback.format_exc(), str(response)]), 'red')\n else:\n try:\n if u'_logged' not in response:\n self.debug(response, 'gray')\n except:\n print(\"Wow something went REAL wrong.\")","title":""},{"docid":"5bdb2843e4ed84aa0a52aee8db3262fc","score":"0.6189986","text":"def _receive_thread(self):\r\n while 1:\r\n try:\r\n message = json.loads(self.ws.recv())\r\n if 'id' in message:\r\n message_id = message['id']\r\n event = self.requests.pop(message_id, None)\r\n if event is not None:\r\n self.results[message_id] = message\r\n event.set()\r\n except Exception:\r\n break","title":""},{"docid":"7cfea8ae99426dd404437a1783fa08c2","score":"0.6182838","text":"async def receive(self):\n\n await self.create_connection()\n\n while True:\n data = json.loads(await self.ws.recv())\n #print(data)\n\n self.sequence_id = data['s'] # Set the sequence id to the one sent by the gateway in case a reconnect is needed\n\n if data['op'] != 0:\n await self.handle_op(data['op'], data)\n\n await self.dispatcher.dispatch(data)","title":""},{"docid":"dbdca3f01cee59c6f4f3e36cf7a15053","score":"0.6154406","text":"def read_loop(self, event): # !TODO timeout to signal\n\n while True:\n try:\n server_ans = recvTlv(self.sock)\n\n if TLV_OK_TAG in server_ans or TLV_FAIL_TAG in server_ans: # ACK / NACK response - main thread is awaiting for it\n # print(\"Message {} has ACK/NACK tag => saving to pipeline for future processing\".format(server_ans))\n self.pipeline.put(server_ans)\n continue\n\n if TLV_NEWTURN_TAG in server_ans:\n client_logger.debug(\"Put TLV_NEWTURN_TAG msg into the pipeline\")\n self.pipeline.put(server_ans)\n continue\n \n # print(\"Message {} is a request from the server\".format(server_ans))\n self.handle_answer(server_ans) # received msg is not a control msg\n except (OSError, EOFError) as e:\n print(\"\\nServer error\\n\")\n client_logger.error(\"Error while reading data: \" + str(e))\n os.kill(os.getpid(), signal.SIGINT) # should raise Keyboard interrupt in the main thread\n event.set() # alarm main thread that program should exit\n return\n except Exception as e:\n client_logger.error(\"Other error while reading data: \" + str(e))\n os.kill(os.getpid(), signal.SIGINT)\n event.set() # alarm main thread that program should exit\n return","title":""},{"docid":"fc5a032b017db13c53f70e791d6eda14","score":"0.61470777","text":"def async_handle(self):\n\n self.raw_requestline = yield from self.rfile.readline()\n if len(self.raw_requestline) > 65536:\n self.requestline = ''\n self.request_version = ''\n self.command = ''\n self.send_error(414)\n return\n\n if not (yield from self.parse_request()): # An error code has been sent, just exit\n return\n\n handler = ServerHandler(\n self.rfile, self.wfile, self.get_stderr(), self.get_environ()\n )\n handler.request_handler = self # backpointer for logging\n yield from handler.run(self.server.get_app())","title":""},{"docid":"2696f3fe24bd9f41cadddc8f6f8dea70","score":"0.614598","text":"def loop(self):\n print(f'Running on {socket.gethostname()}')\n self.mgr.run(self.base_components)\n with cli_connection.server(paths.beeflow_socket()) as server:\n while not self.quit:\n # Handle a message from the client, if there is one\n self.handle_client(server)\n # Poll the components\n self.mgr.poll()\n time.sleep(1)\n # Kill everything, if possible\n self.mgr.kill()","title":""},{"docid":"12ac7d7020d9fad93912e68c129fad75","score":"0.61454654","text":"def handle(self):\n try:\n data = self.request.recv(1024).decode('UTF-8').strip()\n # process the data, i.e. print it:\n\n test = json.loads(data)\n\n # send some 'ok' back\n\n #channels_raw = server.send_command(\"channellist\").data\n #channels = [Channel(**channel) for channel in channels_raw]\n\n #clients_raw = server.clientlist()\n #clients = [Client(**clients_raw[client]) for client in clients_raw]\n #test = Client.clients_json(clients)\n self.request.sendall(bytes(json.dumps(handler.command(test)), 'UTF-8'))\n except Exception as e:\n print(\"Exception wile receiving message: \", e)","title":""},{"docid":"590e650ecd121ef11b701dbd35e058f8","score":"0.61379915","text":"def execute(self):\n self.running = True\n\n # self.log_info(\"Starting...\")\n # while self.running:\n # # self.log(\"Running...\")\n # try:\n # message = self.inbox.get_nowait()\n # self.handle(message)\n\n # # yield to event loop after processing the message\n # self.tick()\n\n # except Empty:\n # # Empty signifies that the queue is empty, so yield to another node\n # self.tick()\n # # pass\n\n # self.tick()\n # self.stop()\n # self.log_info(\"Exiting...\")","title":""},{"docid":"74a95340be1f187d9c069261cfb0bb3b","score":"0.6122714","text":"async def _dispatch_loop(self):\n last_msg_time = time.time()\n async with websockets.connect(self._uri) as ws:\n self._ws = ws\n while self._running:\n msg = await ws.recv()\n msg = self.decode_message(msg)\n\n if msg is None or msg.get_type() == 'BAD_DATA':\n continue\n\n # send a heartbeat message back, since this needs to be\n # constantly sent so the autopilot knows this exists\n if msg.get_type() == 'HEARTBEAT':\n # send -> type, autopilot, base mode, custom mode, system status\n outmsg = self._mav.heartbeat_encode(mavutil.mavlink.MAV_TYPE_GCS,\n mavutil.mavlink.MAV_AUTOPILOT_INVALID, 0, 0,\n mavutil.mavlink.MAV_STATE_ACTIVE)\n await self.send_message(outmsg)\n\n # print('Message received', msg)\n current_time = time.time()\n\n # print(\"Time between messages\", current_time - last_msg_time)\n\n # If we haven't heard a message in a given amount of time\n # terminate connection and event loop.\n if current_time - last_msg_time > self._timeout:\n self.notify_message_listeners(MsgID.CONNECTION_CLOSED, 0)\n self.stop()\n\n # update the time of the last message\n last_msg_time = current_time\n\n dispatch_message(self, msg)\n\n await self._shutdown_event_loop()","title":""},{"docid":"79a20d67e0045810507e29c2fdf46671","score":"0.61191785","text":"def run(self):\n\t\t\n\t\twhile self.running or len(self.outbox) > 0:\n\t\t\tif len(self.outbox) > 0:\n\t\t\t\tif self.debug:\n\t\t\t\t\tprint \"[Notifier] There are\", len(self.outbox), \"messages to send\"\n\t\t\t\tself.obLock.acquire()\n\t\t\t\twhile len(self.outbox) > 0:\n\t\t\t\t\tmessage = self.outbox.pop()\n\t\t\t\t\tif self.debug:\n\t\t\t\t\t\tprint \"[Notifier] Sending queued message to\", message.getTo()\n\t\t\t\t\tself.client.send(message)\n\t\t\t\tself.obLock.release()\n\t\t\tself.client.process(2)\n\t\tif self.debug:\n\t\t\tprint \"[Notifier] Shutting down\"\n\t\tself.client.disconnect()","title":""},{"docid":"4c6df57c5860bd78334c3dfdf7df9dd4","score":"0.6119011","text":"def run(self) -> None:\n\n self.logger.info('Starting Socket consumer.')\n\n while True:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n asyncio.get_event_loop().run_until_complete(self.get())","title":""},{"docid":"9fd8e7b0e5b2ee64fe2f43061b9d1f21","score":"0.61128116","text":"def __runner(self):\n \n conn = pika.BlockingConnection(self.__connection_params)\n chan = conn.channel()\n \n chan.add_on_return_callback(self.__on_returned_msg)\n \n while not self.__shutdown_event.is_set():\n try:\n req = self.__pending_requests.get(True, 5)\n \n self._logger.debug(\"invoking %r\", req)\n \n response = {}\n \n try:\n response['result'] = req.callback(*req.args)\n except:\n self._logger.warn(\"exception invoking %r\", req, exc_info = True)\n \n # the exception value\n response['exception'] = sys.exc_info()[1]\n \n resp_body = serializer_utils.serialize(response, req.content_type)\n\n chan.basic_publish(\n exchange='',\n routing_key = req.reply_to,\n properties = pika.BasicProperties(\n correlation_id = req.correlation_id,\n content_type = req.content_type,\n ),\n body = resp_body\n )\n except Queue.Empty:\n pass\n \n \n conn.close()","title":""},{"docid":"527fd2f96bffa349a49d1479846874bf","score":"0.61126834","text":"def Run(self):\r\n \r\n if (self._server is None): self.PrepareServer()\r\n while not self.quit:\r\n logging.debug('Waiting for request')\r\n self._server.handle_request()\r\n logging.debug('Finished processing requests')","title":""},{"docid":"eef1c0cbee2e2d206f1337c5c8878d6a","score":"0.61029685","text":"def handle(self):\n while True:\n try:\n command = self.fetch_command()\n if not command:\n raise ControlException(\"No command found.\")\n\n for module in self.modules:\n foundWords = []\n for word in module.commandWords:\n if str(word) in command:\n foundWords.append(str(word))\n if len(foundWords) == len(module.commandWords):\n #A command has been said correctly so we can execute the message\n response = module.execute(command)\n #if the command was formatted wrong\n if response == \"ERROR\":\n print(\"Invalid command. Try again.\")\\\n #if we were setting up the token\n if response == \"Setup\":\n print(\"Setup complete.\")\n #otherwise we have the song JSON object returned and we need to parse it and send to MQTT\n else:\n index = response.find('#$#');\n beats = json.loads(response[index+3:])\n index2 = response.find('#%#')\n artist = response[0:index2]\n song = response[index2+3:index]\n print(\"Playing \" + song.upper() + \" by \" + artist.upper())\n self.clientRP.publish(\"raspberrypi/lcd\", response[0:index])\n self.clientRP.publish(\"raspberrypi/led\", response[index+3:])\n except (TypeError, ControlException):\n pass\n except Exception as exc:\n print(\"The request is not included in the API. Try Again.\")\n time.sleep(1)","title":""},{"docid":"4e0b2fd1c100bbe798d7028370db4c15","score":"0.60967106","text":"def run(self):\n while True:\n msg = self.reader.read()\n if msg: # Set event if have new message\n self.log += \"%s\\n\" % msg.decode(DEFAULT_ENCODE)\n self.event.set()","title":""},{"docid":"2da5692ebf649d4b31143786ee31ddb5","score":"0.6090398","text":"def run(self):\n while True:\n socks = select.select(self.sockets.values(), [], [], 0.1)[0]\n for conn in socks:\n try:\n k = conn.recv(65535)\n except:\n # either died on a connection reset, or was SIGTERM's by parent\n return\n if k:\n for sock in self.sockets:\n if self.sockets[sock] == conn:\n srcif = sock\n msg = json.loads(k)\n if not self.handle_packet(srcif, msg):\n self.send_error(conn, msg, srcif)\n else:\n return","title":""},{"docid":"c56b93b51623a2714afc22f43d37e900","score":"0.608456","text":"async def _recv_loop(self):\n try:\n while self._connected:\n try:\n data = await self._recv()\n except asyncio.CancelledError:\n break\n except (IOError, asyncio.IncompleteReadError) as e:\n self._log.warning('Server closed the connection: %s', e)\n await self._recv_queue.put((None, e))\n await self.disconnect()\n except InvalidChecksumError as e:\n self._log.warning('Server response had invalid checksum: %s', e)\n await self._recv_queue.put((None, e))\n except InvalidBufferError as e:\n self._log.warning('Server response had invalid buffer: %s', e)\n await self._recv_queue.put((None, e))\n except Exception as e:\n self._log.exception('Unexpected exception in the receive loop')\n await self._recv_queue.put((None, e))\n await self.disconnect()\n else:\n await self._recv_queue.put((data, None))\n finally:\n await self.disconnect()","title":""},{"docid":"8466187dc60ef8490ce4f06d5a6d4624","score":"0.6083547","text":"def on_msg(self,sock: socket.socket):\n\t\tlogging.info(\"Listening for server messages.\")\n\t\tself.run = True\n\t\tself.on_connect()\n\t\twhile self.run:\n\t\t\ttry:\n\t\t\t\tdata = sock.recv(LEN_OF_HEADER)\n\t\t\t\tif data == b'':\n\t\t\t\t\tself.run = False\n\t\t\t\t\tcontinue\n\t\t\t\tbody_len = get_size_of_msg(data)\n\t\t\t\tbody = b''\n\t\t\t\tif body_len != 0:\n\t\t\t\t\twhile body_len > 0:\n\t\t\t\t\t\tif body_len > MAX_LEN_OF_RECV:\n\t\t\t\t\t\t\tbody += sock.recv(MAX_LEN_OF_RECV)\n\t\t\t\t\t\t\tbody_len -= MAX_LEN_OF_RECV\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tbody += sock.recv(body_len)\n\t\t\t\t\t\t\tbody_len = 0\n\t\t\t\tmsg = Message()\n\t\t\t\tmsg.unpack(data + body)\n\t\t\t\tif msg.type in self.type_callbacks.keys():\n\t\t\t\t\tself.type_callbacks[msg.type](msg)\n\t\t\t\telse:\n\t\t\t\t\tself.run = False\n\t\t\t\t\traise Exception(\"Type: %s isnt defined in the callbacks\" % msg.type)\n\t\t\texcept timeout:\n\t\t\t\tcontinue\n\t\t\texcept Exception as e:\n\t\t\t\tlogging.info(e)\n\t\t\t\tself.run = False\n\t\t\t\tif self.raise_exceptions:\n\t\t\t\t\traise e\n\t\t\t\tbreak\n\t\tself.run = False\n\t\tsock.close()\n\t\tself.on_disconnect()","title":""},{"docid":"33eee1aa590f9e8f422988a42f281ee1","score":"0.60823125","text":"def receiver(conn):\r\n while 1:\r\n msg = conn.recv()\r\n if 'END' in msg:\r\n break\r\n print(F\"Receive the messages: {msg}\")","title":""},{"docid":"0a1c9715fb5a4a91fb083acbb2388d89","score":"0.60811144","text":"def handler(self, timeout=0):\n while True:\n try:\n yield self.recv(timeout=timeout)\n except TimeoutError:\n yield None","title":""},{"docid":"cb2eef4f1256de6bc40ebc26f08c8183","score":"0.6077736","text":"def run(self):\n while True:\n for client in self.clients:\n gevent.spawn(self.send, client)\n gevent.sleep(1)","title":""},{"docid":"bf24b622306ffab9d9a8e6807c386a7b","score":"0.6076663","text":"def catch_data(self):\n while True:\n buf = self.irc.recv(4096)\n lines = buf.split('\\n')\n for data in lines:\n data = str(data).strip()\n \n if len(data) < 2:\n continue\n \n # server ping/pong? \"PING :FFFFFFFF1155571C\"\n if data.startswith('PING'):\n self.send('PONG :' + data.split(':')[1])\n if self.connected == False:\n self.connected = True\n \n if len(data.split()) < 2:\n continue\n \n try:\n if len(data.split()) >= 4:\n line = data.split()\n sender = line[0][1::].split('!',1)[0] # :FM1337!Allen@staff.bouncer.ml\n location = line[2] # Can be channel or user\n text = ' '.join(line[3::])[1::] # Remove the first : then re-join the text\n func = getattr(self, 'trigger_%s' % data.split()[1])\n self.exec_buffer.append({'function': func, 'args': [data, sender, location, text]})\n else:\n func = getattr(self, 'trigger_%s' % data.split()[1])\n self.exec_buffer.append({'function': func, 'args': [data]})\n except AttributeError:\n continue\n except:\n continue","title":""},{"docid":"b1defdd8c5c1c015c4088ed1a8c085f5","score":"0.60731703","text":"def messagesHandler(self):\n try:\n messageArr = self._component.recv()\n for message in messageArr:\n if(message is not None):\n t, m = Parser.splitMessage(message)\n self._container.append(self._component.getName(), message)\n\n except socket.error as error: # Error that happends when TCP client disconect.\n pass","title":""},{"docid":"de4a87699e43cb704c6a3062d7bcb525","score":"0.60722226","text":"def run(self):\n while True:\n try:\n for item in self.reddit.inbox.unread(limit=None):\n if isinstance(item, praw.models.Message):\n try:\n self.process_message(item)\n item.mark_read()\n except Exception as e:\n print(\"Error processing message: \" + str(item))\n traceback.print_exc()\n time.sleep(1)\n except Exception as e:\n print(\"Error reading inbox: \" + str(e))\n traceback.print_exc()","title":""},{"docid":"ecec4930dad9af2a479b0dcdd1fa7416","score":"0.6071832","text":"def __listen_almost_forever(\n self,\n handler: Callable,\n preprocess: Callable = lambda x: x,\n poll_timeout: int = 500,\n ):\n while True:\n with self.__lock:\n if self.__done:\n break\n\n # Don't allow pausing halfway through a message\n with self.__background_lock:\n msg = self.__listener.recv(poll_timeout)\n if msg is None:\n continue\n self.__message_flow(msg, handler, preprocess)\n\n self.__listener.stop()","title":""},{"docid":"1f094aa170b5a803cca8b3b39d929937","score":"0.606992","text":"def run(self) -> None:\n messages = self.server.server_receive_message()\n for message in messages:\n self.progress.emit(message)","title":""},{"docid":"5c267cc4366675389e3706d784d230e0","score":"0.6067176","text":"def handle(self):\n while True:\n chunk = self.connection.recv(4)\n if len(chunk) < 4: break\n slen = struct.unpack(\">L\", chunk)[0]\n chunk = self.connection.recv(slen)\n while len(chunk) < slen:\n chunk = chunk + self.connection.recv(slen - len(chunk))\n obj = pickle.loads(chunk)\n record = logging.makeLogRecord(obj)\n self.handleLogRecord(record)","title":""},{"docid":"bf981b99c960273b6519f5b005ad38cc","score":"0.6050623","text":"async def handle_incoming(self):\n # TODO Deal with other types of messages using flag (currently _)\n\n while True:\n stream_id, flag, message = await self.read_message()\n\n if stream_id is not None and flag is not None and message is not None:\n if stream_id not in self.buffers:\n self.buffers[stream_id] = asyncio.Queue()\n await self.stream_queue.put(stream_id)\n\n if flag is get_flag(True, \"NEW_STREAM\"):\n # new stream detected on connection\n await self.accept_stream()\n\n if message:\n await self.buffers[stream_id].put(message)\n\n # Force context switch\n await asyncio.sleep(0)","title":""},{"docid":"359103b85430f54b77eff232ecec9e02","score":"0.60503155","text":"def handle(self):\n self.ws_addr = \"%s:%s\" % (\n self.ws.environ[\"REMOTE_ADDR\"],\n self.ws.environ[\"REMOTE_PORT\"])\n logger.info(\"%s connected\" % self.ws_addr)\n\n if \"connected\" in actions:\n Handler.currents.append(self.ws)\n actions[\"connected\"]()\n Handler.currents.pop()\n\n go = True\n while go:\n try:\n m = self.ws.receive()\n except:\n m = None\n\n if m is None:\n go = False\n else:\n self.do_message(m)\n\n if \"disconnected\" in actions:\n Handler.currents.append(self.ws)\n actions[\"disconnected\"]()\n Handler.currents.pop()\n\n # remove websocket from connected socket list\n Handler.s.remove(self.ws)\n # check if no more sockets remain and call\n # disconnect functions if so.\n if len(Handler.s) is 0:\n for f in alldisconnected.funcs:\n f()\n logger.info(\"%s disconnected\" % self.ws_addr)","title":""},{"docid":"452b97be6e1bc24b5a229729216a9500","score":"0.60486776","text":"def mainloop(self):\n lverb = True\n is_rfid_scanner = (self.comm_link is not None)\n\n print(\"mainloop begin: is_rfid_server: {}\".format(is_rfid_scanner))\n self.logger.info(\"mainloop begin\")\n # start a random generator thread for testing....\n # self.randTM = Taskmeister.RandomGenerator(self.msgQ, self.logger)\n # self.randTM.set_active(True)\n\n if is_rfid_scanner:\n # send the RFID status to the webclient\n rfid_stat = self.comm_link.get_rfid_state()\n self.send_ws_msg(CommonMSG(CommonMSG.MSG_SV_RFID_STATREP, rfid_stat))\n else:\n # send the stocky server config data\n self.send_server_config()\n # send the QAI update status to the webclient\n self.send_qai_status(None)\n\n do_loop = True\n while do_loop:\n if lverb:\n print(\"YO: before get\")\n msg: CommonMSG = self.msgQ.get()\n self.logger.debug(\"handling msgtype '{}'\".format(msg.msg))\n if lverb:\n print(\"YO: handling msgtype '{}'\".format(msg.msg))\n # handle a EOF separately\n if msg.msg == CommonMSG.MSG_WC_EOF:\n if lverb:\n print(\"mainloop detected WS_EOF... quitting\")\n self.ws = None\n do_loop = False\n print(\"step 2\")\n if msg.is_from_rfid_reader():\n self.logger.debug(\"GOT RFID {}\".format(msg.as_dict()))\n self.activate_rfid_spinner()\n\n is_handled = False\n if self.ws is not None and msg.msg in CommonStockyServer.MSG_FOR_WC_SET:\n is_handled = True\n # print(\"sending to WS...\")\n self.send_ws_msg(msg)\n # print(\"...OK send\")\n if self.tls is not None and msg.msg in CommonStockyServer.MSG_FOR_RFID_SET:\n is_handled = True\n self.tls.send_rfid_msg(msg)\n if msg.msg in CommonStockyServer.MSG_FOR_ME_SET:\n print(\"msg for me: {}\".format(msg.msg))\n is_handled = True\n self.server_handle_msg(msg)\n print(\"done handling\")\n if not is_handled:\n mmm = \"mainloop DID NOT handle msgtype '{}'\".format(msg.msg)\n self.logger.error(mmm)\n print(mmm)\n print(\"end of ML.while\")\n print(\"OUT OF LOOP\")","title":""},{"docid":"46609d36d2efed72778e8638997a9184","score":"0.6048616","text":"def receiver():\n while True:\n msg, future = incoming.get(True)\n if msg == TerminateReceiver:\n break\n else:\n method, args, kw = msg\n future.target = getattr(obj, method)(*args, **kw)\n future.event.set()","title":""},{"docid":"cc8ad9ede0abe26241b991d85f347a2f","score":"0.60392517","text":"def run(self):\n while True:\n recv_data = self.socket.recv(self.buffer_size)\n print(\"Server received data: {}\".format(recv_data))\n packet_info = recv_data.decode('utf-8').strip().split(\",\")\n\n if packet_info[0] == \"rput\" and len(packet_info[1:]) == 2:\n self.__read_file(*packet_info[1:])\n elif packet_info[0] == \"rget\" and len(packet_info[1:]) == 1:\n self.__send_file(*packet_info[1:])\n else:\n if packet_info[0] is not '':\n print(\"Server does not support that check sent command\")\n self.socket.sendall(\"Invalid\".encode('utf-8'))\n\n if not recv_data:\n print(\"Disconnecting from client {}:{}\".format(self.ip, self.port))\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()\n break","title":""}],"string":"[\n {\n \"docid\": \"1f6eb6286e8f5b57133d53eb6f7141b0\",\n \"score\": \"0.73473716\",\n \"text\": \"def handle(self):\\n sock = self.request\\n response = \\\"\\\"\\n\\n while True:\\n try:\\n objects = _read_objects(sock)\\n response = server._callback(objects)\\n except ConnectionClosed:\\n return\\n except ConnectionResetError:\\n sock.close()\\n except Exception:\\n sock.close()\\n\\n _write_objects(sock, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095dffc9db44d375b4a219112a0950f3\",\n \"score\": \"0.7273748\",\n \"text\": \"def run(self):\\n\\n while(True):\\n message = Replyer.receive()\\n method, params = Marshaller.unmarshall(message)\\n response = getattr(self.server, method)(*params)\\n Replyer.send(Marshaller.marshall(response))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6619dd6e3df7946362a387d72331bfa\",\n \"score\": \"0.7205196\",\n \"text\": \"def run_loop(self):\\n\\n while self.pending_calls:\\n args = self.pending_calls.dequeue()\\n\\n method = args.get('method')\\n peer_id = args.get('peer_id')\\n raw_cmd = args.get('raw_cmd')\\n fn = args.get('fn')\\n\\n if method == 'send':\\n self._send_message(peer_id, raw_cmd, fn)\\n elif method in ['response', 'call']:\\n fn()\\n else:\\n raise ValueError('method %s undefined' % (method))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18f2e8211d16ba1b9f8204e458c701f\",\n \"score\": \"0.72048277\",\n \"text\": \"def receive_forever(self):\\n\\n while True:\\n body, addr = self.receive()\\n if body == None and addr == None:\\n # There was an error with the received message\\n continue\\n\\n if body['type'] not in self.handlers:\\n # Provided type is not a registered handler\\n logging.debug('Invalid message type', body)\\n self.send(Error.json(Error.BAD_REQ, 'invalid message type'), addr)\\n continue\\n\\n try:\\n self.handlers[body['type']](self, body, addr)\\n except CloseServer:\\n self.close()\\n return\\n except Exception as e:\\n self.send(Error.json(Error.SERVER_ERR, str(e)), addr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4be43c27814c8c9e804ad8362c3dcf\",\n \"score\": \"0.71238244\",\n \"text\": \"def run(self):\\n self.connect()\\n self.action_helo()\\n i = 200\\n while True:\\n try:\\n message = self.receive_message()\\n except socket.timeout:\\n # no data from server\\n log.debug(\\\"timeout waiting for message\\\")\\n if self.__terminate:\\n return\\n else:\\n continue\\n self.handle_message(message)\\n i = i - 1\\n if i < 0:\\n break\\n self.action_bye()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca1b4f646aad3a2d2ff3da422d50635\",\n \"score\": \"0.70701617\",\n \"text\": \"def run_loop(self):\\n\\n while(1):\\n try:\\n payload = self._recv()\\n except UnicodeError as e:\\n logging.warning(f\\\"Failed to decode : {e.reason}\\\")\\n continue\\n except KeyboardInterrupt:\\n if self._closing:\\n logging.warning(\\\"Forced shutting down now.\\\")\\n os._exit(-1)\\n else:\\n logging.info(\\\"Received KeyboardInterrupt. Ctrl+C again to force shutting down.\\\")\\n self._closing = True\\n continue\\n\\n send_id, recv_id, msg = self._parse_msg(payload)\\n if msg is None:\\n continue\\n else:\\n result, cont = self._execute_cmd(msg.cmd, msg.data)\\n if not cont:\\n logging.info(\\\"Shutting down.\\\")\\n break\\n\\n # Currently not expecting to invoke any callback on ModelServer\\n # side, so second parameter 0\\n self._send_msg(0, send_id, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5823264048390dfb119e082e5e01a8\",\n \"score\": \"0.7060128\",\n \"text\": \"def run(self):\\n channels = self.channel_backend.registry.all_channel_names()\\n while True:\\n channel, message = self.channel_backend.receive_many_blocking(channels)\\n # Handle the message\\n consumer = self.channel_backend.registry.consumer_for_channel(channel)\\n if self.callback:\\n self.callback(channel, message)\\n consumer(channel=channel, **message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad809a85e7fe5fdf9cc756ed66e03a2b\",\n \"score\": \"0.7050459\",\n \"text\": \"async def read(ws: websockets.WebSocketClientProtocol, handler: Callable[[AnyStr], Coroutine]):\\n async for message in ws:\\n await handler(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c83765aef7e6278fc86a7d6545460b\",\n \"score\": \"0.7024942\",\n \"text\": \"def run(self):\\n for data in self.__iter_data():\\n self.on_message(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01e31593ed2d9f13a55251cbf141d500\",\n \"score\": \"0.6971264\",\n \"text\": \"def _process_loop(self):\\n for msg in self._event_queue:\\n self._process_incoming_message(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f96f5742b2606027a53eab8f1987ba5\",\n \"score\": \"0.6967414\",\n \"text\": \"def __callbackHandler(self):\\n while True:\\n callback, msg_obj = SessionManager.callback_queue.get()\\n callback(msg_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b0678be59c9ec58a97fe2f4b6b4a03c\",\n \"score\": \"0.6963102\",\n \"text\": \"def run(self):\\r\\n while True:\\r\\n soc, address = self.queue.get()\\r\\n self.handle_client(soc, address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8520162fe27e75d7d25d3a012960bd\",\n \"score\": \"0.6959939\",\n \"text\": \"def __message_loop(self):\\n\\n # loop until we're told to stop\\n while self.__parsing:\\n # receive message data from the server and pass it along to the\\n # world model as-is. the world model parses it and stores it within\\n # itself for perusal at our leisure.\\n raw_msg = self.__sock.recv()\\n\\n msg_type = self.msg_handler.handle_message(raw_msg)\\n if msg_type is not None:\\n # we send commands all at once every cycle, ie. whenever a\\n # 'sense_body' command is received\\n if msg_type == handler.ActionHandler.CommandType.SENSE_BODY:\\n self.__send_commands = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34813cd5814096560b6b405420741d07\",\n \"score\": \"0.69063914\",\n \"text\": \"def read_commands_loop(self):\\n while True:\\n msg = self.autopilot.recv_msg()\\n if msg:\\n if self.debug: print(f\\\"Receive: {msg}\\\")\\n if msg.id in self.handlers:\\n handle_thread = threading.Thread(\\n target=self.handlers[msg.id],\\n args=(msg,)\\n )\\n handle_thread.start()\\n time.sleep(0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9902643a343dfa8108fa417f34828a\",\n \"score\": \"0.69027877\",\n \"text\": \"def _run(self):\\n self.soc.settimeout(1)\\n while self.connected and not self.shutdown:\\n try:\\n data = self.soc.recv(2**16)\\n data_list = self._check_for_multiple(data)\\n for item in data_list:\\n try:\\n jdata = json.loads(item.decode('utf-8'))\\n func = self.handler[jdata['topic']]\\n handler_thread = Thread(target=func, args=[jdata])\\n handler_thread.start()\\n except json.JSONDecodeError as e:\\n print('\\\\n\\\\n', e)\\n print('Caused by:', item)\\n except socket.timeout:\\n pass\\n except ConnectionResetError:\\n print(self, ':: Master has shut down. Stopping subscriber')\\n self.shutdown = True\\n self.soc.close()\\n self.connected = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31320fa0916e3afe50f3c0701962d3ed\",\n \"score\": \"0.67917025\",\n \"text\": \"def main_loop(self):\\n self.log.info(\\\"Beginning to scan for new inbox messages...\\\")\\n while True:\\n messages = self.r.get_unread()\\n for message in messages:\\n if message.subject == \\\"username mention\\\":\\n self.respond_to_username_mention(message)\\n elif message.subject == \\\"edit request\\\":\\n self.respond_to_edit_request(message)\\n elif message.subject == \\\"delete request\\\":\\n self.respond_to_delete_request(message)\\n elif message.subject == \\\"user translation default request\\\":\\n self.respond_to_user_translation_request(message)\\n elif message.subject == \\\"subreddit translation default request\\\":\\n self.respond_to_subreddit_translation_request(message)\\n message.mark_as_read()\\n sleep(30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4f07e74bb70cb1fb46a81ed9d76c1f\",\n \"score\": \"0.6763541\",\n \"text\": \"def _get_received_messages(self, response_handler):\\n for recvmsg in _utils.getsome(self._channel.poll_received_message):\\n _TRACE(\\\"ECHO_RECV_MSG msgtype: %s msgbody: %s\\\",\\n recvmsg.msgtype, recvmsg.message)\\n if response_handler:\\n response_handler(recvmsg.msgtype, recvmsg.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"449e4b9a3ead12396edcf84b20f414f2\",\n \"score\": \"0.67121315\",\n \"text\": \"def run(self):\\n\\n from twisted.internet import reactor\\n\\n while True:\\n item = self.queue.get()\\n if item.get('json', None) is not None:\\n for json in item['json']:\\n if json['cmd'] == 'refresh':\\n continue\\n client = self.session.get(item['uid'])\\n if client is None:\\n continue\\n if json['cmd'] == 'keepalive':\\n client.updateLastAction()\\n continue\\n if item.get('type', None) is not None:\\n client.type = item['type']\\n client.addResponse(self.protocol.parse(client, json))\\n if callable(item.get('callback', None)):\\n reactor.callFromThread(item['callback'],\\n client.getResponse())\\n self.queue.task_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14942e32f6d81c388ad8dd588906da53\",\n \"score\": \"0.6702382\",\n \"text\": \"def run(self):\\n self.setup_connection()\\n while True:\\n self.send_command()\\n if self.socket_got_data():\\n self.get_next_message()\\n print('received', msg_parser.get_command_name(self.buffer))\\n self.process_message()\\n\\n # This method for debug purpose only\\n # it stop executing when all known blocks processed\\n self.finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9b09c3ecf9efa7dda2f230ce4e63f1\",\n \"score\": \"0.6667263\",\n \"text\": \"def incoming_message_handler(self, running):\\n while running:\\n try:\\n self.unicast_receive()\\n except (socket.timeout, BlockingIOError):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bb9672c224b43a28260ea1adb976ab2\",\n \"score\": \"0.663664\",\n \"text\": \"async def handle_messages(self, /) -> NoReturn:\\n while True:\\n message = await self.read_queue.get()\\n try:\\n await asyncio.to_thread(self.buffer.update, message)\\n async for message in self._emit_responses(message):\\n await self.write_queue.put(message)\\n except (DeviceError, MessageError) as exc:\\n await self.logger.error('Message handling error', exc_info=exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83e840862c5f55eccfa0eed9019fa88\",\n \"score\": \"0.66135174\",\n \"text\": \"async def _receiver(self, websocket):\\n async for msg in websocket:\\n LOGGER.debug(F\\\"Received message: {msg}\\\")\\n self.handle_message(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc9d143c418d7785434439c36290a3c2\",\n \"score\": \"0.660933\",\n \"text\": \"def run(self):\\n try:\\n while True:\\n self.eventHandler()\\n self.poller()\\n except Exception as e:\\n print('Error: %s' % e)\\n finally:\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4309b71dac67ebb1411d1fdbb9228c06\",\n \"score\": \"0.6609241\",\n \"text\": \"def run(self):\\r\\n self.serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\r\\n self.serverSocket.bind((self.hostname, self.server_port))\\r\\n self.serverSocket.listen(1)\\r\\n while not self.done:\\r\\n conn_socket, addr = self.serverSocket.accept()\\r\\n handler = ConnectionHandler(conn_socket, addr, self.timeout)\\r\\n handler.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9d4db5ac2136b1e3e46e2a60fee168\",\n \"score\": \"0.6603911\",\n \"text\": \"def __recv_worker(self):\\n while True:\\n msg = self.atomic_b.wait_for_msg(None)\\n self.msg_handler(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298497e9d6b1e3fc5ad3707f984ad8f9\",\n \"score\": \"0.660326\",\n \"text\": \"def handler_thread(self):\\n while not self.done:\\n try:\\n message = self.message_queue.get(timeout=5)\\n self.handle_message(message)\\n except Queue.Empty:\\n logger.debug(\\\"timeout getting message\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7383fe923d38aa11f8b9032277e7096d\",\n \"score\": \"0.6590043\",\n \"text\": \"def run(self):\\n\\n inputSocketList = []\\n inputSocketList.append(self._clientSocket)\\n\\n while not self.shutdownEvent.is_set():\\n readyToRead, readyToWrite, inputError = select.select(inputSocketList, [], [], self._selectTimeout)\\n\\n for sock in readyToRead:\\n # Read a message off of the socket\\n msgData = MessageHandler.recvMsg(sock)\\n\\n # Process the message\\n if msgData is not None:\\n self.__processMsg(msgData)\\n else:\\n break\\n\\n # Cleanup\\n self.__shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3334f97149eb2abfa7b7a794850272f\",\n \"score\": \"0.65867937\",\n \"text\": \"def handle(self):\\n self.ip = self.client_address[0]\\n self.port = self.client_address[1]\\n self.connection = self.request\\n\\n self.user = User()\\n\\n # Loop that listens for messages from the client\\n while True:\\n\\n received_string = self.connection.recv(4096)\\n \\n message = json.loads(received_string)\\n\\n request = message['request']\\n content = message['content']\\n\\n print 'The request is: ' + request\\n\\n if request == 'login':\\n self.handle_login(content)\\n elif request == 'logout':\\n self.handle_logout()\\n elif request == 'msg':\\n self.handle_msg(content)\\n elif request == 'names':\\n self.handle_names()\\n elif request == 'help':\\n self.handle_help()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ad583343fcfaa2137aba8336b8a5b9\",\n \"score\": \"0.6577251\",\n \"text\": \"def _receiver(self):\\n\\n while self._run_receive:\\n try:\\n if self._something_to_receive():\\n header = self._receive_header()\\n if Response.is_msg_response(header):\\n body = self._receive_data(header[Response.DLEN])\\n self._handle_msg_response(body, header)\\n\\n elif AsyncMsg.is_async_msg(header):\\n header = AsyncMsg.convert_to_async_header(header)\\n body = self._receive_data(header[AsyncMsg.DLEN])\\n self._handle_async_msg(body, header)\\n else:\\n raise SpheroError(\\\"Unknown data received from sphero. Header: {}\\\".format(header))\\n except SpheroError:\\n # TODO release all blocked threads waiting to receive data\\n print \\\"RECEIVER CRASHED\\\"\\n self._receiver_crashed = True\\n #time.sleep(0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac361cd89300750dc1cc55a1216d3c7\",\n \"score\": \"0.6574794\",\n \"text\": \"def listen(self):\\n try:\\n while 1:\\n print \\\"Loop...\\\"\\n # Process incoming XMPP messages.\\n self.cl.Process(5)\\n\\n # Process shoutbox messages.\\n self.process_shoutbox_messages()\\n\\n # Sleep before next loop iteration.\\n #time.sleep(1)\\n\\n # Reconnect to XMPP if necessary.\\n if not self.cl.isConnected():\\n self.cl.reconnectAndReauth()\\n\\n except KeyboardInterrupt:\\n print \\\"Exiting...\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb355fe0a9dc0d205dd475e307982807\",\n \"score\": \"0.65730107\",\n \"text\": \"def main_loop(irc: ssl.SSLSocket):\\n while True:\\n data_ch = irc.recv(1024)\\n raw_message = data_ch.decode('UTF-8')\\n\\n for line in raw_message.splitlines():\\n if line.startswith('PING :tmi.twitch.tv'):\\n send_pong(irc)\\n else:\\n components = line.split()\\n command = components[1]\\n\\n if command == 'PRIVMSG':\\n parse_chat(irc, line)\\n elif command == 'NOTICE':\\n print(\\\"NOTICE: {}\\\".format(line[line.find(\\\":\\\", 1, -1):]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9144766313286dc144f85b816c626ea4\",\n \"score\": \"0.6531432\",\n \"text\": \"def recv_loop(self):\\n while True:\\n recv_msg = self.s.recv(SEND_BUFFER_SIZE).decode(\\\"ISO-8859-1\\\")\\n if recv_msg:\\n message = self.process_received_message(recv_msg)\\n sys.stdout.write(\\\"\\\\t\\\" + message + \\\"\\\\n\\\")\\n sys.stdout.flush()\\n else:\\n os._exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee538df655a759f839d5e644bbfc248c\",\n \"score\": \"0.6527932\",\n \"text\": \"def handle(ws):\\n if ws.path == '/echo':\\n sem = Semaphore()\\n while True:\\n logger.debug(\\\"loop\\\")\\n data = ws.wait()\\n logger.info(\\\"data {}\\\".format(data))\\n pool.apply_async(process, args=(data, ws,sem), callback=log_result)\\n\\n elif ws.path == '/data':\\n for i in xrange(10000):\\n ws.send(\\\"0 %s %s\\\\n\\\" % (i, random.random()))\\n gevent.sleep(0.1)\\n while True:\\n logger.info (\\\"loop\\\")\\n data = ws.recvmsg()\\n pool.apply_async(process, args=(data, ws,sem), callback=log_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ef517931eafa99aa4220ad0ce40d23\",\n \"score\": \"0.6526114\",\n \"text\": \"def serverloop():\\n # Create a regular internet socket (TCP/IP):\\n server_socket = socket.socket()\\n # Bind the socket to listen on a specific port on our computer:\\n server_socket.bind((IP, PORT))\\n # Begin listening on the socket, with a particular queue size:\\n server_socket.listen(MAXIMUM_QUEUE_SIZE)\\n\\n # Do this forever (until server process is killed):\\n while True:\\n # Accept a connection from next client:\\n # for each connection we get the socket and connection details\\n (client_socket, client_ip_and_port) = server_socket.accept()\\n # Process the client's request:\\n respond(client_socket, client_ip_and_port)\\n # Close the client connection:\\n client_socket.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f2c9870f4ab471c1739a2915f7431a\",\n \"score\": \"0.65247244\",\n \"text\": \"def process_receivedMessages():\\n process_receivedMessagesAlertDb=alertsdb(\\\"process_receivedMessages\\\")\\n #print(\\\"Processing the input received from client\\\")\\n #return \\\"received message from clinent:{} \\\\n\\\".format(input_str)\\n while True:\\n input=ClientsToServerMessages.get(block=True)\\n try:\\n inputJson = json.loads(input)\\n functionname=inputJson[\\\"method\\\"]\\n args=inputJson[\\\"params\\\"]\\n FunctionsDictionary[functionname](process_receivedMessagesAlertDb,**args)\\n except Exception as ex:\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\\n log.error(\\\"Exception in process_receivedMessages:{} {} {} {}\\\".format(exc_type, fname, exc_tb.tb_lineno,ex))\\n time.sleep(2)\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c1014430b5ed764c332922c7edb315e\",\n \"score\": \"0.652419\",\n \"text\": \"def run(self):\\n\\n while True:\\n self.wait_on_message()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37898ff6e9bf47450f963867ad4ec8d4\",\n \"score\": \"0.65201706\",\n \"text\": \"def listen(self):\\n\\n while True:\\n self.client, address = self.socket.accept()\\n print(('CONNECTED: @ ' + str(address)))\\n\\n try:\\n ciphertext = self.request_handler(\\n self.client.recv(4096).decode('utf-8'))\\n self.client.sendall(ciphertext[0])\\n self.client.sendall(ciphertext[1])\\n print('file uploaded, awaiting next client')\\n except ValueError:\\n pass\\n print(('DISCONNECTED: @ ' + str(address)))\\n self.socket.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c395d6d04edc0d2b19fe840916c55b16\",\n \"score\": \"0.6504693\",\n \"text\": \"def processMessages(conn, addr):\\n while True:\\n try:\\n # --- receive request --- #\\n data = conn.recv(CHUNK_SIZE) # Receive data\\n if not data:\\n conn.close()\\n msg = data.decode(FORMAT) # Decode incoming data\\n print(\\\"Received: \\\", msg)\\n # ----------------------- #\\n\\n # --- send response --- #\\n time.sleep(SLEEP_INTERVAL) # Intervals \\n print(\\\"sending response\\\")\\n conn.sendall(bytes('Server here.', FORMAT)) # Send data\\n # --------------------- #\\n\\n except:\\n conn.close() # Close connection\\n print(\\\"Connection closed by\\\", addr)\\n sys.exit() # Quit the thread.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a082e5b3af211aebc78cc686f36737\",\n \"score\": \"0.6502024\",\n \"text\": \"def run(self):\\n while True:\\n data, addr = self.sock.recvfrom(1024)\\n self.lock.acquire()\\n try:\\n self.client.server_message.append(data)\\n finally:\\n self.lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b55b76e483a8d749792712b9c50208\",\n \"score\": \"0.64911175\",\n \"text\": \"async def dispatch_queue(self):\\n\\n while True:\\n try:\\n await self.process_one()\\n except Exception:\\n self.log.exception(\\\"Error in message handler\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7349b5acf1678d84d94d2fb418125a40\",\n \"score\": \"0.6489239\",\n \"text\": \"async def event_loop(self, *args: EventHandler):\\n while True:\\n await self.get_event(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9430a1a896924cd29bc32b4783f269d0\",\n \"score\": \"0.64866155\",\n \"text\": \"def listen(self, message_consumer):\\n while not self._rfile.closed:\\n try:\\n request_str = self._read_message()\\n except ValueError:\\n if self._rfile.closed:\\n return\\n else:\\n log.exception(\\\"Failed to read from rfile\\\")\\n\\n if request_str is None:\\n break\\n\\n try:\\n message_consumer(json.loads(request_str.decode('utf-8')))\\n except ValueError:\\n log.exception(\\\"Failed to parse JSON message %s\\\", request_str)\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a4513afc2b0c70d2f7b4bcacde296c\",\n \"score\": \"0.6466923\",\n \"text\": \"def run(self):\\n while self.running:\\n for msg in self.sub.listen():\\n if msg['type'] == 'message':\\n message = msg['data'].decode(\\\"utf-8\\\")\\n event = message.split(';')[0]\\n value = message.split(';')[1]\\n uid = message.split(';')[2]\\n\\n if event == \\\"SonarLeftDetected\\\" or event == \\\"SonarRightDetected\\\":\\n dir = \\\"left\\\" if event == \\\"SonarLeftDetected\\\" else \\\"right\\\"\\n self.obstacle_detected(eval(value), dir)\\n if event == \\\"SonarLeftNothingDetected\\\" or event == \\\"SonarRightNothingDetected\\\":\\n self.obstacle_detected(0, None)\\n if event == \\\"robotIsFalling\\\" or event == \\\"robotHasFallen\\\":\\n self.robot_fallen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"885e51046069dd35403b1f2e4a03c726\",\n \"score\": \"0.64516777\",\n \"text\": \"def run(self):\\n self.poller = select.epoll()\\n self.pollmask = select.EPOLLIN | select.EPOLLHUP | select.EPOLLERR\\n self.poller.register(self.server,self.pollmask)\\n while True:\\n # poll sockets\\n try:\\n fds = self.poller.poll(timeout=1)\\n except:\\n return\\n for (fd,event) in fds:\\n # handle errors\\n if event & (select.POLLHUP | select.POLLERR):\\n self.handleError(fd)\\n continue\\n # handle the server socket\\n if fd == self.server.fileno():\\n self.handleServer()\\n continue\\n # handle client socket\\n self.setTime(fd)\\n self.handleClient(fd)\\n self.markSweep()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de3dc91b368b6ca6812d12fa5e749ea\",\n \"score\": \"0.64459133\",\n \"text\": \"def run(self):\\n\\n try:\\n while True:\\n connection, address = self.serverSock.accept()\\n serverRunThread = threading.Thread(target=self.handler, args=(connection, address))\\n serverRunThread.daemon = True\\n serverRunThread.start()\\n\\n self.users.append((connection, address))\\n print_safe(' [*] {0}:{1} connected'.format(address[0], str(address[1])))\\n\\n except KeyboardInterrupt:\\n self.serverSock.close()\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8cf2ce2c67feda3c3da88e8692947ce\",\n \"score\": \"0.64447606\",\n \"text\": \"def execute(self):\\n self.running = True\\n\\n self.log_info(\\\"Starting...\\\")\\n while self.running:\\n # self.log(\\\"Running...\\\")\\n try:\\n message = self.inbox.get_nowait()\\n self.handle(message)\\n\\n # yield to event loop after processing the message\\n self.tick()\\n\\n except Empty:\\n # Empty signifies that the queue is empty, so yield to another node\\n self.tick()\\n # pass\\n\\n # self.tick()\\n # self.stop()\\n self.log_info(\\\"Exiting...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7687aefa0f91d13a48afd3b09a36519\",\n \"score\": \"0.6433072\",\n \"text\": \"def message_loop(self):\\r\\n\\r\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3785f961604497f7f0684ba96150a91\",\n \"score\": \"0.6401567\",\n \"text\": \"def execute(self):\\n sock = self.sock\\n # Look for responses from all recipients\\n while True:\\n print(sys.stderr, \\\"waiting to receive\\\")\\n try:\\n data, server = sock.recvfrom(16)\\n self.on_receive(data, server)\\n except socket.timeout:\\n print(sys.stderr, \\\"timed out, no more responses\\\")\\n break\\n else:\\n print(sys.stderr, 'received \\\"%s\\\" from %s' % (data, server))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62a599bc9b65d7df0e06aaf861f7241\",\n \"score\": \"0.6397384\",\n \"text\": \"def __callback(self):\\n while self.is_connected:\\n data = self._connection.next()\\n json_data = json.loads(data)\\n\\n event = json_data['ev']\\n\\n if event == 'update':\\n self.on_update(json_data['data'])\\n\\n else:\\n print ('Unknown event: %s' % event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08779d35b580179262609f3925eebd2a\",\n \"score\": \"0.63903886\",\n \"text\": \"async def _run_inbox_handler(self, inbox, handler_name):\\n # Run the handler in a threadpool, so that it cannot block other handlers (from a different task),\\n # or the main client thread. The number of worker threads forms an upper bound on how many instances\\n # of the same handler can be running simultaneously.\\n tp = ThreadPoolExecutor(max_workers=4)\\n while True:\\n handler_arg = await inbox.get()\\n # NOTE: we MUST use getattr here using the handler name, as opposed to directly passing\\n # the handler in order for the handler to be able to be updated without cancelling\\n # the running task created for this coroutine\\n handler = getattr(self, handler_name)\\n if inspect.iscoroutinefunction(handler):\\n # Wrap the coroutine in a function so it can be run in ThreadPool\\n def coro_wrapper(coro, arg):\\n asyncio_compat.run(coro(arg))\\n\\n # TODO: get exceptions to propogate\\n tp.submit(coro_wrapper, handler, handler_arg)\\n else:\\n # Run function directly in ThreadPool\\n # TODO: get exceptions to propogate\\n tp.submit(handler, handler_arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a658766653444a4f21f3c3ac6053762f\",\n \"score\": \"0.63893706\",\n \"text\": \"def runner(self):\\n try:\\n print(\\\"\\\\nWhen a thread is not connected, press Ctrl C \\\" +\\n \\\"with no arguments to shutdown the server\\\\n\\\")\\n while True:\\n print(\\\"Waiting for connection...\\\")\\n client, address = self.server.accept()\\n handler = ClientHandler(client, self.server_name)\\n handler.start()\\n print(\\\"\\\\n... connected from:\\\", address)\\n print(\\\"On IP, Port:\\\", client.getsockname())\\n print(\\\"\\\")\\n except KeyboardInterrupt:\\n self.server.close()\\n sys.exit(0)\\n except OSError:\\n print(\\\"Attempted to get message, however, either a keyboard interrupt caused a stop,\\\\n\\\" +\\n \\\"or arguments given caused an error, server shutting down. Original Error: OSError\\\")\\n self.server.close()\\n except BrokenPipeError:\\n print(\\\"Interrupts caused failed execution, server shutting down. Original Error: BrokenPipeError\\\")\\n self.server.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081cc5dd00998705f785e23d67b30373\",\n \"score\": \"0.6371375\",\n \"text\": \"def loop_forever(self):\\r\\n while True:\\r\\n self.client.wait_msg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7c1d7fc9378a402a684e2ed66b9cd9\",\n \"score\": \"0.63677037\",\n \"text\": \"def run(self):\\n\\n while not self._stop.is_set():\\n sleep(INTERVAL)\\n try:\\n messages = self.socket.recv(BUFSIZE).split('\\\\n')\\n except socket.error as error_:\\n if error_[0] == errno.EWOULDBLOCK:\\n continue\\n elif error_[0] == errno.WSAECONNRESET:\\n print('I tried to recv something after connection closed, like the dumb computer I am.')\\n break\\n else:\\n raise error_\\n for message in messages:\\n if message == '':\\n continue \\n# print message\\n message_dict = loads(message)\\n self.command(message_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f107fa73d5f89984d8ce7c9b2a2654\",\n \"score\": \"0.63586986\",\n \"text\": \"def run(self):\\n try:\\n while True:\\n self.log('Loop start', level=logging.DEBUG)\\n \\n connection = None\\n channel = None\\n \\n try:\\n connection = self.fresh_connection()\\n if connection.connection_open:\\n self.log('Got connection', level=logging.DEBUG)\\n channel = connection.channel()\\n \\n channel.queue_declare(\\n queue=self.__queueName,\\n durable=True,\\n exclusive=False,\\n auto_delete=False)\\n \\n channel.basic_consume(\\n consumer=self.__handle_delivery,\\n queue=self.__queueName)\\n \\n pika.asyncore_loop()\\n \\n self.log('Finished with connection', level=logging.DEBUG)\\n else:\\n self.log('No connection', level=logging.DEBUG)\\n finally:\\n if not channel is None:\\n self.log('Closing channel', level=logging.DEBUG)\\n channel.close()\\n channel = None\\n \\n if not connection is None:\\n self.log('Closing connection', level=logging.DEBUG)\\n connection.close()\\n connection = None\\n \\n time.sleep(self.__loopTimeout)\\n except QuitApplication as e:\\n self.log('Quitting application ({0})'.format(e.signalName))\\n finally:\\n self.log('Log Server stopped')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159c6bfe518dcb2fc6cd713ea7916e36\",\n \"score\": \"0.6350875\",\n \"text\": \"def on_loop(self):\\n data = self.last_read_buffer + self.read()\\n if data:\\n packets = data.split(MSG_END_HEADER)\\n self.last_read_buffer = packets[-1]\\n packets.pop()\\n for packet in packets:\\n self.handle_packet(packet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0bad153c7451bb88dba7b94183ac2d0\",\n \"score\": \"0.63482934\",\n \"text\": \"def run(self):\\n while True:\\n self.handle_ready()\\n p = self.polling_set()\\n # Poll.\\n events = p.poll()\\n\\n self.handle_events(events)\\n self.remove_finished()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fb38004a13c51a6f205c8450e34df4\",\n \"score\": \"0.6304422\",\n \"text\": \"def loop(server):\\n\\n # create logger\\n rootLogger = create_logger(server.logfolder)\\n if rootLogger is not None:\\n server.logging_enabled = True\\n\\n try:\\n # This is the main loop\\n while not server.break_loop:\\n try:\\n server.poll()\\n if server.serving:\\n if not len(server):\\n # The server is serving, but there are no\\n # connections so put in a sleep\\n time.sleep(0.1)\\n else:\\n # if the server is not serving, put a sleep in the loop\\n time.sleep(0.25)\\n except NoService, e:\\n server.log_exception(e)\\n # Unable to bind\\n # GUI will handle this, so loop continues\\n except Exception, e:\\n # log the exception and exit the main loop\\n server.log_exception(e)\\n return 1\\n except KeyboardInterrupt:\\n return 0\\n finally:\\n # shutdown the server\\n server.shutdown()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50fdf448e9ab6994d3e4ea34410f13c\",\n \"score\": \"0.6301584\",\n \"text\": \"async def process_events(self):\\n try:\\n while True:\\n j = await self.recv()\\n await self.process_packet(j)\\n except websockets.ConnectionClosed as err:\\n log.exception('Connection failed')\\n await self.reconnect(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ff189b215ddc2d3ee84e559a78a6fd\",\n \"score\": \"0.62858033\",\n \"text\": \"def handle(self):\\n port = self.request.getpeername()[1]\\n self.session = self.server.mainserver.create_session(session_id=port)\\n self.session.connect(self)\\n\\n while True:\\n try:\\n messages = self.receive_message()\\n if messages:\\n for message in messages:\\n self.handle_message(message)\\n except EOFError:\\n break\\n except IOError:\\n logger.exception(\\\"IOError in CoreAuxRequestHandler\\\")\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af06f5a9882abc2646f200a80bfaf53\",\n \"score\": \"0.625085\",\n \"text\": \"def loop(self, filename, handler):\\n tailer = FileTailer(filename)\\n while True:\\n entry, comment = tailer.poll()\\n if not entry:\\n time.sleep(1.0)\\n continue\\n kos, not_kos, error = self.koscheck_logentry(entry)\\n handler(comment, kos, not_kos, error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4359c2aee0074b225c6e123d386eae\",\n \"score\": \"0.6238515\",\n \"text\": \"def _listen(self):\\n # start server\\n self._socket.bind('tcp://{}:{}'.format(self.zmqconfig.get('host', ZMQ_DEFAULT_HOST), self.zmqconfig.get('port', ZMQ_DEFAULT_PORT)))\\n while True:\\n query = self._socket.recv_pyobj()\\n self._socket.send_pyobj('QUERY_RECEIVED')\\n callback_string = self._socket.recv_pyobj()\\n callback = pickle.loads(callback_string)\\n self._socket.send_pyobj('CALLBACK_RECEIVED')\\n self._execute(query, callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9509b008ee461576c678a379a44dce23\",\n \"score\": \"0.6231346\",\n \"text\": \"def _receive_loop(self, ws_url):\\n\\n if ws_url not in self._conns:\\n self._logr.warning(\\\"<{}> is not an active connection\\\".format(ws_url))\\n return\\n\\n if ws_url not in self._messages:\\n self._messages[ws_url] = {}\\n\\n while not self._receive_stop_events[ws_url].is_set():\\n try:\\n raw_res = yield self._conns[ws_url].read_message()\\n\\n self._logr.debug(\\\"Read message: {}\\\".format(raw_res))\\n\\n if raw_res is None:\\n self._logr.debug(\\\"Cannot read message: Closed WS connection\\\")\\n yield tornado.gen.sleep(self.SLEEP_AFTER_ERR_SECS)\\n continue\\n\\n msg_res = self._parse_msg_response(raw_res)\\n\\n if msg_res:\\n self._messages[ws_url][msg_res.id] = msg_res\\n conditions = self._msg_conditions.get(ws_url, None)\\n\\n if conditions and msg_res.id in conditions:\\n self._logr.debug(\\\"Notifying: {}\\\".format(msg_res.id))\\n conditions[msg_res.id].notify_all()\\n except Exception as ex:\\n self._logr.warning(\\\"Error in read loop: {}\\\".format(ex), exc_info=True)\\n yield tornado.gen.sleep(self.SLEEP_AFTER_ERR_SECS)\\n\\n self._receive_stop_events[ws_url].clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50aefc1f5b8e70679c98785f6f90b75f\",\n \"score\": \"0.6230563\",\n \"text\": \"def incoming(self):\\n with ThreadPoolExecutor(max_workers=self.worker_limit) as executor:\\n with socket(AF_INET, SOCK_STREAM) as sock:\\n sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\\n sock.bind((\\\"0.0.0.0\\\", self.socket_port))\\n sock.listen(10)\\n self.logger.info(\\\"Server listening on local TCP port %s\\\",\\n self.socket_port)\\n while True:\\n conn, addr = sock.accept()\\n executor.submit(self.handler, conn, addr)\\n self.logger.info(\\\"Server shutting down\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdddb1a1c5739a3e99331cace2e216ba\",\n \"score\": \"0.623004\",\n \"text\": \"def loop(self):\\r\\n self.client.check_msg()\\r\\n return;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8564f11c70394715d85e581628f00514\",\n \"score\": \"0.62184477\",\n \"text\": \"def run_main_loop(self):\\n result = self.parse_input()\\n while result:\\n result = self.parse_input()\\n if result == 2:\\n self.respond(\\\"? unknown command\\\\n\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439177dc2306ff1e4df6810bf2c22722\",\n \"score\": \"0.62142485\",\n \"text\": \"def run(self):\\r\\n self.handle_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62daf70752f3e8a10360fecc09b51b78\",\n \"score\": \"0.6211222\",\n \"text\": \"def loop(self):\\n host, port = self._server_init()\\n self._logger.info(\\\"Listener on: {}:{}\\\".format(host, port))\\n downstream_addr = (\\n self._config.get(\\\"Downstream\\\", \\\"Host\\\"),\\n int(self._config.get(\\\"Downstream\\\", \\\"Port\\\")),\\n )\\n self._logger.info(\\n \\\"Connected to downstream: {}:{}\\\".format(\\n downstream_addr[0], downstream_addr[1]\\n )\\n )\\n downstream_cli = socket.create_connection(downstream_addr)\\n self._logger.info(\\\"Converter started.\\\")\\n upstream_conn, client_address = self._server.accept()\\n self._logger.info(\\\"Client connected: {}\\\".format(client_address))\\n self._loop(upstream_conn, downstream_cli, downstream_addr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75fe90ea52757d96af39dd1725be59d\",\n \"score\": \"0.62013793\",\n \"text\": \"def handler ( self ) :\\r\\n\\r\\n\\r\\n # counter\\r\\n \\r\\n self.count = self.count + 1\\r\\n\\r\\n # updates number of events to send\\r\\n \\r\\n if self.number > 0 : self.number = self.number - 1\\r\\n \\r\\n # stops and deletes timer\\r\\n \\r\\n self.stop()\\r\\n \\r\\n## if not self.timer is None : del( self.timer )\\r\\n\\r\\n # next event\\r\\n \\r\\n if self.nextTimeMs is None : self.nextTimeMs = self.clockMs()\\r\\n \\r\\n self.nextTimeMs = self.nextTimeMs + self.periodMs\\r\\n \\r\\n # triggers one shot timer for next event\\r\\n \\r\\n self.trigger()\\r\\n \\r\\n # external call back\\r\\n \\r\\n if not self.command is None :\\r\\n \\r\\n # already busy executing command\\r\\n \\r\\n if self.busy : return\\r\\n \\r\\n self.busy = True\\r\\n \\r\\n self.command()\\r\\n \\r\\n self.busy = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd4fe24cdb425449c42e39abd239be6e\",\n \"score\": \"0.619992\",\n \"text\": \"def _dispatcher(self):\\n while 1:\\n try:\\n response = self._incoming_messages.get(block=True)\\n self._format_incoming(response)\\n now = time.time()\\n # match the event to the best command\\n for command in self.commands[:]:\\n if command.deadline is not False and command.deadline < now:\\n self.unregister_command(command) # silently remove him\\n continue\\n match = command.matches(self, response)\\n if match:\\n command(_SlackBotWrapper(self, response), response, *match.groups())\\n if command.activations is not False and command.activations <= 0:\\n self.unregister_command(command)\\n except Exception:\\n self.debug(u'\\\\n'.join([traceback.format_exc(), str(response)]), 'red')\\n else:\\n try:\\n if u'_logged' not in response:\\n self.debug(response, 'gray')\\n except:\\n print(\\\"Wow something went REAL wrong.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bdb2843e4ed84aa0a52aee8db3262fc\",\n \"score\": \"0.6189986\",\n \"text\": \"def _receive_thread(self):\\r\\n while 1:\\r\\n try:\\r\\n message = json.loads(self.ws.recv())\\r\\n if 'id' in message:\\r\\n message_id = message['id']\\r\\n event = self.requests.pop(message_id, None)\\r\\n if event is not None:\\r\\n self.results[message_id] = message\\r\\n event.set()\\r\\n except Exception:\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfea8ae99426dd404437a1783fa08c2\",\n \"score\": \"0.6182838\",\n \"text\": \"async def receive(self):\\n\\n await self.create_connection()\\n\\n while True:\\n data = json.loads(await self.ws.recv())\\n #print(data)\\n\\n self.sequence_id = data['s'] # Set the sequence id to the one sent by the gateway in case a reconnect is needed\\n\\n if data['op'] != 0:\\n await self.handle_op(data['op'], data)\\n\\n await self.dispatcher.dispatch(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbdca3f01cee59c6f4f3e36cf7a15053\",\n \"score\": \"0.6154406\",\n \"text\": \"def read_loop(self, event): # !TODO timeout to signal\\n\\n while True:\\n try:\\n server_ans = recvTlv(self.sock)\\n\\n if TLV_OK_TAG in server_ans or TLV_FAIL_TAG in server_ans: # ACK / NACK response - main thread is awaiting for it\\n # print(\\\"Message {} has ACK/NACK tag => saving to pipeline for future processing\\\".format(server_ans))\\n self.pipeline.put(server_ans)\\n continue\\n\\n if TLV_NEWTURN_TAG in server_ans:\\n client_logger.debug(\\\"Put TLV_NEWTURN_TAG msg into the pipeline\\\")\\n self.pipeline.put(server_ans)\\n continue\\n \\n # print(\\\"Message {} is a request from the server\\\".format(server_ans))\\n self.handle_answer(server_ans) # received msg is not a control msg\\n except (OSError, EOFError) as e:\\n print(\\\"\\\\nServer error\\\\n\\\")\\n client_logger.error(\\\"Error while reading data: \\\" + str(e))\\n os.kill(os.getpid(), signal.SIGINT) # should raise Keyboard interrupt in the main thread\\n event.set() # alarm main thread that program should exit\\n return\\n except Exception as e:\\n client_logger.error(\\\"Other error while reading data: \\\" + str(e))\\n os.kill(os.getpid(), signal.SIGINT)\\n event.set() # alarm main thread that program should exit\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc5a032b017db13c53f70e791d6eda14\",\n \"score\": \"0.61470777\",\n \"text\": \"def async_handle(self):\\n\\n self.raw_requestline = yield from self.rfile.readline()\\n if len(self.raw_requestline) > 65536:\\n self.requestline = ''\\n self.request_version = ''\\n self.command = ''\\n self.send_error(414)\\n return\\n\\n if not (yield from self.parse_request()): # An error code has been sent, just exit\\n return\\n\\n handler = ServerHandler(\\n self.rfile, self.wfile, self.get_stderr(), self.get_environ()\\n )\\n handler.request_handler = self # backpointer for logging\\n yield from handler.run(self.server.get_app())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2696f3fe24bd9f41cadddc8f6f8dea70\",\n \"score\": \"0.614598\",\n \"text\": \"def loop(self):\\n print(f'Running on {socket.gethostname()}')\\n self.mgr.run(self.base_components)\\n with cli_connection.server(paths.beeflow_socket()) as server:\\n while not self.quit:\\n # Handle a message from the client, if there is one\\n self.handle_client(server)\\n # Poll the components\\n self.mgr.poll()\\n time.sleep(1)\\n # Kill everything, if possible\\n self.mgr.kill()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ac7d7020d9fad93912e68c129fad75\",\n \"score\": \"0.61454654\",\n \"text\": \"def handle(self):\\n try:\\n data = self.request.recv(1024).decode('UTF-8').strip()\\n # process the data, i.e. print it:\\n\\n test = json.loads(data)\\n\\n # send some 'ok' back\\n\\n #channels_raw = server.send_command(\\\"channellist\\\").data\\n #channels = [Channel(**channel) for channel in channels_raw]\\n\\n #clients_raw = server.clientlist()\\n #clients = [Client(**clients_raw[client]) for client in clients_raw]\\n #test = Client.clients_json(clients)\\n self.request.sendall(bytes(json.dumps(handler.command(test)), 'UTF-8'))\\n except Exception as e:\\n print(\\\"Exception wile receiving message: \\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"590e650ecd121ef11b701dbd35e058f8\",\n \"score\": \"0.61379915\",\n \"text\": \"def execute(self):\\n self.running = True\\n\\n # self.log_info(\\\"Starting...\\\")\\n # while self.running:\\n # # self.log(\\\"Running...\\\")\\n # try:\\n # message = self.inbox.get_nowait()\\n # self.handle(message)\\n\\n # # yield to event loop after processing the message\\n # self.tick()\\n\\n # except Empty:\\n # # Empty signifies that the queue is empty, so yield to another node\\n # self.tick()\\n # # pass\\n\\n # self.tick()\\n # self.stop()\\n # self.log_info(\\\"Exiting...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a95340be1f187d9c069261cfb0bb3b\",\n \"score\": \"0.6122714\",\n \"text\": \"async def _dispatch_loop(self):\\n last_msg_time = time.time()\\n async with websockets.connect(self._uri) as ws:\\n self._ws = ws\\n while self._running:\\n msg = await ws.recv()\\n msg = self.decode_message(msg)\\n\\n if msg is None or msg.get_type() == 'BAD_DATA':\\n continue\\n\\n # send a heartbeat message back, since this needs to be\\n # constantly sent so the autopilot knows this exists\\n if msg.get_type() == 'HEARTBEAT':\\n # send -> type, autopilot, base mode, custom mode, system status\\n outmsg = self._mav.heartbeat_encode(mavutil.mavlink.MAV_TYPE_GCS,\\n mavutil.mavlink.MAV_AUTOPILOT_INVALID, 0, 0,\\n mavutil.mavlink.MAV_STATE_ACTIVE)\\n await self.send_message(outmsg)\\n\\n # print('Message received', msg)\\n current_time = time.time()\\n\\n # print(\\\"Time between messages\\\", current_time - last_msg_time)\\n\\n # If we haven't heard a message in a given amount of time\\n # terminate connection and event loop.\\n if current_time - last_msg_time > self._timeout:\\n self.notify_message_listeners(MsgID.CONNECTION_CLOSED, 0)\\n self.stop()\\n\\n # update the time of the last message\\n last_msg_time = current_time\\n\\n dispatch_message(self, msg)\\n\\n await self._shutdown_event_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a20d67e0045810507e29c2fdf46671\",\n \"score\": \"0.61191785\",\n \"text\": \"def run(self):\\n\\t\\t\\n\\t\\twhile self.running or len(self.outbox) > 0:\\n\\t\\t\\tif len(self.outbox) > 0:\\n\\t\\t\\t\\tif self.debug:\\n\\t\\t\\t\\t\\tprint \\\"[Notifier] There are\\\", len(self.outbox), \\\"messages to send\\\"\\n\\t\\t\\t\\tself.obLock.acquire()\\n\\t\\t\\t\\twhile len(self.outbox) > 0:\\n\\t\\t\\t\\t\\tmessage = self.outbox.pop()\\n\\t\\t\\t\\t\\tif self.debug:\\n\\t\\t\\t\\t\\t\\tprint \\\"[Notifier] Sending queued message to\\\", message.getTo()\\n\\t\\t\\t\\t\\tself.client.send(message)\\n\\t\\t\\t\\tself.obLock.release()\\n\\t\\t\\tself.client.process(2)\\n\\t\\tif self.debug:\\n\\t\\t\\tprint \\\"[Notifier] Shutting down\\\"\\n\\t\\tself.client.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6df57c5860bd78334c3dfdf7df9dd4\",\n \"score\": \"0.6119011\",\n \"text\": \"def run(self) -> None:\\n\\n self.logger.info('Starting Socket consumer.')\\n\\n while True:\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\\n asyncio.get_event_loop().run_until_complete(self.get())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd8e7b0e5b2ee64fe2f43061b9d1f21\",\n \"score\": \"0.61128116\",\n \"text\": \"def __runner(self):\\n \\n conn = pika.BlockingConnection(self.__connection_params)\\n chan = conn.channel()\\n \\n chan.add_on_return_callback(self.__on_returned_msg)\\n \\n while not self.__shutdown_event.is_set():\\n try:\\n req = self.__pending_requests.get(True, 5)\\n \\n self._logger.debug(\\\"invoking %r\\\", req)\\n \\n response = {}\\n \\n try:\\n response['result'] = req.callback(*req.args)\\n except:\\n self._logger.warn(\\\"exception invoking %r\\\", req, exc_info = True)\\n \\n # the exception value\\n response['exception'] = sys.exc_info()[1]\\n \\n resp_body = serializer_utils.serialize(response, req.content_type)\\n\\n chan.basic_publish(\\n exchange='',\\n routing_key = req.reply_to,\\n properties = pika.BasicProperties(\\n correlation_id = req.correlation_id,\\n content_type = req.content_type,\\n ),\\n body = resp_body\\n )\\n except Queue.Empty:\\n pass\\n \\n \\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527fd2f96bffa349a49d1479846874bf\",\n \"score\": \"0.61126834\",\n \"text\": \"def Run(self):\\r\\n \\r\\n if (self._server is None): self.PrepareServer()\\r\\n while not self.quit:\\r\\n logging.debug('Waiting for request')\\r\\n self._server.handle_request()\\r\\n logging.debug('Finished processing requests')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef1c0cbee2e2d206f1337c5c8878d6a\",\n \"score\": \"0.61029685\",\n \"text\": \"def handle(self):\\n while True:\\n try:\\n command = self.fetch_command()\\n if not command:\\n raise ControlException(\\\"No command found.\\\")\\n\\n for module in self.modules:\\n foundWords = []\\n for word in module.commandWords:\\n if str(word) in command:\\n foundWords.append(str(word))\\n if len(foundWords) == len(module.commandWords):\\n #A command has been said correctly so we can execute the message\\n response = module.execute(command)\\n #if the command was formatted wrong\\n if response == \\\"ERROR\\\":\\n print(\\\"Invalid command. Try again.\\\")\\\\\\n #if we were setting up the token\\n if response == \\\"Setup\\\":\\n print(\\\"Setup complete.\\\")\\n #otherwise we have the song JSON object returned and we need to parse it and send to MQTT\\n else:\\n index = response.find('#$#');\\n beats = json.loads(response[index+3:])\\n index2 = response.find('#%#')\\n artist = response[0:index2]\\n song = response[index2+3:index]\\n print(\\\"Playing \\\" + song.upper() + \\\" by \\\" + artist.upper())\\n self.clientRP.publish(\\\"raspberrypi/lcd\\\", response[0:index])\\n self.clientRP.publish(\\\"raspberrypi/led\\\", response[index+3:])\\n except (TypeError, ControlException):\\n pass\\n except Exception as exc:\\n print(\\\"The request is not included in the API. Try Again.\\\")\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0b2fd1c100bbe798d7028370db4c15\",\n \"score\": \"0.60967106\",\n \"text\": \"def run(self):\\n while True:\\n msg = self.reader.read()\\n if msg: # Set event if have new message\\n self.log += \\\"%s\\\\n\\\" % msg.decode(DEFAULT_ENCODE)\\n self.event.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da5692ebf649d4b31143786ee31ddb5\",\n \"score\": \"0.6090398\",\n \"text\": \"def run(self):\\n while True:\\n socks = select.select(self.sockets.values(), [], [], 0.1)[0]\\n for conn in socks:\\n try:\\n k = conn.recv(65535)\\n except:\\n # either died on a connection reset, or was SIGTERM's by parent\\n return\\n if k:\\n for sock in self.sockets:\\n if self.sockets[sock] == conn:\\n srcif = sock\\n msg = json.loads(k)\\n if not self.handle_packet(srcif, msg):\\n self.send_error(conn, msg, srcif)\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56b93b51623a2714afc22f43d37e900\",\n \"score\": \"0.608456\",\n \"text\": \"async def _recv_loop(self):\\n try:\\n while self._connected:\\n try:\\n data = await self._recv()\\n except asyncio.CancelledError:\\n break\\n except (IOError, asyncio.IncompleteReadError) as e:\\n self._log.warning('Server closed the connection: %s', e)\\n await self._recv_queue.put((None, e))\\n await self.disconnect()\\n except InvalidChecksumError as e:\\n self._log.warning('Server response had invalid checksum: %s', e)\\n await self._recv_queue.put((None, e))\\n except InvalidBufferError as e:\\n self._log.warning('Server response had invalid buffer: %s', e)\\n await self._recv_queue.put((None, e))\\n except Exception as e:\\n self._log.exception('Unexpected exception in the receive loop')\\n await self._recv_queue.put((None, e))\\n await self.disconnect()\\n else:\\n await self._recv_queue.put((data, None))\\n finally:\\n await self.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8466187dc60ef8490ce4f06d5a6d4624\",\n \"score\": \"0.6083547\",\n \"text\": \"def on_msg(self,sock: socket.socket):\\n\\t\\tlogging.info(\\\"Listening for server messages.\\\")\\n\\t\\tself.run = True\\n\\t\\tself.on_connect()\\n\\t\\twhile self.run:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tdata = sock.recv(LEN_OF_HEADER)\\n\\t\\t\\t\\tif data == b'':\\n\\t\\t\\t\\t\\tself.run = False\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tbody_len = get_size_of_msg(data)\\n\\t\\t\\t\\tbody = b''\\n\\t\\t\\t\\tif body_len != 0:\\n\\t\\t\\t\\t\\twhile body_len > 0:\\n\\t\\t\\t\\t\\t\\tif body_len > MAX_LEN_OF_RECV:\\n\\t\\t\\t\\t\\t\\t\\tbody += sock.recv(MAX_LEN_OF_RECV)\\n\\t\\t\\t\\t\\t\\t\\tbody_len -= MAX_LEN_OF_RECV\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tbody += sock.recv(body_len)\\n\\t\\t\\t\\t\\t\\t\\tbody_len = 0\\n\\t\\t\\t\\tmsg = Message()\\n\\t\\t\\t\\tmsg.unpack(data + body)\\n\\t\\t\\t\\tif msg.type in self.type_callbacks.keys():\\n\\t\\t\\t\\t\\tself.type_callbacks[msg.type](msg)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.run = False\\n\\t\\t\\t\\t\\traise Exception(\\\"Type: %s isnt defined in the callbacks\\\" % msg.type)\\n\\t\\t\\texcept timeout:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tlogging.info(e)\\n\\t\\t\\t\\tself.run = False\\n\\t\\t\\t\\tif self.raise_exceptions:\\n\\t\\t\\t\\t\\traise e\\n\\t\\t\\t\\tbreak\\n\\t\\tself.run = False\\n\\t\\tsock.close()\\n\\t\\tself.on_disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33eee1aa590f9e8f422988a42f281ee1\",\n \"score\": \"0.60823125\",\n \"text\": \"def receiver(conn):\\r\\n while 1:\\r\\n msg = conn.recv()\\r\\n if 'END' in msg:\\r\\n break\\r\\n print(F\\\"Receive the messages: {msg}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1c9715fb5a4a91fb083acbb2388d89\",\n \"score\": \"0.60811144\",\n \"text\": \"def handler(self, timeout=0):\\n while True:\\n try:\\n yield self.recv(timeout=timeout)\\n except TimeoutError:\\n yield None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb2eef4f1256de6bc40ebc26f08c8183\",\n \"score\": \"0.6077736\",\n \"text\": \"def run(self):\\n while True:\\n for client in self.clients:\\n gevent.spawn(self.send, client)\\n gevent.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf24b622306ffab9d9a8e6807c386a7b\",\n \"score\": \"0.6076663\",\n \"text\": \"def catch_data(self):\\n while True:\\n buf = self.irc.recv(4096)\\n lines = buf.split('\\\\n')\\n for data in lines:\\n data = str(data).strip()\\n \\n if len(data) < 2:\\n continue\\n \\n # server ping/pong? \\\"PING :FFFFFFFF1155571C\\\"\\n if data.startswith('PING'):\\n self.send('PONG :' + data.split(':')[1])\\n if self.connected == False:\\n self.connected = True\\n \\n if len(data.split()) < 2:\\n continue\\n \\n try:\\n if len(data.split()) >= 4:\\n line = data.split()\\n sender = line[0][1::].split('!',1)[0] # :FM1337!Allen@staff.bouncer.ml\\n location = line[2] # Can be channel or user\\n text = ' '.join(line[3::])[1::] # Remove the first : then re-join the text\\n func = getattr(self, 'trigger_%s' % data.split()[1])\\n self.exec_buffer.append({'function': func, 'args': [data, sender, location, text]})\\n else:\\n func = getattr(self, 'trigger_%s' % data.split()[1])\\n self.exec_buffer.append({'function': func, 'args': [data]})\\n except AttributeError:\\n continue\\n except:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1defdd8c5c1c015c4088ed1a8c085f5\",\n \"score\": \"0.60731703\",\n \"text\": \"def messagesHandler(self):\\n try:\\n messageArr = self._component.recv()\\n for message in messageArr:\\n if(message is not None):\\n t, m = Parser.splitMessage(message)\\n self._container.append(self._component.getName(), message)\\n\\n except socket.error as error: # Error that happends when TCP client disconect.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de4a87699e43cb704c6a3062d7bcb525\",\n \"score\": \"0.60722226\",\n \"text\": \"def run(self):\\n while True:\\n try:\\n for item in self.reddit.inbox.unread(limit=None):\\n if isinstance(item, praw.models.Message):\\n try:\\n self.process_message(item)\\n item.mark_read()\\n except Exception as e:\\n print(\\\"Error processing message: \\\" + str(item))\\n traceback.print_exc()\\n time.sleep(1)\\n except Exception as e:\\n print(\\\"Error reading inbox: \\\" + str(e))\\n traceback.print_exc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecec4930dad9af2a479b0dcdd1fa7416\",\n \"score\": \"0.6071832\",\n \"text\": \"def __listen_almost_forever(\\n self,\\n handler: Callable,\\n preprocess: Callable = lambda x: x,\\n poll_timeout: int = 500,\\n ):\\n while True:\\n with self.__lock:\\n if self.__done:\\n break\\n\\n # Don't allow pausing halfway through a message\\n with self.__background_lock:\\n msg = self.__listener.recv(poll_timeout)\\n if msg is None:\\n continue\\n self.__message_flow(msg, handler, preprocess)\\n\\n self.__listener.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f094aa170b5a803cca8b3b39d929937\",\n \"score\": \"0.606992\",\n \"text\": \"def run(self) -> None:\\n messages = self.server.server_receive_message()\\n for message in messages:\\n self.progress.emit(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c267cc4366675389e3706d784d230e0\",\n \"score\": \"0.6067176\",\n \"text\": \"def handle(self):\\n while True:\\n chunk = self.connection.recv(4)\\n if len(chunk) < 4: break\\n slen = struct.unpack(\\\">L\\\", chunk)[0]\\n chunk = self.connection.recv(slen)\\n while len(chunk) < slen:\\n chunk = chunk + self.connection.recv(slen - len(chunk))\\n obj = pickle.loads(chunk)\\n record = logging.makeLogRecord(obj)\\n self.handleLogRecord(record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf981b99c960273b6519f5b005ad38cc\",\n \"score\": \"0.6050623\",\n \"text\": \"async def handle_incoming(self):\\n # TODO Deal with other types of messages using flag (currently _)\\n\\n while True:\\n stream_id, flag, message = await self.read_message()\\n\\n if stream_id is not None and flag is not None and message is not None:\\n if stream_id not in self.buffers:\\n self.buffers[stream_id] = asyncio.Queue()\\n await self.stream_queue.put(stream_id)\\n\\n if flag is get_flag(True, \\\"NEW_STREAM\\\"):\\n # new stream detected on connection\\n await self.accept_stream()\\n\\n if message:\\n await self.buffers[stream_id].put(message)\\n\\n # Force context switch\\n await asyncio.sleep(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359103b85430f54b77eff232ecec9e02\",\n \"score\": \"0.60503155\",\n \"text\": \"def handle(self):\\n self.ws_addr = \\\"%s:%s\\\" % (\\n self.ws.environ[\\\"REMOTE_ADDR\\\"],\\n self.ws.environ[\\\"REMOTE_PORT\\\"])\\n logger.info(\\\"%s connected\\\" % self.ws_addr)\\n\\n if \\\"connected\\\" in actions:\\n Handler.currents.append(self.ws)\\n actions[\\\"connected\\\"]()\\n Handler.currents.pop()\\n\\n go = True\\n while go:\\n try:\\n m = self.ws.receive()\\n except:\\n m = None\\n\\n if m is None:\\n go = False\\n else:\\n self.do_message(m)\\n\\n if \\\"disconnected\\\" in actions:\\n Handler.currents.append(self.ws)\\n actions[\\\"disconnected\\\"]()\\n Handler.currents.pop()\\n\\n # remove websocket from connected socket list\\n Handler.s.remove(self.ws)\\n # check if no more sockets remain and call\\n # disconnect functions if so.\\n if len(Handler.s) is 0:\\n for f in alldisconnected.funcs:\\n f()\\n logger.info(\\\"%s disconnected\\\" % self.ws_addr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"452b97be6e1bc24b5a229729216a9500\",\n \"score\": \"0.60486776\",\n \"text\": \"def mainloop(self):\\n lverb = True\\n is_rfid_scanner = (self.comm_link is not None)\\n\\n print(\\\"mainloop begin: is_rfid_server: {}\\\".format(is_rfid_scanner))\\n self.logger.info(\\\"mainloop begin\\\")\\n # start a random generator thread for testing....\\n # self.randTM = Taskmeister.RandomGenerator(self.msgQ, self.logger)\\n # self.randTM.set_active(True)\\n\\n if is_rfid_scanner:\\n # send the RFID status to the webclient\\n rfid_stat = self.comm_link.get_rfid_state()\\n self.send_ws_msg(CommonMSG(CommonMSG.MSG_SV_RFID_STATREP, rfid_stat))\\n else:\\n # send the stocky server config data\\n self.send_server_config()\\n # send the QAI update status to the webclient\\n self.send_qai_status(None)\\n\\n do_loop = True\\n while do_loop:\\n if lverb:\\n print(\\\"YO: before get\\\")\\n msg: CommonMSG = self.msgQ.get()\\n self.logger.debug(\\\"handling msgtype '{}'\\\".format(msg.msg))\\n if lverb:\\n print(\\\"YO: handling msgtype '{}'\\\".format(msg.msg))\\n # handle a EOF separately\\n if msg.msg == CommonMSG.MSG_WC_EOF:\\n if lverb:\\n print(\\\"mainloop detected WS_EOF... quitting\\\")\\n self.ws = None\\n do_loop = False\\n print(\\\"step 2\\\")\\n if msg.is_from_rfid_reader():\\n self.logger.debug(\\\"GOT RFID {}\\\".format(msg.as_dict()))\\n self.activate_rfid_spinner()\\n\\n is_handled = False\\n if self.ws is not None and msg.msg in CommonStockyServer.MSG_FOR_WC_SET:\\n is_handled = True\\n # print(\\\"sending to WS...\\\")\\n self.send_ws_msg(msg)\\n # print(\\\"...OK send\\\")\\n if self.tls is not None and msg.msg in CommonStockyServer.MSG_FOR_RFID_SET:\\n is_handled = True\\n self.tls.send_rfid_msg(msg)\\n if msg.msg in CommonStockyServer.MSG_FOR_ME_SET:\\n print(\\\"msg for me: {}\\\".format(msg.msg))\\n is_handled = True\\n self.server_handle_msg(msg)\\n print(\\\"done handling\\\")\\n if not is_handled:\\n mmm = \\\"mainloop DID NOT handle msgtype '{}'\\\".format(msg.msg)\\n self.logger.error(mmm)\\n print(mmm)\\n print(\\\"end of ML.while\\\")\\n print(\\\"OUT OF LOOP\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46609d36d2efed72778e8638997a9184\",\n \"score\": \"0.6048616\",\n \"text\": \"def receiver():\\n while True:\\n msg, future = incoming.get(True)\\n if msg == TerminateReceiver:\\n break\\n else:\\n method, args, kw = msg\\n future.target = getattr(obj, method)(*args, **kw)\\n future.event.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8ad9ede0abe26241b991d85f347a2f\",\n \"score\": \"0.60392517\",\n \"text\": \"def run(self):\\n while True:\\n recv_data = self.socket.recv(self.buffer_size)\\n print(\\\"Server received data: {}\\\".format(recv_data))\\n packet_info = recv_data.decode('utf-8').strip().split(\\\",\\\")\\n\\n if packet_info[0] == \\\"rput\\\" and len(packet_info[1:]) == 2:\\n self.__read_file(*packet_info[1:])\\n elif packet_info[0] == \\\"rget\\\" and len(packet_info[1:]) == 1:\\n self.__send_file(*packet_info[1:])\\n else:\\n if packet_info[0] is not '':\\n print(\\\"Server does not support that check sent command\\\")\\n self.socket.sendall(\\\"Invalid\\\".encode('utf-8'))\\n\\n if not recv_data:\\n print(\\\"Disconnecting from client {}:{}\\\".format(self.ip, self.port))\\n self.socket.shutdown(socket.SHUT_RDWR)\\n self.socket.close()\\n break\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994473,"cells":{"query_id":{"kind":"string","value":"36afa34acf326a9c7053b818cecc01ff"},"query":{"kind":"string","value":"Convert list of tiles as returned by SH WFS into list of time ranges whose time deltas are not greater than max_timedelta."},"positive_passages":{"kind":"list like","value":[{"docid":"c32b8a492c9b5dc658dfb7c466e6b815","score":"0.7689309","text":"def tile_features_to_time_ranges(cls, tile_features, max_timedelta: Union[str, pd.Timedelta] = '1H') \\\n -> List[Tuple[pd.Timestamp, pd.Timestamp]]:\n max_timedelta = pd.to_timedelta(max_timedelta) if isinstance(max_timedelta, str) else max_timedelta\n feature_properties = [feature[\"properties\"] for feature in tile_features]\n timestamps = [pd.to_datetime(f'{properties[\"date\"]}T{properties[\"time\"]}', utc=True)\n for properties in feature_properties]\n timestamps.sort()\n num_timestamps = len(timestamps)\n time_ranges = []\n i = 0\n while i < num_timestamps:\n timestamp1 = timestamp2 = timestamps[i]\n while i < num_timestamps:\n timestamp = timestamps[i]\n if timestamp - timestamp1 >= max_timedelta:\n break\n timestamp2 = timestamp\n i += 1\n time_ranges.append((timestamp1, timestamp2))\n return time_ranges","title":""}],"string":"[\n {\n \"docid\": \"c32b8a492c9b5dc658dfb7c466e6b815\",\n \"score\": \"0.7689309\",\n \"text\": \"def tile_features_to_time_ranges(cls, tile_features, max_timedelta: Union[str, pd.Timedelta] = '1H') \\\\\\n -> List[Tuple[pd.Timestamp, pd.Timestamp]]:\\n max_timedelta = pd.to_timedelta(max_timedelta) if isinstance(max_timedelta, str) else max_timedelta\\n feature_properties = [feature[\\\"properties\\\"] for feature in tile_features]\\n timestamps = [pd.to_datetime(f'{properties[\\\"date\\\"]}T{properties[\\\"time\\\"]}', utc=True)\\n for properties in feature_properties]\\n timestamps.sort()\\n num_timestamps = len(timestamps)\\n time_ranges = []\\n i = 0\\n while i < num_timestamps:\\n timestamp1 = timestamp2 = timestamps[i]\\n while i < num_timestamps:\\n timestamp = timestamps[i]\\n if timestamp - timestamp1 >= max_timedelta:\\n break\\n timestamp2 = timestamp\\n i += 1\\n time_ranges.append((timestamp1, timestamp2))\\n return time_ranges\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"322f66d335834d6e7191aa041611eaff","score":"0.6455336","text":"def split(self, max_delta):\n assert isinstance(max_delta, timedelta)\n chunks = []\n chunk_since = self.since\n while chunk_since < self.until:\n chunk_delta = min(max_delta, self.until - chunk_since)\n chunks.append(TimeWindow.from_timedelta(chunk_since, chunk_delta))\n chunk_since += chunk_delta\n return chunks","title":""},{"docid":"177ea67f424b310a56547c9177c0aa70","score":"0.59138876","text":"def expand_time_windows(periods: List[TimeWindow]) -> List[pd.Timestamp]:\n days_exclude = []\n for window in periods:\n days_exclude += pd.date_range(window.first_day, window.last_day, freq='D')\n\n return list(set(days_exclude))","title":""},{"docid":"712dcf88b0eb24498d5f1c2fdca179a0","score":"0.5832873","text":"def _tiff_timestamp_ranges(self) -> list:\n return self.frame_timestamp_ranges()","title":""},{"docid":"0ab84714f060edc3c064b7b1e57131ed","score":"0.58066446","text":"def _getTimeGrid(self, tMin, tMax, nPoi):\n return [tMin + float(i) / (nPoi - 1) * (tMax - tMin) for i in range(nPoi)]","title":""},{"docid":"d4caf76c7e1f162971066e91d002be57","score":"0.57903236","text":"def getNonEntries(self):\n entryList = self.entryList\n invertedEntryList = [\n (entryList[i][1], entryList[i + 1][0], \"\")\n for i in range(len(entryList) - 1)\n ]\n\n # Remove entries that have no duration (ie lie between two entries\n # that share a border)\n invertedEntryList = [\n entry for entry in invertedEntryList if entry[0] < entry[1]\n ]\n\n if entryList[0][0] > 0:\n invertedEntryList.insert(0, (0, entryList[0][0], \"\"))\n\n if entryList[-1][1] < self.maxTimestamp:\n invertedEntryList.append((entryList[-1][1], self.maxTimestamp, \"\"))\n\n invertedEntryList = [\n entry if isinstance(entry, Interval) else Interval(*entry)\n for entry in invertedEntryList\n ]\n\n return invertedEntryList","title":""},{"docid":"81862dbc17a81dd4152c5180c4e037e1","score":"0.5698723","text":"def window_time_range(incident_time, threshold=5, unit=\"second\"):\n\n if unit == \"second\":\n second_index = incident_time.rfind(\":\")\n seconds = int(incident_time[second_index + 1: second_index + 3])\n min_sec, max_sec = check_time(str(max(seconds - threshold, 0))), check_time(str(min(seconds + threshold, 59)))\n min_time = incident_time[:second_index + 1] + min_sec\n max_time = incident_time[:second_index + 1] + max_sec\n return [min_time, max_time]\n\n elif unit == \"minute\":\n minute_index = incident_time.find(\":\")\n minutes = int(incident_time[minute_index + 1: minute_index + 3])\n min_min, max_min = check_time(str(max(minutes - threshold, 0))), check_time(str(min(minutes + threshold, 59)))\n min_time = incident_time[:minute_index + 1] + min_min + incident_time[minute_index + 3:]\n max_time = incident_time[:minute_index + 1] + max_min + incident_time[minute_index + 3:]\n return [min_time, max_time]\n\n elif unit == \"hour\":\n hour_index = incident_time.find(\"T\")\n hour = int(incident_time[hour_index + 1: hour_index + 3])\n min_hour, max_hour = check_time(str(max(hour - threshold, 0))), check_time(str(min(hour + threshold, 23)))\n min_time = incident_time[:hour_index + 1] + min_hour + incident_time[hour_index + 3:]\n max_time = incident_time[:hour_index + 1] + max_hour + incident_time[hour_index + 3:]\n return [min_time, max_time]\n\n return None","title":""},{"docid":"655bda73fa96acb31cc7faac9093f9be","score":"0.5638426","text":"def get_tilearea_range(self):\n return [x ** 2 for x in xrange(1, self.tilearea_limit + 1)]","title":""},{"docid":"cfade30e1a645e36d80b3c17c01479c2","score":"0.5628009","text":"def tiles(self) -> List[HipsTile]:\n return [self.tile(ipix) for ipix in range(self.n_tiles)]","title":""},{"docid":"7dbc7431fc8c5d7464c86b21301a5041","score":"0.56226414","text":"def _convert_range_to_time_units(time_windows):\n start, end = time_windows\n tw_range = (end - start) * 60\n return (0, tw_range)","title":""},{"docid":"b854e1e39e165df7e3aaccb3e3303a7c","score":"0.5593613","text":"def __getTimeRange_ut(self): \n for f in self.__fileList:\n regex = ( r'^' + self.runName + '_mhd_' +\n r'(\\d{4})\\-(\\d{2})\\-(\\d{2})' +\n r'T' +\n r'(\\d{2})\\-(\\d{2})-(\\d{2})' +\n r'Z'+\n self.runExt + r'$' )\n \n r = re.match(regex, os.path.basename(f))\n \n assert( len(r.groups()) == 6 )\n t = [ int(match) for match in r.groups() ]\n self.__timeRange.append( datetime.datetime(year=t[0],\n month=t[1],\n day=t[2],\n hour=t[3],\n minute=t[4],\n second=t[5]) )\n \n return self.__timeRange","title":""},{"docid":"3bb5f3b385b7cfece8820710af72ecf7","score":"0.5544131","text":"def MaxToMin(min_lev, max_lev, datetime_maxes):\r\n y_res_mins = []\r\n deltaT_ranges = []\r\n for row in range(len(max_lev)-1):\r\n range_length = (datetime_maxes.iloc[row+1]-datetime_maxes.iloc[row]).seconds/3600 # hours\r\n if ~np.isnan(max_lev.iloc[row]) & (range_length>=3.5) & (range_length<=7.5): # peak to trough should take 6.2 hours in ocean\r\n y_res_mins.append(min_lev.iloc[row+1])\r\n deltaT_ranges.append(datetime_maxes.iloc[row+1]-datetime_maxes.iloc[row])\r\n elif ~np.isnan(max_lev.iloc[row]): # if peak lag is more than 4 hours, erroneous data\r\n y_res_mins.append(np.nan)\r\n deltaT_ranges.append(np.nan)\r\n # if last value in oceanside array is not nan, append another nan on the return arrays\r\n if (datetime_maxes.iloc[row+1]==datetime_maxes.iloc[-1]) & ~np.isnan(max_lev.iloc[row+1]): \r\n y_res_mins.append(np.nan)\r\n deltaT_ranges.append(np.nan)\r\n y_res_mins = np.array(y_res_mins)\r\n deltaT_ranges = np.array(deltaT_ranges)\r\n return deltaT_ranges, y_res_mins","title":""},{"docid":"41b1dbe4f8cd9d2568d9d3386c4a4859","score":"0.5513535","text":"def resamplify_ranges(ranges, scored_hz, target_hz=200):\n\n ranges_copy = []\n\n for start, stop in ranges:\n corrected_start = (start * scored_hz) / target_hz\n corrected_stop = (stop * scored_hz) / target_hz\n\n ranges_copy.append((corrected_start, corrected_stop))\n return ranges_copy","title":""},{"docid":"1215f220a238d9e91fcf0eb5d6bc0b77","score":"0.5413744","text":"def get_bb_max_times(self):\n return [scipy.optimize.fminbound(\n (lambda x: -f(x)), self.t_initial, self.t_final) for f in self.fps]","title":""},{"docid":"d5b44bb030acc7b8a7b386e7bc2a4cce","score":"0.5357053","text":"def time_slicer(time_coordinate, query):\n ref_time = time_coordinate.values[0]\n # next step is parsing out the times\n max_hours = max(query['hours'])\n assert int(max_hours) == max_hours\n max_time = ref_time + np.timedelta64(int(max_hours), 'h')\n max_ind = np.max(np.nonzero(time_coordinate.values <= max_time)[0])\n assert max_ind > 0\n return slice(0, max_ind + 1)","title":""},{"docid":"5e561d6f32e5841cd68875f60c76edc2","score":"0.5344265","text":"def _get_breaks(n_items: int, n_workers: int) -> List[Tuple[int, int]]:\n div, remainder = divmod(n_items, n_workers)\n slices = []\n bottom = 0\n for i in range(n_workers):\n top = bottom + div\n top += int(i < remainder)\n slices.append((bottom, top))\n bottom = top\n return slices","title":""},{"docid":"a9fb2b396b170ebe52fbe5b2edba0a75","score":"0.5329466","text":"def _trim(self): \n trimmed = []\n for start, end in self.stamps:\n if not (end < self.tStart or start > self.tEnd):\n trimmed.append((max(start, self.tStart), min(end, self.tEnd)))\n self.stamps = trimmed","title":""},{"docid":"be7a8dcd65fb033ac172b5d1663b3e95","score":"0.531007","text":"def calculate_deltas(self, start: datetime, end: datetime) -> List[datetime]:\n deltas = []\n curr = start\n while curr < end:\n deltas.append(curr)\n curr += self._time_granularity_\n return deltas","title":""},{"docid":"4ea10c59d602c9340d024ca922c1324c","score":"0.5305902","text":"def ConvertRanges2ExpandedRanges( ranges, max_length ):\n \n new_ranges = []\n \n for r in ranges:\n new_ranges.append( ( max(int(r[0] * param_resolution) + 1, 1),\n min(int(r[1] * param_resolution) + 1, max_length)) )\n return new_ranges","title":""},{"docid":"9fc78a21d79b5448f6c15ae4793876ac","score":"0.5299879","text":"def timeseries_to_batch(x: npt.NDArray[T], len_t: int = 3) -> list[npt.NDArray[T]]:\n subviews = np.moveaxis(\n np.lib.stride_tricks.sliding_window_view( # type: ignore [no-untyped-call]\n x, len_t, axis=0\n ),\n -1,\n 1,\n )\n\n list_subviews = np.split( # type: ignore [no-untyped-call]\n subviews, subviews.shape[0], axis=0\n )\n return [np.take(subview, 0, axis=0) for subview in list_subviews]","title":""},{"docid":"91a8c7658c29c3f725b15cf24411adda","score":"0.5297275","text":"def build_climatology_bounds(da: xr.DataArray) -> list[str]:\n n = len(da.time)\n return da.time[0 :: n - 1].dt.strftime(\"%Y-%m-%d\").values.tolist()","title":""},{"docid":"614df040cb216430b45604215397a9fc","score":"0.5269239","text":"def absolute_timings(self) -> List[Tuple[int, int, int, int]]:\n schedules = []\n for schedule in self.absolute_schedules.all():\n event_time = schedule.event_time\n num_seconds = event_time.minute * 60 + event_time.hour * 3600\n schedules.append([event_time.year, event_time.month, event_time.day, num_seconds])\n return schedules","title":""},{"docid":"577e53c3a47159674c2908c7a2b3ec3a","score":"0.526138","text":"def get_unstable_range(\n self, limit: int = None, range_threshold: float = None, **kwargs\n ) -> typing.List[VideoCutRange]:\n change_range_list = sorted(\n [i for i in self.range_list if not i.is_stable(**kwargs)],\n key=lambda x: x.start,\n )\n\n # video can be totally stable ( nothing changed )\n # or only one unstable range\n if len(change_range_list) <= 1:\n return change_range_list\n\n # merge\n i = 0\n merged_change_range_list = list()\n while i < len(change_range_list) - 1:\n cur = change_range_list[i]\n while cur.can_merge(change_range_list[i + 1], **kwargs):\n # can be merged\n i += 1\n cur = cur.merge(change_range_list[i], **kwargs)\n\n # out of range\n if i + 1 >= len(change_range_list):\n break\n merged_change_range_list.append(cur)\n i += 1\n if change_range_list[-1].start > merged_change_range_list[-1].end:\n merged_change_range_list.append(change_range_list[-1])\n\n if limit:\n merged_change_range_list = self._length_filter(\n merged_change_range_list, limit\n )\n # merged range check\n if range_threshold:\n merged_change_range_list = [\n i for i in merged_change_range_list if not i.is_loop(range_threshold)\n ]\n logger.debug(\n f\"unstable range of [{self.video.path}]: {merged_change_range_list}\"\n )\n return merged_change_range_list","title":""},{"docid":"5d55876f5627898e2b71c7df9ffda943","score":"0.5261111","text":"def compressed(self):\n time_windows = self.time_windows_sorted_by_since\n if not time_windows:\n return TimeWindowsCollection([])\n stack = []\n\n latest = {\n 'since': None,\n 'until': None\n }\n\n for current in time_windows:\n if latest['until'] is None:\n latest['since'] = current.since\n latest['until'] = current.until\n elif latest['until'] >= current.since:\n latest['until'] = max(latest['until'], current.until)\n else:\n stack.append(TimeWindow(latest['since'], latest['until']))\n latest['since'] = current.since\n latest['until'] = current.until\n\n stack.append(TimeWindow(latest['since'], latest['until']))\n return TimeWindowsCollection(stack, sorted_since=True)","title":""},{"docid":"857893fc52595e1667f57e57d035f309","score":"0.5246155","text":"def test_global_tiles_clamped():\n tiles = list(mercantile.tiles(-180, -90, 180, 90, [1]))\n assert len(tiles) == 4\n assert min(t.y for t in tiles) == 0\n assert max(t.y for t in tiles) == 1","title":""},{"docid":"2adf7bdc91bddc71de0cd1901d0d236e","score":"0.5245297","text":"def filter_out_non_working_hours(times): # TODO: what's this?\n for start, end in times:\n pass","title":""},{"docid":"4f313cc3fae33ef1cf4351c6a838e65a","score":"0.5245202","text":"def adjust_list(ticks: list, ttask: int) -> list:\n for _ in range(ttask - 1):\n ticks.append(0)\n return ticks","title":""},{"docid":"8d0feb1999602273e6c9cfd37b24189c","score":"0.52408475","text":"def get_gtopo30_tile_list(self):\n\n logger = logging.getLogger(__name__)\n\n # GTOPO30 tile information\n NORTH_LON_LOCATIONS = np.array([-180.0, -140.0, -100.0, -60.0, -20.0,\n 20.0, 60.0, 100.0, 140.0])\n\n SOUTH_LON_LOCATIONS = np.array([-180.0, -120.0, -60.0,\n 0.0, 60.0, 120.0])\n\n LAT_LOCATIONS = np.array([90.0, 40.0, -10.0, -60.0])\n\n GTOPO30_TILE_SET_CUTOFF_LATITUDE = -60.0\n\n '''\n The tiles do not necessarily go all the way to the boundary. For\n example, the w100n90 tile only goes to -99.995833333334 longitude.\n Thus, if our UL longitude falls between -99.995833333334 and -100.0\n then we will need to use w140n90 to get the far left edge. To\n account for this we'll artificially pad the East and West boundaries\n by 1 degree.\n\n Note: this does not seem to cause problems on the North and South\n borders, so we will no worry about them.\n '''\n ul_lon = Math.longitude_norm(self.bounding_west_longitude -\n self.gtopo30_padding)\n lr_lon = Math.longitude_norm(self.bounding_east_longitude +\n self.gtopo30_padding)\n logger.debug('ul_lon = {0}'.format(ul_lon))\n logger.debug('lr_lon = {0}'.format(lr_lon))\n\n '''\n There is a condition which could cause problems on the North and South\n boundaries as well. This is if a scene comes very near the edge, but\n does not quite get there. It could be that adding 1.5 to nrows before\n taking the ceiling (see calculation of nrows in get_gtopo30_data.c)\n causes the nrows to be large enough that we need data from the tile to\n the South, even though the scene stopped just before that tile. To\n address this we will grow North and South by 1 degree as we did in the\n East and West directions above. Note, the same problem can occur in\n the East and West directions, but was already addressed when adding\n one to the longitudes above.\n '''\n ul_lat = min((self.bounding_north_latitude + self.gtopo30_padding),\n self.north_latitude_limit)\n lr_lat = max((self.bounding_south_latitude - self.gtopo30_padding),\n self.south_latitude_limit)\n logger.debug('ul_lat = {0}'.format(ul_lat))\n logger.debug('lr_lat = {0}'.format(lr_lat))\n\n # Determine the unique latitudes where the input data resides\n lat_list = list(set([min(LAT_LOCATIONS[\n np.where(ul_lat < LAT_LOCATIONS)]),\n min(LAT_LOCATIONS[\n np.where(lr_lat < LAT_LOCATIONS)])]))\n logger.debug('lat_list = {0}'.format(lat_list))\n\n tile_list = list()\n # Determine the unique longitudes where the input data resides\n for lat in lat_list:\n lon_list = list()\n if lat > GTOPO30_TILE_SET_CUTOFF_LATITUDE:\n lon_list.append(\n max(NORTH_LON_LOCATIONS[\n np.where(ul_lon > NORTH_LON_LOCATIONS)]))\n lon_list.append(\n max(NORTH_LON_LOCATIONS[\n np.where(lr_lon > NORTH_LON_LOCATIONS)]))\n else:\n lon_list.append(\n max(SOUTH_LON_LOCATIONS[\n np.where(ul_lon > SOUTH_LON_LOCATIONS)]))\n lon_list.append(\n max(SOUTH_LON_LOCATIONS[\n np.where(lr_lon > SOUTH_LON_LOCATIONS)]))\n lon_list = list(set(lon_list))\n logger.debug('lon_list = {0}'.format(lon_list))\n\n n_s = 'n'\n if lat < 0:\n n_s = 's'\n\n for lon in lon_list:\n e_w = 'e'\n if lon <= 0:\n e_w = 'w'\n\n # Already know if north/south, east/west so we just need\n # the positive value for the filename\n abs_lat = abs(lat)\n abs_lon = abs(lon)\n\n tile_list.append('{0}{1:03}{2}{3:02}'\n .format(e_w, abs_lon, n_s, abs_lat))\n\n return tile_list","title":""},{"docid":"9015767027dbd353a22eee05789ed134","score":"0.5227308","text":"def _removeUltrashortIntervals(tier, minLength, minTimestamp):\n\n # First, remove tiny intervals\n newEntryList = []\n j = 0 # index to newEntryList\n for start, stop, label in tier.entryList:\n\n if stop - start < minLength:\n # Correct ultra-short entries\n if len(newEntryList) > 0:\n lastStart, _, lastLabel = newEntryList[j - 1]\n newEntryList[j - 1] = (lastStart, stop, lastLabel)\n else:\n # Special case: the first entry in oldEntryList was ultra-short\n if len(newEntryList) == 0 and start != minTimestamp:\n newEntryList.append((minTimestamp, stop, label))\n # Normal case\n else:\n newEntryList.append((start, stop, label))\n j += 1\n\n # Next, shift near equivalent tiny boundaries\n # This will link intervals that were connected by an interval\n # that was shorter than minLength\n j = 0\n while j < len(newEntryList) - 1:\n diff = abs(newEntryList[j][1] - newEntryList[j + 1][0])\n if diff > 0 and diff < minLength:\n newEntryList[j] = (\n newEntryList[j][0],\n newEntryList[j + 1][0],\n newEntryList[j][2],\n )\n j += 1\n\n return tier.new(entryList=newEntryList)","title":""},{"docid":"181fc0a6659862eed9e9293c98c5cfd1","score":"0.5219579","text":"def cut(times, dt=1):\n time_list = times.values\n if not time_list.size:\n return []\n\n jumps = time_list[1:] != time_list[:-1]+dt\n nb_jumps = sum(jumps) # numbers of jumps\n\n dates = np.zeros((nb_jumps+1, 2))\n # Fist time value\n dates[0, 0] = time_list[0]\n # Last time value\n dates[-1, -1] = time_list[-1]\n # Add all other jumps\n dates[1:, 0] = time_list[1:][jumps]\n dates[:-1, 1] = time_list[:-1][jumps]\n return dates.tolist()","title":""},{"docid":"c5b164778c0d2897adea2c63719902f8","score":"0.52050847","text":"def time_range(self):\n if len(self._time_range) == 0:\n for band in self.bands_from_grib_file:\n self._time_range.append(self.timestep_for_band(band))\n return self._time_range","title":""},{"docid":"d9e912e74144156504e701b78250b167","score":"0.5189701","text":"def range_maps(self) -> List[RangeMap]:\n range_maps_list = []\n for dest in self._state.attributes[SERVICE_RANGEMAP][\"rangemaps\"]:\n range_maps_list.append(RangeMap(dest))\n return range_maps_list","title":""},{"docid":"db126b5d56db778a58e6dd0fcf370c93","score":"0.5189438","text":"def tile_limits(x_min, x_max, y_min, y_max, x_tile, y_tile):\n # modify the bounding box to be a multiple of the tile dimension\n x_pad = (x_max - x_min) % x_tile\n x_min += math.ceil(x_pad/2)\n x_max += math.floor(x_pad/2) \n x_num_tiles = int((x_max - x_min) // x_tile)\n\n y_pad = (y_max - y_min) % y_tile\n y_min += math.ceil(y_pad/2)\n y_max += math.floor(y_pad/2) \n y_num_tiles = int((y_max - y_min) // y_tile)\n\n # generate tile bboxes\n tiles = []\n for ii in range(x_num_tiles):\n for jj in range(y_num_tiles):\n tile = {}\n tile['x_min'] = x_min + ii*x_tile\n tile['x_max'] = tile['x_min'] + x_tile\n tile['y_min'] = y_min + jj*y_tile\n tile['y_max'] = tile['y_min'] + y_tile\n tiles.append(tile)\n\n return tiles","title":""},{"docid":"6200c7c574041bc8ed3f27af2e7e8923","score":"0.5184637","text":"def get_bb_max_times(self):\n return [sympyutils.poly_fminbound(\n -poly, self.t_initial, self.t_final) for poly in self.polys]","title":""},{"docid":"9f72df2508cbe1b32761abaf6284d332","score":"0.51813865","text":"def scale_timings(timelist, input_units, output_units, time_repetition):\r\n if input_units==output_units:\r\n _scalefactor = 1.\r\n if (input_units == 'scans') and (output_units == 'secs'):\r\n _scalefactor = time_repetition\r\n if (input_units == 'secs') and (output_units == 'scans'):\r\n _scalefactor = 1./time_repetition\r\n timelist = [np.max([0., _scalefactor*t]) for t in timelist]\r\n return timelist","title":""},{"docid":"93c02940a180c73e6c46faf4dbb625a2","score":"0.517908","text":"def _get_empty_timeslots(list_of_booked_timings, date):\n day = datetime.strptime((details[\"date\"]), '%d-%b-%Y').strftime('%A')\n \n #weekday 8.30 to 10.30\n #weekend 8.30 to 5\n \n list_of_available_timings = []\n \n list_of_booked_timings.sort()\n \n for i in range(len(list_of_booked_timings)-1):\n time_range1 = list_of_booked_timings[i].split('-')\n start_time1 = int(time_range1[0].replace(':', \"\"))\n end_time1 = int(time_range1[1].replace(':',\"\"))\n \n \n time_range2 = list_of_booked_timings[i+1].split('-')\n start_time2 = int(time_range2[0].replace(':', \"\"))\n end_time2 = int(time_range2[1].replace(':',\"\"))\n \n if i == 0:\n check = start_time1 - 800\n if check > 0:\n first = '08:30' + '-' + time_range1[0]\n list_of_available_timings.append(first)\n \n \n if i == len(list_of_booked_timings)-2: \n check = 2230 - end_time2\n if check > 0: \n if day == 'Saturday' or day == 'Sunday':\n last = time_range2[1] + '-' + '17:00'\n list_of_available_timings.append(last)\n else:\n last = time_range2[1] + '-' + '22:30'\n list_of_available_timings.append(last)\n \n if start_time2 - end_time1 > 0:\n add = time_range1[1] + '-' + time_range2[0]\n list_of_available_timings.append(add)\n \n list_of_available_timings.sort()\n \n return list_of_available_timings","title":""},{"docid":"a7f63a31a2f5b2ff8e8b3be377aa448a","score":"0.51764965","text":"def get_filenames_by_time_range(\n self, start: datetime, stop: datetime\n ) -> List[Path]:\n filenames = []\n time = start\n while time <= stop:\n filenames.append(self.get_filename_by_time(time))\n time += timedelta(hours=1)\n\n # Cast to set then back to list to remove duplicates.\n return sorted(list(set(filenames)))","title":""},{"docid":"1c7a7d3d459bbda361500bda8065fe5c","score":"0.517434","text":"def _calculate_min_max(self):\n\n min_max_list = []\n\n for band in self.bands:\n\n file_path = ('%s/%s_B%s.TIF' % (self.warp_path,\n self.image, band))\n\n if os.path.exists(file_path):\n print ('Starting the Min/Max process with designated -percent '\n 'cut- for band %s of %s' % (band, self.image))\n print '...'\n\n # Open images in the warp folder\n ds = gdal.Open(file_path)\n\n # converting raster to numpy array\n values = numpy.array(ds.GetRasterBand(1).ReadAsArray())\n to_list = values.tolist()\n\n full_list = [item for sublist in to_list for item in sublist]\n\n # removing zeros\n value_list = filter(lambda x: x != 0, full_list)\n list_len = len(value_list)\n\n value_list.sort()\n\n # determining number of integers to cut from bottom of list\n cut_value_bottom = int(float(self.btm_prct) /\n float(100) * float(list_len))\n\n # determining number of integers to cut from top of list\n cut_value_top = int(float(self.top_prct) /\n float(100) * float(list_len))\n\n # establishing new min and max with percent cut\n cut_list = value_list[\n (cut_value_bottom + 1):(list_len - cut_value_top)]\n\n # adding min and max with percent cut values to list\n min_max_list.extend([cut_list[0], cut_list[-1]])\n\n print 'Finished processing band %s of %s' % (band, self.image)\n\n return [min(min_max_list), max(min_max_list)]","title":""},{"docid":"4ee63c0439a4476e10a117915e35af8f","score":"0.51733375","text":"def create_list(dig):\n max_range = pow(10, dig) - 1\n min_range = pow(10, dig-1) - 1\n unfiltered_list = list(range(max_range, min_range, -1))\n return unfiltered_list","title":""},{"docid":"ceb2b2c28adc79a6cde5bb05107097ce","score":"0.5172149","text":"def timedelta_with_itself(time_list: List) -> List:\n temp_list = []\n for index , time in enumerate(time_list):\n try:\n if index==0:\n temp_list.append((0.0))\n else:\n temp_list.append(time_list[index]-time_list[index-1])\n except IndexError:\n pass\n return temp_list","title":""},{"docid":"a182e850796228a52ff5605a5b9ac531","score":"0.5169623","text":"def get_time_range_from_relayoutData(relayoutData):\n time_range = [df_start, df_end]\n if relayoutData is None:\n pass\n else:\n if 'xaxis.range[0]' in relayoutData:\n time_range = [pd.to_datetime(relayoutData['xaxis.range[0]']),\n pd.to_datetime(relayoutData['xaxis.range[1]'])]\n return time_range","title":""},{"docid":"4944d0d6bcc8c043ed50f3349dfe72aa","score":"0.5168822","text":"def list_of_length_scales(bottom_bound, top_bound, steps):\n length_scales = []\n for i in range(bottom_bound, top_bound, steps):\n length_scales.append(i)\n return length_scales","title":""},{"docid":"4d857517b86e9b30fd371be8f3085f93","score":"0.5161929","text":"def _generate_time_windows(od_dist, time_windows):\n # initialize time matrix as empty list\n time_matrix = []\n # if time_windows is set in database this means it is a list of lits.\n # so iterate each list of lists and add it to time matrix.\n # else, do it from slider.\n if len(time_windows) > 2:\n for ti_wi in time_windows:\n tw = _convert_range_to_time_units(ti_wi)\n time_matrix.append(tw)\n else:\n tw = _convert_range_to_time_units(time_windows)\n time_matrix = [tw for elem in range(len(od_dist))]\n return time_matrix","title":""},{"docid":"5440cbfc84a53cd8db90e087315b083b","score":"0.51561314","text":"def make_consistent(orig_list, delta, max_value=None, target='datetimefield'):\n if not orig_list:\n return\n\n prev = orig_list[0]\n\n for i in orig_list:\n while abs(prev[target]-i[target]).total_seconds() / delta > 1:\n if prev[target] > i[target]:\n prev[target] -= dt.timedelta(seconds=delta)\n else:\n prev[target] += dt.timedelta(seconds=delta)\n\n yield prev, True\n\n prev = i\n yield i, False\n\n # append new elements at the end until reach max value\n while max_value and abs(prev[target] - max_value).total_seconds()/delta > 1:\n if prev[target] > max_value:\n prev[target] -= dt.timedelta(seconds=delta)\n else:\n prev[target] += dt.timedelta(seconds=delta)\n\n yield prev, True","title":""},{"docid":"1657797e8e149e49f2e0ac34a224b3e1","score":"0.5152248","text":"def maxvals4timeslice(self, name, start, stop):\n return self.reduce_lonlat_slice(name, np.nanmax, start, stop)","title":""},{"docid":"f4ce06af851daedef0460dddba5849bf","score":"0.51467496","text":"def date_range_groups(data):\n\tranges = []\n\tfor k, g in groupby(enumerate(data), lambda i_x: i_x[1]+relativedelta(days=-i_x[0])):\n\t\tgroup = list(map(itemgetter(1), g))\n\t\tranges.append((group[0], group[-1]))\n\n\treturn ranges","title":""},{"docid":"e7cf2433b1c582744f45361d622915e2","score":"0.513196","text":"def split_tranges(tranges, width, tres):\n newtranges = []\n for trange in tranges:\n t0, t1 = trange\n assert width < (t1 - t0)\n # calculate left and right edges of subtranges that fall within trange:\n # This is tricky: find maximum left edge such that the corresponding maximum right\n # edge goes as close as possible to t1 without exceeding it:\n tend = (t1-width+tres) // tres*tres # there might be a nicer way, but this works\n ledges = np.arange(t0, tend, tres)\n redges = ledges + width\n subtranges = [ (le, re) for le, re in zip(ledges, redges) ]\n newtranges.append(subtranges)\n return np.vstack(newtranges)","title":""},{"docid":"76ff79eef98757a88f01cd7b9a35eb2e","score":"0.5129836","text":"def bounds_to_tiles(self, bounds):\n grid_ys, grid_xs = self._frame_bounds(bounds)\n return self._yield_tiles(grid_ys, grid_xs, bounds)","title":""},{"docid":"8c3013f098873b4f6568da362e0ac566","score":"0.5124893","text":"def make_last_days_y_list(time_tuple_lst):\n temp_list = []\n for tpl in time_tuple_lst:\n temp_list.append(tpl[1]) #do not use \"extend\"...says \"numpy.float64 object is not iterable\" \n return temp_list","title":""},{"docid":"0aed682abebd6d1b897d1aefd011477e","score":"0.51233643","text":"def filter_annots_using_minimum_timedelta(ibs, aid_list, min_timedelta):\n import vtool as vt\n #min_timedelta = 60 * 60 * 24\n #min_timedelta = 60 * 10\n grouped_aids = ibs.group_annots_by_name(aid_list)[0]\n unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)\n # Find the maximum size subset such that all timedeltas are less than a given value\n chosen_idxs_list = [\n ut.maximin_distance_subset1d(unixtimes, min_thresh=min_timedelta)[0]\n for unixtimes in unixtimes_list]\n filtered_groups = vt.ziptake(grouped_aids, chosen_idxs_list)\n filtered_aids = ut.flatten(filtered_groups)\n if ut.DEBUG2:\n timedeltas = ibs.get_unflat_annots_timedelta_list(filtered_groups)\n min_timedeltas = np.array([np.nan if dists is None else\n np.nanmin(dists) for dists in timedeltas])\n min_name_timedelta_stats = ut.get_stats(min_timedeltas, use_nan=True)\n print('min_name_timedelta_stats = %s' % (ut.dict_str(min_name_timedelta_stats),))\n return filtered_aids","title":""},{"docid":"a50af269ca2843fabd52d605ffea8f36","score":"0.5115767","text":"def get_pools(all_trips, total_trips, pool_size):\n pools_per_day = (24 * 60) / pool_size\n pools_per_hour = 60 / pool_size\n pools = [ [] for i in range(pools_per_day)]\n\n pool_start_time = 0\n prev_hour = 0\n trips_pooled = 0\n for i in range(pools_per_day):\n if pool_start_time >= 60:\n pool_start_time = 0\n\n for j in range(trips_pooled, total_trips):\n trip_start_minutes = int(all_trips[j][5].split(\":\")[1])\n trip_start_hour = int(all_trips[j][5].split(\":\")[0])\n\n if prev_hour == trip_start_hour:\n if trip_start_minutes >= pool_start_time and trip_start_minutes < pool_start_time + pool_size:\n pools[i].append(all_trips[j][0])\n trips_pooled += 1\n\n pool_start_time += pool_size\n if ((i + 1) % pools_per_hour) == 0:\n prev_hour += 1\n\n return pools","title":""},{"docid":"3e9ecde636bc49bcaf64c556581184ee","score":"0.5112423","text":"def prepare_time_list_according_to_format(self):\n final_time_list = []\n actual_data = list(self.get_demand_data()[\"Time\"])\n for each in actual_data:\n if \"pm\" in each and \"am\" not in each:\n sub_str = []\n res = re.sub('pm', '', each)\n res = res.split(\"-\")\n res = list(map(int, res))\n for each_split in res:\n if each_split < 12:\n each_split += 12\n sub_str.append(str(each_split))\n final_time_list.append(\"-\".join(sub_str))\n if \"am\" in each and \"pm\" not in each:\n res = re.sub('am', '', each)\n final_time_list.append(res)\n if \"am\" in each and \"pm\" in each:\n res = re.sub('am|pm', '', each)\n final_time_list.append(res)\n return final_time_list","title":""},{"docid":"66a54225e72ced6c14df1220027ae310","score":"0.51037484","text":"def time_window_bins(self, t0, tend):\n indexes = []\n for level in self.dmd_tree.levels:\n for leaf in self.dmd_tree.index_leaves(level):\n local_times = self.partial_time_interval(level, leaf)\n if (\n local_times[\"t0\"] <= t0 < local_times[\"tend\"]\n or local_times[\"t0\"] < tend <= local_times[\"tend\"]\n or (t0 <= local_times[\"t0\"] and tend >= local_times[\"tend\"])\n ):\n indexes.append((level, leaf))\n\n indexes = np.unique(indexes, axis=0)\n return indexes","title":""},{"docid":"b86808c5107e7c11acca27c250317eb4","score":"0.5089789","text":"def build_tiles():\n return [[ Tile(y)\n for y in range(MAP_HEIGHT) ]\n for x in range(MAP_WIDTH) ]","title":""},{"docid":"319ecf77e84f33a9f80f6c301e08d815","score":"0.5086945","text":"def time_window_bins(self, t0, tend):\n indexes = []\n for level in self.dmd_tree.levels:\n for leaf in self.dmd_tree.index_leaves(level):\n\n local_times = self.partial_time_interval(level, leaf)\n if (local_times[\"t0\"] <= t0 < local_times[\"tend\"] or\n local_times[\"t0\"] < tend <= local_times[\"tend\"] or\n (\n t0 <= local_times[\"t0\"] and\n tend >= local_times[\"tend\"]\n )):\n indexes.append((level, leaf))\n\n indexes = np.unique(indexes, axis=0)\n return indexes","title":""},{"docid":"be5290842e10ad069370a00909f50151","score":"0.50859946","text":"def my_time_slice(self, t_start, t_stop):\n \n #new_VmList = VmList(self.raw_data(), self.id_list(), self.dt, t_start, t_stop, self.dimensions)\n #for id in self.id_list():\n # new_VmList[id]=self.analog_signals[id].time_slice(t_start, t_stop)\n \n #return new_AnalogSignalList\n \n \n new_VmList=super( MyVmList, self ).time_slice(t_start, t_stop)\n \n\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n new_MyVmList=convert_super_to_sub_class(new_VmList, MyVmList)\n \n return new_MyVmList","title":""},{"docid":"aec236a2203f6d6ab8547420ca854a24","score":"0.5085477","text":"def cut_tim(tim,tmin=None,tmax=None,output=None):\n tmin = tmin or -np.inf\n tmax = tmax or np.inf\n tim_strings = deque()\n lines = file(tim).readlines()\n if 'FORMAT 1' not in lines[0]:\n raise ValueError('Cannot parse non-tempo2 style TOA files.')\n for line in lines:\n tim_strings.append(line)\n if id_toa_line(line):\n mjd = float(line.split()[2])\n if not((mjd >= tmin) and (mjd < tmax)):\n tim_strings.pop()\n if output is not None:\n file(output,'w').write(''.join(tim_strings))\n return tim_strings","title":""},{"docid":"d1e06ce12d5a516c54c8252f9696ac70","score":"0.5085181","text":"def _list_from_ranges(data):\n if not isinstance(data, list):\n data = [data]\n result = []\n for ranges in data:\n if (isinstance(ranges, int)):\n ranges = str(ranges)\n num_start = 0\n num_end = 0\n if '-' in ranges:\n split_start, split_end = ranges.split('-')\n if (split_start.isdigit()) and (split_end.isdigit()):\n # Regular numeric ranges\n num_start, num_end = int(split_start), int(split_end)\n else:\n # X10 device ranges, presumably\n num_start = X10.housecode_to_int(split_start)\n num_end = X10.housecode_to_int(split_end)\n if (num_start is None) or (num_end is None):\n continue\n range_start = num_start - 1\n range_end = num_end - 1\n result.extend(list(range(range_start, range_end + 1)))\n else:\n range_start = None\n num_start = 0\n if ranges.isdigit():\n num_start = int(ranges)\n else:\n num_start = X10.housecode_to_int(ranges)\n range_start = num_start - 1\n result.append(num_start)\n return result","title":""},{"docid":"56caadf17b3fc3f6d8f29ac6c56eadd9","score":"0.5083477","text":"def filter_time_points2(tot_incidlist, incl_min, incl_max):\n\n\t# calculate cumulative number of cases at each time step\n\tcum_ct = np.cumsum(tot_incidlist)\n\t# generate list of time steps where cumulative number of cases is between incl_min and incl_max proportion of total cases in epidemic\n\tincl_tsteps = [i for i, ct in enumerate(cum_ct) if ct/float(max(cum_ct)) > incl_min and ct/float(max(cum_ct)) < incl_max]\n\t\n\tif incl_tsteps:\n\t\treturn min(incl_tsteps), max(incl_tsteps)\n\telse:\n\t\treturn 0, 0","title":""},{"docid":"12e033cdd1ee70b7be6d941672a0fd79","score":"0.5078448","text":"def list_time_range(self, limit: int = DEFAULT_LIMIT, offset: int = None) -> dict[str, Any]:\n return self._http_request(\n method='GET',\n url_suffix='api/objects/timeranges',\n params=assign_params(limit=limit, offset=offset),\n )","title":""},{"docid":"a2d13d1a16d44b4ff22c9b351a1a1d51","score":"0.50747746","text":"def filter_temps_by_monitoring(temp_range, start_time, end_time):\n monitored_temps = []\n for row in temp_range:\n try:\n # Check if the datetime in the row's first column is in between\n # the start and end monitoring time\n temp_date = datetime.strptime(row[0].value, '%Y-%m-%d %H:%M:%S')\n except TypeError:\n # We're parsing a sheet that has fewer than 1440 rows of temp data\n break\n if start_time - timedelta(minutes=15) <= temp_date <= end_time + timedelta(minutes=15):\n monitored_temps.append(row[1].value)\n\n return monitored_temps","title":""},{"docid":"ec7ae7e98fcee6a804922e53460278f3","score":"0.506944","text":"def _getTimeVsPotObjsForEachTimeHillAdded(inpObj, timeRange=None, timeTol=1e-4, minTimeDiff=1e-3):\n\tuseObj = copy.deepcopy(inpObj)\n\tuseObj.sortTimes()\n\n\t#Step 0: Figure out the time range\n\tif timeRange is None:\n\t\ttimeRange = [-1, np.inf]\n\n\t#Step 1: We need to figure out the time ranges for each\n\tminTime, maxTime = timeRange\n\tspawnTimes = [t for t in useObj.times if t>minTime and t<maxTime] #this will be in order\n\n\n\n\t#Find the INDICES corresponding to times where hills were essentially spawned\n\tspawnIndices, outTimeVals = [0], [spawnTimes[0]]\n\tfor idx,t in enumerate(spawnTimes[1:],1):\n\t\tif t>outTimeVals[-1]+minTimeDiff:\n\t\t\tspawnIndices.append(idx)\n\t\t\toutTimeVals.append(spawnTimes[idx])\n\n\t#Now use minTime to get timeRanges (we need to do a load of -1 things tho)\n\toutTimeRanges = list()\n\tfor idx in spawnIndices[1:]:\n\t\toutTimeRanges.append( [minTime,spawnTimes[idx-1]] )\n\n\t#We need to figure out the last one now; which just runs from minTime to maxTime\n\toutTimeRanges.append([minTime, maxTime])\n\n\t#Step 2: get potential objs from these times\n\toutObjs = list()\n\tfor tRange in outTimeRanges:\n\t\tcurrObj = useObj.createGroupedHills(timeRange=tRange, timeTol=timeTol)\n\t\toutObjs.append(currObj)\n\n\treturn [ [t,obj] for t,obj in it.zip_longest(outTimeVals,outObjs)]","title":""},{"docid":"cd3c50f892de2f31e880c579a7ca1a40","score":"0.5066422","text":"def extent(lst):\n vecs = zip(*lst)\n return [max(v) - min(v) for v in vecs]","title":""},{"docid":"efa36aacda44aadc979f32710257ddef","score":"0.5055729","text":"def partition(self, max_size):\n if max_size <= 0:\n raise ValueError('max_size must be > 0: {}'.format(max_size))\n\n for interval in self:\n refname = interval.reference_name\n for pos in range(interval.start, interval.end, max_size):\n yield make_range(refname, pos, min(interval.end, pos + max_size))","title":""},{"docid":"f39f0f1464966f351e73d2179b201f21","score":"0.50527614","text":"def get_table_sizes(task_list: list) -> list:\n x = 0\n time_slot = 0\n for ele in task_list:\n if len(ele[1]) > x:\n x = len(ele[1])\n if len(ele[0]) > time_slot:\n time_slot = len(ele[0])\n if x < 5:\n x = 5\n return [x + 15, time_slot - 7]","title":""},{"docid":"0415c966cb35f35eb0f7e00f868d8c41","score":"0.5042304","text":"def _partition_daterange(self, start, end, step: datetime.timedelta):\n get_start_time = operator.itemgetter(self.partition_field_start.eval(self.config))\n get_end_time = operator.itemgetter(self.partition_field_end.eval(self.config))\n date_range = [\n dr\n for dr in super(DatetimeIncrementalSyncComponent, self)._partition_daterange(start, end, step)\n if get_start_time(dr) < get_end_time(dr)\n ]\n for i, _slice in enumerate(date_range):\n start_time = self._parser.parse(get_start_time(_slice), self.start_datetime.datetime_format, self._timezone)\n end_time = self._parser.parse(get_end_time(_slice), self.end_datetime.datetime_format, self._timezone)\n _slice[self.stream_slice_field_step.eval(self.config)] = (end_time + datetime.timedelta(days=int(bool(i))) - start_time).days\n return date_range","title":""},{"docid":"e683c73106742273faa2edf2ba559dce","score":"0.5034739","text":"def space_timesteps(num_timesteps, section_counts):\n if isinstance(section_counts, str):\n if section_counts.startswith(\"ddim\"):\n desired_count = int(section_counts[len(\"ddim\") :])\n for i in range(1, num_timesteps):\n if len(range(0, num_timesteps, i)) == desired_count:\n return set(range(0, num_timesteps, i))\n raise ValueError(\n f\"cannot create exactly {num_timesteps} steps with an integer stride\"\n )\n elif section_counts == \"fast27\":\n steps = space_timesteps(num_timesteps, \"10,10,3,2,2\")\n # Help reduce DDIM artifacts from noisiest timesteps.\n steps.remove(num_timesteps - 1)\n steps.add(num_timesteps - 3)\n return steps\n section_counts = [int(x) for x in section_counts.split(\",\")]\n size_per = num_timesteps // len(section_counts)\n extra = num_timesteps % len(section_counts)\n start_idx = 0\n all_steps = []\n for i, section_count in enumerate(section_counts):\n size = size_per + (1 if i < extra else 0)\n if size < section_count:\n raise ValueError(\n f\"cannot divide section of {size} steps into {section_count}\"\n )\n if section_count <= 1:\n frac_stride = 1\n else:\n frac_stride = (size - 1) / (section_count - 1)\n cur_idx = 0.0\n taken_steps = []\n for _ in range(section_count):\n taken_steps.append(start_idx + round(cur_idx))\n cur_idx += frac_stride\n all_steps += taken_steps\n start_idx += size\n return set(all_steps)","title":""},{"docid":"82cb93efc92f41642cefa81f0ac5569c","score":"0.5032564","text":"def get_tile_tups(self):\n return [(t.left, t.right) for t in self.tiles]","title":""},{"docid":"025c5c185d042648a990f4b6809785d9","score":"0.5026414","text":"def candidates_for_task_timeslots(t, ul):\r\n \r\n lt = []\r\n ls = []\r\n \r\n ts = t[4] / t[6] * 60\r\n \r\n # time\r\n for i in ul:\r\n if(i[3] >= t[3] and i[3] <= (t[3] + ts)):\r\n lt.append(i)\r\n \r\n # space\r\n for j in lt:\r\n t1 = (t[1], t[2])\r\n t2 = (j[1], j[2])\r\n distance = hv(t1, t2, 'm')\r\n # distance factor of user-i for task-j\r\n dMax = 1 - (distance / t[5])\r\n if(distance >= 0):\r\n ls.append(j)\r\n \r\n return ls","title":""},{"docid":"58eb9de2d75206bffdf50be740d057ed","score":"0.5026111","text":"def filter_list(lst):\n return [t for t in lst if -1 < t[0] < 7 and -1 < t[1] < 6]","title":""},{"docid":"748545a69cd4bafaacc1d4c482043ac3","score":"0.5024284","text":"def getByTime(self):\n width, height, nChannels, nSlices, nFrames = self.image.getDimensions() \n dendritesByTime = [[] for x in range(0, nFrames)] \n for element in self.elements.values():\n t = element.getFrame()\n dendritesByTime[t-1].append(element)\n return dendritesByTime","title":""},{"docid":"2f84812e91adadc09dd84f03caf72ff6","score":"0.5023871","text":"def from_to(tstart, tend, tdelta):\r\n if not type(tstart) == dt.datetime:\r\n tstart = dt.datetime.strptime(tstart, \"%Y-%m-%d %H:%M:%S\")\r\n if not type(tend) == dt.datetime:\r\n tend = dt.datetime.strptime(tend, \"%Y-%m-%d %H:%M:%S\")\r\n tdelta = dt.timedelta(seconds=tdelta)\r\n tsteps = [tstart, ]\r\n tmptime = tstart\r\n while True:\r\n tmptime = tmptime + tdelta\r\n if tmptime > tend:\r\n break\r\n else:\r\n tsteps.append(tmptime)\r\n return tsteps","title":""},{"docid":"c7c3b5ff40b7b42776ad469c126ba892","score":"0.50130343","text":"def convertToTiles(surface, width, height):\n \n ret = []\n \n sz = surface.get_size()\n if sz[0] == width and sz[1] == height: return [surface]\n \n xstep = int(ceil(float(sz[0]) / float(width)))\n ystep = int(ceil(float(sz[1]) / float(height)))\n \n for dy in xrange(ystep):\n for dx in xrange(xstep):\n surf = pygame.Surface((width, height)).convert_alpha()\n surf.fill((0,0,0,0))\n surf.blit(surface, (0,0), (dx*width, dy*height, width, height))\n ret.append(surf)\n \n print \"Converted surface with dimensions (%d x %d) into %d tiles of dimension (%d x %d)\" % \\\n (surface.get_width(), surface.get_height(), len(ret), width, height)\n \n return ret","title":""},{"docid":"62d0bf21aee6fffc967a7297ce23c102","score":"0.50098825","text":"def _group_ranges(self, units):\n for _, group in itertools.groupby(enumerate(units), lambda q: (q[1] - q[0])):\n group = list(group)\n yield group[0][1], group[-1][1]","title":""},{"docid":"b4204692b566a58a0f716553571839d8","score":"0.5003235","text":"def ranges(self) -> typing.List[ezcoach.range.Range]:","title":""},{"docid":"d0f500fb1cfb8a4f438c9e4eee38f754","score":"0.50002885","text":"def _concatenate_timestreams(cls, ts_lst, ts_rounding_error=0.6, ts_interp_threshold=0):\n #check for contiguous timestreams\n for i in range(1, len(ts_lst)):\n ts_sep = (ts_lst[i].start.time - ts_lst[i-1].stop.time) * ts_lst[i].sample_rate\n if numpy.abs(ts_sep - 1) > ts_rounding_error:\n if (ts_interp_threshold > 0) and ((ts_sep - 1) < ts_interp_threshold):\n log_warn(\"Timestreams are not contiguous: timestreams %d and %d \"\n \"separated by %f samples (%s). Interpolating.\" %\n (i, i-1, ts_sep - 1, str(ts_lst[i].start)))\n v = numpy.linspace(ts_lst[i-1][-1], ts_lst[i][0], ts_sep + 1)[1:-1]\n ts_interp = cls(v)\n ts_interp.units = ts_lst[0].units\n ts_interp.start = ts_lst[i-1].stop + 1. / ts_lst[i].sample_rate\n ts_interp.stop = ts_lst[i].start - 1. / ts_lst[i].sample_rate\n ts_lst = ts_lst[:i] + [ts_interp] + ts_lst[i:]\n else:\n log_fatal(\"Timestreams are not contiguous: timestreams %d and %d \"\n \"separated by %f samples (%s)\" % (i, i-1, ts_sep - 1, str(ts_lst[i].start)))\n if (ts_lst[i].units != ts_lst[0].units):\n log_fatal(\"Timestreams are not the same units\")\n out_ts = cls(numpy.concatenate(ts_lst))\n out_ts.units = ts_lst[0].units\n out_ts.start = ts_lst[0].start\n out_ts.stop = ts_lst[-1].stop\n return out_ts","title":""},{"docid":"c05187abbe42bb87e7bc8e06136bbeb3","score":"0.49992543","text":"def copy_tiles(self):\n new_tiles = []\n for l in self.tiles:\n new_row = l[:]\n new_tiles.append(new_row)\n \n return new_tiles","title":""},{"docid":"6598aea487edac0f3e68cda27c5ce7cc","score":"0.49947765","text":"def range_mapping(pattern, data):\n\n squished_data = []\n\n for entry in data:\n min_diff = entry - min(data)\n normalized_diff = min_diff / (max(data) - min(data))\n squished_entry = min(pattern) + normalized_diff * (max(pattern) - min(pattern))\n\n squished_data.append(squished_entry)\n\n return squished_data","title":""},{"docid":"8b1d3e03c8c1ce3e8cf00cc5523aabf5","score":"0.49899983","text":"def set_limit_list(msid):\n \n [udict, ddict] = read_unit_list()\n tchk = 0\n try:\n unit = udict[msid.lower()]\n if unit.lower() in ['k', 'degc']:\n tchk = 1\n elif unit.lower() == 'degf':\n tchk = 2\n elif unit.lower() == 'psia':\n tchk = 3\n except:\n pass\n \n l_list = gsr.read_glimmon(msid, tchk)\n \n if len(l_list) == 0:\n try:\n l_list = mta_db[msid]\n except:\n l_list = []\n#\n#--- if there is no limt given, set dummy limits\n#\n if len(l_list) == 0:\n tstart = 31536000 #---- 1999:001:00:00:00\n tstop = find_current_stime()\n l_list = [[tstart, tstop, -998, 998, -999, 999]]\n\n return l_list\n\n cleaned = []\n for alist in l_list:\n if alist[0] == alist[1]:\n continue\n else:\n cleaned.append(alist)\n \n cleaned2 = []\n alist = cleaned[0]\n for k in range(1, len(cleaned)):\n blist = cleaned[k]\n if (alist[2] == blist[2]) and (alist[3] == blist[3]) and (alist[4] == blist[4]) and (alist[5] == blist[5]):\n alist[1] = blist[1]\n else:\n cleaned2.append(alist)\n alist = blist\n\n cleaned2.append(alist)\n \n return cleaned2","title":""},{"docid":"6528631120c2513d135bffc2dc03a111","score":"0.49865663","text":"def _slice_time_series(time_series, start, end):\n # start must be greater than 1, this is asserted in genex_databse._process_loi\n rtn = list()\n\n for ts in time_series:\n ts_id = ts[0]\n ts_data = ts[1]\n # we take min because min can be math.inf\n for i in range(start, min(end, len(ts_data))):\n rtn += _get_sublist_as_sequences(single_time_series=ts_data, data_id=ts_id, length=i)\n return rtn","title":""},{"docid":"49417de99f92c728d2bc00c1c9e0afb1","score":"0.49861628","text":"def pruneList(list_):\r\n i = 0\r\n while i < len(list_):\r\n while True:\r\n if len(list_[i][1]) == 0:\r\n list_.pop(i)\r\n i -= 1\r\n break\r\n if list_[i][1][0] + numHours - 300 < time(): # 5 minute leeway\r\n break\r\n list_[i][1].pop(0)\r\n i += 1","title":""},{"docid":"f1845541e8dd065f7ab58b8bbeaa633c","score":"0.49857557","text":"def sanitizeMultiStopLongFlights(flightList, maxDuration=21600):\n ## TODO: add type checking; this method should only accept dictionaries\n\n modifiedLists = []\n\n #Remove flights with more than 1 stop and with a total duration of 5 or more hours\n logging.debug('sanitizeMultiStopLongFlights: ' + 'Sanitizing list to remove multi-stop and long duration flights...')\n sanitizedList = {k:v for k,v in flightList.items() if v['count'] <= 2 and v['duration'] <= maxDuration }\n logging.debug('sanitizeMultiStopLongFlights: ' + 'Size of result set reduced from ' + str(len(flightList)) + ' to ' + str(len(sanitizedList)))\n\n #We need to use this list to filter the separate outbound/inbound lists to make sure we arent pulling them in any future queries\n discardedList = {k:v for k,v in flightList.items() if v['count'] > 2 or v['duration'] > maxDuration }\n logging.debug('sanitizeMultistopLongFlights: ' + 'Size of discarded list is ' + str(len(discardedList)))\n\n modifiedLists.append(sanitizedList)\n modifiedLists.append(discardedList)\n return modifiedLists","title":""},{"docid":"8772e634c002e4023b663ab2dae854e7","score":"0.49856314","text":"def _apply_gti_to_live_time(self):\n\n # First negate all FT2 entries\n\n filter_idx = np.zeros_like(self._livetime, dtype=bool)\n\n # now loop through each GTI interval\n\n for start, stop in zip(self._gti_start, self._gti_stop):\n\n # create an index of all the FT2 bins falling within this interval\n\n tmp_idx = np.logical_and(start <= self._ft2_tstart, self._ft2_tstop <= stop)\n\n # add them to the already selected idx\n filter_idx = np.logical_or(filter_idx, tmp_idx)\n\n # Now filter the whole list\n self._ft2_tstart = self._ft2_tstart[filter_idx]\n self._ft2_tstop = self._ft2_tstop[filter_idx]\n self._livetime = self._livetime[filter_idx]","title":""},{"docid":"b16f9b726498665a74bb821945f66fd4","score":"0.4980329","text":"def split_ranges(start, stop, count=None, step=None):\n if count is not None and count < 1:\n return []\n\n if count is None:\n if isinstance(step, timedelta):\n time_range = stop - start\n time_in_ms = time_range.microseconds + 1e6 * (\n time_range.seconds + 86400 * time_range.days\n )\n step_in_ms = step.microseconds + 1e6 * (step.seconds + 86400 * step.days)\n count = time_in_ms / step_in_ms\n else:\n count = (stop - start) / step\n if count < 1:\n count = 1\n\n if step is None:\n step = (stop - start) / count\n\n return [(start + i * step, start + (i + 1) * step) for i in range(int(count))]","title":""},{"docid":"d626e409424faea89c7be257685c4818","score":"0.49720186","text":"def build_timestamp_list(start, end, time_step_second=15*60):\n timestamp_list = [start]\n\n start = libdt.datetime.strptime(start, \"%Y-%m-%d %H:%M:%S\")\n end = libdt.datetime.strptime(end, \"%Y-%m-%d %H:%M:%S\") #+ libdt.timedelta(minutes=time_step_second)\n\n while start < end:\n start = start + libdt.timedelta(0, time_step_second)\n timestamp_list.append(libdt.datetime.strftime(start, \"%Y-%m-%d %H:%M:%S\"))\n return timestamp_list","title":""},{"docid":"48314a638d8b3df4d261317a3813ed40","score":"0.49701375","text":"def build_streched_workloads(workloads: list):\n assert isinstance(workloads[0], pd.DataFrame)\n target_workloads = []\n\n for workload in workloads:\n w = np.array(workload.as_matrix(), dtype=\"float32\")\n\n streched_workload = np.zeros((2 * w.shape[0], w.shape[1]))\n n_time_steps = w.shape[0]\n \n for t in range(n_time_steps):\n k = t*2\n streched_workload[k:k + 2, :] = w[[t], :]\n\n target_workloads.append(streched_workload)\n\n return target_workloads","title":""},{"docid":"c42a655a7ba60730f33a7ce555a993c1","score":"0.49635893","text":"def to_ranges(self):\n\t\tranges = []\n\t\tfor di in range(self.ndet):\n\t\t\ts1, s2 = self.stack_bounds[di:di+2]\n\t\t\tinds = self.index_stack[s1:s2]\n\t\t\tmask = np.any(self.flag_stack[s1:s2],1)\n\t\t\tmask = np.concatenate([[False],mask]).astype(int)\n\t\t\tedges = mask[1:]-mask[:-1]\n\t\t\tstarts = np.where(edges>0)[0]\n\t\t\tends = np.where(edges<0)[0]\n\t\t\tif len(starts) == 0:\n\t\t\t\tranges.append(np.zeros([0,2],int))\n\t\t\telse:\n\t\t\t\tr = np.zeros([len(starts),2],int)\n\t\t\t\tr[:,0] = inds[starts]\n\t\t\t\tr[:len(ends),1] = inds[ends]\n\t\t\t\tif len(ends) < len(starts): r[-1,1] = self.nsamp\n\t\t\t\tranges.append(r)\n\t\treturn ranges","title":""},{"docid":"ed648d55d34ecc869551f1376e5e2b63","score":"0.49619672","text":"def potentialRange(self):\n values = []\n for candidate in self.root.output():\n values.append(dateutil.parser.parse(candidate.get(self.attribute)).time())\n return (min(values), max(values))","title":""},{"docid":"98279742d758d892ea1f40b1a161ed50","score":"0.4961269","text":"def relative_timings(self) -> List[Tuple[int, int, int]]:\n schedules = []\n for schedule in self.relative_schedules.all():\n num_seconds = schedule.minute * 60 + schedule.hour * 3600\n schedules.append([schedule.intervention.id, schedule.days_after, num_seconds])\n return schedules","title":""},{"docid":"906152c6823f82fe93d6b99d52c0b8e8","score":"0.49586782","text":"def image_range_to_tiles(start_ind, end_ind, display=False, save_summary=True, save_data=True, save_top_tiles=True):\n image_num_list = list()\n tile_summaries_dict = dict()\n for slide_num in range(start_ind, end_ind + 1):\n tile_summary = summary_and_tiles(slide_num, display, save_summary, save_data, save_top_tiles)\n image_num_list.append(slide_num)\n tile_summaries_dict[slide_num] = tile_summary\n return image_num_list, tile_summaries_dict","title":""},{"docid":"ee240255ce46e4d6b8e4dcc37ffba8d6","score":"0.49579102","text":"def calc_date_chunks(start, end):\n chunks = []\n\n start = datetime.datetime.strptime(start, '%m-%d-%Y-%H:%M')\n prev = start\n end = datetime.datetime.strptime(end, '%m-%d-%Y-%H:%M')\n\n while (prev <= end):\n # Increment 6 hours\n curr = prev + datetime.timedelta(seconds=21600)\n\n chunks.append((prev.strftime('%m-%d-%Y-%H:%M'), curr.strftime('%m-%d-%Y-%H:%M')))\n prev = curr\n\n \"\"\"\n Adjust the last tuple incase the time period doesnt divide evenly into\n 6-hr periods\n\n Ex\n ---\n Unadjusted:\n in: calc_date_chunks('09-01-2019-15:00', '09-06-2019-13:00')[-1]\n out: ('09-06-2019-09:00', '09-06-2019-15:00')\n\n Adjusted:\n in: calc_date_chunks('09-01-2019-15:00', '09-06-2019-13:00')[-1]\n out: ('09-06-2019-09:00', '09-06-2019-13:00')\n\n \"\"\"\n if (datetime.datetime.strptime(chunks[-1][1], '%m-%d-%Y-%H:%M') > end):\n chunks = chunks[:-1]\n prev = prev - datetime.timedelta(seconds=21600)\n chunks.append((prev.strftime('%m-%d-%Y-%H:%M'), end.strftime('%m-%d-%Y-%H:%M')))\n\n return chunks","title":""},{"docid":"c21b7d1a6531aaae04bbf366e5f55498","score":"0.4957181","text":"def intervals(\n nms #list of indices\n ):\n return list(zip(nms[:],nms[1:]))","title":""},{"docid":"c0d25e19bfcbfb65a188aefb80f6ea9c","score":"0.49563673","text":"def range_and_interval_to_list(start_time_unix_sec=None, end_time_unix_sec=None,\n time_interval_sec=None, include_endpoint=True):\n\n error_checking.assert_is_integer(start_time_unix_sec)\n error_checking.assert_is_not_nan(start_time_unix_sec)\n error_checking.assert_is_integer(end_time_unix_sec)\n error_checking.assert_is_not_nan(end_time_unix_sec)\n error_checking.assert_is_integer(time_interval_sec)\n error_checking.assert_is_boolean(include_endpoint)\n\n if include_endpoint:\n error_checking.assert_is_geq(end_time_unix_sec, start_time_unix_sec)\n else:\n error_checking.assert_is_greater(end_time_unix_sec, start_time_unix_sec)\n\n start_time_unix_sec = int(rounder.floor_to_nearest(\n float(start_time_unix_sec), time_interval_sec\n ))\n end_time_unix_sec = int(rounder.ceiling_to_nearest(\n float(end_time_unix_sec), time_interval_sec\n ))\n\n if not include_endpoint:\n end_time_unix_sec -= time_interval_sec\n\n num_time_steps = 1 + int(numpy.round(\n (end_time_unix_sec - start_time_unix_sec) / time_interval_sec\n ))\n\n return numpy.linspace(start_time_unix_sec, end_time_unix_sec,\n num=num_time_steps, dtype=int)","title":""},{"docid":"adde6045a88d6db8a6b268355a4c0a71","score":"0.49558872","text":"def get_time_slot_set(lecture: Lecture):\n time_slot_set = [[], [], [], [], [], []]\n for time_slot in lecture.time_slots.all():\n day_index = day_to_index[time_slot.day_of_week]\n start_int = time_to_int(time_slot.start_time)\n end_int = time_to_int(time_slot.end_time)\n time_slot_set[day_index].append((start_int, end_int))\n for i, time_slots in enumerate(time_slot_set):\n if not time_slots:\n continue\n time_slots.sort()\n new_time_slots = []\n\n start, end = time_slots[0]\n for time_slot in time_slots[1:]:\n if end < time_slot[0]:\n new_time_slots.append((start, end))\n start, end = time_slot\n else:\n end = max(end, time_slot[1])\n new_time_slots.append((start, end))\n time_slot_set[i] = new_time_slots\n return time_slot_set","title":""},{"docid":"7cadecc119e73dfdba63313974c2802c","score":"0.49555343","text":"def subtract(self, other_time_range):\n output = []\n if (self.t1 < other_time_range.t1):\n output.append(TimeRange(self.t1, other_time_range.t1))\n \n if (self.t2 > other_time_range.t2):\n output.append(TimeRange(other_time_range.t2, self.t2))\n\n return output","title":""},{"docid":"a63625b0bb5de267eaada4d345153519","score":"0.49435526","text":"def reshape_spiking_times(my_spikemon:SpikeMonitor, spiking_index:int=0, lower_threshold:float=0, upper_threshold=np.infty) -> Tuple[list, int, int]:\n result = []\n min_ = inf\n max_ = -1.\n\n argmin_ = 0\n argmax_ = 0\n\n n = len(my_spikemon.values('t'))\n for i in range(n):\n if len(my_spikemon.values('t')[i] / ms) <= spiking_index:\n result.append(nan)\n else:\n spike_time = my_spikemon.values('t')[i][spiking_index] / ms\n if spike_time >= lower_threshold and spike_time <= upper_threshold:\n result.append(spike_time)\n if spike_time <= min_:\n argmin_ = i\n min_ = spike_time\n elif spike_time >= max_:\n argmax_ = i\n max_ = spike_time\n else:\n result.append(nan)\n\n if len(result) == 0:\n raise Warning(\"No spikes\")\n\n return result, argmin_, argmax_","title":""},{"docid":"99334b47854fa95a9d27ab178a23171e","score":"0.4935655","text":"def roi_to_tiles(self, roi):\n bounds = BoundingBox(*roi.bounds)\n grid_ys, grid_xs = self._frame_bounds(bounds)\n return self._yield_tiles(grid_ys, grid_xs, roi)","title":""},{"docid":"e79b61abf5cef6b6cfe8e677f517c093","score":"0.4922719","text":"def extract_intervals(request_history):\n if not request_history:\n return []\n\n # first - sort by request start time and end time, this allows us to order the\n # requests on the actual timeline.\n timings = sorted(\n [req.timing for req in request_history],\n key=lambda timing: (timing[0], timing[1]),\n )\n\n distinct_intervals = []\n min_start, max_end = timings[0][:2]\n\n for timing in timings:\n start, end = timing[0], timing[1]\n if start <= max_end and end > max_end:\n max_end = timing[1]\n\n if start > max_end:\n distinct_intervals.append((min_start, max_end))\n min_start, max_end = timing[:2]\n\n # last loop iteration doesn't append distinct interval\n distinct_intervals.append((min_start, max_end))\n\n return distinct_intervals","title":""},{"docid":"9f9835b199461d6318d7395f49526ff7","score":"0.4919626","text":"def tiles(filepath, tile_ids):\n generated_tiles = []\n\n for tile_id in tile_ids:\n tile_id_parts = tile_id.split(\".\")\n tile_position = list(map(int, tile_id_parts[1:3]))\n\n (dense, mins, maxs) = get_data(\n h5py.File(filepath), tile_position[0], tile_position[1]\n )\n\n \"\"\"\n if len(dense):\n max_dense = max(dense)\n min_dense = min(dense)\n else:\n max_dense = 0\n min_dense = 0\n has_nan = len([d for d in dense if np.isnan(d)]) > 0\n if (\n not has_nan and\n max_dense > min_f16 and max_dense < max_f16 and\n min_dense > min_f16 and min_dense < max_f16\n ):\n tile_value = {\n 'dense': base64.b64encode(dense.astype('float16')).decode('utf-8'),\n 'mins': base64.b64encode(mins.astype('float16')).decode('utf-8'),\n 'maxs': base64.b64encode(mins.astype('float16')).decode('utf-8'),\n 'dtype': 'float16'\n }\n else:\n \"\"\"\n tile_value = {\n \"dense\": base64.b64encode(dense.astype(\"float32\")).decode(\"utf-8\"),\n \"mins\": base64.b64encode(mins.astype(\"float32\")).decode(\"utf-8\"),\n \"maxs\": base64.b64encode(maxs.astype(\"float32\")).decode(\"utf-8\"),\n \"dtype\": \"float32\",\n }\n\n generated_tiles += [(tile_id, tile_value)]\n\n return generated_tiles","title":""},{"docid":"e85acb35406628678d0ba05818c7abd2","score":"0.49142137","text":"def frame_timestamp_ranges(self, *, include_dead_time=False):\n\n ts_min = self._timestamps(reduce=np.min)\n ts_max = self._timestamps(reduce=np.max)\n delta_ts = int(1e9 / self.infowave.sample_rate) # We want the sample beyond the end\n if ts_min.ndim == 2:\n return [(np.min(ts_min), np.max(ts_max) + delta_ts)]\n else:\n if include_dead_time:\n frame_time = ts_min[1, 0, 0] - ts_min[0, 0, 0]\n return [(t, t + frame_time) for t in ts_min[:, 0, 0]]\n else:\n maximum_timestamp = np.max(ts_max, axis=tuple(range(1, ts_max.ndim)))\n return [(t1, t2) for t1, t2 in zip(ts_min[:, 0, 0], maximum_timestamp + delta_ts)]","title":""},{"docid":"9bd3bfbe9d726c83df5a6325b34b8300","score":"0.4913405","text":"def take_all_remaining_tiles(self):\r\n result = self._my_tiles\r\n self._my_tiles = []\r\n return result","title":""}],"string":"[\n {\n \"docid\": \"322f66d335834d6e7191aa041611eaff\",\n \"score\": \"0.6455336\",\n \"text\": \"def split(self, max_delta):\\n assert isinstance(max_delta, timedelta)\\n chunks = []\\n chunk_since = self.since\\n while chunk_since < self.until:\\n chunk_delta = min(max_delta, self.until - chunk_since)\\n chunks.append(TimeWindow.from_timedelta(chunk_since, chunk_delta))\\n chunk_since += chunk_delta\\n return chunks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177ea67f424b310a56547c9177c0aa70\",\n \"score\": \"0.59138876\",\n \"text\": \"def expand_time_windows(periods: List[TimeWindow]) -> List[pd.Timestamp]:\\n days_exclude = []\\n for window in periods:\\n days_exclude += pd.date_range(window.first_day, window.last_day, freq='D')\\n\\n return list(set(days_exclude))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712dcf88b0eb24498d5f1c2fdca179a0\",\n \"score\": \"0.5832873\",\n \"text\": \"def _tiff_timestamp_ranges(self) -> list:\\n return self.frame_timestamp_ranges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab84714f060edc3c064b7b1e57131ed\",\n \"score\": \"0.58066446\",\n \"text\": \"def _getTimeGrid(self, tMin, tMax, nPoi):\\n return [tMin + float(i) / (nPoi - 1) * (tMax - tMin) for i in range(nPoi)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4caf76c7e1f162971066e91d002be57\",\n \"score\": \"0.57903236\",\n \"text\": \"def getNonEntries(self):\\n entryList = self.entryList\\n invertedEntryList = [\\n (entryList[i][1], entryList[i + 1][0], \\\"\\\")\\n for i in range(len(entryList) - 1)\\n ]\\n\\n # Remove entries that have no duration (ie lie between two entries\\n # that share a border)\\n invertedEntryList = [\\n entry for entry in invertedEntryList if entry[0] < entry[1]\\n ]\\n\\n if entryList[0][0] > 0:\\n invertedEntryList.insert(0, (0, entryList[0][0], \\\"\\\"))\\n\\n if entryList[-1][1] < self.maxTimestamp:\\n invertedEntryList.append((entryList[-1][1], self.maxTimestamp, \\\"\\\"))\\n\\n invertedEntryList = [\\n entry if isinstance(entry, Interval) else Interval(*entry)\\n for entry in invertedEntryList\\n ]\\n\\n return invertedEntryList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81862dbc17a81dd4152c5180c4e037e1\",\n \"score\": \"0.5698723\",\n \"text\": \"def window_time_range(incident_time, threshold=5, unit=\\\"second\\\"):\\n\\n if unit == \\\"second\\\":\\n second_index = incident_time.rfind(\\\":\\\")\\n seconds = int(incident_time[second_index + 1: second_index + 3])\\n min_sec, max_sec = check_time(str(max(seconds - threshold, 0))), check_time(str(min(seconds + threshold, 59)))\\n min_time = incident_time[:second_index + 1] + min_sec\\n max_time = incident_time[:second_index + 1] + max_sec\\n return [min_time, max_time]\\n\\n elif unit == \\\"minute\\\":\\n minute_index = incident_time.find(\\\":\\\")\\n minutes = int(incident_time[minute_index + 1: minute_index + 3])\\n min_min, max_min = check_time(str(max(minutes - threshold, 0))), check_time(str(min(minutes + threshold, 59)))\\n min_time = incident_time[:minute_index + 1] + min_min + incident_time[minute_index + 3:]\\n max_time = incident_time[:minute_index + 1] + max_min + incident_time[minute_index + 3:]\\n return [min_time, max_time]\\n\\n elif unit == \\\"hour\\\":\\n hour_index = incident_time.find(\\\"T\\\")\\n hour = int(incident_time[hour_index + 1: hour_index + 3])\\n min_hour, max_hour = check_time(str(max(hour - threshold, 0))), check_time(str(min(hour + threshold, 23)))\\n min_time = incident_time[:hour_index + 1] + min_hour + incident_time[hour_index + 3:]\\n max_time = incident_time[:hour_index + 1] + max_hour + incident_time[hour_index + 3:]\\n return [min_time, max_time]\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"655bda73fa96acb31cc7faac9093f9be\",\n \"score\": \"0.5638426\",\n \"text\": \"def get_tilearea_range(self):\\n return [x ** 2 for x in xrange(1, self.tilearea_limit + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfade30e1a645e36d80b3c17c01479c2\",\n \"score\": \"0.5628009\",\n \"text\": \"def tiles(self) -> List[HipsTile]:\\n return [self.tile(ipix) for ipix in range(self.n_tiles)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dbc7431fc8c5d7464c86b21301a5041\",\n \"score\": \"0.56226414\",\n \"text\": \"def _convert_range_to_time_units(time_windows):\\n start, end = time_windows\\n tw_range = (end - start) * 60\\n return (0, tw_range)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b854e1e39e165df7e3aaccb3e3303a7c\",\n \"score\": \"0.5593613\",\n \"text\": \"def __getTimeRange_ut(self): \\n for f in self.__fileList:\\n regex = ( r'^' + self.runName + '_mhd_' +\\n r'(\\\\d{4})\\\\-(\\\\d{2})\\\\-(\\\\d{2})' +\\n r'T' +\\n r'(\\\\d{2})\\\\-(\\\\d{2})-(\\\\d{2})' +\\n r'Z'+\\n self.runExt + r'$' )\\n \\n r = re.match(regex, os.path.basename(f))\\n \\n assert( len(r.groups()) == 6 )\\n t = [ int(match) for match in r.groups() ]\\n self.__timeRange.append( datetime.datetime(year=t[0],\\n month=t[1],\\n day=t[2],\\n hour=t[3],\\n minute=t[4],\\n second=t[5]) )\\n \\n return self.__timeRange\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb5f3b385b7cfece8820710af72ecf7\",\n \"score\": \"0.5544131\",\n \"text\": \"def MaxToMin(min_lev, max_lev, datetime_maxes):\\r\\n y_res_mins = []\\r\\n deltaT_ranges = []\\r\\n for row in range(len(max_lev)-1):\\r\\n range_length = (datetime_maxes.iloc[row+1]-datetime_maxes.iloc[row]).seconds/3600 # hours\\r\\n if ~np.isnan(max_lev.iloc[row]) & (range_length>=3.5) & (range_length<=7.5): # peak to trough should take 6.2 hours in ocean\\r\\n y_res_mins.append(min_lev.iloc[row+1])\\r\\n deltaT_ranges.append(datetime_maxes.iloc[row+1]-datetime_maxes.iloc[row])\\r\\n elif ~np.isnan(max_lev.iloc[row]): # if peak lag is more than 4 hours, erroneous data\\r\\n y_res_mins.append(np.nan)\\r\\n deltaT_ranges.append(np.nan)\\r\\n # if last value in oceanside array is not nan, append another nan on the return arrays\\r\\n if (datetime_maxes.iloc[row+1]==datetime_maxes.iloc[-1]) & ~np.isnan(max_lev.iloc[row+1]): \\r\\n y_res_mins.append(np.nan)\\r\\n deltaT_ranges.append(np.nan)\\r\\n y_res_mins = np.array(y_res_mins)\\r\\n deltaT_ranges = np.array(deltaT_ranges)\\r\\n return deltaT_ranges, y_res_mins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b1dbe4f8cd9d2568d9d3386c4a4859\",\n \"score\": \"0.5513535\",\n \"text\": \"def resamplify_ranges(ranges, scored_hz, target_hz=200):\\n\\n ranges_copy = []\\n\\n for start, stop in ranges:\\n corrected_start = (start * scored_hz) / target_hz\\n corrected_stop = (stop * scored_hz) / target_hz\\n\\n ranges_copy.append((corrected_start, corrected_stop))\\n return ranges_copy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1215f220a238d9e91fcf0eb5d6bc0b77\",\n \"score\": \"0.5413744\",\n \"text\": \"def get_bb_max_times(self):\\n return [scipy.optimize.fminbound(\\n (lambda x: -f(x)), self.t_initial, self.t_final) for f in self.fps]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5b44bb030acc7b8a7b386e7bc2a4cce\",\n \"score\": \"0.5357053\",\n \"text\": \"def time_slicer(time_coordinate, query):\\n ref_time = time_coordinate.values[0]\\n # next step is parsing out the times\\n max_hours = max(query['hours'])\\n assert int(max_hours) == max_hours\\n max_time = ref_time + np.timedelta64(int(max_hours), 'h')\\n max_ind = np.max(np.nonzero(time_coordinate.values <= max_time)[0])\\n assert max_ind > 0\\n return slice(0, max_ind + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e561d6f32e5841cd68875f60c76edc2\",\n \"score\": \"0.5344265\",\n \"text\": \"def _get_breaks(n_items: int, n_workers: int) -> List[Tuple[int, int]]:\\n div, remainder = divmod(n_items, n_workers)\\n slices = []\\n bottom = 0\\n for i in range(n_workers):\\n top = bottom + div\\n top += int(i < remainder)\\n slices.append((bottom, top))\\n bottom = top\\n return slices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9fb2b396b170ebe52fbe5b2edba0a75\",\n \"score\": \"0.5329466\",\n \"text\": \"def _trim(self): \\n trimmed = []\\n for start, end in self.stamps:\\n if not (end < self.tStart or start > self.tEnd):\\n trimmed.append((max(start, self.tStart), min(end, self.tEnd)))\\n self.stamps = trimmed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be7a8dcd65fb033ac172b5d1663b3e95\",\n \"score\": \"0.531007\",\n \"text\": \"def calculate_deltas(self, start: datetime, end: datetime) -> List[datetime]:\\n deltas = []\\n curr = start\\n while curr < end:\\n deltas.append(curr)\\n curr += self._time_granularity_\\n return deltas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea10c59d602c9340d024ca922c1324c\",\n \"score\": \"0.5305902\",\n \"text\": \"def ConvertRanges2ExpandedRanges( ranges, max_length ):\\n \\n new_ranges = []\\n \\n for r in ranges:\\n new_ranges.append( ( max(int(r[0] * param_resolution) + 1, 1),\\n min(int(r[1] * param_resolution) + 1, max_length)) )\\n return new_ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc78a21d79b5448f6c15ae4793876ac\",\n \"score\": \"0.5299879\",\n \"text\": \"def timeseries_to_batch(x: npt.NDArray[T], len_t: int = 3) -> list[npt.NDArray[T]]:\\n subviews = np.moveaxis(\\n np.lib.stride_tricks.sliding_window_view( # type: ignore [no-untyped-call]\\n x, len_t, axis=0\\n ),\\n -1,\\n 1,\\n )\\n\\n list_subviews = np.split( # type: ignore [no-untyped-call]\\n subviews, subviews.shape[0], axis=0\\n )\\n return [np.take(subview, 0, axis=0) for subview in list_subviews]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a8c7658c29c3f725b15cf24411adda\",\n \"score\": \"0.5297275\",\n \"text\": \"def build_climatology_bounds(da: xr.DataArray) -> list[str]:\\n n = len(da.time)\\n return da.time[0 :: n - 1].dt.strftime(\\\"%Y-%m-%d\\\").values.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614df040cb216430b45604215397a9fc\",\n \"score\": \"0.5269239\",\n \"text\": \"def absolute_timings(self) -> List[Tuple[int, int, int, int]]:\\n schedules = []\\n for schedule in self.absolute_schedules.all():\\n event_time = schedule.event_time\\n num_seconds = event_time.minute * 60 + event_time.hour * 3600\\n schedules.append([event_time.year, event_time.month, event_time.day, num_seconds])\\n return schedules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577e53c3a47159674c2908c7a2b3ec3a\",\n \"score\": \"0.526138\",\n \"text\": \"def get_unstable_range(\\n self, limit: int = None, range_threshold: float = None, **kwargs\\n ) -> typing.List[VideoCutRange]:\\n change_range_list = sorted(\\n [i for i in self.range_list if not i.is_stable(**kwargs)],\\n key=lambda x: x.start,\\n )\\n\\n # video can be totally stable ( nothing changed )\\n # or only one unstable range\\n if len(change_range_list) <= 1:\\n return change_range_list\\n\\n # merge\\n i = 0\\n merged_change_range_list = list()\\n while i < len(change_range_list) - 1:\\n cur = change_range_list[i]\\n while cur.can_merge(change_range_list[i + 1], **kwargs):\\n # can be merged\\n i += 1\\n cur = cur.merge(change_range_list[i], **kwargs)\\n\\n # out of range\\n if i + 1 >= len(change_range_list):\\n break\\n merged_change_range_list.append(cur)\\n i += 1\\n if change_range_list[-1].start > merged_change_range_list[-1].end:\\n merged_change_range_list.append(change_range_list[-1])\\n\\n if limit:\\n merged_change_range_list = self._length_filter(\\n merged_change_range_list, limit\\n )\\n # merged range check\\n if range_threshold:\\n merged_change_range_list = [\\n i for i in merged_change_range_list if not i.is_loop(range_threshold)\\n ]\\n logger.debug(\\n f\\\"unstable range of [{self.video.path}]: {merged_change_range_list}\\\"\\n )\\n return merged_change_range_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d55876f5627898e2b71c7df9ffda943\",\n \"score\": \"0.5261111\",\n \"text\": \"def compressed(self):\\n time_windows = self.time_windows_sorted_by_since\\n if not time_windows:\\n return TimeWindowsCollection([])\\n stack = []\\n\\n latest = {\\n 'since': None,\\n 'until': None\\n }\\n\\n for current in time_windows:\\n if latest['until'] is None:\\n latest['since'] = current.since\\n latest['until'] = current.until\\n elif latest['until'] >= current.since:\\n latest['until'] = max(latest['until'], current.until)\\n else:\\n stack.append(TimeWindow(latest['since'], latest['until']))\\n latest['since'] = current.since\\n latest['until'] = current.until\\n\\n stack.append(TimeWindow(latest['since'], latest['until']))\\n return TimeWindowsCollection(stack, sorted_since=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857893fc52595e1667f57e57d035f309\",\n \"score\": \"0.5246155\",\n \"text\": \"def test_global_tiles_clamped():\\n tiles = list(mercantile.tiles(-180, -90, 180, 90, [1]))\\n assert len(tiles) == 4\\n assert min(t.y for t in tiles) == 0\\n assert max(t.y for t in tiles) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2adf7bdc91bddc71de0cd1901d0d236e\",\n \"score\": \"0.5245297\",\n \"text\": \"def filter_out_non_working_hours(times): # TODO: what's this?\\n for start, end in times:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f313cc3fae33ef1cf4351c6a838e65a\",\n \"score\": \"0.5245202\",\n \"text\": \"def adjust_list(ticks: list, ttask: int) -> list:\\n for _ in range(ttask - 1):\\n ticks.append(0)\\n return ticks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d0feb1999602273e6c9cfd37b24189c\",\n \"score\": \"0.52408475\",\n \"text\": \"def get_gtopo30_tile_list(self):\\n\\n logger = logging.getLogger(__name__)\\n\\n # GTOPO30 tile information\\n NORTH_LON_LOCATIONS = np.array([-180.0, -140.0, -100.0, -60.0, -20.0,\\n 20.0, 60.0, 100.0, 140.0])\\n\\n SOUTH_LON_LOCATIONS = np.array([-180.0, -120.0, -60.0,\\n 0.0, 60.0, 120.0])\\n\\n LAT_LOCATIONS = np.array([90.0, 40.0, -10.0, -60.0])\\n\\n GTOPO30_TILE_SET_CUTOFF_LATITUDE = -60.0\\n\\n '''\\n The tiles do not necessarily go all the way to the boundary. For\\n example, the w100n90 tile only goes to -99.995833333334 longitude.\\n Thus, if our UL longitude falls between -99.995833333334 and -100.0\\n then we will need to use w140n90 to get the far left edge. To\\n account for this we'll artificially pad the East and West boundaries\\n by 1 degree.\\n\\n Note: this does not seem to cause problems on the North and South\\n borders, so we will no worry about them.\\n '''\\n ul_lon = Math.longitude_norm(self.bounding_west_longitude -\\n self.gtopo30_padding)\\n lr_lon = Math.longitude_norm(self.bounding_east_longitude +\\n self.gtopo30_padding)\\n logger.debug('ul_lon = {0}'.format(ul_lon))\\n logger.debug('lr_lon = {0}'.format(lr_lon))\\n\\n '''\\n There is a condition which could cause problems on the North and South\\n boundaries as well. This is if a scene comes very near the edge, but\\n does not quite get there. It could be that adding 1.5 to nrows before\\n taking the ceiling (see calculation of nrows in get_gtopo30_data.c)\\n causes the nrows to be large enough that we need data from the tile to\\n the South, even though the scene stopped just before that tile. To\\n address this we will grow North and South by 1 degree as we did in the\\n East and West directions above. Note, the same problem can occur in\\n the East and West directions, but was already addressed when adding\\n one to the longitudes above.\\n '''\\n ul_lat = min((self.bounding_north_latitude + self.gtopo30_padding),\\n self.north_latitude_limit)\\n lr_lat = max((self.bounding_south_latitude - self.gtopo30_padding),\\n self.south_latitude_limit)\\n logger.debug('ul_lat = {0}'.format(ul_lat))\\n logger.debug('lr_lat = {0}'.format(lr_lat))\\n\\n # Determine the unique latitudes where the input data resides\\n lat_list = list(set([min(LAT_LOCATIONS[\\n np.where(ul_lat < LAT_LOCATIONS)]),\\n min(LAT_LOCATIONS[\\n np.where(lr_lat < LAT_LOCATIONS)])]))\\n logger.debug('lat_list = {0}'.format(lat_list))\\n\\n tile_list = list()\\n # Determine the unique longitudes where the input data resides\\n for lat in lat_list:\\n lon_list = list()\\n if lat > GTOPO30_TILE_SET_CUTOFF_LATITUDE:\\n lon_list.append(\\n max(NORTH_LON_LOCATIONS[\\n np.where(ul_lon > NORTH_LON_LOCATIONS)]))\\n lon_list.append(\\n max(NORTH_LON_LOCATIONS[\\n np.where(lr_lon > NORTH_LON_LOCATIONS)]))\\n else:\\n lon_list.append(\\n max(SOUTH_LON_LOCATIONS[\\n np.where(ul_lon > SOUTH_LON_LOCATIONS)]))\\n lon_list.append(\\n max(SOUTH_LON_LOCATIONS[\\n np.where(lr_lon > SOUTH_LON_LOCATIONS)]))\\n lon_list = list(set(lon_list))\\n logger.debug('lon_list = {0}'.format(lon_list))\\n\\n n_s = 'n'\\n if lat < 0:\\n n_s = 's'\\n\\n for lon in lon_list:\\n e_w = 'e'\\n if lon <= 0:\\n e_w = 'w'\\n\\n # Already know if north/south, east/west so we just need\\n # the positive value for the filename\\n abs_lat = abs(lat)\\n abs_lon = abs(lon)\\n\\n tile_list.append('{0}{1:03}{2}{3:02}'\\n .format(e_w, abs_lon, n_s, abs_lat))\\n\\n return tile_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9015767027dbd353a22eee05789ed134\",\n \"score\": \"0.5227308\",\n \"text\": \"def _removeUltrashortIntervals(tier, minLength, minTimestamp):\\n\\n # First, remove tiny intervals\\n newEntryList = []\\n j = 0 # index to newEntryList\\n for start, stop, label in tier.entryList:\\n\\n if stop - start < minLength:\\n # Correct ultra-short entries\\n if len(newEntryList) > 0:\\n lastStart, _, lastLabel = newEntryList[j - 1]\\n newEntryList[j - 1] = (lastStart, stop, lastLabel)\\n else:\\n # Special case: the first entry in oldEntryList was ultra-short\\n if len(newEntryList) == 0 and start != minTimestamp:\\n newEntryList.append((minTimestamp, stop, label))\\n # Normal case\\n else:\\n newEntryList.append((start, stop, label))\\n j += 1\\n\\n # Next, shift near equivalent tiny boundaries\\n # This will link intervals that were connected by an interval\\n # that was shorter than minLength\\n j = 0\\n while j < len(newEntryList) - 1:\\n diff = abs(newEntryList[j][1] - newEntryList[j + 1][0])\\n if diff > 0 and diff < minLength:\\n newEntryList[j] = (\\n newEntryList[j][0],\\n newEntryList[j + 1][0],\\n newEntryList[j][2],\\n )\\n j += 1\\n\\n return tier.new(entryList=newEntryList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181fc0a6659862eed9e9293c98c5cfd1\",\n \"score\": \"0.5219579\",\n \"text\": \"def cut(times, dt=1):\\n time_list = times.values\\n if not time_list.size:\\n return []\\n\\n jumps = time_list[1:] != time_list[:-1]+dt\\n nb_jumps = sum(jumps) # numbers of jumps\\n\\n dates = np.zeros((nb_jumps+1, 2))\\n # Fist time value\\n dates[0, 0] = time_list[0]\\n # Last time value\\n dates[-1, -1] = time_list[-1]\\n # Add all other jumps\\n dates[1:, 0] = time_list[1:][jumps]\\n dates[:-1, 1] = time_list[:-1][jumps]\\n return dates.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b164778c0d2897adea2c63719902f8\",\n \"score\": \"0.52050847\",\n \"text\": \"def time_range(self):\\n if len(self._time_range) == 0:\\n for band in self.bands_from_grib_file:\\n self._time_range.append(self.timestep_for_band(band))\\n return self._time_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9e912e74144156504e701b78250b167\",\n \"score\": \"0.5189701\",\n \"text\": \"def range_maps(self) -> List[RangeMap]:\\n range_maps_list = []\\n for dest in self._state.attributes[SERVICE_RANGEMAP][\\\"rangemaps\\\"]:\\n range_maps_list.append(RangeMap(dest))\\n return range_maps_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db126b5d56db778a58e6dd0fcf370c93\",\n \"score\": \"0.5189438\",\n \"text\": \"def tile_limits(x_min, x_max, y_min, y_max, x_tile, y_tile):\\n # modify the bounding box to be a multiple of the tile dimension\\n x_pad = (x_max - x_min) % x_tile\\n x_min += math.ceil(x_pad/2)\\n x_max += math.floor(x_pad/2) \\n x_num_tiles = int((x_max - x_min) // x_tile)\\n\\n y_pad = (y_max - y_min) % y_tile\\n y_min += math.ceil(y_pad/2)\\n y_max += math.floor(y_pad/2) \\n y_num_tiles = int((y_max - y_min) // y_tile)\\n\\n # generate tile bboxes\\n tiles = []\\n for ii in range(x_num_tiles):\\n for jj in range(y_num_tiles):\\n tile = {}\\n tile['x_min'] = x_min + ii*x_tile\\n tile['x_max'] = tile['x_min'] + x_tile\\n tile['y_min'] = y_min + jj*y_tile\\n tile['y_max'] = tile['y_min'] + y_tile\\n tiles.append(tile)\\n\\n return tiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6200c7c574041bc8ed3f27af2e7e8923\",\n \"score\": \"0.5184637\",\n \"text\": \"def get_bb_max_times(self):\\n return [sympyutils.poly_fminbound(\\n -poly, self.t_initial, self.t_final) for poly in self.polys]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f72df2508cbe1b32761abaf6284d332\",\n \"score\": \"0.51813865\",\n \"text\": \"def scale_timings(timelist, input_units, output_units, time_repetition):\\r\\n if input_units==output_units:\\r\\n _scalefactor = 1.\\r\\n if (input_units == 'scans') and (output_units == 'secs'):\\r\\n _scalefactor = time_repetition\\r\\n if (input_units == 'secs') and (output_units == 'scans'):\\r\\n _scalefactor = 1./time_repetition\\r\\n timelist = [np.max([0., _scalefactor*t]) for t in timelist]\\r\\n return timelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c02940a180c73e6c46faf4dbb625a2\",\n \"score\": \"0.517908\",\n \"text\": \"def _get_empty_timeslots(list_of_booked_timings, date):\\n day = datetime.strptime((details[\\\"date\\\"]), '%d-%b-%Y').strftime('%A')\\n \\n #weekday 8.30 to 10.30\\n #weekend 8.30 to 5\\n \\n list_of_available_timings = []\\n \\n list_of_booked_timings.sort()\\n \\n for i in range(len(list_of_booked_timings)-1):\\n time_range1 = list_of_booked_timings[i].split('-')\\n start_time1 = int(time_range1[0].replace(':', \\\"\\\"))\\n end_time1 = int(time_range1[1].replace(':',\\\"\\\"))\\n \\n \\n time_range2 = list_of_booked_timings[i+1].split('-')\\n start_time2 = int(time_range2[0].replace(':', \\\"\\\"))\\n end_time2 = int(time_range2[1].replace(':',\\\"\\\"))\\n \\n if i == 0:\\n check = start_time1 - 800\\n if check > 0:\\n first = '08:30' + '-' + time_range1[0]\\n list_of_available_timings.append(first)\\n \\n \\n if i == len(list_of_booked_timings)-2: \\n check = 2230 - end_time2\\n if check > 0: \\n if day == 'Saturday' or day == 'Sunday':\\n last = time_range2[1] + '-' + '17:00'\\n list_of_available_timings.append(last)\\n else:\\n last = time_range2[1] + '-' + '22:30'\\n list_of_available_timings.append(last)\\n \\n if start_time2 - end_time1 > 0:\\n add = time_range1[1] + '-' + time_range2[0]\\n list_of_available_timings.append(add)\\n \\n list_of_available_timings.sort()\\n \\n return list_of_available_timings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f63a31a2f5b2ff8e8b3be377aa448a\",\n \"score\": \"0.51764965\",\n \"text\": \"def get_filenames_by_time_range(\\n self, start: datetime, stop: datetime\\n ) -> List[Path]:\\n filenames = []\\n time = start\\n while time <= stop:\\n filenames.append(self.get_filename_by_time(time))\\n time += timedelta(hours=1)\\n\\n # Cast to set then back to list to remove duplicates.\\n return sorted(list(set(filenames)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7a7d3d459bbda361500bda8065fe5c\",\n \"score\": \"0.517434\",\n \"text\": \"def _calculate_min_max(self):\\n\\n min_max_list = []\\n\\n for band in self.bands:\\n\\n file_path = ('%s/%s_B%s.TIF' % (self.warp_path,\\n self.image, band))\\n\\n if os.path.exists(file_path):\\n print ('Starting the Min/Max process with designated -percent '\\n 'cut- for band %s of %s' % (band, self.image))\\n print '...'\\n\\n # Open images in the warp folder\\n ds = gdal.Open(file_path)\\n\\n # converting raster to numpy array\\n values = numpy.array(ds.GetRasterBand(1).ReadAsArray())\\n to_list = values.tolist()\\n\\n full_list = [item for sublist in to_list for item in sublist]\\n\\n # removing zeros\\n value_list = filter(lambda x: x != 0, full_list)\\n list_len = len(value_list)\\n\\n value_list.sort()\\n\\n # determining number of integers to cut from bottom of list\\n cut_value_bottom = int(float(self.btm_prct) /\\n float(100) * float(list_len))\\n\\n # determining number of integers to cut from top of list\\n cut_value_top = int(float(self.top_prct) /\\n float(100) * float(list_len))\\n\\n # establishing new min and max with percent cut\\n cut_list = value_list[\\n (cut_value_bottom + 1):(list_len - cut_value_top)]\\n\\n # adding min and max with percent cut values to list\\n min_max_list.extend([cut_list[0], cut_list[-1]])\\n\\n print 'Finished processing band %s of %s' % (band, self.image)\\n\\n return [min(min_max_list), max(min_max_list)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee63c0439a4476e10a117915e35af8f\",\n \"score\": \"0.51733375\",\n \"text\": \"def create_list(dig):\\n max_range = pow(10, dig) - 1\\n min_range = pow(10, dig-1) - 1\\n unfiltered_list = list(range(max_range, min_range, -1))\\n return unfiltered_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb2b2c28adc79a6cde5bb05107097ce\",\n \"score\": \"0.5172149\",\n \"text\": \"def timedelta_with_itself(time_list: List) -> List:\\n temp_list = []\\n for index , time in enumerate(time_list):\\n try:\\n if index==0:\\n temp_list.append((0.0))\\n else:\\n temp_list.append(time_list[index]-time_list[index-1])\\n except IndexError:\\n pass\\n return temp_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a182e850796228a52ff5605a5b9ac531\",\n \"score\": \"0.5169623\",\n \"text\": \"def get_time_range_from_relayoutData(relayoutData):\\n time_range = [df_start, df_end]\\n if relayoutData is None:\\n pass\\n else:\\n if 'xaxis.range[0]' in relayoutData:\\n time_range = [pd.to_datetime(relayoutData['xaxis.range[0]']),\\n pd.to_datetime(relayoutData['xaxis.range[1]'])]\\n return time_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4944d0d6bcc8c043ed50f3349dfe72aa\",\n \"score\": \"0.5168822\",\n \"text\": \"def list_of_length_scales(bottom_bound, top_bound, steps):\\n length_scales = []\\n for i in range(bottom_bound, top_bound, steps):\\n length_scales.append(i)\\n return length_scales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d857517b86e9b30fd371be8f3085f93\",\n \"score\": \"0.5161929\",\n \"text\": \"def _generate_time_windows(od_dist, time_windows):\\n # initialize time matrix as empty list\\n time_matrix = []\\n # if time_windows is set in database this means it is a list of lits.\\n # so iterate each list of lists and add it to time matrix.\\n # else, do it from slider.\\n if len(time_windows) > 2:\\n for ti_wi in time_windows:\\n tw = _convert_range_to_time_units(ti_wi)\\n time_matrix.append(tw)\\n else:\\n tw = _convert_range_to_time_units(time_windows)\\n time_matrix = [tw for elem in range(len(od_dist))]\\n return time_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5440cbfc84a53cd8db90e087315b083b\",\n \"score\": \"0.51561314\",\n \"text\": \"def make_consistent(orig_list, delta, max_value=None, target='datetimefield'):\\n if not orig_list:\\n return\\n\\n prev = orig_list[0]\\n\\n for i in orig_list:\\n while abs(prev[target]-i[target]).total_seconds() / delta > 1:\\n if prev[target] > i[target]:\\n prev[target] -= dt.timedelta(seconds=delta)\\n else:\\n prev[target] += dt.timedelta(seconds=delta)\\n\\n yield prev, True\\n\\n prev = i\\n yield i, False\\n\\n # append new elements at the end until reach max value\\n while max_value and abs(prev[target] - max_value).total_seconds()/delta > 1:\\n if prev[target] > max_value:\\n prev[target] -= dt.timedelta(seconds=delta)\\n else:\\n prev[target] += dt.timedelta(seconds=delta)\\n\\n yield prev, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1657797e8e149e49f2e0ac34a224b3e1\",\n \"score\": \"0.5152248\",\n \"text\": \"def maxvals4timeslice(self, name, start, stop):\\n return self.reduce_lonlat_slice(name, np.nanmax, start, stop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ce06af851daedef0460dddba5849bf\",\n \"score\": \"0.51467496\",\n \"text\": \"def date_range_groups(data):\\n\\tranges = []\\n\\tfor k, g in groupby(enumerate(data), lambda i_x: i_x[1]+relativedelta(days=-i_x[0])):\\n\\t\\tgroup = list(map(itemgetter(1), g))\\n\\t\\tranges.append((group[0], group[-1]))\\n\\n\\treturn ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7cf2433b1c582744f45361d622915e2\",\n \"score\": \"0.513196\",\n \"text\": \"def split_tranges(tranges, width, tres):\\n newtranges = []\\n for trange in tranges:\\n t0, t1 = trange\\n assert width < (t1 - t0)\\n # calculate left and right edges of subtranges that fall within trange:\\n # This is tricky: find maximum left edge such that the corresponding maximum right\\n # edge goes as close as possible to t1 without exceeding it:\\n tend = (t1-width+tres) // tres*tres # there might be a nicer way, but this works\\n ledges = np.arange(t0, tend, tres)\\n redges = ledges + width\\n subtranges = [ (le, re) for le, re in zip(ledges, redges) ]\\n newtranges.append(subtranges)\\n return np.vstack(newtranges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ff79eef98757a88f01cd7b9a35eb2e\",\n \"score\": \"0.5129836\",\n \"text\": \"def bounds_to_tiles(self, bounds):\\n grid_ys, grid_xs = self._frame_bounds(bounds)\\n return self._yield_tiles(grid_ys, grid_xs, bounds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3013f098873b4f6568da362e0ac566\",\n \"score\": \"0.5124893\",\n \"text\": \"def make_last_days_y_list(time_tuple_lst):\\n temp_list = []\\n for tpl in time_tuple_lst:\\n temp_list.append(tpl[1]) #do not use \\\"extend\\\"...says \\\"numpy.float64 object is not iterable\\\" \\n return temp_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aed682abebd6d1b897d1aefd011477e\",\n \"score\": \"0.51233643\",\n \"text\": \"def filter_annots_using_minimum_timedelta(ibs, aid_list, min_timedelta):\\n import vtool as vt\\n #min_timedelta = 60 * 60 * 24\\n #min_timedelta = 60 * 10\\n grouped_aids = ibs.group_annots_by_name(aid_list)[0]\\n unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)\\n # Find the maximum size subset such that all timedeltas are less than a given value\\n chosen_idxs_list = [\\n ut.maximin_distance_subset1d(unixtimes, min_thresh=min_timedelta)[0]\\n for unixtimes in unixtimes_list]\\n filtered_groups = vt.ziptake(grouped_aids, chosen_idxs_list)\\n filtered_aids = ut.flatten(filtered_groups)\\n if ut.DEBUG2:\\n timedeltas = ibs.get_unflat_annots_timedelta_list(filtered_groups)\\n min_timedeltas = np.array([np.nan if dists is None else\\n np.nanmin(dists) for dists in timedeltas])\\n min_name_timedelta_stats = ut.get_stats(min_timedeltas, use_nan=True)\\n print('min_name_timedelta_stats = %s' % (ut.dict_str(min_name_timedelta_stats),))\\n return filtered_aids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a50af269ca2843fabd52d605ffea8f36\",\n \"score\": \"0.5115767\",\n \"text\": \"def get_pools(all_trips, total_trips, pool_size):\\n pools_per_day = (24 * 60) / pool_size\\n pools_per_hour = 60 / pool_size\\n pools = [ [] for i in range(pools_per_day)]\\n\\n pool_start_time = 0\\n prev_hour = 0\\n trips_pooled = 0\\n for i in range(pools_per_day):\\n if pool_start_time >= 60:\\n pool_start_time = 0\\n\\n for j in range(trips_pooled, total_trips):\\n trip_start_minutes = int(all_trips[j][5].split(\\\":\\\")[1])\\n trip_start_hour = int(all_trips[j][5].split(\\\":\\\")[0])\\n\\n if prev_hour == trip_start_hour:\\n if trip_start_minutes >= pool_start_time and trip_start_minutes < pool_start_time + pool_size:\\n pools[i].append(all_trips[j][0])\\n trips_pooled += 1\\n\\n pool_start_time += pool_size\\n if ((i + 1) % pools_per_hour) == 0:\\n prev_hour += 1\\n\\n return pools\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9ecde636bc49bcaf64c556581184ee\",\n \"score\": \"0.5112423\",\n \"text\": \"def prepare_time_list_according_to_format(self):\\n final_time_list = []\\n actual_data = list(self.get_demand_data()[\\\"Time\\\"])\\n for each in actual_data:\\n if \\\"pm\\\" in each and \\\"am\\\" not in each:\\n sub_str = []\\n res = re.sub('pm', '', each)\\n res = res.split(\\\"-\\\")\\n res = list(map(int, res))\\n for each_split in res:\\n if each_split < 12:\\n each_split += 12\\n sub_str.append(str(each_split))\\n final_time_list.append(\\\"-\\\".join(sub_str))\\n if \\\"am\\\" in each and \\\"pm\\\" not in each:\\n res = re.sub('am', '', each)\\n final_time_list.append(res)\\n if \\\"am\\\" in each and \\\"pm\\\" in each:\\n res = re.sub('am|pm', '', each)\\n final_time_list.append(res)\\n return final_time_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a54225e72ced6c14df1220027ae310\",\n \"score\": \"0.51037484\",\n \"text\": \"def time_window_bins(self, t0, tend):\\n indexes = []\\n for level in self.dmd_tree.levels:\\n for leaf in self.dmd_tree.index_leaves(level):\\n local_times = self.partial_time_interval(level, leaf)\\n if (\\n local_times[\\\"t0\\\"] <= t0 < local_times[\\\"tend\\\"]\\n or local_times[\\\"t0\\\"] < tend <= local_times[\\\"tend\\\"]\\n or (t0 <= local_times[\\\"t0\\\"] and tend >= local_times[\\\"tend\\\"])\\n ):\\n indexes.append((level, leaf))\\n\\n indexes = np.unique(indexes, axis=0)\\n return indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86808c5107e7c11acca27c250317eb4\",\n \"score\": \"0.5089789\",\n \"text\": \"def build_tiles():\\n return [[ Tile(y)\\n for y in range(MAP_HEIGHT) ]\\n for x in range(MAP_WIDTH) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319ecf77e84f33a9f80f6c301e08d815\",\n \"score\": \"0.5086945\",\n \"text\": \"def time_window_bins(self, t0, tend):\\n indexes = []\\n for level in self.dmd_tree.levels:\\n for leaf in self.dmd_tree.index_leaves(level):\\n\\n local_times = self.partial_time_interval(level, leaf)\\n if (local_times[\\\"t0\\\"] <= t0 < local_times[\\\"tend\\\"] or\\n local_times[\\\"t0\\\"] < tend <= local_times[\\\"tend\\\"] or\\n (\\n t0 <= local_times[\\\"t0\\\"] and\\n tend >= local_times[\\\"tend\\\"]\\n )):\\n indexes.append((level, leaf))\\n\\n indexes = np.unique(indexes, axis=0)\\n return indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be5290842e10ad069370a00909f50151\",\n \"score\": \"0.50859946\",\n \"text\": \"def my_time_slice(self, t_start, t_stop):\\n \\n #new_VmList = VmList(self.raw_data(), self.id_list(), self.dt, t_start, t_stop, self.dimensions)\\n #for id in self.id_list():\\n # new_VmList[id]=self.analog_signals[id].time_slice(t_start, t_stop)\\n \\n #return new_AnalogSignalList\\n \\n \\n new_VmList=super( MyVmList, self ).time_slice(t_start, t_stop)\\n \\n\\n warnings.filterwarnings(\\\"ignore\\\", category=DeprecationWarning)\\n new_MyVmList=convert_super_to_sub_class(new_VmList, MyVmList)\\n \\n return new_MyVmList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec236a2203f6d6ab8547420ca854a24\",\n \"score\": \"0.5085477\",\n \"text\": \"def cut_tim(tim,tmin=None,tmax=None,output=None):\\n tmin = tmin or -np.inf\\n tmax = tmax or np.inf\\n tim_strings = deque()\\n lines = file(tim).readlines()\\n if 'FORMAT 1' not in lines[0]:\\n raise ValueError('Cannot parse non-tempo2 style TOA files.')\\n for line in lines:\\n tim_strings.append(line)\\n if id_toa_line(line):\\n mjd = float(line.split()[2])\\n if not((mjd >= tmin) and (mjd < tmax)):\\n tim_strings.pop()\\n if output is not None:\\n file(output,'w').write(''.join(tim_strings))\\n return tim_strings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e06ce12d5a516c54c8252f9696ac70\",\n \"score\": \"0.5085181\",\n \"text\": \"def _list_from_ranges(data):\\n if not isinstance(data, list):\\n data = [data]\\n result = []\\n for ranges in data:\\n if (isinstance(ranges, int)):\\n ranges = str(ranges)\\n num_start = 0\\n num_end = 0\\n if '-' in ranges:\\n split_start, split_end = ranges.split('-')\\n if (split_start.isdigit()) and (split_end.isdigit()):\\n # Regular numeric ranges\\n num_start, num_end = int(split_start), int(split_end)\\n else:\\n # X10 device ranges, presumably\\n num_start = X10.housecode_to_int(split_start)\\n num_end = X10.housecode_to_int(split_end)\\n if (num_start is None) or (num_end is None):\\n continue\\n range_start = num_start - 1\\n range_end = num_end - 1\\n result.extend(list(range(range_start, range_end + 1)))\\n else:\\n range_start = None\\n num_start = 0\\n if ranges.isdigit():\\n num_start = int(ranges)\\n else:\\n num_start = X10.housecode_to_int(ranges)\\n range_start = num_start - 1\\n result.append(num_start)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56caadf17b3fc3f6d8f29ac6c56eadd9\",\n \"score\": \"0.5083477\",\n \"text\": \"def filter_time_points2(tot_incidlist, incl_min, incl_max):\\n\\n\\t# calculate cumulative number of cases at each time step\\n\\tcum_ct = np.cumsum(tot_incidlist)\\n\\t# generate list of time steps where cumulative number of cases is between incl_min and incl_max proportion of total cases in epidemic\\n\\tincl_tsteps = [i for i, ct in enumerate(cum_ct) if ct/float(max(cum_ct)) > incl_min and ct/float(max(cum_ct)) < incl_max]\\n\\t\\n\\tif incl_tsteps:\\n\\t\\treturn min(incl_tsteps), max(incl_tsteps)\\n\\telse:\\n\\t\\treturn 0, 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e033cdd1ee70b7be6d941672a0fd79\",\n \"score\": \"0.5078448\",\n \"text\": \"def list_time_range(self, limit: int = DEFAULT_LIMIT, offset: int = None) -> dict[str, Any]:\\n return self._http_request(\\n method='GET',\\n url_suffix='api/objects/timeranges',\\n params=assign_params(limit=limit, offset=offset),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d13d1a16d44b4ff22c9b351a1a1d51\",\n \"score\": \"0.50747746\",\n \"text\": \"def filter_temps_by_monitoring(temp_range, start_time, end_time):\\n monitored_temps = []\\n for row in temp_range:\\n try:\\n # Check if the datetime in the row's first column is in between\\n # the start and end monitoring time\\n temp_date = datetime.strptime(row[0].value, '%Y-%m-%d %H:%M:%S')\\n except TypeError:\\n # We're parsing a sheet that has fewer than 1440 rows of temp data\\n break\\n if start_time - timedelta(minutes=15) <= temp_date <= end_time + timedelta(minutes=15):\\n monitored_temps.append(row[1].value)\\n\\n return monitored_temps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7ae7e98fcee6a804922e53460278f3\",\n \"score\": \"0.506944\",\n \"text\": \"def _getTimeVsPotObjsForEachTimeHillAdded(inpObj, timeRange=None, timeTol=1e-4, minTimeDiff=1e-3):\\n\\tuseObj = copy.deepcopy(inpObj)\\n\\tuseObj.sortTimes()\\n\\n\\t#Step 0: Figure out the time range\\n\\tif timeRange is None:\\n\\t\\ttimeRange = [-1, np.inf]\\n\\n\\t#Step 1: We need to figure out the time ranges for each\\n\\tminTime, maxTime = timeRange\\n\\tspawnTimes = [t for t in useObj.times if t>minTime and t<maxTime] #this will be in order\\n\\n\\n\\n\\t#Find the INDICES corresponding to times where hills were essentially spawned\\n\\tspawnIndices, outTimeVals = [0], [spawnTimes[0]]\\n\\tfor idx,t in enumerate(spawnTimes[1:],1):\\n\\t\\tif t>outTimeVals[-1]+minTimeDiff:\\n\\t\\t\\tspawnIndices.append(idx)\\n\\t\\t\\toutTimeVals.append(spawnTimes[idx])\\n\\n\\t#Now use minTime to get timeRanges (we need to do a load of -1 things tho)\\n\\toutTimeRanges = list()\\n\\tfor idx in spawnIndices[1:]:\\n\\t\\toutTimeRanges.append( [minTime,spawnTimes[idx-1]] )\\n\\n\\t#We need to figure out the last one now; which just runs from minTime to maxTime\\n\\toutTimeRanges.append([minTime, maxTime])\\n\\n\\t#Step 2: get potential objs from these times\\n\\toutObjs = list()\\n\\tfor tRange in outTimeRanges:\\n\\t\\tcurrObj = useObj.createGroupedHills(timeRange=tRange, timeTol=timeTol)\\n\\t\\toutObjs.append(currObj)\\n\\n\\treturn [ [t,obj] for t,obj in it.zip_longest(outTimeVals,outObjs)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3c50f892de2f31e880c579a7ca1a40\",\n \"score\": \"0.5066422\",\n \"text\": \"def extent(lst):\\n vecs = zip(*lst)\\n return [max(v) - min(v) for v in vecs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa36aacda44aadc979f32710257ddef\",\n \"score\": \"0.5055729\",\n \"text\": \"def partition(self, max_size):\\n if max_size <= 0:\\n raise ValueError('max_size must be > 0: {}'.format(max_size))\\n\\n for interval in self:\\n refname = interval.reference_name\\n for pos in range(interval.start, interval.end, max_size):\\n yield make_range(refname, pos, min(interval.end, pos + max_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39f0f1464966f351e73d2179b201f21\",\n \"score\": \"0.50527614\",\n \"text\": \"def get_table_sizes(task_list: list) -> list:\\n x = 0\\n time_slot = 0\\n for ele in task_list:\\n if len(ele[1]) > x:\\n x = len(ele[1])\\n if len(ele[0]) > time_slot:\\n time_slot = len(ele[0])\\n if x < 5:\\n x = 5\\n return [x + 15, time_slot - 7]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0415c966cb35f35eb0f7e00f868d8c41\",\n \"score\": \"0.5042304\",\n \"text\": \"def _partition_daterange(self, start, end, step: datetime.timedelta):\\n get_start_time = operator.itemgetter(self.partition_field_start.eval(self.config))\\n get_end_time = operator.itemgetter(self.partition_field_end.eval(self.config))\\n date_range = [\\n dr\\n for dr in super(DatetimeIncrementalSyncComponent, self)._partition_daterange(start, end, step)\\n if get_start_time(dr) < get_end_time(dr)\\n ]\\n for i, _slice in enumerate(date_range):\\n start_time = self._parser.parse(get_start_time(_slice), self.start_datetime.datetime_format, self._timezone)\\n end_time = self._parser.parse(get_end_time(_slice), self.end_datetime.datetime_format, self._timezone)\\n _slice[self.stream_slice_field_step.eval(self.config)] = (end_time + datetime.timedelta(days=int(bool(i))) - start_time).days\\n return date_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e683c73106742273faa2edf2ba559dce\",\n \"score\": \"0.5034739\",\n \"text\": \"def space_timesteps(num_timesteps, section_counts):\\n if isinstance(section_counts, str):\\n if section_counts.startswith(\\\"ddim\\\"):\\n desired_count = int(section_counts[len(\\\"ddim\\\") :])\\n for i in range(1, num_timesteps):\\n if len(range(0, num_timesteps, i)) == desired_count:\\n return set(range(0, num_timesteps, i))\\n raise ValueError(\\n f\\\"cannot create exactly {num_timesteps} steps with an integer stride\\\"\\n )\\n elif section_counts == \\\"fast27\\\":\\n steps = space_timesteps(num_timesteps, \\\"10,10,3,2,2\\\")\\n # Help reduce DDIM artifacts from noisiest timesteps.\\n steps.remove(num_timesteps - 1)\\n steps.add(num_timesteps - 3)\\n return steps\\n section_counts = [int(x) for x in section_counts.split(\\\",\\\")]\\n size_per = num_timesteps // len(section_counts)\\n extra = num_timesteps % len(section_counts)\\n start_idx = 0\\n all_steps = []\\n for i, section_count in enumerate(section_counts):\\n size = size_per + (1 if i < extra else 0)\\n if size < section_count:\\n raise ValueError(\\n f\\\"cannot divide section of {size} steps into {section_count}\\\"\\n )\\n if section_count <= 1:\\n frac_stride = 1\\n else:\\n frac_stride = (size - 1) / (section_count - 1)\\n cur_idx = 0.0\\n taken_steps = []\\n for _ in range(section_count):\\n taken_steps.append(start_idx + round(cur_idx))\\n cur_idx += frac_stride\\n all_steps += taken_steps\\n start_idx += size\\n return set(all_steps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82cb93efc92f41642cefa81f0ac5569c\",\n \"score\": \"0.5032564\",\n \"text\": \"def get_tile_tups(self):\\n return [(t.left, t.right) for t in self.tiles]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025c5c185d042648a990f4b6809785d9\",\n \"score\": \"0.5026414\",\n \"text\": \"def candidates_for_task_timeslots(t, ul):\\r\\n \\r\\n lt = []\\r\\n ls = []\\r\\n \\r\\n ts = t[4] / t[6] * 60\\r\\n \\r\\n # time\\r\\n for i in ul:\\r\\n if(i[3] >= t[3] and i[3] <= (t[3] + ts)):\\r\\n lt.append(i)\\r\\n \\r\\n # space\\r\\n for j in lt:\\r\\n t1 = (t[1], t[2])\\r\\n t2 = (j[1], j[2])\\r\\n distance = hv(t1, t2, 'm')\\r\\n # distance factor of user-i for task-j\\r\\n dMax = 1 - (distance / t[5])\\r\\n if(distance >= 0):\\r\\n ls.append(j)\\r\\n \\r\\n return ls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58eb9de2d75206bffdf50be740d057ed\",\n \"score\": \"0.5026111\",\n \"text\": \"def filter_list(lst):\\n return [t for t in lst if -1 < t[0] < 7 and -1 < t[1] < 6]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748545a69cd4bafaacc1d4c482043ac3\",\n \"score\": \"0.5024284\",\n \"text\": \"def getByTime(self):\\n width, height, nChannels, nSlices, nFrames = self.image.getDimensions() \\n dendritesByTime = [[] for x in range(0, nFrames)] \\n for element in self.elements.values():\\n t = element.getFrame()\\n dendritesByTime[t-1].append(element)\\n return dendritesByTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f84812e91adadc09dd84f03caf72ff6\",\n \"score\": \"0.5023871\",\n \"text\": \"def from_to(tstart, tend, tdelta):\\r\\n if not type(tstart) == dt.datetime:\\r\\n tstart = dt.datetime.strptime(tstart, \\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n if not type(tend) == dt.datetime:\\r\\n tend = dt.datetime.strptime(tend, \\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n tdelta = dt.timedelta(seconds=tdelta)\\r\\n tsteps = [tstart, ]\\r\\n tmptime = tstart\\r\\n while True:\\r\\n tmptime = tmptime + tdelta\\r\\n if tmptime > tend:\\r\\n break\\r\\n else:\\r\\n tsteps.append(tmptime)\\r\\n return tsteps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c3b5ff40b7b42776ad469c126ba892\",\n \"score\": \"0.50130343\",\n \"text\": \"def convertToTiles(surface, width, height):\\n \\n ret = []\\n \\n sz = surface.get_size()\\n if sz[0] == width and sz[1] == height: return [surface]\\n \\n xstep = int(ceil(float(sz[0]) / float(width)))\\n ystep = int(ceil(float(sz[1]) / float(height)))\\n \\n for dy in xrange(ystep):\\n for dx in xrange(xstep):\\n surf = pygame.Surface((width, height)).convert_alpha()\\n surf.fill((0,0,0,0))\\n surf.blit(surface, (0,0), (dx*width, dy*height, width, height))\\n ret.append(surf)\\n \\n print \\\"Converted surface with dimensions (%d x %d) into %d tiles of dimension (%d x %d)\\\" % \\\\\\n (surface.get_width(), surface.get_height(), len(ret), width, height)\\n \\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62d0bf21aee6fffc967a7297ce23c102\",\n \"score\": \"0.50098825\",\n \"text\": \"def _group_ranges(self, units):\\n for _, group in itertools.groupby(enumerate(units), lambda q: (q[1] - q[0])):\\n group = list(group)\\n yield group[0][1], group[-1][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4204692b566a58a0f716553571839d8\",\n \"score\": \"0.5003235\",\n \"text\": \"def ranges(self) -> typing.List[ezcoach.range.Range]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f500fb1cfb8a4f438c9e4eee38f754\",\n \"score\": \"0.50002885\",\n \"text\": \"def _concatenate_timestreams(cls, ts_lst, ts_rounding_error=0.6, ts_interp_threshold=0):\\n #check for contiguous timestreams\\n for i in range(1, len(ts_lst)):\\n ts_sep = (ts_lst[i].start.time - ts_lst[i-1].stop.time) * ts_lst[i].sample_rate\\n if numpy.abs(ts_sep - 1) > ts_rounding_error:\\n if (ts_interp_threshold > 0) and ((ts_sep - 1) < ts_interp_threshold):\\n log_warn(\\\"Timestreams are not contiguous: timestreams %d and %d \\\"\\n \\\"separated by %f samples (%s). Interpolating.\\\" %\\n (i, i-1, ts_sep - 1, str(ts_lst[i].start)))\\n v = numpy.linspace(ts_lst[i-1][-1], ts_lst[i][0], ts_sep + 1)[1:-1]\\n ts_interp = cls(v)\\n ts_interp.units = ts_lst[0].units\\n ts_interp.start = ts_lst[i-1].stop + 1. / ts_lst[i].sample_rate\\n ts_interp.stop = ts_lst[i].start - 1. / ts_lst[i].sample_rate\\n ts_lst = ts_lst[:i] + [ts_interp] + ts_lst[i:]\\n else:\\n log_fatal(\\\"Timestreams are not contiguous: timestreams %d and %d \\\"\\n \\\"separated by %f samples (%s)\\\" % (i, i-1, ts_sep - 1, str(ts_lst[i].start)))\\n if (ts_lst[i].units != ts_lst[0].units):\\n log_fatal(\\\"Timestreams are not the same units\\\")\\n out_ts = cls(numpy.concatenate(ts_lst))\\n out_ts.units = ts_lst[0].units\\n out_ts.start = ts_lst[0].start\\n out_ts.stop = ts_lst[-1].stop\\n return out_ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05187abbe42bb87e7bc8e06136bbeb3\",\n \"score\": \"0.49992543\",\n \"text\": \"def copy_tiles(self):\\n new_tiles = []\\n for l in self.tiles:\\n new_row = l[:]\\n new_tiles.append(new_row)\\n \\n return new_tiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6598aea487edac0f3e68cda27c5ce7cc\",\n \"score\": \"0.49947765\",\n \"text\": \"def range_mapping(pattern, data):\\n\\n squished_data = []\\n\\n for entry in data:\\n min_diff = entry - min(data)\\n normalized_diff = min_diff / (max(data) - min(data))\\n squished_entry = min(pattern) + normalized_diff * (max(pattern) - min(pattern))\\n\\n squished_data.append(squished_entry)\\n\\n return squished_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1d3e03c8c1ce3e8cf00cc5523aabf5\",\n \"score\": \"0.49899983\",\n \"text\": \"def set_limit_list(msid):\\n \\n [udict, ddict] = read_unit_list()\\n tchk = 0\\n try:\\n unit = udict[msid.lower()]\\n if unit.lower() in ['k', 'degc']:\\n tchk = 1\\n elif unit.lower() == 'degf':\\n tchk = 2\\n elif unit.lower() == 'psia':\\n tchk = 3\\n except:\\n pass\\n \\n l_list = gsr.read_glimmon(msid, tchk)\\n \\n if len(l_list) == 0:\\n try:\\n l_list = mta_db[msid]\\n except:\\n l_list = []\\n#\\n#--- if there is no limt given, set dummy limits\\n#\\n if len(l_list) == 0:\\n tstart = 31536000 #---- 1999:001:00:00:00\\n tstop = find_current_stime()\\n l_list = [[tstart, tstop, -998, 998, -999, 999]]\\n\\n return l_list\\n\\n cleaned = []\\n for alist in l_list:\\n if alist[0] == alist[1]:\\n continue\\n else:\\n cleaned.append(alist)\\n \\n cleaned2 = []\\n alist = cleaned[0]\\n for k in range(1, len(cleaned)):\\n blist = cleaned[k]\\n if (alist[2] == blist[2]) and (alist[3] == blist[3]) and (alist[4] == blist[4]) and (alist[5] == blist[5]):\\n alist[1] = blist[1]\\n else:\\n cleaned2.append(alist)\\n alist = blist\\n\\n cleaned2.append(alist)\\n \\n return cleaned2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6528631120c2513d135bffc2dc03a111\",\n \"score\": \"0.49865663\",\n \"text\": \"def _slice_time_series(time_series, start, end):\\n # start must be greater than 1, this is asserted in genex_databse._process_loi\\n rtn = list()\\n\\n for ts in time_series:\\n ts_id = ts[0]\\n ts_data = ts[1]\\n # we take min because min can be math.inf\\n for i in range(start, min(end, len(ts_data))):\\n rtn += _get_sublist_as_sequences(single_time_series=ts_data, data_id=ts_id, length=i)\\n return rtn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49417de99f92c728d2bc00c1c9e0afb1\",\n \"score\": \"0.49861628\",\n \"text\": \"def pruneList(list_):\\r\\n i = 0\\r\\n while i < len(list_):\\r\\n while True:\\r\\n if len(list_[i][1]) == 0:\\r\\n list_.pop(i)\\r\\n i -= 1\\r\\n break\\r\\n if list_[i][1][0] + numHours - 300 < time(): # 5 minute leeway\\r\\n break\\r\\n list_[i][1].pop(0)\\r\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1845541e8dd065f7ab58b8bbeaa633c\",\n \"score\": \"0.49857557\",\n \"text\": \"def sanitizeMultiStopLongFlights(flightList, maxDuration=21600):\\n ## TODO: add type checking; this method should only accept dictionaries\\n\\n modifiedLists = []\\n\\n #Remove flights with more than 1 stop and with a total duration of 5 or more hours\\n logging.debug('sanitizeMultiStopLongFlights: ' + 'Sanitizing list to remove multi-stop and long duration flights...')\\n sanitizedList = {k:v for k,v in flightList.items() if v['count'] <= 2 and v['duration'] <= maxDuration }\\n logging.debug('sanitizeMultiStopLongFlights: ' + 'Size of result set reduced from ' + str(len(flightList)) + ' to ' + str(len(sanitizedList)))\\n\\n #We need to use this list to filter the separate outbound/inbound lists to make sure we arent pulling them in any future queries\\n discardedList = {k:v for k,v in flightList.items() if v['count'] > 2 or v['duration'] > maxDuration }\\n logging.debug('sanitizeMultistopLongFlights: ' + 'Size of discarded list is ' + str(len(discardedList)))\\n\\n modifiedLists.append(sanitizedList)\\n modifiedLists.append(discardedList)\\n return modifiedLists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8772e634c002e4023b663ab2dae854e7\",\n \"score\": \"0.49856314\",\n \"text\": \"def _apply_gti_to_live_time(self):\\n\\n # First negate all FT2 entries\\n\\n filter_idx = np.zeros_like(self._livetime, dtype=bool)\\n\\n # now loop through each GTI interval\\n\\n for start, stop in zip(self._gti_start, self._gti_stop):\\n\\n # create an index of all the FT2 bins falling within this interval\\n\\n tmp_idx = np.logical_and(start <= self._ft2_tstart, self._ft2_tstop <= stop)\\n\\n # add them to the already selected idx\\n filter_idx = np.logical_or(filter_idx, tmp_idx)\\n\\n # Now filter the whole list\\n self._ft2_tstart = self._ft2_tstart[filter_idx]\\n self._ft2_tstop = self._ft2_tstop[filter_idx]\\n self._livetime = self._livetime[filter_idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16f9b726498665a74bb821945f66fd4\",\n \"score\": \"0.4980329\",\n \"text\": \"def split_ranges(start, stop, count=None, step=None):\\n if count is not None and count < 1:\\n return []\\n\\n if count is None:\\n if isinstance(step, timedelta):\\n time_range = stop - start\\n time_in_ms = time_range.microseconds + 1e6 * (\\n time_range.seconds + 86400 * time_range.days\\n )\\n step_in_ms = step.microseconds + 1e6 * (step.seconds + 86400 * step.days)\\n count = time_in_ms / step_in_ms\\n else:\\n count = (stop - start) / step\\n if count < 1:\\n count = 1\\n\\n if step is None:\\n step = (stop - start) / count\\n\\n return [(start + i * step, start + (i + 1) * step) for i in range(int(count))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d626e409424faea89c7be257685c4818\",\n \"score\": \"0.49720186\",\n \"text\": \"def build_timestamp_list(start, end, time_step_second=15*60):\\n timestamp_list = [start]\\n\\n start = libdt.datetime.strptime(start, \\\"%Y-%m-%d %H:%M:%S\\\")\\n end = libdt.datetime.strptime(end, \\\"%Y-%m-%d %H:%M:%S\\\") #+ libdt.timedelta(minutes=time_step_second)\\n\\n while start < end:\\n start = start + libdt.timedelta(0, time_step_second)\\n timestamp_list.append(libdt.datetime.strftime(start, \\\"%Y-%m-%d %H:%M:%S\\\"))\\n return timestamp_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48314a638d8b3df4d261317a3813ed40\",\n \"score\": \"0.49701375\",\n \"text\": \"def build_streched_workloads(workloads: list):\\n assert isinstance(workloads[0], pd.DataFrame)\\n target_workloads = []\\n\\n for workload in workloads:\\n w = np.array(workload.as_matrix(), dtype=\\\"float32\\\")\\n\\n streched_workload = np.zeros((2 * w.shape[0], w.shape[1]))\\n n_time_steps = w.shape[0]\\n \\n for t in range(n_time_steps):\\n k = t*2\\n streched_workload[k:k + 2, :] = w[[t], :]\\n\\n target_workloads.append(streched_workload)\\n\\n return target_workloads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c42a655a7ba60730f33a7ce555a993c1\",\n \"score\": \"0.49635893\",\n \"text\": \"def to_ranges(self):\\n\\t\\tranges = []\\n\\t\\tfor di in range(self.ndet):\\n\\t\\t\\ts1, s2 = self.stack_bounds[di:di+2]\\n\\t\\t\\tinds = self.index_stack[s1:s2]\\n\\t\\t\\tmask = np.any(self.flag_stack[s1:s2],1)\\n\\t\\t\\tmask = np.concatenate([[False],mask]).astype(int)\\n\\t\\t\\tedges = mask[1:]-mask[:-1]\\n\\t\\t\\tstarts = np.where(edges>0)[0]\\n\\t\\t\\tends = np.where(edges<0)[0]\\n\\t\\t\\tif len(starts) == 0:\\n\\t\\t\\t\\tranges.append(np.zeros([0,2],int))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tr = np.zeros([len(starts),2],int)\\n\\t\\t\\t\\tr[:,0] = inds[starts]\\n\\t\\t\\t\\tr[:len(ends),1] = inds[ends]\\n\\t\\t\\t\\tif len(ends) < len(starts): r[-1,1] = self.nsamp\\n\\t\\t\\t\\tranges.append(r)\\n\\t\\treturn ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed648d55d34ecc869551f1376e5e2b63\",\n \"score\": \"0.49619672\",\n \"text\": \"def potentialRange(self):\\n values = []\\n for candidate in self.root.output():\\n values.append(dateutil.parser.parse(candidate.get(self.attribute)).time())\\n return (min(values), max(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98279742d758d892ea1f40b1a161ed50\",\n \"score\": \"0.4961269\",\n \"text\": \"def relative_timings(self) -> List[Tuple[int, int, int]]:\\n schedules = []\\n for schedule in self.relative_schedules.all():\\n num_seconds = schedule.minute * 60 + schedule.hour * 3600\\n schedules.append([schedule.intervention.id, schedule.days_after, num_seconds])\\n return schedules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"906152c6823f82fe93d6b99d52c0b8e8\",\n \"score\": \"0.49586782\",\n \"text\": \"def image_range_to_tiles(start_ind, end_ind, display=False, save_summary=True, save_data=True, save_top_tiles=True):\\n image_num_list = list()\\n tile_summaries_dict = dict()\\n for slide_num in range(start_ind, end_ind + 1):\\n tile_summary = summary_and_tiles(slide_num, display, save_summary, save_data, save_top_tiles)\\n image_num_list.append(slide_num)\\n tile_summaries_dict[slide_num] = tile_summary\\n return image_num_list, tile_summaries_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee240255ce46e4d6b8e4dcc37ffba8d6\",\n \"score\": \"0.49579102\",\n \"text\": \"def calc_date_chunks(start, end):\\n chunks = []\\n\\n start = datetime.datetime.strptime(start, '%m-%d-%Y-%H:%M')\\n prev = start\\n end = datetime.datetime.strptime(end, '%m-%d-%Y-%H:%M')\\n\\n while (prev <= end):\\n # Increment 6 hours\\n curr = prev + datetime.timedelta(seconds=21600)\\n\\n chunks.append((prev.strftime('%m-%d-%Y-%H:%M'), curr.strftime('%m-%d-%Y-%H:%M')))\\n prev = curr\\n\\n \\\"\\\"\\\"\\n Adjust the last tuple incase the time period doesnt divide evenly into\\n 6-hr periods\\n\\n Ex\\n ---\\n Unadjusted:\\n in: calc_date_chunks('09-01-2019-15:00', '09-06-2019-13:00')[-1]\\n out: ('09-06-2019-09:00', '09-06-2019-15:00')\\n\\n Adjusted:\\n in: calc_date_chunks('09-01-2019-15:00', '09-06-2019-13:00')[-1]\\n out: ('09-06-2019-09:00', '09-06-2019-13:00')\\n\\n \\\"\\\"\\\"\\n if (datetime.datetime.strptime(chunks[-1][1], '%m-%d-%Y-%H:%M') > end):\\n chunks = chunks[:-1]\\n prev = prev - datetime.timedelta(seconds=21600)\\n chunks.append((prev.strftime('%m-%d-%Y-%H:%M'), end.strftime('%m-%d-%Y-%H:%M')))\\n\\n return chunks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21b7d1a6531aaae04bbf366e5f55498\",\n \"score\": \"0.4957181\",\n \"text\": \"def intervals(\\n nms #list of indices\\n ):\\n return list(zip(nms[:],nms[1:]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d25e19bfcbfb65a188aefb80f6ea9c\",\n \"score\": \"0.49563673\",\n \"text\": \"def range_and_interval_to_list(start_time_unix_sec=None, end_time_unix_sec=None,\\n time_interval_sec=None, include_endpoint=True):\\n\\n error_checking.assert_is_integer(start_time_unix_sec)\\n error_checking.assert_is_not_nan(start_time_unix_sec)\\n error_checking.assert_is_integer(end_time_unix_sec)\\n error_checking.assert_is_not_nan(end_time_unix_sec)\\n error_checking.assert_is_integer(time_interval_sec)\\n error_checking.assert_is_boolean(include_endpoint)\\n\\n if include_endpoint:\\n error_checking.assert_is_geq(end_time_unix_sec, start_time_unix_sec)\\n else:\\n error_checking.assert_is_greater(end_time_unix_sec, start_time_unix_sec)\\n\\n start_time_unix_sec = int(rounder.floor_to_nearest(\\n float(start_time_unix_sec), time_interval_sec\\n ))\\n end_time_unix_sec = int(rounder.ceiling_to_nearest(\\n float(end_time_unix_sec), time_interval_sec\\n ))\\n\\n if not include_endpoint:\\n end_time_unix_sec -= time_interval_sec\\n\\n num_time_steps = 1 + int(numpy.round(\\n (end_time_unix_sec - start_time_unix_sec) / time_interval_sec\\n ))\\n\\n return numpy.linspace(start_time_unix_sec, end_time_unix_sec,\\n num=num_time_steps, dtype=int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adde6045a88d6db8a6b268355a4c0a71\",\n \"score\": \"0.49558872\",\n \"text\": \"def get_time_slot_set(lecture: Lecture):\\n time_slot_set = [[], [], [], [], [], []]\\n for time_slot in lecture.time_slots.all():\\n day_index = day_to_index[time_slot.day_of_week]\\n start_int = time_to_int(time_slot.start_time)\\n end_int = time_to_int(time_slot.end_time)\\n time_slot_set[day_index].append((start_int, end_int))\\n for i, time_slots in enumerate(time_slot_set):\\n if not time_slots:\\n continue\\n time_slots.sort()\\n new_time_slots = []\\n\\n start, end = time_slots[0]\\n for time_slot in time_slots[1:]:\\n if end < time_slot[0]:\\n new_time_slots.append((start, end))\\n start, end = time_slot\\n else:\\n end = max(end, time_slot[1])\\n new_time_slots.append((start, end))\\n time_slot_set[i] = new_time_slots\\n return time_slot_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cadecc119e73dfdba63313974c2802c\",\n \"score\": \"0.49555343\",\n \"text\": \"def subtract(self, other_time_range):\\n output = []\\n if (self.t1 < other_time_range.t1):\\n output.append(TimeRange(self.t1, other_time_range.t1))\\n \\n if (self.t2 > other_time_range.t2):\\n output.append(TimeRange(other_time_range.t2, self.t2))\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63625b0bb5de267eaada4d345153519\",\n \"score\": \"0.49435526\",\n \"text\": \"def reshape_spiking_times(my_spikemon:SpikeMonitor, spiking_index:int=0, lower_threshold:float=0, upper_threshold=np.infty) -> Tuple[list, int, int]:\\n result = []\\n min_ = inf\\n max_ = -1.\\n\\n argmin_ = 0\\n argmax_ = 0\\n\\n n = len(my_spikemon.values('t'))\\n for i in range(n):\\n if len(my_spikemon.values('t')[i] / ms) <= spiking_index:\\n result.append(nan)\\n else:\\n spike_time = my_spikemon.values('t')[i][spiking_index] / ms\\n if spike_time >= lower_threshold and spike_time <= upper_threshold:\\n result.append(spike_time)\\n if spike_time <= min_:\\n argmin_ = i\\n min_ = spike_time\\n elif spike_time >= max_:\\n argmax_ = i\\n max_ = spike_time\\n else:\\n result.append(nan)\\n\\n if len(result) == 0:\\n raise Warning(\\\"No spikes\\\")\\n\\n return result, argmin_, argmax_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99334b47854fa95a9d27ab178a23171e\",\n \"score\": \"0.4935655\",\n \"text\": \"def roi_to_tiles(self, roi):\\n bounds = BoundingBox(*roi.bounds)\\n grid_ys, grid_xs = self._frame_bounds(bounds)\\n return self._yield_tiles(grid_ys, grid_xs, roi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e79b61abf5cef6b6cfe8e677f517c093\",\n \"score\": \"0.4922719\",\n \"text\": \"def extract_intervals(request_history):\\n if not request_history:\\n return []\\n\\n # first - sort by request start time and end time, this allows us to order the\\n # requests on the actual timeline.\\n timings = sorted(\\n [req.timing for req in request_history],\\n key=lambda timing: (timing[0], timing[1]),\\n )\\n\\n distinct_intervals = []\\n min_start, max_end = timings[0][:2]\\n\\n for timing in timings:\\n start, end = timing[0], timing[1]\\n if start <= max_end and end > max_end:\\n max_end = timing[1]\\n\\n if start > max_end:\\n distinct_intervals.append((min_start, max_end))\\n min_start, max_end = timing[:2]\\n\\n # last loop iteration doesn't append distinct interval\\n distinct_intervals.append((min_start, max_end))\\n\\n return distinct_intervals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f9835b199461d6318d7395f49526ff7\",\n \"score\": \"0.4919626\",\n \"text\": \"def tiles(filepath, tile_ids):\\n generated_tiles = []\\n\\n for tile_id in tile_ids:\\n tile_id_parts = tile_id.split(\\\".\\\")\\n tile_position = list(map(int, tile_id_parts[1:3]))\\n\\n (dense, mins, maxs) = get_data(\\n h5py.File(filepath), tile_position[0], tile_position[1]\\n )\\n\\n \\\"\\\"\\\"\\n if len(dense):\\n max_dense = max(dense)\\n min_dense = min(dense)\\n else:\\n max_dense = 0\\n min_dense = 0\\n has_nan = len([d for d in dense if np.isnan(d)]) > 0\\n if (\\n not has_nan and\\n max_dense > min_f16 and max_dense < max_f16 and\\n min_dense > min_f16 and min_dense < max_f16\\n ):\\n tile_value = {\\n 'dense': base64.b64encode(dense.astype('float16')).decode('utf-8'),\\n 'mins': base64.b64encode(mins.astype('float16')).decode('utf-8'),\\n 'maxs': base64.b64encode(mins.astype('float16')).decode('utf-8'),\\n 'dtype': 'float16'\\n }\\n else:\\n \\\"\\\"\\\"\\n tile_value = {\\n \\\"dense\\\": base64.b64encode(dense.astype(\\\"float32\\\")).decode(\\\"utf-8\\\"),\\n \\\"mins\\\": base64.b64encode(mins.astype(\\\"float32\\\")).decode(\\\"utf-8\\\"),\\n \\\"maxs\\\": base64.b64encode(maxs.astype(\\\"float32\\\")).decode(\\\"utf-8\\\"),\\n \\\"dtype\\\": \\\"float32\\\",\\n }\\n\\n generated_tiles += [(tile_id, tile_value)]\\n\\n return generated_tiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85acb35406628678d0ba05818c7abd2\",\n \"score\": \"0.49142137\",\n \"text\": \"def frame_timestamp_ranges(self, *, include_dead_time=False):\\n\\n ts_min = self._timestamps(reduce=np.min)\\n ts_max = self._timestamps(reduce=np.max)\\n delta_ts = int(1e9 / self.infowave.sample_rate) # We want the sample beyond the end\\n if ts_min.ndim == 2:\\n return [(np.min(ts_min), np.max(ts_max) + delta_ts)]\\n else:\\n if include_dead_time:\\n frame_time = ts_min[1, 0, 0] - ts_min[0, 0, 0]\\n return [(t, t + frame_time) for t in ts_min[:, 0, 0]]\\n else:\\n maximum_timestamp = np.max(ts_max, axis=tuple(range(1, ts_max.ndim)))\\n return [(t1, t2) for t1, t2 in zip(ts_min[:, 0, 0], maximum_timestamp + delta_ts)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd3bfbe9d726c83df5a6325b34b8300\",\n \"score\": \"0.4913405\",\n \"text\": \"def take_all_remaining_tiles(self):\\r\\n result = self._my_tiles\\r\\n self._my_tiles = []\\r\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994474,"cells":{"query_id":{"kind":"string","value":"a7c90f3bc19198d782a98754c8220f05"},"query":{"kind":"string","value":"calculate a and b of y=a.x+b before including in the point p"},"positive_passages":{"kind":"list like","value":[{"docid":"d6e7219911aa3e8d7f7c2c10c2fa4687","score":"0.6427087","text":"def evalPoint(self, p):\n if not self._points:\n raise EvalPointError(\"\"\"Cannot calculate slope\n and intercept with a single point.\n \"\"\")\n x = p.x\n y = p.y\n w = float(p.w)\n\n wixiyi = self._wixiyi + w*x*y\n wixi = self._wixi + w*x\n wiyi = self._wiyi + w*y\n wixi2 = self._wixi2 + w*x*x\n wi = self._wi + w\n\n if wixi2 == wixi**2/wi:\n a = float(\"inf\")\n b = float(\"inf\")\n return (a, b)\n a = (wixiyi - wixi*wiyi/wi)/(wixi2 - wixi**2/wi)\n b = (wiyi - a * wixi)/wi\n return (a, b)","title":""}],"string":"[\n {\n \"docid\": \"d6e7219911aa3e8d7f7c2c10c2fa4687\",\n \"score\": \"0.6427087\",\n \"text\": \"def evalPoint(self, p):\\n if not self._points:\\n raise EvalPointError(\\\"\\\"\\\"Cannot calculate slope\\n and intercept with a single point.\\n \\\"\\\"\\\")\\n x = p.x\\n y = p.y\\n w = float(p.w)\\n\\n wixiyi = self._wixiyi + w*x*y\\n wixi = self._wixi + w*x\\n wiyi = self._wiyi + w*y\\n wixi2 = self._wixi2 + w*x*x\\n wi = self._wi + w\\n\\n if wixi2 == wixi**2/wi:\\n a = float(\\\"inf\\\")\\n b = float(\\\"inf\\\")\\n return (a, b)\\n a = (wixiyi - wixi*wiyi/wi)/(wixi2 - wixi**2/wi)\\n b = (wiyi - a * wixi)/wi\\n return (a, b)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3420fc09cc7a6c8ccb3050887f7e8572","score":"0.7422088","text":"def point_add(a, b, p, x0, y0, x1, y1):\n\n # ADD YOUR CODE BELOW\n assert isinstance(a, Bn)\n assert isinstance(b, Bn)\n assert isinstance(p, Bn) and p > 0\n assert (isinstance(x0, Bn) and isinstance(y0, Bn)) or (x0 == None and y0 == None)\n assert (isinstance(x1, Bn) and isinstance(y1, Bn)) or (x1 == None and y1 == None)\n\n if (x0 is not None and y0 is not None) and (x1 is None and y1 is None):\n return (x0, y0)\n if (x0 is None and y0 is None) and (x1 is not None and y1 is not None):\n return (x1, y1)\n if (x0 is x1) and (y0 is not y1):\n return (None, None)\n if (x0 is not x1) and (y0 is not y1):\n lam = (y1.mod_sub(y0, p)).mod_mul(((x1.mod_sub(x0, p)).mod_inverse(p)), p)\n xr = ((lam.mod_pow(2, p)).mod_sub(x0, p)).mod_sub(x1, p)\n yr = (lam.mod_mul(x0.mod_sub(xr, p), p)).mod_sub(y0, p)\n return (xr, yr) \n raise ArithmeticError(\"EC Points must not be equal\")","title":""},{"docid":"6e9f565833cf303d8390ec92753e1af3","score":"0.74085206","text":"def point_add(a, b, p, x0, y0, x1, y1):\n\n if ((x0, y0) == (x1, y1)):\n raise Exception('EC Points must not be equal')\n\n # if x1 == x0 then there is no inverse, also check both points are on curve\n if ((x0 == x1) or\n (not is_point_on_curve(a, b, p, x0, y0)) or\n (not is_point_on_curve(a, b, p, x1, y1))):\n return(None, None)\n\n if (x0, y0) == (None, None):\n return (x1, y1)\n\n if (x1, y1) == (None, None):\n return (x0, y0)\n\n # calculate lam in stages using Bn methods\n xqminxp = x1.mod_sub(x0, p)\n yqminyp = y1.mod_sub(y0, p)\n\n xqminxpmodinv = xqminxp.mod_inverse(m=p)\n lam = xqminxpmodinv.mod_mul(yqminyp, p)\n\n # calculate xr\n lamsq = lam.mod_mul(lam, p)\n lamsqmin = lamsq.mod_sub(x0, p)\n xr = lamsqmin.mod_sub(x1, p)\n\n # calculate yr\n xpminxr = x0.mod_sub(xr, p)\n lamxpxr = lam.mod_mul(xpminxr, p)\n yr = lamxpxr.mod_sub(y0, p)\n\n return (xr, yr)","title":""},{"docid":"b83fd6662528e49c276dd1d0280731f5","score":"0.7015354","text":"def __add__(self,b):\n return Punto(self.x + b.x, self.y + b.y)","title":""},{"docid":"2412afaf4905a0427016faa6e08c0927","score":"0.6985416","text":"def add(self, p1, p2):\n x1, y1 = p1\n x2, y2 = p2\n a, p = self.a, self.p\n\n if p1 == self.POINT_AT_INFINITY:\n return p2\n elif p2 == self.POINT_AT_INFINITY:\n return p1\n elif x1 == x2 and y1 != y2:\n # We can rewrite this condition as\n # x1 == x2 and find_inverse(-y1, p) == y2\n # but, y1 != y2 is more efficient.\n # This is correct because vertical line intersects\n # the elliptic curve in one or two points.\n return self.POINT_AT_INFINITY\n\n if p1 == p2:\n m = (3 * x1**2 + a) * find_inverse(2 * y1, p) % p\n else:\n m = (y2 - y1) * find_inverse(x2 - x1, p) % p\n\n x3 = (m**2 - x1 - x2) % p\n y3 = (m * (x1 - x3) - y1) % p\n result = (x3, y3)\n\n return result","title":""},{"docid":"72ce35e9e7c0dae0a8178ffa222c2fed","score":"0.6642534","text":"def point_double(a, b, p, x, y): \n\n # ADD YOUR CODE BELOW\n assert isinstance(a, Bn)\n assert isinstance(b, Bn)\n assert isinstance(p, Bn) and p > 0\n assert (isinstance(x, Bn) and isinstance(y, Bn)) or (x == None and y == None)\n\n if (x is not None) and (y is not None):\n lam = (((x.mod_pow(2, p)).mod_mul(3, p)).mod_add(a, p)).mod_mul((y.mod_mul(2, p)).mod_inverse(p), p)\n xr = (lam.mod_pow(2, p)).mod_sub(x.mod_mul(2, p), p)\n yr = ((x.mod_sub(xr, p)).mod_mul(lam, p)).mod_sub(y, p)\n return (xr, yr)\n return (None, None)","title":""},{"docid":"43fee64b56075c2f93ca88f5101ce039","score":"0.66143286","text":"def __add__(self, other):\n if self == PAI:\n return other\n if other == PAI:\n return self\n if self.x == other.x and self.y != other.y:\n return PAI\n\n # Compute slope y = mx + v\n if self.x == other.x and self.y == other.y:\n # Two Points are the same -> Compute tangent on curve\n m = (3 * self.x**2) * inv(2 * self.y, self.curve.p)\n else:\n # Compute slope between two points\n m = (other.y - self.y) * inv(other.x - self.x, self.curve.p)\n\n v = self.y - m * self.x\n\n # New Point is at the intersection of the slop and the curve\n new_x = (m**2 - self.x - other.x) % self.curve.p\n new_y = (-(m * new_x + v)) % self.curve.p\n\n return Point(self.curve, new_x, new_y)","title":""},{"docid":"202c705f07d067dd507f1814053a60a7","score":"0.65900207","text":"def makeLine(p1,p2):\n x1 = p1[0]\n y1 = p1[1]\n\n x2 = p2[0]\n y2 = p2[1]\n\n a = (y1 - y2) / (x1 - x2)\n b = (x1 * y2 - x2 * y1) / (x1 - x2)\n\n return a,b","title":""},{"docid":"a8012eb38404f0ab4ab915d8ed550ad0","score":"0.6575684","text":"def point_double(a, b, p, x, y):\n\n if x is None and y is None:\n return None, None\n\n xsq = x.mod_mul(x, p)\n xsq3 = Bn(3).mod_mul(xsq, p)\n num = xsq3.mod_add(a, p)\n y2 = Bn(2).mod_mul(y, p)\n y2inv = y2.mod_inverse(m=p)\n lam = num.mod_mul(y2inv, p)\n\n xr = lam.mod_mul(lam, p)\n xr = xr.mod_sub(x, p)\n xr = xr.mod_sub(x, p)\n\n yr = lam.mod_mul(x.mod_sub(xr, p), p)\n yr = yr.mod_sub(y, p)\n\n return (xr, yr)","title":""},{"docid":"fddf12737ff87b09db1618354dad3456","score":"0.656293","text":"def yp(self,a,b):\n \n y_prime = np.array([-np.sin(a),np.cos(a),0])\n return y_prime","title":""},{"docid":"95ae92515c68d0ee85f5dbf718f817a6","score":"0.65425587","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n rhs = 2 * (y - y ** 2) + 2 * (x - x ** 2)\n return rhs","title":""},{"docid":"d146142695eac0628804c7b6d3d7a175","score":"0.6453014","text":"def point(self, p, x):\n return self.plec(x, p)","title":""},{"docid":"244fae6563ee6f0b919e4df35fabe410","score":"0.63918453","text":"def restar(self, b):\n return Punto(self.x - b.x, self.y - b.y)","title":""},{"docid":"244fae6563ee6f0b919e4df35fabe410","score":"0.63918453","text":"def restar(self, b):\n return Punto(self.x - b.x, self.y - b.y)","title":""},{"docid":"8ef3cf169cab513e5ae413a8d4477610","score":"0.63806015","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n a = 10000 * (x ** 2 + y ** 2) ** 2\n rhs = -100 * (4 - 4 * a) / (a + 1) ** 2\n return rhs","title":""},{"docid":"158fbd965428d8f1d088bf80536f91ae","score":"0.6314499","text":"def __add__(self, other):\r\n return Point2d(self.x + other.x, self.y + other.y)","title":""},{"docid":"5ed7d1da2004cf73c5ec2aeece598215","score":"0.6312248","text":"def __add__(self, otherPoint) :\n\t\treturn point(self.x + otherPoint.x, self.y + otherPoint.y, self.z + otherPoint.z)","title":""},{"docid":"6dcafd5403f3b44cefd7d0411b47b349","score":"0.6309287","text":"def bresa(p1, p2):\n xi, yi = p1\n x2, y2 = p2\n cells = [(xi, yi)]\n\n # Slope is calculated once only if it's possible\n if xi != x2:\n m = (y2 - yi) / (x2 - xi)\n delta_y = m\n x1 = xi\n y1 = yi + 0.5 - m * 0.5\n\n if x1 < x2 and yi <= y2:\n x = x1 + 1\n y = yi\n inc_y = y1 + delta_y\n while x < x2 + 1 or y < y2:\n if inc_y > y + 1:\n y += 1\n elif inc_y == y + 1:\n y += 1\n x += 1\n delta_y += m\n else:\n x += 1\n delta_y += m\n cells.append((x - 1, y))\n inc_y = y1 + delta_y\n\n elif x1 < x2 and yi >= y2:\n x = x1 + 1\n y = yi\n inc_y = y1 + delta_y\n while x < x2 + 1 or y > y2:\n if inc_y < y:\n y -= 1\n elif inc_y == y:\n y -= 1\n x += 1\n delta_y += m\n else:\n x += 1\n delta_y += m\n cells.append((x - 1, y))\n inc_y = y1 + delta_y\n\n elif x1 > x2 and yi > y2:\n x = x1\n y = yi\n inc_y = y1\n delta_y = 0\n while x > x2 or y > y2:\n if inc_y < y:\n y -= 1\n elif inc_y == y:\n y -= 1\n x -= 1\n delta_y -= m\n else:\n x -= 1\n delta_y -= m\n cells.append((x, y))\n inc_y = y1 + delta_y\n\n elif x1 > x2 and yi <= y2:\n x = x1\n y = yi\n inc_y = y1\n delta_y = 0\n while x > x2 or y < y2:\n if inc_y > y + 1:\n y += 1\n elif inc_y == y + 1:\n y += 1\n x -= 1\n delta_y -= m\n else:\n x -= 1\n delta_y -= m\n cells.append((x, y))\n inc_y = y1 + delta_y\n\n else:\n y = yi\n if y2 >= yi:\n while y < y2 + 1:\n cells.append((xi, y))\n y += 1\n else:\n while y > y2:\n cells.append((xi, y - 1))\n y -= 1\n\n return np.array(cells)","title":""},{"docid":"2771385a126aa4e9c6a9056d6f911d1c","score":"0.63012713","text":"def project_y(self, p):\r\n raise NotImplementedError","title":""},{"docid":"e63fb2ff37a255a7a1d19527ca8d72a7","score":"0.6294456","text":"def _reduce_xyfp(x, y):\n a = 420.0\n return -x/a, y/a","title":""},{"docid":"f98e7f30356d534c4d0069a867e65bde","score":"0.6290059","text":"def pos_line(p):\n # y = x + c\n # c = (p.y-p.x)\n pos_y_intersects.add(p.y - p.x)","title":""},{"docid":"3e21035726b43dd91ebf6d03a380bae4","score":"0.62183064","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n val = -2 * (x ** 2 + y ** 2)\n return val","title":""},{"docid":"7ee3ec6caa31e10021ba8e29b0a32a1a","score":"0.62158895","text":"def Interpolate(a, b, p):\n\treturn a + (b - a) * p","title":""},{"docid":"aa26bfc911d4bc0bc5aabba98c59e24d","score":"0.62128115","text":"def get_line_to(self, p2):\n \"\"\" y1- ax1 = y2 - ax2\n a = (y1-y2)/(x1-x2)\n b = y1 - a * x1\"\"\"\n if self.x == p2.x:\n return 'x = {0}'.format(self.x)\n elif self.y == p2.y:\n return 'y = {0}'.format(self.y)\n else:\n a = (self.y - p2.y)/(self.x - p2.x)\n b = self.y - a * self.x\n return (a, b)","title":""},{"docid":"f659810350a6341efa94a6a38e1cd310","score":"0.6169263","text":"def sum (self, other):\n new_pt = Point(self.x + other.x, self.y + other.y)\n return new_pt","title":""},{"docid":"58992d107efdd8acddb93f580aa7ed44","score":"0.61668736","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n val = -0.16 * (x ** 2 + y ** 2) ** (-0.8)\n return val","title":""},{"docid":"de5e4121550330c7ab097e607b56c785","score":"0.61619216","text":"def __add__(self, other):\n return Point(self.x+other.x, self.y+other.y)","title":""},{"docid":"552c20c11fb4155927a32b66419b0b91","score":"0.61449575","text":"def axpy(alpha,x,y):\n for i in range(0,len(x)):\n y[i]=alpha*x[i]+y[i]","title":""},{"docid":"2f66e3ba0296e7bfeee3f8d6650be372","score":"0.61183864","text":"def skalarni_produkt(self, other):\n return self.x * other.x + self.y * other.y","title":""},{"docid":"dc53b3f08439aa631a5033e14379ea6d","score":"0.608645","text":"def adding_points(self, other):\n sum = Point()\n sum.x = self.x + other.x\n sum.y = self.y + other.y\n return sum","title":""},{"docid":"a937fd5606f64d05e064b7a37e097f75","score":"0.6071962","text":"def crossover(self, p1, p2):\n return (p1 + p2) / 2","title":""},{"docid":"277c55565ed833bb815fa50baf7d0c41","score":"0.60685253","text":"def Double(self, p: EcPoint) -> EcPoint:\n if p == INFINITY:\n return p\n x, y = p\n num = (3 * x * x + self.a) % self.mod\n den = 2 * y\n t = num * gmpy.invert(den, self.mod) % self.mod\n x2 = (t * t - 2 * x) % self.mod\n y2 = (t * (x - x2) - y) % self.mod\n return (x2, y2)","title":""},{"docid":"bdb4e83d2db4e6222cc3d3402046ec8d","score":"0.6058135","text":"def h(p1, p2):\n x1, y1 = p1\n x2, y2 = p2\n return abs(x1 - x2) + abs(y1 - y2)","title":""},{"docid":"7ba25ba56832f05342cc5bc0952fac81","score":"0.6055224","text":"def himmelblau(p):\n x, y = p\n a = x*x + y - 11\n b = x + y*y - 7\n return a*a + b*b","title":""},{"docid":"efc07df32e014b7da7b5402653d74f73","score":"0.6045281","text":"def y_value(a: Point, b: Point, x: Union[int, float]) -> float:\n assert isinstance(x, (int, float))\n\n if a.x == b.x:\n raise ValueError('Cannot return y-value for x=const.')\n\n return (a.y - b.y) * (x - b.x) / (a.x - b.x) + b.y","title":""},{"docid":"34ceeb34368086ac841be2a8e165528c","score":"0.59830785","text":"def appendPoint(self, p):\n self._points.append(p)\n self._wixiyi += p.w * p.x * p.y\n self._wixi += p.w * p.x\n self._wiyi += p.w * p.y\n self._wixi2 += p.w * p.x * p.x\n self._wi += p.w\n if len(self._points) > 1:\n # if points are aligned with exactly the same x, line is vertical\n # a and b would be infinity\n if self._wixi2 == self._wixi**2/self._wi:\n self.a = float(\"inf\")\n self.b = float(\"inf\")\n else:\n self.a = (self._wixiyi - self._wixi*self._wiyi/self._wi)\\\n / (self._wixi2 - self._wixi**2/self._wi)\n self.b = (self._wiyi - self.a * self._wixi)/self._wi\n self._rss = self.calcRSS(self.a, self.b)","title":""},{"docid":"3c79bd48cf4c73b4eb7e84d4736c1c75","score":"0.597987","text":"def __or__(self,other):\n if self.a==other.a:\n return None\n x=(self.b-other.b)/(other.a-self.a)\n y=self.a*x+self.b\n return Point(x,y)","title":""},{"docid":"4765f1b0722b4b6aa8ca09eeff1fbdb9","score":"0.5977134","text":"def __sub__(self,b):\n return Punto(self.x - b.x, self.y - b.y)","title":""},{"docid":"18ee7218cf11d06e075703ed6d4f4692","score":"0.59696823","text":"def _project_point_onto_line(self, p, ep0, ep1):\n dx, dy = ep1[0] - ep0[0], ep1[1] - ep0[1]\n determinant = dx * dx + dy * dy\n coeff = (dy * (p[1] - ep0[1]) + dx * (p[0] - ep0[0])) / determinant\n\n return ep0[0] + coeff * dx, ep0[1] + coeff * dy","title":""},{"docid":"df4ec24f049e9baad160dcfac340525c","score":"0.5966919","text":"def lerp(x, y, a):\n\treturn (1 - a) * x + a * y","title":""},{"docid":"4edf564c718338183be214c4221cc8ab","score":"0.59647214","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n rhs = -(4 * x ** 2 + 4 * y ** 2 + 4) * (np.exp(x ** 2 + y ** 2))\n return rhs","title":""},{"docid":"73362f6ce969028d4dcd64d98d59c5df","score":"0.5933852","text":"def sum(self):\n return self.x + self.y","title":""},{"docid":"d98a1f1b20feadc260d2824c1d07ccd3","score":"0.59313613","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n t0 = (x + 0.5) ** 2 + (y - 0.5) ** 2 + 0.01\n t1 = (x - 0.5) ** 2 + (y + 0.5) ** 2 + 0.01\n val = (\n (2 * x - 1.0) * (4 * x - 2.0) / t1 ** 3 -\n (2 * x + 1.0) * (4 * x + 2.0) / t0 ** 3 -\n (2 * y - 1.0) * (4 * y - 2.0) / t0 ** 3 +\n (2 * y + 1.0) * (4 * y + 2.0) / t1 ** 3 + 4 / t0 ** 2 - 4 / t1 ** 2\n )\n\n return val","title":""},{"docid":"4fb7697f9750b0eed1c2bf141d533b82","score":"0.5914811","text":"def __add__(self, b):\n temp_y = self.y - b.y\n result = Sweep()\n result.LoadArrays(self.x, temp_y)\n return result","title":""},{"docid":"494cf9ec50748b2c7b59d47b42189639","score":"0.5908124","text":"def Cp_a(a, y):\n #return (a - y) * Sigmoid.prime(z)\n return (a - y)","title":""},{"docid":"b8563422fb987b44c6d663a28cbb5972","score":"0.5891777","text":"def phi_b(x):\n y = np.copy(x)\n x1, x2 = x[:2]\n y[0], y[1] = x1, x2 + b*x1**2 - 100*b\n return y","title":""},{"docid":"872335f4a037e02e249eb68d9d7e4f1c","score":"0.5886332","text":"def _reduce_point(self, R, p):\n if R.is_zero():\n return R.curve().change_ring(rings.GF(p))(0)\n x, y = R.xy()\n d = lcm(x.denominator(), y.denominator())\n return R.curve().change_ring(rings.GF(p))([x*d, y*d, d])","title":""},{"docid":"518c03a8dc0ee11e655efed0ac1d6cba","score":"0.5879374","text":"def B(self):\n\t\ta = self.points[1].x-self.points[0].x\n\t\tb = self.points[2].x-self.points[0].x\n\t\tc = self.points[1].y-self.points[0].y\n\t\td = self.points[2].y-self.points[0].y\n\t\treturn 1/(a*d-c*b)*(np.array([[d,-c],[-b,a]]))","title":""},{"docid":"3a8edbf29e8c83b00ddb3a3250a614e8","score":"0.58390766","text":"def pcoord(x, y):\n r = np.sqrt( x**2 + y**2 )\n az = np.degrees( np.arctan2(x, y) )\n # az[where(az<0.)[0]] += 360.\n az = (az+360.)%360.\n return r, az","title":""},{"docid":"65d381933f0fea732e72c5d8043c7384","score":"0.58352846","text":"def add(x, y):","title":""},{"docid":"35befa5d8a46c43bd1f28d387ac30740","score":"0.58334136","text":"def __radd__(self,\n point: IPoint) -> IPoint:","title":""},{"docid":"776b08038104a9ca64c5f3b8c113cc70","score":"0.5827073","text":"def calc_target_point_rect(p1, p2):\n tp = [0, 0] # coordinates of target point\n # determine position on frame of p1\n frame_pos_p1 = calc_frame_pos(p1)\n\n # opposite border position\n frame_pos_opp = (frame_pos_p1 + 2) % 4\n\n # handle vertical lines, because steepness is infinity\n if p1[0] == p2[0]:\n tp[0] = p1[0]\n tp[1] = frame_borders[frame_pos_opp]\n return tp\n # otherwise calculate line equation y = mx + b\n m = calc_steepness(p1, p2)\n b = calc_bias(p1, p2)\n # if left or right\n if frame_pos_p1 % 2 == 0:\n tp[0] = frame_borders[frame_pos_opp]\n tp[1] = m * frame_borders[frame_pos_opp] + b\n if tp[1] < top:\n tp[0] = (top - b) / m\n tp[1] = top\n elif tp[1] > bot:\n tp[0] = (bot - b) / m\n tp[1] = bot\n # if top or bottom\n else:\n tp[0] = (frame_borders[frame_pos_opp] - b) / m\n tp[1] = frame_borders[frame_pos_opp]\n if tp[0] < left:\n tp[0] = left\n tp[1] = m * left + b\n elif tp[0] > right:\n tp[0] = right\n tp[1] = m * right + b\n return tp","title":""},{"docid":"2ae9fde7dea3c24e881ae8766627a379","score":"0.58093","text":"def xp(self,a,b):\n \n x_prime = np.array([np.cos(a)*np.cos(b),np.sin(a)*np.cos(b),np.sin(b)])\n return x_prime","title":""},{"docid":"a8729f947df81e1efbc60f50eba4a77e","score":"0.58075106","text":"def __mul__(self, otherPoint) :\n\t\treturn sum(map((lambda x,y : x*y), self.coords, otherPoint.coords))","title":""},{"docid":"cb8d7535f339acd913b65332b0bde6bb","score":"0.5805177","text":"def gradient(self, p):\n x = p[..., 0]\n y = p[..., 1]\n pi = np.pi\n uprime = np.zeros(p.shape, dtype=np.float64)\n uprime[..., 0] = (1 - 2 * x) * (y - y ** 2)\n uprime[..., 1] = (1 - 2 * y) * (x - x ** 2)\n return uprime","title":""},{"docid":"6c02a92f8619527547764cbcced482be","score":"0.5803821","text":"def __add__(self,other):\n if not isinstance(other,Point):\n return NotImplemented\n return Point( self._x + other._x, self._y + other._y)","title":""},{"docid":"abfc4447e5e1e67def94427c995a1997","score":"0.5770588","text":"def __abs__(self):\n return (self.x ** 2 + self.y ** 2) ** (1 / 2)","title":""},{"docid":"f41e1d0af0d3dfb80e89a27129e1f558","score":"0.5759189","text":"def __add__(self,\n point: IPoint) -> IPoint:","title":""},{"docid":"31100fa7ca9e9d5fa23e345fdb9fe407","score":"0.57477564","text":"def suma(x, y):\n return x + y","title":""},{"docid":"9d4c44f230f4ba2af87c06fdaf32b293","score":"0.5747229","text":"def slope(p):\n try:\n return float(p.y - p0.y)/(p.x - p0.x)\n except ZeroDivisionError:\n return 0","title":""},{"docid":"81b52e91f520215135082cfa0b54ddd2","score":"0.57468235","text":"def f(x, y):\n return x/y + y/x","title":""},{"docid":"c9dcb30df328a9de8dbdb2942dc39ead","score":"0.573359","text":"def neg_line(p):\n # y = -x + c\n # c = (p.x+p.y)\n neg_y_intersects.add(p.x + p.y)","title":""},{"docid":"3e2de4f62d2409ace1fde62ed0ea11c6","score":"0.5733273","text":"def test_inplace_plus(self):\n result = coords.Cartesian(self.p1.x + self.p2.x,\n self.p1.y + self.p2.y,\n self.p1.z + self.p2.z)\n a = self.p1\n a += self.p2\n self.assertTrue(result == a)","title":""},{"docid":"4a0bf35bbb3abe08bdddbc99c6100d94","score":"0.57209086","text":"def itp(x1,x2,y):\n x=float(y-x1)/float(x2-x1)\n assert x>=0\n return x","title":""},{"docid":"d504193153683f8dc9ea2c9aa365b561","score":"0.571949","text":"def projectOnLine(v, p):\n l = 1./linalg.norm(v)\n vn = v*l\n a = float(vn.T*p)\n u = a*vn\n return u,a*l","title":""},{"docid":"04ab41675635ea696132d70722e87a1b","score":"0.57041645","text":"def gradient(self, p):\n x = p[..., 0]\n y = p[..., 1]\n pi = np.pi\n uprime = np.zeros(p.shape, dtype=np.float64)\n uprime[..., 0] = 2 * x * (np.exp(x ** 2 + y ** 2))\n uprime[..., 1] = 2 * y * (np.exp(x ** 2 + y ** 2))\n return uprime","title":""},{"docid":"7cdf30cfc07b6c6c2b2e170cac563f7e","score":"0.5699533","text":"def x_value(a: Point, b: Point, y: Union[int, float]) -> float:\n assert isinstance(y, (int, float))\n\n if a.y == b.y:\n raise ValueError('Cannot return x-value for y=const.')\n\n return (a.x - b.x) * (y - b.y) / (a.y - b.y) + b.x","title":""},{"docid":"6d6d2b2beb7d175c43d471a2e083279c","score":"0.56977385","text":"def __add__(self, other):\n return Coordinates(self.x + other.x, self.y + other.y)","title":""},{"docid":"84159ce0e9e85121f092428bb9969467","score":"0.5693522","text":"def project_on_line(l, p):\n\ta, b = l\n\tseg_length = distance(a, b)\n\tap = (p[0] - a[0], p[1] - a[1])\n\tab = (b[0] - a[0], b[1] - a[1])\n\tab = (ab[0] / seg_length, ab[1] / seg_length)\n\tdot = ab[0]*ap[0] + ab[1]*ap[1]\n\treturn (a[0] + dot*ab[0], a[1] + dot*ab[1])","title":""},{"docid":"6eece127ad362069f9610f9f6bd8c195","score":"0.56822526","text":"def f(x, y):\n return -2 * y + 4 * x","title":""},{"docid":"0abf750bd7774049b28a1567d9aa54df","score":"0.5680937","text":"def _lhs(self, P):\n P = self._process_regularizer(P)\n return P, self._AtA + (P.T @ P)","title":""},{"docid":"38c583b19369311175bcdab375b20e65","score":"0.5678734","text":"def rel(self, x, y):","title":""},{"docid":"2cdacb439206cb7aeef31813e1a021e5","score":"0.56757486","text":"def test_add_pipoints_current_value(self, pi_point):\n total = pi_point.point + pi_point.point\n assert (\n round(\n total.current_value - (pi_point.values[-1] + pi_point.values[-1]),\n ndigits=7,\n )\n == 0\n )","title":""},{"docid":"739d0e774817b1585634aa5713bafcfb","score":"0.5671398","text":"def adding_tuple(self, other):\n sum = Point()\n sum.x = self.x + other[0]\n sum.y = self.y + other[1]\n return sum","title":""},{"docid":"abf5108e14381d3dc672daf7cda0d107","score":"0.5663466","text":"def ta_ori(self, r, p, y) -> Tuple[float, float, float]:\n pass","title":""},{"docid":"793be6fb4b77d466b8687c5a1bd9f193","score":"0.56602883","text":"def __iadd__(self,other):\n # print('in-place addition')\n if not isinstance(other,Point):\n return NotImplemented\n self._x += other._x\n self._y += other._y\n\n return self","title":""},{"docid":"e2deafbff70ff0a3140ecbe39d216bb0","score":"0.565977","text":"def cross_point(self,aXY1,aXY2,bXY1,bXY2):\n\t\tA1 = self.find_A(aXY1,aXY2)\n\t\tA2 = self.find_A(bXY1,bXY2)\n\t\tB1 = self.find_B(aXY1,aXY2)\n\t\tB2 = self.find_B(bXY1,bXY2)\n\t\tC1 = self.find_C(aXY1,aXY2)\n\t\tC2 = self.find_C(bXY1,bXY2)\n\t\tif (A1==A2==0) or (B1==B2==0):\n\t\t\treturn None\n\t\treturn [(B1*C2-B2*C1)/float(A1*B2-A2*B1),(C1*A2-C2*A1)/float(A1*B2-A2*B1)]","title":""},{"docid":"d2becc084ada73abf8e41ebd454ffeb8","score":"0.56586313","text":"def Perp2(a, b):\n\n return a[0] * b[1] - a[1] * b[0]","title":""},{"docid":"2eda7ec654e9b5b746946bd5d80367bf","score":"0.5655645","text":"def compute(x, y):\n return x + y","title":""},{"docid":"28440b6046e11cec8b4b8308b92c5953","score":"0.5649141","text":"def source(self, p):\n x = p[..., 0]\n y = p[..., 1]\n val = 1.0\n return val","title":""},{"docid":"5e8c876dc790ecbe6e0652b16faf2239","score":"0.56401366","text":"def transform_point(p,R,t):\r\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\r\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\r\n return [x,y]","title":""},{"docid":"0cec334aee987d84fee3c64141f12e07","score":"0.56382406","text":"def point(self,p):\n return self.token(p[0]).token(p[1])","title":""},{"docid":"39973c7a7dd4c33658de4272361217e8","score":"0.56363815","text":"def heuristic(a, b):\n (x1, y1) = a\n (x2, y2) = b\n return abs(x1 - x2) + abs(y1 - y2)","title":""},{"docid":"d45cd9b7fc4df5f88aa0af094bafa0e8","score":"0.5632378","text":"def recta(x,a,b):\r\n fa=log(f(a))\r\n return ((fa-log(f(b)))/(a-b))*(x-a) + fa","title":""},{"docid":"00e4134e46a1d496bd5d6af534314661","score":"0.56241816","text":"def gradient(self, p):\n x = p[..., 0]\n y = p[..., 1]\n\n val = np.zeros(p.shape, dtype=p.dtype)\n pi = np.pi\n cos = np.cos\n sin = np.sin\n gamma = 0.1\n sigma = -14.9225565104455152\n rho = pi / 4\n theta = np.arctan2(y, x)\n theta = (theta >= 0) * theta + (theta < 0) * (theta + 2 * pi)\n t = 1 + (y / x) ** 2\n r = np.sqrt(x ** 2 + y ** 2)\n rg = r ** gamma\n\n ux1 = ((x >= 0.0) & (y >= 0.0)) * (\n gamma * rg * cos((pi / 2 - sigma) * gamma) * (x * cos((theta - pi / 2 + rho) * gamma) / (r * r)\n + y * sin((theta - pi / 2 + rho) * gamma) / (x * x * t))\n )\n\n uy1 = ((x >= 0.0) & (y >= 0.0)) * (gamma * rg * cos((pi / 2 - sigma) * gamma) * (\n y * cos((theta - pi / 2 + rho) * gamma) / (r * r) - sin((theta - pi / 2 + rho) * gamma) / (x * t)))\n\n ux2 = ((x <= 0.0) & (y >= 0.0)) * (gamma * rg * cos(rho * gamma) * (\n x * cos((theta - pi + sigma) * gamma) / (r * r) + y * sin((theta - pi + sigma) * gamma) / (x * x * t)))\n\n uy2 = ((x <= 0.0) & (y >= 0.0)) * (gamma * rg * cos(rho * gamma) * (\n y * cos((theta - pi + sigma) * gamma) / (r * r) - sin((theta - pi + sigma) * gamma) / (x * t)))\n\n ux3 = ((x <= 0.0) & (y <= 0.0)) * (gamma * rg * cos(sigma * gamma) * (\n x * cos((theta - pi - rho) * gamma) / (r * r) + y * sin((theta - pi - rho) * gamma) / (x * x * t)))\n\n uy3 = ((x <= 0.0) & (y <= 0.0)) * (gamma * rg * cos(sigma * gamma) * (\n y * cos((theta - pi - rho) * gamma) / (r * r) - sin((theta - pi - rho) * gamma) / (x * t)))\n\n ux4 = ((x >= 0.0) & (y <= 0.0)) * (gamma * rg * cos((pi / 2 - rho) * gamma) * (\n x * cos((theta - 3 * pi / 2 - sigma) * gamma) / (r * r) + y * sin((theta - 3 * pi / 2 - sigma) * gamma) / (\n x * x * t)))\n\n uy4 = ((x >= 0.0) & (y <= 0.0)) * (gamma * rg * cos((pi / 2 - rho) * gamma) * (\n y * cos((theta - 3 * pi / 2 - sigma) * gamma) / (r * r) - sin((theta - 3 * pi / 2 - sigma) * gamma) / (\n x * t)))\n\n val[..., 0] = ux1 + ux2 + ux3 + ux4\n val[..., 1] = uy1 + uy2 + uy3 + uy4\n return val","title":""},{"docid":"a01bc1b94f1dfa71666c80e1d1066811","score":"0.5613631","text":"def __call__(self, x: float) -> float:\n return (self.slope * (x - self.point.x)) + self.point.y","title":""},{"docid":"878235a2110faaeec17b93b20c5819c9","score":"0.5610729","text":"def gradient(self, p):\n x = p[..., 0]\n y = p[..., 1]\n a = 10000 * (x ** 2 + y ** 2) ** 2\n uprime = np.zeros(p.shape, dtype=np.float64)\n uprime[..., 0] = 200 * x / (1 + a)\n uprime[..., 1] = 200 * y / (1 + a)\n return uprime","title":""},{"docid":"0abd15fb337df1adb4ef9c662a72f4c8","score":"0.5609698","text":"def __radd__(self,\n point: IPoint) -> IPoint:\n return self + point","title":""},{"docid":"af1fe4c9d0b2bec548eb95d9dee153c3","score":"0.5596991","text":"def vecteur_points (p1,p2):\n return ( p2 [0] - p1 [0], p2 [1] - p1 [1])","title":""},{"docid":"bd017d95c56b36d5e5e07f9882046ecf","score":"0.5595749","text":"def _parabola_3points(x1,y1,x2,y2,x3,y3):\n delta = (x1 - x2)*(x1 - x3)*(x2 - x3)\n a = (x3 * (y2 - y1) + x2 * (y1 - y3) + x1 * (y3 - y2)) / delta\n b = (x3**2 * (y1 - y2) + x2**2 * (y3 - y1) + x1**2 * (y2 - y3)) / delta\n c = (x2 * x3 * (x2 - x3) * y1 + x3 * x1 * (x3 - x1) * y2 + x1 * x2 * (x1 - x2) * y3) / delta\n return a, b, c","title":""},{"docid":"73ea1535c6544471b0494e1e00930daa","score":"0.55935794","text":"def find_slope2(m, x, b):\n m = float(m)\n x = float(x)\n b = float(b)\n y = m * x + b\n\n return y","title":""},{"docid":"6419ca50ad4e8a0fdefaeb36cb9aa990","score":"0.5592216","text":"def _line(x, a, b):\n return a*x + b","title":""},{"docid":"329d5e28df9331ef10840b50765217d7","score":"0.55885667","text":"def zp(self,a,b):\n\n z_prime = np.array([-np.cos(a)*np.sin(b),-np.sin(a)*np.sin(b),np.cos(b)])\n return z_prime","title":""},{"docid":"aff1bbeb9862dc36923f7978e7c18951","score":"0.5587336","text":"def _point_interpolation(self, p1, p2):\n\n # Find the middle point of p1-p2\n p = p1 + (p2 - p1)/2\n # Project the point onto the ellipsoid\n p = self._point_ellipsoid_projection(p)\n # Return\n return p","title":""},{"docid":"979d7a1b9ee5f0974836f14e49f5e10a","score":"0.5586449","text":"def polyval(p,x):\n y = 0\n for i in range(len(p)):\n y = x*y + p[i]\n return y","title":""},{"docid":"6a898a753e61dedf1e5757551a7ce9b3","score":"0.55851406","text":"def cost(a, b, all_points):\n\n total_cost = 0\n\n pointA = all_points[a]\n pointB = all_points[b]\n\n x1 = pointA['coord'][0]\n x2 = pointB['coord'][0]\n y1 = pointA['coord'][1]\n y2 = pointB['coord'][1]\n\n # Travel time\n total_cost += math.hypot(x2 - x1, y2 - y1) / ROBOT_SPEED\n\n # Penalties incurred for skipping\n for i in range(a+1, b):\n total_cost += all_points[i]['penalty']\n\n # Add waiting time\n total_cost += ROBOT_WAIT_TIME\n\n return total_cost","title":""},{"docid":"5300dd422945dec99c5799f6813c6b47","score":"0.5582936","text":"def is_point_on_curve(a, b, p, x, y):\n assert isinstance(a, Bn)\n assert isinstance(b, Bn)\n assert isinstance(p, Bn) and p > 0\n assert (isinstance(x, Bn) and\n isinstance(y, Bn)) or (x is None and y is None)\n\n if x is None and y is None:\n return True\n\n lhs = (y * y) % p\n rhs = ((x * x * x) + (a * x) + b) % p\n on_curve = (lhs == rhs)\n\n return on_curve","title":""},{"docid":"55a2bd6dd953a6c201c289f25dde65b6","score":"0.5582299","text":"def point_return(point):\n return Point(0, point.y + 1)","title":""},{"docid":"ea582fa8a8a20153aed6796289a4d02b","score":"0.5582241","text":"def _interpolate_p(p, r, v):\n\n # interpolate p (v should be in table)\n # if .5 < p < .75 use linear interpolation in q\n # if p > .75 use quadratic interpolation in log(y + r/v)\n # by -1. / (1. + 1.5 * _phi((1. + p)/2.))\n\n # find the 3 closest v values\n p0, p1, p2 = _select_ps(p)\n try:\n y0 = _func(A[(p0, v)], p0, r, v) + 1.\n except:\n print(p,r,v)\n raise\n y1 = _func(A[(p1, v)], p1, r, v) + 1.\n y2 = _func(A[(p2, v)], p2, r, v) + 1.\n\n y_log0 = math.log(y0 + float(r)/float(v))\n y_log1 = math.log(y1 + float(r)/float(v))\n y_log2 = math.log(y2 + float(r)/float(v))\n\n # If p < .85 apply only the ordinate transformation\n # if p > .85 apply the ordinate and the abcissa transformation\n # In both cases apply quadratic interpolation\n if p > .85:\n p_t = _ptransform(p)\n p0_t = _ptransform(p0)\n p1_t = _ptransform(p1)\n p2_t = _ptransform(p2)\n\n # calculate derivatives for quadratic interpolation\n d2 = 2*((y_log2-y_log1)/(p2_t-p1_t) - \\\n (y_log1-y_log0)/(p1_t-p0_t))/(p2_t-p0_t)\n if (p2+p0)>=(p1+p1):\n d1 = (y_log2-y_log1)/(p2_t-p1_t) - 0.5*d2*(p2_t-p1_t)\n else:\n d1 = (y_log1-y_log0)/(p1_t-p0_t) + 0.5*d2*(p1_t-p0_t)\n d0 = y_log1\n\n # interpolate value\n y_log = (d2/2.) * (p_t-p1_t)**2. + d1 * (p_t-p1_t) + d0\n\n # transform back to y\n y = math.exp(y_log) - float(r)/float(v)\n\n elif p > .5:\n # calculate derivatives for quadratic interpolation\n d2 = 2*((y_log2-y_log1)/(p2-p1) - \\\n (y_log1-y_log0)/(p1-p0))/(p2-p0)\n if (p2+p0)>=(p1+p1):\n d1 = (y_log2-y_log1)/(p2-p1) - 0.5*d2*(p2-p1)\n else:\n d1 = (y_log1-y_log0)/(p1-p0) + 0.5*d2*(p1-p0)\n d0 = y_log1\n\n # interpolate values\n y_log = (d2/2.) * (p-p1)**2. + d1 * (p-p1) + d0\n\n # transform back to y\n y = math.exp(y_log) - float(r)/float(v)\n\n else:\n # linear interpolation in q and p\n v = min(v, 1e38)\n q0 = math.sqrt(2) * -y0 * \\\n scipy.stats.t.isf((1.+p0)/2., v)\n q1 = math.sqrt(2) * -y1 * \\\n scipy.stats.t.isf((1.+p1)/2., v)\n\n d1 = (q1-q0)/(p1-p0)\n d0 = q0\n\n # interpolate values\n q = d1 * (p-p0) + d0\n\n # transform back to y\n y = -q / (math.sqrt(2) * scipy.stats.t.isf((1.+p)/2., v))\n\n return y","title":""},{"docid":"853ea1b03c03dae2f30016a13b2e2f3c","score":"0.5577537","text":"def is_point_on_curve(a, b, p, x, y):\n assert isinstance(a, Bn)\n assert isinstance(b, Bn)\n assert isinstance(p, Bn) and p > 0\n assert (isinstance(x, Bn) and isinstance(y, Bn)) or (x == None and y == None)\n\n if (x is not None) and (y is not None):\n lhs = (y * y) % p\n rhs = (x*x*x + a*x + b) % p\n on_curve = (lhs == rhs)\n return on_curve\n return True","title":""},{"docid":"5b04d1630e595eeca93b4421531a9318","score":"0.55759376","text":"def __add__(self, p):\n\n\tnew_poly = list\n\tif len(self.coeff) <= len(p.coeff):\n\t\tnew_poly = p.coeff[:]\n\t\tfor i in range(len(self.coeff)):\n\t\t\tnew_poly[i] = p.coeff[i] + self.coeff[i]\n\telse:\n\t\tnew_poly = self.coeff[:]\n\t\tfor i in range(len(p.coeff)):\n\t\t\tnew_poly[i] = self.coeff[i] + p.coeff[i]","title":""}],"string":"[\n {\n \"docid\": \"3420fc09cc7a6c8ccb3050887f7e8572\",\n \"score\": \"0.7422088\",\n \"text\": \"def point_add(a, b, p, x0, y0, x1, y1):\\n\\n # ADD YOUR CODE BELOW\\n assert isinstance(a, Bn)\\n assert isinstance(b, Bn)\\n assert isinstance(p, Bn) and p > 0\\n assert (isinstance(x0, Bn) and isinstance(y0, Bn)) or (x0 == None and y0 == None)\\n assert (isinstance(x1, Bn) and isinstance(y1, Bn)) or (x1 == None and y1 == None)\\n\\n if (x0 is not None and y0 is not None) and (x1 is None and y1 is None):\\n return (x0, y0)\\n if (x0 is None and y0 is None) and (x1 is not None and y1 is not None):\\n return (x1, y1)\\n if (x0 is x1) and (y0 is not y1):\\n return (None, None)\\n if (x0 is not x1) and (y0 is not y1):\\n lam = (y1.mod_sub(y0, p)).mod_mul(((x1.mod_sub(x0, p)).mod_inverse(p)), p)\\n xr = ((lam.mod_pow(2, p)).mod_sub(x0, p)).mod_sub(x1, p)\\n yr = (lam.mod_mul(x0.mod_sub(xr, p), p)).mod_sub(y0, p)\\n return (xr, yr) \\n raise ArithmeticError(\\\"EC Points must not be equal\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9f565833cf303d8390ec92753e1af3\",\n \"score\": \"0.74085206\",\n \"text\": \"def point_add(a, b, p, x0, y0, x1, y1):\\n\\n if ((x0, y0) == (x1, y1)):\\n raise Exception('EC Points must not be equal')\\n\\n # if x1 == x0 then there is no inverse, also check both points are on curve\\n if ((x0 == x1) or\\n (not is_point_on_curve(a, b, p, x0, y0)) or\\n (not is_point_on_curve(a, b, p, x1, y1))):\\n return(None, None)\\n\\n if (x0, y0) == (None, None):\\n return (x1, y1)\\n\\n if (x1, y1) == (None, None):\\n return (x0, y0)\\n\\n # calculate lam in stages using Bn methods\\n xqminxp = x1.mod_sub(x0, p)\\n yqminyp = y1.mod_sub(y0, p)\\n\\n xqminxpmodinv = xqminxp.mod_inverse(m=p)\\n lam = xqminxpmodinv.mod_mul(yqminyp, p)\\n\\n # calculate xr\\n lamsq = lam.mod_mul(lam, p)\\n lamsqmin = lamsq.mod_sub(x0, p)\\n xr = lamsqmin.mod_sub(x1, p)\\n\\n # calculate yr\\n xpminxr = x0.mod_sub(xr, p)\\n lamxpxr = lam.mod_mul(xpminxr, p)\\n yr = lamxpxr.mod_sub(y0, p)\\n\\n return (xr, yr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83fd6662528e49c276dd1d0280731f5\",\n \"score\": \"0.7015354\",\n \"text\": \"def __add__(self,b):\\n return Punto(self.x + b.x, self.y + b.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2412afaf4905a0427016faa6e08c0927\",\n \"score\": \"0.6985416\",\n \"text\": \"def add(self, p1, p2):\\n x1, y1 = p1\\n x2, y2 = p2\\n a, p = self.a, self.p\\n\\n if p1 == self.POINT_AT_INFINITY:\\n return p2\\n elif p2 == self.POINT_AT_INFINITY:\\n return p1\\n elif x1 == x2 and y1 != y2:\\n # We can rewrite this condition as\\n # x1 == x2 and find_inverse(-y1, p) == y2\\n # but, y1 != y2 is more efficient.\\n # This is correct because vertical line intersects\\n # the elliptic curve in one or two points.\\n return self.POINT_AT_INFINITY\\n\\n if p1 == p2:\\n m = (3 * x1**2 + a) * find_inverse(2 * y1, p) % p\\n else:\\n m = (y2 - y1) * find_inverse(x2 - x1, p) % p\\n\\n x3 = (m**2 - x1 - x2) % p\\n y3 = (m * (x1 - x3) - y1) % p\\n result = (x3, y3)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ce35e9e7c0dae0a8178ffa222c2fed\",\n \"score\": \"0.6642534\",\n \"text\": \"def point_double(a, b, p, x, y): \\n\\n # ADD YOUR CODE BELOW\\n assert isinstance(a, Bn)\\n assert isinstance(b, Bn)\\n assert isinstance(p, Bn) and p > 0\\n assert (isinstance(x, Bn) and isinstance(y, Bn)) or (x == None and y == None)\\n\\n if (x is not None) and (y is not None):\\n lam = (((x.mod_pow(2, p)).mod_mul(3, p)).mod_add(a, p)).mod_mul((y.mod_mul(2, p)).mod_inverse(p), p)\\n xr = (lam.mod_pow(2, p)).mod_sub(x.mod_mul(2, p), p)\\n yr = ((x.mod_sub(xr, p)).mod_mul(lam, p)).mod_sub(y, p)\\n return (xr, yr)\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43fee64b56075c2f93ca88f5101ce039\",\n \"score\": \"0.66143286\",\n \"text\": \"def __add__(self, other):\\n if self == PAI:\\n return other\\n if other == PAI:\\n return self\\n if self.x == other.x and self.y != other.y:\\n return PAI\\n\\n # Compute slope y = mx + v\\n if self.x == other.x and self.y == other.y:\\n # Two Points are the same -> Compute tangent on curve\\n m = (3 * self.x**2) * inv(2 * self.y, self.curve.p)\\n else:\\n # Compute slope between two points\\n m = (other.y - self.y) * inv(other.x - self.x, self.curve.p)\\n\\n v = self.y - m * self.x\\n\\n # New Point is at the intersection of the slop and the curve\\n new_x = (m**2 - self.x - other.x) % self.curve.p\\n new_y = (-(m * new_x + v)) % self.curve.p\\n\\n return Point(self.curve, new_x, new_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"202c705f07d067dd507f1814053a60a7\",\n \"score\": \"0.65900207\",\n \"text\": \"def makeLine(p1,p2):\\n x1 = p1[0]\\n y1 = p1[1]\\n\\n x2 = p2[0]\\n y2 = p2[1]\\n\\n a = (y1 - y2) / (x1 - x2)\\n b = (x1 * y2 - x2 * y1) / (x1 - x2)\\n\\n return a,b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8012eb38404f0ab4ab915d8ed550ad0\",\n \"score\": \"0.6575684\",\n \"text\": \"def point_double(a, b, p, x, y):\\n\\n if x is None and y is None:\\n return None, None\\n\\n xsq = x.mod_mul(x, p)\\n xsq3 = Bn(3).mod_mul(xsq, p)\\n num = xsq3.mod_add(a, p)\\n y2 = Bn(2).mod_mul(y, p)\\n y2inv = y2.mod_inverse(m=p)\\n lam = num.mod_mul(y2inv, p)\\n\\n xr = lam.mod_mul(lam, p)\\n xr = xr.mod_sub(x, p)\\n xr = xr.mod_sub(x, p)\\n\\n yr = lam.mod_mul(x.mod_sub(xr, p), p)\\n yr = yr.mod_sub(y, p)\\n\\n return (xr, yr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fddf12737ff87b09db1618354dad3456\",\n \"score\": \"0.656293\",\n \"text\": \"def yp(self,a,b):\\n \\n y_prime = np.array([-np.sin(a),np.cos(a),0])\\n return y_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ae92515c68d0ee85f5dbf718f817a6\",\n \"score\": \"0.65425587\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n rhs = 2 * (y - y ** 2) + 2 * (x - x ** 2)\\n return rhs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d146142695eac0628804c7b6d3d7a175\",\n \"score\": \"0.6453014\",\n \"text\": \"def point(self, p, x):\\n return self.plec(x, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244fae6563ee6f0b919e4df35fabe410\",\n \"score\": \"0.63918453\",\n \"text\": \"def restar(self, b):\\n return Punto(self.x - b.x, self.y - b.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244fae6563ee6f0b919e4df35fabe410\",\n \"score\": \"0.63918453\",\n \"text\": \"def restar(self, b):\\n return Punto(self.x - b.x, self.y - b.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef3cf169cab513e5ae413a8d4477610\",\n \"score\": \"0.63806015\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n a = 10000 * (x ** 2 + y ** 2) ** 2\\n rhs = -100 * (4 - 4 * a) / (a + 1) ** 2\\n return rhs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158fbd965428d8f1d088bf80536f91ae\",\n \"score\": \"0.6314499\",\n \"text\": \"def __add__(self, other):\\r\\n return Point2d(self.x + other.x, self.y + other.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed7d1da2004cf73c5ec2aeece598215\",\n \"score\": \"0.6312248\",\n \"text\": \"def __add__(self, otherPoint) :\\n\\t\\treturn point(self.x + otherPoint.x, self.y + otherPoint.y, self.z + otherPoint.z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dcafd5403f3b44cefd7d0411b47b349\",\n \"score\": \"0.6309287\",\n \"text\": \"def bresa(p1, p2):\\n xi, yi = p1\\n x2, y2 = p2\\n cells = [(xi, yi)]\\n\\n # Slope is calculated once only if it's possible\\n if xi != x2:\\n m = (y2 - yi) / (x2 - xi)\\n delta_y = m\\n x1 = xi\\n y1 = yi + 0.5 - m * 0.5\\n\\n if x1 < x2 and yi <= y2:\\n x = x1 + 1\\n y = yi\\n inc_y = y1 + delta_y\\n while x < x2 + 1 or y < y2:\\n if inc_y > y + 1:\\n y += 1\\n elif inc_y == y + 1:\\n y += 1\\n x += 1\\n delta_y += m\\n else:\\n x += 1\\n delta_y += m\\n cells.append((x - 1, y))\\n inc_y = y1 + delta_y\\n\\n elif x1 < x2 and yi >= y2:\\n x = x1 + 1\\n y = yi\\n inc_y = y1 + delta_y\\n while x < x2 + 1 or y > y2:\\n if inc_y < y:\\n y -= 1\\n elif inc_y == y:\\n y -= 1\\n x += 1\\n delta_y += m\\n else:\\n x += 1\\n delta_y += m\\n cells.append((x - 1, y))\\n inc_y = y1 + delta_y\\n\\n elif x1 > x2 and yi > y2:\\n x = x1\\n y = yi\\n inc_y = y1\\n delta_y = 0\\n while x > x2 or y > y2:\\n if inc_y < y:\\n y -= 1\\n elif inc_y == y:\\n y -= 1\\n x -= 1\\n delta_y -= m\\n else:\\n x -= 1\\n delta_y -= m\\n cells.append((x, y))\\n inc_y = y1 + delta_y\\n\\n elif x1 > x2 and yi <= y2:\\n x = x1\\n y = yi\\n inc_y = y1\\n delta_y = 0\\n while x > x2 or y < y2:\\n if inc_y > y + 1:\\n y += 1\\n elif inc_y == y + 1:\\n y += 1\\n x -= 1\\n delta_y -= m\\n else:\\n x -= 1\\n delta_y -= m\\n cells.append((x, y))\\n inc_y = y1 + delta_y\\n\\n else:\\n y = yi\\n if y2 >= yi:\\n while y < y2 + 1:\\n cells.append((xi, y))\\n y += 1\\n else:\\n while y > y2:\\n cells.append((xi, y - 1))\\n y -= 1\\n\\n return np.array(cells)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2771385a126aa4e9c6a9056d6f911d1c\",\n \"score\": \"0.63012713\",\n \"text\": \"def project_y(self, p):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63fb2ff37a255a7a1d19527ca8d72a7\",\n \"score\": \"0.6294456\",\n \"text\": \"def _reduce_xyfp(x, y):\\n a = 420.0\\n return -x/a, y/a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f98e7f30356d534c4d0069a867e65bde\",\n \"score\": \"0.6290059\",\n \"text\": \"def pos_line(p):\\n # y = x + c\\n # c = (p.y-p.x)\\n pos_y_intersects.add(p.y - p.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e21035726b43dd91ebf6d03a380bae4\",\n \"score\": \"0.62183064\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n val = -2 * (x ** 2 + y ** 2)\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee3ec6caa31e10021ba8e29b0a32a1a\",\n \"score\": \"0.62158895\",\n \"text\": \"def Interpolate(a, b, p):\\n\\treturn a + (b - a) * p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa26bfc911d4bc0bc5aabba98c59e24d\",\n \"score\": \"0.62128115\",\n \"text\": \"def get_line_to(self, p2):\\n \\\"\\\"\\\" y1- ax1 = y2 - ax2\\n a = (y1-y2)/(x1-x2)\\n b = y1 - a * x1\\\"\\\"\\\"\\n if self.x == p2.x:\\n return 'x = {0}'.format(self.x)\\n elif self.y == p2.y:\\n return 'y = {0}'.format(self.y)\\n else:\\n a = (self.y - p2.y)/(self.x - p2.x)\\n b = self.y - a * self.x\\n return (a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f659810350a6341efa94a6a38e1cd310\",\n \"score\": \"0.6169263\",\n \"text\": \"def sum (self, other):\\n new_pt = Point(self.x + other.x, self.y + other.y)\\n return new_pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58992d107efdd8acddb93f580aa7ed44\",\n \"score\": \"0.61668736\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n val = -0.16 * (x ** 2 + y ** 2) ** (-0.8)\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5e4121550330c7ab097e607b56c785\",\n \"score\": \"0.61619216\",\n \"text\": \"def __add__(self, other):\\n return Point(self.x+other.x, self.y+other.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552c20c11fb4155927a32b66419b0b91\",\n \"score\": \"0.61449575\",\n \"text\": \"def axpy(alpha,x,y):\\n for i in range(0,len(x)):\\n y[i]=alpha*x[i]+y[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f66e3ba0296e7bfeee3f8d6650be372\",\n \"score\": \"0.61183864\",\n \"text\": \"def skalarni_produkt(self, other):\\n return self.x * other.x + self.y * other.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc53b3f08439aa631a5033e14379ea6d\",\n \"score\": \"0.608645\",\n \"text\": \"def adding_points(self, other):\\n sum = Point()\\n sum.x = self.x + other.x\\n sum.y = self.y + other.y\\n return sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a937fd5606f64d05e064b7a37e097f75\",\n \"score\": \"0.6071962\",\n \"text\": \"def crossover(self, p1, p2):\\n return (p1 + p2) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277c55565ed833bb815fa50baf7d0c41\",\n \"score\": \"0.60685253\",\n \"text\": \"def Double(self, p: EcPoint) -> EcPoint:\\n if p == INFINITY:\\n return p\\n x, y = p\\n num = (3 * x * x + self.a) % self.mod\\n den = 2 * y\\n t = num * gmpy.invert(den, self.mod) % self.mod\\n x2 = (t * t - 2 * x) % self.mod\\n y2 = (t * (x - x2) - y) % self.mod\\n return (x2, y2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb4e83d2db4e6222cc3d3402046ec8d\",\n \"score\": \"0.6058135\",\n \"text\": \"def h(p1, p2):\\n x1, y1 = p1\\n x2, y2 = p2\\n return abs(x1 - x2) + abs(y1 - y2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba25ba56832f05342cc5bc0952fac81\",\n \"score\": \"0.6055224\",\n \"text\": \"def himmelblau(p):\\n x, y = p\\n a = x*x + y - 11\\n b = x + y*y - 7\\n return a*a + b*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc07df32e014b7da7b5402653d74f73\",\n \"score\": \"0.6045281\",\n \"text\": \"def y_value(a: Point, b: Point, x: Union[int, float]) -> float:\\n assert isinstance(x, (int, float))\\n\\n if a.x == b.x:\\n raise ValueError('Cannot return y-value for x=const.')\\n\\n return (a.y - b.y) * (x - b.x) / (a.x - b.x) + b.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ceeb34368086ac841be2a8e165528c\",\n \"score\": \"0.59830785\",\n \"text\": \"def appendPoint(self, p):\\n self._points.append(p)\\n self._wixiyi += p.w * p.x * p.y\\n self._wixi += p.w * p.x\\n self._wiyi += p.w * p.y\\n self._wixi2 += p.w * p.x * p.x\\n self._wi += p.w\\n if len(self._points) > 1:\\n # if points are aligned with exactly the same x, line is vertical\\n # a and b would be infinity\\n if self._wixi2 == self._wixi**2/self._wi:\\n self.a = float(\\\"inf\\\")\\n self.b = float(\\\"inf\\\")\\n else:\\n self.a = (self._wixiyi - self._wixi*self._wiyi/self._wi)\\\\\\n / (self._wixi2 - self._wixi**2/self._wi)\\n self.b = (self._wiyi - self.a * self._wixi)/self._wi\\n self._rss = self.calcRSS(self.a, self.b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c79bd48cf4c73b4eb7e84d4736c1c75\",\n \"score\": \"0.597987\",\n \"text\": \"def __or__(self,other):\\n if self.a==other.a:\\n return None\\n x=(self.b-other.b)/(other.a-self.a)\\n y=self.a*x+self.b\\n return Point(x,y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4765f1b0722b4b6aa8ca09eeff1fbdb9\",\n \"score\": \"0.5977134\",\n \"text\": \"def __sub__(self,b):\\n return Punto(self.x - b.x, self.y - b.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ee7218cf11d06e075703ed6d4f4692\",\n \"score\": \"0.59696823\",\n \"text\": \"def _project_point_onto_line(self, p, ep0, ep1):\\n dx, dy = ep1[0] - ep0[0], ep1[1] - ep0[1]\\n determinant = dx * dx + dy * dy\\n coeff = (dy * (p[1] - ep0[1]) + dx * (p[0] - ep0[0])) / determinant\\n\\n return ep0[0] + coeff * dx, ep0[1] + coeff * dy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4ec24f049e9baad160dcfac340525c\",\n \"score\": \"0.5966919\",\n \"text\": \"def lerp(x, y, a):\\n\\treturn (1 - a) * x + a * y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4edf564c718338183be214c4221cc8ab\",\n \"score\": \"0.59647214\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n rhs = -(4 * x ** 2 + 4 * y ** 2 + 4) * (np.exp(x ** 2 + y ** 2))\\n return rhs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73362f6ce969028d4dcd64d98d59c5df\",\n \"score\": \"0.5933852\",\n \"text\": \"def sum(self):\\n return self.x + self.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d98a1f1b20feadc260d2824c1d07ccd3\",\n \"score\": \"0.59313613\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n t0 = (x + 0.5) ** 2 + (y - 0.5) ** 2 + 0.01\\n t1 = (x - 0.5) ** 2 + (y + 0.5) ** 2 + 0.01\\n val = (\\n (2 * x - 1.0) * (4 * x - 2.0) / t1 ** 3 -\\n (2 * x + 1.0) * (4 * x + 2.0) / t0 ** 3 -\\n (2 * y - 1.0) * (4 * y - 2.0) / t0 ** 3 +\\n (2 * y + 1.0) * (4 * y + 2.0) / t1 ** 3 + 4 / t0 ** 2 - 4 / t1 ** 2\\n )\\n\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb7697f9750b0eed1c2bf141d533b82\",\n \"score\": \"0.5914811\",\n \"text\": \"def __add__(self, b):\\n temp_y = self.y - b.y\\n result = Sweep()\\n result.LoadArrays(self.x, temp_y)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494cf9ec50748b2c7b59d47b42189639\",\n \"score\": \"0.5908124\",\n \"text\": \"def Cp_a(a, y):\\n #return (a - y) * Sigmoid.prime(z)\\n return (a - y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8563422fb987b44c6d663a28cbb5972\",\n \"score\": \"0.5891777\",\n \"text\": \"def phi_b(x):\\n y = np.copy(x)\\n x1, x2 = x[:2]\\n y[0], y[1] = x1, x2 + b*x1**2 - 100*b\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"872335f4a037e02e249eb68d9d7e4f1c\",\n \"score\": \"0.5886332\",\n \"text\": \"def _reduce_point(self, R, p):\\n if R.is_zero():\\n return R.curve().change_ring(rings.GF(p))(0)\\n x, y = R.xy()\\n d = lcm(x.denominator(), y.denominator())\\n return R.curve().change_ring(rings.GF(p))([x*d, y*d, d])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518c03a8dc0ee11e655efed0ac1d6cba\",\n \"score\": \"0.5879374\",\n \"text\": \"def B(self):\\n\\t\\ta = self.points[1].x-self.points[0].x\\n\\t\\tb = self.points[2].x-self.points[0].x\\n\\t\\tc = self.points[1].y-self.points[0].y\\n\\t\\td = self.points[2].y-self.points[0].y\\n\\t\\treturn 1/(a*d-c*b)*(np.array([[d,-c],[-b,a]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8edbf29e8c83b00ddb3a3250a614e8\",\n \"score\": \"0.58390766\",\n \"text\": \"def pcoord(x, y):\\n r = np.sqrt( x**2 + y**2 )\\n az = np.degrees( np.arctan2(x, y) )\\n # az[where(az<0.)[0]] += 360.\\n az = (az+360.)%360.\\n return r, az\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d381933f0fea732e72c5d8043c7384\",\n \"score\": \"0.58352846\",\n \"text\": \"def add(x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35befa5d8a46c43bd1f28d387ac30740\",\n \"score\": \"0.58334136\",\n \"text\": \"def __radd__(self,\\n point: IPoint) -> IPoint:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776b08038104a9ca64c5f3b8c113cc70\",\n \"score\": \"0.5827073\",\n \"text\": \"def calc_target_point_rect(p1, p2):\\n tp = [0, 0] # coordinates of target point\\n # determine position on frame of p1\\n frame_pos_p1 = calc_frame_pos(p1)\\n\\n # opposite border position\\n frame_pos_opp = (frame_pos_p1 + 2) % 4\\n\\n # handle vertical lines, because steepness is infinity\\n if p1[0] == p2[0]:\\n tp[0] = p1[0]\\n tp[1] = frame_borders[frame_pos_opp]\\n return tp\\n # otherwise calculate line equation y = mx + b\\n m = calc_steepness(p1, p2)\\n b = calc_bias(p1, p2)\\n # if left or right\\n if frame_pos_p1 % 2 == 0:\\n tp[0] = frame_borders[frame_pos_opp]\\n tp[1] = m * frame_borders[frame_pos_opp] + b\\n if tp[1] < top:\\n tp[0] = (top - b) / m\\n tp[1] = top\\n elif tp[1] > bot:\\n tp[0] = (bot - b) / m\\n tp[1] = bot\\n # if top or bottom\\n else:\\n tp[0] = (frame_borders[frame_pos_opp] - b) / m\\n tp[1] = frame_borders[frame_pos_opp]\\n if tp[0] < left:\\n tp[0] = left\\n tp[1] = m * left + b\\n elif tp[0] > right:\\n tp[0] = right\\n tp[1] = m * right + b\\n return tp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ae9fde7dea3c24e881ae8766627a379\",\n \"score\": \"0.58093\",\n \"text\": \"def xp(self,a,b):\\n \\n x_prime = np.array([np.cos(a)*np.cos(b),np.sin(a)*np.cos(b),np.sin(b)])\\n return x_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8729f947df81e1efbc60f50eba4a77e\",\n \"score\": \"0.58075106\",\n \"text\": \"def __mul__(self, otherPoint) :\\n\\t\\treturn sum(map((lambda x,y : x*y), self.coords, otherPoint.coords))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb8d7535f339acd913b65332b0bde6bb\",\n \"score\": \"0.5805177\",\n \"text\": \"def gradient(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n pi = np.pi\\n uprime = np.zeros(p.shape, dtype=np.float64)\\n uprime[..., 0] = (1 - 2 * x) * (y - y ** 2)\\n uprime[..., 1] = (1 - 2 * y) * (x - x ** 2)\\n return uprime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c02a92f8619527547764cbcced482be\",\n \"score\": \"0.5803821\",\n \"text\": \"def __add__(self,other):\\n if not isinstance(other,Point):\\n return NotImplemented\\n return Point( self._x + other._x, self._y + other._y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abfc4447e5e1e67def94427c995a1997\",\n \"score\": \"0.5770588\",\n \"text\": \"def __abs__(self):\\n return (self.x ** 2 + self.y ** 2) ** (1 / 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41e1d0af0d3dfb80e89a27129e1f558\",\n \"score\": \"0.5759189\",\n \"text\": \"def __add__(self,\\n point: IPoint) -> IPoint:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31100fa7ca9e9d5fa23e345fdb9fe407\",\n \"score\": \"0.57477564\",\n \"text\": \"def suma(x, y):\\n return x + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d4c44f230f4ba2af87c06fdaf32b293\",\n \"score\": \"0.5747229\",\n \"text\": \"def slope(p):\\n try:\\n return float(p.y - p0.y)/(p.x - p0.x)\\n except ZeroDivisionError:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81b52e91f520215135082cfa0b54ddd2\",\n \"score\": \"0.57468235\",\n \"text\": \"def f(x, y):\\n return x/y + y/x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9dcb30df328a9de8dbdb2942dc39ead\",\n \"score\": \"0.573359\",\n \"text\": \"def neg_line(p):\\n # y = -x + c\\n # c = (p.x+p.y)\\n neg_y_intersects.add(p.x + p.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2de4f62d2409ace1fde62ed0ea11c6\",\n \"score\": \"0.5733273\",\n \"text\": \"def test_inplace_plus(self):\\n result = coords.Cartesian(self.p1.x + self.p2.x,\\n self.p1.y + self.p2.y,\\n self.p1.z + self.p2.z)\\n a = self.p1\\n a += self.p2\\n self.assertTrue(result == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a0bf35bbb3abe08bdddbc99c6100d94\",\n \"score\": \"0.57209086\",\n \"text\": \"def itp(x1,x2,y):\\n x=float(y-x1)/float(x2-x1)\\n assert x>=0\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d504193153683f8dc9ea2c9aa365b561\",\n \"score\": \"0.571949\",\n \"text\": \"def projectOnLine(v, p):\\n l = 1./linalg.norm(v)\\n vn = v*l\\n a = float(vn.T*p)\\n u = a*vn\\n return u,a*l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ab41675635ea696132d70722e87a1b\",\n \"score\": \"0.57041645\",\n \"text\": \"def gradient(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n pi = np.pi\\n uprime = np.zeros(p.shape, dtype=np.float64)\\n uprime[..., 0] = 2 * x * (np.exp(x ** 2 + y ** 2))\\n uprime[..., 1] = 2 * y * (np.exp(x ** 2 + y ** 2))\\n return uprime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cdf30cfc07b6c6c2b2e170cac563f7e\",\n \"score\": \"0.5699533\",\n \"text\": \"def x_value(a: Point, b: Point, y: Union[int, float]) -> float:\\n assert isinstance(y, (int, float))\\n\\n if a.y == b.y:\\n raise ValueError('Cannot return x-value for y=const.')\\n\\n return (a.x - b.x) * (y - b.y) / (a.y - b.y) + b.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6d2b2beb7d175c43d471a2e083279c\",\n \"score\": \"0.56977385\",\n \"text\": \"def __add__(self, other):\\n return Coordinates(self.x + other.x, self.y + other.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84159ce0e9e85121f092428bb9969467\",\n \"score\": \"0.5693522\",\n \"text\": \"def project_on_line(l, p):\\n\\ta, b = l\\n\\tseg_length = distance(a, b)\\n\\tap = (p[0] - a[0], p[1] - a[1])\\n\\tab = (b[0] - a[0], b[1] - a[1])\\n\\tab = (ab[0] / seg_length, ab[1] / seg_length)\\n\\tdot = ab[0]*ap[0] + ab[1]*ap[1]\\n\\treturn (a[0] + dot*ab[0], a[1] + dot*ab[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eece127ad362069f9610f9f6bd8c195\",\n \"score\": \"0.56822526\",\n \"text\": \"def f(x, y):\\n return -2 * y + 4 * x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0abf750bd7774049b28a1567d9aa54df\",\n \"score\": \"0.5680937\",\n \"text\": \"def _lhs(self, P):\\n P = self._process_regularizer(P)\\n return P, self._AtA + (P.T @ P)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c583b19369311175bcdab375b20e65\",\n \"score\": \"0.5678734\",\n \"text\": \"def rel(self, x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cdacb439206cb7aeef31813e1a021e5\",\n \"score\": \"0.56757486\",\n \"text\": \"def test_add_pipoints_current_value(self, pi_point):\\n total = pi_point.point + pi_point.point\\n assert (\\n round(\\n total.current_value - (pi_point.values[-1] + pi_point.values[-1]),\\n ndigits=7,\\n )\\n == 0\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739d0e774817b1585634aa5713bafcfb\",\n \"score\": \"0.5671398\",\n \"text\": \"def adding_tuple(self, other):\\n sum = Point()\\n sum.x = self.x + other[0]\\n sum.y = self.y + other[1]\\n return sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf5108e14381d3dc672daf7cda0d107\",\n \"score\": \"0.5663466\",\n \"text\": \"def ta_ori(self, r, p, y) -> Tuple[float, float, float]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793be6fb4b77d466b8687c5a1bd9f193\",\n \"score\": \"0.56602883\",\n \"text\": \"def __iadd__(self,other):\\n # print('in-place addition')\\n if not isinstance(other,Point):\\n return NotImplemented\\n self._x += other._x\\n self._y += other._y\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2deafbff70ff0a3140ecbe39d216bb0\",\n \"score\": \"0.565977\",\n \"text\": \"def cross_point(self,aXY1,aXY2,bXY1,bXY2):\\n\\t\\tA1 = self.find_A(aXY1,aXY2)\\n\\t\\tA2 = self.find_A(bXY1,bXY2)\\n\\t\\tB1 = self.find_B(aXY1,aXY2)\\n\\t\\tB2 = self.find_B(bXY1,bXY2)\\n\\t\\tC1 = self.find_C(aXY1,aXY2)\\n\\t\\tC2 = self.find_C(bXY1,bXY2)\\n\\t\\tif (A1==A2==0) or (B1==B2==0):\\n\\t\\t\\treturn None\\n\\t\\treturn [(B1*C2-B2*C1)/float(A1*B2-A2*B1),(C1*A2-C2*A1)/float(A1*B2-A2*B1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2becc084ada73abf8e41ebd454ffeb8\",\n \"score\": \"0.56586313\",\n \"text\": \"def Perp2(a, b):\\n\\n return a[0] * b[1] - a[1] * b[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eda7ec654e9b5b746946bd5d80367bf\",\n \"score\": \"0.5655645\",\n \"text\": \"def compute(x, y):\\n return x + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28440b6046e11cec8b4b8308b92c5953\",\n \"score\": \"0.5649141\",\n \"text\": \"def source(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n val = 1.0\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8c876dc790ecbe6e0652b16faf2239\",\n \"score\": \"0.56401366\",\n \"text\": \"def transform_point(p,R,t):\\r\\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\\r\\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\\r\\n return [x,y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cec334aee987d84fee3c64141f12e07\",\n \"score\": \"0.56382406\",\n \"text\": \"def point(self,p):\\n return self.token(p[0]).token(p[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39973c7a7dd4c33658de4272361217e8\",\n \"score\": \"0.56363815\",\n \"text\": \"def heuristic(a, b):\\n (x1, y1) = a\\n (x2, y2) = b\\n return abs(x1 - x2) + abs(y1 - y2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45cd9b7fc4df5f88aa0af094bafa0e8\",\n \"score\": \"0.5632378\",\n \"text\": \"def recta(x,a,b):\\r\\n fa=log(f(a))\\r\\n return ((fa-log(f(b)))/(a-b))*(x-a) + fa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e4134e46a1d496bd5d6af534314661\",\n \"score\": \"0.56241816\",\n \"text\": \"def gradient(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n\\n val = np.zeros(p.shape, dtype=p.dtype)\\n pi = np.pi\\n cos = np.cos\\n sin = np.sin\\n gamma = 0.1\\n sigma = -14.9225565104455152\\n rho = pi / 4\\n theta = np.arctan2(y, x)\\n theta = (theta >= 0) * theta + (theta < 0) * (theta + 2 * pi)\\n t = 1 + (y / x) ** 2\\n r = np.sqrt(x ** 2 + y ** 2)\\n rg = r ** gamma\\n\\n ux1 = ((x >= 0.0) & (y >= 0.0)) * (\\n gamma * rg * cos((pi / 2 - sigma) * gamma) * (x * cos((theta - pi / 2 + rho) * gamma) / (r * r)\\n + y * sin((theta - pi / 2 + rho) * gamma) / (x * x * t))\\n )\\n\\n uy1 = ((x >= 0.0) & (y >= 0.0)) * (gamma * rg * cos((pi / 2 - sigma) * gamma) * (\\n y * cos((theta - pi / 2 + rho) * gamma) / (r * r) - sin((theta - pi / 2 + rho) * gamma) / (x * t)))\\n\\n ux2 = ((x <= 0.0) & (y >= 0.0)) * (gamma * rg * cos(rho * gamma) * (\\n x * cos((theta - pi + sigma) * gamma) / (r * r) + y * sin((theta - pi + sigma) * gamma) / (x * x * t)))\\n\\n uy2 = ((x <= 0.0) & (y >= 0.0)) * (gamma * rg * cos(rho * gamma) * (\\n y * cos((theta - pi + sigma) * gamma) / (r * r) - sin((theta - pi + sigma) * gamma) / (x * t)))\\n\\n ux3 = ((x <= 0.0) & (y <= 0.0)) * (gamma * rg * cos(sigma * gamma) * (\\n x * cos((theta - pi - rho) * gamma) / (r * r) + y * sin((theta - pi - rho) * gamma) / (x * x * t)))\\n\\n uy3 = ((x <= 0.0) & (y <= 0.0)) * (gamma * rg * cos(sigma * gamma) * (\\n y * cos((theta - pi - rho) * gamma) / (r * r) - sin((theta - pi - rho) * gamma) / (x * t)))\\n\\n ux4 = ((x >= 0.0) & (y <= 0.0)) * (gamma * rg * cos((pi / 2 - rho) * gamma) * (\\n x * cos((theta - 3 * pi / 2 - sigma) * gamma) / (r * r) + y * sin((theta - 3 * pi / 2 - sigma) * gamma) / (\\n x * x * t)))\\n\\n uy4 = ((x >= 0.0) & (y <= 0.0)) * (gamma * rg * cos((pi / 2 - rho) * gamma) * (\\n y * cos((theta - 3 * pi / 2 - sigma) * gamma) / (r * r) - sin((theta - 3 * pi / 2 - sigma) * gamma) / (\\n x * t)))\\n\\n val[..., 0] = ux1 + ux2 + ux3 + ux4\\n val[..., 1] = uy1 + uy2 + uy3 + uy4\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01bc1b94f1dfa71666c80e1d1066811\",\n \"score\": \"0.5613631\",\n \"text\": \"def __call__(self, x: float) -> float:\\n return (self.slope * (x - self.point.x)) + self.point.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878235a2110faaeec17b93b20c5819c9\",\n \"score\": \"0.5610729\",\n \"text\": \"def gradient(self, p):\\n x = p[..., 0]\\n y = p[..., 1]\\n a = 10000 * (x ** 2 + y ** 2) ** 2\\n uprime = np.zeros(p.shape, dtype=np.float64)\\n uprime[..., 0] = 200 * x / (1 + a)\\n uprime[..., 1] = 200 * y / (1 + a)\\n return uprime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0abd15fb337df1adb4ef9c662a72f4c8\",\n \"score\": \"0.5609698\",\n \"text\": \"def __radd__(self,\\n point: IPoint) -> IPoint:\\n return self + point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1fe4c9d0b2bec548eb95d9dee153c3\",\n \"score\": \"0.5596991\",\n \"text\": \"def vecteur_points (p1,p2):\\n return ( p2 [0] - p1 [0], p2 [1] - p1 [1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd017d95c56b36d5e5e07f9882046ecf\",\n \"score\": \"0.5595749\",\n \"text\": \"def _parabola_3points(x1,y1,x2,y2,x3,y3):\\n delta = (x1 - x2)*(x1 - x3)*(x2 - x3)\\n a = (x3 * (y2 - y1) + x2 * (y1 - y3) + x1 * (y3 - y2)) / delta\\n b = (x3**2 * (y1 - y2) + x2**2 * (y3 - y1) + x1**2 * (y2 - y3)) / delta\\n c = (x2 * x3 * (x2 - x3) * y1 + x3 * x1 * (x3 - x1) * y2 + x1 * x2 * (x1 - x2) * y3) / delta\\n return a, b, c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ea1535c6544471b0494e1e00930daa\",\n \"score\": \"0.55935794\",\n \"text\": \"def find_slope2(m, x, b):\\n m = float(m)\\n x = float(x)\\n b = float(b)\\n y = m * x + b\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6419ca50ad4e8a0fdefaeb36cb9aa990\",\n \"score\": \"0.5592216\",\n \"text\": \"def _line(x, a, b):\\n return a*x + b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329d5e28df9331ef10840b50765217d7\",\n \"score\": \"0.55885667\",\n \"text\": \"def zp(self,a,b):\\n\\n z_prime = np.array([-np.cos(a)*np.sin(b),-np.sin(a)*np.sin(b),np.cos(b)])\\n return z_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff1bbeb9862dc36923f7978e7c18951\",\n \"score\": \"0.5587336\",\n \"text\": \"def _point_interpolation(self, p1, p2):\\n\\n # Find the middle point of p1-p2\\n p = p1 + (p2 - p1)/2\\n # Project the point onto the ellipsoid\\n p = self._point_ellipsoid_projection(p)\\n # Return\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979d7a1b9ee5f0974836f14e49f5e10a\",\n \"score\": \"0.5586449\",\n \"text\": \"def polyval(p,x):\\n y = 0\\n for i in range(len(p)):\\n y = x*y + p[i]\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a898a753e61dedf1e5757551a7ce9b3\",\n \"score\": \"0.55851406\",\n \"text\": \"def cost(a, b, all_points):\\n\\n total_cost = 0\\n\\n pointA = all_points[a]\\n pointB = all_points[b]\\n\\n x1 = pointA['coord'][0]\\n x2 = pointB['coord'][0]\\n y1 = pointA['coord'][1]\\n y2 = pointB['coord'][1]\\n\\n # Travel time\\n total_cost += math.hypot(x2 - x1, y2 - y1) / ROBOT_SPEED\\n\\n # Penalties incurred for skipping\\n for i in range(a+1, b):\\n total_cost += all_points[i]['penalty']\\n\\n # Add waiting time\\n total_cost += ROBOT_WAIT_TIME\\n\\n return total_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5300dd422945dec99c5799f6813c6b47\",\n \"score\": \"0.5582936\",\n \"text\": \"def is_point_on_curve(a, b, p, x, y):\\n assert isinstance(a, Bn)\\n assert isinstance(b, Bn)\\n assert isinstance(p, Bn) and p > 0\\n assert (isinstance(x, Bn) and\\n isinstance(y, Bn)) or (x is None and y is None)\\n\\n if x is None and y is None:\\n return True\\n\\n lhs = (y * y) % p\\n rhs = ((x * x * x) + (a * x) + b) % p\\n on_curve = (lhs == rhs)\\n\\n return on_curve\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a2bd6dd953a6c201c289f25dde65b6\",\n \"score\": \"0.5582299\",\n \"text\": \"def point_return(point):\\n return Point(0, point.y + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea582fa8a8a20153aed6796289a4d02b\",\n \"score\": \"0.5582241\",\n \"text\": \"def _interpolate_p(p, r, v):\\n\\n # interpolate p (v should be in table)\\n # if .5 < p < .75 use linear interpolation in q\\n # if p > .75 use quadratic interpolation in log(y + r/v)\\n # by -1. / (1. + 1.5 * _phi((1. + p)/2.))\\n\\n # find the 3 closest v values\\n p0, p1, p2 = _select_ps(p)\\n try:\\n y0 = _func(A[(p0, v)], p0, r, v) + 1.\\n except:\\n print(p,r,v)\\n raise\\n y1 = _func(A[(p1, v)], p1, r, v) + 1.\\n y2 = _func(A[(p2, v)], p2, r, v) + 1.\\n\\n y_log0 = math.log(y0 + float(r)/float(v))\\n y_log1 = math.log(y1 + float(r)/float(v))\\n y_log2 = math.log(y2 + float(r)/float(v))\\n\\n # If p < .85 apply only the ordinate transformation\\n # if p > .85 apply the ordinate and the abcissa transformation\\n # In both cases apply quadratic interpolation\\n if p > .85:\\n p_t = _ptransform(p)\\n p0_t = _ptransform(p0)\\n p1_t = _ptransform(p1)\\n p2_t = _ptransform(p2)\\n\\n # calculate derivatives for quadratic interpolation\\n d2 = 2*((y_log2-y_log1)/(p2_t-p1_t) - \\\\\\n (y_log1-y_log0)/(p1_t-p0_t))/(p2_t-p0_t)\\n if (p2+p0)>=(p1+p1):\\n d1 = (y_log2-y_log1)/(p2_t-p1_t) - 0.5*d2*(p2_t-p1_t)\\n else:\\n d1 = (y_log1-y_log0)/(p1_t-p0_t) + 0.5*d2*(p1_t-p0_t)\\n d0 = y_log1\\n\\n # interpolate value\\n y_log = (d2/2.) * (p_t-p1_t)**2. + d1 * (p_t-p1_t) + d0\\n\\n # transform back to y\\n y = math.exp(y_log) - float(r)/float(v)\\n\\n elif p > .5:\\n # calculate derivatives for quadratic interpolation\\n d2 = 2*((y_log2-y_log1)/(p2-p1) - \\\\\\n (y_log1-y_log0)/(p1-p0))/(p2-p0)\\n if (p2+p0)>=(p1+p1):\\n d1 = (y_log2-y_log1)/(p2-p1) - 0.5*d2*(p2-p1)\\n else:\\n d1 = (y_log1-y_log0)/(p1-p0) + 0.5*d2*(p1-p0)\\n d0 = y_log1\\n\\n # interpolate values\\n y_log = (d2/2.) * (p-p1)**2. + d1 * (p-p1) + d0\\n\\n # transform back to y\\n y = math.exp(y_log) - float(r)/float(v)\\n\\n else:\\n # linear interpolation in q and p\\n v = min(v, 1e38)\\n q0 = math.sqrt(2) * -y0 * \\\\\\n scipy.stats.t.isf((1.+p0)/2., v)\\n q1 = math.sqrt(2) * -y1 * \\\\\\n scipy.stats.t.isf((1.+p1)/2., v)\\n\\n d1 = (q1-q0)/(p1-p0)\\n d0 = q0\\n\\n # interpolate values\\n q = d1 * (p-p0) + d0\\n\\n # transform back to y\\n y = -q / (math.sqrt(2) * scipy.stats.t.isf((1.+p)/2., v))\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853ea1b03c03dae2f30016a13b2e2f3c\",\n \"score\": \"0.5577537\",\n \"text\": \"def is_point_on_curve(a, b, p, x, y):\\n assert isinstance(a, Bn)\\n assert isinstance(b, Bn)\\n assert isinstance(p, Bn) and p > 0\\n assert (isinstance(x, Bn) and isinstance(y, Bn)) or (x == None and y == None)\\n\\n if (x is not None) and (y is not None):\\n lhs = (y * y) % p\\n rhs = (x*x*x + a*x + b) % p\\n on_curve = (lhs == rhs)\\n return on_curve\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b04d1630e595eeca93b4421531a9318\",\n \"score\": \"0.55759376\",\n \"text\": \"def __add__(self, p):\\n\\n\\tnew_poly = list\\n\\tif len(self.coeff) <= len(p.coeff):\\n\\t\\tnew_poly = p.coeff[:]\\n\\t\\tfor i in range(len(self.coeff)):\\n\\t\\t\\tnew_poly[i] = p.coeff[i] + self.coeff[i]\\n\\telse:\\n\\t\\tnew_poly = self.coeff[:]\\n\\t\\tfor i in range(len(p.coeff)):\\n\\t\\t\\tnew_poly[i] = self.coeff[i] + p.coeff[i]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994475,"cells":{"query_id":{"kind":"string","value":"5c23c20f83b7e5f6c4fde5503aa792ee"},"query":{"kind":"string","value":"Run aflshowmap on the target and get the coverage bitmap for a particular testcase."},"positive_passages":{"kind":"list like","value":[{"docid":"7976f8ba9da94bc5b4cef400affa0d06","score":"0.7753865","text":"def run_afl_showmap(target: str, afl_stats: FuzzerStats, testcase: str) -> bytes:\n # Base afl-showmap options and arguements on afl-fuzz options and arguments\n afl_showmap_opts = ['-b', '-q']\n for opt, arg in afl_stats.afl_cmdline:\n if opt in ('-m', '-Q', '-t'):\n afl_showmap_opts.extend([opt, arg])\n\n # Default timeout of 1 sec\n if '-t' not in afl_showmap_opts:\n afl_showmap_opts.extend(['-t', '1000'])\n\n # Generate the target command-line for a testcase popped off the task\n # queue. Replace the original (uninstrumented) target binary with the\n # instrumented version\n target_cmdline, target_input = afl_stats.gen_command_line(testcase)\n target_cmdline[0] = target\n\n with NamedTemporaryFile() as temp:\n # Run afl-showmap\n args = ['afl-showmap', *afl_showmap_opts, '-o', temp.name, '--',\n *target_cmdline]\n subprocess.run(args, input=target_input, check=False,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n # Read the coverage bitmap\n with open(temp.name, 'rb') as showmap_out:\n return showmap_out.read()","title":""}],"string":"[\n {\n \"docid\": \"7976f8ba9da94bc5b4cef400affa0d06\",\n \"score\": \"0.7753865\",\n \"text\": \"def run_afl_showmap(target: str, afl_stats: FuzzerStats, testcase: str) -> bytes:\\n # Base afl-showmap options and arguements on afl-fuzz options and arguments\\n afl_showmap_opts = ['-b', '-q']\\n for opt, arg in afl_stats.afl_cmdline:\\n if opt in ('-m', '-Q', '-t'):\\n afl_showmap_opts.extend([opt, arg])\\n\\n # Default timeout of 1 sec\\n if '-t' not in afl_showmap_opts:\\n afl_showmap_opts.extend(['-t', '1000'])\\n\\n # Generate the target command-line for a testcase popped off the task\\n # queue. Replace the original (uninstrumented) target binary with the\\n # instrumented version\\n target_cmdline, target_input = afl_stats.gen_command_line(testcase)\\n target_cmdline[0] = target\\n\\n with NamedTemporaryFile() as temp:\\n # Run afl-showmap\\n args = ['afl-showmap', *afl_showmap_opts, '-o', temp.name, '--',\\n *target_cmdline]\\n subprocess.run(args, input=target_input, check=False,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\n # Read the coverage bitmap\\n with open(temp.name, 'rb') as showmap_out:\\n return showmap_out.read()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"40e66fffa9607e4942f5d9355e8da9d7","score":"0.56286484","text":"def show_coverage(self, viewer_id=None, table_group='main'):\n\n view_type = 'coverImage'\n cid = viewer_id if viewer_id else (\"%s-%s\" % (LO_VIEW_DICT[view_type], table_group))\n payload = {'viewerType': LO_VIEW_DICT[view_type], 'cellId': cid}\n return self.dispatch(ACTION_DICT['ShowCoverage'], payload)","title":""},{"docid":"8c6f366d7fc37e29f39aaeb437017c28","score":"0.55119926","text":"def __main__():\n\n args = setup_parser().parse_args()\n\n args = check_args(args)\n\n if args.verbose:\n print('{a} image(s) at {pos}'.format(\n a='Writing' if not args.coadd else 'Coadding', pos=args.skypos))\n print('\t\tof dimensions {w}x{h} degrees'.format(\n w=args.skyrange[0], h=args.skyrange[1]))\n print('\t\twith a virtual detector {d} degrees across'.format(\n d=args.detsize))\n print('\t\tin time range(s): {t}'.format(t=repr(args.trange)))\n\n gmap(band=args.band, cntfile=args.cntfile,\n coadd=args.coadd, detsize=args.detsize, intfile=args.intfile,\n skypos=args.skypos, maxgap=args.maxgap,\n memlight=args.memlight, minexp=args.minexp, overwrite=args.overwrite,\n retries=args.retries, skyrange=args.skyrange, stepsz=args.stepsz,\n trange=args.trange, verbose=args.verbose,\n cntcoaddfile=args.cntcoaddfile, intcoaddfile=args.intcoaddfile)","title":""},{"docid":"73c3aa142f471d5478c926535b65627c","score":"0.5394963","text":"def test_print_all_details_runs():\n image_info.print_all_details()","title":""},{"docid":"67748086c5c9b818e2f154b2850b68d3","score":"0.5338853","text":"def capture_unittest_coverage(self):\n assert self.m.chromium.c.BUILD_CONFIG == 'Coverage'\n generate_coverage_py = self.public_scripts_dir.join(\n 'generate_coverage.py')\n args = ['--verbose',\n '--syzygy',\n '--build-dir', self.output_dir]\n return self.m.python(\n 'capture_unittest_coverage', generate_coverage_py, args)","title":""},{"docid":"84ef8c1ac0d4292307c52db81ba0b272","score":"0.5305564","text":"def coverage():\n run('coverage run --source chanjo-report setup.py test')\n run('coverage report -m')\n run('coverage html')\n run('open htmlcov/index.html')\n log.info('collected test coverage stats')","title":""},{"docid":"aa7afde0e7f48c97e6b975eca418320d","score":"0.5225435","text":"def main(args, utils_impl=Utils):\n\n if args.date:\n resolved_date = args.date\n else:\n resolved_date = MapPreview.get_most_recent_date(\n args.source, args.signal, args.geo_type, args.geo_value)\n print(f'using latest data reported for date {resolved_date}')\n\n screen_size = (\n args.width + MapPreview.SCREEN_PADDING[0],\n args.height + MapPreview.SCREEN_PADDING[1],\n )\n with utils_impl.run_screenshot_stack(screen_size) as driver:\n MapPreview.take_screenshot(driver, args, resolved_date)","title":""},{"docid":"e2267171cef3143f3cb1a1ac8898f3ff","score":"0.52149725","text":"def test_simple_all_run(msa_fasta_file: Path, tmp_path: Path):\n mv = MsaViz(msa_fasta_file)\n\n fig_outfile = tmp_path / \"test.png\"\n mv.savefig(fig_outfile)\n\n assert fig_outfile.exists()","title":""},{"docid":"abe8241e4df36202ceae5c089531dd30","score":"0.52022386","text":"def coverage(self):\n return self._hitmap.viewkeys()","title":""},{"docid":"7c2eb324267b8a33077e93aa723e6888","score":"0.51690245","text":"def main():\n\n with its.device.ItsSession() as cam:\n # Arbitrary capture request exposure values; image content is not\n # important for this test, only the metadata.\n props = cam.get_camera_properties()\n if not its.caps.manual_sensor(props):\n print \"Test skipped\"\n return\n\n req,fmt = its.objects.get_fastest_manual_capture_settings(props)\n cap = cam.do_capture(req, fmt)\n pprint.pprint(cap[\"metadata\"])\n\n # No pass/fail check; test passes if it completes.","title":""},{"docid":"3323706ea25cc11b6931c6845bbde71c","score":"0.5165554","text":"def test_all_run_with_options(msa_fasta_file: Path, tmp_path: Path):\n mv = MsaViz(\n msa_fasta_file,\n color_scheme=\"Identity\",\n wrap_length=50,\n show_label=False,\n show_seq_char=False,\n sort=True,\n )\n mv.set_highlight_pos([1, 5, (10, 13), 18])\n mv.set_highlight_pos_by_ident_thr(min_thr=80, max_thr=100)\n mv.add_markers([50, 51, 52, (60, 70), 80], marker=\"x\", color=\"blue\", size=6)\n mv.add_text_annotation(\n (100, 120), text=\"test\", text_color=\"blue\", text_size=10, range_color=\"blue\"\n )\n\n fig_outfile = tmp_path / \"test.png\"\n mv.savefig(fig_outfile)\n\n assert fig_outfile.exists()","title":""},{"docid":"fb3b6f858afa060ed7599f2f18be574f","score":"0.51270723","text":"def main():\n NAME = os.path.basename(__file__).split(\".\")[0]\n\n with its.device.ItsSession() as cam:\n props = cam.get_camera_properties()\n if not its.caps.full(props):\n print \"Test skipped\"\n return\n\n _,fmt = its.objects.get_fastest_manual_capture_settings(props)\n e, s = its.target.get_target_exposure_combos(cam)[\"midExposureTime\"]\n e /= 2.0\n\n r_means = []\n g_means = []\n b_means = []\n\n reqs = [\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s*2,e, True),\n its.objects.manual_capture_request(s*2,e, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s, e*2, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s*2,e, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s, e*2, True),\n its.objects.manual_capture_request(s, e, True),\n its.objects.manual_capture_request(s, e*2, True),\n its.objects.manual_capture_request(s, e*2, True),\n ]\n\n caps = cam.do_capture(reqs, fmt)\n for i,cap in enumerate(caps):\n img = its.image.convert_capture_to_rgb_image(cap)\n its.image.write_image(img, \"%s_i=%02d.jpg\" % (NAME, i))\n tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)\n rgb_means = its.image.compute_image_means(tile)\n r_means.append(rgb_means[0])\n g_means.append(rgb_means[1])\n b_means.append(rgb_means[2])\n\n # Draw a plot.\n idxs = range(len(r_means))\n pylab.plot(idxs, r_means, 'r')\n pylab.plot(idxs, g_means, 'g')\n pylab.plot(idxs, b_means, 'b')\n pylab.ylim([0,1])\n matplotlib.pyplot.savefig(\"%s_plot_means.png\" % (NAME))\n\n g_avg = sum(g_means) / len(g_means)\n g_ratios = [g / g_avg for g in g_means]\n g_hilo = [g>1.0 for g in g_ratios]\n assert(g_hilo == [False, False, True, True, False, False, True,\n False, True, False, True, False, True, True])","title":""},{"docid":"164cc4024a9866da376a18a660601ea3","score":"0.5114869","text":"def view_coverage():\n coverage_file = os.path.join(SCRIPTDIR, '.coverage')\n if not os.path.exists(coverage_file):\n raise InvalidArg('No coverage file found, run coverage: {}'.format(\n coverage_file,\n ))\n # Show console report.\n print(C('').join(C('\\nCoverage Report', 'cyan'), ':'))\n\n covreportcmd = [\n 'coverage',\n 'report',\n ]\n try:\n output = subprocess.check_output(\n covreportcmd,\n stderr=subprocess.STDOUT,\n ).decode()\n except subprocess.CalledProcessError:\n return 1\n if not output.startswith('Name'):\n print_err(output)\n return 1\n\n divline = C('-' * 45, 'dimgrey')\n for line in output.splitlines():\n if line.startswith('--'):\n # Divider line.\n print(divline)\n continue\n name, statements, miss, cover = line.split()\n namefmt = C(name).ljust(25)\n statementsfmt = C(statements, (46, 137, 255)).rjust(5)\n try:\n miss = int(miss)\n if miss == 0:\n missfmt = C(miss, 'lightgreen', style='bright').rjust(6)\n else:\n missfmt = C(miss, 'red').rjust(6)\n except ValueError:\n # Actual 'Miss' header.\n missfmt = C(miss, 'red').rjust(6)\n try:\n cover = int(cover.rstrip('%'))\n if cover == 100:\n covercolr = {'fore': 'lightgreen', 'style': 'bright'}\n elif cover > 49:\n covercolr = {'fore': 'green'}\n else:\n covercolr = {'fore': 'red'}\n coverfmt = C('{}%'.format(cover), **covercolr).rjust(6)\n except ValueError:\n # Actual 'Cover' header.\n coverfmt = C(cover, 'green').rjust(6)\n print(C(' ').join(namefmt, statementsfmt, missfmt, coverfmt))\n return 0","title":""},{"docid":"87f81b4d465d34c90833cc53640041db","score":"0.5109988","text":"def _run_test(self, test_info):\n self._ensure_dump_render_tree_is_running()\n # The pixel_hash is used to avoid doing an image dump if the\n # checksums match, so it should be set to a blank value if we\n # are generating a new baseline. (Otherwise, an image from a\n # previous run will be copied into the baseline.)\n image_hash = test_info.image_hash()\n if image_hash and self._test_args.new_baseline:\n image_hash = \"\"\n start = time.time()\n crash, timeout, actual_checksum, output, error = \\\n self._driver.run_test(test_info.uri, test_info.timeout, image_hash)\n end = time.time()\n\n result = process_output(self._port, test_info, self._test_types,\n self._test_args, self._options.configuration,\n self._options.results_directory, crash,\n timeout, end - start, actual_checksum,\n output, error)\n self._test_results.append(result)\n return result","title":""},{"docid":"72db4b1979c7a2c3f6eca99720f4c95b","score":"0.5108259","text":"def run(self):\n \n run_afl(\n indir = self.srcdir, \n outdir = self.afloutdir, \n tgtcmd = self.cfg.tgtcmd, \n cfg = self.cfg, \n cores = self.cores, \n persist_tgt = False,\n verbose = self.verbose,\n dry_run = self.dry_run,\n )\n\n self.test_img_creation()","title":""},{"docid":"d50a0d5c8bd81d1faa8c1ea7e4df00ab","score":"0.5093121","text":"def runAnalysis(aif):\n\n aif.runDefaultAnalysis()\n\n for ttest in aif.getTTestsForTargetAndExperiment():\n aif.runHammingDistanceAnalysis(ttest.randomTraceSet, \"di1\",\"di2\")","title":""},{"docid":"91ab0be9bde1a12283152c4e2cf6ddd7","score":"0.5092178","text":"def cov():\n local(\"coverage report -m\")\n local(\"coverage html\")\n local(\"coverage xml\")","title":""},{"docid":"8be185c0911acbba7dc88c66a9ba23cd","score":"0.50723636","text":"def main():\r\n calibrate_camera_to_DMD_mapping()\r\n goto_flagged_images()","title":""},{"docid":"67e98532384640f184daed30d2585fb3","score":"0.50558","text":"def focalCover(targetfile = pc4layer, cbscode='40', outputfile='coverageparks.shp'):\n\n return focalsum","title":""},{"docid":"3a2c433efab6746b8930a29837880397","score":"0.5052849","text":"async def showMap(self,ctx,*argv):\n if(len(argv)==3):\n zoom = int(argv[0])\n lat_deg = float(argv[1])\n lon_deg = float(argv[2])\n elif(len(argv)==1):\n zll = argv[0][argv[0].find(\"map=\")+4:]\n zoom=int(zll[:zll.find('/')])\n ll = zll[zll.find('/')+1:]\n lat_deg = float(ll[:ll.find('/')])\n lon_deg = float(ll[ll.find('/')+1:])\n #print(f'{zoom},{lat_deg},{lon_deg}')\n else:\n await ctx.send(f\"Either enter the zoom, latitude and longditude, or url, like this: `€showMap 15 60.3912 5.3242`\")\n return\n if(zoom>19):\n zoom=19\n await ImageCluster(ctx, lat_deg, lon_deg, zoom)","title":""},{"docid":"8fb328b24c61edcedb51025df4e23fd7","score":"0.50147796","text":"def print_target_maps(target_maps):\n for index, target_map in enumerate(target_maps):\n print('Target Map {}/{}'.format(index + 1, len(target_maps)))\n print_target_map(target_map)","title":""},{"docid":"3e92840211c6f9d99624e7daa1699b6f","score":"0.5008252","text":"def test_mosaic_to_workspace_FGDB(self):\n raster1 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic1')\n raster2 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic2')\n raster3 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic3')\n self.request['params'][0]['response']['docs'][0]['path'] = raster1\n self.request['params'][0]['response']['docs'][1]['path'] = raster2\n self.request['params'][0]['response']['docs'][2]['path'] = raster3\n self.request['params'][3]['value'] = 'Mosaic'\n self.request['params'][5]['value'] = 'FileGDB'\n self.request['params'][2]['value'] = self.target_ws\n getattr(sys.modules[self.request['task']], \"execute\")(self.request)\n dsc = arcpy.Describe(os.path.join(self.target_ws, 'Mosaic'))\n self.assertEquals([dsc.bandcount, dsc.pixeltype], [1, 'U8'])","title":""},{"docid":"2ee7635916f0ae14e5a441824aa522fa","score":"0.49968728","text":"def test_get_status_maps1(self):\n pass","title":""},{"docid":"5c3c2a04b06933684a37f94dfdd9aa9e","score":"0.4987271","text":"def display(self):\n if not self.initialized:\n return\n\n cv2.imshow(\"explored map\", self._map_exploration)\n cv2.imshow(\"exploration_zone\", self._exploration_zone)\n cv2.waitKey(1)","title":""},{"docid":"bee6748ea45ad623eccec5c4deb37f6f","score":"0.4959971","text":"def do_test(self):\n exe_name = \"a.out\"\n exe = self.getBuildArtifact(exe_name)\n\n # Create the target\n target = self.dbg.CreateTarget(exe)\n self.assertTrue(target, VALID_TARGET)\n\n self.expect(\"target modules dump symtab -m a.out\",\n patterns=['Metadata.*_TMC1a3Foo'])\n\n self.expect(\"target modules dump symtab a.out\",\n patterns=['Metadata.*type metadata for'])","title":""},{"docid":"9c5291000db13eea6a693d77a4f905e0","score":"0.4957987","text":"def coverage():\n tests = unittest.TestLoader().discover('live/tests')\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n COV.html_report()\n COV.erase()\n return 0\n return 1","title":""},{"docid":"be8dd53a8b29eeb641e9681835a9d094","score":"0.4954103","text":"def testGetOneMAPWithMatchingGroundtruthAndDetections(self):\n coco_evaluator = coco_evaluation.CocoDetectionEvaluator(\n _get_categories_list())\n coco_evaluator.add_single_ground_truth_image_info(\n image_id='image1',\n groundtruth_dict={\n standard_fields.InputDataFields.groundtruth_boxes:\n np.array([[100., 100., 200., 200.]]),\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\n })\n coco_evaluator.add_single_detected_image_info(\n image_id='image1',\n detections_dict={\n standard_fields.DetectionResultFields.detection_boxes:\n np.array([[100., 100., 200., 200.]]),\n standard_fields.DetectionResultFields.detection_scores:\n np.array([.8]),\n standard_fields.DetectionResultFields.detection_classes:\n np.array([1])\n })\n coco_evaluator.add_single_ground_truth_image_info(\n image_id='image2',\n groundtruth_dict={\n standard_fields.InputDataFields.groundtruth_boxes:\n np.array([[50., 50., 100., 100.]]),\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\n })\n coco_evaluator.add_single_detected_image_info(\n image_id='image2',\n detections_dict={\n standard_fields.DetectionResultFields.detection_boxes:\n np.array([[50., 50., 100., 100.]]),\n standard_fields.DetectionResultFields.detection_scores:\n np.array([.8]),\n standard_fields.DetectionResultFields.detection_classes:\n np.array([1])\n })\n coco_evaluator.add_single_ground_truth_image_info(\n image_id='image3',\n groundtruth_dict={\n standard_fields.InputDataFields.groundtruth_boxes:\n np.array([[25., 25., 50., 50.]]),\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\n })\n coco_evaluator.add_single_detected_image_info(\n image_id='image3',\n detections_dict={\n standard_fields.DetectionResultFields.detection_boxes:\n np.array([[25., 25., 50., 50.]]),\n standard_fields.DetectionResultFields.detection_scores:\n np.array([.8]),\n standard_fields.DetectionResultFields.detection_classes:\n np.array([1])\n })\n metrics = coco_evaluator.evaluate()\n self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)","title":""},{"docid":"913613823815f627e1d6e9515eba0ba3","score":"0.49520984","text":"def main(self):\n if len(sys.argv) > 2:\n for i in range(2, len(sys.argv)):\n if sys.argv[i].lower() == \"p\":\n self.ploter.show_compareImages(self.imageProcessor.result_image())\n elif sys.argv[i].lower() == \"s\":\n self.__create_image()\n else:\n print(\"This flag is unknown\\n\\n\")\n self.__use()\n \n print(\"Cloud Coverage Index: \" + str(self.imageProcessor.get_CCI()))","title":""},{"docid":"a5202515237df3b09d013f4afb7bb80f","score":"0.49435407","text":"def test_and_run_command(self):\n self.build()\n self.breakOnCtor()\n\n raw_output = self.res.GetOutput()\n frameRE = re.compile(r\"\"\"\n ^\\s\\sframe # heading for the frame info,\n .* # wildcard, and\n 0x[0-9a-f]{16} # the frame pc, and\n \\sa.out`(.+) # module`function, and\n \\s\\+\\s # the rest ' + ....'\n \"\"\", re.VERBOSE)\n for line in raw_output.split(os.linesep):\n match = frameRE.search(line)\n if match:\n function = match.group(1)\n #print(\"line:\", line)\n #print(\"function:\", function)\n self.runCmd(\"disassemble -n '%s'\" % function)","title":""},{"docid":"7a460dc47b3c644d4964978664fd3b8f","score":"0.4939229","text":"def test_get_status_maps(self):\n pass","title":""},{"docid":"743d5f34f19134bbca906bea7845a65f","score":"0.49347323","text":"def _analyze_anchors_coverage(self, anchors: Anchors, image_size: int, labels: list, title: str):\n\n fig = plt.figure(figsize=(12, 5))\n fig.suptitle(f\"{title} anchors coverage\")\n\n # box style plot\n ax = fig.add_subplot(121)\n ax.set_xlabel(\"W\", fontsize=STAT_LOGGER_FONT_SIZE)\n ax.set_ylabel(\"H\", fontsize=STAT_LOGGER_FONT_SIZE)\n ax.set_xlim([0, image_size])\n ax.set_ylim([0, image_size])\n\n anchors_boxes = anchors.anchors.cpu().numpy()\n anchors_len = anchors.num_anchors\n\n anchors_boxes = anchors_boxes.reshape(-1, 2)\n\n for i in range(anchors_len):\n rect = self._get_rect(anchors_boxes[i][0], anchors_boxes[i][1])\n rect.set_alpha(0.3)\n rect.set_facecolor([random.random(), random.random(), random.random(), 0.3])\n ax.add_patch(rect)\n\n # distance from anchor plot\n ax = fig.add_subplot(122)\n ax.set_xlabel(\"W\", fontsize=STAT_LOGGER_FONT_SIZE)\n ax.set_ylabel(\"H\", fontsize=STAT_LOGGER_FONT_SIZE)\n\n x = np.arange(1, image_size, 1)\n y = np.arange(1, image_size, 1)\n\n xx, yy = np.meshgrid(x, y, sparse=False, indexing=\"xy\")\n points = np.concatenate([xx.reshape(1, -1), yy.reshape(1, -1)])\n\n color = self._get_score(anchors_boxes, points, image_size)\n\n ax.set_xlabel(\"W\", fontsize=STAT_LOGGER_FONT_SIZE)\n ax.set_ylabel(\"H\", fontsize=STAT_LOGGER_FONT_SIZE)\n plt.imshow(color, interpolation=\"nearest\", origin=\"lower\", extent=[0, image_size, 0, image_size])\n\n # calculate the coverage for the dataset labels\n cover_masks = []\n for i in range(anchors_len):\n w_max = (anchors_boxes[i][0] / image_size) * 4\n w_min = (anchors_boxes[i][0] / image_size) * 0.25\n h_max = (anchors_boxes[i][1] / image_size) * 4\n h_min = (anchors_boxes[i][1] / image_size) * 0.25\n cover_masks.append(\n np.logical_and(np.logical_and(np.logical_and(labels[:, 3] < w_max, labels[:, 3] > w_min), labels[:, 4] < h_max), labels[:, 4] > h_min)\n )\n cover_masks = np.stack(cover_masks)\n coverage = np.count_nonzero(np.any(cover_masks, axis=0)) / len(labels)\n\n self.sg_logger.add_figure(tag=f\"{title} anchors coverage\", figure=fig)\n return coverage","title":""},{"docid":"47644e7446b102e28b498746ab865996","score":"0.49319172","text":"def exportMap():\r\n global OD\r\n iface.mapCanvas().saveAsImage('D:/Users/63707/Documents/python3/C_TWN_road_attribute/test/{}_{}.png'.format(OD[0], OD[1]))\r\n QTimer.singleShot(500, next_point)","title":""},{"docid":"3b13f0d421a009b0aa89c97c41e99864","score":"0.49302006","text":"def test_CAS7697(self):\n myia = iatool()\n imagename = \"CAS7697.im\"\n myia.fromshape(imagename, [100,100, 10])\n myia.addnoise()\n bb = myia.getchunk()\n bb[:,:,9] = 40\n myia.putchunk(bb)\n myia.calcmask(imagename + \"<30\")\n for clmethod in [\"framework\", \"tiled\"]:\n stats = myia.statistics(axes=[0,1], clmethod=clmethod)\n self.assertTrue(stats['max'][9] == 0)\n self.assertTrue(stats['min'][9] == 0)\n myia.done()","title":""},{"docid":"d4828e1818bd19cd4abb5f78fe07cd29","score":"0.49240312","text":"def test_plot_65_qubit_gate_map(self):\n # getting the mock backend from FakeProvider\n\n backend = FakeManhattan()\n\n fname = \"65_qubit_gate_map.png\"\n self.graph_plot_gate_map(backend=backend, filename=fname)\n\n ratio = VisualTestUtilities._save_diff(\n self._image_path(fname),\n self._reference_path(fname),\n fname,\n FAILURE_DIFF_DIR,\n FAILURE_PREFIX,\n )\n self.assertGreaterEqual(ratio, 0.99)","title":""},{"docid":"8aac14006c4b3e3f041e01f950be2f9c","score":"0.49185798","text":"def test_arcs(self):\r\n image = self.design.layout.layers[0].images[0]\r\n assert len(image.traces) == 2","title":""},{"docid":"c8323cdd29be54f591199d64662be6a9","score":"0.4911458","text":"def mapper(self, sample):\n if sample.general.bestassemblyfile != \"NA\":\n # Define the Qualimap log and report files\n reportfile = os.path.join(sample.general.QualimapResults, 'genome_results.txt')\n # Define the Qualimap call\n qualimapcall = 'qualimap bamqc -bam {} -outdir {}'.format(sample.general.sortedbam,\n sample.general.QualimapResults)\n sample.commands.qualimap = qualimapcall\n # Initialise a dictionary to hold the Qualimap results\n qdict = dict()\n # If the report file doesn't exist, run Qualimap, and print logs to the log file\n if not os.path.isfile(reportfile):\n tlock = threading.Lock()\n out, err = run_subprocess(sample.commands.qualimap)\n tlock.acquire()\n write_to_logfile(sample.commands.qualimap, sample.commands.qualimap, self.logfile,\n sample.general.logout, sample.general.logerr, None, None)\n write_to_logfile(out, err, self.logfile, sample.general.logout, sample.general.logerr, None, None)\n tlock.release()\n # Initialise a genobject to store the coverage dictionaries\n sample.depth = GenObject()\n sample.depth.length = dict()\n sample.depth.bases = dict()\n sample.depth.coverage = dict()\n sample.depth.stddev = dict()\n try:\n with open(reportfile) as report:\n # Read the report\n for line in report:\n # Sanitise the keys and values using self.analyze\n key, value = self.analyze(line)\n # If the keys and values exist, enter them into the dictionary\n if (key, value) != (None, None):\n qdict[key] = value\n if 'Coverage per contig' in line:\n for contigline in report:\n try:\n _, name, length, bases, coverage, stddev = contigline.rstrip().split('\\t')\n sample.depth.length.update({name: length})\n sample.depth.bases.update({name: bases})\n sample.depth.coverage.update({name: coverage})\n sample.depth.stddev.update({name: stddev})\n except ValueError:\n pass\n\n except (IOError, FileNotFoundError):\n pass\n # If there are values in the dictionary\n if qdict:\n # Make new category for Qualimap results and populate this category with the report data\n for attribute in qdict:\n # Remove the 'X' from the depth values e.g. 40.238X\n setattr(sample.mapping, attribute, qdict[attribute].rstrip('X'))","title":""},{"docid":"50a492e4dde4246cfc7a5b12985d9b7e","score":"0.49097607","text":"def main():\n\n\tprint \"Doing stuff...\\n\"\n\n\ttest=map.Map()\n\ttest.updateMap()\n\twhile(1):\n\t\tcv2.namedWindow('map hector', 0)\n\t \t# refresh the image on the screen\n\t\tcv2.imshow('map hector',test._map)\n\t\tcv2.waitKey(3)","title":""},{"docid":"06fd5c3921452fe3a7206ac8452d9488","score":"0.49032006","text":"def test_simple(self):\r\n image = self.design.layout.layers[0].images[0]\r\n assert len(image.traces) == 2","title":""},{"docid":"ff9777612899284a917d16162efc5e2d","score":"0.4892885","text":"def test_get_image_information_runs():\n image_info.get_image_information(5)","title":""},{"docid":"d6d586398c52a87597d7f66bdb921021","score":"0.48847738","text":"def identify_confusion(fname, base_dir, gt_dir):\n tiles = open(fname).read().split(\"\\n\")\n tiles = [t for t in tiles if t!= \"\"]\n sums = numpy.zeros(17)\n for tile in tiles:\n pred = misc.imread(base_dir + tile + \".png\")\n target = misc.imread(gt_dir + tile + \".png\")\n target[pred!=1] = 17 # so that only for road (label=1) can the two be equal\n for i in range(17):\n sums[i] += numpy.count_nonzero(target==i)\n\n for i in range(17):\n print(f'{i}: {sums[i]}')","title":""},{"docid":"eabdc2adf59282bf0f3d1cc72eae406c","score":"0.4877715","text":"def testCoverage_0(self):\n mtt.makeTempDirParent()\n tmpDir = os.path.abspath(mtt.makeTempDir('testCoverage_0'))\n parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n customOpts = mafval.GenericValidationOptions()\n for target, mafSeq, solutionDict in g_knownGood:\n testMaf = mtt.testFile(os.path.join(os.path.abspath(tmpDir), 'test.maf'),\n mafSeq, g_headers)\n cmd = [os.path.abspath(os.path.join(parent, 'test', 'mafCoverage'))]\n cmd += ['--maf', os.path.abspath(os.path.join(tmpDir, 'test.maf')),\n '--species', target]\n outpipes = [os.path.join(tmpDir, 'output.txt')]\n mtt.recordCommands([cmd], tmpDir, outPipes=outpipes)\n mtt.runCommandsS([cmd], tmpDir, outPipes=outpipes)\n self.assertTrue(mafval.validateMaf(os.path.join(tmpDir, 'test.maf'),\n customOpts))\n self.assertTrue(coverageIsCorrect(os.path.join(tmpDir, 'output.txt'),\n solutionDict))\n mtt.removeDir(tmpDir)","title":""},{"docid":"f4ef77b99e077457106c74116ec8a0b6","score":"0.4867624","text":"def test_pokemon_map_output(self, mock_stdout):\n expected_output = \"🐱 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\\\n \"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\n\"\n pokemon_map()\n self.assertEqual(mock_stdout.getvalue(), expected_output)","title":""},{"docid":"c2db9d9d76a3fee17e2c5c301c723790","score":"0.48535594","text":"def main():\n args = argument_parser()\n args.mbtiles_file.close()\n mbtiles_file = args.mbtiles_file.name\n zoom = args.zoom\n tile_x = args.tilex\n tile_y = args.tiley\n mvt_content = run(tile_x, tile_y, zoom, mbtiles_file)\n print(mvt_content, end='')","title":""},{"docid":"62f8bc2455db32b045117a3787dd6713","score":"0.48534715","text":"def _binclass_print_teststats(self,pmodel,targets,imode):\n popts = helpers.Struct()\n popts.imode = imode\n popts.ptype = 3\n (h_q, rho_q, logz, h_p, rho_p) = self.predict(pmodel,popts)\n nte = targets.shape[0]\n acc = 100.*float((np.sign(h_q)==targets).sum())/nte\n loglh = logz.sum()/nte\n print ('Test set predictions: Accuracy: %4.2f%%, '\n 'log likelihood: %.6f') % (acc, loglh)","title":""},{"docid":"34d5df76f4da061f2daa4865cca78aba","score":"0.48515138","text":"def _BuilderRunsTestOfInterest(self, test_map: Dict[str, Any]) -> bool:\n raise NotImplementedError()","title":""},{"docid":"cd94393951093aaab4d4db1a73b30e1f","score":"0.4850536","text":"def test_plot_1_qubit_gate_map(self):\n # getting the mock backend from FakeProvider\n\n backend = FakeArmonk()\n\n fname = \"1_qubit_gate_map.png\"\n self.graph_plot_gate_map(backend=backend, filename=fname)\n\n ratio = VisualTestUtilities._save_diff(\n self._image_path(fname),\n self._reference_path(fname),\n fname,\n FAILURE_DIFF_DIR,\n FAILURE_PREFIX,\n )\n self.assertGreaterEqual(ratio, 0.99)","title":""},{"docid":"06449bd1248ff80e999ddbec03cb704f","score":"0.48434246","text":"def show_map_batch(sample_batched, img_to_show=3, save_file_path=None, as_numpy=False):\n\n # just select 6 images to show per batch\n sat_img_batch, map_img_batch = sample_batched['sat_img'][:img_to_show, :, :, :],\\\n sample_batched['map_img'][:img_to_show, :, :, :]\n batch_size = len(sat_img_batch)\n\n f, ax = plt.subplots(int(np.ceil(batch_size / 3)), 3, figsize=(15, int(np.ceil(batch_size / 3)) * 5))\n f.tight_layout()\n f.subplots_adjust(hspace=.05, wspace=.05)\n ax = ax.ravel()\n\n # unorm = UnNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n\n for i in range(batch_size):\n ax[i].axis('off')\n show_map(sat_img=sat_img_batch.cpu().numpy()[i, :, :, :].transpose((1, 2, 0)),\n map_img=map_img_batch.cpu().numpy()[i, 0, :, :], axis=ax[i])\n\n if save_file_path is not None:\n f.savefig(save_file_path)\n\n if as_numpy:\n f.canvas.draw()\n width, height = f.get_size_inches() * f.get_dpi()\n mplimage = np.frombuffer(f.canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n plt.cla()\n plt.close(f)\n\n return mplimage","title":""},{"docid":"968e8429c1fa23ffe0ac80617f446638","score":"0.4835228","text":"def main():\n\n options, configs = parse_args(sys.argv)\n ip_reader = csv.reader(open(options.ips, 'rb'), delimiter=',')\n ip_converter = IpConverter(configs)\n width = options.width\n height = options.height\n color = 'black'\n # Here we apply ratios, since the map will be croped later on.\n width = int(width * 2.5)\n height = int(height * 1.33333333) \n world_map = Map(width, height, color)\n\n roll = options.gen\n current_roll = None\n\n for row in ip_reader:\n try:\n ip = proxy_filter(row[0])\n timestamp = parse(row[1])\n if current_roll is None:\n current_roll = getattr(timestamp, roll)\n elif current_roll != getattr(timestamp, roll) and\\\n getattr(timestamp, roll) % options.gen_accuracy == 0:\n current_roll = getattr(timestamp, roll)\n # print current map to file, and generate new one.\n world_map.render('%s/%s.jpg' % (configs['maps']['folder'],\n timestamp))\n\n data = ip_converter.get_gps_from_ip(ip)\n if data is not None:\n world_map.add_gps_to_map(data) \n except pygeoip.GeoIPError, error:\n logging.error(error)\n except ValueError, error:\n logging.error(error)\n\n world_map.render('%s/%s.jpg' % (configs['maps']['folder'],\n timestamp))\n \n # generates video oO\n video = Video(width, height) \n video.build(configs['maps']['folder'])","title":""},{"docid":"40b8cde4718350bb315854d64300d7f6","score":"0.48325807","text":"def view_coverage_browser():\n indexfile = os.path.join(COVERAGE_DIR, 'index.html')\n if not os.path.exists(indexfile):\n print_err(C(': ').join(\n C('Missing coverage report file', 'red'),\n C(indexfile, 'blue'),\n ))\n print_err(C(' ').join(\n C('Run', 'red'),\n C('./runtests.py -R', 'blue').join('`', '`'),\n C('to generate it.', 'red'),\n ))\n return 1\n\n cmd = ['google-chrome', indexfile]\n print(C(': ').join(\n C('Running', 'cyan'),\n C(' ').join(\n C(cmd[0], 'blue', style='bright'),\n C(' '.join(cmd[1:]), 'blue'),\n ),\n ))\n subprocess.Popen(cmd, stderr=subprocess.DEVNULL)\n return 0","title":""},{"docid":"e3a313c8be0e2e8b2704386ff1ccbc7e","score":"0.48314133","text":"def main():\n NAME = os.path.basename(__file__).split(\".\")[0]\n\n with its.device.ItsSession() as cam:\n props = cam.get_camera_properties()\n if (not its.caps.raw(props) or\n not its.caps.read_3a(props)):\n print \"Test skipped\"\n return\n\n cam.do_3a()\n\n req = its.objects.auto_capture_request()\n cap_dng, cap_yuv = cam.do_capture(req, cam.CAP_DNG_YUV)\n\n img = its.image.convert_capture_to_rgb_image(cap_yuv)\n its.image.write_image(img, \"%s.jpg\" % (NAME))\n\n with open(\"%s.dng\"%(NAME), \"wb\") as f:\n f.write(cap_dng[\"data\"])\n\n # No specific pass/fail check; test is assumed to have succeeded if\n # it completes.","title":""},{"docid":"eec7c2399041533fb56193b89ffdbba6","score":"0.4824971","text":"def main():\n cur_dir = os.path.dirname(os.path.realpath(sys.argv[0]))\n input_tif = cur_dir + \"/../tests/data/Jamaica_dem.tif\"\n rst = RasterUtilClass.read_raster(input_tif)\n # metadata information\n print(\"rows: %d, cols: %d\" % (rst.nRows, rst.nCols))\n print(\"LLCornerX: %.2f, LLCornerY: %.2f\" % (rst.xMin, rst.yMin))\n print(\"cell size: %.1f\" % rst.dx)\n # basic statistics, nodata is excluded\n print(\"mean: %.2f, max: %.2f, min: %.2f\" % (rst.get_average(), rst.get_max(), rst.get_min()))\n print(\"std: %.2f, sum: %.2f\" % (rst.get_std(), rst.get_sum()))","title":""},{"docid":"5e5c060fc70675e7deef53904d2e9cbe","score":"0.4817491","text":"def test_vis(layer_to_img_dict, model_fn, output_dir =\"./zeiler_fergus_vis\" ):","title":""},{"docid":"d0a6bbf904f11b4cce066edcd8f802e9","score":"0.48165083","text":"def coverage(ctx):\n path = (_COVERAGE_PATH / \"index.html\").absolute()\n webbrowser.open(f\"file:///{path}\")","title":""},{"docid":"5c09238ae978e4d6eed66c8fd3ecc767","score":"0.48047954","text":"def analyze(display=True):","title":""},{"docid":"ec70ece3f91c8315b5ad8fbf08a67ab0","score":"0.48042879","text":"def inspect(output_dir):\n #log = getLog(\"tmp.log\")\n log.info(\"Check parcellation\")\n\n reg_path = output_dir\n\n cmdstr = op.join(reg_path, 'fsmask_1mm.nii.gz ')\n\n for p in lausanne_spec.keys():\n cmdstr += \" \" + op.join(reg_path, p, 'ROIv_HR_th.nii.gz')\n\n freeview_view_cmd = 'freeview %s' % cmdstr\n runCmd( freeview_view_cmd, log )","title":""},{"docid":"9b2ed09837a856dfe770b7cc93a9a214","score":"0.47905618","text":"def task_coverage() -> DoitTask:\n path_tests = DG.test.path_tests\n cov_dir = DG.test.path_coverage_index.parent\n cov_html = f'--cov-report=html:\"{cov_dir}\" --html=\"{DG.test.path_test_report}\" --self-contained-html'\n diff_html = f'--html-report {DG.test.path_diff_test_report}'\n return debug_task([\n Interactive(\n f'poetry run python -m pytest \"{path_tests}\" {DG.test.args_pytest} --cov={DG.meta.pkg_name} {cov_html}',\n ),\n 'poetry run python -m coverage json', # Create coverage.json file for \"_write_coverage_to_md\"\n 'poetry run python -m coverage xml',\n Interactive(f'poetry run diff-cover coverage.xml {DG.test.args_diff} {diff_html}'),\n ])","title":""},{"docid":"230ff2c38e3141dc68f6a32bcc0e6686","score":"0.4780019","text":"def test_processing():\n for cleantopo_process in [\n \"testdata/cleantopo_tl.mapchete\", \"testdata/cleantopo_br.mapchete\"\n ]:\n mp = mapchete.open(os.path.join(SCRIPTDIR, cleantopo_process))\n for zoom in range(6):\n tiles = []\n for tile in mp.get_process_tiles(zoom):\n output = mp.execute(tile)\n tiles.append(output)\n assert isinstance(output, BufferedTile)\n assert isinstance(output.data, ma.MaskedArray)\n assert output.data.shape == output.shape\n assert not ma.all(output.data.mask)\n mp.write(output)\n mosaic, mosaic_affine = create_mosaic(tiles)\n try:\n temp_vrt = os.path.join(OUT_DIR, str(zoom)+\".vrt\")\n gdalbuildvrt = \"gdalbuildvrt %s %s/%s/*/*.tif > /dev/null\" % (\n temp_vrt, OUT_DIR, zoom)\n os.system(gdalbuildvrt)\n with rasterio.open(temp_vrt, \"r\") as testfile:\n for file_item, mosaic_item in zip(\n testfile.meta[\"transform\"], mosaic_affine\n ):\n assert file_item == mosaic_item\n band = testfile.read(1, masked=True)\n assert band.shape == mosaic.shape\n assert ma.allclose(band, mosaic)\n assert ma.allclose(band.mask, mosaic.mask)\n finally:\n shutil.rmtree(OUT_DIR, ignore_errors=True)","title":""},{"docid":"0a730a490732d7e97e279455c2300003","score":"0.47758254","text":"def test_with_dwarf(self):\n self.buildDwarf()\n self.do_my_test()","title":""},{"docid":"65a2bdd7a6815c519e4162f477e867fb","score":"0.47702894","text":"def tile_classifier__simple_test(ctx, self, img):\n return tile_classifier__simple_test_impl(self, img)","title":""},{"docid":"07fb06daeacde1a62deb09fc5f923e3f","score":"0.47621217","text":"def runAnalysis(aif):\n \n for blob in aif.getTraceSetBlobsForTargetAndExperiment():\n\n aif.runAverageTraceForTraceSetBlob(blob)\n\n di1_xor_di2 = aif.opXor(blob, \"di1\", \"di2\")\n di3_xor_di4 = aif.opXor(blob, \"di3\", \"di4\")\n di5_xor_di6 = aif.opXor(blob, \"di5\", \"di6\")\n\n aif.runHammingDistanceAnalysis(blob,\n di1_xor_di2, di3_xor_di4, \"HD(d1^d2,d3^d4)\")\n \n aif.runHammingDistanceAnalysis(blob,\n di1_xor_di2, di5_xor_di6, \"HD(d1^d2,d5^d6)\")\n \n aif.runHammingDistanceAnalysis(blob,\n di3_xor_di4, di5_xor_di6, \"HD(d3^d4,d5^d6)\")\n\n aif.runHammingDistanceAnalysis(blob, \"di1\",\"di2\")\n aif.runHammingDistanceAnalysis(blob, \"di1\",\"di3\")\n aif.runHammingDistanceAnalysis(blob, \"di1\",\"di4\")\n aif.runHammingDistanceAnalysis(blob, \"di1\",\"di5\")\n aif.runHammingDistanceAnalysis(blob, \"di1\",\"di6\")\n\n aif.runHammingDistanceAnalysis(blob, \"di2\",\"di3\")\n aif.runHammingDistanceAnalysis(blob, \"di2\",\"di4\")\n aif.runHammingDistanceAnalysis(blob, \"di2\",\"di5\")\n aif.runHammingDistanceAnalysis(blob, \"di2\",\"di6\")\n\n aif.runHammingDistanceAnalysis(blob, \"di3\",\"di4\")\n aif.runHammingDistanceAnalysis(blob, \"di3\",\"di5\")\n aif.runHammingDistanceAnalysis(blob, \"di3\",\"di6\")\n \n aif.runHammingDistanceAnalysis(blob, \"di4\",\"di5\")\n aif.runHammingDistanceAnalysis(blob, \"di4\",\"di6\")\n \n aif.runHammingDistanceAnalysis(blob, \"di5\",\"di6\")\n\n aif.runHammingWeightAnalysis(blob, \"di1\")\n aif.runHammingWeightAnalysis(blob, \"di2\")\n aif.runHammingWeightAnalysis(blob, \"di3\")\n aif.runHammingWeightAnalysis(blob, \"di4\")\n aif.runHammingWeightAnalysis(blob, \"di5\")\n aif.runHammingWeightAnalysis(blob, \"di6\")","title":""},{"docid":"224d4f1415b6079fc8f2ce273019715c","score":"0.47593078","text":"def show(self):\n c_map=copy.deepcopy(self.map[:])\n c_map[self.agent.pos[0]][self.agent.pos[1]]=\"I\"\n for e in self.enemies:\n c_map[e[0]][e[1]]=\"E\"\n #print(c_map)\n print(\"---------------------------\")\n for i in range(len(c_map)):\n str=\"\"\n for c in c_map[i]:\n str=str+c\n print(\"|\"+str+\"|\")\n \n print(\"---------------------------\")\n str2=\"\"\n for i in range(self.agent.energy):\n str2=str2+\"H\"\n print(str2)","title":""},{"docid":"4a1ad359f0f9776b598853ee7050b816","score":"0.47584385","text":"def print_hitmap(hitmap):\r\n lines = 0\r\n for i in xrange(256):\r\n if (i % 16) == 0:\r\n line = [\"%02X: \"% (i)]\r\n if i not in hitmap or hitmap[i][List([])] == 0:\r\n line.append(\" \")\r\n else:\r\n line.append(\"%03i\" % hitmap[i][List([])])\r\n\r\n if (i % 16) == 15:\r\n line = \" \".join(line)\r\n if len(line.strip()) > 3:\r\n lines += 1\r\n print line\r\n if lines == 0:\r\n print \" <nothing>\"\r\n return","title":""},{"docid":"b5565c18e384263a07d53db2ee08c17b","score":"0.4757261","text":"def test_imshow():\n s = numpy.random.random((10, 10))\n return imshow(s, colormap='gist_earth')","title":""},{"docid":"bd252b434d78687d5b9316d1131e0285","score":"0.47531605","text":"def test_plot_27_qubit_gate_map(self):\n # getting the mock backend from FakeProvider\n\n backend = FakeMumbai()\n\n fname = \"27_qubit_gate_map.png\"\n self.graph_plot_gate_map(backend=backend, filename=fname)\n\n ratio = VisualTestUtilities._save_diff(\n self._image_path(fname),\n self._reference_path(fname),\n fname,\n FAILURE_DIFF_DIR,\n FAILURE_PREFIX,\n )\n self.assertGreaterEqual(ratio, 0.99)","title":""},{"docid":"c97a9e81b5269a159f2d3baa8ad997a6","score":"0.474789","text":"def cvg_main(mappedlocations, conflictlocations, bamfile, reference, outdir, prefix):\n newpath = 'coverage'\n os.makedirs(os.path.join(outdir,newpath))\n bar = progressbar.ProgressBar(widgets = ['Running SAMtools: ', progressbar.Bar(), '(', progressbar.ETA(),')'])\n for i in bar(range(1)):\n make_bed(mappedlocations, conflictlocations, reference, outdir, prefix)\n sam(bamfile, outdir, prefix)\n output(outdir, prefix)","title":""},{"docid":"a911a47541d3d0b60f7d5ec64134ca42","score":"0.4746671","text":"def show(img):\n remap = \" .*#\"+\"#\"*100\n img = (img.flatten()+.5)*3\n if len(img) != 784: return\n print(\"START\")\n for i in range(28):\n print(\"\".join([remap[int(round(x))] for x in img[i*28:i*28+28]]))","title":""},{"docid":"00fdd08672d2f1b9bd69a34939389e67","score":"0.47436896","text":"def test_init() -> None:\n # check that the map start with no info\n map_ = sm.SepalMap()\n\n # add a fullscreenControl\n control = sm.FullScreenControl(map_)\n map_.add(control)\n\n assert isinstance(control, sm.FullScreenControl)\n assert control in map_.controls\n assert control.zoomed is False\n assert \"fa-solid fa-expand\" in control.w_btn.children[0].children\n\n return","title":""},{"docid":"2dad434907b996751cbbf6b0012a96f6","score":"0.47427168","text":"def test_run(self):\n CalibrationTest.setup_flags(self)\n log_file = os.path.join(self.io_args.log_dir, \"CalibrationLibMain.INFO\")\n\n # CalibrationLibMain assumes the operating frame to be 000000\n lib_main_input = self.io_args.color_full + \"_000000\"\n if not os.path.exists(lib_main_input):\n shutil.copytree(self.io_args.color_full, lib_main_input)\n for cam in os.listdir(lib_main_input):\n cur_img = os.path.join(lib_main_input, cam, f\"{self.io_args.first}.png\")\n new_img = os.path.join(lib_main_input, cam, \"000000.png\")\n os.rename(cur_img, new_img)\n\n self.run_app(\n \"CalibrationLibMain\",\n args=f\"{self.io_args.rig_out} {self.io_args.matches} {self.io_args.rig_in} {lib_main_input}\",\n log_file=log_file,\n )\n record = parse_calibration_results(\n self.io_args.log_dir, bin_name=\"CalibrationLibMain\"\n )\n self.check_metrics(record)","title":""},{"docid":"3e7687404d80ae6c33acab4583d0a717","score":"0.47350043","text":"def test_imshow():\n s = np.random.random((10, 10))\n return imshow(s, colormap='gist_earth')","title":""},{"docid":"dbacaf1685129d12771b8a3bafeb9645","score":"0.47331163","text":"def test_glyph(self):\r\n\r\n glyph = \"a\"\r\n self.put_text(glyph)\r\n\r\n #found = list(scrape.str_from_img(self.img))\r\n #assert len(found) == 1, found\r\n\r\n #self._show()\r","title":""},{"docid":"9634646b116b42a3395be9c9594257ab","score":"0.47267002","text":"def display_mapping(mapping):\n img_size = mapping[\"frame_size\"]\n win_name = \"mappings\"\n win = cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)\n\n for frame, cmd in izip(mapping[\"frames\"], mapping[\"commands\"]):\n read_cmd = command_readable_mapping[cmd]\n msg = read_cmd\n img = frame.reshape(img_size)\n cv2.putText(img, msg, (10, 10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.6, (0, 0, 0))\n cv2.imshow(win_name, img)\n if cv2.waitKey(0) & ord(\"q\") == ord(\"q\"):\n break\n\n cv2.destroyAllWindows()","title":""},{"docid":"0a35767f00285b47d024b9be766389b6","score":"0.47253826","text":"def ShowRasterRef(self) -> bool:","title":""},{"docid":"0a35767f00285b47d024b9be766389b6","score":"0.47253826","text":"def ShowRasterRef(self) -> bool:","title":""},{"docid":"63091d4aa3ccba4681ade7b286339f3c","score":"0.47246438","text":"def get_map_and_measure(png_a):\n\n import os\n from CPAC.qc.utils import get_map_id\n\n measure_name = None\n map_name = None\n\n if '_fwhm_' in png_a:\n measure_name = os.path.basename(os.path.dirname(os.path.dirname(png_a)))\n else:\n measure_name = os.path.basename(os.path.dirname((png_a)))\n\n str_ = os.path.basename(png_a)\n\n if 'sca_tempreg' in png_a:\n map_name = get_map_id(str_, 'maps_roi_')\n\n if 'sca_roi' in png_a:\n map_name = get_map_id(str_, 'ROI_')\n\n if 'dr_tempreg' in png_a:\n map_name = get_map_id(str_, 'temp_reg_map_')\n\n if 'centrality' in png_a:\n map_name = get_map_id(str_, 'centrality_')\n\n return map_name, measure_name","title":""},{"docid":"36b65bab80c3c41304d6668ee9995edb","score":"0.47228912","text":"def test_run(self):\n in_bam = 'test.bam'\n index = 'test.bam.bai'\n picard_memory = '58G'\n picard_path = 'path/to/picard'\n temp_dir = 'path/to/temp/dir'\n lines = ps.general._picard_index(in_bam, index, picard_memory,\n picard_path, temp_dir)","title":""},{"docid":"a68f3ca07821c27980fea0d3f693a686","score":"0.47226566","text":"def test_get_summary_heat(self):\n pass","title":""},{"docid":"c5d6fb16a706bab3f4f1da4c77b58cff","score":"0.4718451","text":"def simple_drizzle(images, coverage_map, wcs_file, filt):\n\n # This calculates how many of the input images cover each\n # pixel of the output image grid. Used to determine best\n # median algorithm for drizzle\n median_coverage = np.median(coverage_map[coverage_map>0])\n out_name = '{}_final'.format(filt)\n if median_coverage>4:\n med_alg = 'median'\n combine_nhigh = 1\n else:\n med_alg = 'minmed'\n combine_nhigh = 0\n\n # Due to bug in astrodrizzle, this can't yet be optimized\n # to use the mdriztab\n\n astrodrizzle.AstroDrizzle(images, clean=True, build=True,\n context=False, preserve=False,\n combine_type=med_alg,\n combine_nhigh=combine_nhigh,\n in_memory=False, final_wcs=True,\n final_refimage=wcs_file,\n output=out_name)","title":""},{"docid":"dc0cb0aca6f70c1f84aad52a73bf46fc","score":"0.47070697","text":"def coverage(context):\n cmd = \"pytest -s --color no --cov=function_pipe/core --cov-report html\"\n context.run(cmd, echo=True)\n import webbrowser\n webbrowser.open(\"htmlcov/index.html\")","title":""},{"docid":"bb189ab1309927ca956683d07f55e194","score":"0.470688","text":"def run_demo() -> None:\n qc3 = StandardImagingQC3.from_demo_image()\n qc3.analyze()\n qc3.plot_analyzed_image()","title":""},{"docid":"4e9a88ce9716dee489e09726409ec209","score":"0.47065702","text":"def display_image(heatmap, class_index):\n\tplt.imshow(heatmap[:,:,class_index])\n\tplt.show()","title":""},{"docid":"dca23b9533a2413249880941ce1aebda","score":"0.47005153","text":"def test_image_processing():\n src_path = '/media/bryce/1TB Samsung/ml_datasets/cityscapes_data/gtFine/train/aachen'\n inst_path = os.path.join(src_path, 'aachen_000000_000019_gtFine_instanceIds.png')\n seg_path = os.path.join(src_path, 'aachen_000000_000019_gtFine_labelIds.png')\n\n inst_img = np.array(Image.open(inst_path), dtype=np.int16)\n\n base_img = seg_cv2_bgr(seg_path)\n\n bboxes = bbox_info_from_instance(inst_img)\n\n # assert all(bbox['train_id'] < 19 for bbox in bboxes), f\"Invalid Train ID found: {inst_path}.\"\n\n write_info_json(inst_path, bboxes)\n\n visualise_output(base_img, bboxes)","title":""},{"docid":"647493bcbca791430c7c9fcd16eaa5d4","score":"0.46926156","text":"def _run_tests(test_suite, package_name):\n count = test_suite.countTestCases()\n print(\"########\")\n print(\"%s tests has been discovered in %s\" % (count, package_name))\n print(\"Python GDAL : %s\" % gdal.VersionInfo(\"VERSION_NUM\"))\n print(\"########\")\n\n cov = coverage.Coverage(\n source=[\"/tests_directory/quick_api\"],\n omit=[\n \"*/quick_api_dialog_base.py\",\n \"*/quick_api.py\",\n \"*/__init__.py\",\n \"*/tests/*\",\n \"*/test_suite.py\",\n ],\n )\n cov.start()\n\n unittest.TextTestRunner(verbosity=3, stream=sys.stdout).run(test_suite)\n\n cov.stop()\n cov.save()\n cov.report(file=sys.stdout)","title":""},{"docid":"031f25a2afd44ea1ea8f8265161d8513","score":"0.46898025","text":"def pcap_analysis(self, pcap_md5: str) -> requests.Response:\n response = requests.get(\n self.base_url + self.version + '/pcaps/{0}/analysis'.format(pcap_md5),\n headers=self.headers\n )\n\n return response","title":""},{"docid":"675a48decb00ebf576ee9f4babf7b254","score":"0.4689187","text":"def test_CAS8357(self):\n myia = iatool()\n myia.fromshape(\"\",[30,30])\n reg = \"circle[[-1pix,-1pix], 10pix]\"\n res = myia.statistics(region=reg)\n myia.done()\n self.assertTrue(res['npts'] == 70, 'Wrong number of points')","title":""},{"docid":"d5b3f000570cc3add717dac71290504e","score":"0.46868145","text":"def show_cmaps():\n x = np.linspace(0, 100, 100)[None, :]\n fig, axs = plt.subplots(int(np.ceil(len(crameri_cmaps) / 7)), 7, figsize=(22, 10))\n fig.subplots_adjust(hspace=.8, wspace=.08)\n axs = axs.ravel()\n for ax in axs:\n ax.axis('off')\n for c, cmap_selected in enumerate(sorted(crameri_cmaps.keys())):\n colourmap = crameri_cmaps[cmap_selected]\n axs[c].pcolor(x, cmap=colourmap)\n axs[c].text(5, -0.3, cmap_selected, fontsize=26)","title":""},{"docid":"54cd0ff294bfe914096081fa6d7e21fb","score":"0.4684422","text":"def main():\n\n # Parse arguments ---\n args = parse_arguments()\n\n # Load log ---\n if args.debug:\n log_level = logging.DEBUG\n elif args.quiet:\n log_level = logging.NOTSET\n else:\n log_level = logging.INFO\n\n log = load_log(log_level)\n log.info('\\n 2D Map Extractor')\n log.info(' by {0}'.format(__author__))\n log.info(' Version {0} - {1}\\n'.format(__version__, __date__))\n\n # Let's start to count the time ---\n tstart = datetime.datetime.now()\n log.debug(' [{0}] Script Start'.format(tstart.strftime('%H:%M:%S')))\n\n # Perform the 2D-Map Extraction ---\n results = perform_2dmap_extraction(\n args.filename, log, args.pool_size, args.algorithm\n )\n\n # Write the results to a FITS file ---\n write_results(\n results, args.filename, args.output, args.algorithm,\n wavelength=args.wavelength\n )\n\n # Now I am good. The script is already done ---\n tend = datetime.datetime.now()\n delta_t = tend - tstart\n\n log.info('')\n log.debug(' [{0}] Script finished.'.format(tend.strftime('%H:%M:%S')))\n log.debug(' Total time elapsed: {:s}'.format(str(delta_t)))\n log.info('All done.')","title":""},{"docid":"92df6a69d9ad3315fd73e1a4faae6fe9","score":"0.4675138","text":"def inspect(component_run_id, address: str = \"\"):\n # Set address\n if address and len(address) > 0:\n set_address(address)\n\n show_info_card(component_run_id)","title":""},{"docid":"07710d40870a400585b6a1bca36914b0","score":"0.46741608","text":"def show_contour():\n draw = ImageDraw.Draw(OutputImageObj)\n for list in PixelLocations:\n for pixel in list:\n i = pixel[0]\n j = pixel[1]\n draw.point((i,j), fill=128)\n print \"Optimal Contour:\"\n for i in range(1, len(Contour)):\n draw.line(Contour[i-1] + Contour[i], fill=256)\n print \"\"\" %d %d \"\"\" % (Contour[i-1][0], Contour[i-1][1])\n OutputImageObj.show()","title":""},{"docid":"fbdb203951ddb22d24aac5a5dc9075e5","score":"0.46704444","text":"def show_sample():\n # an example to show difference between grayscale image and binary image\n license_plate = imread(\"data/crop_h1/I00000.png\", as_grey=True)/255.0\n print(license_plate.shape)\n # see the difference between gray scale and binary image\n gray_car_image = license_plate * 255\n _, (ax1, ax2) = plt.subplots(1, 2)\n ax1.imshow(gray_car_image, cmap=\"gray\")\n # threshold_otsu is an algorithm to reduce grayscale image to binary image\n threshold_value = threshold_otsu(gray_car_image)\n binary_car_image = gray_car_image > threshold_value\n ax2.imshow(binary_car_image, cmap=\"gray\")\n print(binary_car_image)","title":""},{"docid":"701ff4791192cd7a6b81dd73cbefbdae","score":"0.4669743","text":"def test(ctx, debug=False, maxfail=0):\n print_header(\"RUNNING TESTS\")\n flags = [\n \"--verbose\",\n f\"--cov={package.__name__}\",\n \"--cov-branch\",\n f'--cov-report=\"html:{_COVERAGE_PATH}\"',\n ]\n if debug:\n flags.append(\"--capture=no\")\n if maxfail:\n flags.append(f\"--maxfail={int(maxfail)}\")\n\n ctx.run(f\"pytest {' '.join(flags)} tests/\", pty=True)\n print(f\"\\nCoverage: {get_total_coverage(ctx)}\")","title":""},{"docid":"ea29f2e54879adda842e12aca35d5477","score":"0.466707","text":"def main():\n parser = argparse.ArgumentParser(allow_abbrev=False)\n\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\"-v\", \"--verbose\", help=\"display all files processed\",\n action=\"store_const\", const=logging.INFO, dest='loglevel')\n group.add_argument(\"-d\", \"--debug\", help=\"display debugging information\",\n action=\"store_const\", const=logging.DEBUG, dest='loglevel')\n\n parser.add_argument('-o', '--output', default='pinmap.pcf',\n help='output file with .pcf extension')\n parser.add_argument(\"-n\", \"--dry-run\", help=\"examine files without changing\",\n action=\"store_true\")\n parser.add_argument(\"file\", nargs=1, help=\"file to check\")\n args = parser.parse_args()\n logging.basicConfig(level=args.loglevel or logging.WARNING)\n\n if args.dry_run:\n logging.info(\"Dry run -- no files will be overwritten\")\n\n symtable = []\n for fname in args.file:\n with open(fname) as source_file:\n logging.info(r\"in: %s\", fname)\n symtable = [mapping for line in source_file if (\n mapping := get_mapping(line)) is not None]\n\n if not symtable:\n logging.warning('No @MAP_IO statements found for %s', args.output)\n\n if not args.dry_run:\n logging.info(r'out: %s', args.output)\n with open(args.output, mode='wt') as pcf_file:\n pcf_file.write(PCF_HEADER)\n for item in symtable:\n pcf_file.write(item.to_set_io())","title":""},{"docid":"f5bfc5a98918592f1bca67e35dc0b50c","score":"0.4666668","text":"def test(coverage=False):\n if coverage and not os.environ.get('FLASK_COVERAGE'):\n import sys\n os.environ['FLASK_COVERAGE'] = '1'\n os.execvp(sys.executable, [sys.executable] + sys.argv)\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)\n if COV:\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n COV.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n COV.erase()","title":""},{"docid":"9fcc91119c9cbfea625259d7d7afa261","score":"0.46650156","text":"def run_canica(params):\n dataset = datasets.fetch_adhd()\n func_files = dataset.func\n output_dir = osp.abspath(params.pop('output_folder'))\n prepare_report_directory(output_dir)\n run(func_files, params, output_dir)\n json.dump(params, open(osp.join(output_dir, 'params.json'), 'w'))\n img_src_filenames = [osp.join(output_dir, 'images', fname) for fname in\n os.listdir(osp.join(output_dir, 'images'))\n if fname.startswith('IC_')]\n report = generate_report(params, img_src_filenames)\n reportindex = osp.abspath(osp.join(output_dir, 'index.html'))\n report.save_html(reportindex)\n return ('file', 'file://{}'.format(reportindex))","title":""},{"docid":"9254448f3f41a8594beba8a2005890e5","score":"0.46635103","text":"def run(self, showDetails=False):\n \n pass","title":""},{"docid":"b6eff3134ed4f97c8f6b63d9cc6bfe1e","score":"0.46633792","text":"def show_cartoon(contest):\n image = mpimg.imread(INFO_DIR+str(contest)+\"/\"+str(contest)+\".jpg\")\n plt.figure(figsize=(12, 12))\n plt.imshow(image, cmap='Greys_r')\n plt.axis('off')","title":""},{"docid":"79287e033e60334c42fe8373afbdcf8f","score":"0.46548027","text":"async def fetch_coverage() -> coverage.Coverage:\n client = TestingCupid()\n data = await client.coverage()\n await client.close()\n return data","title":""},{"docid":"29024d67bc14cf9d4dea2504f87630f0","score":"0.46501207","text":"def test(coverage=False):\n if coverage and not os.environ.get('FLASK_COVERAGE'):\n import sys\n os.environ['FLASK_COVERAGE'] = '1'\n os.execvp(sys.executable, [sys.executable] + sys.argv)\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)\n if COV:\n COV.stop()\n COV.save()\n print('Test coverage summary:')\n COV.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n COV.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n COV.erase()","title":""},{"docid":"cb35b605a9b9f440b5fc3814cd8443ef","score":"0.46497378","text":"def test_show_command(self, capsys):\n main(\"show pydicom::MR_small_RLE.dcm\".split())\n out, err = capsys.readouterr()\n\n assert \"Instance Creation Date DA: '20040826'\" in out\n assert out.endswith(\"OB: Array of 126 elements\\n\")\n assert err == \"\"\n\n # Get a specific data element\n main(\"show pydicom::MR_small_RLE.dcm::LargestImagePixelValue\".split())\n out, _ = capsys.readouterr()\n assert \"4000\" == out.strip()","title":""},{"docid":"aa5c851f00dddf23a1fba3b5955740df","score":"0.46470836","text":"def info(test_func):\n\n @wraps(test_func)\n def test_func_wrapper(nom, test):\n print '\\n============================================================='\n print 'Starting test:', test_func.__name__\n print 'Testing highland analysis:', nom.name\n print 'originally processed with:', nom.input_file\n print 'nominal version:', nom.input_type, nom.version\n print 'test version:', test.input_type, test.version, '\\n'\n test_func(nom, test)\n\n return test_func_wrapper","title":""},{"docid":"437a5ac03fddaa97b642ffba4f22bbc8","score":"0.46434614","text":"def show_map(self, rbt_pos):\n # normalise values in numpy matrix occ_grid.m to values between 0 and 255, + RGB.\n # img_mat = uint8(self.m * 255) # multiply by 255 from 0-1 to 0-255\n # img_mat = stack((img_mat, img_mat, img_mat), 2) # convert to RGB (3rd dim)\n # print(img_mat.shape) # (100, 100, 3)\n \n # color the robot position as a crosshair\n rbt_idx = self.pos2idx(rbt_pos) # robot index\n # img_mat[rbt_idx[0], rbt_idx[1], :] = (0, 255, 0) # green\n img_mat = self.m.copy()\n img_mat[rbt_idx] = 1 # white\n img_mat[rbt_idx] = 0\n img_mat[rbt_idx[0] - 1, rbt_idx[1]] = 1\n img_mat[rbt_idx[0] + 1, rbt_idx[1]] = 1\n img_mat[rbt_idx[0], rbt_idx[1] - 1] = 1\n img_mat[rbt_idx[0], rbt_idx[1] + 1] = 1\n \n # print to a window 'img'\n cv2.imshow('img', img_mat)\n cv2.waitKey(10);","title":""},{"docid":"7f88957f0e8d8f4c413bf8b3de4c55a9","score":"0.46375304","text":"def test_cmap2act(self):\n colors.cmap2act('viridis', filename=self.f)\n ref = os.path.join(self.ref_dir, 'viridis.act')\n\n assert filecmp.cmp(self.f, ref)","title":""}],"string":"[\n {\n \"docid\": \"40e66fffa9607e4942f5d9355e8da9d7\",\n \"score\": \"0.56286484\",\n \"text\": \"def show_coverage(self, viewer_id=None, table_group='main'):\\n\\n view_type = 'coverImage'\\n cid = viewer_id if viewer_id else (\\\"%s-%s\\\" % (LO_VIEW_DICT[view_type], table_group))\\n payload = {'viewerType': LO_VIEW_DICT[view_type], 'cellId': cid}\\n return self.dispatch(ACTION_DICT['ShowCoverage'], payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6f366d7fc37e29f39aaeb437017c28\",\n \"score\": \"0.55119926\",\n \"text\": \"def __main__():\\n\\n args = setup_parser().parse_args()\\n\\n args = check_args(args)\\n\\n if args.verbose:\\n print('{a} image(s) at {pos}'.format(\\n a='Writing' if not args.coadd else 'Coadding', pos=args.skypos))\\n print('\\t\\tof dimensions {w}x{h} degrees'.format(\\n w=args.skyrange[0], h=args.skyrange[1]))\\n print('\\t\\twith a virtual detector {d} degrees across'.format(\\n d=args.detsize))\\n print('\\t\\tin time range(s): {t}'.format(t=repr(args.trange)))\\n\\n gmap(band=args.band, cntfile=args.cntfile,\\n coadd=args.coadd, detsize=args.detsize, intfile=args.intfile,\\n skypos=args.skypos, maxgap=args.maxgap,\\n memlight=args.memlight, minexp=args.minexp, overwrite=args.overwrite,\\n retries=args.retries, skyrange=args.skyrange, stepsz=args.stepsz,\\n trange=args.trange, verbose=args.verbose,\\n cntcoaddfile=args.cntcoaddfile, intcoaddfile=args.intcoaddfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c3aa142f471d5478c926535b65627c\",\n \"score\": \"0.5394963\",\n \"text\": \"def test_print_all_details_runs():\\n image_info.print_all_details()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67748086c5c9b818e2f154b2850b68d3\",\n \"score\": \"0.5338853\",\n \"text\": \"def capture_unittest_coverage(self):\\n assert self.m.chromium.c.BUILD_CONFIG == 'Coverage'\\n generate_coverage_py = self.public_scripts_dir.join(\\n 'generate_coverage.py')\\n args = ['--verbose',\\n '--syzygy',\\n '--build-dir', self.output_dir]\\n return self.m.python(\\n 'capture_unittest_coverage', generate_coverage_py, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ef8c1ac0d4292307c52db81ba0b272\",\n \"score\": \"0.5305564\",\n \"text\": \"def coverage():\\n run('coverage run --source chanjo-report setup.py test')\\n run('coverage report -m')\\n run('coverage html')\\n run('open htmlcov/index.html')\\n log.info('collected test coverage stats')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7afde0e7f48c97e6b975eca418320d\",\n \"score\": \"0.5225435\",\n \"text\": \"def main(args, utils_impl=Utils):\\n\\n if args.date:\\n resolved_date = args.date\\n else:\\n resolved_date = MapPreview.get_most_recent_date(\\n args.source, args.signal, args.geo_type, args.geo_value)\\n print(f'using latest data reported for date {resolved_date}')\\n\\n screen_size = (\\n args.width + MapPreview.SCREEN_PADDING[0],\\n args.height + MapPreview.SCREEN_PADDING[1],\\n )\\n with utils_impl.run_screenshot_stack(screen_size) as driver:\\n MapPreview.take_screenshot(driver, args, resolved_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2267171cef3143f3cb1a1ac8898f3ff\",\n \"score\": \"0.52149725\",\n \"text\": \"def test_simple_all_run(msa_fasta_file: Path, tmp_path: Path):\\n mv = MsaViz(msa_fasta_file)\\n\\n fig_outfile = tmp_path / \\\"test.png\\\"\\n mv.savefig(fig_outfile)\\n\\n assert fig_outfile.exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe8241e4df36202ceae5c089531dd30\",\n \"score\": \"0.52022386\",\n \"text\": \"def coverage(self):\\n return self._hitmap.viewkeys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2eb324267b8a33077e93aa723e6888\",\n \"score\": \"0.51690245\",\n \"text\": \"def main():\\n\\n with its.device.ItsSession() as cam:\\n # Arbitrary capture request exposure values; image content is not\\n # important for this test, only the metadata.\\n props = cam.get_camera_properties()\\n if not its.caps.manual_sensor(props):\\n print \\\"Test skipped\\\"\\n return\\n\\n req,fmt = its.objects.get_fastest_manual_capture_settings(props)\\n cap = cam.do_capture(req, fmt)\\n pprint.pprint(cap[\\\"metadata\\\"])\\n\\n # No pass/fail check; test passes if it completes.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3323706ea25cc11b6931c6845bbde71c\",\n \"score\": \"0.5165554\",\n \"text\": \"def test_all_run_with_options(msa_fasta_file: Path, tmp_path: Path):\\n mv = MsaViz(\\n msa_fasta_file,\\n color_scheme=\\\"Identity\\\",\\n wrap_length=50,\\n show_label=False,\\n show_seq_char=False,\\n sort=True,\\n )\\n mv.set_highlight_pos([1, 5, (10, 13), 18])\\n mv.set_highlight_pos_by_ident_thr(min_thr=80, max_thr=100)\\n mv.add_markers([50, 51, 52, (60, 70), 80], marker=\\\"x\\\", color=\\\"blue\\\", size=6)\\n mv.add_text_annotation(\\n (100, 120), text=\\\"test\\\", text_color=\\\"blue\\\", text_size=10, range_color=\\\"blue\\\"\\n )\\n\\n fig_outfile = tmp_path / \\\"test.png\\\"\\n mv.savefig(fig_outfile)\\n\\n assert fig_outfile.exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3b6f858afa060ed7599f2f18be574f\",\n \"score\": \"0.51270723\",\n \"text\": \"def main():\\n NAME = os.path.basename(__file__).split(\\\".\\\")[0]\\n\\n with its.device.ItsSession() as cam:\\n props = cam.get_camera_properties()\\n if not its.caps.full(props):\\n print \\\"Test skipped\\\"\\n return\\n\\n _,fmt = its.objects.get_fastest_manual_capture_settings(props)\\n e, s = its.target.get_target_exposure_combos(cam)[\\\"midExposureTime\\\"]\\n e /= 2.0\\n\\n r_means = []\\n g_means = []\\n b_means = []\\n\\n reqs = [\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s*2,e, True),\\n its.objects.manual_capture_request(s*2,e, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s, e*2, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s*2,e, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s, e*2, True),\\n its.objects.manual_capture_request(s, e, True),\\n its.objects.manual_capture_request(s, e*2, True),\\n its.objects.manual_capture_request(s, e*2, True),\\n ]\\n\\n caps = cam.do_capture(reqs, fmt)\\n for i,cap in enumerate(caps):\\n img = its.image.convert_capture_to_rgb_image(cap)\\n its.image.write_image(img, \\\"%s_i=%02d.jpg\\\" % (NAME, i))\\n tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)\\n rgb_means = its.image.compute_image_means(tile)\\n r_means.append(rgb_means[0])\\n g_means.append(rgb_means[1])\\n b_means.append(rgb_means[2])\\n\\n # Draw a plot.\\n idxs = range(len(r_means))\\n pylab.plot(idxs, r_means, 'r')\\n pylab.plot(idxs, g_means, 'g')\\n pylab.plot(idxs, b_means, 'b')\\n pylab.ylim([0,1])\\n matplotlib.pyplot.savefig(\\\"%s_plot_means.png\\\" % (NAME))\\n\\n g_avg = sum(g_means) / len(g_means)\\n g_ratios = [g / g_avg for g in g_means]\\n g_hilo = [g>1.0 for g in g_ratios]\\n assert(g_hilo == [False, False, True, True, False, False, True,\\n False, True, False, True, False, True, True])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"164cc4024a9866da376a18a660601ea3\",\n \"score\": \"0.5114869\",\n \"text\": \"def view_coverage():\\n coverage_file = os.path.join(SCRIPTDIR, '.coverage')\\n if not os.path.exists(coverage_file):\\n raise InvalidArg('No coverage file found, run coverage: {}'.format(\\n coverage_file,\\n ))\\n # Show console report.\\n print(C('').join(C('\\\\nCoverage Report', 'cyan'), ':'))\\n\\n covreportcmd = [\\n 'coverage',\\n 'report',\\n ]\\n try:\\n output = subprocess.check_output(\\n covreportcmd,\\n stderr=subprocess.STDOUT,\\n ).decode()\\n except subprocess.CalledProcessError:\\n return 1\\n if not output.startswith('Name'):\\n print_err(output)\\n return 1\\n\\n divline = C('-' * 45, 'dimgrey')\\n for line in output.splitlines():\\n if line.startswith('--'):\\n # Divider line.\\n print(divline)\\n continue\\n name, statements, miss, cover = line.split()\\n namefmt = C(name).ljust(25)\\n statementsfmt = C(statements, (46, 137, 255)).rjust(5)\\n try:\\n miss = int(miss)\\n if miss == 0:\\n missfmt = C(miss, 'lightgreen', style='bright').rjust(6)\\n else:\\n missfmt = C(miss, 'red').rjust(6)\\n except ValueError:\\n # Actual 'Miss' header.\\n missfmt = C(miss, 'red').rjust(6)\\n try:\\n cover = int(cover.rstrip('%'))\\n if cover == 100:\\n covercolr = {'fore': 'lightgreen', 'style': 'bright'}\\n elif cover > 49:\\n covercolr = {'fore': 'green'}\\n else:\\n covercolr = {'fore': 'red'}\\n coverfmt = C('{}%'.format(cover), **covercolr).rjust(6)\\n except ValueError:\\n # Actual 'Cover' header.\\n coverfmt = C(cover, 'green').rjust(6)\\n print(C(' ').join(namefmt, statementsfmt, missfmt, coverfmt))\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f81b4d465d34c90833cc53640041db\",\n \"score\": \"0.5109988\",\n \"text\": \"def _run_test(self, test_info):\\n self._ensure_dump_render_tree_is_running()\\n # The pixel_hash is used to avoid doing an image dump if the\\n # checksums match, so it should be set to a blank value if we\\n # are generating a new baseline. (Otherwise, an image from a\\n # previous run will be copied into the baseline.)\\n image_hash = test_info.image_hash()\\n if image_hash and self._test_args.new_baseline:\\n image_hash = \\\"\\\"\\n start = time.time()\\n crash, timeout, actual_checksum, output, error = \\\\\\n self._driver.run_test(test_info.uri, test_info.timeout, image_hash)\\n end = time.time()\\n\\n result = process_output(self._port, test_info, self._test_types,\\n self._test_args, self._options.configuration,\\n self._options.results_directory, crash,\\n timeout, end - start, actual_checksum,\\n output, error)\\n self._test_results.append(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72db4b1979c7a2c3f6eca99720f4c95b\",\n \"score\": \"0.5108259\",\n \"text\": \"def run(self):\\n \\n run_afl(\\n indir = self.srcdir, \\n outdir = self.afloutdir, \\n tgtcmd = self.cfg.tgtcmd, \\n cfg = self.cfg, \\n cores = self.cores, \\n persist_tgt = False,\\n verbose = self.verbose,\\n dry_run = self.dry_run,\\n )\\n\\n self.test_img_creation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50a0d5c8bd81d1faa8c1ea7e4df00ab\",\n \"score\": \"0.5093121\",\n \"text\": \"def runAnalysis(aif):\\n\\n aif.runDefaultAnalysis()\\n\\n for ttest in aif.getTTestsForTargetAndExperiment():\\n aif.runHammingDistanceAnalysis(ttest.randomTraceSet, \\\"di1\\\",\\\"di2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ab0be9bde1a12283152c4e2cf6ddd7\",\n \"score\": \"0.5092178\",\n \"text\": \"def cov():\\n local(\\\"coverage report -m\\\")\\n local(\\\"coverage html\\\")\\n local(\\\"coverage xml\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be185c0911acbba7dc88c66a9ba23cd\",\n \"score\": \"0.50723636\",\n \"text\": \"def main():\\r\\n calibrate_camera_to_DMD_mapping()\\r\\n goto_flagged_images()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e98532384640f184daed30d2585fb3\",\n \"score\": \"0.50558\",\n \"text\": \"def focalCover(targetfile = pc4layer, cbscode='40', outputfile='coverageparks.shp'):\\n\\n return focalsum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2c433efab6746b8930a29837880397\",\n \"score\": \"0.5052849\",\n \"text\": \"async def showMap(self,ctx,*argv):\\n if(len(argv)==3):\\n zoom = int(argv[0])\\n lat_deg = float(argv[1])\\n lon_deg = float(argv[2])\\n elif(len(argv)==1):\\n zll = argv[0][argv[0].find(\\\"map=\\\")+4:]\\n zoom=int(zll[:zll.find('/')])\\n ll = zll[zll.find('/')+1:]\\n lat_deg = float(ll[:ll.find('/')])\\n lon_deg = float(ll[ll.find('/')+1:])\\n #print(f'{zoom},{lat_deg},{lon_deg}')\\n else:\\n await ctx.send(f\\\"Either enter the zoom, latitude and longditude, or url, like this: `€showMap 15 60.3912 5.3242`\\\")\\n return\\n if(zoom>19):\\n zoom=19\\n await ImageCluster(ctx, lat_deg, lon_deg, zoom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb328b24c61edcedb51025df4e23fd7\",\n \"score\": \"0.50147796\",\n \"text\": \"def print_target_maps(target_maps):\\n for index, target_map in enumerate(target_maps):\\n print('Target Map {}/{}'.format(index + 1, len(target_maps)))\\n print_target_map(target_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e92840211c6f9d99624e7daa1699b6f\",\n \"score\": \"0.5008252\",\n \"text\": \"def test_mosaic_to_workspace_FGDB(self):\\n raster1 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic1')\\n raster2 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic2')\\n raster3 = os.path.join(os.getcwd(), 'test-data', 'raster', 'mosaic3')\\n self.request['params'][0]['response']['docs'][0]['path'] = raster1\\n self.request['params'][0]['response']['docs'][1]['path'] = raster2\\n self.request['params'][0]['response']['docs'][2]['path'] = raster3\\n self.request['params'][3]['value'] = 'Mosaic'\\n self.request['params'][5]['value'] = 'FileGDB'\\n self.request['params'][2]['value'] = self.target_ws\\n getattr(sys.modules[self.request['task']], \\\"execute\\\")(self.request)\\n dsc = arcpy.Describe(os.path.join(self.target_ws, 'Mosaic'))\\n self.assertEquals([dsc.bandcount, dsc.pixeltype], [1, 'U8'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee7635916f0ae14e5a441824aa522fa\",\n \"score\": \"0.49968728\",\n \"text\": \"def test_get_status_maps1(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3c2a04b06933684a37f94dfdd9aa9e\",\n \"score\": \"0.4987271\",\n \"text\": \"def display(self):\\n if not self.initialized:\\n return\\n\\n cv2.imshow(\\\"explored map\\\", self._map_exploration)\\n cv2.imshow(\\\"exploration_zone\\\", self._exploration_zone)\\n cv2.waitKey(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee6748ea45ad623eccec5c4deb37f6f\",\n \"score\": \"0.4959971\",\n \"text\": \"def do_test(self):\\n exe_name = \\\"a.out\\\"\\n exe = self.getBuildArtifact(exe_name)\\n\\n # Create the target\\n target = self.dbg.CreateTarget(exe)\\n self.assertTrue(target, VALID_TARGET)\\n\\n self.expect(\\\"target modules dump symtab -m a.out\\\",\\n patterns=['Metadata.*_TMC1a3Foo'])\\n\\n self.expect(\\\"target modules dump symtab a.out\\\",\\n patterns=['Metadata.*type metadata for'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5291000db13eea6a693d77a4f905e0\",\n \"score\": \"0.4957987\",\n \"text\": \"def coverage():\\n tests = unittest.TestLoader().discover('live/tests')\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n COV.html_report()\\n COV.erase()\\n return 0\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8dd53a8b29eeb641e9681835a9d094\",\n \"score\": \"0.4954103\",\n \"text\": \"def testGetOneMAPWithMatchingGroundtruthAndDetections(self):\\n coco_evaluator = coco_evaluation.CocoDetectionEvaluator(\\n _get_categories_list())\\n coco_evaluator.add_single_ground_truth_image_info(\\n image_id='image1',\\n groundtruth_dict={\\n standard_fields.InputDataFields.groundtruth_boxes:\\n np.array([[100., 100., 200., 200.]]),\\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\\n })\\n coco_evaluator.add_single_detected_image_info(\\n image_id='image1',\\n detections_dict={\\n standard_fields.DetectionResultFields.detection_boxes:\\n np.array([[100., 100., 200., 200.]]),\\n standard_fields.DetectionResultFields.detection_scores:\\n np.array([.8]),\\n standard_fields.DetectionResultFields.detection_classes:\\n np.array([1])\\n })\\n coco_evaluator.add_single_ground_truth_image_info(\\n image_id='image2',\\n groundtruth_dict={\\n standard_fields.InputDataFields.groundtruth_boxes:\\n np.array([[50., 50., 100., 100.]]),\\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\\n })\\n coco_evaluator.add_single_detected_image_info(\\n image_id='image2',\\n detections_dict={\\n standard_fields.DetectionResultFields.detection_boxes:\\n np.array([[50., 50., 100., 100.]]),\\n standard_fields.DetectionResultFields.detection_scores:\\n np.array([.8]),\\n standard_fields.DetectionResultFields.detection_classes:\\n np.array([1])\\n })\\n coco_evaluator.add_single_ground_truth_image_info(\\n image_id='image3',\\n groundtruth_dict={\\n standard_fields.InputDataFields.groundtruth_boxes:\\n np.array([[25., 25., 50., 50.]]),\\n standard_fields.InputDataFields.groundtruth_classes: np.array([1])\\n })\\n coco_evaluator.add_single_detected_image_info(\\n image_id='image3',\\n detections_dict={\\n standard_fields.DetectionResultFields.detection_boxes:\\n np.array([[25., 25., 50., 50.]]),\\n standard_fields.DetectionResultFields.detection_scores:\\n np.array([.8]),\\n standard_fields.DetectionResultFields.detection_classes:\\n np.array([1])\\n })\\n metrics = coco_evaluator.evaluate()\\n self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913613823815f627e1d6e9515eba0ba3\",\n \"score\": \"0.49520984\",\n \"text\": \"def main(self):\\n if len(sys.argv) > 2:\\n for i in range(2, len(sys.argv)):\\n if sys.argv[i].lower() == \\\"p\\\":\\n self.ploter.show_compareImages(self.imageProcessor.result_image())\\n elif sys.argv[i].lower() == \\\"s\\\":\\n self.__create_image()\\n else:\\n print(\\\"This flag is unknown\\\\n\\\\n\\\")\\n self.__use()\\n \\n print(\\\"Cloud Coverage Index: \\\" + str(self.imageProcessor.get_CCI()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5202515237df3b09d013f4afb7bb80f\",\n \"score\": \"0.49435407\",\n \"text\": \"def test_and_run_command(self):\\n self.build()\\n self.breakOnCtor()\\n\\n raw_output = self.res.GetOutput()\\n frameRE = re.compile(r\\\"\\\"\\\"\\n ^\\\\s\\\\sframe # heading for the frame info,\\n .* # wildcard, and\\n 0x[0-9a-f]{16} # the frame pc, and\\n \\\\sa.out`(.+) # module`function, and\\n \\\\s\\\\+\\\\s # the rest ' + ....'\\n \\\"\\\"\\\", re.VERBOSE)\\n for line in raw_output.split(os.linesep):\\n match = frameRE.search(line)\\n if match:\\n function = match.group(1)\\n #print(\\\"line:\\\", line)\\n #print(\\\"function:\\\", function)\\n self.runCmd(\\\"disassemble -n '%s'\\\" % function)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a460dc47b3c644d4964978664fd3b8f\",\n \"score\": \"0.4939229\",\n \"text\": \"def test_get_status_maps(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"743d5f34f19134bbca906bea7845a65f\",\n \"score\": \"0.49347323\",\n \"text\": \"def _analyze_anchors_coverage(self, anchors: Anchors, image_size: int, labels: list, title: str):\\n\\n fig = plt.figure(figsize=(12, 5))\\n fig.suptitle(f\\\"{title} anchors coverage\\\")\\n\\n # box style plot\\n ax = fig.add_subplot(121)\\n ax.set_xlabel(\\\"W\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n ax.set_ylabel(\\\"H\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n ax.set_xlim([0, image_size])\\n ax.set_ylim([0, image_size])\\n\\n anchors_boxes = anchors.anchors.cpu().numpy()\\n anchors_len = anchors.num_anchors\\n\\n anchors_boxes = anchors_boxes.reshape(-1, 2)\\n\\n for i in range(anchors_len):\\n rect = self._get_rect(anchors_boxes[i][0], anchors_boxes[i][1])\\n rect.set_alpha(0.3)\\n rect.set_facecolor([random.random(), random.random(), random.random(), 0.3])\\n ax.add_patch(rect)\\n\\n # distance from anchor plot\\n ax = fig.add_subplot(122)\\n ax.set_xlabel(\\\"W\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n ax.set_ylabel(\\\"H\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n\\n x = np.arange(1, image_size, 1)\\n y = np.arange(1, image_size, 1)\\n\\n xx, yy = np.meshgrid(x, y, sparse=False, indexing=\\\"xy\\\")\\n points = np.concatenate([xx.reshape(1, -1), yy.reshape(1, -1)])\\n\\n color = self._get_score(anchors_boxes, points, image_size)\\n\\n ax.set_xlabel(\\\"W\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n ax.set_ylabel(\\\"H\\\", fontsize=STAT_LOGGER_FONT_SIZE)\\n plt.imshow(color, interpolation=\\\"nearest\\\", origin=\\\"lower\\\", extent=[0, image_size, 0, image_size])\\n\\n # calculate the coverage for the dataset labels\\n cover_masks = []\\n for i in range(anchors_len):\\n w_max = (anchors_boxes[i][0] / image_size) * 4\\n w_min = (anchors_boxes[i][0] / image_size) * 0.25\\n h_max = (anchors_boxes[i][1] / image_size) * 4\\n h_min = (anchors_boxes[i][1] / image_size) * 0.25\\n cover_masks.append(\\n np.logical_and(np.logical_and(np.logical_and(labels[:, 3] < w_max, labels[:, 3] > w_min), labels[:, 4] < h_max), labels[:, 4] > h_min)\\n )\\n cover_masks = np.stack(cover_masks)\\n coverage = np.count_nonzero(np.any(cover_masks, axis=0)) / len(labels)\\n\\n self.sg_logger.add_figure(tag=f\\\"{title} anchors coverage\\\", figure=fig)\\n return coverage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47644e7446b102e28b498746ab865996\",\n \"score\": \"0.49319172\",\n \"text\": \"def exportMap():\\r\\n global OD\\r\\n iface.mapCanvas().saveAsImage('D:/Users/63707/Documents/python3/C_TWN_road_attribute/test/{}_{}.png'.format(OD[0], OD[1]))\\r\\n QTimer.singleShot(500, next_point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b13f0d421a009b0aa89c97c41e99864\",\n \"score\": \"0.49302006\",\n \"text\": \"def test_CAS7697(self):\\n myia = iatool()\\n imagename = \\\"CAS7697.im\\\"\\n myia.fromshape(imagename, [100,100, 10])\\n myia.addnoise()\\n bb = myia.getchunk()\\n bb[:,:,9] = 40\\n myia.putchunk(bb)\\n myia.calcmask(imagename + \\\"<30\\\")\\n for clmethod in [\\\"framework\\\", \\\"tiled\\\"]:\\n stats = myia.statistics(axes=[0,1], clmethod=clmethod)\\n self.assertTrue(stats['max'][9] == 0)\\n self.assertTrue(stats['min'][9] == 0)\\n myia.done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4828e1818bd19cd4abb5f78fe07cd29\",\n \"score\": \"0.49240312\",\n \"text\": \"def test_plot_65_qubit_gate_map(self):\\n # getting the mock backend from FakeProvider\\n\\n backend = FakeManhattan()\\n\\n fname = \\\"65_qubit_gate_map.png\\\"\\n self.graph_plot_gate_map(backend=backend, filename=fname)\\n\\n ratio = VisualTestUtilities._save_diff(\\n self._image_path(fname),\\n self._reference_path(fname),\\n fname,\\n FAILURE_DIFF_DIR,\\n FAILURE_PREFIX,\\n )\\n self.assertGreaterEqual(ratio, 0.99)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aac14006c4b3e3f041e01f950be2f9c\",\n \"score\": \"0.49185798\",\n \"text\": \"def test_arcs(self):\\r\\n image = self.design.layout.layers[0].images[0]\\r\\n assert len(image.traces) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8323cdd29be54f591199d64662be6a9\",\n \"score\": \"0.4911458\",\n \"text\": \"def mapper(self, sample):\\n if sample.general.bestassemblyfile != \\\"NA\\\":\\n # Define the Qualimap log and report files\\n reportfile = os.path.join(sample.general.QualimapResults, 'genome_results.txt')\\n # Define the Qualimap call\\n qualimapcall = 'qualimap bamqc -bam {} -outdir {}'.format(sample.general.sortedbam,\\n sample.general.QualimapResults)\\n sample.commands.qualimap = qualimapcall\\n # Initialise a dictionary to hold the Qualimap results\\n qdict = dict()\\n # If the report file doesn't exist, run Qualimap, and print logs to the log file\\n if not os.path.isfile(reportfile):\\n tlock = threading.Lock()\\n out, err = run_subprocess(sample.commands.qualimap)\\n tlock.acquire()\\n write_to_logfile(sample.commands.qualimap, sample.commands.qualimap, self.logfile,\\n sample.general.logout, sample.general.logerr, None, None)\\n write_to_logfile(out, err, self.logfile, sample.general.logout, sample.general.logerr, None, None)\\n tlock.release()\\n # Initialise a genobject to store the coverage dictionaries\\n sample.depth = GenObject()\\n sample.depth.length = dict()\\n sample.depth.bases = dict()\\n sample.depth.coverage = dict()\\n sample.depth.stddev = dict()\\n try:\\n with open(reportfile) as report:\\n # Read the report\\n for line in report:\\n # Sanitise the keys and values using self.analyze\\n key, value = self.analyze(line)\\n # If the keys and values exist, enter them into the dictionary\\n if (key, value) != (None, None):\\n qdict[key] = value\\n if 'Coverage per contig' in line:\\n for contigline in report:\\n try:\\n _, name, length, bases, coverage, stddev = contigline.rstrip().split('\\\\t')\\n sample.depth.length.update({name: length})\\n sample.depth.bases.update({name: bases})\\n sample.depth.coverage.update({name: coverage})\\n sample.depth.stddev.update({name: stddev})\\n except ValueError:\\n pass\\n\\n except (IOError, FileNotFoundError):\\n pass\\n # If there are values in the dictionary\\n if qdict:\\n # Make new category for Qualimap results and populate this category with the report data\\n for attribute in qdict:\\n # Remove the 'X' from the depth values e.g. 40.238X\\n setattr(sample.mapping, attribute, qdict[attribute].rstrip('X'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a492e4dde4246cfc7a5b12985d9b7e\",\n \"score\": \"0.49097607\",\n \"text\": \"def main():\\n\\n\\tprint \\\"Doing stuff...\\\\n\\\"\\n\\n\\ttest=map.Map()\\n\\ttest.updateMap()\\n\\twhile(1):\\n\\t\\tcv2.namedWindow('map hector', 0)\\n\\t \\t# refresh the image on the screen\\n\\t\\tcv2.imshow('map hector',test._map)\\n\\t\\tcv2.waitKey(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06fd5c3921452fe3a7206ac8452d9488\",\n \"score\": \"0.49032006\",\n \"text\": \"def test_simple(self):\\r\\n image = self.design.layout.layers[0].images[0]\\r\\n assert len(image.traces) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9777612899284a917d16162efc5e2d\",\n \"score\": \"0.4892885\",\n \"text\": \"def test_get_image_information_runs():\\n image_info.get_image_information(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6d586398c52a87597d7f66bdb921021\",\n \"score\": \"0.48847738\",\n \"text\": \"def identify_confusion(fname, base_dir, gt_dir):\\n tiles = open(fname).read().split(\\\"\\\\n\\\")\\n tiles = [t for t in tiles if t!= \\\"\\\"]\\n sums = numpy.zeros(17)\\n for tile in tiles:\\n pred = misc.imread(base_dir + tile + \\\".png\\\")\\n target = misc.imread(gt_dir + tile + \\\".png\\\")\\n target[pred!=1] = 17 # so that only for road (label=1) can the two be equal\\n for i in range(17):\\n sums[i] += numpy.count_nonzero(target==i)\\n\\n for i in range(17):\\n print(f'{i}: {sums[i]}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabdc2adf59282bf0f3d1cc72eae406c\",\n \"score\": \"0.4877715\",\n \"text\": \"def testCoverage_0(self):\\n mtt.makeTempDirParent()\\n tmpDir = os.path.abspath(mtt.makeTempDir('testCoverage_0'))\\n parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\\n customOpts = mafval.GenericValidationOptions()\\n for target, mafSeq, solutionDict in g_knownGood:\\n testMaf = mtt.testFile(os.path.join(os.path.abspath(tmpDir), 'test.maf'),\\n mafSeq, g_headers)\\n cmd = [os.path.abspath(os.path.join(parent, 'test', 'mafCoverage'))]\\n cmd += ['--maf', os.path.abspath(os.path.join(tmpDir, 'test.maf')),\\n '--species', target]\\n outpipes = [os.path.join(tmpDir, 'output.txt')]\\n mtt.recordCommands([cmd], tmpDir, outPipes=outpipes)\\n mtt.runCommandsS([cmd], tmpDir, outPipes=outpipes)\\n self.assertTrue(mafval.validateMaf(os.path.join(tmpDir, 'test.maf'),\\n customOpts))\\n self.assertTrue(coverageIsCorrect(os.path.join(tmpDir, 'output.txt'),\\n solutionDict))\\n mtt.removeDir(tmpDir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ef77b99e077457106c74116ec8a0b6\",\n \"score\": \"0.4867624\",\n \"text\": \"def test_pokemon_map_output(self, mock_stdout):\\n expected_output = \\\"🐱 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\\\\\n \\\"🌲 🌲 🌲 🌲 🌲 🌲 🌲 🌲\\\\n\\\"\\n pokemon_map()\\n self.assertEqual(mock_stdout.getvalue(), expected_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2db9d9d76a3fee17e2c5c301c723790\",\n \"score\": \"0.48535594\",\n \"text\": \"def main():\\n args = argument_parser()\\n args.mbtiles_file.close()\\n mbtiles_file = args.mbtiles_file.name\\n zoom = args.zoom\\n tile_x = args.tilex\\n tile_y = args.tiley\\n mvt_content = run(tile_x, tile_y, zoom, mbtiles_file)\\n print(mvt_content, end='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f8bc2455db32b045117a3787dd6713\",\n \"score\": \"0.48534715\",\n \"text\": \"def _binclass_print_teststats(self,pmodel,targets,imode):\\n popts = helpers.Struct()\\n popts.imode = imode\\n popts.ptype = 3\\n (h_q, rho_q, logz, h_p, rho_p) = self.predict(pmodel,popts)\\n nte = targets.shape[0]\\n acc = 100.*float((np.sign(h_q)==targets).sum())/nte\\n loglh = logz.sum()/nte\\n print ('Test set predictions: Accuracy: %4.2f%%, '\\n 'log likelihood: %.6f') % (acc, loglh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d5df76f4da061f2daa4865cca78aba\",\n \"score\": \"0.48515138\",\n \"text\": \"def _BuilderRunsTestOfInterest(self, test_map: Dict[str, Any]) -> bool:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd94393951093aaab4d4db1a73b30e1f\",\n \"score\": \"0.4850536\",\n \"text\": \"def test_plot_1_qubit_gate_map(self):\\n # getting the mock backend from FakeProvider\\n\\n backend = FakeArmonk()\\n\\n fname = \\\"1_qubit_gate_map.png\\\"\\n self.graph_plot_gate_map(backend=backend, filename=fname)\\n\\n ratio = VisualTestUtilities._save_diff(\\n self._image_path(fname),\\n self._reference_path(fname),\\n fname,\\n FAILURE_DIFF_DIR,\\n FAILURE_PREFIX,\\n )\\n self.assertGreaterEqual(ratio, 0.99)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06449bd1248ff80e999ddbec03cb704f\",\n \"score\": \"0.48434246\",\n \"text\": \"def show_map_batch(sample_batched, img_to_show=3, save_file_path=None, as_numpy=False):\\n\\n # just select 6 images to show per batch\\n sat_img_batch, map_img_batch = sample_batched['sat_img'][:img_to_show, :, :, :],\\\\\\n sample_batched['map_img'][:img_to_show, :, :, :]\\n batch_size = len(sat_img_batch)\\n\\n f, ax = plt.subplots(int(np.ceil(batch_size / 3)), 3, figsize=(15, int(np.ceil(batch_size / 3)) * 5))\\n f.tight_layout()\\n f.subplots_adjust(hspace=.05, wspace=.05)\\n ax = ax.ravel()\\n\\n # unorm = UnNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\\n\\n for i in range(batch_size):\\n ax[i].axis('off')\\n show_map(sat_img=sat_img_batch.cpu().numpy()[i, :, :, :].transpose((1, 2, 0)),\\n map_img=map_img_batch.cpu().numpy()[i, 0, :, :], axis=ax[i])\\n\\n if save_file_path is not None:\\n f.savefig(save_file_path)\\n\\n if as_numpy:\\n f.canvas.draw()\\n width, height = f.get_size_inches() * f.get_dpi()\\n mplimage = np.frombuffer(f.canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\\n plt.cla()\\n plt.close(f)\\n\\n return mplimage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968e8429c1fa23ffe0ac80617f446638\",\n \"score\": \"0.4835228\",\n \"text\": \"def main():\\n\\n options, configs = parse_args(sys.argv)\\n ip_reader = csv.reader(open(options.ips, 'rb'), delimiter=',')\\n ip_converter = IpConverter(configs)\\n width = options.width\\n height = options.height\\n color = 'black'\\n # Here we apply ratios, since the map will be croped later on.\\n width = int(width * 2.5)\\n height = int(height * 1.33333333) \\n world_map = Map(width, height, color)\\n\\n roll = options.gen\\n current_roll = None\\n\\n for row in ip_reader:\\n try:\\n ip = proxy_filter(row[0])\\n timestamp = parse(row[1])\\n if current_roll is None:\\n current_roll = getattr(timestamp, roll)\\n elif current_roll != getattr(timestamp, roll) and\\\\\\n getattr(timestamp, roll) % options.gen_accuracy == 0:\\n current_roll = getattr(timestamp, roll)\\n # print current map to file, and generate new one.\\n world_map.render('%s/%s.jpg' % (configs['maps']['folder'],\\n timestamp))\\n\\n data = ip_converter.get_gps_from_ip(ip)\\n if data is not None:\\n world_map.add_gps_to_map(data) \\n except pygeoip.GeoIPError, error:\\n logging.error(error)\\n except ValueError, error:\\n logging.error(error)\\n\\n world_map.render('%s/%s.jpg' % (configs['maps']['folder'],\\n timestamp))\\n \\n # generates video oO\\n video = Video(width, height) \\n video.build(configs['maps']['folder'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b8cde4718350bb315854d64300d7f6\",\n \"score\": \"0.48325807\",\n \"text\": \"def view_coverage_browser():\\n indexfile = os.path.join(COVERAGE_DIR, 'index.html')\\n if not os.path.exists(indexfile):\\n print_err(C(': ').join(\\n C('Missing coverage report file', 'red'),\\n C(indexfile, 'blue'),\\n ))\\n print_err(C(' ').join(\\n C('Run', 'red'),\\n C('./runtests.py -R', 'blue').join('`', '`'),\\n C('to generate it.', 'red'),\\n ))\\n return 1\\n\\n cmd = ['google-chrome', indexfile]\\n print(C(': ').join(\\n C('Running', 'cyan'),\\n C(' ').join(\\n C(cmd[0], 'blue', style='bright'),\\n C(' '.join(cmd[1:]), 'blue'),\\n ),\\n ))\\n subprocess.Popen(cmd, stderr=subprocess.DEVNULL)\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a313c8be0e2e8b2704386ff1ccbc7e\",\n \"score\": \"0.48314133\",\n \"text\": \"def main():\\n NAME = os.path.basename(__file__).split(\\\".\\\")[0]\\n\\n with its.device.ItsSession() as cam:\\n props = cam.get_camera_properties()\\n if (not its.caps.raw(props) or\\n not its.caps.read_3a(props)):\\n print \\\"Test skipped\\\"\\n return\\n\\n cam.do_3a()\\n\\n req = its.objects.auto_capture_request()\\n cap_dng, cap_yuv = cam.do_capture(req, cam.CAP_DNG_YUV)\\n\\n img = its.image.convert_capture_to_rgb_image(cap_yuv)\\n its.image.write_image(img, \\\"%s.jpg\\\" % (NAME))\\n\\n with open(\\\"%s.dng\\\"%(NAME), \\\"wb\\\") as f:\\n f.write(cap_dng[\\\"data\\\"])\\n\\n # No specific pass/fail check; test is assumed to have succeeded if\\n # it completes.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec7c2399041533fb56193b89ffdbba6\",\n \"score\": \"0.4824971\",\n \"text\": \"def main():\\n cur_dir = os.path.dirname(os.path.realpath(sys.argv[0]))\\n input_tif = cur_dir + \\\"/../tests/data/Jamaica_dem.tif\\\"\\n rst = RasterUtilClass.read_raster(input_tif)\\n # metadata information\\n print(\\\"rows: %d, cols: %d\\\" % (rst.nRows, rst.nCols))\\n print(\\\"LLCornerX: %.2f, LLCornerY: %.2f\\\" % (rst.xMin, rst.yMin))\\n print(\\\"cell size: %.1f\\\" % rst.dx)\\n # basic statistics, nodata is excluded\\n print(\\\"mean: %.2f, max: %.2f, min: %.2f\\\" % (rst.get_average(), rst.get_max(), rst.get_min()))\\n print(\\\"std: %.2f, sum: %.2f\\\" % (rst.get_std(), rst.get_sum()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5c060fc70675e7deef53904d2e9cbe\",\n \"score\": \"0.4817491\",\n \"text\": \"def test_vis(layer_to_img_dict, model_fn, output_dir =\\\"./zeiler_fergus_vis\\\" ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0a6bbf904f11b4cce066edcd8f802e9\",\n \"score\": \"0.48165083\",\n \"text\": \"def coverage(ctx):\\n path = (_COVERAGE_PATH / \\\"index.html\\\").absolute()\\n webbrowser.open(f\\\"file:///{path}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c09238ae978e4d6eed66c8fd3ecc767\",\n \"score\": \"0.48047954\",\n \"text\": \"def analyze(display=True):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec70ece3f91c8315b5ad8fbf08a67ab0\",\n \"score\": \"0.48042879\",\n \"text\": \"def inspect(output_dir):\\n #log = getLog(\\\"tmp.log\\\")\\n log.info(\\\"Check parcellation\\\")\\n\\n reg_path = output_dir\\n\\n cmdstr = op.join(reg_path, 'fsmask_1mm.nii.gz ')\\n\\n for p in lausanne_spec.keys():\\n cmdstr += \\\" \\\" + op.join(reg_path, p, 'ROIv_HR_th.nii.gz')\\n\\n freeview_view_cmd = 'freeview %s' % cmdstr\\n runCmd( freeview_view_cmd, log )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2ed09837a856dfe770b7cc93a9a214\",\n \"score\": \"0.47905618\",\n \"text\": \"def task_coverage() -> DoitTask:\\n path_tests = DG.test.path_tests\\n cov_dir = DG.test.path_coverage_index.parent\\n cov_html = f'--cov-report=html:\\\"{cov_dir}\\\" --html=\\\"{DG.test.path_test_report}\\\" --self-contained-html'\\n diff_html = f'--html-report {DG.test.path_diff_test_report}'\\n return debug_task([\\n Interactive(\\n f'poetry run python -m pytest \\\"{path_tests}\\\" {DG.test.args_pytest} --cov={DG.meta.pkg_name} {cov_html}',\\n ),\\n 'poetry run python -m coverage json', # Create coverage.json file for \\\"_write_coverage_to_md\\\"\\n 'poetry run python -m coverage xml',\\n Interactive(f'poetry run diff-cover coverage.xml {DG.test.args_diff} {diff_html}'),\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230ff2c38e3141dc68f6a32bcc0e6686\",\n \"score\": \"0.4780019\",\n \"text\": \"def test_processing():\\n for cleantopo_process in [\\n \\\"testdata/cleantopo_tl.mapchete\\\", \\\"testdata/cleantopo_br.mapchete\\\"\\n ]:\\n mp = mapchete.open(os.path.join(SCRIPTDIR, cleantopo_process))\\n for zoom in range(6):\\n tiles = []\\n for tile in mp.get_process_tiles(zoom):\\n output = mp.execute(tile)\\n tiles.append(output)\\n assert isinstance(output, BufferedTile)\\n assert isinstance(output.data, ma.MaskedArray)\\n assert output.data.shape == output.shape\\n assert not ma.all(output.data.mask)\\n mp.write(output)\\n mosaic, mosaic_affine = create_mosaic(tiles)\\n try:\\n temp_vrt = os.path.join(OUT_DIR, str(zoom)+\\\".vrt\\\")\\n gdalbuildvrt = \\\"gdalbuildvrt %s %s/%s/*/*.tif > /dev/null\\\" % (\\n temp_vrt, OUT_DIR, zoom)\\n os.system(gdalbuildvrt)\\n with rasterio.open(temp_vrt, \\\"r\\\") as testfile:\\n for file_item, mosaic_item in zip(\\n testfile.meta[\\\"transform\\\"], mosaic_affine\\n ):\\n assert file_item == mosaic_item\\n band = testfile.read(1, masked=True)\\n assert band.shape == mosaic.shape\\n assert ma.allclose(band, mosaic)\\n assert ma.allclose(band.mask, mosaic.mask)\\n finally:\\n shutil.rmtree(OUT_DIR, ignore_errors=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a730a490732d7e97e279455c2300003\",\n \"score\": \"0.47758254\",\n \"text\": \"def test_with_dwarf(self):\\n self.buildDwarf()\\n self.do_my_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a2bdd7a6815c519e4162f477e867fb\",\n \"score\": \"0.47702894\",\n \"text\": \"def tile_classifier__simple_test(ctx, self, img):\\n return tile_classifier__simple_test_impl(self, img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fb06daeacde1a62deb09fc5f923e3f\",\n \"score\": \"0.47621217\",\n \"text\": \"def runAnalysis(aif):\\n \\n for blob in aif.getTraceSetBlobsForTargetAndExperiment():\\n\\n aif.runAverageTraceForTraceSetBlob(blob)\\n\\n di1_xor_di2 = aif.opXor(blob, \\\"di1\\\", \\\"di2\\\")\\n di3_xor_di4 = aif.opXor(blob, \\\"di3\\\", \\\"di4\\\")\\n di5_xor_di6 = aif.opXor(blob, \\\"di5\\\", \\\"di6\\\")\\n\\n aif.runHammingDistanceAnalysis(blob,\\n di1_xor_di2, di3_xor_di4, \\\"HD(d1^d2,d3^d4)\\\")\\n \\n aif.runHammingDistanceAnalysis(blob,\\n di1_xor_di2, di5_xor_di6, \\\"HD(d1^d2,d5^d6)\\\")\\n \\n aif.runHammingDistanceAnalysis(blob,\\n di3_xor_di4, di5_xor_di6, \\\"HD(d3^d4,d5^d6)\\\")\\n\\n aif.runHammingDistanceAnalysis(blob, \\\"di1\\\",\\\"di2\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di1\\\",\\\"di3\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di1\\\",\\\"di4\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di1\\\",\\\"di5\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di1\\\",\\\"di6\\\")\\n\\n aif.runHammingDistanceAnalysis(blob, \\\"di2\\\",\\\"di3\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di2\\\",\\\"di4\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di2\\\",\\\"di5\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di2\\\",\\\"di6\\\")\\n\\n aif.runHammingDistanceAnalysis(blob, \\\"di3\\\",\\\"di4\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di3\\\",\\\"di5\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di3\\\",\\\"di6\\\")\\n \\n aif.runHammingDistanceAnalysis(blob, \\\"di4\\\",\\\"di5\\\")\\n aif.runHammingDistanceAnalysis(blob, \\\"di4\\\",\\\"di6\\\")\\n \\n aif.runHammingDistanceAnalysis(blob, \\\"di5\\\",\\\"di6\\\")\\n\\n aif.runHammingWeightAnalysis(blob, \\\"di1\\\")\\n aif.runHammingWeightAnalysis(blob, \\\"di2\\\")\\n aif.runHammingWeightAnalysis(blob, \\\"di3\\\")\\n aif.runHammingWeightAnalysis(blob, \\\"di4\\\")\\n aif.runHammingWeightAnalysis(blob, \\\"di5\\\")\\n aif.runHammingWeightAnalysis(blob, \\\"di6\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"224d4f1415b6079fc8f2ce273019715c\",\n \"score\": \"0.47593078\",\n \"text\": \"def show(self):\\n c_map=copy.deepcopy(self.map[:])\\n c_map[self.agent.pos[0]][self.agent.pos[1]]=\\\"I\\\"\\n for e in self.enemies:\\n c_map[e[0]][e[1]]=\\\"E\\\"\\n #print(c_map)\\n print(\\\"---------------------------\\\")\\n for i in range(len(c_map)):\\n str=\\\"\\\"\\n for c in c_map[i]:\\n str=str+c\\n print(\\\"|\\\"+str+\\\"|\\\")\\n \\n print(\\\"---------------------------\\\")\\n str2=\\\"\\\"\\n for i in range(self.agent.energy):\\n str2=str2+\\\"H\\\"\\n print(str2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1ad359f0f9776b598853ee7050b816\",\n \"score\": \"0.47584385\",\n \"text\": \"def print_hitmap(hitmap):\\r\\n lines = 0\\r\\n for i in xrange(256):\\r\\n if (i % 16) == 0:\\r\\n line = [\\\"%02X: \\\"% (i)]\\r\\n if i not in hitmap or hitmap[i][List([])] == 0:\\r\\n line.append(\\\" \\\")\\r\\n else:\\r\\n line.append(\\\"%03i\\\" % hitmap[i][List([])])\\r\\n\\r\\n if (i % 16) == 15:\\r\\n line = \\\" \\\".join(line)\\r\\n if len(line.strip()) > 3:\\r\\n lines += 1\\r\\n print line\\r\\n if lines == 0:\\r\\n print \\\" <nothing>\\\"\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5565c18e384263a07d53db2ee08c17b\",\n \"score\": \"0.4757261\",\n \"text\": \"def test_imshow():\\n s = numpy.random.random((10, 10))\\n return imshow(s, colormap='gist_earth')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd252b434d78687d5b9316d1131e0285\",\n \"score\": \"0.47531605\",\n \"text\": \"def test_plot_27_qubit_gate_map(self):\\n # getting the mock backend from FakeProvider\\n\\n backend = FakeMumbai()\\n\\n fname = \\\"27_qubit_gate_map.png\\\"\\n self.graph_plot_gate_map(backend=backend, filename=fname)\\n\\n ratio = VisualTestUtilities._save_diff(\\n self._image_path(fname),\\n self._reference_path(fname),\\n fname,\\n FAILURE_DIFF_DIR,\\n FAILURE_PREFIX,\\n )\\n self.assertGreaterEqual(ratio, 0.99)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97a9e81b5269a159f2d3baa8ad997a6\",\n \"score\": \"0.474789\",\n \"text\": \"def cvg_main(mappedlocations, conflictlocations, bamfile, reference, outdir, prefix):\\n newpath = 'coverage'\\n os.makedirs(os.path.join(outdir,newpath))\\n bar = progressbar.ProgressBar(widgets = ['Running SAMtools: ', progressbar.Bar(), '(', progressbar.ETA(),')'])\\n for i in bar(range(1)):\\n make_bed(mappedlocations, conflictlocations, reference, outdir, prefix)\\n sam(bamfile, outdir, prefix)\\n output(outdir, prefix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a911a47541d3d0b60f7d5ec64134ca42\",\n \"score\": \"0.4746671\",\n \"text\": \"def show(img):\\n remap = \\\" .*#\\\"+\\\"#\\\"*100\\n img = (img.flatten()+.5)*3\\n if len(img) != 784: return\\n print(\\\"START\\\")\\n for i in range(28):\\n print(\\\"\\\".join([remap[int(round(x))] for x in img[i*28:i*28+28]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00fdd08672d2f1b9bd69a34939389e67\",\n \"score\": \"0.47436896\",\n \"text\": \"def test_init() -> None:\\n # check that the map start with no info\\n map_ = sm.SepalMap()\\n\\n # add a fullscreenControl\\n control = sm.FullScreenControl(map_)\\n map_.add(control)\\n\\n assert isinstance(control, sm.FullScreenControl)\\n assert control in map_.controls\\n assert control.zoomed is False\\n assert \\\"fa-solid fa-expand\\\" in control.w_btn.children[0].children\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dad434907b996751cbbf6b0012a96f6\",\n \"score\": \"0.47427168\",\n \"text\": \"def test_run(self):\\n CalibrationTest.setup_flags(self)\\n log_file = os.path.join(self.io_args.log_dir, \\\"CalibrationLibMain.INFO\\\")\\n\\n # CalibrationLibMain assumes the operating frame to be 000000\\n lib_main_input = self.io_args.color_full + \\\"_000000\\\"\\n if not os.path.exists(lib_main_input):\\n shutil.copytree(self.io_args.color_full, lib_main_input)\\n for cam in os.listdir(lib_main_input):\\n cur_img = os.path.join(lib_main_input, cam, f\\\"{self.io_args.first}.png\\\")\\n new_img = os.path.join(lib_main_input, cam, \\\"000000.png\\\")\\n os.rename(cur_img, new_img)\\n\\n self.run_app(\\n \\\"CalibrationLibMain\\\",\\n args=f\\\"{self.io_args.rig_out} {self.io_args.matches} {self.io_args.rig_in} {lib_main_input}\\\",\\n log_file=log_file,\\n )\\n record = parse_calibration_results(\\n self.io_args.log_dir, bin_name=\\\"CalibrationLibMain\\\"\\n )\\n self.check_metrics(record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7687404d80ae6c33acab4583d0a717\",\n \"score\": \"0.47350043\",\n \"text\": \"def test_imshow():\\n s = np.random.random((10, 10))\\n return imshow(s, colormap='gist_earth')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbacaf1685129d12771b8a3bafeb9645\",\n \"score\": \"0.47331163\",\n \"text\": \"def test_glyph(self):\\r\\n\\r\\n glyph = \\\"a\\\"\\r\\n self.put_text(glyph)\\r\\n\\r\\n #found = list(scrape.str_from_img(self.img))\\r\\n #assert len(found) == 1, found\\r\\n\\r\\n #self._show()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9634646b116b42a3395be9c9594257ab\",\n \"score\": \"0.47267002\",\n \"text\": \"def display_mapping(mapping):\\n img_size = mapping[\\\"frame_size\\\"]\\n win_name = \\\"mappings\\\"\\n win = cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)\\n\\n for frame, cmd in izip(mapping[\\\"frames\\\"], mapping[\\\"commands\\\"]):\\n read_cmd = command_readable_mapping[cmd]\\n msg = read_cmd\\n img = frame.reshape(img_size)\\n cv2.putText(img, msg, (10, 10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.6, (0, 0, 0))\\n cv2.imshow(win_name, img)\\n if cv2.waitKey(0) & ord(\\\"q\\\") == ord(\\\"q\\\"):\\n break\\n\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a35767f00285b47d024b9be766389b6\",\n \"score\": \"0.47253826\",\n \"text\": \"def ShowRasterRef(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a35767f00285b47d024b9be766389b6\",\n \"score\": \"0.47253826\",\n \"text\": \"def ShowRasterRef(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63091d4aa3ccba4681ade7b286339f3c\",\n \"score\": \"0.47246438\",\n \"text\": \"def get_map_and_measure(png_a):\\n\\n import os\\n from CPAC.qc.utils import get_map_id\\n\\n measure_name = None\\n map_name = None\\n\\n if '_fwhm_' in png_a:\\n measure_name = os.path.basename(os.path.dirname(os.path.dirname(png_a)))\\n else:\\n measure_name = os.path.basename(os.path.dirname((png_a)))\\n\\n str_ = os.path.basename(png_a)\\n\\n if 'sca_tempreg' in png_a:\\n map_name = get_map_id(str_, 'maps_roi_')\\n\\n if 'sca_roi' in png_a:\\n map_name = get_map_id(str_, 'ROI_')\\n\\n if 'dr_tempreg' in png_a:\\n map_name = get_map_id(str_, 'temp_reg_map_')\\n\\n if 'centrality' in png_a:\\n map_name = get_map_id(str_, 'centrality_')\\n\\n return map_name, measure_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b65bab80c3c41304d6668ee9995edb\",\n \"score\": \"0.47228912\",\n \"text\": \"def test_run(self):\\n in_bam = 'test.bam'\\n index = 'test.bam.bai'\\n picard_memory = '58G'\\n picard_path = 'path/to/picard'\\n temp_dir = 'path/to/temp/dir'\\n lines = ps.general._picard_index(in_bam, index, picard_memory,\\n picard_path, temp_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a68f3ca07821c27980fea0d3f693a686\",\n \"score\": \"0.47226566\",\n \"text\": \"def test_get_summary_heat(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d6fb16a706bab3f4f1da4c77b58cff\",\n \"score\": \"0.4718451\",\n \"text\": \"def simple_drizzle(images, coverage_map, wcs_file, filt):\\n\\n # This calculates how many of the input images cover each\\n # pixel of the output image grid. Used to determine best\\n # median algorithm for drizzle\\n median_coverage = np.median(coverage_map[coverage_map>0])\\n out_name = '{}_final'.format(filt)\\n if median_coverage>4:\\n med_alg = 'median'\\n combine_nhigh = 1\\n else:\\n med_alg = 'minmed'\\n combine_nhigh = 0\\n\\n # Due to bug in astrodrizzle, this can't yet be optimized\\n # to use the mdriztab\\n\\n astrodrizzle.AstroDrizzle(images, clean=True, build=True,\\n context=False, preserve=False,\\n combine_type=med_alg,\\n combine_nhigh=combine_nhigh,\\n in_memory=False, final_wcs=True,\\n final_refimage=wcs_file,\\n output=out_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0cb0aca6f70c1f84aad52a73bf46fc\",\n \"score\": \"0.47070697\",\n \"text\": \"def coverage(context):\\n cmd = \\\"pytest -s --color no --cov=function_pipe/core --cov-report html\\\"\\n context.run(cmd, echo=True)\\n import webbrowser\\n webbrowser.open(\\\"htmlcov/index.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb189ab1309927ca956683d07f55e194\",\n \"score\": \"0.470688\",\n \"text\": \"def run_demo() -> None:\\n qc3 = StandardImagingQC3.from_demo_image()\\n qc3.analyze()\\n qc3.plot_analyzed_image()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9a88ce9716dee489e09726409ec209\",\n \"score\": \"0.47065702\",\n \"text\": \"def display_image(heatmap, class_index):\\n\\tplt.imshow(heatmap[:,:,class_index])\\n\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca23b9533a2413249880941ce1aebda\",\n \"score\": \"0.47005153\",\n \"text\": \"def test_image_processing():\\n src_path = '/media/bryce/1TB Samsung/ml_datasets/cityscapes_data/gtFine/train/aachen'\\n inst_path = os.path.join(src_path, 'aachen_000000_000019_gtFine_instanceIds.png')\\n seg_path = os.path.join(src_path, 'aachen_000000_000019_gtFine_labelIds.png')\\n\\n inst_img = np.array(Image.open(inst_path), dtype=np.int16)\\n\\n base_img = seg_cv2_bgr(seg_path)\\n\\n bboxes = bbox_info_from_instance(inst_img)\\n\\n # assert all(bbox['train_id'] < 19 for bbox in bboxes), f\\\"Invalid Train ID found: {inst_path}.\\\"\\n\\n write_info_json(inst_path, bboxes)\\n\\n visualise_output(base_img, bboxes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647493bcbca791430c7c9fcd16eaa5d4\",\n \"score\": \"0.46926156\",\n \"text\": \"def _run_tests(test_suite, package_name):\\n count = test_suite.countTestCases()\\n print(\\\"########\\\")\\n print(\\\"%s tests has been discovered in %s\\\" % (count, package_name))\\n print(\\\"Python GDAL : %s\\\" % gdal.VersionInfo(\\\"VERSION_NUM\\\"))\\n print(\\\"########\\\")\\n\\n cov = coverage.Coverage(\\n source=[\\\"/tests_directory/quick_api\\\"],\\n omit=[\\n \\\"*/quick_api_dialog_base.py\\\",\\n \\\"*/quick_api.py\\\",\\n \\\"*/__init__.py\\\",\\n \\\"*/tests/*\\\",\\n \\\"*/test_suite.py\\\",\\n ],\\n )\\n cov.start()\\n\\n unittest.TextTestRunner(verbosity=3, stream=sys.stdout).run(test_suite)\\n\\n cov.stop()\\n cov.save()\\n cov.report(file=sys.stdout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031f25a2afd44ea1ea8f8265161d8513\",\n \"score\": \"0.46898025\",\n \"text\": \"def pcap_analysis(self, pcap_md5: str) -> requests.Response:\\n response = requests.get(\\n self.base_url + self.version + '/pcaps/{0}/analysis'.format(pcap_md5),\\n headers=self.headers\\n )\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675a48decb00ebf576ee9f4babf7b254\",\n \"score\": \"0.4689187\",\n \"text\": \"def test_CAS8357(self):\\n myia = iatool()\\n myia.fromshape(\\\"\\\",[30,30])\\n reg = \\\"circle[[-1pix,-1pix], 10pix]\\\"\\n res = myia.statistics(region=reg)\\n myia.done()\\n self.assertTrue(res['npts'] == 70, 'Wrong number of points')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5b3f000570cc3add717dac71290504e\",\n \"score\": \"0.46868145\",\n \"text\": \"def show_cmaps():\\n x = np.linspace(0, 100, 100)[None, :]\\n fig, axs = plt.subplots(int(np.ceil(len(crameri_cmaps) / 7)), 7, figsize=(22, 10))\\n fig.subplots_adjust(hspace=.8, wspace=.08)\\n axs = axs.ravel()\\n for ax in axs:\\n ax.axis('off')\\n for c, cmap_selected in enumerate(sorted(crameri_cmaps.keys())):\\n colourmap = crameri_cmaps[cmap_selected]\\n axs[c].pcolor(x, cmap=colourmap)\\n axs[c].text(5, -0.3, cmap_selected, fontsize=26)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cd0ff294bfe914096081fa6d7e21fb\",\n \"score\": \"0.4684422\",\n \"text\": \"def main():\\n\\n # Parse arguments ---\\n args = parse_arguments()\\n\\n # Load log ---\\n if args.debug:\\n log_level = logging.DEBUG\\n elif args.quiet:\\n log_level = logging.NOTSET\\n else:\\n log_level = logging.INFO\\n\\n log = load_log(log_level)\\n log.info('\\\\n 2D Map Extractor')\\n log.info(' by {0}'.format(__author__))\\n log.info(' Version {0} - {1}\\\\n'.format(__version__, __date__))\\n\\n # Let's start to count the time ---\\n tstart = datetime.datetime.now()\\n log.debug(' [{0}] Script Start'.format(tstart.strftime('%H:%M:%S')))\\n\\n # Perform the 2D-Map Extraction ---\\n results = perform_2dmap_extraction(\\n args.filename, log, args.pool_size, args.algorithm\\n )\\n\\n # Write the results to a FITS file ---\\n write_results(\\n results, args.filename, args.output, args.algorithm,\\n wavelength=args.wavelength\\n )\\n\\n # Now I am good. The script is already done ---\\n tend = datetime.datetime.now()\\n delta_t = tend - tstart\\n\\n log.info('')\\n log.debug(' [{0}] Script finished.'.format(tend.strftime('%H:%M:%S')))\\n log.debug(' Total time elapsed: {:s}'.format(str(delta_t)))\\n log.info('All done.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92df6a69d9ad3315fd73e1a4faae6fe9\",\n \"score\": \"0.4675138\",\n \"text\": \"def inspect(component_run_id, address: str = \\\"\\\"):\\n # Set address\\n if address and len(address) > 0:\\n set_address(address)\\n\\n show_info_card(component_run_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07710d40870a400585b6a1bca36914b0\",\n \"score\": \"0.46741608\",\n \"text\": \"def show_contour():\\n draw = ImageDraw.Draw(OutputImageObj)\\n for list in PixelLocations:\\n for pixel in list:\\n i = pixel[0]\\n j = pixel[1]\\n draw.point((i,j), fill=128)\\n print \\\"Optimal Contour:\\\"\\n for i in range(1, len(Contour)):\\n draw.line(Contour[i-1] + Contour[i], fill=256)\\n print \\\"\\\"\\\" %d %d \\\"\\\"\\\" % (Contour[i-1][0], Contour[i-1][1])\\n OutputImageObj.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdb203951ddb22d24aac5a5dc9075e5\",\n \"score\": \"0.46704444\",\n \"text\": \"def show_sample():\\n # an example to show difference between grayscale image and binary image\\n license_plate = imread(\\\"data/crop_h1/I00000.png\\\", as_grey=True)/255.0\\n print(license_plate.shape)\\n # see the difference between gray scale and binary image\\n gray_car_image = license_plate * 255\\n _, (ax1, ax2) = plt.subplots(1, 2)\\n ax1.imshow(gray_car_image, cmap=\\\"gray\\\")\\n # threshold_otsu is an algorithm to reduce grayscale image to binary image\\n threshold_value = threshold_otsu(gray_car_image)\\n binary_car_image = gray_car_image > threshold_value\\n ax2.imshow(binary_car_image, cmap=\\\"gray\\\")\\n print(binary_car_image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"701ff4791192cd7a6b81dd73cbefbdae\",\n \"score\": \"0.4669743\",\n \"text\": \"def test(ctx, debug=False, maxfail=0):\\n print_header(\\\"RUNNING TESTS\\\")\\n flags = [\\n \\\"--verbose\\\",\\n f\\\"--cov={package.__name__}\\\",\\n \\\"--cov-branch\\\",\\n f'--cov-report=\\\"html:{_COVERAGE_PATH}\\\"',\\n ]\\n if debug:\\n flags.append(\\\"--capture=no\\\")\\n if maxfail:\\n flags.append(f\\\"--maxfail={int(maxfail)}\\\")\\n\\n ctx.run(f\\\"pytest {' '.join(flags)} tests/\\\", pty=True)\\n print(f\\\"\\\\nCoverage: {get_total_coverage(ctx)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea29f2e54879adda842e12aca35d5477\",\n \"score\": \"0.466707\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(allow_abbrev=False)\\n\\n group = parser.add_mutually_exclusive_group()\\n group.add_argument(\\\"-v\\\", \\\"--verbose\\\", help=\\\"display all files processed\\\",\\n action=\\\"store_const\\\", const=logging.INFO, dest='loglevel')\\n group.add_argument(\\\"-d\\\", \\\"--debug\\\", help=\\\"display debugging information\\\",\\n action=\\\"store_const\\\", const=logging.DEBUG, dest='loglevel')\\n\\n parser.add_argument('-o', '--output', default='pinmap.pcf',\\n help='output file with .pcf extension')\\n parser.add_argument(\\\"-n\\\", \\\"--dry-run\\\", help=\\\"examine files without changing\\\",\\n action=\\\"store_true\\\")\\n parser.add_argument(\\\"file\\\", nargs=1, help=\\\"file to check\\\")\\n args = parser.parse_args()\\n logging.basicConfig(level=args.loglevel or logging.WARNING)\\n\\n if args.dry_run:\\n logging.info(\\\"Dry run -- no files will be overwritten\\\")\\n\\n symtable = []\\n for fname in args.file:\\n with open(fname) as source_file:\\n logging.info(r\\\"in: %s\\\", fname)\\n symtable = [mapping for line in source_file if (\\n mapping := get_mapping(line)) is not None]\\n\\n if not symtable:\\n logging.warning('No @MAP_IO statements found for %s', args.output)\\n\\n if not args.dry_run:\\n logging.info(r'out: %s', args.output)\\n with open(args.output, mode='wt') as pcf_file:\\n pcf_file.write(PCF_HEADER)\\n for item in symtable:\\n pcf_file.write(item.to_set_io())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5bfc5a98918592f1bca67e35dc0b50c\",\n \"score\": \"0.4666668\",\n \"text\": \"def test(coverage=False):\\n if coverage and not os.environ.get('FLASK_COVERAGE'):\\n import sys\\n os.environ['FLASK_COVERAGE'] = '1'\\n os.execvp(sys.executable, [sys.executable] + sys.argv)\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\\n if COV:\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n COV.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n COV.erase()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fcc91119c9cbfea625259d7d7afa261\",\n \"score\": \"0.46650156\",\n \"text\": \"def run_canica(params):\\n dataset = datasets.fetch_adhd()\\n func_files = dataset.func\\n output_dir = osp.abspath(params.pop('output_folder'))\\n prepare_report_directory(output_dir)\\n run(func_files, params, output_dir)\\n json.dump(params, open(osp.join(output_dir, 'params.json'), 'w'))\\n img_src_filenames = [osp.join(output_dir, 'images', fname) for fname in\\n os.listdir(osp.join(output_dir, 'images'))\\n if fname.startswith('IC_')]\\n report = generate_report(params, img_src_filenames)\\n reportindex = osp.abspath(osp.join(output_dir, 'index.html'))\\n report.save_html(reportindex)\\n return ('file', 'file://{}'.format(reportindex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9254448f3f41a8594beba8a2005890e5\",\n \"score\": \"0.46635103\",\n \"text\": \"def run(self, showDetails=False):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6eff3134ed4f97c8f6b63d9cc6bfe1e\",\n \"score\": \"0.46633792\",\n \"text\": \"def show_cartoon(contest):\\n image = mpimg.imread(INFO_DIR+str(contest)+\\\"/\\\"+str(contest)+\\\".jpg\\\")\\n plt.figure(figsize=(12, 12))\\n plt.imshow(image, cmap='Greys_r')\\n plt.axis('off')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79287e033e60334c42fe8373afbdcf8f\",\n \"score\": \"0.46548027\",\n \"text\": \"async def fetch_coverage() -> coverage.Coverage:\\n client = TestingCupid()\\n data = await client.coverage()\\n await client.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29024d67bc14cf9d4dea2504f87630f0\",\n \"score\": \"0.46501207\",\n \"text\": \"def test(coverage=False):\\n if coverage and not os.environ.get('FLASK_COVERAGE'):\\n import sys\\n os.environ['FLASK_COVERAGE'] = '1'\\n os.execvp(sys.executable, [sys.executable] + sys.argv)\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\\n if COV:\\n COV.stop()\\n COV.save()\\n print('Test coverage summary:')\\n COV.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n COV.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n COV.erase()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb35b605a9b9f440b5fc3814cd8443ef\",\n \"score\": \"0.46497378\",\n \"text\": \"def test_show_command(self, capsys):\\n main(\\\"show pydicom::MR_small_RLE.dcm\\\".split())\\n out, err = capsys.readouterr()\\n\\n assert \\\"Instance Creation Date DA: '20040826'\\\" in out\\n assert out.endswith(\\\"OB: Array of 126 elements\\\\n\\\")\\n assert err == \\\"\\\"\\n\\n # Get a specific data element\\n main(\\\"show pydicom::MR_small_RLE.dcm::LargestImagePixelValue\\\".split())\\n out, _ = capsys.readouterr()\\n assert \\\"4000\\\" == out.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5c851f00dddf23a1fba3b5955740df\",\n \"score\": \"0.46470836\",\n \"text\": \"def info(test_func):\\n\\n @wraps(test_func)\\n def test_func_wrapper(nom, test):\\n print '\\\\n============================================================='\\n print 'Starting test:', test_func.__name__\\n print 'Testing highland analysis:', nom.name\\n print 'originally processed with:', nom.input_file\\n print 'nominal version:', nom.input_type, nom.version\\n print 'test version:', test.input_type, test.version, '\\\\n'\\n test_func(nom, test)\\n\\n return test_func_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437a5ac03fddaa97b642ffba4f22bbc8\",\n \"score\": \"0.46434614\",\n \"text\": \"def show_map(self, rbt_pos):\\n # normalise values in numpy matrix occ_grid.m to values between 0 and 255, + RGB.\\n # img_mat = uint8(self.m * 255) # multiply by 255 from 0-1 to 0-255\\n # img_mat = stack((img_mat, img_mat, img_mat), 2) # convert to RGB (3rd dim)\\n # print(img_mat.shape) # (100, 100, 3)\\n \\n # color the robot position as a crosshair\\n rbt_idx = self.pos2idx(rbt_pos) # robot index\\n # img_mat[rbt_idx[0], rbt_idx[1], :] = (0, 255, 0) # green\\n img_mat = self.m.copy()\\n img_mat[rbt_idx] = 1 # white\\n img_mat[rbt_idx] = 0\\n img_mat[rbt_idx[0] - 1, rbt_idx[1]] = 1\\n img_mat[rbt_idx[0] + 1, rbt_idx[1]] = 1\\n img_mat[rbt_idx[0], rbt_idx[1] - 1] = 1\\n img_mat[rbt_idx[0], rbt_idx[1] + 1] = 1\\n \\n # print to a window 'img'\\n cv2.imshow('img', img_mat)\\n cv2.waitKey(10);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f88957f0e8d8f4c413bf8b3de4c55a9\",\n \"score\": \"0.46375304\",\n \"text\": \"def test_cmap2act(self):\\n colors.cmap2act('viridis', filename=self.f)\\n ref = os.path.join(self.ref_dir, 'viridis.act')\\n\\n assert filecmp.cmp(self.f, ref)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994476,"cells":{"query_id":{"kind":"string","value":"7de2e432a41ce01f92bdcf930bbdbe45"},"query":{"kind":"string","value":"A rule statement that defines a string match search for AWS WAF to apply to web requests. See Byte Match Statement below for details."},"positive_passages":{"kind":"list like","value":[{"docid":"588cd4e70cd75d6bde629d1c0c44578a","score":"0.54430264","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""}],"string":"[\n {\n \"docid\": \"588cd4e70cd75d6bde629d1c0c44578a\",\n \"score\": \"0.54430264\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"90ea33f01c95e611308c46f7671fe928","score":"0.6055283","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"37b1bade8a13772a1924d586704bd672","score":"0.60350204","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"c07caf852cad3f61887cc0c04acf80d3","score":"0.60124177","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"46b5cb9235185a3e27fe5006d9e51615","score":"0.5988622","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"1e94d06db038d8401e3bba08848f9e27","score":"0.5986877","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"b185edbbcf2fddc1e7c66b74474f46e2","score":"0.59791476","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"4d60b5e41f96c8acc8626113f3af3137","score":"0.5976147","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"187f497294343371bdf70da54614d4c0","score":"0.5970547","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"211d0c9f82c3481006caa81549caee72","score":"0.5967579","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"06ef80d3ae0ee352594ad4496b624769","score":"0.59659845","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"25432b072bf96b04f097e91d94180f72","score":"0.5965506","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"08af4ef69d822a81d4bbeaaa1ed47e45","score":"0.59638906","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"7cae839a37a3c67205d80db18810ef2c","score":"0.59469104","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"660436ffd9e7f0ea30168c3ea987c8cb","score":"0.5943673","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"2d9eabcd9fb79cdd6b3b8634007147b6","score":"0.5931858","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"302cc600cd10c159077449d556fb40b7","score":"0.59254676","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"1465b0769d5079e441ae518a7f9f9eea","score":"0.5920594","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"650cd976e29b4cfd9858e770e2d1234b","score":"0.59054774","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"eef65009e0116bbd7ca730775c8c0c1b","score":"0.5894218","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"2da3c86c2e1228b1375bdd1286c6d317","score":"0.58768034","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"f3f309c946a0376cfbdc1eab97ef51cf","score":"0.5875306","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"75dd1e3bbb23a92627f42c2e9f3199e9","score":"0.5872796","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"943b8d14175e786a1d650c4db39a9053","score":"0.5872302","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"a837434a4f2ac0c998ea4feb15c85ec7","score":"0.58694166","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"45cfbaf06ab2254a077d47e289d2ac86","score":"0.58691984","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"d6caf2260d1330d7af7cfcef165e4be6","score":"0.5864394","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"88c09673c44eef79f4e3647eac5fa96e","score":"0.58545715","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"d659677e7e5145648a3b2fa0e783dea3","score":"0.5851143","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"e3b9ffa7c147cc3c90002cce76c4c9f4","score":"0.5846507","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"8fdf75f3739b5b94ac38dd438be85efd","score":"0.584298","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"83d6ed5e1b6673a0190e99f492fe8cc0","score":"0.5842126","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"dd2805c501a696b0fe4709b4006433d4","score":"0.58298934","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"4f9b1825dd3289049e15e6e4de98b53d","score":"0.58291286","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"96cedc9ba3b3baf89d96d514deb85e37","score":"0.58158344","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"70d3f90055878a9e124852de47964bf2","score":"0.5815436","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"aaf97b938073f2bcdddb86a961127576","score":"0.5811115","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"1e521d1cc80b9dc654de31911454ecc4","score":"0.5806521","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"79eb9179fcb4c8d3a594704f4c5030e0","score":"0.5804012","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"0f7fdde4e6735e967771ed68453b88c8","score":"0.57720006","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"3bd855293e70252b24a72f3e713837ae","score":"0.5767267","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"15fc1837ee33de50497b8bceca1164c3","score":"0.5600881","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"43be85b96aae0ec5dd79ceb03bf0e12a","score":"0.5579652","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"b27aaafcf484652887e96a9d34bb9769","score":"0.55768216","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"28d07d5c9b0ae297fe07ce16626ce3c1","score":"0.5560359","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"c243d741d07111cde308f1b6a05474b3","score":"0.55501586","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"f254bb94ffba4eb972e32f852beb2154","score":"0.5530985","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"0a922fd098a717bc390cac3a3e441b2e","score":"0.55158365","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"d96be1e51e119585f7ecb9d43e8b6085","score":"0.55146426","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"167f755d574ca4fc212f5708f3c51728","score":"0.5513237","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"a295695a3436291c629430a76f67368b","score":"0.5510537","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"f2aa7417970f67542931bf89248d304e","score":"0.55066735","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"d3559e170ee208656a83ec7109a46526","score":"0.54610586","text":"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"c86e1248854060cd206a0c4ec1c33981","score":"0.54358566","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"b1baf88753686e0a92fcc3abd27f0daf","score":"0.5433733","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"15065d1c53d38dd998bc2da392246ee6","score":"0.543036","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"3c640962ecbb251be98c908fb990a117","score":"0.541499","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"6fc9d0fabe5f2360fa60eb279d7920a3","score":"0.54079485","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"68b784c51444fa5c9e15e8adf3f1e5f2","score":"0.53983206","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"22ab98b8b10e07b9711d415f0d47f9f6","score":"0.53966856","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"2309c88dfa1c7a08bd6a494db8368a54","score":"0.53933","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"729e9efdbc9600f29c89bdb1f3e577ad","score":"0.5392608","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"aeaa52f5b62ee0deb660364b64344323","score":"0.5385919","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"06d7b9052fc55b372ee9d959fcf4dfb7","score":"0.5384918","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"cfdb290985e565a10675182947500265","score":"0.5382792","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"353228a97d3b84627252e1d850905208","score":"0.53812593","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"646a1625afc630a4726c031635613b10","score":"0.5379696","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"0f4235da4d750fff0260c4f1ffbcf48a","score":"0.5377892","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"ab7fdddc1a407643c4ef565890ebc013","score":"0.5367528","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"a8f3142f60fd1ee48b35127166cd9b93","score":"0.5365676","text":"def match(self, match):\r\n pass","title":""},{"docid":"991670b48a340f62646bb9a6768f29b9","score":"0.53637457","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"443a7401789eeff44d82a158661d4caa","score":"0.5361841","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"d25b138c90c9ddac471edaf78ba0a442","score":"0.5358515","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"b8425ebe37784f485f0c12022b12be5d","score":"0.53569084","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"5a904b4eebe66a211c5fb8a6cd57c1f0","score":"0.53545016","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"7c0390f734eaa35906dedc2eeaeef98e","score":"0.5352244","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"c5765610e2df617ca75788b506933a52","score":"0.5348085","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"3fd669c7cbdcff82ae5d35fe5ce7c6da","score":"0.5343908","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"659e9ee9281454861721792bc1dc8fb8","score":"0.5341646","text":"def test_payload_match(self):\n # Case 1 possible ssrf\n self.ssrf_obj.payloads = [\"http://2852039166/\"]\n result=self.ssrf_obj.payload_match(\"http://2852039166/\")\n self.assertTrue(result)\n\n # Case 2 No ssrf\n\n self.ssrf_obj.payloads = [\"http://2852039166/\"]\n result=self.ssrf_obj.payload_match(\"https://www.google.com\")\n self.assertFalse(result)","title":""},{"docid":"acf94326e92f9d6e57043f5e3665db5f","score":"0.5340817","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"31e532949f9361df5cef538ed413a5a0","score":"0.5337767","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"e7b434d4a7331ef1984a2ce9567b396f","score":"0.53277296","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"7cce27036648076d9ad592d200c7d49f","score":"0.53257835","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"910994d8493a1bbbde352ae9c8872838","score":"0.5324175","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"693b942f107724743a82c34779d47a24","score":"0.5322797","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"372023cbbbfa7d6fba7816e2d54012d1","score":"0.532265","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"458c45a761a74f26221a60bf3cfa66da","score":"0.5322235","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"73b88662c7ded1344a5b8013b816b1e8","score":"0.5316091","text":"def test_event_match_body(self) -> None:\n\n # if the key is `content.body`, the pattern matches substrings.\n\n # non-wildcards should match\n condition = {\n \"kind\": \"event_match\",\n \"key\": \"content.body\",\n \"pattern\": \"foobaz\",\n }\n self._assert_matches(\n condition,\n {\"body\": \"aaa FoobaZ zzz\"},\n \"patterns should match and be case-insensitive\",\n )\n self._assert_not_matches(\n condition,\n {\"body\": \"aa xFoobaZ yy\"},\n \"pattern should only match at word boundaries\",\n )\n self._assert_not_matches(\n condition,\n {\"body\": \"aa foobazx yy\"},\n \"pattern should only match at word boundaries\",\n )\n\n # wildcards should match\n condition = {\n \"kind\": \"event_match\",\n \"key\": \"content.body\",\n \"pattern\": \"f?o*baz\",\n }\n\n self._assert_matches(\n condition,\n {\"body\": \"aaa FoobarbaZ zzz\"},\n \"* should match string and pattern should be case-insensitive\",\n )\n self._assert_matches(\n condition, {\"body\": \"aa foobaz yy\"}, \"* should match 0 characters\"\n )\n self._assert_not_matches(\n condition, {\"body\": \"aa fobbaz yy\"}, \"? should not match 0 characters\"\n )\n self._assert_not_matches(\n condition, {\"body\": \"aa fiiobaz yy\"}, \"? should not match 2 characters\"\n )\n self._assert_not_matches(\n condition,\n {\"body\": \"aa xfooxbaz yy\"},\n \"pattern should only match at word boundaries\",\n )\n self._assert_not_matches(\n condition,\n {\"body\": \"aa fooxbazx yy\"},\n \"pattern should only match at word boundaries\",\n )\n\n # test backslashes\n condition = {\n \"kind\": \"event_match\",\n \"key\": \"content.body\",\n \"pattern\": r\"f\\oobaz\",\n }\n self._assert_matches(\n condition,\n {\"body\": r\"F\\oobaz\"},\n \"backslash should match itself\",\n )\n condition = {\n \"kind\": \"event_match\",\n \"key\": \"content.body\",\n \"pattern\": r\"f\\?obaz\",\n }\n self._assert_matches(\n condition,\n {\"body\": r\"F\\oobaz\"},\n r\"? after \\ should match any character\",\n )","title":""},{"docid":"39be4150c64f856db770eafa362ce6c8","score":"0.53157616","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"d7ecc99a9f73409d632e59a2237646a8","score":"0.5315204","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"c350a64a8521fafa1f824c003ce9e7e8","score":"0.5312725","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"dd5b817e4a305eeab000c216d4226707","score":"0.5309885","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"7d42d0fc43aa76a97fc60f2a3782c5a1","score":"0.53063047","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"fe7c81fd554e3af7d3e1036854c3b3e9","score":"0.52973425","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"9aa1062d7f0bc12b1caef8a6d3d5be6c","score":"0.5296215","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"38a33be35b3e795cf0167070e26311e3","score":"0.5291868","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"e5ec5a8043555885ea6e73fcda540cb3","score":"0.5290918","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"52b91d36a44976add6808041ba60a730","score":"0.5290032","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"cb577aa298501244e45529ab9b618479","score":"0.52869815","text":"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")","title":""},{"docid":"fde75b566773ae8889e163a2bfd2f7bd","score":"0.5284116","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""},{"docid":"cf89773910a299ff2a3e40c3cd66a87b","score":"0.5281293","text":"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\n return pulumi.get(self, \"field_to_match\")","title":""}],"string":"[\n {\n \"docid\": \"90ea33f01c95e611308c46f7671fe928\",\n \"score\": \"0.6055283\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b1bade8a13772a1924d586704bd672\",\n \"score\": \"0.60350204\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07caf852cad3f61887cc0c04acf80d3\",\n \"score\": \"0.60124177\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b5cb9235185a3e27fe5006d9e51615\",\n \"score\": \"0.5988622\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e94d06db038d8401e3bba08848f9e27\",\n \"score\": \"0.5986877\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b185edbbcf2fddc1e7c66b74474f46e2\",\n \"score\": \"0.59791476\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d60b5e41f96c8acc8626113f3af3137\",\n \"score\": \"0.5976147\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187f497294343371bdf70da54614d4c0\",\n \"score\": \"0.5970547\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211d0c9f82c3481006caa81549caee72\",\n \"score\": \"0.5967579\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ef80d3ae0ee352594ad4496b624769\",\n \"score\": \"0.59659845\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25432b072bf96b04f097e91d94180f72\",\n \"score\": \"0.5965506\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08af4ef69d822a81d4bbeaaa1ed47e45\",\n \"score\": \"0.59638906\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cae839a37a3c67205d80db18810ef2c\",\n \"score\": \"0.59469104\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"660436ffd9e7f0ea30168c3ea987c8cb\",\n \"score\": \"0.5943673\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9eabcd9fb79cdd6b3b8634007147b6\",\n \"score\": \"0.5931858\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302cc600cd10c159077449d556fb40b7\",\n \"score\": \"0.59254676\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1465b0769d5079e441ae518a7f9f9eea\",\n \"score\": \"0.5920594\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650cd976e29b4cfd9858e770e2d1234b\",\n \"score\": \"0.59054774\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef65009e0116bbd7ca730775c8c0c1b\",\n \"score\": \"0.5894218\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da3c86c2e1228b1375bdd1286c6d317\",\n \"score\": \"0.58768034\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f309c946a0376cfbdc1eab97ef51cf\",\n \"score\": \"0.5875306\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75dd1e3bbb23a92627f42c2e9f3199e9\",\n \"score\": \"0.5872796\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943b8d14175e786a1d650c4db39a9053\",\n \"score\": \"0.5872302\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a837434a4f2ac0c998ea4feb15c85ec7\",\n \"score\": \"0.58694166\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45cfbaf06ab2254a077d47e289d2ac86\",\n \"score\": \"0.58691984\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6caf2260d1330d7af7cfcef165e4be6\",\n \"score\": \"0.5864394\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c09673c44eef79f4e3647eac5fa96e\",\n \"score\": \"0.58545715\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d659677e7e5145648a3b2fa0e783dea3\",\n \"score\": \"0.5851143\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b9ffa7c147cc3c90002cce76c4c9f4\",\n \"score\": \"0.5846507\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdf75f3739b5b94ac38dd438be85efd\",\n \"score\": \"0.584298\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d6ed5e1b6673a0190e99f492fe8cc0\",\n \"score\": \"0.5842126\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2805c501a696b0fe4709b4006433d4\",\n \"score\": \"0.58298934\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9b1825dd3289049e15e6e4de98b53d\",\n \"score\": \"0.58291286\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cedc9ba3b3baf89d96d514deb85e37\",\n \"score\": \"0.58158344\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d3f90055878a9e124852de47964bf2\",\n \"score\": \"0.5815436\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf97b938073f2bcdddb86a961127576\",\n \"score\": \"0.5811115\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e521d1cc80b9dc654de31911454ecc4\",\n \"score\": \"0.5806521\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79eb9179fcb4c8d3a594704f4c5030e0\",\n \"score\": \"0.5804012\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7fdde4e6735e967771ed68453b88c8\",\n \"score\": \"0.57720006\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd855293e70252b24a72f3e713837ae\",\n \"score\": \"0.5767267\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15fc1837ee33de50497b8bceca1164c3\",\n \"score\": \"0.5600881\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43be85b96aae0ec5dd79ceb03bf0e12a\",\n \"score\": \"0.5579652\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b27aaafcf484652887e96a9d34bb9769\",\n \"score\": \"0.55768216\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d07d5c9b0ae297fe07ce16626ce3c1\",\n \"score\": \"0.5560359\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c243d741d07111cde308f1b6a05474b3\",\n \"score\": \"0.55501586\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f254bb94ffba4eb972e32f852beb2154\",\n \"score\": \"0.5530985\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a922fd098a717bc390cac3a3e441b2e\",\n \"score\": \"0.55158365\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d96be1e51e119585f7ecb9d43e8b6085\",\n \"score\": \"0.55146426\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167f755d574ca4fc212f5708f3c51728\",\n \"score\": \"0.5513237\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a295695a3436291c629430a76f67368b\",\n \"score\": \"0.5510537\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2aa7417970f67542931bf89248d304e\",\n \"score\": \"0.55066735\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3559e170ee208656a83ec7109a46526\",\n \"score\": \"0.54610586\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86e1248854060cd206a0c4ec1c33981\",\n \"score\": \"0.54358566\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1baf88753686e0a92fcc3abd27f0daf\",\n \"score\": \"0.5433733\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15065d1c53d38dd998bc2da392246ee6\",\n \"score\": \"0.543036\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c640962ecbb251be98c908fb990a117\",\n \"score\": \"0.541499\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc9d0fabe5f2360fa60eb279d7920a3\",\n \"score\": \"0.54079485\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b784c51444fa5c9e15e8adf3f1e5f2\",\n \"score\": \"0.53983206\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22ab98b8b10e07b9711d415f0d47f9f6\",\n \"score\": \"0.53966856\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2309c88dfa1c7a08bd6a494db8368a54\",\n \"score\": \"0.53933\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729e9efdbc9600f29c89bdb1f3e577ad\",\n \"score\": \"0.5392608\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeaa52f5b62ee0deb660364b64344323\",\n \"score\": \"0.5385919\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d7b9052fc55b372ee9d959fcf4dfb7\",\n \"score\": \"0.5384918\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfdb290985e565a10675182947500265\",\n \"score\": \"0.5382792\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"353228a97d3b84627252e1d850905208\",\n \"score\": \"0.53812593\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"646a1625afc630a4726c031635613b10\",\n \"score\": \"0.5379696\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4235da4d750fff0260c4f1ffbcf48a\",\n \"score\": \"0.5377892\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab7fdddc1a407643c4ef565890ebc013\",\n \"score\": \"0.5367528\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f3142f60fd1ee48b35127166cd9b93\",\n \"score\": \"0.5365676\",\n \"text\": \"def match(self, match):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991670b48a340f62646bb9a6768f29b9\",\n \"score\": \"0.53637457\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443a7401789eeff44d82a158661d4caa\",\n \"score\": \"0.5361841\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25b138c90c9ddac471edaf78ba0a442\",\n \"score\": \"0.5358515\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8425ebe37784f485f0c12022b12be5d\",\n \"score\": \"0.53569084\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a904b4eebe66a211c5fb8a6cd57c1f0\",\n \"score\": \"0.53545016\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c0390f734eaa35906dedc2eeaeef98e\",\n \"score\": \"0.5352244\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5765610e2df617ca75788b506933a52\",\n \"score\": \"0.5348085\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd669c7cbdcff82ae5d35fe5ce7c6da\",\n \"score\": \"0.5343908\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659e9ee9281454861721792bc1dc8fb8\",\n \"score\": \"0.5341646\",\n \"text\": \"def test_payload_match(self):\\n # Case 1 possible ssrf\\n self.ssrf_obj.payloads = [\\\"http://2852039166/\\\"]\\n result=self.ssrf_obj.payload_match(\\\"http://2852039166/\\\")\\n self.assertTrue(result)\\n\\n # Case 2 No ssrf\\n\\n self.ssrf_obj.payloads = [\\\"http://2852039166/\\\"]\\n result=self.ssrf_obj.payload_match(\\\"https://www.google.com\\\")\\n self.assertFalse(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf94326e92f9d6e57043f5e3665db5f\",\n \"score\": \"0.5340817\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31e532949f9361df5cef538ed413a5a0\",\n \"score\": \"0.5337767\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b434d4a7331ef1984a2ce9567b396f\",\n \"score\": \"0.53277296\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cce27036648076d9ad592d200c7d49f\",\n \"score\": \"0.53257835\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementAndStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"910994d8493a1bbbde352ae9c8872838\",\n \"score\": \"0.5324175\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"693b942f107724743a82c34779d47a24\",\n \"score\": \"0.5322797\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372023cbbbfa7d6fba7816e2d54012d1\",\n \"score\": \"0.532265\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementOrStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458c45a761a74f26221a60bf3cfa66da\",\n \"score\": \"0.5322235\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b88662c7ded1344a5b8013b816b1e8\",\n \"score\": \"0.5316091\",\n \"text\": \"def test_event_match_body(self) -> None:\\n\\n # if the key is `content.body`, the pattern matches substrings.\\n\\n # non-wildcards should match\\n condition = {\\n \\\"kind\\\": \\\"event_match\\\",\\n \\\"key\\\": \\\"content.body\\\",\\n \\\"pattern\\\": \\\"foobaz\\\",\\n }\\n self._assert_matches(\\n condition,\\n {\\\"body\\\": \\\"aaa FoobaZ zzz\\\"},\\n \\\"patterns should match and be case-insensitive\\\",\\n )\\n self._assert_not_matches(\\n condition,\\n {\\\"body\\\": \\\"aa xFoobaZ yy\\\"},\\n \\\"pattern should only match at word boundaries\\\",\\n )\\n self._assert_not_matches(\\n condition,\\n {\\\"body\\\": \\\"aa foobazx yy\\\"},\\n \\\"pattern should only match at word boundaries\\\",\\n )\\n\\n # wildcards should match\\n condition = {\\n \\\"kind\\\": \\\"event_match\\\",\\n \\\"key\\\": \\\"content.body\\\",\\n \\\"pattern\\\": \\\"f?o*baz\\\",\\n }\\n\\n self._assert_matches(\\n condition,\\n {\\\"body\\\": \\\"aaa FoobarbaZ zzz\\\"},\\n \\\"* should match string and pattern should be case-insensitive\\\",\\n )\\n self._assert_matches(\\n condition, {\\\"body\\\": \\\"aa foobaz yy\\\"}, \\\"* should match 0 characters\\\"\\n )\\n self._assert_not_matches(\\n condition, {\\\"body\\\": \\\"aa fobbaz yy\\\"}, \\\"? should not match 0 characters\\\"\\n )\\n self._assert_not_matches(\\n condition, {\\\"body\\\": \\\"aa fiiobaz yy\\\"}, \\\"? should not match 2 characters\\\"\\n )\\n self._assert_not_matches(\\n condition,\\n {\\\"body\\\": \\\"aa xfooxbaz yy\\\"},\\n \\\"pattern should only match at word boundaries\\\",\\n )\\n self._assert_not_matches(\\n condition,\\n {\\\"body\\\": \\\"aa fooxbazx yy\\\"},\\n \\\"pattern should only match at word boundaries\\\",\\n )\\n\\n # test backslashes\\n condition = {\\n \\\"kind\\\": \\\"event_match\\\",\\n \\\"key\\\": \\\"content.body\\\",\\n \\\"pattern\\\": r\\\"f\\\\oobaz\\\",\\n }\\n self._assert_matches(\\n condition,\\n {\\\"body\\\": r\\\"F\\\\oobaz\\\"},\\n \\\"backslash should match itself\\\",\\n )\\n condition = {\\n \\\"kind\\\": \\\"event_match\\\",\\n \\\"key\\\": \\\"content.body\\\",\\n \\\"pattern\\\": r\\\"f\\\\?obaz\\\",\\n }\\n self._assert_matches(\\n condition,\\n {\\\"body\\\": r\\\"F\\\\oobaz\\\"},\\n r\\\"? after \\\\ should match any character\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39be4150c64f856db770eafa362ce6c8\",\n \"score\": \"0.53157616\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ecc99a9f73409d632e59a2237646a8\",\n \"score\": \"0.5315204\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c350a64a8521fafa1f824c003ce9e7e8\",\n \"score\": \"0.5312725\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5b817e4a305eeab000c216d4226707\",\n \"score\": \"0.5309885\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d42d0fc43aa76a97fc60f2a3782c5a1\",\n \"score\": \"0.53063047\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7c81fd554e3af7d3e1036854c3b3e9\",\n \"score\": \"0.52973425\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aa1062d7f0bc12b1caef8a6d3d5be6c\",\n \"score\": \"0.5296215\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a33be35b3e795cf0167070e26311e3\",\n \"score\": \"0.5291868\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementOrStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5ec5a8043555885ea6e73fcda540cb3\",\n \"score\": \"0.5290918\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b91d36a44976add6808041ba60a730\",\n \"score\": \"0.5290032\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementNotStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb577aa298501244e45529ab9b618479\",\n \"score\": \"0.52869815\",\n \"text\": \"def byte_match_statement(self) -> Optional[pulumi.Input['RuleGroupRuleStatementNotStatementStatementNotStatementStatementByteMatchStatementArgs']]:\\n return pulumi.get(self, \\\"byte_match_statement\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde75b566773ae8889e163a2bfd2f7bd\",\n \"score\": \"0.5284116\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementNotStatementStatementAndStatementStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf89773910a299ff2a3e40c3cd66a87b\",\n \"score\": \"0.5281293\",\n \"text\": \"def field_to_match(self) -> Optional[pulumi.Input['WebAclRuleStatementOrStatementStatementByteMatchStatementFieldToMatchArgs']]:\\n return pulumi.get(self, \\\"field_to_match\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994477,"cells":{"query_id":{"kind":"string","value":"bce6738a1a6462f06b945c022ba3e93f"},"query":{"kind":"string","value":"Read solution from a binary file. Please refer to README.md for description of the binary file format used here."},"positive_passages":{"kind":"list like","value":[{"docid":"eea1f3696bea425597d502f3e1952a69","score":"0.6074145","text":"def read_solution_from_file(path_to_data):\n\n data = open(path_to_data, \"rb\").read()\n\n # nx: number of x points\n # ----------\n\n start = 0\n end = 4*3\n (_, nx, _) = struct.unpack(\"@iii\", data[start: end])\n\n # nt: number of t points\n # ----------\n\n start = end\n end = start + 4*3\n (_, nt, _) = struct.unpack(\"@iii\", data[start: end])\n\n # x values\n # ---------\n\n start = end + 4\n end = start + nx * 8\n x_values = array.array(\"d\")\n x_values.frombytes(data[start:end])\n\n # t values\n # ---------\n\n start = end + 8\n end = start + nt * 8\n t_values = array.array(\"d\")\n t_values.frombytes(data[start:end])\n\n # Solution: 2D array\n # ---------\n\n start = end + 8\n end = start + nx * nt * 8\n solution = array.array(\"d\")\n solution.frombytes(data[start:end])\n solution = np.reshape(solution, (nt, nx), order='C')\n\n return (x_values, t_values, solution)","title":""}],"string":"[\n {\n \"docid\": \"eea1f3696bea425597d502f3e1952a69\",\n \"score\": \"0.6074145\",\n \"text\": \"def read_solution_from_file(path_to_data):\\n\\n data = open(path_to_data, \\\"rb\\\").read()\\n\\n # nx: number of x points\\n # ----------\\n\\n start = 0\\n end = 4*3\\n (_, nx, _) = struct.unpack(\\\"@iii\\\", data[start: end])\\n\\n # nt: number of t points\\n # ----------\\n\\n start = end\\n end = start + 4*3\\n (_, nt, _) = struct.unpack(\\\"@iii\\\", data[start: end])\\n\\n # x values\\n # ---------\\n\\n start = end + 4\\n end = start + nx * 8\\n x_values = array.array(\\\"d\\\")\\n x_values.frombytes(data[start:end])\\n\\n # t values\\n # ---------\\n\\n start = end + 8\\n end = start + nt * 8\\n t_values = array.array(\\\"d\\\")\\n t_values.frombytes(data[start:end])\\n\\n # Solution: 2D array\\n # ---------\\n\\n start = end + 8\\n end = start + nx * nt * 8\\n solution = array.array(\\\"d\\\")\\n solution.frombytes(data[start:end])\\n solution = np.reshape(solution, (nt, nx), order='C')\\n\\n return (x_values, t_values, solution)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"54d9052ff2375d0421dc2be1610b999e","score":"0.65914464","text":"def read_binary(filename):\n with FileBinaryReader(filename) as f:\n return f.read()","title":""},{"docid":"83beb9771c450aa37bce0e79e62b5fcc","score":"0.6584203","text":"def load_solution(filepath):\n with open(filepath) as f:\n solution = pickle.load(f)\n return solution","title":""},{"docid":"081ddc5df69ce0eeccfae1756e551372","score":"0.6413085","text":"def load_binary(path):\n try:\n with open(path, \"rb\") as f:\n loaded_data = pickle.load(f)\n print(f\"Data binary file loaded from {path}\")\n return loaded_data\n except Exception as e:\n print(\"Binary file couldn't be loaded.\")","title":""},{"docid":"e3eebf94bda4497f169fc6cecd832960","score":"0.6381922","text":"def open_for_reading_binary(file_path):\n try:\n f = open(file_path, 'r')\n return f\n except IOError as e:\n raise _io_load_error(e, file_path)","title":""},{"docid":"c83e78e91312904f676408d421479e08","score":"0.6330716","text":"def load(raw):\n return BinFile(raw[:0x20], raw[0x20:])","title":""},{"docid":"7ad1b97c48f1973bf44bb60403ee3be3","score":"0.62161446","text":"def read_binary_file(file_name):\n return read_file(file_name, 'rb')","title":""},{"docid":"5833686738a2e2586680303015219694","score":"0.6203299","text":"def load_bin_file(filepath):\n datas = []\n f = open(filepath, 'rb')\n s = f.read()\n for x in s:\n rdata = struct.unpack(\"B\", x)[0]\n datas.append(rdata)\n return datas","title":""},{"docid":"0232133f58de643f09d0050bc7120f01","score":"0.61890787","text":"def read_binary(self, filename):\n return self._read_file_or_404(filename, False)","title":""},{"docid":"be4a4123358aab2f659222ceea275d1f","score":"0.61867404","text":"def read(path_to_file):","title":""},{"docid":"83f68aa4d8f22c96017e59dedd80e9b4","score":"0.61727834","text":"def open_for_reading_binary(filename):\n return FileBinaryReader(filename)","title":""},{"docid":"1fa5301b50a7a026be3c790a1bc1643e","score":"0.61461914","text":"def readSol(filename):\r\n\r\n if not isinstance(filename, str):\r\n raise TypeError(\"argument 1 to readSol must be a string\")\r\n #end if\r\n data_type=sol_data_type()\r\n record_length=numpy.empty(0, dtype=data_type).itemsize\r\n header_length_noCheck=numpy.empty(0,\r\n dtype=numpy.dtype(sol_header_types()[:-1])).itemsize\r\n #print(data_type)\r\n #Calculate checksum for first record. This is done again later on the whole\r\n #data set. This is just here so if it is clearly rubbish I don't read the\r\n #whole file into memory\r\n fd=open(filename, 'r', encoding=\"utf-8\",errors=\"ignore\")\r\n header_size=numpy.empty(0, dtype=numpy.dtype(sol_header_types())).itemsize\r\n header=fd.read(header_size)\r\n fd.close()\r\n # Calculate the checksum - TODO: NOT SURE IF WORKING WITH PYTHON 3\r\n checksum = 0\r\n # Do a cumulative bitwise xor on all elements of the header bytearray\r\n for byte in bytearray(header[:-1],encoding=\"utf8\"):\r\n # ^ means xor\r\n checksum ^= byte\r\n # ADDED THIS TRY TO AVOID ERRORS IN SOME SOL FILES, SEEMS TO WORK OK(kevin mcguigan, AGRG)\r\n try:\r\n if checksum != ord(header[header_size-1]):\r\n raise IOError(\"Header checksum does not match\")\r\n except:\r\n pass\r\n #this is the error that would occasinally appear, such as when running:\r\n # testSol = r\"Q:\\CHII\\1_IE\\20170828174507_SH_AR\\proc\\t200_TC_MULTI_RAPID_BART_LTWN.sol\"\r\n # solDf = readSol(testSol)\r\n # print solDf\r\n # '''Traceback (most recent call last):\r\n # File \"Q:\\CHII\\_meta\\_dev\\read_nav_file.py\", line 177, in readSol\r\n # if checksum != ord(header[header_size-1]):\r\n # IndexError: string index out of range'''\r\n\r\n\r\n #Read file into memory, is a numpy array of tuples (not really tuples, these\r\n #are mutable, it looks like a tuple though)\r\n sol_data=numpy.fromfile(filename, dtype=data_type)\r\n #Check that the header checksum is correct for all records\r\n #The checksum is the bitwise xor of all elements in the header excluding the\r\n #checksum itself.\r\n #Do a bitwise xor on all header records, want to reduce along axis 1\r\n #to give an array of checksums\r\n if (not (numpy.bitwise_xor.reduce(\r\n #numpy.view gives the view of the array in the given data type,\r\n #and becuase we are doing a bitwise xor needs to be in unsigned 8bit\r\n #integers\r\n #reshape the array so that it is in records\r\n sol_data.view(numpy.uint8).reshape(sol_data.shape[0], record_length)\\\r\n #Want just the header records up to the\r\n #checksum\r\n [:, :header_length_noCheck], axis=1)\\\r\n ==\\\r\n #Check that the checksums are correct\r\n sol_data['header_checksum']).all()):\r\n raise IOError(\"Header checksum does not match, may not be a sol file\")\r\n #end if\r\n return sol_data","title":""},{"docid":"eac38bb353f0b881e02bf8caa797b2ff","score":"0.6142402","text":"def read_file_bin():\n\tif not isfile(binpath):\n\t\twrite_file_bin([\"Hello\"], [\"Welcome!\"])\n\ttry:\n\t\tf = open(binpath, \"br\")\n\t\tdata = pickle.load(f)\n\t\tf.close()\n\t\tret_pages = []\n\t\tfor i,d in enumerate(data[0]):\n\t\t\tret_pages.append([d, data[1][i]])\n\t\treturn ret_pages\n\texcept:\n\t\tprint(\"Error reading file:\")\n\t\tprint(sys.exc_info())\n\t\texit(1)","title":""},{"docid":"4d9a936f5a7923d72754eb54dc5257a3","score":"0.60778","text":"def import_bin_file(file_path):\n with open(file_path, 'rb') as file:\n data = pickle.load(file)\n return data","title":""},{"docid":"22d6b335fef0ba95602885b246934d8d","score":"0.6052145","text":"def puzzle_02() -> None:\n\n with open(INPUT_FILE_PATH, \"r\") as f:\n document = loads(f.read())\n print_puzzle_solution(_object_sum(document))","title":""},{"docid":"5863bfb14f50e9983918c9201312e3a9","score":"0.6046847","text":"def load_binary(binary_path=\"dictionary.dat\"):\r\n try:\r\n with open(binary_path, 'rb') as file:\r\n my_depickler = pickle.Unpickler(file)\r\n dictionary = my_depickler.load()\r\n return dictionary\r\n except:\r\n return {}","title":""},{"docid":"50c72d58a0e1035a343bdf9cf22ae4f0","score":"0.6030863","text":"def load_from_bin(file: str) -> Any:\n t = time()\n with open(file, 'rb') as f:\n obj = load(f)\n print('loaded from binary in ' + timer(t))\n return obj","title":""},{"docid":"743e91c8bf7dd88cf236572b856c9c0e","score":"0.600608","text":"def read_from_binary_file(file_name, lines='All'):\r\n with open(file_name, mode='rb') as f:\r\n global commands\r\n commands = pickle.loads(f.read())\r\n\r\n num_of_lines = len(commands) if lines == 'All' else int(lines)\r\n printed_commands = commands[-num_of_lines:]\r\n for command in printed_commands:\r\n print(command)","title":""},{"docid":"1fc1d5463383615fa70253882f4bab37","score":"0.5980304","text":"def read_file(self, file_name):\n cur_dir = os.path.dirname(os.path.abspath(__file__))\n path = Path(cur_dir)\n cur_dir = path.parent\n file_name = os.path.join(cur_dir, \"bin\", file_name)\n try:\n with open(file_name, 'r') as fileread:\n data = fileread.read()\n return file_name, data\n except IOError as error:\n print(error)","title":""},{"docid":"8b1a5a329db491ecd915b0ffa0d70373","score":"0.5975809","text":"def load_bin(infile):\n\treturn np.load(infile,allow_pickle='TRUE')","title":""},{"docid":"17575ce47aeea8722154bd8d24e88e73","score":"0.5974111","text":"def read_binary_file(filename, offset=0):\n read_buffer = open(filename, 'rb')\n read_buffer.seek(int(offset), 0)\n header = struct.unpack('<xcccc', read_buffer.read(5))\n if header[0] != 'B':\n print(\"Input .ark file is not binary\")\n sys.exit(-1)\n if header[1] == 'C':\n print(\"Input .ark file is compressed, exist now.\")\n sys.exit(-1)\n\n rows = 0; cols= 0\n _, rows = struct.unpack('<bi', read_buffer.read(5))\n _, cols = struct.unpack('<bi', read_buffer.read(5))\n\n if header[1] == \"F\":\n tmp_mat = np.frombuffer(read_buffer.read(rows * cols * 4),\n dtype=np.float32)\n elif header[1] == \"D\":\n tmp_mat = np.frombuffer(read_buffer.read(rows * cols * 8),\n dtype=np.float64)\n mat = np.reshape(tmp_mat, (rows, cols))\n\n read_buffer.close()\n\n return mat","title":""},{"docid":"d6113ee473da2a279178fc743e473559","score":"0.5967676","text":"def read_bin_input_spec(f):\n with open(f, \"rb\") as ff:\n l = ff.readline().strip()\n if l == \"#v1\":\n x = BinInputSpecV1()\n else:\n print >>sys.stderr, \"Unknown file version for input/binary spec\", l\n \n x.read(ff)\n\n return x","title":""},{"docid":"e85fa1660644f6e447cf79f684996e01","score":"0.59667325","text":"def load_bin(file_name: str, **params: Any) -> pd.DataFrame:\n file_path = (_PROJECT_DIR / file_name).resolve()\n info(f\"Reading from file {file_path}\")\n df = pd.read_feather(file_path, **params)\n return df","title":""},{"docid":"96768f34615a64d3d96e395c62d29903","score":"0.5932118","text":"def decode_binary_file(self, file_path):\n if not os.path.exists(file_path):\n log.error(\"Missing: {}\".format(file_path))\n raise FileNotFoundError(\"Missing: {}\".format(file_path))\n log.info(\"Decoding file: {}\".format(file_path))\n return self.decode(open(file_path, \"rb\").read())","title":""},{"docid":"1fe538ef4116dbe30eaa1c78ffdc07bd","score":"0.5915349","text":"def read_binary(self):\n with self.open('rb') as f:\n return f.read()","title":""},{"docid":"2b96b9c2a6ec59e0034c4c2b652066d8","score":"0.5899989","text":"def read(filename='DOS'):\n from struct import pack,unpack,calcsize\n intSize = calcsize('<i')\n dubSize = calcsize('<d')\n strSize = calcsize('<s')\n version=1\n\n f=open(filename,'r').read()\n i = 0\n filetype, = unpack('<64s',f[i:i+64*strSize]) ; i += 64*strSize\n version, = unpack('<i',f[i:i+intSize]) ; i += intSize\n comment, = unpack('<1024s',f[i:i+1024*strSize]) ; i += 1024*strSize\n N_Bins, = unpack('<i',f[i:i+intSize]) ; i += intSize\n dE, = unpack('<d',f[i:i+dubSize]) ; i += dubSize\n DOS = unpack('<%id' % (N_Bins),f[i:])\n DOS = numpy.array(DOS)\n E = numpy.arange(0,N_Bins*dE,dE)\n return (filetype.strip('\\x00'),version,comment.strip('\\x00')),E,DOS","title":""},{"docid":"7ee7f758e0988ae1341a55e32ca3da37","score":"0.5864759","text":"def load_file(path):\n with open(path, 'rb') as file:\n header = file.read(0x20)\n raw = file.read()\n return BinFile(header, raw)","title":""},{"docid":"8302f81b1d862f5562c6ef0303446110","score":"0.5856217","text":"def testReadFile(self):\n definitions_file = self._GetTestFilePath(['definitions', 'integers.yaml'])\n self._SkipIfPathNotExists(definitions_file)\n\n definitions_registry = registry.DataTypeDefinitionsRegistry()\n definitions_reader = reader.DataTypeDefinitionsFileReader()\n\n definitions_reader.ReadFile(definitions_registry, definitions_file)","title":""},{"docid":"baeb28f9c15a634b6ea9e6142545a4e4","score":"0.58441705","text":"def read():\n path = pathlib.Path(RAW_EVALATIN_DATA)\n files = fileinput.input(path.glob(\"*.conllu\"))\n return pyconll.unit.conll.Conll(files)","title":""},{"docid":"45827cd91933db92f06db4adff4e308c","score":"0.58408415","text":"def load_binary_fixture(filename: str) -> bytes:\n path = get_fixture_path(filename)\n return path.read_bytes()","title":""},{"docid":"7c82f10e8cf104b39926b6d4b920d3fc","score":"0.5837009","text":"def readSpecFile(fileName):\n fl = open(fileName, 'rb') # TODO wyjatek nie ma pliku\n A = pickle.load(fl)\n fl.close()\n return A","title":""},{"docid":"ae6eacd24095b8c090c0ca86a28b50cf","score":"0.58316576","text":"def load(self):\n starting_memory = 0\n # debug = 1\n file_name = sys.argv[1]\n with open(file_name) as f:\n for line in f:\n # WE DIVIDE LINE IF THERE IS A COMMENT\n comment_split = line.split('#')\n # WE TAKE THE LEFT PART OF THE ARRAY, AND STRIP SPACE AT THE END\n instruction = comment_split[0].strip()\n # print(f'Instruction: {instruction} in line {debug}')\n if instruction == '':\n continue\n # WE CONVERT STRING WITH BINARY CODE INTO BINARY VALUE\n binary_code = int(instruction, 2)\n # WE SAVE EACH INSTRUCTION INTO RAM\n self.ram_write(starting_memory, binary_code)\n # WE INCREMENT THE ADDRESS IN MEMORE SO WE CAN ADD THE NEXT INSTRUCTION IN THE NEXT SLOT\n starting_memory += 1\n # debug += 1","title":""},{"docid":"1708ef77c72d25d3c21b0ac67449c1c4","score":"0.58202326","text":"def readFiles( self ):\n\n # Read in the BIN file\n self._printer.printStatus( 0, 'Opening BIN file \"%s\" ...' % self.fileNameBin )\n if not os.path.isfile(self.fileNameBin):\n self._printer.errorExit( 2, 'File \"' + self.fileNameBin + '\" is not a readable file.' )\n binFile = open( self.fileNameBin, 'rb' )\n self.binData = binFile.read()\n binFile.close()\n if len(self.binData) <= 0:\n self._printer.errorExit( 2, 'File \"' + self.fileNameBin + '\" contains no data' )\n \n # Read in the raw CFG file\n self._printer.printStatus( 0, 'Opening CFG file \"%s\" ...' % self.fileNameCfg )\n if not os.path.isfile(self.fileNameCfg):\n self._printer.errorExit( 2, 'File \"' + self.fileNameCfg + '\" is not a readable file.' )\n cfgFile = open( self.fileNameCfg, 'r' )\n cfgData = cfgFile.read()\n cfgFile.close()\n \n #\n # Parse the CFG file (at least partially)\n #\n # Mostly, lines after a section (ex: [vars]) are associated with that section. Here are the exceptions:\n # 1. Contiguous comments immediately BEFORE a section as associated that section.\n # Blank lines and lines with non-comment data break contiguousness (i.e. these lines go with the PRIOR section, as per normal).\n # 2. [mapping] section is treated special since it is the only section that is truly parsed.\n # All other sections are just plain lines of text.\n # 3. Corner case: blank and comment lines that appear before the 1st section are associated with that section.\n #\n reSectionName = re.compile(r\"\\[([^\\]]*)\\]\")\n reMappingLine = re.compile(r\"\\$([0-9A-Fa-f]{1,4})\\s*-\\s*\\$([0-9A-Fa-f]{1,4})\\s*=\\s*\\$([0-9A-Fa-f]{1,4})\\s*(.*)\")\n sectionCurrent = self.__KEY_NONE\n cfgLines = cfgData.splitlines(False)\n for i in range(0, len(cfgLines)):\n cfgLine = cfgLines[i]\n appendLine = False\n hasComment = self.lineHasComment( cfgLine )\n cfgLineTrimmed = self.trimCommentAndWhitespace( cfgLine )\n isEmpty = len( cfgLineTrimmed ) == 0\n onlyComment = hasComment and isEmpty\n \n # Core parsing block\n reSectionMatch = reSectionName.fullmatch( cfgLineTrimmed )\n if reSectionMatch != None:\n # Handler for parsing a section header (ex: [vars] or [mapping])\n sectionCurrent = reSectionMatch.group(1)\n if sectionCurrent not in self.cfgSections:\n self.cfgSections[sectionCurrent] = []\n elif not isEmpty and sectionCurrent == self.__KEY_MAPPING:\n # Handler for lines in the [mapping] section\n reMappingMatch = reMappingLine.fullmatch( cfgLineTrimmed )\n if reMappingMatch == None:\n self._printer.errorPrint( 'Unrecognized [mapping] line: ' + cfgLine )\n self._printer.errorExit( 2, 'Unrecognized [mapping] line:\\n' + cfgLine )\n if reMappingMatch.group(4) != None and len( reMappingMatch.group(4) ) > 0:\n self._printer.errorExit( 2, 'Parser does not handle [mapping] lines with the extension of: \"%s\"' % reMappingMatch.group(4) )\n startOffset = int( reMappingMatch.group(1), 16)\n lenInBytes = ( int( reMappingMatch.group(2), 16) - startOffset + 1 ) * 2\n startAddress = int( reMappingMatch.group(3), 16)\n startOffset *= 2 # The 2x converts from WORD index into BYTE index\n self.memoryMap.writeByteBuffer( startAddress, self.binData, startOffset, lenInBytes, cfgLine )\n else:\n if sectionCurrent == self.__KEY_NONE and not self.__KEY_NONE in self.cfgSections:\n self.cfgSections[sectionCurrent] = []\n self.cfgSections[sectionCurrent].append( cfgLine )","title":""},{"docid":"7cc97c8eadfac017949628e92f1d7595","score":"0.58198744","text":"def read_file(self) -> str:\r\n with open(self.path) as file_descriptor:\r\n try:\r\n contents = file_descriptor.read()\r\n return contents\r\n except UnicodeDecodeError:\r\n InputFile.logger.warning('\"%s\" seems to be a binary file and will be ignored.',\r\n self.path)","title":""},{"docid":"c2f703e680ee2331d4a90632d199e17c","score":"0.58051693","text":"def read_binary_file(self, path: PATH_TYPE) -> bytes:\n with open(path, \"rb\") as fd:\n return fd.read()","title":""},{"docid":"b2d06700deb6502e2012060d29b0c8f4","score":"0.57962126","text":"def load_binary_file(path: str) -> bytes:\n return Path(path).read_bytes()","title":""},{"docid":"4eeac7b5b86dc318900c7006a0380491","score":"0.5787983","text":"def read(fname, mode = \"binary\"):\n \n fin = InputFile(fname, mode)\n\n return fin.readObject()","title":""},{"docid":"11d012bb7a6ed682962194edad16a77a","score":"0.5784898","text":"def binary_file(fn):\r\n return binary(open(fn, 'rb').read(4096))","title":""},{"docid":"39e09cd3e7e26700113bca790ed81d0d","score":"0.5742841","text":"def readbin(type, file):\n return unpack(type, file.read(calcsize(type)))","title":""},{"docid":"859c632a64fdd5bba3147bc91b069a72","score":"0.5739511","text":"def load_from_binary_stram(path = \"Anaheim.graph\"):\n FIn = snap.TFIn(path)\n G = snap.TNEANet.Load(FIn)\n return G","title":""},{"docid":"152cf4806de08e230fc094f6e6320349","score":"0.57388985","text":"def read_binary(filename,shape=(1),cplex=True,big=True):\n \n # open the file and get the data\n f = open(filename)\n data = get_data(f,big=big)\n\n # complexify if needed\n if cplex:\n data = complexify_data(data)\n\n # create dictionary\n dic = {\"FILE_SIZE\":os.stat(filename).st_size}\n\n # reshape if possible\n try:\n return dic,data.reshape(shape)\n\n except ValueError:\n print \"Warning:\",data.shape,\"cannot be shaped into\",shape\n return dic,data","title":""},{"docid":"f17395302f5919ef115adddcc2219701","score":"0.5735585","text":"def readModel(fname, ):\n \n if (fname.endswith('wnd')):\n return bladed(fname,)\n elif (fname.endswith('bl')):\n return bladed(fname,)\n elif (fname.endswith('bts')):\n return turbsim(fname,)\n\n # Otherwise try reading it as a .wnd file.\n bladed(fname) # This will raise an error if it doesn't work. ","title":""},{"docid":"ffe75d1f0571428ae1a2e4ddb0177d9b","score":"0.57136637","text":"def read(self):\n with open(self.path, \"rb\") as inputfile:\n return pickle.load(inputfile)","title":""},{"docid":"955e1515d53d2d464c7adfbf9ff36b92","score":"0.57072","text":"def open_bin_data(path_to_file, file_name):\r\n with open(os.path.join(path_to_file, file_name + \".bin\"),'r') as current_file:\r\n data_type = np.dtype('>d') # assign data type: big-endian ordered 64 bit long data format \r\n raw_data = np.fromfile(current_file, dtype = data_type) # use numpy module to assign current trace data to [raw_data]\r\n raw_current = raw_data[0::2] # array of full baseline (current) data with first second and trailing values removed\r\n raw_voltage = raw_data[1::2]\r\n x_data_index_master = np.linspace(0,len(raw_current), len(raw_current), endpoint = True, dtype = 'int')\r\n current_file.close() # closes large data file\r\n del current_file\r\n \r\n return(raw_current, raw_voltage, x_data_index_master)","title":""},{"docid":"69b26c33304302414b6d22d47d9334da","score":"0.5701565","text":"def load(self):\n\n address = 0\n load_address = 0\n\n program = [0] * 256\n\n if len(sys.argv) != 2:\n print(f\"Usage:\\npython3 {sys.argv[0]} filename.ls8\")\n exit()\n try:\n with open(sys.argv[1]) as f:\n for line in f:\n possible_num = line[:line.find('#')] # strip comments\n if possible_num == '': # strip blank lines\n continue\n # convert \"binary\" string into a number\n program[load_address] = (int(possible_num, 2))\n load_address += 1\n if load_address == 256:\n raise Exception(\"Out of memory. Program is too large.\")\n\n except FileNotFoundError:\n print(f\"{sys.argv[1]} not found.\")\n exit()\n\n for instruction in program:\n self.ram[address] = instruction\n address += 1","title":""},{"docid":"ae7e9ed273700f07793c53d5752f8b8f","score":"0.568671","text":"def read_data_from_bin_file(fileName):\n with open(fileName, mode='rb') as file: # b is important -> binary\n fileContent = file.read()\n\n (ChannelData, LenOf1Channel,\n NumOfChannels, SampleTime) = read_data_from_bytes(fileContent)\n \n return ChannelData, LenOf1Channel, NumOfChannels, SampleTime","title":""},{"docid":"0fab1e55afca23d2e869bf99fd5dbd5f","score":"0.568458","text":"def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"0fab1e55afca23d2e869bf99fd5dbd5f","score":"0.568458","text":"def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"0fab1e55afca23d2e869bf99fd5dbd5f","score":"0.568458","text":"def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"0fab1e55afca23d2e869bf99fd5dbd5f","score":"0.568458","text":"def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"71d96782d57c91209adfd704d793fc6a","score":"0.5680559","text":"def read_is(filename, verbose=None): # noqa\n with open(filename, \"rb\") as f:\n logger.info(f\"Reading {filename}\")\n logger.info(f\"Reading Header...\")\n is_type = [\n struct.unpack(\"c\", f.read(1))[0].decode(\"utf-8\") for i in range(4)\n ]\n is_type = \"\".join(is_type)\n if is_type not in [\"IS01\", \"IS02\", \"IS03\"]:\n raise ValueError(\n f\"{is_type} : Invalid IS type, please check that \"\n \"input file is a Inverse Solution matrix\"\n )\n logger.info(f\"IS type: {is_type}\")\n n_channels = struct.unpack(\"I\", f.read(4))[0]\n logger.info(f\"n_channels: {n_channels}\")\n numsolutionpoints = struct.unpack(\"I\", f.read(4))[0]\n logger.info(f\"n_solutionpoints: {numsolutionpoints}\")\n numregularizations = struct.unpack(\"I\", f.read(4))[0]\n logger.info(f\"n_regularizations: {numregularizations}\")\n isinversescalar = struct.unpack(\"c\", f.read(1))[0]\n if isinversescalar == b\"\\x01\":\n n_dim = 1\n logger.info(f\"Inverse solution is Scalar\")\n elif isinversescalar == b\"\\x00\":\n logger.info(f\"Inverse solution is Vectorial\")\n n_dim = 3\n else:\n raise ValueError(\n f\"isinversescalar must be either 1 for scalar, \"\n f\"either 0 for vectorial, but \"\n f\"{ord(isinversescalar)} found.\"\n )\n\n if is_type in [\"IS01\", \"IS02\"]:\n buf = f.read(n_dim * numsolutionpoints * n_channels * 4)\n data = np.frombuffer(buf, dtype=np.float32)\n data = data.reshape(numsolutionpoints, n_dim, n_channels)\n data = np.array([data])\n data = np.swapaxes(data, 1, 2)\n\n elif is_type == \"IS03\":\n logger.info(f\"Reading Variable Header...\")\n\n ch_names = []\n for _ in range(n_channels):\n name = [\n char for char in f.read(32).split(b\"\\x00\") if char != b\"\"\n ][0]\n ch_names.append(name.decode(\"utf-8\"))\n\n solutionpoints_names = []\n for _ in range(numsolutionpoints):\n name = [\n char for char in f.read(16).split(b\"\\x00\") if char != b\"\"\n ][0]\n solutionpoints_names.append(name.decode(\"utf-8\"))\n\n regularizations_values = []\n for _ in range(numregularizations):\n value = struct.unpack(\"d\", f.read(8))[0]\n regularizations_values.append(value)\n logger.info(f\"Regularizations values: {regularizations_values}\")\n\n regularizations_names = []\n for _ in range(numregularizations):\n name = [\n char for char in f.read(32).split(b\"\\x00\") if char != b\"\"\n ][0]\n regularizations_names.append(name.decode(\"utf-8\"))\n logger.info(f\"Regularizations names: {regularizations_names}\")\n\n regularisation_solutions = []\n buf = f.read(\n numregularizations * n_dim * numsolutionpoints * n_channels * 4\n )\n data = np.frombuffer(buf, dtype=np.float32)\n data = data.reshape(\n numregularizations, numsolutionpoints, n_dim, n_channels\n )\n data = np.swapaxes(data, 1, 2)\n\n regularisation_solutions = np.array(regularisation_solutions)\n inverse_solution = {\n \"is_type\": is_type,\n \"is_scalar\": True if isinversescalar == \"0\" else False,\n \"ch_names\": ch_names,\n \"solutionpoints_names\": solutionpoints_names,\n \"regularizations_values\": regularizations_values,\n \"regularizations_names\": regularizations_names,\n \"regularisation_solutions\": data,\n }\n return inverse_solution","title":""},{"docid":"ae93c608217144a7a805d253f2a70c76","score":"0.5676097","text":"def test_decode_from_file(tempdir):\n job = Decode()\n tempdir.write(\"input.md\", b\"### My heading\")\n result = job.run(tempdir.getpath(\"input.md\"),)\n assert result[\"result\"] == dict(\n type=\"Article\", content=[dict(type=\"Heading\", depth=3, content=[\"My heading\"])]\n )\n assert result[\"log\"] == []","title":""},{"docid":"5572c6dc19de076c943ed068d85f839a","score":"0.5675747","text":"def read(dir=\".\",bin_file=None,acqus_files=None,pprog_file=None,shape=None,\n cplex=None,big=None,read_prog=True,read_acqus=True):\n\n if os.path.isdir(dir) != True:\n raise IOError,\"directory %s does not exist\"%(dir)\n\n # determind parameter automatically\n if bin_file == None:\n if os.path.isfile(os.path.join(dir,\"fid\")):\n bin_file = \"fid\"\n elif os.path.isfile(os.path.join(dir,\"ser\")):\n bin_file = \"ser\"\n else:\n raise IOError,\"no Bruker binary file could be found in %s\"%(dir)\n\n if acqus_files == None:\n acqus_files = []\n for f in [\"acqus\",\"acqu2s\",\"acqu3s\",\"acqu4s\"]:\n if os.path.isfile(os.path.join(dir,f)):\n acqus_files.append(f)\n\n if pprog_file == None:\n pprog_file = \"pulseprogram\"\n\n # create an empty dictionary\n dic = dict()\n\n # read the acqus_files and add to the dictionary\n if read_acqus:\n for f in acqus_files:\n dic[f] = read_jcamp(os.path.join(dir,f))\n\n # read the pulse program and add to the dictionary\n if read_prog:\n dic[\"pprog\"] = read_pprog(os.path.join(dir,pprog_file))\n\n # determind file size and add to the dictionary\n dic[\"FILE_SIZE\"] = os.stat(os.path.join(dir,bin_file)).st_size\n\n # determind shape and complexity for direct dim if needed\n if shape == None or cplex == None:\n gshape,gcplex = guess_shape(dic)\n if gcplex==True: # divide last dim by 2 if complex\n t = list(gshape)\n t[-1]=t[-1]/2\n gshape = tuple(t)\n if shape == None:\n shape = gshape\n if cplex == None:\n cplex = gcplex\n \n # determind endianness (assume little-endian unless BYTORDA is 1)\n if big == None:\n big = False # default value\n if \"acqus\" in dic and \"BYTORDA\" in dic[\"acqus\"]:\n if dic[\"acqus\"][\"BYTORDA\"] == 1:\n big = True\n else:\n big = False\n\n # read the binary file\n f = os.path.join(dir,bin_file)\n null,data = read_binary(f,shape=shape,cplex=cplex,big=big)\n return dic,data","title":""},{"docid":"3a5280a68a5f3e0d6d76dcb240ff59ad","score":"0.56719095","text":"def test_read(file_system):\n workfolder = file_system[\"main\"][\"dir\"]\n target = file_system[\"main\"][\"file1\"].name\n instructions = {\n \"KeyName\": \"Host Name\",\n \"EnableLineNumber\": \"False\"\n }\n var = ReadFullDocument(workfolder, target, instructions)\n result = var.read()\n\n assert len(result.container) == 1\n data = result.container[0]\n\n assert data.key == \"Host Name\"\n assert data.values == (\"Beolab90\",)","title":""},{"docid":"1ea1e41b13cc1d372d6d4db483f47d4b","score":"0.56717515","text":"def load(self):\n if STLMode.ascii == self._mode:\n if not os.path.exists(self._path):\n raise Exception(\"File not found: [%s]\" % self._path)\n with open(self._path) as f:\n ln = f.readline()\n while ln != '':\n line = ln.strip()\n self._process(line)\n\n # increment reader\n ln = f.readline()\n self._cur_line += 1\n elif STLMode.binary == self._mode:\n raise Exception(\"Not implemented yet\")\n else:\n raise Exception('No mode specified')","title":""},{"docid":"92cab9d4106d20138619687a2ef59253","score":"0.5668465","text":"def binary_format_reader(filename):\n header_part = \"\"\n rawVectorData = None\n header = None\n with open(filename, 'rb') as f:\n x = f.readline()\n while x != b'# End: Header\\n':\n header_part += str(x)\n x = f.readline()\n\n header = ParsingUtils.process_header(header_part)\n\n byte_type = f.readline()\n while byte_type == b'#\\n':\n byte_type = f.readline()\n # compile struct byte type\n fmt, buff_size, val = ParsingUtils.decode_byte_size(byte_type)\n struct_object = struct.Struct(fmt)\n test_val = struct_object.unpack(f.read(buff_size))[0]\n if test_val != val:\n raise ValueError(\"Invalid file format with validation {} value, \\\n should be {}\".format(test_val, val))\n\n k = int(header['xnodes']*header['ynodes']*header['znodes'])\n rawVectorData = ParsingUtils.standard_vertex_mode(\n f, k, struct_object, buff_size)\n f.close()\n assert rawVectorData is not None\n assert header is not None\n return header, rawVectorData","title":""},{"docid":"4a9ada01c65385b2871007056fa8103a","score":"0.56642765","text":"def read_file(file):","title":""},{"docid":"cc75af9bb6c59c6776055e0fda3dcaba","score":"0.5662355","text":"def read_data(self, filepath):","title":""},{"docid":"b7da863fec1eabd3641740f447a79c7b","score":"0.5660204","text":"def ibe_read_from_file(filename):\n with open(filename, \"rb\") as f:\n res = f.read()\n return res \n \n return None","title":""},{"docid":"99a41036869100beed68325254c3bb7f","score":"0.565705","text":"def Read(self):\n\n\t\tself.data = np.loadtxt(self.fpath)","title":""},{"docid":"e0af20f8b5a74bcd37c0631c68a536f7","score":"0.5652648","text":"def file_read(path):\n with open(path, mode='rb') as f:\n return pickle.load(f)","title":""},{"docid":"766029f1d9d0d8fa560746e8577e78e4","score":"0.56470966","text":"def _read(self):\n with open(self.filename, 'r') as f:\n# self.title = f.read()\n # initializng data dictionary\n print('Opening: %s'%self.filename)\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 \n self.plot()","title":""},{"docid":"827bfa72bf571703d29d5c2abb1e029a","score":"0.56453896","text":"def deserialize(infile):\n raise NotImplementedError","title":""},{"docid":"212fbd56b6f9c334a3ce54ea263447dc","score":"0.5645035","text":"def read_binary(filename, dim, dtype = tf.float32):\n\n bin_file = tf.read_file(filename)\n bin_tensor = tf.decode_raw(bin_file, dtype)\n bin_tensor = tf.to_float(bin_tensor)\n bin_tensor = tf.reshape(bin_tensor,[dim])\n # bin_tensor = normalize_tensor(bin_tensor)\n\n return bin_tensor","title":""},{"docid":"9b8bf689314b19d81b273c74c6c0d0f8","score":"0.5644736","text":"def read_nifti_file(filepath):\n\tscan = nib.load(filepath)\n\tscan = scan.get_data()\n\treturn scan","title":""},{"docid":"232aa8aaba074414f04b2c6406e9d2a3","score":"0.56351805","text":"def read_binary_ivector(fname):\n \n f= open(fname, \"rb\")\n ivector, n_data, metadata = string_to_ivector(f)\n\n return ivector, n_data, metadata","title":""},{"docid":"d6332b75c57f8e316b0e99a1ef17d8b1","score":"0.56314677","text":"def read_file(self):\n try:\n data = pickle.load(self.open_diary)\n return data\n except EOFError:\n #print('Your diary is empty!')\n pass","title":""},{"docid":"127b3c6dbe7b24997d709620119ba0b7","score":"0.562332","text":"def ReadFile(self, filename):","title":""},{"docid":"1def917729181ba740a36d735e3c43c3","score":"0.5613102","text":"def test_task2_with_input_file():\n with gzip.open(INPUT_FILE_PATH, \"rt\", encoding=\"ascii\") as file:\n result = solve_task2(file)\n assert result == 9533698720","title":""},{"docid":"9ee5160e18af0baaea0694a85741f2ab","score":"0.5610461","text":"def test_embedded_bin_chars(self):\n file = io.StringIO(\"line\\x00\\x03\\x04\\x0c\\x17\\x1a\\x1c1\")\n self.reader._read_file_obj(file)\n\n self.assertEqual(len(self.parser.lines), 1);\n self.assertEqual(self.parser.lines[0].get_filename(), Helpers.TEST_FNAME)\n self.assertEqual(self.parser.lines[0].get_line(), 1)\n self.assertEqual(self.parser.lines[0].get_text(), \"line\\x00\\x03\\x04\\x0c\\x17\\x1a\\x1c1\")","title":""},{"docid":"8a8b757ca487c46314722a37a08f69ad","score":"0.56001437","text":"def read_file(self, file):","title":""},{"docid":"d38e85afed3be4e44fdf50d7433c0adc","score":"0.55997443","text":"def read_from_file():\n\tparser = argparse.ArgumentParser()\n\t# command cannot be executed without a path\n\tparser.add_argument(\"--input\", required=True,help=\"you need to parse a link to a file with instructions, please see the docs\")\n\targs = (parser.parse_args())\n\n\turi = str(args.input)\n\treq = urllib.request.urlopen(uri)\n\tdata = req.read().decode('utf-8')\n\t# first line is the size of the led board\n\tled_box_len = int(data.splitlines()[0])\n\t# create new board\n\tboard = (create_board(led_box_len))\n\t# the actions are the rest of the file\n\tcommands = data.splitlines()[1:]\n\t# cleaned up data\n\tfinal_data = clean_up_input_file(commands)\n\t# perform actions on the board\n\tresult = board_plotter(final_data, board)\n\tprint(print_lights_totals(result))","title":""},{"docid":"1606243fa969203a08d8265a06349208","score":"0.55972725","text":"def _read_solution(self,\n symbols_filename,\n solution_filename):\n\n # parse the symbol map\n symbol_map = {}\n with open(symbols_filename) as f:\n for line in f:\n lp_symbol, scenario_tree_id = line.strip().split()\n symbol_map[lp_symbol] = scenario_tree_id\n\n results = SPSolverResults()\n results.status = None\n results.solver.status = None\n results.solver.termination_condition = None\n results.solver.message = None\n xhat = {}\n try:\n with open(solution_filename, 'r') as f:\n line = f.readline()\n assert line.startswith(\"Problem:\")\n assert line.split()[1].strip() == \"pysp_model\"\n line = f.readline()\n assert line.startswith(\"First Stage Rows:\")\n line = f.readline()\n assert line.startswith(\"First Stage Columns:\")\n line = f.readline()\n assert line.startswith(\"First Stage Non-zeros:\")\n line = f.readline()\n assert line.startswith(\"Replication No.\") or \\\n line.startswith(\"Number of replications:\")\n line = f.readline()\n assert line.startswith(\"Status:\")\n results.solver.message = line.split(\":\")[1].strip()\n (results.status,\n results.solver.status,\n results.solver.termination_condition) = \\\n _sd_status_map.get(results.solver.message,\n (SolutionStatus.unknown,\n SolverStatus.unknown,\n TerminationCondition.unknown))\n\n #\n # Objective and Bound\n #\n\n line = f.readline()\n assert line.startswith(\"Total Objective Function Upper Bound:\")\n line = line.split(':')\n if line[1].strip() == '':\n pass\n else:\n assert len(line) == 4\n line = line[1]\n if \"half-width\" in line:\n # we are given confidence intervals on the objective\n line = line.split(',')\n assert len(line) == 4\n results.objective = float(line[0])\n assert line[1].startswith('[')\n assert line[2].endswith(']')\n results.objective_interval = (float(line[1][1:]),\n float(line[2][:-1]))\n else:\n results.objective = float(line[1])\n line = f.readline()\n assert line.startswith(\"Total Objective Function Lower Bound:\")\n line = line.split(':')\n if line[1].strip() == '':\n pass\n else:\n if \"half-width\" in line[1]:\n # we are given confidence intervals on the bound\n line = line[1].split(',')\n assert len(line) == 4\n results.bound = float(line[0])\n assert line[1].startswith('[')\n assert line[2].endswith(']')\n results.bound_interval = (float(line[1][1:]),\n float(line[2][:-1]))\n else:\n results.bound = float(line[1].strip())\n\n #\n # Xhat\n #\n\n line = f.readline()\n assert line.strip() == ''\n line = f.readline()\n assert line.startswith('First Stage Solutions:')\n line = f.readline()\n assert line.startswith(' No. Row name Activity Lower bound Upper bound Dual Dual STDEV')\n line = f.readline()\n assert line.startswith('------ ------------ ------------- ------------- ------------- ------------- -------------')\n\n xhat_start_line = ' No. Column name Activity Lower bound Upper bound Reduced Cost RC STDEV'\n line = f.readline()\n while not line.startswith(xhat_start_line):\n line = f.readline()\n line = f.readline()\n assert line.startswith('------ ------------ ------------- ------------- ------------- ------------- -------------')\n line = f.readline().strip().split()\n while line:\n varlabel, varvalue = line[1:3]\n varlabel = varlabel.strip()\n varvalue = float(varvalue)\n xhat[symbol_map[varlabel]] = varvalue\n line = f.readline().strip().split()\n\n except (IOError, OSError):\n logger.warn(\n \"Exception encountered while parsing sd \"\n \"solution file '%s':\\n%s'\"\n % (solution_filename, traceback.format_exc()))\n xhat = None\n\n return xhat, results","title":""},{"docid":"548368c802df754b17a79ed2c4295068","score":"0.5592868","text":"def testReadFileObjectFormatVersion1(self):\n output_writer = test_lib.TestOutputWriter()\n test_file = recycle_bin.RecycleBinMetadataFile(\n debug=True, output_writer=output_writer)\n\n test_file_path = self._GetTestFilePath(['$II3DF3L.zip'])\n self._SkipIfPathNotExists(test_file_path)\n\n test_file.Open(test_file_path)","title":""},{"docid":"bf106ada03f16d5af6d5606c2d9ef68b","score":"0.5589384","text":"def read(self, filename, nscale=1):\n reader = SHTextReader(filename)\n if filename.endswith(\".bdo\") or filename.endswith(\".bdox\"):\n reader = SHBinaryReader(filename)\n # find better way to discover if file comes from Fluka\n elif \"_fort\" in filename:\n reader = FlukaBinaryReader(filename)\n reader.read(self, nscale)\n self.counter = 1","title":""},{"docid":"a8db1d58aa7dbcc575b0fab2e0449b79","score":"0.55843747","text":"def read(self, fileobj):\n raise NotImplementedError","title":""},{"docid":"b82f75cdfe1e8ceff468ac2c426769f0","score":"0.5572124","text":"def read(file_path):\n\n data = numpy.load(file_path)\n return data","title":""},{"docid":"637704aa0b14c2204edfb6f70530bc46","score":"0.5569049","text":"def read_input(fname=\"day14.in\"):\n with open(fname) as f:\n return lines2recipe(f.readlines())","title":""},{"docid":"c700e2233d8a793c95a99b844fbf8c25","score":"0.5567073","text":"def read_label_file(label_file):\n with open(label_file,'rb') as f:\n data = f.read(4)\n magic_no = struct.unpack('>L',data)\n if magic_no==2049 or magic_no==2051:\n print 'Incorrectly parsing files'\n print ' magic no = %d '%magic_no\n data = f.read(4)\n num_data, = struct.unpack('>L',data)\n print ' Number of data points = %d '% num_data\n unpacked_data = np.zeros((num_data),np.uint8)\n for i in range(num_data):\n temp_data, = struct.unpack('>B',f.read(1))\n unpacked_data[i] = temp_data\n\n return unpacked_data","title":""},{"docid":"7982bf506d30a156ccde787310f635b4","score":"0.5565414","text":"def readFlow(fn):\n # Code adapted from:\n # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy\n\n # WARNING: this will work on little-endian architectures (eg Intel x86) only!\n # print 'fn = %s'%(fn)\n with open(fn, 'rb') as f:\n magic = np.fromfile(f, np.float32, count=1)\n if 202021.25 != magic:\n print('Magic number incorrect. Invalid .flo file')\n return None\n else:\n w = np.fromfile(f, np.int32, count=1)\n h = np.fromfile(f, np.int32, count=1)\n # print 'Reading %d x %d flo file\\n' % (w, h)\n data = np.fromfile(f, np.float32, count=2*int(w)*int(h))\n # Reshape data into 3D array (columns, rows, bands)\n # The reshape here is for visualization, the original code is (w,h,2)\n return np.resize(data, (int(h), int(w), 2))","title":""},{"docid":"7982bf506d30a156ccde787310f635b4","score":"0.5565414","text":"def readFlow(fn):\n # Code adapted from:\n # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy\n\n # WARNING: this will work on little-endian architectures (eg Intel x86) only!\n # print 'fn = %s'%(fn)\n with open(fn, 'rb') as f:\n magic = np.fromfile(f, np.float32, count=1)\n if 202021.25 != magic:\n print('Magic number incorrect. Invalid .flo file')\n return None\n else:\n w = np.fromfile(f, np.int32, count=1)\n h = np.fromfile(f, np.int32, count=1)\n # print 'Reading %d x %d flo file\\n' % (w, h)\n data = np.fromfile(f, np.float32, count=2*int(w)*int(h))\n # Reshape data into 3D array (columns, rows, bands)\n # The reshape here is for visualization, the original code is (w,h,2)\n return np.resize(data, (int(h), int(w), 2))","title":""},{"docid":"287e2d6c4fd39a889b210abc1a842643","score":"0.55653274","text":"def read_highd_binary(filename, shape, perm, dtype = tf.float32):\n\n bin_file = tf.read_file(filename)\n bin_tensor = tf.decode_raw(bin_file, dtype)\n bin_tensor = tf.to_float(bin_tensor)\n bin_tensor = tf.reshape(bin_tensor, shape)\n bin_tensor = tf.transpose(bin_tensor, perm= perm)\n return bin_tensor","title":""},{"docid":"e3839f09e15da7f48ea2ff8793a231b3","score":"0.55636847","text":"def load_from_file(self):\n pass","title":""},{"docid":"62707ee689da802dfae878980a1ae478","score":"0.55600953","text":"def readBinaryFiles(file, dimensions=(100, 100), saveFITS=True, output='tmp.fits'):\n fh = open(file, 'rb')\n #use numpy to read the binary format, the data is 16bit unsigned int\n a = np.fromfile(fh, dtype=np.uint16)\n fh.close()\n\n try:\n #note the transpose\n image = a.reshape(dimensions).T\n except:\n print 'Image shape as not expected'\n print a.shape\n return None\n\n if saveFITS:\n fileIO.writeFITS(image, output)\n\n return image","title":""},{"docid":"6e080715b5f6482e47acabc414bf77fc","score":"0.55571693","text":"def importBinary(self):\n ...","title":""},{"docid":"8b7e59093ab25aeedd2b28447a62e763","score":"0.5556787","text":"def read_vec_int(file_or_fd):\n fd = open_or_fd(file_or_fd)\n binary = fd.read(2)\n if binary == '\\0B': # binary flag\n assert(fd.read(1) == '\\4'); # int-size\n vec_size = struct.unpack('<i', fd.read(4))[0] # vector dim\n ans = np.zeros(vec_size, dtype=int)\n for i in range(vec_size):\n assert(fd.read(1) == '\\4'); # int-size\n ans[i] = struct.unpack('<i', fd.read(4))[0] #data\n return ans\n else: # ascii,\n arr = (binary + fd.readline()).strip().split()\n try:\n arr.remove('['); arr.remove(']') # optionally\n except ValueError:\n pass\n ans = np.array(arr, dtype=int)\n if fd is not file_or_fd : fd.close() # cleanup\n return ans","title":""},{"docid":"476546683990b5670c60367797c425be","score":"0.55560607","text":"def structread(fromfile, decode_struct) : \n return struct.unpack(decode_struct, fromfile.read(struct.calcsize(decode_struct)))","title":""},{"docid":"2e9fbdde41f75881fc42362c02755f4b","score":"0.55545557","text":"def readBinary(zTop=0, zBot=1200,\n min_utme=None, max_utme=None,\n min_utmn=None, max_utmn=None,\n newModelName='subModel.nc',\n binModelName='',\n saveFull='N'):\n\n # The following lines describe the size of the binary file values.\n #\n # Layer 1 0-1200m depth\n # 3271 in EW direction\n # 5367 in NS direction\n # 13 in z\n # dx=dy= 200m, dz= 100m\n #\n if 'l1' in binModelName:\n nx = 3271; ny = 5367; nz = 13\n dx = 200; dy = 200; dz = 100\n zmin = 0; zmax = 1200\n #\n # Layer 2 1500-9900m depth\n # 2181 in EW\n # 3578 in NS\n # 29 in z\n # dx=dy=dz=300m\n #\n if 'l2' in binModelName:\n nx = 2181; ny = 3578; nz = 29\n dx = 300; dy = 300; dz = 300\n zmin = 1500; zmax = 9900\n #\n # Layer 3 10800-59400m depth\n # 727 in EW\n # 1193 NS\n # 55 in z\n # dx=dy=dz=900m\n #\n if 'l3' in binModelName:\n nx = 727; ny = 1193; nz = 55\n dx = 900; dy = 900; dz = 900\n zmin = 10800; zmax = 59400\n #\n # The SW corner of the velocity model is -10800m East, 4467300m N Zone 10.\n SWcornerFull = [-10800, 4467300]\n #\n\n # Read in binary file\n v = np.fromfile(binModelName, dtype='<f4')\n\n # Generate arrays for x, y, & z locations\n z = np.linspace(zmin, zmax, nz)\n z = np.repeat(z, (np.ones(len(z))*nx*ny).astype(int))\n z = z[::-1] # Reverse array\n\n y = np.linspace(SWcornerFull[1], SWcornerFull[1]+ny*dy, ny, endpoint=False)\n y = np.repeat(y, (np.ones(len(y))*nx).astype(int))\n y = np.tile(y, nz) # Repeat array for each depth\n\n x = np.linspace(SWcornerFull[0], SWcornerFull[0]+nx*dx, nx, endpoint=False)\n x = np.tile(x, ny*nz)\n\n # Convert CVM to dataframe\n model = pd.DataFrame(np.column_stack((x,y,z,v)), columns=['utme','utmn','z',(binModelName.split('/'))[1].split('_16')[0]])\n\n if saveFull == 'N' or saveFull == 'n':\n # Subset model (speed things up)\n if min_utme==None:\n # Use full horizontal model extent\n subModel = model[(model[\"z\"] >= zTop) & (model[\"z\"] <= zBot)]\n else:\n subModel = model[(model[\"z\"] >= zTop) & (model[\"z\"] <= zBot)\n & (model[\"utme\"] >= min_utme) & (model[\"utme\"] <= max_utme)\n & (model[\"utmn\"] >= min_utmn) & (model[\"utmn\"] <= max_utmn)]\n subModel = subModel.set_index(['utme','utmn','z']) # Set these parameters as coordinates\n\n # Convert to xarray (slow)\n subModelxr = subModel.to_xarray()\n\n # Save xarray for faster reload later\n subModelxr.to_netcdf('../output/' + newModelName)\n\n return subModelxr\n\n # This can be VERY slow (~ 1 hour)\n if saveFull == 'Y' or saveFull == 'y':\n model = model.set_index(['utme','utmn','z'])\n modelxr = model.to_xarray()\n modelxr.to_netcdf('../output/' + (binModelName.split('/'))[1].split('.bin')[0]+'.nc')\n\n return modelxr","title":""},{"docid":"2b7d2c3d0629055aabf0bb03abe5134f","score":"0.5550434","text":"def read_low(filepath):\n pass","title":""},{"docid":"5d0bd63a786585d392b5683fea245461","score":"0.55393505","text":"def load_from_File(self, filePath):\n # Create file object\n fileObject = open(filePath, 'rb')\n # Load from the file object\n b = pickle.load(fileObject)\n return b","title":""},{"docid":"702e0c9544e686a6ebcb974163ad5c12","score":"0.5539064","text":"def load_bitstream(self, bitfile):\n if not os.path.isfile(bitfile):\n raise ValueError(\"Cannot find specified bitfile {}\".format(bitfile))\n\n bitstream = open(bitfile, \"rb\")\n self._llint.FPGAProgram(bitstream)\n pass","title":""},{"docid":"6dc76223ce23e3f476893a9fa85491ca","score":"0.55328214","text":"def read(filename):\n with open(filename, \"rb\") as f:\n mesh = read_buffer(f)\n return mesh","title":""},{"docid":"ccc01eb139326e1f13ad57ca6ad5e5b4","score":"0.55316436","text":"def test_task1_with_input_file():\n with gzip.open(INPUT_FILE_PATH, \"rt\", encoding=\"ascii\") as file:\n trees = solve_task1(file)\n assert trees == 230","title":""},{"docid":"b8315e84a83d80709f997ea77dfffacb","score":"0.55310977","text":"def loadbin(self, fobj, offset=0):\n fread = getattr(fobj, \"read\", None)\n if fread is None:\n f = open(fobj, \"rb\")\n fread = f.read\n fclose = f.close\n else:\n fclose = None\n\n try:\n self.frombytes(array('B', asbytes(fread())), offset=offset)\n finally:\n if fclose:\n fclose()","title":""},{"docid":"629af03410e0d164a23edd6dee98d557","score":"0.5530605","text":"def load(self, filename):\n\n address = 0\n\n # In `load()`, you will now want to use those command line arguments to open a\n # file, read in its contents line by line, and save appropriate data into RAM. \n with open(filename) as file:\n\n # As you process lines from the file, you should be on the lookout for blank lines\n # (ignore them), and you should ignore everything after a `#`, since that's a\n # comment.\n for line in file:\n comment_split = line.split('#')\n instruction = comment_split[0]\n\n if instruction == '':\n continue\n \n first_bit = instruction[0]\n\n # You'll have to convert the binary strings to integer values to store in RAM. The\n # built-in `int()` function can do that when you specify a number base as the\n # second argument:\n if first_bit == '0' or first_bit == '1':\n self.ram[address] = int(instruction[:8], 2)\n address += 1","title":""},{"docid":"482779e5cd2fa61dc4c94498d875f598","score":"0.55265105","text":"def load(self, path, offset=0):\n\n # Read the file into main memory byte-by-byte\n with open(path, \"rb\") as f:\n b = f.read(1)\n while b:\n self.memory[offset] = struct.unpack(\"B\", b)[0]\n offset += 1\n b = f.read(1)","title":""},{"docid":"2740dda98dcf8ae0a6ed8066acc72a05","score":"0.55191106","text":"def load_solution(f):\n return json.load(f)","title":""},{"docid":"a8db5b57ee26df6ff644366f13ff3578","score":"0.551412","text":"def _read_file_from_archive(cls, read_obj, src_file):\n with read_obj.open(src_file, 'r') as file_obj:\n return file_obj.read()","title":""},{"docid":"dfb91ee7ec3032545954fa5472f4172a","score":"0.55100393","text":"def load_binary_data(filename):\n\n input_data = open(filename,\"rb\")\n grid = dill.load(input_data)\n input_data.close()\n retessellate = grid.remove_tracks(config.track_threshold) # filter out incomplete tracks\n if (config.distort_grid):\n grid.distort_grid()\n retessellate = True\n if (retessellate or config.retessellate):\n grid.tessellate()\n\n if (grid.user_params != config.user_params):\n print(\"WARNING: mismatch between the user_params in the binary grid file\")\n print(\" and in AIMS_configure.py. Will overwrite user_params.\")\n print(\" Binary grid file: \",grid.user_params)\n print(\" AIMS_configure.py: \",config.user_params)\n\n # manually correct relevant variables:\n config.user_params = grid.user_params\n model.nglb = 8 + len(config.user_params)\n model.nlin = 5 + len(config.user_params)\n model.ifreq_ref = 5 + len(config.user_params)\n model.iradius = 6 + len(config.user_params)\n model.iluminosity = 7 + len(config.user_params)\n model.init_user_param_dict()\n\n return grid","title":""},{"docid":"0d404a72286b82bbcae00bffcded3153","score":"0.55079895","text":"def load(fname):\n try:\n with open(fname, 'rb') as fin:\n return fin.read()\n except FileNotFoundError:\n print(\"Couldn't read file\", fname)\n exit(1)","title":""},{"docid":"792418a879f4b424fe407d0b8d29bcae","score":"0.5493646","text":"def load(self):\n\n params = sys.argv\n if len(params) != 2: # if no filename is passed\n print(\"usage: file.py filename\") # print error and exit\n sys.exit(1)\n if len(params) == 2: # if filename passed\n try:\n with open(params[1]) as f: # read filename\n address = 0\n for line in f: # for each line\n comment_split = line.split(\"#\") # SPlit at comments\n num = comment_split[0].strip() # strip whiteSPace\n if num == '': # if line is blank, continue\n continue\n val = int(\"0b\"+num,2) # otherwise convert oPCode to binary\n self.ram_write(address, val) # write the op code to memory\n address += 1 # increment the address to the next one in ram\n except FileNotFoundError: # if try fails, filename doesn't exist\n print(\"ERROR: File not found\")\n sys.exit(2)","title":""}],"string":"[\n {\n \"docid\": \"54d9052ff2375d0421dc2be1610b999e\",\n \"score\": \"0.65914464\",\n \"text\": \"def read_binary(filename):\\n with FileBinaryReader(filename) as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83beb9771c450aa37bce0e79e62b5fcc\",\n \"score\": \"0.6584203\",\n \"text\": \"def load_solution(filepath):\\n with open(filepath) as f:\\n solution = pickle.load(f)\\n return solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081ddc5df69ce0eeccfae1756e551372\",\n \"score\": \"0.6413085\",\n \"text\": \"def load_binary(path):\\n try:\\n with open(path, \\\"rb\\\") as f:\\n loaded_data = pickle.load(f)\\n print(f\\\"Data binary file loaded from {path}\\\")\\n return loaded_data\\n except Exception as e:\\n print(\\\"Binary file couldn't be loaded.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3eebf94bda4497f169fc6cecd832960\",\n \"score\": \"0.6381922\",\n \"text\": \"def open_for_reading_binary(file_path):\\n try:\\n f = open(file_path, 'r')\\n return f\\n except IOError as e:\\n raise _io_load_error(e, file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83e78e91312904f676408d421479e08\",\n \"score\": \"0.6330716\",\n \"text\": \"def load(raw):\\n return BinFile(raw[:0x20], raw[0x20:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad1b97c48f1973bf44bb60403ee3be3\",\n \"score\": \"0.62161446\",\n \"text\": \"def read_binary_file(file_name):\\n return read_file(file_name, 'rb')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5833686738a2e2586680303015219694\",\n \"score\": \"0.6203299\",\n \"text\": \"def load_bin_file(filepath):\\n datas = []\\n f = open(filepath, 'rb')\\n s = f.read()\\n for x in s:\\n rdata = struct.unpack(\\\"B\\\", x)[0]\\n datas.append(rdata)\\n return datas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0232133f58de643f09d0050bc7120f01\",\n \"score\": \"0.61890787\",\n \"text\": \"def read_binary(self, filename):\\n return self._read_file_or_404(filename, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4a4123358aab2f659222ceea275d1f\",\n \"score\": \"0.61867404\",\n \"text\": \"def read(path_to_file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f68aa4d8f22c96017e59dedd80e9b4\",\n \"score\": \"0.61727834\",\n \"text\": \"def open_for_reading_binary(filename):\\n return FileBinaryReader(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa5301b50a7a026be3c790a1bc1643e\",\n \"score\": \"0.61461914\",\n \"text\": \"def readSol(filename):\\r\\n\\r\\n if not isinstance(filename, str):\\r\\n raise TypeError(\\\"argument 1 to readSol must be a string\\\")\\r\\n #end if\\r\\n data_type=sol_data_type()\\r\\n record_length=numpy.empty(0, dtype=data_type).itemsize\\r\\n header_length_noCheck=numpy.empty(0,\\r\\n dtype=numpy.dtype(sol_header_types()[:-1])).itemsize\\r\\n #print(data_type)\\r\\n #Calculate checksum for first record. This is done again later on the whole\\r\\n #data set. This is just here so if it is clearly rubbish I don't read the\\r\\n #whole file into memory\\r\\n fd=open(filename, 'r', encoding=\\\"utf-8\\\",errors=\\\"ignore\\\")\\r\\n header_size=numpy.empty(0, dtype=numpy.dtype(sol_header_types())).itemsize\\r\\n header=fd.read(header_size)\\r\\n fd.close()\\r\\n # Calculate the checksum - TODO: NOT SURE IF WORKING WITH PYTHON 3\\r\\n checksum = 0\\r\\n # Do a cumulative bitwise xor on all elements of the header bytearray\\r\\n for byte in bytearray(header[:-1],encoding=\\\"utf8\\\"):\\r\\n # ^ means xor\\r\\n checksum ^= byte\\r\\n # ADDED THIS TRY TO AVOID ERRORS IN SOME SOL FILES, SEEMS TO WORK OK(kevin mcguigan, AGRG)\\r\\n try:\\r\\n if checksum != ord(header[header_size-1]):\\r\\n raise IOError(\\\"Header checksum does not match\\\")\\r\\n except:\\r\\n pass\\r\\n #this is the error that would occasinally appear, such as when running:\\r\\n # testSol = r\\\"Q:\\\\CHII\\\\1_IE\\\\20170828174507_SH_AR\\\\proc\\\\t200_TC_MULTI_RAPID_BART_LTWN.sol\\\"\\r\\n # solDf = readSol(testSol)\\r\\n # print solDf\\r\\n # '''Traceback (most recent call last):\\r\\n # File \\\"Q:\\\\CHII\\\\_meta\\\\_dev\\\\read_nav_file.py\\\", line 177, in readSol\\r\\n # if checksum != ord(header[header_size-1]):\\r\\n # IndexError: string index out of range'''\\r\\n\\r\\n\\r\\n #Read file into memory, is a numpy array of tuples (not really tuples, these\\r\\n #are mutable, it looks like a tuple though)\\r\\n sol_data=numpy.fromfile(filename, dtype=data_type)\\r\\n #Check that the header checksum is correct for all records\\r\\n #The checksum is the bitwise xor of all elements in the header excluding the\\r\\n #checksum itself.\\r\\n #Do a bitwise xor on all header records, want to reduce along axis 1\\r\\n #to give an array of checksums\\r\\n if (not (numpy.bitwise_xor.reduce(\\r\\n #numpy.view gives the view of the array in the given data type,\\r\\n #and becuase we are doing a bitwise xor needs to be in unsigned 8bit\\r\\n #integers\\r\\n #reshape the array so that it is in records\\r\\n sol_data.view(numpy.uint8).reshape(sol_data.shape[0], record_length)\\\\\\r\\n #Want just the header records up to the\\r\\n #checksum\\r\\n [:, :header_length_noCheck], axis=1)\\\\\\r\\n ==\\\\\\r\\n #Check that the checksums are correct\\r\\n sol_data['header_checksum']).all()):\\r\\n raise IOError(\\\"Header checksum does not match, may not be a sol file\\\")\\r\\n #end if\\r\\n return sol_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac38bb353f0b881e02bf8caa797b2ff\",\n \"score\": \"0.6142402\",\n \"text\": \"def read_file_bin():\\n\\tif not isfile(binpath):\\n\\t\\twrite_file_bin([\\\"Hello\\\"], [\\\"Welcome!\\\"])\\n\\ttry:\\n\\t\\tf = open(binpath, \\\"br\\\")\\n\\t\\tdata = pickle.load(f)\\n\\t\\tf.close()\\n\\t\\tret_pages = []\\n\\t\\tfor i,d in enumerate(data[0]):\\n\\t\\t\\tret_pages.append([d, data[1][i]])\\n\\t\\treturn ret_pages\\n\\texcept:\\n\\t\\tprint(\\\"Error reading file:\\\")\\n\\t\\tprint(sys.exc_info())\\n\\t\\texit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9a936f5a7923d72754eb54dc5257a3\",\n \"score\": \"0.60778\",\n \"text\": \"def import_bin_file(file_path):\\n with open(file_path, 'rb') as file:\\n data = pickle.load(file)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22d6b335fef0ba95602885b246934d8d\",\n \"score\": \"0.6052145\",\n \"text\": \"def puzzle_02() -> None:\\n\\n with open(INPUT_FILE_PATH, \\\"r\\\") as f:\\n document = loads(f.read())\\n print_puzzle_solution(_object_sum(document))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5863bfb14f50e9983918c9201312e3a9\",\n \"score\": \"0.6046847\",\n \"text\": \"def load_binary(binary_path=\\\"dictionary.dat\\\"):\\r\\n try:\\r\\n with open(binary_path, 'rb') as file:\\r\\n my_depickler = pickle.Unpickler(file)\\r\\n dictionary = my_depickler.load()\\r\\n return dictionary\\r\\n except:\\r\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c72d58a0e1035a343bdf9cf22ae4f0\",\n \"score\": \"0.6030863\",\n \"text\": \"def load_from_bin(file: str) -> Any:\\n t = time()\\n with open(file, 'rb') as f:\\n obj = load(f)\\n print('loaded from binary in ' + timer(t))\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"743e91c8bf7dd88cf236572b856c9c0e\",\n \"score\": \"0.600608\",\n \"text\": \"def read_from_binary_file(file_name, lines='All'):\\r\\n with open(file_name, mode='rb') as f:\\r\\n global commands\\r\\n commands = pickle.loads(f.read())\\r\\n\\r\\n num_of_lines = len(commands) if lines == 'All' else int(lines)\\r\\n printed_commands = commands[-num_of_lines:]\\r\\n for command in printed_commands:\\r\\n print(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc1d5463383615fa70253882f4bab37\",\n \"score\": \"0.5980304\",\n \"text\": \"def read_file(self, file_name):\\n cur_dir = os.path.dirname(os.path.abspath(__file__))\\n path = Path(cur_dir)\\n cur_dir = path.parent\\n file_name = os.path.join(cur_dir, \\\"bin\\\", file_name)\\n try:\\n with open(file_name, 'r') as fileread:\\n data = fileread.read()\\n return file_name, data\\n except IOError as error:\\n print(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1a5a329db491ecd915b0ffa0d70373\",\n \"score\": \"0.5975809\",\n \"text\": \"def load_bin(infile):\\n\\treturn np.load(infile,allow_pickle='TRUE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17575ce47aeea8722154bd8d24e88e73\",\n \"score\": \"0.5974111\",\n \"text\": \"def read_binary_file(filename, offset=0):\\n read_buffer = open(filename, 'rb')\\n read_buffer.seek(int(offset), 0)\\n header = struct.unpack('<xcccc', read_buffer.read(5))\\n if header[0] != 'B':\\n print(\\\"Input .ark file is not binary\\\")\\n sys.exit(-1)\\n if header[1] == 'C':\\n print(\\\"Input .ark file is compressed, exist now.\\\")\\n sys.exit(-1)\\n\\n rows = 0; cols= 0\\n _, rows = struct.unpack('<bi', read_buffer.read(5))\\n _, cols = struct.unpack('<bi', read_buffer.read(5))\\n\\n if header[1] == \\\"F\\\":\\n tmp_mat = np.frombuffer(read_buffer.read(rows * cols * 4),\\n dtype=np.float32)\\n elif header[1] == \\\"D\\\":\\n tmp_mat = np.frombuffer(read_buffer.read(rows * cols * 8),\\n dtype=np.float64)\\n mat = np.reshape(tmp_mat, (rows, cols))\\n\\n read_buffer.close()\\n\\n return mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6113ee473da2a279178fc743e473559\",\n \"score\": \"0.5967676\",\n \"text\": \"def read_bin_input_spec(f):\\n with open(f, \\\"rb\\\") as ff:\\n l = ff.readline().strip()\\n if l == \\\"#v1\\\":\\n x = BinInputSpecV1()\\n else:\\n print >>sys.stderr, \\\"Unknown file version for input/binary spec\\\", l\\n \\n x.read(ff)\\n\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85fa1660644f6e447cf79f684996e01\",\n \"score\": \"0.59667325\",\n \"text\": \"def load_bin(file_name: str, **params: Any) -> pd.DataFrame:\\n file_path = (_PROJECT_DIR / file_name).resolve()\\n info(f\\\"Reading from file {file_path}\\\")\\n df = pd.read_feather(file_path, **params)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96768f34615a64d3d96e395c62d29903\",\n \"score\": \"0.5932118\",\n \"text\": \"def decode_binary_file(self, file_path):\\n if not os.path.exists(file_path):\\n log.error(\\\"Missing: {}\\\".format(file_path))\\n raise FileNotFoundError(\\\"Missing: {}\\\".format(file_path))\\n log.info(\\\"Decoding file: {}\\\".format(file_path))\\n return self.decode(open(file_path, \\\"rb\\\").read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe538ef4116dbe30eaa1c78ffdc07bd\",\n \"score\": \"0.5915349\",\n \"text\": \"def read_binary(self):\\n with self.open('rb') as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b96b9c2a6ec59e0034c4c2b652066d8\",\n \"score\": \"0.5899989\",\n \"text\": \"def read(filename='DOS'):\\n from struct import pack,unpack,calcsize\\n intSize = calcsize('<i')\\n dubSize = calcsize('<d')\\n strSize = calcsize('<s')\\n version=1\\n\\n f=open(filename,'r').read()\\n i = 0\\n filetype, = unpack('<64s',f[i:i+64*strSize]) ; i += 64*strSize\\n version, = unpack('<i',f[i:i+intSize]) ; i += intSize\\n comment, = unpack('<1024s',f[i:i+1024*strSize]) ; i += 1024*strSize\\n N_Bins, = unpack('<i',f[i:i+intSize]) ; i += intSize\\n dE, = unpack('<d',f[i:i+dubSize]) ; i += dubSize\\n DOS = unpack('<%id' % (N_Bins),f[i:])\\n DOS = numpy.array(DOS)\\n E = numpy.arange(0,N_Bins*dE,dE)\\n return (filetype.strip('\\\\x00'),version,comment.strip('\\\\x00')),E,DOS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee7f758e0988ae1341a55e32ca3da37\",\n \"score\": \"0.5864759\",\n \"text\": \"def load_file(path):\\n with open(path, 'rb') as file:\\n header = file.read(0x20)\\n raw = file.read()\\n return BinFile(header, raw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8302f81b1d862f5562c6ef0303446110\",\n \"score\": \"0.5856217\",\n \"text\": \"def testReadFile(self):\\n definitions_file = self._GetTestFilePath(['definitions', 'integers.yaml'])\\n self._SkipIfPathNotExists(definitions_file)\\n\\n definitions_registry = registry.DataTypeDefinitionsRegistry()\\n definitions_reader = reader.DataTypeDefinitionsFileReader()\\n\\n definitions_reader.ReadFile(definitions_registry, definitions_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baeb28f9c15a634b6ea9e6142545a4e4\",\n \"score\": \"0.58441705\",\n \"text\": \"def read():\\n path = pathlib.Path(RAW_EVALATIN_DATA)\\n files = fileinput.input(path.glob(\\\"*.conllu\\\"))\\n return pyconll.unit.conll.Conll(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45827cd91933db92f06db4adff4e308c\",\n \"score\": \"0.58408415\",\n \"text\": \"def load_binary_fixture(filename: str) -> bytes:\\n path = get_fixture_path(filename)\\n return path.read_bytes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c82f10e8cf104b39926b6d4b920d3fc\",\n \"score\": \"0.5837009\",\n \"text\": \"def readSpecFile(fileName):\\n fl = open(fileName, 'rb') # TODO wyjatek nie ma pliku\\n A = pickle.load(fl)\\n fl.close()\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6eacd24095b8c090c0ca86a28b50cf\",\n \"score\": \"0.58316576\",\n \"text\": \"def load(self):\\n starting_memory = 0\\n # debug = 1\\n file_name = sys.argv[1]\\n with open(file_name) as f:\\n for line in f:\\n # WE DIVIDE LINE IF THERE IS A COMMENT\\n comment_split = line.split('#')\\n # WE TAKE THE LEFT PART OF THE ARRAY, AND STRIP SPACE AT THE END\\n instruction = comment_split[0].strip()\\n # print(f'Instruction: {instruction} in line {debug}')\\n if instruction == '':\\n continue\\n # WE CONVERT STRING WITH BINARY CODE INTO BINARY VALUE\\n binary_code = int(instruction, 2)\\n # WE SAVE EACH INSTRUCTION INTO RAM\\n self.ram_write(starting_memory, binary_code)\\n # WE INCREMENT THE ADDRESS IN MEMORE SO WE CAN ADD THE NEXT INSTRUCTION IN THE NEXT SLOT\\n starting_memory += 1\\n # debug += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1708ef77c72d25d3c21b0ac67449c1c4\",\n \"score\": \"0.58202326\",\n \"text\": \"def readFiles( self ):\\n\\n # Read in the BIN file\\n self._printer.printStatus( 0, 'Opening BIN file \\\"%s\\\" ...' % self.fileNameBin )\\n if not os.path.isfile(self.fileNameBin):\\n self._printer.errorExit( 2, 'File \\\"' + self.fileNameBin + '\\\" is not a readable file.' )\\n binFile = open( self.fileNameBin, 'rb' )\\n self.binData = binFile.read()\\n binFile.close()\\n if len(self.binData) <= 0:\\n self._printer.errorExit( 2, 'File \\\"' + self.fileNameBin + '\\\" contains no data' )\\n \\n # Read in the raw CFG file\\n self._printer.printStatus( 0, 'Opening CFG file \\\"%s\\\" ...' % self.fileNameCfg )\\n if not os.path.isfile(self.fileNameCfg):\\n self._printer.errorExit( 2, 'File \\\"' + self.fileNameCfg + '\\\" is not a readable file.' )\\n cfgFile = open( self.fileNameCfg, 'r' )\\n cfgData = cfgFile.read()\\n cfgFile.close()\\n \\n #\\n # Parse the CFG file (at least partially)\\n #\\n # Mostly, lines after a section (ex: [vars]) are associated with that section. Here are the exceptions:\\n # 1. Contiguous comments immediately BEFORE a section as associated that section.\\n # Blank lines and lines with non-comment data break contiguousness (i.e. these lines go with the PRIOR section, as per normal).\\n # 2. [mapping] section is treated special since it is the only section that is truly parsed.\\n # All other sections are just plain lines of text.\\n # 3. Corner case: blank and comment lines that appear before the 1st section are associated with that section.\\n #\\n reSectionName = re.compile(r\\\"\\\\[([^\\\\]]*)\\\\]\\\")\\n reMappingLine = re.compile(r\\\"\\\\$([0-9A-Fa-f]{1,4})\\\\s*-\\\\s*\\\\$([0-9A-Fa-f]{1,4})\\\\s*=\\\\s*\\\\$([0-9A-Fa-f]{1,4})\\\\s*(.*)\\\")\\n sectionCurrent = self.__KEY_NONE\\n cfgLines = cfgData.splitlines(False)\\n for i in range(0, len(cfgLines)):\\n cfgLine = cfgLines[i]\\n appendLine = False\\n hasComment = self.lineHasComment( cfgLine )\\n cfgLineTrimmed = self.trimCommentAndWhitespace( cfgLine )\\n isEmpty = len( cfgLineTrimmed ) == 0\\n onlyComment = hasComment and isEmpty\\n \\n # Core parsing block\\n reSectionMatch = reSectionName.fullmatch( cfgLineTrimmed )\\n if reSectionMatch != None:\\n # Handler for parsing a section header (ex: [vars] or [mapping])\\n sectionCurrent = reSectionMatch.group(1)\\n if sectionCurrent not in self.cfgSections:\\n self.cfgSections[sectionCurrent] = []\\n elif not isEmpty and sectionCurrent == self.__KEY_MAPPING:\\n # Handler for lines in the [mapping] section\\n reMappingMatch = reMappingLine.fullmatch( cfgLineTrimmed )\\n if reMappingMatch == None:\\n self._printer.errorPrint( 'Unrecognized [mapping] line: ' + cfgLine )\\n self._printer.errorExit( 2, 'Unrecognized [mapping] line:\\\\n' + cfgLine )\\n if reMappingMatch.group(4) != None and len( reMappingMatch.group(4) ) > 0:\\n self._printer.errorExit( 2, 'Parser does not handle [mapping] lines with the extension of: \\\"%s\\\"' % reMappingMatch.group(4) )\\n startOffset = int( reMappingMatch.group(1), 16)\\n lenInBytes = ( int( reMappingMatch.group(2), 16) - startOffset + 1 ) * 2\\n startAddress = int( reMappingMatch.group(3), 16)\\n startOffset *= 2 # The 2x converts from WORD index into BYTE index\\n self.memoryMap.writeByteBuffer( startAddress, self.binData, startOffset, lenInBytes, cfgLine )\\n else:\\n if sectionCurrent == self.__KEY_NONE and not self.__KEY_NONE in self.cfgSections:\\n self.cfgSections[sectionCurrent] = []\\n self.cfgSections[sectionCurrent].append( cfgLine )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc97c8eadfac017949628e92f1d7595\",\n \"score\": \"0.58198744\",\n \"text\": \"def read_file(self) -> str:\\r\\n with open(self.path) as file_descriptor:\\r\\n try:\\r\\n contents = file_descriptor.read()\\r\\n return contents\\r\\n except UnicodeDecodeError:\\r\\n InputFile.logger.warning('\\\"%s\\\" seems to be a binary file and will be ignored.',\\r\\n self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2f703e680ee2331d4a90632d199e17c\",\n \"score\": \"0.58051693\",\n \"text\": \"def read_binary_file(self, path: PATH_TYPE) -> bytes:\\n with open(path, \\\"rb\\\") as fd:\\n return fd.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d06700deb6502e2012060d29b0c8f4\",\n \"score\": \"0.57962126\",\n \"text\": \"def load_binary_file(path: str) -> bytes:\\n return Path(path).read_bytes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eeac7b5b86dc318900c7006a0380491\",\n \"score\": \"0.5787983\",\n \"text\": \"def read(fname, mode = \\\"binary\\\"):\\n \\n fin = InputFile(fname, mode)\\n\\n return fin.readObject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d012bb7a6ed682962194edad16a77a\",\n \"score\": \"0.5784898\",\n \"text\": \"def binary_file(fn):\\r\\n return binary(open(fn, 'rb').read(4096))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e09cd3e7e26700113bca790ed81d0d\",\n \"score\": \"0.5742841\",\n \"text\": \"def readbin(type, file):\\n return unpack(type, file.read(calcsize(type)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"859c632a64fdd5bba3147bc91b069a72\",\n \"score\": \"0.5739511\",\n \"text\": \"def load_from_binary_stram(path = \\\"Anaheim.graph\\\"):\\n FIn = snap.TFIn(path)\\n G = snap.TNEANet.Load(FIn)\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152cf4806de08e230fc094f6e6320349\",\n \"score\": \"0.57388985\",\n \"text\": \"def read_binary(filename,shape=(1),cplex=True,big=True):\\n \\n # open the file and get the data\\n f = open(filename)\\n data = get_data(f,big=big)\\n\\n # complexify if needed\\n if cplex:\\n data = complexify_data(data)\\n\\n # create dictionary\\n dic = {\\\"FILE_SIZE\\\":os.stat(filename).st_size}\\n\\n # reshape if possible\\n try:\\n return dic,data.reshape(shape)\\n\\n except ValueError:\\n print \\\"Warning:\\\",data.shape,\\\"cannot be shaped into\\\",shape\\n return dic,data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17395302f5919ef115adddcc2219701\",\n \"score\": \"0.5735585\",\n \"text\": \"def readModel(fname, ):\\n \\n if (fname.endswith('wnd')):\\n return bladed(fname,)\\n elif (fname.endswith('bl')):\\n return bladed(fname,)\\n elif (fname.endswith('bts')):\\n return turbsim(fname,)\\n\\n # Otherwise try reading it as a .wnd file.\\n bladed(fname) # This will raise an error if it doesn't work. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffe75d1f0571428ae1a2e4ddb0177d9b\",\n \"score\": \"0.57136637\",\n \"text\": \"def read(self):\\n with open(self.path, \\\"rb\\\") as inputfile:\\n return pickle.load(inputfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"955e1515d53d2d464c7adfbf9ff36b92\",\n \"score\": \"0.57072\",\n \"text\": \"def open_bin_data(path_to_file, file_name):\\r\\n with open(os.path.join(path_to_file, file_name + \\\".bin\\\"),'r') as current_file:\\r\\n data_type = np.dtype('>d') # assign data type: big-endian ordered 64 bit long data format \\r\\n raw_data = np.fromfile(current_file, dtype = data_type) # use numpy module to assign current trace data to [raw_data]\\r\\n raw_current = raw_data[0::2] # array of full baseline (current) data with first second and trailing values removed\\r\\n raw_voltage = raw_data[1::2]\\r\\n x_data_index_master = np.linspace(0,len(raw_current), len(raw_current), endpoint = True, dtype = 'int')\\r\\n current_file.close() # closes large data file\\r\\n del current_file\\r\\n \\r\\n return(raw_current, raw_voltage, x_data_index_master)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b26c33304302414b6d22d47d9334da\",\n \"score\": \"0.5701565\",\n \"text\": \"def load(self):\\n\\n address = 0\\n load_address = 0\\n\\n program = [0] * 256\\n\\n if len(sys.argv) != 2:\\n print(f\\\"Usage:\\\\npython3 {sys.argv[0]} filename.ls8\\\")\\n exit()\\n try:\\n with open(sys.argv[1]) as f:\\n for line in f:\\n possible_num = line[:line.find('#')] # strip comments\\n if possible_num == '': # strip blank lines\\n continue\\n # convert \\\"binary\\\" string into a number\\n program[load_address] = (int(possible_num, 2))\\n load_address += 1\\n if load_address == 256:\\n raise Exception(\\\"Out of memory. Program is too large.\\\")\\n\\n except FileNotFoundError:\\n print(f\\\"{sys.argv[1]} not found.\\\")\\n exit()\\n\\n for instruction in program:\\n self.ram[address] = instruction\\n address += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7e9ed273700f07793c53d5752f8b8f\",\n \"score\": \"0.568671\",\n \"text\": \"def read_data_from_bin_file(fileName):\\n with open(fileName, mode='rb') as file: # b is important -> binary\\n fileContent = file.read()\\n\\n (ChannelData, LenOf1Channel,\\n NumOfChannels, SampleTime) = read_data_from_bytes(fileContent)\\n \\n return ChannelData, LenOf1Channel, NumOfChannels, SampleTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab1e55afca23d2e869bf99fd5dbd5f\",\n \"score\": \"0.568458\",\n \"text\": \"def main() -> None:\\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab1e55afca23d2e869bf99fd5dbd5f\",\n \"score\": \"0.568458\",\n \"text\": \"def main() -> None:\\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab1e55afca23d2e869bf99fd5dbd5f\",\n \"score\": \"0.568458\",\n \"text\": \"def main() -> None:\\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab1e55afca23d2e869bf99fd5dbd5f\",\n \"score\": \"0.568458\",\n \"text\": \"def main() -> None:\\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d96782d57c91209adfd704d793fc6a\",\n \"score\": \"0.5680559\",\n \"text\": \"def read_is(filename, verbose=None): # noqa\\n with open(filename, \\\"rb\\\") as f:\\n logger.info(f\\\"Reading {filename}\\\")\\n logger.info(f\\\"Reading Header...\\\")\\n is_type = [\\n struct.unpack(\\\"c\\\", f.read(1))[0].decode(\\\"utf-8\\\") for i in range(4)\\n ]\\n is_type = \\\"\\\".join(is_type)\\n if is_type not in [\\\"IS01\\\", \\\"IS02\\\", \\\"IS03\\\"]:\\n raise ValueError(\\n f\\\"{is_type} : Invalid IS type, please check that \\\"\\n \\\"input file is a Inverse Solution matrix\\\"\\n )\\n logger.info(f\\\"IS type: {is_type}\\\")\\n n_channels = struct.unpack(\\\"I\\\", f.read(4))[0]\\n logger.info(f\\\"n_channels: {n_channels}\\\")\\n numsolutionpoints = struct.unpack(\\\"I\\\", f.read(4))[0]\\n logger.info(f\\\"n_solutionpoints: {numsolutionpoints}\\\")\\n numregularizations = struct.unpack(\\\"I\\\", f.read(4))[0]\\n logger.info(f\\\"n_regularizations: {numregularizations}\\\")\\n isinversescalar = struct.unpack(\\\"c\\\", f.read(1))[0]\\n if isinversescalar == b\\\"\\\\x01\\\":\\n n_dim = 1\\n logger.info(f\\\"Inverse solution is Scalar\\\")\\n elif isinversescalar == b\\\"\\\\x00\\\":\\n logger.info(f\\\"Inverse solution is Vectorial\\\")\\n n_dim = 3\\n else:\\n raise ValueError(\\n f\\\"isinversescalar must be either 1 for scalar, \\\"\\n f\\\"either 0 for vectorial, but \\\"\\n f\\\"{ord(isinversescalar)} found.\\\"\\n )\\n\\n if is_type in [\\\"IS01\\\", \\\"IS02\\\"]:\\n buf = f.read(n_dim * numsolutionpoints * n_channels * 4)\\n data = np.frombuffer(buf, dtype=np.float32)\\n data = data.reshape(numsolutionpoints, n_dim, n_channels)\\n data = np.array([data])\\n data = np.swapaxes(data, 1, 2)\\n\\n elif is_type == \\\"IS03\\\":\\n logger.info(f\\\"Reading Variable Header...\\\")\\n\\n ch_names = []\\n for _ in range(n_channels):\\n name = [\\n char for char in f.read(32).split(b\\\"\\\\x00\\\") if char != b\\\"\\\"\\n ][0]\\n ch_names.append(name.decode(\\\"utf-8\\\"))\\n\\n solutionpoints_names = []\\n for _ in range(numsolutionpoints):\\n name = [\\n char for char in f.read(16).split(b\\\"\\\\x00\\\") if char != b\\\"\\\"\\n ][0]\\n solutionpoints_names.append(name.decode(\\\"utf-8\\\"))\\n\\n regularizations_values = []\\n for _ in range(numregularizations):\\n value = struct.unpack(\\\"d\\\", f.read(8))[0]\\n regularizations_values.append(value)\\n logger.info(f\\\"Regularizations values: {regularizations_values}\\\")\\n\\n regularizations_names = []\\n for _ in range(numregularizations):\\n name = [\\n char for char in f.read(32).split(b\\\"\\\\x00\\\") if char != b\\\"\\\"\\n ][0]\\n regularizations_names.append(name.decode(\\\"utf-8\\\"))\\n logger.info(f\\\"Regularizations names: {regularizations_names}\\\")\\n\\n regularisation_solutions = []\\n buf = f.read(\\n numregularizations * n_dim * numsolutionpoints * n_channels * 4\\n )\\n data = np.frombuffer(buf, dtype=np.float32)\\n data = data.reshape(\\n numregularizations, numsolutionpoints, n_dim, n_channels\\n )\\n data = np.swapaxes(data, 1, 2)\\n\\n regularisation_solutions = np.array(regularisation_solutions)\\n inverse_solution = {\\n \\\"is_type\\\": is_type,\\n \\\"is_scalar\\\": True if isinversescalar == \\\"0\\\" else False,\\n \\\"ch_names\\\": ch_names,\\n \\\"solutionpoints_names\\\": solutionpoints_names,\\n \\\"regularizations_values\\\": regularizations_values,\\n \\\"regularizations_names\\\": regularizations_names,\\n \\\"regularisation_solutions\\\": data,\\n }\\n return inverse_solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae93c608217144a7a805d253f2a70c76\",\n \"score\": \"0.5676097\",\n \"text\": \"def test_decode_from_file(tempdir):\\n job = Decode()\\n tempdir.write(\\\"input.md\\\", b\\\"### My heading\\\")\\n result = job.run(tempdir.getpath(\\\"input.md\\\"),)\\n assert result[\\\"result\\\"] == dict(\\n type=\\\"Article\\\", content=[dict(type=\\\"Heading\\\", depth=3, content=[\\\"My heading\\\"])]\\n )\\n assert result[\\\"log\\\"] == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5572c6dc19de076c943ed068d85f839a\",\n \"score\": \"0.5675747\",\n \"text\": \"def read(dir=\\\".\\\",bin_file=None,acqus_files=None,pprog_file=None,shape=None,\\n cplex=None,big=None,read_prog=True,read_acqus=True):\\n\\n if os.path.isdir(dir) != True:\\n raise IOError,\\\"directory %s does not exist\\\"%(dir)\\n\\n # determind parameter automatically\\n if bin_file == None:\\n if os.path.isfile(os.path.join(dir,\\\"fid\\\")):\\n bin_file = \\\"fid\\\"\\n elif os.path.isfile(os.path.join(dir,\\\"ser\\\")):\\n bin_file = \\\"ser\\\"\\n else:\\n raise IOError,\\\"no Bruker binary file could be found in %s\\\"%(dir)\\n\\n if acqus_files == None:\\n acqus_files = []\\n for f in [\\\"acqus\\\",\\\"acqu2s\\\",\\\"acqu3s\\\",\\\"acqu4s\\\"]:\\n if os.path.isfile(os.path.join(dir,f)):\\n acqus_files.append(f)\\n\\n if pprog_file == None:\\n pprog_file = \\\"pulseprogram\\\"\\n\\n # create an empty dictionary\\n dic = dict()\\n\\n # read the acqus_files and add to the dictionary\\n if read_acqus:\\n for f in acqus_files:\\n dic[f] = read_jcamp(os.path.join(dir,f))\\n\\n # read the pulse program and add to the dictionary\\n if read_prog:\\n dic[\\\"pprog\\\"] = read_pprog(os.path.join(dir,pprog_file))\\n\\n # determind file size and add to the dictionary\\n dic[\\\"FILE_SIZE\\\"] = os.stat(os.path.join(dir,bin_file)).st_size\\n\\n # determind shape and complexity for direct dim if needed\\n if shape == None or cplex == None:\\n gshape,gcplex = guess_shape(dic)\\n if gcplex==True: # divide last dim by 2 if complex\\n t = list(gshape)\\n t[-1]=t[-1]/2\\n gshape = tuple(t)\\n if shape == None:\\n shape = gshape\\n if cplex == None:\\n cplex = gcplex\\n \\n # determind endianness (assume little-endian unless BYTORDA is 1)\\n if big == None:\\n big = False # default value\\n if \\\"acqus\\\" in dic and \\\"BYTORDA\\\" in dic[\\\"acqus\\\"]:\\n if dic[\\\"acqus\\\"][\\\"BYTORDA\\\"] == 1:\\n big = True\\n else:\\n big = False\\n\\n # read the binary file\\n f = os.path.join(dir,bin_file)\\n null,data = read_binary(f,shape=shape,cplex=cplex,big=big)\\n return dic,data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5280a68a5f3e0d6d76dcb240ff59ad\",\n \"score\": \"0.56719095\",\n \"text\": \"def test_read(file_system):\\n workfolder = file_system[\\\"main\\\"][\\\"dir\\\"]\\n target = file_system[\\\"main\\\"][\\\"file1\\\"].name\\n instructions = {\\n \\\"KeyName\\\": \\\"Host Name\\\",\\n \\\"EnableLineNumber\\\": \\\"False\\\"\\n }\\n var = ReadFullDocument(workfolder, target, instructions)\\n result = var.read()\\n\\n assert len(result.container) == 1\\n data = result.container[0]\\n\\n assert data.key == \\\"Host Name\\\"\\n assert data.values == (\\\"Beolab90\\\",)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea1e41b13cc1d372d6d4db483f47d4b\",\n \"score\": \"0.56717515\",\n \"text\": \"def load(self):\\n if STLMode.ascii == self._mode:\\n if not os.path.exists(self._path):\\n raise Exception(\\\"File not found: [%s]\\\" % self._path)\\n with open(self._path) as f:\\n ln = f.readline()\\n while ln != '':\\n line = ln.strip()\\n self._process(line)\\n\\n # increment reader\\n ln = f.readline()\\n self._cur_line += 1\\n elif STLMode.binary == self._mode:\\n raise Exception(\\\"Not implemented yet\\\")\\n else:\\n raise Exception('No mode specified')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cab9d4106d20138619687a2ef59253\",\n \"score\": \"0.5668465\",\n \"text\": \"def binary_format_reader(filename):\\n header_part = \\\"\\\"\\n rawVectorData = None\\n header = None\\n with open(filename, 'rb') as f:\\n x = f.readline()\\n while x != b'# End: Header\\\\n':\\n header_part += str(x)\\n x = f.readline()\\n\\n header = ParsingUtils.process_header(header_part)\\n\\n byte_type = f.readline()\\n while byte_type == b'#\\\\n':\\n byte_type = f.readline()\\n # compile struct byte type\\n fmt, buff_size, val = ParsingUtils.decode_byte_size(byte_type)\\n struct_object = struct.Struct(fmt)\\n test_val = struct_object.unpack(f.read(buff_size))[0]\\n if test_val != val:\\n raise ValueError(\\\"Invalid file format with validation {} value, \\\\\\n should be {}\\\".format(test_val, val))\\n\\n k = int(header['xnodes']*header['ynodes']*header['znodes'])\\n rawVectorData = ParsingUtils.standard_vertex_mode(\\n f, k, struct_object, buff_size)\\n f.close()\\n assert rawVectorData is not None\\n assert header is not None\\n return header, rawVectorData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9ada01c65385b2871007056fa8103a\",\n \"score\": \"0.56642765\",\n \"text\": \"def read_file(file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc75af9bb6c59c6776055e0fda3dcaba\",\n \"score\": \"0.5662355\",\n \"text\": \"def read_data(self, filepath):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7da863fec1eabd3641740f447a79c7b\",\n \"score\": \"0.5660204\",\n \"text\": \"def ibe_read_from_file(filename):\\n with open(filename, \\\"rb\\\") as f:\\n res = f.read()\\n return res \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a41036869100beed68325254c3bb7f\",\n \"score\": \"0.565705\",\n \"text\": \"def Read(self):\\n\\n\\t\\tself.data = np.loadtxt(self.fpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0af20f8b5a74bcd37c0631c68a536f7\",\n \"score\": \"0.5652648\",\n \"text\": \"def file_read(path):\\n with open(path, mode='rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766029f1d9d0d8fa560746e8577e78e4\",\n \"score\": \"0.56470966\",\n \"text\": \"def _read(self):\\n with open(self.filename, 'r') as f:\\n# self.title = f.read()\\n # initializng data dictionary\\n print('Opening: %s'%self.filename)\\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 \\n self.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827bfa72bf571703d29d5c2abb1e029a\",\n \"score\": \"0.56453896\",\n \"text\": \"def deserialize(infile):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212fbd56b6f9c334a3ce54ea263447dc\",\n \"score\": \"0.5645035\",\n \"text\": \"def read_binary(filename, dim, dtype = tf.float32):\\n\\n bin_file = tf.read_file(filename)\\n bin_tensor = tf.decode_raw(bin_file, dtype)\\n bin_tensor = tf.to_float(bin_tensor)\\n bin_tensor = tf.reshape(bin_tensor,[dim])\\n # bin_tensor = normalize_tensor(bin_tensor)\\n\\n return bin_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8bf689314b19d81b273c74c6c0d0f8\",\n \"score\": \"0.5644736\",\n \"text\": \"def read_nifti_file(filepath):\\n\\tscan = nib.load(filepath)\\n\\tscan = scan.get_data()\\n\\treturn scan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232aa8aaba074414f04b2c6406e9d2a3\",\n \"score\": \"0.56351805\",\n \"text\": \"def read_binary_ivector(fname):\\n \\n f= open(fname, \\\"rb\\\")\\n ivector, n_data, metadata = string_to_ivector(f)\\n\\n return ivector, n_data, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6332b75c57f8e316b0e99a1ef17d8b1\",\n \"score\": \"0.56314677\",\n \"text\": \"def read_file(self):\\n try:\\n data = pickle.load(self.open_diary)\\n return data\\n except EOFError:\\n #print('Your diary is empty!')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127b3c6dbe7b24997d709620119ba0b7\",\n \"score\": \"0.562332\",\n \"text\": \"def ReadFile(self, filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1def917729181ba740a36d735e3c43c3\",\n \"score\": \"0.5613102\",\n \"text\": \"def test_task2_with_input_file():\\n with gzip.open(INPUT_FILE_PATH, \\\"rt\\\", encoding=\\\"ascii\\\") as file:\\n result = solve_task2(file)\\n assert result == 9533698720\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee5160e18af0baaea0694a85741f2ab\",\n \"score\": \"0.5610461\",\n \"text\": \"def test_embedded_bin_chars(self):\\n file = io.StringIO(\\\"line\\\\x00\\\\x03\\\\x04\\\\x0c\\\\x17\\\\x1a\\\\x1c1\\\")\\n self.reader._read_file_obj(file)\\n\\n self.assertEqual(len(self.parser.lines), 1);\\n self.assertEqual(self.parser.lines[0].get_filename(), Helpers.TEST_FNAME)\\n self.assertEqual(self.parser.lines[0].get_line(), 1)\\n self.assertEqual(self.parser.lines[0].get_text(), \\\"line\\\\x00\\\\x03\\\\x04\\\\x0c\\\\x17\\\\x1a\\\\x1c1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a8b757ca487c46314722a37a08f69ad\",\n \"score\": \"0.56001437\",\n \"text\": \"def read_file(self, file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d38e85afed3be4e44fdf50d7433c0adc\",\n \"score\": \"0.55997443\",\n \"text\": \"def read_from_file():\\n\\tparser = argparse.ArgumentParser()\\n\\t# command cannot be executed without a path\\n\\tparser.add_argument(\\\"--input\\\", required=True,help=\\\"you need to parse a link to a file with instructions, please see the docs\\\")\\n\\targs = (parser.parse_args())\\n\\n\\turi = str(args.input)\\n\\treq = urllib.request.urlopen(uri)\\n\\tdata = req.read().decode('utf-8')\\n\\t# first line is the size of the led board\\n\\tled_box_len = int(data.splitlines()[0])\\n\\t# create new board\\n\\tboard = (create_board(led_box_len))\\n\\t# the actions are the rest of the file\\n\\tcommands = data.splitlines()[1:]\\n\\t# cleaned up data\\n\\tfinal_data = clean_up_input_file(commands)\\n\\t# perform actions on the board\\n\\tresult = board_plotter(final_data, board)\\n\\tprint(print_lights_totals(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1606243fa969203a08d8265a06349208\",\n \"score\": \"0.55972725\",\n \"text\": \"def _read_solution(self,\\n symbols_filename,\\n solution_filename):\\n\\n # parse the symbol map\\n symbol_map = {}\\n with open(symbols_filename) as f:\\n for line in f:\\n lp_symbol, scenario_tree_id = line.strip().split()\\n symbol_map[lp_symbol] = scenario_tree_id\\n\\n results = SPSolverResults()\\n results.status = None\\n results.solver.status = None\\n results.solver.termination_condition = None\\n results.solver.message = None\\n xhat = {}\\n try:\\n with open(solution_filename, 'r') as f:\\n line = f.readline()\\n assert line.startswith(\\\"Problem:\\\")\\n assert line.split()[1].strip() == \\\"pysp_model\\\"\\n line = f.readline()\\n assert line.startswith(\\\"First Stage Rows:\\\")\\n line = f.readline()\\n assert line.startswith(\\\"First Stage Columns:\\\")\\n line = f.readline()\\n assert line.startswith(\\\"First Stage Non-zeros:\\\")\\n line = f.readline()\\n assert line.startswith(\\\"Replication No.\\\") or \\\\\\n line.startswith(\\\"Number of replications:\\\")\\n line = f.readline()\\n assert line.startswith(\\\"Status:\\\")\\n results.solver.message = line.split(\\\":\\\")[1].strip()\\n (results.status,\\n results.solver.status,\\n results.solver.termination_condition) = \\\\\\n _sd_status_map.get(results.solver.message,\\n (SolutionStatus.unknown,\\n SolverStatus.unknown,\\n TerminationCondition.unknown))\\n\\n #\\n # Objective and Bound\\n #\\n\\n line = f.readline()\\n assert line.startswith(\\\"Total Objective Function Upper Bound:\\\")\\n line = line.split(':')\\n if line[1].strip() == '':\\n pass\\n else:\\n assert len(line) == 4\\n line = line[1]\\n if \\\"half-width\\\" in line:\\n # we are given confidence intervals on the objective\\n line = line.split(',')\\n assert len(line) == 4\\n results.objective = float(line[0])\\n assert line[1].startswith('[')\\n assert line[2].endswith(']')\\n results.objective_interval = (float(line[1][1:]),\\n float(line[2][:-1]))\\n else:\\n results.objective = float(line[1])\\n line = f.readline()\\n assert line.startswith(\\\"Total Objective Function Lower Bound:\\\")\\n line = line.split(':')\\n if line[1].strip() == '':\\n pass\\n else:\\n if \\\"half-width\\\" in line[1]:\\n # we are given confidence intervals on the bound\\n line = line[1].split(',')\\n assert len(line) == 4\\n results.bound = float(line[0])\\n assert line[1].startswith('[')\\n assert line[2].endswith(']')\\n results.bound_interval = (float(line[1][1:]),\\n float(line[2][:-1]))\\n else:\\n results.bound = float(line[1].strip())\\n\\n #\\n # Xhat\\n #\\n\\n line = f.readline()\\n assert line.strip() == ''\\n line = f.readline()\\n assert line.startswith('First Stage Solutions:')\\n line = f.readline()\\n assert line.startswith(' No. Row name Activity Lower bound Upper bound Dual Dual STDEV')\\n line = f.readline()\\n assert line.startswith('------ ------------ ------------- ------------- ------------- ------------- -------------')\\n\\n xhat_start_line = ' No. Column name Activity Lower bound Upper bound Reduced Cost RC STDEV'\\n line = f.readline()\\n while not line.startswith(xhat_start_line):\\n line = f.readline()\\n line = f.readline()\\n assert line.startswith('------ ------------ ------------- ------------- ------------- ------------- -------------')\\n line = f.readline().strip().split()\\n while line:\\n varlabel, varvalue = line[1:3]\\n varlabel = varlabel.strip()\\n varvalue = float(varvalue)\\n xhat[symbol_map[varlabel]] = varvalue\\n line = f.readline().strip().split()\\n\\n except (IOError, OSError):\\n logger.warn(\\n \\\"Exception encountered while parsing sd \\\"\\n \\\"solution file '%s':\\\\n%s'\\\"\\n % (solution_filename, traceback.format_exc()))\\n xhat = None\\n\\n return xhat, results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548368c802df754b17a79ed2c4295068\",\n \"score\": \"0.5592868\",\n \"text\": \"def testReadFileObjectFormatVersion1(self):\\n output_writer = test_lib.TestOutputWriter()\\n test_file = recycle_bin.RecycleBinMetadataFile(\\n debug=True, output_writer=output_writer)\\n\\n test_file_path = self._GetTestFilePath(['$II3DF3L.zip'])\\n self._SkipIfPathNotExists(test_file_path)\\n\\n test_file.Open(test_file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf106ada03f16d5af6d5606c2d9ef68b\",\n \"score\": \"0.5589384\",\n \"text\": \"def read(self, filename, nscale=1):\\n reader = SHTextReader(filename)\\n if filename.endswith(\\\".bdo\\\") or filename.endswith(\\\".bdox\\\"):\\n reader = SHBinaryReader(filename)\\n # find better way to discover if file comes from Fluka\\n elif \\\"_fort\\\" in filename:\\n reader = FlukaBinaryReader(filename)\\n reader.read(self, nscale)\\n self.counter = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8db1d58aa7dbcc575b0fab2e0449b79\",\n \"score\": \"0.55843747\",\n \"text\": \"def read(self, fileobj):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82f75cdfe1e8ceff468ac2c426769f0\",\n \"score\": \"0.5572124\",\n \"text\": \"def read(file_path):\\n\\n data = numpy.load(file_path)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637704aa0b14c2204edfb6f70530bc46\",\n \"score\": \"0.5569049\",\n \"text\": \"def read_input(fname=\\\"day14.in\\\"):\\n with open(fname) as f:\\n return lines2recipe(f.readlines())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c700e2233d8a793c95a99b844fbf8c25\",\n \"score\": \"0.5567073\",\n \"text\": \"def read_label_file(label_file):\\n with open(label_file,'rb') as f:\\n data = f.read(4)\\n magic_no = struct.unpack('>L',data)\\n if magic_no==2049 or magic_no==2051:\\n print 'Incorrectly parsing files'\\n print ' magic no = %d '%magic_no\\n data = f.read(4)\\n num_data, = struct.unpack('>L',data)\\n print ' Number of data points = %d '% num_data\\n unpacked_data = np.zeros((num_data),np.uint8)\\n for i in range(num_data):\\n temp_data, = struct.unpack('>B',f.read(1))\\n unpacked_data[i] = temp_data\\n\\n return unpacked_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7982bf506d30a156ccde787310f635b4\",\n \"score\": \"0.5565414\",\n \"text\": \"def readFlow(fn):\\n # Code adapted from:\\n # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy\\n\\n # WARNING: this will work on little-endian architectures (eg Intel x86) only!\\n # print 'fn = %s'%(fn)\\n with open(fn, 'rb') as f:\\n magic = np.fromfile(f, np.float32, count=1)\\n if 202021.25 != magic:\\n print('Magic number incorrect. Invalid .flo file')\\n return None\\n else:\\n w = np.fromfile(f, np.int32, count=1)\\n h = np.fromfile(f, np.int32, count=1)\\n # print 'Reading %d x %d flo file\\\\n' % (w, h)\\n data = np.fromfile(f, np.float32, count=2*int(w)*int(h))\\n # Reshape data into 3D array (columns, rows, bands)\\n # The reshape here is for visualization, the original code is (w,h,2)\\n return np.resize(data, (int(h), int(w), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7982bf506d30a156ccde787310f635b4\",\n \"score\": \"0.5565414\",\n \"text\": \"def readFlow(fn):\\n # Code adapted from:\\n # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy\\n\\n # WARNING: this will work on little-endian architectures (eg Intel x86) only!\\n # print 'fn = %s'%(fn)\\n with open(fn, 'rb') as f:\\n magic = np.fromfile(f, np.float32, count=1)\\n if 202021.25 != magic:\\n print('Magic number incorrect. Invalid .flo file')\\n return None\\n else:\\n w = np.fromfile(f, np.int32, count=1)\\n h = np.fromfile(f, np.int32, count=1)\\n # print 'Reading %d x %d flo file\\\\n' % (w, h)\\n data = np.fromfile(f, np.float32, count=2*int(w)*int(h))\\n # Reshape data into 3D array (columns, rows, bands)\\n # The reshape here is for visualization, the original code is (w,h,2)\\n return np.resize(data, (int(h), int(w), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e2d6c4fd39a889b210abc1a842643\",\n \"score\": \"0.55653274\",\n \"text\": \"def read_highd_binary(filename, shape, perm, dtype = tf.float32):\\n\\n bin_file = tf.read_file(filename)\\n bin_tensor = tf.decode_raw(bin_file, dtype)\\n bin_tensor = tf.to_float(bin_tensor)\\n bin_tensor = tf.reshape(bin_tensor, shape)\\n bin_tensor = tf.transpose(bin_tensor, perm= perm)\\n return bin_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3839f09e15da7f48ea2ff8793a231b3\",\n \"score\": \"0.55636847\",\n \"text\": \"def load_from_file(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62707ee689da802dfae878980a1ae478\",\n \"score\": \"0.55600953\",\n \"text\": \"def readBinaryFiles(file, dimensions=(100, 100), saveFITS=True, output='tmp.fits'):\\n fh = open(file, 'rb')\\n #use numpy to read the binary format, the data is 16bit unsigned int\\n a = np.fromfile(fh, dtype=np.uint16)\\n fh.close()\\n\\n try:\\n #note the transpose\\n image = a.reshape(dimensions).T\\n except:\\n print 'Image shape as not expected'\\n print a.shape\\n return None\\n\\n if saveFITS:\\n fileIO.writeFITS(image, output)\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e080715b5f6482e47acabc414bf77fc\",\n \"score\": \"0.55571693\",\n \"text\": \"def importBinary(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7e59093ab25aeedd2b28447a62e763\",\n \"score\": \"0.5556787\",\n \"text\": \"def read_vec_int(file_or_fd):\\n fd = open_or_fd(file_or_fd)\\n binary = fd.read(2)\\n if binary == '\\\\0B': # binary flag\\n assert(fd.read(1) == '\\\\4'); # int-size\\n vec_size = struct.unpack('<i', fd.read(4))[0] # vector dim\\n ans = np.zeros(vec_size, dtype=int)\\n for i in range(vec_size):\\n assert(fd.read(1) == '\\\\4'); # int-size\\n ans[i] = struct.unpack('<i', fd.read(4))[0] #data\\n return ans\\n else: # ascii,\\n arr = (binary + fd.readline()).strip().split()\\n try:\\n arr.remove('['); arr.remove(']') # optionally\\n except ValueError:\\n pass\\n ans = np.array(arr, dtype=int)\\n if fd is not file_or_fd : fd.close() # cleanup\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476546683990b5670c60367797c425be\",\n \"score\": \"0.55560607\",\n \"text\": \"def structread(fromfile, decode_struct) : \\n return struct.unpack(decode_struct, fromfile.read(struct.calcsize(decode_struct)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9fbdde41f75881fc42362c02755f4b\",\n \"score\": \"0.55545557\",\n \"text\": \"def readBinary(zTop=0, zBot=1200,\\n min_utme=None, max_utme=None,\\n min_utmn=None, max_utmn=None,\\n newModelName='subModel.nc',\\n binModelName='',\\n saveFull='N'):\\n\\n # The following lines describe the size of the binary file values.\\n #\\n # Layer 1 0-1200m depth\\n # 3271 in EW direction\\n # 5367 in NS direction\\n # 13 in z\\n # dx=dy= 200m, dz= 100m\\n #\\n if 'l1' in binModelName:\\n nx = 3271; ny = 5367; nz = 13\\n dx = 200; dy = 200; dz = 100\\n zmin = 0; zmax = 1200\\n #\\n # Layer 2 1500-9900m depth\\n # 2181 in EW\\n # 3578 in NS\\n # 29 in z\\n # dx=dy=dz=300m\\n #\\n if 'l2' in binModelName:\\n nx = 2181; ny = 3578; nz = 29\\n dx = 300; dy = 300; dz = 300\\n zmin = 1500; zmax = 9900\\n #\\n # Layer 3 10800-59400m depth\\n # 727 in EW\\n # 1193 NS\\n # 55 in z\\n # dx=dy=dz=900m\\n #\\n if 'l3' in binModelName:\\n nx = 727; ny = 1193; nz = 55\\n dx = 900; dy = 900; dz = 900\\n zmin = 10800; zmax = 59400\\n #\\n # The SW corner of the velocity model is -10800m East, 4467300m N Zone 10.\\n SWcornerFull = [-10800, 4467300]\\n #\\n\\n # Read in binary file\\n v = np.fromfile(binModelName, dtype='<f4')\\n\\n # Generate arrays for x, y, & z locations\\n z = np.linspace(zmin, zmax, nz)\\n z = np.repeat(z, (np.ones(len(z))*nx*ny).astype(int))\\n z = z[::-1] # Reverse array\\n\\n y = np.linspace(SWcornerFull[1], SWcornerFull[1]+ny*dy, ny, endpoint=False)\\n y = np.repeat(y, (np.ones(len(y))*nx).astype(int))\\n y = np.tile(y, nz) # Repeat array for each depth\\n\\n x = np.linspace(SWcornerFull[0], SWcornerFull[0]+nx*dx, nx, endpoint=False)\\n x = np.tile(x, ny*nz)\\n\\n # Convert CVM to dataframe\\n model = pd.DataFrame(np.column_stack((x,y,z,v)), columns=['utme','utmn','z',(binModelName.split('/'))[1].split('_16')[0]])\\n\\n if saveFull == 'N' or saveFull == 'n':\\n # Subset model (speed things up)\\n if min_utme==None:\\n # Use full horizontal model extent\\n subModel = model[(model[\\\"z\\\"] >= zTop) & (model[\\\"z\\\"] <= zBot)]\\n else:\\n subModel = model[(model[\\\"z\\\"] >= zTop) & (model[\\\"z\\\"] <= zBot)\\n & (model[\\\"utme\\\"] >= min_utme) & (model[\\\"utme\\\"] <= max_utme)\\n & (model[\\\"utmn\\\"] >= min_utmn) & (model[\\\"utmn\\\"] <= max_utmn)]\\n subModel = subModel.set_index(['utme','utmn','z']) # Set these parameters as coordinates\\n\\n # Convert to xarray (slow)\\n subModelxr = subModel.to_xarray()\\n\\n # Save xarray for faster reload later\\n subModelxr.to_netcdf('../output/' + newModelName)\\n\\n return subModelxr\\n\\n # This can be VERY slow (~ 1 hour)\\n if saveFull == 'Y' or saveFull == 'y':\\n model = model.set_index(['utme','utmn','z'])\\n modelxr = model.to_xarray()\\n modelxr.to_netcdf('../output/' + (binModelName.split('/'))[1].split('.bin')[0]+'.nc')\\n\\n return modelxr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7d2c3d0629055aabf0bb03abe5134f\",\n \"score\": \"0.5550434\",\n \"text\": \"def read_low(filepath):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0bd63a786585d392b5683fea245461\",\n \"score\": \"0.55393505\",\n \"text\": \"def load_from_File(self, filePath):\\n # Create file object\\n fileObject = open(filePath, 'rb')\\n # Load from the file object\\n b = pickle.load(fileObject)\\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702e0c9544e686a6ebcb974163ad5c12\",\n \"score\": \"0.5539064\",\n \"text\": \"def load_bitstream(self, bitfile):\\n if not os.path.isfile(bitfile):\\n raise ValueError(\\\"Cannot find specified bitfile {}\\\".format(bitfile))\\n\\n bitstream = open(bitfile, \\\"rb\\\")\\n self._llint.FPGAProgram(bitstream)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc76223ce23e3f476893a9fa85491ca\",\n \"score\": \"0.55328214\",\n \"text\": \"def read(filename):\\n with open(filename, \\\"rb\\\") as f:\\n mesh = read_buffer(f)\\n return mesh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc01eb139326e1f13ad57ca6ad5e5b4\",\n \"score\": \"0.55316436\",\n \"text\": \"def test_task1_with_input_file():\\n with gzip.open(INPUT_FILE_PATH, \\\"rt\\\", encoding=\\\"ascii\\\") as file:\\n trees = solve_task1(file)\\n assert trees == 230\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8315e84a83d80709f997ea77dfffacb\",\n \"score\": \"0.55310977\",\n \"text\": \"def loadbin(self, fobj, offset=0):\\n fread = getattr(fobj, \\\"read\\\", None)\\n if fread is None:\\n f = open(fobj, \\\"rb\\\")\\n fread = f.read\\n fclose = f.close\\n else:\\n fclose = None\\n\\n try:\\n self.frombytes(array('B', asbytes(fread())), offset=offset)\\n finally:\\n if fclose:\\n fclose()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629af03410e0d164a23edd6dee98d557\",\n \"score\": \"0.5530605\",\n \"text\": \"def load(self, filename):\\n\\n address = 0\\n\\n # In `load()`, you will now want to use those command line arguments to open a\\n # file, read in its contents line by line, and save appropriate data into RAM. \\n with open(filename) as file:\\n\\n # As you process lines from the file, you should be on the lookout for blank lines\\n # (ignore them), and you should ignore everything after a `#`, since that's a\\n # comment.\\n for line in file:\\n comment_split = line.split('#')\\n instruction = comment_split[0]\\n\\n if instruction == '':\\n continue\\n \\n first_bit = instruction[0]\\n\\n # You'll have to convert the binary strings to integer values to store in RAM. The\\n # built-in `int()` function can do that when you specify a number base as the\\n # second argument:\\n if first_bit == '0' or first_bit == '1':\\n self.ram[address] = int(instruction[:8], 2)\\n address += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482779e5cd2fa61dc4c94498d875f598\",\n \"score\": \"0.55265105\",\n \"text\": \"def load(self, path, offset=0):\\n\\n # Read the file into main memory byte-by-byte\\n with open(path, \\\"rb\\\") as f:\\n b = f.read(1)\\n while b:\\n self.memory[offset] = struct.unpack(\\\"B\\\", b)[0]\\n offset += 1\\n b = f.read(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2740dda98dcf8ae0a6ed8066acc72a05\",\n \"score\": \"0.55191106\",\n \"text\": \"def load_solution(f):\\n return json.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8db5b57ee26df6ff644366f13ff3578\",\n \"score\": \"0.551412\",\n \"text\": \"def _read_file_from_archive(cls, read_obj, src_file):\\n with read_obj.open(src_file, 'r') as file_obj:\\n return file_obj.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb91ee7ec3032545954fa5472f4172a\",\n \"score\": \"0.55100393\",\n \"text\": \"def load_binary_data(filename):\\n\\n input_data = open(filename,\\\"rb\\\")\\n grid = dill.load(input_data)\\n input_data.close()\\n retessellate = grid.remove_tracks(config.track_threshold) # filter out incomplete tracks\\n if (config.distort_grid):\\n grid.distort_grid()\\n retessellate = True\\n if (retessellate or config.retessellate):\\n grid.tessellate()\\n\\n if (grid.user_params != config.user_params):\\n print(\\\"WARNING: mismatch between the user_params in the binary grid file\\\")\\n print(\\\" and in AIMS_configure.py. Will overwrite user_params.\\\")\\n print(\\\" Binary grid file: \\\",grid.user_params)\\n print(\\\" AIMS_configure.py: \\\",config.user_params)\\n\\n # manually correct relevant variables:\\n config.user_params = grid.user_params\\n model.nglb = 8 + len(config.user_params)\\n model.nlin = 5 + len(config.user_params)\\n model.ifreq_ref = 5 + len(config.user_params)\\n model.iradius = 6 + len(config.user_params)\\n model.iluminosity = 7 + len(config.user_params)\\n model.init_user_param_dict()\\n\\n return grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d404a72286b82bbcae00bffcded3153\",\n \"score\": \"0.55079895\",\n \"text\": \"def load(fname):\\n try:\\n with open(fname, 'rb') as fin:\\n return fin.read()\\n except FileNotFoundError:\\n print(\\\"Couldn't read file\\\", fname)\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792418a879f4b424fe407d0b8d29bcae\",\n \"score\": \"0.5493646\",\n \"text\": \"def load(self):\\n\\n params = sys.argv\\n if len(params) != 2: # if no filename is passed\\n print(\\\"usage: file.py filename\\\") # print error and exit\\n sys.exit(1)\\n if len(params) == 2: # if filename passed\\n try:\\n with open(params[1]) as f: # read filename\\n address = 0\\n for line in f: # for each line\\n comment_split = line.split(\\\"#\\\") # SPlit at comments\\n num = comment_split[0].strip() # strip whiteSPace\\n if num == '': # if line is blank, continue\\n continue\\n val = int(\\\"0b\\\"+num,2) # otherwise convert oPCode to binary\\n self.ram_write(address, val) # write the op code to memory\\n address += 1 # increment the address to the next one in ram\\n except FileNotFoundError: # if try fails, filename doesn't exist\\n print(\\\"ERROR: File not found\\\")\\n sys.exit(2)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994478,"cells":{"query_id":{"kind":"string","value":"16d29fd4457273d8c9392acf2e3ee714"},"query":{"kind":"string","value":"Builds predefined pages and puts them into the paginator."},"positive_passages":{"kind":"list like","value":[{"docid":"13d00e00d4e316949f7f6b0d7981e6b5","score":"0.758468","text":"async def build_pages(self):\n paginator = LinePaginator(prefix=\"\", suffix=\"\")\n\n # Add general info page\n paginator.add_page(Page(\n strings.Info.bot_welcome,\n \"\",\n strings.Info.bot_tutorial,\n \"\",\n strings.Info.bot_browse,\n \"\",\n strings.Info.bot_output\n ))\n\n # Add rules page\n paginator.add_page(Page(\n strings.Info.rules_welcome,\n \"\",\n strings.Info.rules_first,\n strings.Info.rules_second,\n strings.Info.rules_third,\n strings.Info.rules_fourth,\n strings.Info.rules_fifth,\n strings.Info.rules_sixth\n ))\n\n # Add links page\n paginator.add_page(Page(\n strings.Info.links_welcome,\n \"\",\n strings.Info.links_ts,\n \"\",\n strings.Info.links_website,\n \"\",\n strings.Info.links_public_steam,\n \"\",\n strings.Info.links_private_steam\n ))\n\n # Add authors page\n paginator.add_page(Page(\n strings.Info.authors_welcome,\n \"\",\n strings.Info.authors_bertalicious,\n strings.Info.authors_white_noise,\n \"\",\n strings.Info.authors_support,\n \"\",\n strings.Info.authors_link\n ))\n\n # Save organised pages to session\n self.pages = paginator.pages","title":""}],"string":"[\n {\n \"docid\": \"13d00e00d4e316949f7f6b0d7981e6b5\",\n \"score\": \"0.758468\",\n \"text\": \"async def build_pages(self):\\n paginator = LinePaginator(prefix=\\\"\\\", suffix=\\\"\\\")\\n\\n # Add general info page\\n paginator.add_page(Page(\\n strings.Info.bot_welcome,\\n \\\"\\\",\\n strings.Info.bot_tutorial,\\n \\\"\\\",\\n strings.Info.bot_browse,\\n \\\"\\\",\\n strings.Info.bot_output\\n ))\\n\\n # Add rules page\\n paginator.add_page(Page(\\n strings.Info.rules_welcome,\\n \\\"\\\",\\n strings.Info.rules_first,\\n strings.Info.rules_second,\\n strings.Info.rules_third,\\n strings.Info.rules_fourth,\\n strings.Info.rules_fifth,\\n strings.Info.rules_sixth\\n ))\\n\\n # Add links page\\n paginator.add_page(Page(\\n strings.Info.links_welcome,\\n \\\"\\\",\\n strings.Info.links_ts,\\n \\\"\\\",\\n strings.Info.links_website,\\n \\\"\\\",\\n strings.Info.links_public_steam,\\n \\\"\\\",\\n strings.Info.links_private_steam\\n ))\\n\\n # Add authors page\\n paginator.add_page(Page(\\n strings.Info.authors_welcome,\\n \\\"\\\",\\n strings.Info.authors_bertalicious,\\n strings.Info.authors_white_noise,\\n \\\"\\\",\\n strings.Info.authors_support,\\n \\\"\\\",\\n strings.Info.authors_link\\n ))\\n\\n # Save organised pages to session\\n self.pages = paginator.pages\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d4904c1eae48a04ca36df142f4678fa7","score":"0.71009964","text":"def generate_all_pages():\n fetcher = getattr(parent, fetcher_str)\n\n first_page = fetcher.get(**kwargs)\n kwargs['page_size'] = len(first_page)\n count = fetcher.current_total_count\n yield first_page\n\n if count > kwargs['page_size']:\n num_pages = int(ceil(float(count) / kwargs['page_size']))\n for page in range(1, num_pages):\n kwargs['page'] = page\n yield fetcher.get(**kwargs)","title":""},{"docid":"d8eec126418e333d5e2c9d0a81577084","score":"0.68907714","text":"def _setup_pages(self):\n stdkwargs = {\n 'template': 'nav_playground.html',\n 'language': 'en',\n 'published': True,\n 'in_navigation': True,\n }\n page_1 = create_page(\"page_1\", **stdkwargs) # first page slug is /\n page_2 = create_page(\"page_2\", parent=page_1, **stdkwargs)\n page_3 = create_page(\"page_3\", parent=page_2, **stdkwargs)\n page_4 = create_page(\"page_4\", parent=page_3, **stdkwargs)\n page_5 = create_page(\"page_5\", parent=page_1, **stdkwargs)\n page_6 = create_page(\"page_6\", parent=page_5, **stdkwargs)\n return [page_1,\n page_2,\n page_3,\n page_4,\n page_5,\n page_6,\n ]","title":""},{"docid":"2944cb58de08e6eb89206ca25af8c078","score":"0.67420703","text":"def build_pages(n):\n pages = list()\n template = jinja_env.get_template('template.html')\n # i is the current page and we want the current\n # page to point to the next page and all of the \n # pages before it if it isn't the last page (page n).\n # Add 1 to n because we start from 1.\n for i in range(1, n + 1):\n links = list(range(1, i))\n # Add the next page if (i + 1) if it isn't the last page (n + 1)\n if i + 1 != n + 1:\n links.append(i + 1)\n context = dict(title='Page{}'.format(i), links=links)\n pages.append(template.render(context))\n\n return pages","title":""},{"docid":"ff579bb2917c912ed7f607b6573dc837","score":"0.6695581","text":"def generate(self, **options):\n from pyClanSphere.application import url_for, get_application, \\\n DEFAULT_THEME_SETTINGS\n\n if self._skip_theme_defaults:\n settings = DEFAULT_THEME_SETTINGS\n else:\n settings = get_application().theme.settings\n\n def _getopt(name):\n value = options.pop(name, None)\n if value is not None:\n return value\n return settings['pagination.' + name]\n normal = _getopt('normal')\n active = _getopt('active')\n commata = _getopt('commata')\n ellipsis = _getopt('ellipsis')\n threshold = _getopt('threshold')\n left_threshold = _getopt('left_threshold')\n right_threshold = _getopt('right_threshold')\n prev_link = _getopt('prev_link')\n next_link = _getopt('next_link')\n gray_prev_link = _getopt('gray_prev_link')\n gray_next_link = _getopt('gray_next_link')\n simple = _getopt('simple')\n if options:\n raise TypeError('generate() got an unexpected keyword '\n 'argument %r' % iter(options).next())\n\n was_ellipsis = False\n result = []\n prev = None\n next = None\n get_link = lambda x: url_for(self.endpoint, page=x,\n per_page=self.per_page,\n post_id=self.post_id, **self.url_args)\n\n if simple:\n result.append(active % {\n 'url': get_link(self.page),\n 'page': self.page\n })\n if self.page > 1:\n prev = self.page - 1\n if self.page < self.pages:\n next = self.page + 1\n else:\n for num in xrange(1, self.pages + 1):\n if num == self.page:\n was_ellipsis = False\n if num - 1 == self.page:\n next = num\n if num + 1 == self.page:\n prev = num\n if num <= left_threshold or \\\n num > self.pages - right_threshold or \\\n abs(self.page - num) < threshold:\n if result and result[-1] != ellipsis:\n result.append(commata)\n link = get_link(num)\n template = num == self.page and active or normal\n result.append(template % {\n 'url': link,\n 'page': num\n })\n elif not was_ellipsis:\n was_ellipsis = True\n result.append(ellipsis)\n\n if next_link:\n if next is not None:\n result.append(u' <a href=\"%s\" class=\"next\">%s</a>' %\n (get_link(next), _(u'Next »')))\n elif gray_next_link:\n result.append(u' <span class=\"disabled next\">%s</span>' %\n _(u'Next »'))\n if prev_link:\n if prev is not None:\n result.insert(0, u'<a href=\"%s\" class=\"prev\">%s</a> ' %\n (get_link(prev), _(u'« Previous')))\n elif gray_prev_link:\n result.insert(0, u'<span class=\"disabled prev\">%s</span> ' %\n _(u'« Previous'))\n\n return Markup(u''.join(result))","title":""},{"docid":"a3945a6a1b440b882a1afb72f01db9c9","score":"0.66792846","text":"def _create_pages(self):\n\n\t\tself.sidebar_modules = [] # add extra sidebar links to this\n\n\t\t# create a page for each class -- recursive for subclasses/subfunctions\n\t\tif len(self.data['classes']) > 0:\n\t\t\t# start a subelement list\n\t\t\tself.sidebar_modules.append('<span>Classes:</span>')\n\t\t\tself.sidebar_modules.append('<ul>')\n\n\t\t\tfor class_item in self.data['classes']:\n\t\t\t\tself._doc_class(class_item)\n\n\t\t\tself.sidebar_modules.append('</ul>')\n\n\n\t\t# create a page for each function\n\t\tif len(self.data['functions']) > 0:\n\t\t\tself.sidebar_modules.append('<span>Functions:</span>')\n\t\t\tself.sidebar_modules.append('<ul>')\n\t\t\t\n\t\t\tfor function_item in self.data['functions']:\n\t\t\t\tself._doc_function(function_item)\n\n\t\t\tself.sidebar_modules.append('</ul>')\n\n\n\t\t# end\n\t\tif len(self.sidebar_modules) > 0:\n\t\t\tself.sidebar += \"<p>Modules:</p>\\n\"\n\t\t\tfor item in self.sidebar_modules:\n\t\t\t\tself.sidebar += item + \"\\n\"\n\n\t\tpass","title":""},{"docid":"ba06ff441157039f6355e13e8514b22b","score":"0.65176576","text":"def generate_pages(self, node, depth, kwargs={}):\n for child in node.children.all():\n \n \n page = self.page_class(child, depth, \n exporter = self,\n has_children=child.children.exists(),\n **kwargs)\n \n last_page = self.pages[-1] if self.pages else None\n if last_page:\n page.prev_page = last_page\n last_page.next_page = page\n self.pages.append(page)\n self.generate_pages(child, depth + 1)","title":""},{"docid":"3b91fd69ceb1e16b3b51e4070e8c8f63","score":"0.6274178","text":"def generate_index_page_list(self, response):\n total_houses_str = response.css('h2.total.fl span::text').extract_first().strip()\n page_count = int(int(total_houses_str) / self.default_house_count + 1)\n for i in range(1, page_count + 1):\n index_url = self.index_url_template.format(str(i))\n self.start_urls.append(index_url)\n return self.initialized()","title":""},{"docid":"171b8daf2ffedafa10102ff9b453c6a8","score":"0.62389433","text":"def generate_pages(self, writer):\n write = partial(writer.write_file,\n relative_urls=self.settings['RELATIVE_URLS'],\n override_output=True)\n\n # to minimize the number of relative path stuff modification\n # in writer, articles pass first\n # self.generate_articles(write)\n self.generate_period_archives(write)\n self.generate_direct_templates(write)\n\n # and subfolders after that\n self.generate_categories(write)\n self.generate_authors(write)","title":""},{"docid":"02710a96eb532d702f34996c92665368","score":"0.62368083","text":"def page_links(paginator, current, view_name, suffix=\"?page=\"):\n num_pages = paginator.num_pages\n current = int(current)\n page_numbers = [current]\n for num in [1, 2, current - 1, current + 1, num_pages, num_pages - 1]:\n if num not in page_numbers and num > 0 and num <= num_pages:\n page_numbers.append(num)\n page_numbers.sort()\n\n new_page_numbers = [page_numbers[0]]\n j = 1\n for i in range(1, len(page_numbers)):\n if (page_numbers[i] - page_numbers[i-1] > 1):\n new_page_numbers.append(0)\n new_page_numbers.append(page_numbers[i])\n page_numbers = new_page_numbers\n\n return {\n \"page_numbers\": page_numbers,\n \"current\": current,\n \"view_name\": view_name,\n \"suffix\": suffix,\n }","title":""},{"docid":"9f38c0ed0641ed446af60de3e71527e3","score":"0.621711","text":"def pagination(context, page):\n paginator, page_num = page.paginator, page.number\n \n pagination_required = paginator.num_pages > 1\n if not pagination_required:\n page_range = []\n else:\n ON_EACH_SIDE = 3\n ON_ENDS = 2\n \n # If there are 10 or fewer pages, display links to every page.\n # Otherwise, do some fancy\n if paginator.num_pages <= 10:\n page_range = range(paginator.num_pages)\n else:\n # Insert \"smart\" pagination links, so that there are always ON_ENDS\n # links at either end of the list of pages, and there are always\n # ON_EACH_SIDE links at either end of the \"current page\" link.\n page_range = []\n if page_num > (ON_EACH_SIDE + ON_ENDS):\n page_range.extend(range(0, ON_EACH_SIDE - 1))\n page_range.append(DOT)\n page_range.extend(range(page_num - ON_EACH_SIDE, page_num + 1))\n else:\n page_range.extend(range(0, page_num + 1))\n if page_num < (paginator.num_pages - ON_EACH_SIDE - ON_ENDS - 1):\n page_range.extend(range(page_num + 1, page_num + ON_EACH_SIDE + 1))\n page_range.append(DOT)\n page_range.extend(range(paginator.num_pages - ON_ENDS, paginator.num_pages))\n else:\n page_range.extend(range(page_num + 1, paginator.num_pages))\n \n return {\n 'context': context,\n 'page': page,\n 'pagination_required': pagination_required,\n 'page_range': page_range,\n }","title":""},{"docid":"eb4759640ebab8a5ce27c16d5840907d","score":"0.61539274","text":"def test_paginator(self):\n nine = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n ten = nine + [10]\n eleven = ten + [11]\n tests = (\n # Each item is two tuples:\n # First tuple is pagination.Paginator parameters - object_list, per_page,\n # orphans, and allow_empty_first_page.\n # Second tuple is resulting pagination.Paginator attributes - count,\n # num_pages, and page_range.\n # Ten items, varying orphans, no empty first page.\n ((ten, 4, 0, False), (10, 3, [1, 2, 3])),\n ((ten, 4, 1, False), (10, 3, [1, 2, 3])),\n ((ten, 4, 2, False), (10, 2, [1, 2])),\n ((ten, 4, 5, False), (10, 2, [1, 2])),\n ((ten, 4, 6, False), (10, 1, [1])),\n # Ten items, varying orphans, allow empty first page.\n ((ten, 4, 0, True), (10, 3, [1, 2, 3])),\n ((ten, 4, 1, True), (10, 3, [1, 2, 3])),\n ((ten, 4, 2, True), (10, 2, [1, 2])),\n ((ten, 4, 5, True), (10, 2, [1, 2])),\n ((ten, 4, 6, True), (10, 1, [1])),\n # One item, varying orphans, no empty first page.\n (([1], 4, 0, False), (1, 1, [1])),\n (([1], 4, 1, False), (1, 1, [1])),\n (([1], 4, 2, False), (1, 1, [1])),\n # One item, varying orphans, allow empty first page.\n (([1], 4, 0, True), (1, 1, [1])),\n (([1], 4, 1, True), (1, 1, [1])),\n (([1], 4, 2, True), (1, 1, [1])),\n # Zero items, varying orphans, no empty first page.\n (([], 4, 0, False), (0, 0, [])),\n (([], 4, 1, False), (0, 0, [])),\n (([], 4, 2, False), (0, 0, [])),\n # Zero items, varying orphans, allow empty first page.\n (([], 4, 0, True), (0, 1, [1])),\n (([], 4, 1, True), (0, 1, [1])),\n (([], 4, 2, True), (0, 1, [1])),\n # Number if items one less than per_page.\n (([], 1, 0, True), (0, 1, [1])),\n (([], 1, 0, False), (0, 0, [])),\n (([1], 2, 0, True), (1, 1, [1])),\n ((nine, 10, 0, True), (9, 1, [1])),\n # Number if items equal to per_page.\n (([1], 1, 0, True), (1, 1, [1])),\n (([1, 2], 2, 0, True), (2, 1, [1])),\n ((ten, 10, 0, True), (10, 1, [1])),\n # Number if items one more than per_page.\n (([1, 2], 1, 0, True), (2, 2, [1, 2])),\n (([1, 2, 3], 2, 0, True), (3, 2, [1, 2])),\n ((eleven, 10, 0, True), (11, 2, [1, 2])),\n # Number if items one more than per_page with one orphan.\n (([1, 2], 1, 1, True), (2, 1, [1])),\n (([1, 2, 3], 2, 1, True), (3, 1, [1])),\n ((eleven, 10, 1, True), (11, 1, [1])),\n )\n for params, output in tests:\n self.check_paginator(params, output)","title":""},{"docid":"a25bc5a2c73617805c76987b229f332d","score":"0.6152125","text":"def get_pages(self):\n raise NotImplementedError(\"get_pages\")","title":""},{"docid":"58a3f64b467a15dde5fbf886ba5c60a2","score":"0.6131596","text":"def test_page_indexes(self):\n ten = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n tests = (\n # Each item is three tuples:\n # First tuple is pagination.Paginator parameters - object_list, per_page,\n # orphans, and allow_empty_first_page.\n # Second tuple is the start and end indexes of the first page.\n # Third tuple is the start and end indexes of the last page.\n # Ten items, varying per_page, no orphans.\n ((ten, 1, 0, True), (1, 1), (10, 10)),\n ((ten, 2, 0, True), (1, 2), (9, 10)),\n ((ten, 3, 0, True), (1, 3), (10, 10)),\n ((ten, 5, 0, True), (1, 5), (6, 10)),\n # Ten items, varying per_page, with orphans.\n ((ten, 1, 1, True), (1, 1), (9, 10)),\n ((ten, 1, 2, True), (1, 1), (8, 10)),\n ((ten, 3, 1, True), (1, 3), (7, 10)),\n ((ten, 3, 2, True), (1, 3), (7, 10)),\n ((ten, 3, 4, True), (1, 3), (4, 10)),\n ((ten, 5, 1, True), (1, 5), (6, 10)),\n ((ten, 5, 2, True), (1, 5), (6, 10)),\n ((ten, 5, 5, True), (1, 10), (1, 10)),\n # One item, varying orphans, no empty first page.\n (([1], 4, 0, False), (1, 1), (1, 1)),\n (([1], 4, 1, False), (1, 1), (1, 1)),\n (([1], 4, 2, False), (1, 1), (1, 1)),\n # One item, varying orphans, allow empty first page.\n (([1], 4, 0, True), (1, 1), (1, 1)),\n (([1], 4, 1, True), (1, 1), (1, 1)),\n (([1], 4, 2, True), (1, 1), (1, 1)),\n # Zero items, varying orphans, allow empty first page.\n (([], 4, 0, True), (0, 0), (0, 0)),\n (([], 4, 1, True), (0, 0), (0, 0)),\n (([], 4, 2, True), (0, 0), (0, 0)),\n )\n for params, first, last in tests:\n self.check_indexes(params, 'first', first)\n self.check_indexes(params, 'last', last)\n # When no items and no empty first page, we should get pagination.EmptyPage error.\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 0, False), 1, None)\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 1, False), 1, None)\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 2, False), 1, None)","title":""},{"docid":"589fcbcf6705d08c3a728772ae0ba330","score":"0.60818326","text":"def _get_gen_pages(self, gen, count: int = None, site=None):\n original_iter = iter(gen)\n\n gen = itertools.islice(original_iter, 0, count)\n\n gen_pages = list(gen)\n\n with suppress(StopIteration):\n gen_pages.append(next(original_iter))\n next(original_iter)\n if not site:\n site = gen_pages[0].site\n gen_pages.append(pywikibot.Page(site, '...'))\n\n for page in gen_pages:\n self.assertIsInstance(page, pywikibot.Page)\n if site:\n self.assertEqual(page.site, site)\n\n return gen_pages","title":""},{"docid":"10cc4137c45385bea2f3f26c1e656bfd","score":"0.60435414","text":"def _get_pages(self, verb, route, params, model):\n response_list = []\n\n if params is None:\n params = {}\n params['per_page'] = 100\n\n if 'per_page' not in params:\n params['per_page'] = 100\n\n if 'page' in params:\n response = self._request(verb, route, params)\n if response.status_code != 200:\n logging.error(\"Error::{}\".format(response.text))\n return None\n return CBWParser().parse_response(model, response)\n\n response = self._request(verb, route, params)\n\n if response.status_code != 200:\n logging.error(\"Error::{}\".format(response.text))\n return None\n\n response_list.extend(CBWParser().parse_response(model, response))\n\n while 'next' in response.links:\n next_url = urlparse(response.links['next']['url'])\n params['page'] = parse_qs(next_url .query)['page'][0]\n response = self._request(verb, route, params)\n response_list.extend(CBWParser().parse_response(model, response))\n return response_list","title":""},{"docid":"447e591b96bdb8e94c521f23429f22b0","score":"0.59836143","text":"def make_pages_list(self, pages):\n pages_list = \"const pages = [\"\n for page in pages:\n pages_list += '\\n \"pages/{}/index.html,\"'.format(page)\n pages_list += \"\\n];\\n\"\n return pages_list","title":""},{"docid":"9e2b447f17b53e3918c1a541a0be1072","score":"0.59680253","text":"def create_pages(self, item, vbox):\n adapters = self.get_adapters(item)\n\n first = True\n for _, name, adapter in adapters:\n try:\n page = adapter.construct()\n if page is None:\n continue\n if first:\n vbox.pack_start(page, False, True, 0)\n first = False\n else:\n expander = Gtk.Expander()\n expander.set_use_markup(True)\n expander.set_label(f\"<b>{name}</b>\")\n expander.add(page)\n expander.show_all()\n vbox.pack_start(expander, False, True, 0)\n except Exception:\n log.error(\n \"Could not construct property page for \" + name, exc_info=True\n )","title":""},{"docid":"b5f5074c12d95d44c3d668e6e386016e","score":"0.5956614","text":"def makePages(self, pageCnt, pn=1, template=None, name=None, w=None, h=None, **kwargs):\n for n in range(pageCnt): # First page is n + pn\n # Parent is forced to self.\n self.newPage(pn=pn+n, template=template, name=name, w=w, h=h, **kwargs)","title":""},{"docid":"909d71dc88d5a2fb030f624f3da1c4a8","score":"0.5955188","text":"def _fill_pages(self):\n\t\tif self.pages is None:\n\t\t\tself.log.info(\"Couldn't get any information about the module.\")\n\t\t\treturn\n\n\t\t# unless this is the index file, it needs to go in the html subdirectory\n\t\tnewpages = {}\n\t\tfor page in self.pages:\n\t\t\tif page != \"/index.html\":\n\t\t\t\tnewpages[\"html/\" + page] = self.pages[page]\n\t\tnewpages[\"/index.html\"] = self.pages[\"/index.html\"]\n\n\t\t# grab the updated version of pages\n\t\tself.pages = newpages\n\n\t\t# iterate through pages and add html frame\n\t\tfor page in self.pages:\n\t\t\t# dont stick the css into an html file\n\t\t\tif page == \"style.css\":\n\t\t\t\tcontinue\n\n\t\t\t# see how many subdirectory levels the page is\n\t\t\tsubdirnum = len(ghtml.get_subdirs(page))\n\n\t\t\t# take the page body information and fill a base html page with it\n\t\t\tself.pages[page] = ghtml.fill_base( title = self.title,\n\t\t\t\t\t\t\t\t\t\t\t\troot_url = (subdirnum - 1) * '../',\n\t\t\t\t\t\t\t\t\t\t\t\tbody = self.pages[page],\n\t\t\t\t\t\t\t\t\t\t\t\tsidebar = self.sidebar,\n\t\t\t\t\t\t\t\t\t\t\t\tfooter = self.footer)\n\n\t\tpass","title":""},{"docid":"ed30a5201ffd260586cf61a83fd26b51","score":"0.5953235","text":"def _generatePaginationLinks(\n self, offsetval, returned_count, number_found, params):\n\n doc_limit = self._getDocLimit()\n pcopy = params.copy()\n if offsetval - doc_limit >= 0:\n pcopy['offset'] = offsetval - doc_limit\n prev_link = '/psearch?' + urllib.urlencode(pcopy)\n else:\n prev_link = None\n if ((offsetval + doc_limit <= self._OFFSET_LIMIT)\n and (returned_count == doc_limit)\n and (offsetval + returned_count < number_found)):\n pcopy['offset'] = offsetval + doc_limit\n next_link = '/psearch?' + urllib.urlencode(pcopy)\n else:\n next_link = None\n return (prev_link, next_link)","title":""},{"docid":"73a2eccccb520236572d48d29034c924","score":"0.59350693","text":"def get_pagination_parameters(request, paginator, paginated):\n LEADING_PAGE_RANGE_DISPLAYED = TRAILING_PAGE_RANGE_DISPLAYED = 10\n LEADING_PAGE_RANGE = TRAILING_PAGE_RANGE = 8\n NUM_PAGES_OUTSIDE_RANGE = 2\n ADJACENT_PAGES = 4\n\n pages = paginator.num_pages\n page = paginated.number\n in_leading_range = in_trailing_range = False\n pages_outside_leading_range = pages_outside_trailing_range = range(0)\n if pages <= LEADING_PAGE_RANGE_DISPLAYED + NUM_PAGES_OUTSIDE_RANGE + 1:\n in_leading_range = in_trailing_range = True\n page_range = [n for n in range(1, pages + 1)]\n elif page <= LEADING_PAGE_RANGE:\n in_leading_range = True\n page_range = [n for n in range(1, LEADING_PAGE_RANGE_DISPLAYED + 1)]\n pages_outside_leading_range = [\n n + pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1)]\n elif page > pages - TRAILING_PAGE_RANGE:\n in_trailing_range = True\n page_range = [n for n in range(\n pages - TRAILING_PAGE_RANGE_DISPLAYED + 1, pages + 1)\n if n > 0 and n <= pages]\n pages_outside_trailing_range = [\n n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE)]\n else:\n page_range = [n for n in range(\n page - ADJACENT_PAGES, page + ADJACENT_PAGES + 1)\n if n > 0 and n <= pages]\n pages_outside_leading_range = [\n n + pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1)]\n pages_outside_trailing_range = [\n n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE)]\n\n # Now try to retain GET params, except for 'page'\n params = request.GET.copy()\n if 'page' in params:\n del(params['page'])\n get_params = params.urlencode()\n prev = paginated.previous_page_number() if paginated.has_previous() else \"\"\n\n return {\n 'pages': pages,\n 'page': page,\n 'previous': prev,\n 'next': paginated.next_page_number() if paginated.has_next() else \"\",\n 'has_previous': paginated.has_previous(),\n 'has_next': paginated.has_next(),\n 'page_range': page_range,\n 'in_leading_range': in_leading_range,\n 'in_trailing_range': in_trailing_range,\n 'pages_outside_leading_range': pages_outside_leading_range,\n 'pages_outside_trailing_range': pages_outside_trailing_range,\n 'get_params': get_params,\n 'count': paginator.count,\n }","title":""},{"docid":"520186a2ff96f069d212438fd3609c57","score":"0.5923028","text":"def loadpages(self):\n newpages = {}\n for page in listfiles(PAGES_DIR):\n (path,ext) = os.path.splitext(page)\n if ext == '.markdown' or ext == '.html':\n path = os.path.join(PAGES_DIR,page)\n (rest,ext) = os.path.splitext(page)\n # reuse existing pages where we can\n if rest in self.pages:\n newpages[rest] = self.pages[rest]\n else:\n p = Page(self,path)\n for decorators in self.page_decorators:\n decorators.extend_page()\n newpages[rest] = p\n self.pages = newpages","title":""},{"docid":"e727adfb5953ef2317650250a05855fc","score":"0.59183145","text":"def pagination_management(paginator, selected_page):\n \n try:\n objects = paginator.page(selected_page)\n except PageNotAnInteger:\n objects = paginator.page(1)\n except EmptyPage:\n objects = paginator.page(paginator.num_pages)\n return objects","title":""},{"docid":"21a1480319a474bc49a85a94c9b8918c","score":"0.5917556","text":"def paginator(context, adjacent_pages=3):\n \n # Get the base query string to be used in the pagination\n request = context[\"request\"]\n base_query_string_data = {}\n for (arg_key, arg_value) in request.GET.items():\n if arg_key != PAGE_NUMBER_KEY:\n arg_key_encoded = arg_key.encode(\"utf8\")\n arg_value_encoded = arg_value.encode(\"utf8\")\n base_query_string_data[arg_key_encoded] = arg_value_encoded\n\n paginator_object = context[\"paginator\"]\n page_count = paginator_object.num_pages\n current_page = int(request.GET.get(PAGE_NUMBER_KEY, 1))\n\n # Get the links to the pages surrounding the current one\n bottom_page = max(current_page - adjacent_pages, 1)\n top_page = min(current_page + adjacent_pages, page_count) \n page_numbers = range(bottom_page, top_page + 1)\n\n return {\n \"current_page\": current_page,\n \"page_count\": page_count,\n \"page_numbers\": page_numbers,\n \"show_first\": 1 not in page_numbers,\n \"show_last\": page_count not in page_numbers,\n \"previous_page_number\": current_page - 1,\n \"next_page_number\": current_page + 1,\n \"base_query_string_data\": base_query_string_data,\n }","title":""},{"docid":"aac55713cf835e0fc027ac72d982fb9f","score":"0.5898167","text":"def __init__(self) -> None:\n self.pages = []","title":""},{"docid":"cf1c702aa080aefce14205cc430d6b4e","score":"0.5882981","text":"def pages(self):\n if self._pages is None:\n context = layout.LayoutContext(\n self.enable_hinting, self.style_for, self.get_image_from_uri)\n self._pages = list(layout.layout_document(\n context, self.formatting_structure))\n return self._pages","title":""},{"docid":"24e59978d90f1a146323b0c1397c3242","score":"0.58063865","text":"def show_paginator(context, paginator, page, current_page, request, anchor=\"\", non_grouped_number_of_results = -1 ):\r\n \r\n adjacent_pages = 3\r\n total_wanted = adjacent_pages * 2 + 1\r\n min_page_num = max(current_page - adjacent_pages, 1)\r\n max_page_num = min(current_page + adjacent_pages + 1, paginator.num_pages + 1)\r\n\r\n num_items = max_page_num - min_page_num\r\n\r\n if num_items < total_wanted and num_items < paginator.num_pages:\r\n if min_page_num == 1:\r\n # we're at the start, increment max_page_num\r\n max_page_num += min(total_wanted - num_items, paginator.num_pages - num_items)\r\n else:\r\n # we're at the end, decrement\r\n min_page_num -= min(total_wanted - num_items, paginator.num_pages - num_items)\r\n\r\n # although paginator objects are 0-based, we use 1-based paging\r\n page_numbers = [n for n in range(min_page_num, max_page_num) if n > 0 and n <= paginator.num_pages]\r\n \r\n params = urllib.urlencode([(key, value.encode('utf-8')) for (key, value) in request.GET.items() if key.lower() != u\"page\"])\r\n \r\n if params == \"\":\r\n url = request.path + u\"?page=\"\r\n else:\r\n url = request.path + u\"?\" + params + u\"&page=\"\r\n\r\n return {\r\n \"page\": page,\r\n \"paginator\": paginator,\r\n \"current_page\": current_page,\r\n \"page_numbers\": page_numbers,\r\n \"show_first\": 1 not in page_numbers,\r\n \"show_last\": paginator.num_pages not in page_numbers,\r\n \"url\" : url,\r\n \"media_url\": context['media_url'],\r\n \"anchor\": anchor,\r\n \"non_grouped_number_of_results\": non_grouped_number_of_results\r\n }","title":""},{"docid":"d9d08fe4ea612250f5ffc97f408d901a","score":"0.5784741","text":"def parse(self, response):\n s = Selector(response)\n pagination = s.css(\".pagination\")\n pagelinks = pagination.xpath('//a[contains(@href, \"&page=\")]/@href').extract()\n for pagelink in pagelinks:\n request = Request(\n urljoin(self.root, pagelink),\n callback=self.parse_jobspage,\n dont_filter=True,\n )\n yield request","title":""},{"docid":"08998b293ef44108c4d93b015d1e23c7","score":"0.57800114","text":"def build_page(self):\n if self.query:\n try:\n page_no = int(self.request.GET.get('page', 1))\n except (TypeError, ValueError):\n raise Http404(\"Not a valid number for page.\")\n\n if page_no < 1:\n raise Http404(\"Pages should be 1 or greater.\")\n\n start_offset = (page_no - 1) * self.results_per_page\n self.results[start_offset:start_offset + self.results_per_page]\n\n paginator = Paginator(self.results, self.results_per_page)\n\n try:\n page = paginator.page(page_no)\n except InvalidPage:\n raise Http404(\"No such page!\")\n\n return (paginator, page)\n else:\n return (None, None)","title":""},{"docid":"8ca2361f00c677ef6c375454c72e564a","score":"0.5776995","text":"def generator(self):\n cat = pywikibot.Category(self.site,\n \"%s:%s\"\n % (self.site.namespaces.CATEGORY.custom_name,\n self.source))\n gen = pagegenerators.CategorizedPageGenerator(cat)\n gen = pagegenerators.NamespaceFilterPageGenerator(\n gen, self.site.namespaces.FILE.custom_name)\n if not self.filter:\n gen = pagegenerators.PreloadingGenerator(gen)\n # gen = pagegenerators.ImageGenerator(gen)\n for item in gen:\n page = DUP_Image(item.site, item.title(),\n not self.filter and item.get() or None,\n item.editTime())\n if self.filter and page.hasRefs:\n continue\n if not self.filter and not page.validReasons:\n continue\n yield page","title":""},{"docid":"b332c06add8221f8a4cff58e7ba47f87","score":"0.57711005","text":"def gen_pages_for_shop(shop_id):\n for i in range(1, 2):\n uri = ''.join((url_shop, shop_id,\n '/#reviews?ref=pagination&page=', str(i)))\n print(uri)\n yield uri","title":""},{"docid":"c9744d40d4cf44d65760b67dd57b0b76","score":"0.5763879","text":"def allpages(self, start=None, prefix=None, namespace='0', filterredir='all',\n minsize=None, maxsize=None, prtype=None, prlevel=None,\n limit=None, dir='ascending', filterlanglinks='all', generator=True,\n end=None):\n\n pfx = listing.List.get_prefix('ap', generator)\n kwargs = dict(listing.List.generate_kwargs(\n pfx, ('from', start), ('to', end), prefix=prefix,\n minsize=minsize, maxsize=maxsize, prtype=prtype, prlevel=prlevel,\n namespace=namespace, filterredir=filterredir, dir=dir,\n filterlanglinks=filterlanglinks,\n ))\n return listing.List.get_list(generator)(self, 'allpages', 'ap',\n limit=limit, return_values='title',\n **kwargs)","title":""},{"docid":"8a080ddb3221c9160acdd4fc5e48e91d","score":"0.576003","text":"def GetPageCount(self):","title":""},{"docid":"e783672e3d5a5d38787eea8e528fafd5","score":"0.57418764","text":"def pages(per_page=1, show_page=True):\n def page_source(coro):\n async def create_page_header(self, menu, entry):\n result = await discord.utils.maybe_coroutine(coro, self, menu, entry)\n return menu.generate_page(result, self._max_pages)\n\n def __init__(self, list_pages):\n super(self.__class__, self).__init__(list_pages, per_page=per_page)\n kwargs = {\n '__init__': __init__,\n 'format_page': (coro, create_page_header)[show_page]\n }\n return type(coro.__name__, (menus.ListPageSource,), kwargs)\n return page_source","title":""},{"docid":"119d5b173254b7d2f06149bbaf91528b","score":"0.57410246","text":"def paginate(self):\n raise NotImplementedError","title":""},{"docid":"30775c9803d6cbe75b6b3b08ea2e22e7","score":"0.573554","text":"def parse_page(self):\n self._nav_to_header_parent()\n self._nav_to_posts_parent()\n self.parse_library()\n self.parse_last_pg_url()\n self.compile_records()","title":""},{"docid":"24b88d6214f0138d7abd58c251665b1f","score":"0.57333153","text":"def paginate(self, url):\n #browser = Browser(\"chrome\")\n r = requests.get(\"http://localhost:8950/render.html?\", params= {\"url\":url})\n for i in range(10):\n try:\n browser.find_by_css(\".PagerLinks > a\")[-2].click()\n pages.append(browser.html)\n except:\n break\n return pages","title":""},{"docid":"86045b49fa09d83bdbcb9154019baf9f","score":"0.5726248","text":"def _get_all_pages(cls, start_url, params=None):\n entries = []\n # Composing URL in advance since the link to the next page already have the params of the\n # first request and using `get_soup` with the params argument creates duplicate params\n next_url = ds_utils.compose_url_get(start_url, params)\n while next_url:\n soup = cls._get_soup(next_url)\n for entry in soup.feed.find_all('entry'):\n entries.append(cls(cls._parse_entry(entry)))\n next_link = soup.find('link', rel=\"next\")\n next_url = next_link and next_link.attrs.get('href', None)\n return entries","title":""},{"docid":"6494149e83f9c5877bfd1cfb278abc61","score":"0.5702473","text":"def allpages(self, limit=100, namespace=0,\n prefix=None, getinfo=None, **evil):\n params = {\n 'action': 'query',\n 'list': 'allpages',\n 'aplimit': limit,\n 'apprefix': prefix,\n 'apnamespace': namespace,\n }\n params.update(evil)\n return self._generate(\n params,\n Page,\n ('query', 'allpages'),\n getinfo\n )","title":""},{"docid":"4f6153aa78f96b69d3086a47417e0c71","score":"0.5702232","text":"def querypages(self,tag=None,category=None,maxitems=None,order=None):\n \n if self.preview_mode:\n for p in self.pages.values():\n p.refresh()\n \n pages = []\n for p in self.pages.values():\n if tag is not None and tag not in p.tags:\n continue\n if category is not None and category != p.category:\n continue \n pages.append(p)\n if order:\n if order == 'date_created':\n pages.sort(key=lambda p:p.date_created())\n pages.reverse()\n elif order == 'date_modified':\n pages.sort(key=lambda p:p.date_modified())\n pages.reverse()\n elif order == 'title':\n pages.sort(key=lambda p:p.title)\n \n if maxitems is not None:\n pages = pages[0:maxitems]\n return pages","title":""},{"docid":"2061573eaf01340af95789722f841b36","score":"0.5683553","text":"def _setup_pages(cls, config):\n from cms.exceptions import NoHomeFound\n from cms.models import Page\n from cms.utils import get_language_list\n from django.conf import settings\n from django.utils.translation import override\n\n app_page = None\n get_url = False\n if getattr(settings, \"ALDRYN_SEARCH_CMS_PAGE\", False):\n from aldryn_search.search_indexes import TitleIndex\n\n def fake_url(self, obj):\n return \"\"\n\n get_url = TitleIndex.get_url\n TitleIndex.get_url = fake_url\n site = Site.objects.get_current()\n auto_sites = cls.auto_setup.get(\"sites\", True)\n if auto_sites is True or site.pk in auto_sites:\n if getattr(cls, \"app_config\", False):\n configs = cls.app_config.objects.all()\n if not configs.exists():\n config = cls._create_config()\n else:\n config = configs.first()\n\n langs = get_language_list(site.pk)\n if not Page.objects.on_site(site.pk).filter(application_urls=cls.__name__).exists():\n for lang in langs:\n with override(lang):\n if config:\n if cls.auto_setup[\"config_translated_fields\"]:\n cls._create_config_translation(config, lang)\n namespace = config.namespace\n elif cls.app_name:\n namespace = cls.app_name\n else:\n namespace = None\n try:\n home = Page.objects.get_home(site.pk).get_draft_object()\n except NoHomeFound:\n home = None\n set_home = hasattr(Page, \"set_as_homepage\")\n home = cls._create_page(home, lang, cls.auto_setup[\"home title\"], site=site, set_home=set_home)\n app_page = cls._create_page(\n app_page, lang, cls.auto_setup[\"page title\"], cls.__name__, home, namespace, site=site\n )\n if get_url:\n TitleIndex.get_url = get_url","title":""},{"docid":"c9a5511d8c86c396c1982627dcc07741","score":"0.5668219","text":"def generatePageImages(self):","title":""},{"docid":"4c97dfdf4d85e08e46c436c499df279f","score":"0.56672287","text":"def page_range(self):\n b_size = self._BATCH_SIZE\n range_start = max(self.page_number() - 5, 0)\n range_stop = min(max(self.page_number() + 5, 10), self.page_count())\n\n pages = []\n for p in range(range_start, range_stop):\n b_start = p * b_size\n pages.append({'number': p + 1,\n 'url': self._getNavigationURL(b_start)})\n return pages","title":""},{"docid":"93df7f6c3692b8dcd223550674198c28","score":"0.56533444","text":"def get_pages(self):\n # Get base url\n base_url = self.chapter_url\n max_retries = deepcopy(self.max_retries)\n wait_retry_time = deepcopy(self.wait_time)\n\n while True:\n # Get javascript blocks\n r = requests.get(base_url, verify=self.verify_https)\n soup = bsoup.BeautifulSoup(r.text, 'html.parser')\n scripts = [script for script in soup.find_all(\n 'script', attrs={'type': 'text/javascript'})]\n\n if scripts:\n # Get total pages\n for script in scripts:\n if script.contents:\n matched_groups = re.search(\n 'var total_pages\\s?=\\s?(\\d*)\\s?;',\n script.contents[0])\n if matched_groups:\n total_pages = int(matched_groups.group(1))\n break\n # Get page urls\n page_urls = [\"%s/%d.html\" % (self.chapter_url, i + 1)\n for i in range(total_pages)]\n page_num = [i + 1 for i in range(total_pages)]\n pages = list(zip(page_urls, page_num))\n shuffle(pages)\n\n return True, pages\n\n elif (max_retries > 0):\n # Idea from manga_downloader (which in turn was from wget)\n sleep(uniform(0.5 * wait_retry_time, 1.5 * wait_retry_time))\n max_retries -= 1\n else:\n return False, None","title":""},{"docid":"09ca102245f15657942a1c554de5d3c4","score":"0.56481713","text":"def apply_paginators(self, paginators, max_size=None):\n pagination = {\n 'limit': self.DEFAULT_LIMIT,\n 'offset': self.DEFAULT_OFFSET\n }\n for paginator in paginators:\n try:\n value = int(paginator.value)\n\n # Raise if the maximum page size was exceeded.\n if max_size is not None and \\\n paginator.strategy in ['limit', 'number'] and \\\n value > max_size:\n raise ValueError('Maximum query size exceeded.')\n\n pagination[paginator.strategy] = value\n except ValueError:\n raise errors.InvalidPaginationValue(item=paginator)\n if 'number' in pagination:\n limit = pagination['limit']\n pagination['offset'] = pagination['number'] * limit - limit\n return self.limit(pagination['limit']).offset(pagination['offset'])","title":""},{"docid":"78e3ea1e0415c435376af4176808b171","score":"0.5643073","text":"def paginator(parser, token):\n args = token.split_contents()\n\n # Parameters\n page = args[1]\n\n style=None\n preload=True\n for a in args[1:]:\n if a[0:len('style=')] == 'style=':\n style = a[len('style='):]\n if a == 'nopreload':\n preload = False\n\n # Read nodelist\n nodelist = parser.parse(('endpaginate',))\n parser.delete_first_token()\n\n # Return meta node\n return PaginateNode(page, nodelist, style, preload)","title":""},{"docid":"0a91e8db52121f3910dd0372ba5ef0ba","score":"0.56149274","text":"def __build_pages_matrices(self, keypoint_data_pages, label_data_pages):\n pages = []\n for keypoints, labels in zip(keypoint_data_pages, label_data_pages):\n pages.append(self.__build_page_matrix(keypoints, labels))\n return pages","title":""},{"docid":"892648a228137fec2beaf03eebed7b94","score":"0.5593152","text":"def paginate(objects, page_num, items_per_page=20, padding_pages=3):\n\n context = {}\n paginate = Paginator(objects, items_per_page)\n\n try:\n page = paginate.page(page_num)\n context['current_page'] = int(page_num)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n page = paginate.page(1)\n context['current_page'] = 1\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n page = paginate.page(paginate.num_pages)\n context['current_page'] = paginate.num_pages\n\n context['objects'] = page.object_list\n context['totalPages'] = paginate.num_pages\n\n startPage = max(context['current_page'] - padding_pages - 1, 0)\n endPage = context['current_page'] + padding_pages\n context['pages'] = paginate.page_range[startPage:endPage]\n\n if page.has_next():\n context['nextPage'] = page.next_page_number()\n context['lastPage'] = paginate.num_pages\n else:\n context['nextPage'] = False\n context['lastPage'] = False\n\n if page.has_previous():\n context['prevPage'] = page.previous_page_number()\n context['firstPage'] = 1\n else:\n context['prevPage'] = False\n context['firstPage'] = False\n\n if not 1 in context['pages']:\n context['pages'].insert(0, '...')\n if not paginate.num_pages in context['pages']:\n context['pages'].append('...')\n\n return context","title":""},{"docid":"07d667d9743e74f6e1954708ec6ee21f","score":"0.55680984","text":"def get_pages(context):\n # set the current search context\n if context is None:\n context = invoke_context()\n print('invoked context: %s' % context)\n limit = request.args.get('limit', None)\n return jsonify([page.to_json() for page in Page.all(index=context, limit=limit)])","title":""},{"docid":"162c62851ddce79676488d4c7c8f6bb4","score":"0.5564884","text":"def _page0(self, page=None, pageSize=None):\n yield self.cpage\n self.npage = page\n url0 = self.mkUrl(page=self.npage, pageSize=self.pageSize)\n r = self.fetch(url=url0)\n self.cpage = self.extract(list=r.read())","title":""},{"docid":"a694f6aebd5ee1b49db857fc03c39ffd","score":"0.5563814","text":"def pages(self, pages):\n\n self._pages = pages","title":""},{"docid":"1af89ac1720d4323cce7f21ab3477e31","score":"0.55627286","text":"def build(self,flowables,onFirstPage=_doNothing, onLaterPages=_doNothing, canvasmaker=canvas.Canvas):\n self._calc() #in case we changed margins sizes etc\n frameT = Frame(self.leftMargin, self.bottomMargin, self.width, self.height, id='normal')\n self.addPageTemplates([PageTemplate(id='First',frames=frameT, onPage=onFirstPage,pagesize=self.pagesize),\n PageTemplate(id='Later',frames=frameT, onPage=onLaterPages,pagesize=self.pagesize)])\n if onFirstPage is _doNothing and hasattr(self,'onFirstPage'):\n self.pageTemplates[0].beforeDrawPage = self.onFirstPage\n if onLaterPages is _doNothing and hasattr(self,'onLaterPages'):\n self.pageTemplates[1].beforeDrawPage = self.onLaterPages\n BaseDocTemplate.build(self,flowables, canvasmaker=canvasmaker)","title":""},{"docid":"18a72d8ef682f834541ee089f4705d11","score":"0.5561412","text":"def __init__(self, prop, **kwargs):\n QueryGenerator.__init__(self, prop=prop, **kwargs)\n self.resultkey = \"pages\"","title":""},{"docid":"3b628462d31f4bb7c6c233c08d1ef138","score":"0.55603456","text":"def main_paginate():\n import argparse\n\n parser = argparse.ArgumentParser(prog='latexpages-paginate',\n description='Computes and updates start page numbers in compiled parts and contents')\n\n parser.add_argument('--version', action='version',\n version='%%(prog)s %s' % __version__)\n\n parser.add_argument('filename',\n help='INI file configuring the parts and paginate options')\n\n args = parser.parse_args()\n paginate(args.filename)","title":""},{"docid":"42815a9482b425f90187ee638272a70c","score":"0.555997","text":"def get_main_pages_urls(self) -> Generator[str, None, None]:\n return (f\"{self.main_url}p={page}\" for page in range(1, self.pages_number + 1))","title":""},{"docid":"1c6c1ec39a0fc6ab05220b4de04c594f","score":"0.55559534","text":"def _getPages(request, filter_regex=None):\n filter = None\n if filter_regex:\n filter = re.compile(filter_regex).match\n pages = request.rootpage.getPageList(filter=filter)\n pages.sort()\n return pages","title":""},{"docid":"8e3001056fd0cc3bebf94aa1ef39f1df","score":"0.55523103","text":"def pagination(page, objects_caption, query_string=''):\n\n if not hasattr(page, 'has_other_pages') or not page.has_other_pages():\n return {\n 'page_object': page,\n }\n\n padding = settings.PAGINATION_PADDING\n tail = settings.PAGINATION_TAIL\n\n current_page = page.number\n pages_count = page.paginator.num_pages + 1\n\n # Create a list of pages which are within +/- `padding` items around the current one\n main_range_from = max(current_page - padding, 1)\n main_range_to = min(main_range_from + padding * 2 + 1, pages_count)\n main_range = range(main_range_from, main_range_to)\n\n # Create a list of `tail` pages located at the beginning of the navigation block\n left_range_from = 1\n left_range_to = min(left_range_from + tail, main_range_from)\n left_range = range(left_range_from, left_range_to)\n\n # If the lists main_range and left_range overlap, join them\n # to avoid a discontinuity in the navigation block\n if (main_range[0] - 1) <= tail:\n main_range = left_range + main_range\n left_range = []\n\n # Same for the right side of the navigation block\n right_range_to = pages_count\n right_range_from = max(right_range_to - tail, main_range_to)\n right_range = range(right_range_from, right_range_to)\n\n if (main_range[-1] + 1) >= (pages_count - tail):\n main_range = main_range + right_range\n right_range = []\n\n if query_string:\n query_string = '&' + query_string\n\n return {\n 'left_range': left_range,\n 'main_range': main_range,\n 'right_range': right_range,\n 'page': page,\n 'query_string': query_string,\n 'objects_caption': objects_caption,\n }","title":""},{"docid":"7b5eeb014aa59cbc9acd49b4a2c0f902","score":"0.5549387","text":"def make_pagination(query, offset, list_name=\"objects\"):\n if type(query) == list:\n objs = len(query)\n else:\n objs = query.count()\n if objs == 0:\n return dict()\n if offset >= objs or offset%5 != 0:\n raise Exception(\"Bad request\")\n\n page = offset/5 + 1\n prev_pages = list(enumerate(range(0, offset, 5), start=1))\n next_pages = list(enumerate(range(offset+5, objs, 5), start=page+1))\n\n d = {\n list_name: query[offset:offset+5],\n \"page\": page,\n \"next_pages\": next_pages,\n \"prev_pages\": prev_pages,\n }\n\n return d","title":""},{"docid":"686729ab98feb7d50d5d73706ca7b6a3","score":"0.553319","text":"def before():\n g.pages = PagesRepository(pages)","title":""},{"docid":"48778d836e6ec813e8fcc57ed67db601","score":"0.5519491","text":"def process_multi_page_menu(pages): \n date = get_date(pages[0])\n lunch = get_meal(multi_page_classify,pages[1])\n dinner = get_meal(multi_page_classify,pages[2])\n\n return {\"dinner\":dinner,\"lunch\":lunch,\"base_date\":date}","title":""},{"docid":"4cb829c1b7d9c5db3149c1c8e838bdf3","score":"0.55104864","text":"def add_pagination_context(context): \n per_page = context[\"paginator\"].per_page\n page_obj = context[\"page_obj\"]\n context[\"showing_first\"] = per_page * (page_obj.number - 1) + 1 if page_obj else 0\n context[\"showing_end\"] = (\n context[\"showing_first\"] + len(page_obj) - 1 if page_obj else 0\n )\n return context","title":""},{"docid":"65c7b0d0ae3a88f4475c25398df9344c","score":"0.55101967","text":"def __init__(self, generator, g_content=False, **kwargs):\n # get some basic information about every page generated\n if 'prop' in kwargs:\n kwargs['prop'] += \"|info|imageinfo|categoryinfo\"\n else:\n kwargs['prop'] = 'info|imageinfo|categoryinfo'\n if g_content:\n # retrieve the current revision\n kwargs['prop'] += \"|revisions\"\n if \"rvprop\" in kwargs:\n kwargs[\"rvprop\"] += \"ids|timestamp|flags|comment|user|content\"\n else:\n kwargs[\"rvprop\"] = \"ids|timestamp|flags|comment|user|content\"\n if \"inprop\" in kwargs:\n if \"protection\" not in kwargs[\"inprop\"]:\n kwargs[\"inprop\"] += \"|protection\"\n else:\n kwargs['inprop'] = 'protection'\n if \"iiprop\" in kwargs:\n kwargs[\"iiprop\"] += 'timestamp|user|comment|url|size|sha1|metadata'\n else:\n kwargs['iiprop'] = 'timestamp|user|comment|url|size|sha1|metadata'\n QueryGenerator.__init__(self, generator=generator, **kwargs)\n self.resultkey = \"pages\" # element to look for in result","title":""},{"docid":"8e2934ef3a85377fbc0a457a8517d0b8","score":"0.5501297","text":"def render_pagination(pagination: Optional[Dict[str, Any]]) -> None:\n if pagination and (int(pagination[\"current\"]) + 1 <= int(pagination[\"total\"])):\n kwargs = {\"page\": int(pagination[\"current\"]) + 1}\n if plugin.settings.exclude_anime == \"true\" and \"start_from\" in pagination:\n kwargs[\"start_from\"] = pagination[\"start_from\"]\n img = plugin.routing.build_icon_path(\"next_page\")\n # Next\n li = plugin.list_item(\n name=f\"[COLOR FFFFF000]{localize(32016)}[/COLOR]\", iconImage=img, thumbnailImage=img\n )\n url = plugin.routing.add_kwargs_to_url(**kwargs)\n xbmcplugin.addDirectoryItem(plugin.handle, url, li, True)\n img = plugin.routing.build_icon_path(\"home\")\n home_url = plugin.routing.build_url(\"/\")\n # Home\n li = plugin.list_item(\n name=f\"[COLOR FFFFF000]{localize(32017)}[/COLOR]\", iconImage=img, thumbnailImage=img\n )\n xbmcplugin.addDirectoryItem(plugin.handle, home_url, li, True)\n xbmcplugin.endOfDirectory(plugin.handle)","title":""},{"docid":"2cdd07cf54df71c53bc87060436c9aac","score":"0.5495602","text":"def gen_tasks(self):\n self.site.scan_posts()\n yield self.group_task()\n\n # Cache classification sets per language for taxonomies where\n # add_other_languages_variable is True.\n classification_set_per_lang = {}\n for taxonomy in self.site.taxonomy_plugins.values():\n if taxonomy.add_other_languages_variable:\n lookup = self.site.posts_per_classification[taxonomy.classification_name]\n cspl = {lang: set(lookup[lang].keys()) for lang in lookup}\n classification_set_per_lang[taxonomy.classification_name] = cspl\n\n # Collect post lists for classification pages and determine whether\n # they should be generated.\n post_lists_per_lang = {}\n for taxonomy in self.site.taxonomy_plugins.values():\n plpl = {}\n for lang in self.site.config[\"TRANSLATIONS\"]:\n result = {}\n for classification, posts in self.site.posts_per_classification[taxonomy.classification_name][lang].items():\n # Filter list\n filtered_posts = self._filter_list(posts, lang)\n if len(filtered_posts) == 0 and taxonomy.omit_empty_classifications:\n generate_list = generate_rss = generate_atom = False\n else:\n # Should we create this list?\n generate_list = taxonomy.should_generate_classification_page(classification, filtered_posts, lang)\n generate_rss = taxonomy.should_generate_rss_for_classification_page(classification, filtered_posts, lang)\n generate_atom = taxonomy.should_generate_atom_for_classification_page(classification, filtered_posts, lang)\n result[classification] = (filtered_posts, generate_list, generate_rss, generate_atom)\n plpl[lang] = result\n post_lists_per_lang[taxonomy.classification_name] = plpl\n\n # Now generate pages\n for lang in self.site.config[\"TRANSLATIONS\"]:\n # To support that tag and category classifications share the same overview,\n # we explicitly detect this case:\n ignore_plugins_for_overview = set()\n if 'tag' in self.site.taxonomy_plugins and 'category' in self.site.taxonomy_plugins and self.site.link(\"tag_index\", None, lang) == self.site.link(\"category_index\", None, lang):\n # Block both plugins from creating overviews\n ignore_plugins_for_overview.add(self.site.taxonomy_plugins['tag'])\n ignore_plugins_for_overview.add(self.site.taxonomy_plugins['category'])\n for taxonomy in self.site.taxonomy_plugins.values():\n if not taxonomy.is_enabled(lang):\n continue\n # Generate list of classifications (i.e. classification overview)\n if taxonomy not in ignore_plugins_for_overview:\n if taxonomy.template_for_classification_overview is not None:\n for task in self._generate_classification_overview(taxonomy, lang):\n yield task\n\n # Process classifications\n for classification, (filtered_posts, generate_list, generate_rss, generate_atom) in post_lists_per_lang[taxonomy.classification_name][lang].items():\n for task in self._generate_classification_page(taxonomy, classification, filtered_posts,\n generate_list, generate_rss, generate_atom, lang,\n post_lists_per_lang[taxonomy.classification_name],\n classification_set_per_lang.get(taxonomy.classification_name)):\n yield task\n # In case we are ignoring plugins for overview, we must have a collision for\n # tags and categories. Handle this special case with extra code.\n if ignore_plugins_for_overview:\n for task in self._generate_tag_and_category_overview(self.site.taxonomy_plugins['tag'], self.site.taxonomy_plugins['category'], lang):\n yield task","title":""},{"docid":"cd4dd2f9827651f7a5ae593b6b6026e5","score":"0.54944646","text":"def scraping_pages_links(self, void: str) -> List[str]:\r\n # Create link\r\n link = self.page + void\r\n\r\n try:\r\n # Read website, encode and create HTML parser\r\n soup_pages = self.enterPage_parser(link)\r\n\r\n # Extract max page number\r\n res = soup_pages.findAll('script')\r\n lengths = [len(str(el)) for el in res]\r\n json_object = json.loads(res[lengths.index(max(lengths))].contents[0])\r\n max_page_num = json_object[\"props\"][\"pageProps\"][\"tracking\"]['listing']['page_count']\r\n\r\n # Create all pages links\r\n all_pages_links = [link + '?page=' + str(page) for page in range(1, max_page_num + 1)]\r\n\r\n except:\r\n all_pages_links = link\r\n\r\n return all_pages_links","title":""},{"docid":"926c4f2c038863745b487488d4021e1f","score":"0.54927444","text":"def paginator(context, adjacent_pages=2):\n if not 'page' in context:\n # improper use of paginator tag, bail out\n return {}\n\n page = context['page']\n page_no = int(page.number)\n\n s = max(1, page_no - adjacent_pages - max(0, page_no+adjacent_pages-page.paginator.num_pages))\n page_numbers = range(s, min(page.paginator.num_pages, s+2*adjacent_pages)+1)\n\n return {\n 'page': page,\n 'results_per_page': context['results_per_page'],\n 'page_numbers': page_numbers,\n 'show_first': 1 not in page_numbers,\n 'show_last': page.paginator.num_pages not in page_numbers,\n }","title":""},{"docid":"b7d0c81e3c4385fe99299f1a54e5735b","score":"0.54913855","text":"def load_pages(self):\n\t\t#A dict of dicts to store the contents of _ directories. The first-\n\t\t# level key is the parent directory of the _ dir; the second-level\n\t\t# key is the name of the _ dir without the '_'. The value is a\n\t\t# list of rendered pages.\n\t\textravars = defaultdict(dict)\n\n\t\t#Walk through the directory bottom-up so that we get any\n\t\t# subdirectories with extra variables first.\n\t\tfor root, dirs, files in os.walk(self.options['content_dir'],\n\t\t\ttopdown=False, followlinks=True):\n\t\t\t#Per-directory environment to get templates from current\n\t\t\t# directory or its parents\n\t\t\tenvironment = jinja2.Environment(\n\t\t\t\tloader=ParentLoader(root, stop=self.root,\n\t\t\t\t\tdefault=self.options['default_template']),\n\t\t\t\textensions=self.options.get('jinja2_extensions', []))\n\n\t\t\t#Add any filters specified in options\n\t\t\tenvironment.filters.update(self.options.get('jinja2_filters', {}))\n\n\t\t\troot_basename = os.path.basename(root) #The name only of the current directory\n\t\t\tparent_dir = os.path.split(root)[0] #The full path of the parent directory\n\n\t\t\t#If the subdirectory starts with _, read and parse all .md files\n\t\t\t# within it and add them as variables with the directory name\n\t\t\t# (without the _).\n\t\t\tin_subfiles = root_basename.startswith('_')\n\t\t\tif in_subfiles:\n\t\t\t\troot_basename = root_basename[1:]\n\t\t\t\textravars[parent_dir][root_basename] = [] #Drop the _\n\n\t\t\t#Go through each file in the current directory (root)\n\t\t\tfor f in files:\n\t\t\t\trname, ext = os.path.splitext(f)\n\n\t\t\t\t#Skip hidden and template files\n\t\t\t\tif f.startswith('.') or ext == '.jinja':\n\t\t\t\t\tcontinue\n\n\t\t\t\t#Figure out where it should go in output for files to be\n\t\t\t\t# copied or written\n\t\t\t\tdestroot = os.path.relpath(\n\t\t\t\t\tos.path.join(\n\t\t\t\t\t\tos.path.join(parent_dir, root_basename) if in_subfiles else root,\n\t\t\t\t\t\trname), #Filename with no extension\n\t\t\t\t\tstart=self.options['content_dir'])\n\t\t\t\tif destroot.split(os.path.sep, 1)[0] == self.options['root_subdir']:\n\t\t\t\t\tdestroot = os.path.relpath(destroot, start=self.options['root_subdir'])\n\n\t\t\t\t#If it's a .md, we should render it\n\t\t\t\tif ext == '.md':\n\t\t\t\t\there = os.getcwd()\n\t\t\t\t\tos.chdir(root) #Be sure we're in root for relative paths\n\t\t\t\t\tmeta = self.render(f, environment, extravars.get(root, {}))\n\t\t\t\t\tos.chdir(here)\n\t\t\t\t\t#If we're in a _ dir, put the rendered file in extravars,\n\t\t\t\t\t# otherwise write the rendered result to disk\n\t\t\t\t\tif in_subfiles:\n\t\t\t\t\t\textravars[parent_dir][root_basename].append(meta)\n\t\t\t\t\telif meta['content']:\n\t\t\t\t\t\t\tself.write(meta['content'], destroot + '.html')\n\n\t\t\t\t#Otherwise copy it\n\t\t\t\telse:\n\t\t\t\t\tsrc = os.path.join(root, f)\n\t\t\t\t\tdst = os.path.join(self.options['output_dir'], destroot + ext)\n\t\t\t\t\ttry:\n\t\t\t\t\t\tos.makedirs(os.path.dirname(dst))\n\t\t\t\t\texcept OSError as e:\n\t\t\t\t\t\tpass\n\t\t\t\t\tlogging.info(\"Copy file {0} to {1}\".format(src, dst))\n\t\t\t\t\tshutil.copy(src, dst)","title":""},{"docid":"209d366f074719763b074282c9c3a85b","score":"0.54750156","text":"def populate(self):\n # type: () -> None\n self.container.clear_children()\n self.recent_pages = GUI.PagedContainer((20, 0), width=self.width - 40, height=60, hideControls=True)\n self.recent_pages.add_page(self.recent_pages.generate_page())\n self.btn_left = GUI.Button((0, 0), \"<\", state.color_palette.get_color(GUI.Palette.accent),\n state.color_palette.get_color(GUI.Palette.item), 20, width=20, height=60,\n onClick=self.recent_pages.page_left)\n self.btn_right = GUI.Button((self.width - 20, 0), \">\", state.color_palette.get_color(GUI.Palette.accent),\n state.color_palette.get_color(GUI.Palette.item), 20, width=20, height=60,\n onClick=self.recent_pages.page_right)\n per_app = (self.width - 40) / 4\n current = 0\n for app in state.application_list.active_applications:\n if app is not state.active_application and app.parameters.get(\"persist\", True) and app.name != \"home\":\n if current >= 4:\n current = 0\n self.recent_pages.add_page(self.recent_pages.generate_page())\n cont = GUI.Container((per_app * current, 0), transparent=True, width=per_app, height=self.height,\n border=1, borderColor=state.color_palette.get_color(GUI.Palette.item),\n onClick=self.activate, onClickData=(app,), onLongClick=self.close_ask,\n onLongClickData=(app,))\n cont.SKIP_CHILD_CHECK = True\n icon = app.getIcon()\n if not icon:\n icon = state.icons.get_loaded_icon(\"unknown\")\n img = GUI.Image((0, 5), surface=icon)\n img.position[0] = GUI.get_centered_coordinates(img, cont)[0]\n name = GUI.Text((0, 45), app.title, state.color_palette.get_color(GUI.Palette.item), 10)\n name.position[0] = GUI.get_centered_coordinates(name, cont)[0]\n cont.add_child(img)\n cont.add_child(name)\n self.recent_pages.add_child(cont)\n current += 1\n if len(self.recent_pages.get_page(0).child_components) == 0:\n notxt = GUI.Text((0, 0), \"No Recent Apps\", state.color_palette.get_color(GUI.Palette.item), 16)\n notxt.position = GUI.get_centered_coordinates(notxt, self.recent_pages.get_page(0))\n self.recent_pages.add_child(notxt)\n self.recent_pages.go_to_page()\n self.add_child(self.recent_pages)\n self.add_child(self.btn_left)\n self.add_child(self.btn_right)","title":""},{"docid":"3718ba5b83568394a4038dea4d13f7dc","score":"0.5471489","text":"def get_all(parent, fetcher_str, **kwargs):\n # overwrite parameters that may interfere\n kwargs.update({'commit': True, 'page': 0})\n\n def generate_all_pages():\n \"\"\"generator for all pages.\"\"\"\n fetcher = getattr(parent, fetcher_str)\n\n first_page = fetcher.get(**kwargs)\n kwargs['page_size'] = len(first_page)\n count = fetcher.current_total_count\n yield first_page\n\n if count > kwargs['page_size']:\n num_pages = int(ceil(float(count) / kwargs['page_size']))\n for page in range(1, num_pages):\n kwargs['page'] = page\n yield fetcher.get(**kwargs)\n\n # flatten the pages into one generator for just the objects\n return chain.from_iterable(generate_all_pages())","title":""},{"docid":"e26a017ac4f628e4de52230fc64d2684","score":"0.5469787","text":"def get_pages(self) -> List[str]:\r\n\r\n # Scrape all links for voivodeships in self.voivodeship variable\r\n results_pages = self.scraping_all_links(self.scraping_pages_links, self.voivodeships)\r\n results_pages = self.flatten(results_pages)\r\n\r\n # Verify weather there are some missing oferts\r\n missed_pages = [oferts for oferts in results_pages if \"page\" not in oferts]\r\n\r\n if len(missed_pages) != 0:\r\n results_pages = self.flatten(\r\n [properties for properties in results_pages if (properties != None) & (\"page\" in properties)])\r\n\r\n # Try to scrape missing links once again and join them with scraped before\r\n missed_pages_list = self.missed_links_all(missed_offers=missed_pages, func=self.missed_offers_pages,\r\n details=False, offers=False,\r\n func_pages_or_offers=self.scraping_pages_links)\r\n results_pages = self.join_missed_with_scraped(missed_pages_list, results_pages)\r\n\r\n return self.flatten(results_pages)","title":""},{"docid":"b3bbcd891336824ba4d54606f47acf42","score":"0.54651093","text":"def page(self, *criterion, limit=None, offset=None):\n raise NotImplementedError(\"Subclasses should overwrite this Method.\")","title":""},{"docid":"b3bbcd891336824ba4d54606f47acf42","score":"0.54651093","text":"def page(self, *criterion, limit=None, offset=None):\n raise NotImplementedError(\"Subclasses should overwrite this Method.\")","title":""},{"docid":"4fa7e5b96053b854a6db20fdd919c089","score":"0.54617846","text":"def chunked_pages(self, tags_list: list, n: int):\n pages = []\n for chunk in self.chunk_list(tags_list, n):\n content = ''\n for col in self.chunk_list(chunk, 3):\n # the columns wont have the perfect number of elements every time, we need to append spaces if\n # the list entries is less then the number of columns\n while len(col) < 3:\n col.append(' ')\n\n # Concatenate the formatted column string to the page content string\n content += \"{: <20} {: <20} {: <20}\\n\".format(*col)\n\n # Append the content string to the list of pages to send to the paginator\n # Marked as a code block to ensure a monospaced font and even columns\n pages.append(f'```{content}```')\n return pages","title":""},{"docid":"6176b69bdc6ee17ebf18f4d304fb119d","score":"0.545429","text":"def make_poss(current_page):\n print \"fetching possibilities\"\n possibilities = []\n\n for title in current_page.links():\n try:\n if debug: print \"fetching \" + str(title) \n possibilities.append(Page(title))\n except Exception, msg:\n if debug: print \"caught an error: \" + str(msg)\n return possibilities","title":""},{"docid":"0ec86b4f5ccf3ba9711e952db01bfb86","score":"0.54528755","text":"def _fill_pages(self):\n tif = self._new_tif()\n draw = ImageDraw.Draw(tif)\n page_nb = 0\n x_pos = self.start_x\n y_pos = self.start_y\n if self.verbose:\n print('Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\n for true_type in self.true_type_list:\n if x_pos != self.start_x or y_pos != self.start_y:\n x_pos = self.start_x\n y_pos = self.start_y\n self._save_tif(tif, page_nb) # save individual tif\n page_nb += 1\n if self.verbose:\n print('Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\n tif = self._new_tif() # new page\n draw = ImageDraw.Draw(tif) # write on this new page\n for word in self.text:\n word += ' ' # add a space between each word\n wordsize_w, wordsize_h = true_type.getsize(word)\n # Check if word can fit the line, if not, newline\n # if newline, check if the newline fits the page\n # if not, save the current page and create a new one\n if not word_fits_in_line(self.W, x_pos, wordsize_w):\n if newline_fits_in_page(self.H, y_pos, wordsize_h):\n # newline\n x_pos = self.start_x\n y_pos += wordsize_h\n else:\n # newline AND newpage\n x_pos = self.start_x\n y_pos = self.start_y\n self._save_tif(tif, page_nb) # save individual tif\n page_nb += 1\n if self.verbose:\n print(\n 'Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\n tif = self._new_tif() # new page\n draw = ImageDraw.Draw(tif) # write on this new page\n # write word\n for char in word:\n char_w, char_h = true_type.getsize(char) # get character height / width\n offset_x, offset_y = true_type.getoffset(char)\n top_left = (x_pos + offset_x, y_pos + offset_y) # character top-left corner coordinates\n bottom_right = (x_pos + char_w, y_pos + char_h) # character bottom-roght corner coordinates\n draw.text((x_pos, y_pos), char, fill=\"black\", font=true_type) # write character in tif file\n if char != ' ':\n # draw.rectangle([(char_x0, char_y0),(char_x1, char_y1)], outline=\"red\")\n self._write_boxline(char, top_left, bottom_right, self.H, page_nb) # add coordinates to boxfile\n x_pos += char_w\n self._save_tif(tif, page_nb) # save last tif","title":""},{"docid":"386a714133811877b0279e2577fe78e2","score":"0.54449975","text":"def generate(root_url):\n \n result = []\n for c in Conference.objects.order_by('-start')[1:9999]:\n print '*** generating page for:', c\n year = str(c.start.year)\n\n result.append(\n build_page(parts={'title':year,'meta':'','fragment':''}, data=fields, url=root_url+'/'+year))\n\n return result","title":""},{"docid":"d13b1f571c9265ef7f19a9102d5e55c2","score":"0.5439712","text":"def paginate(self, *args, **kwargs):\r\n op_dict = {'ListObjects': self.list_objects}\r\n yield op_dict[self.name](kwargs)","title":""},{"docid":"2a63d69402f59db5cf475640a697079e","score":"0.5438786","text":"def make_pages(plugins, src, dst, layout, **params):\n items = []\n\n # Compute a \"base path\" from the first wildcard in src.\n # This will be used to compose a slug that includes subdirectories.\n basepath = get_base_path(src)\n for src_path in glob.glob(src):\n content = read_content(src_path, basepath)\n\n page_params = dict(params, **content)\n\n if page_params.get('draft') != True:\n # Populate placeholders in content if content-rendering is enabled.\n if page_params.get('render') == True:\n rendered_content = render(plugins, page_params['content'], **page_params)\n page_params['content'] = rendered_content\n content['content'] = rendered_content\n\n items.append(content)\n\n dst_path = render(plugins, dst, **page_params)\n # Certain meta params need processing\n # if page_params.get('guests')\n output = render(plugins, layout, **page_params)\n\n log('Rendering {} => {} ...', src_path, dst_path)\n fwrite(dst_path, output)\n\n comparison_fun = lambda x: datetime.datetime.strptime(x['date'], '%Y-%m-%d')\n return sorted(items, key=comparison_fun, reverse=True)","title":""},{"docid":"f2a7d18560abfacd9b1edcd9b03a63be","score":"0.54367083","text":"def build_menus():\n # call the extract_iterations() method\n # to identify the number of pages to\n # be visited\n iterations = extract_iterations()\n print(\"Building menus...\")\n for page_num in range(1, iterations+1):\n # append page number to base_url\n # to get a specific page\n url = base_url + str(page_num)\n r = requests.get(url)\n data = r.json()\n menus_value = data['menus']\n for menu_entry in menus_value:\n # adding another field 'visited'\n # to each of the nodes, so as to\n # manage their visit status\n menu_entry['visited'] = False\n menus.insert(menu_entry)\n\n # the menus table is now complete\n # (with the additional 'visited' field)","title":""},{"docid":"c7ea197640891059792fe168f1d036ac","score":"0.5434656","text":"def available_pagination_pages(self):\n chunked_posts = list(chunk_list(self.get_posts(), PAGINATION_PAGE_MAX))\n return list(range(1, len(chunked_posts) + 1))","title":""},{"docid":"c1499067b8abdacb86aa2e1e9fc3dab5","score":"0.54340935","text":"def render(self, templ_vars=None):\n if not templ_vars:\n templ_vars = {}\n\n if 'pagination' in self.meta and 'list' in self.meta['pagination']:\n extra_pages = self.paginate()\n else:\n extra_pages = []\n\n if 'page' in templ_vars:\n logging.debug('Found defaulted page data.')\n templ_vars['page'].update(self.meta)\n else:\n templ_vars['page'] = self.meta\n\n if 'pagination' in templ_vars:\n templ_vars['pagination'].update(self.meta['pagination'])\n else:\n templ_vars['pagination'] = self.meta['pagination']\n\n logging.debug('templ_vars.keys(): ' + repr(templ_vars.keys()))\n self.rendered = self.template.render(templ_vars)\n\n logging.debug('extra pages is: ' + repr(extra_pages))\n return extra_pages","title":""},{"docid":"9ae6a1906ad53393e6eededa46b95073","score":"0.54241955","text":"def generate_data(self):\n page_home_id = Page.objects.create(title='home').id\n page_section1_id = Page.objects.create(title='section1', parent_id=page_home_id, url='section1').id\n page_section2_id = Page.objects.create(title='section2', parent_id=page_home_id, url='section2').id\n page_abc_id = Page.objects.create(title='abc', parent_id=page_section1_id, url='abc').id\n Page.objects.create(title='xyz', parent_id=page_abc_id, url='xyz')\n page_def_id = Page.objects.create(title='def', parent_id=page_section2_id, url='/def/').id # absolute url\n page_ghi_id = Page.objects.create(title='ghi', parent_id=page_section2_id, url='ghi').id\n Page.objects.create(title='example', url='http://example.com')\n\n page_def = Page.objects.get(id=page_def_id)\n page_ghi = Page.objects.get(id=page_ghi_id)\n page_ghi.move_to(page_def, 'right')","title":""},{"docid":"a1bbb24ffd7c59cbc31e634298f757f7","score":"0.54222685","text":"def getPages(\n self,\n pageNumSpec,\n refreshConfig=False,\n doRules=True,\n doFilter=True,\n onlyFnRules=False,\n ):\n\n if not self.good:\n print(\"SKIPPING because of config errors\")\n return\n\n fnRules = self.fnRules\n spaces = self.spaces\n columns = self.columns\n\n self.doRules = doRules\n self.doFilter = doFilter\n\n ruleIndex = self.ruleIndex\n rulesApplied = self.rulesApplied\n\n if refreshConfig:\n self.getCharConfig()\n\n for rn in ruleIndex:\n rulesApplied[rn] = collections.Counter()\n\n for (i, pageNum) in enumerate(self.parsePageNums(pageNumSpec)):\n self.pageNum = pageNum\n rep = (\n f\"{i + 1:>4} (page {pageNum:>4})\"\n if pageNum != i + 1\n else (f\"{i + 1:>4}\" + \" \" * 12)\n )\n sys.stdout.write(f\"\\r\\t{rep}\")\n sys.stdout.flush()\n doc = self.doc\n page = doc[pageNum - 1]\n\n theseFnRules = set()\n\n for fnRule in page.getDrawings():\n if RECT in fnRule and fnRule.get(COLOR, None):\n rect = fnRule[RECT]\n width = rect.x1 - rect.x0\n if width > FNRULE_WIDTH:\n theseFnRules.add(int(round(rect.y1)))\n\n fnRules[pageNum] = tuple(sorted(theseFnRules))\n spaces[pageNum] = {}\n columns[pageNum] = {}\n\n if onlyFnRules:\n continue\n\n textPage = page.getTextPage()\n data = textPage.extractRAWDICT()\n self.collectPage(data)","title":""},{"docid":"4d8c1125ccddccd90b01594e1e488a68","score":"0.54105824","text":"def get_paginator(self, search, per_page, orphans=0, **kwargs):\n return self.paginator_class(search, per_page, orphans=orphans, **kwargs)","title":""},{"docid":"8cd4c55d3d8e6a0cbd7ab492c072acbb","score":"0.5408659","text":"def _pagination_iter(self, more_func, *args, **kwargs):\n\n\t\toffset = 0\n\t\tpage = []\n\n\t\twhile True:\n\t\t\tif not page:\n\t\t\t\tpage = more_func(offset, *args, **kwargs)\n\t\t\t\tif not page:\n\t\t\t\t\tbreak\n\n\t\t\t\toffset += len(page)\n\n\t\t\tyield page.pop()","title":""},{"docid":"e55c94dbac8f075e797497f3c215a458","score":"0.5406382","text":"def render_pages(pages, dry=False):\n filenames = [(page, dry) for page in pages]\n open_pool(create_page, filenames)","title":""},{"docid":"1261bd1dbe04abcfaf08bec6f45f087c","score":"0.5386752","text":"def search_page_wise(self):\n results = {}\n for page in range(0,self.pages):\n args = {'q' : self.query,\n 'v' : '1.0',\n 'start' : page,\n 'rsz': RSZ_LARGE,\n 'safe' : SAFE_OFF, \n 'filter' : FILTER_ON, \n }\n q = urlencode(args)\n search_results = self.__urlopen(URL+q)\n data = json.loads((search_results.read() or '').decode(\"utf-8\"))\n urls = []\n if 'responseData' in data:\n for result in data['responseData'].get('results', []):\n if result and 'unescapedUrl' in result:\n url = urllib2.unquote(result['unescapedUrl'])\n urls.append(url) \n else:\n self.logger.error('no responseData key found in response')\n results[page] = urls\n return results","title":""},{"docid":"b41fbf7098052f1e7bec48f62ff125bb","score":"0.5370654","text":"def build_data(api_key, query, begin_date, end_date, page='0'):\n \n dat = get_articles(api_key, query, begin_date, end_date, page='0')\n time.sleep(1)\n articles_data = dat['response']['docs']\n pages = ceil(dat['response']['meta']['hits']/10)\n for page in range(1, int(pages)):\n print('Gathering article page {} of {}'.format(page, pages))\n p = get_articles(api_key, query, begin_date, end_date, page=str(page))\n articles_data.extend(p['response']['docs'])\n time.sleep(0.75)\n \n return(articles_data)","title":""},{"docid":"7f6973135c5a3bc750032e28ccc4d517","score":"0.53705144","text":"def annotation_pages(result: Optional[dict]) -> Optional[str]:\n if result:\n if result[\"total\"] > 0:\n last = urlparse(result[\"last\"])\n last_page = parse_qs(last.query)[\"page\"][0]\n for p in range(0, int(last_page) + 1):\n page = set_query_field(result[\"last\"], field=\"page\", value=p, replace=True)\n yield page\n else:\n return\n else:\n return","title":""},{"docid":"2e850a43224fbc695d52eafc681d3d0a","score":"0.53601015","text":"def get_list(self, *args, **kwargs):\n \n # Set up the mongo connection.\n mongo_object = MongoConnection(db=self.db_name, collection=self.collection_name, auth=self.auth_string)\n connection = mongo_object.connect()\n \n # Get the total count and the number of pages.\n total_count = connection.find(self.query_dict).count()\n \n if (total_count % self.pagination_limit) > 0:\n self.pages = (total_count / self.pagination_limit) + 1\n else:\n self.pages = (total_count / self.pagination_limit)\n \n if self.pages == 1:\n self.page_range = [1]\n else:\n self.page_range = range(1,self.pages+1)\n \n try:\n query_kwargs = self.query_filter['kwargs']\n query_filter = { query_kwargs['mongo_field']: self.kwargs[query_kwargs['url_kwarg']] }\n \n except Exception, e:\n print Exception, e\n query_filter = None\n \n try:\n # Try to get the page from the URL.\n self.page = int(self.request.GET['page'])\n \n # If we can get the page from the URL, calculate the offset.\n \n # First, test if the current page is 0.\n if self.page == 0:\n \n # If so, short circuit the process and return a redirect URL while setting the type as a redirect.\n redirect_url = '%s?page=%s' % (self.request.META['PATH_INFO'], 1)\n return redirect_url, 'redirect'\n \n # Next, check if the current page is less than the total number of pages.\n elif self.page <= self.pages:\n \n # If so, set the offset to the 1-indexed page number * the pagination limit.\n self.offset = (self.page-1) * self.pagination_limit\n \n # Set the previous/next page numbers.\n if self.page == 1 and self.page == self.pages:\n pass\n \n elif self.page == 1:\n self.next_page_number = 2\n \n elif self.page == self.pages:\n self.previous_page_number = self.page - 1\n \n else:\n self.next_page_number = self.page + 1\n self.previous_page_number = self.page - 1\n \n # Finally, check if the current page is greater than the total number of pages.\n elif self.page > self.pages:\n \n # If so, short circuit the process and return a redirect URL while setting the type as a redirect.\n redirect_url = '%s?page=%s' % (self.request.META['PATH_INFO'], self.pages)\n return redirect_url, 'redirect'\n \n # Check if the sort is absent.\n if self.query_sort == None:\n \n # If there's no sort, just send along the connection query.\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset)\n \n # Otherwise, if the sort is present ...\n else:\n \n # ... send along the connection query with the sort attached.\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset).sort(self.query_sort['field'], direction=self.query_sort['direction'])\n \n except Exception, e:\n \n # If there's no page in the URL, set the page to 1.\n self.page = 1\n self.offset = 0\n \n # If so, set the offset to the 1-indexed page number * the pagination limit.\n self.offset = (self.page-1) * self.pagination_limit\n \n # Set the previous/next page numbers.\n if self.page == 1 and self.page == self.pages:\n pass\n \n elif self.page == 1:\n self.next_page_number = 2\n \n elif self.page == self.pages:\n self.previous_page_number = self.page - 1\n \n else:\n self.next_page_number = self.page + 1\n self.previous_page_number = self.page - 1\n \n # Check if the sort is absent.\n if self.query_sort == None:\n \n # If there's no sort, just send along the connection query.\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset)\n \n # Otherwise, if the sort is present ...\n else:\n \n # ... send along the connection query with the sort attached.\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset).sort(self.query_sort['field'], direction=self.query_sort['direction']) \n \n # Set up a query list.\n query_list = []\n \n # Append each item to the query_list.\n for item in query:\n item_dict = {}\n item_as_kwargs = dict(item)\n item_dict.update(**item_as_kwargs)\n item_dict['id'] = str(item['_id'])\n item_dict.pop('_id')\n query_list.append(item_dict)\n \n # Return the query list, and set the type of return to query.\n return query_list, 'query'","title":""},{"docid":"756189bdfc65eac7274551cacdedd181","score":"0.53503144","text":"def aggregate_pages(roots):\n aggregate = {}\n req = requests.get(get_url(0))\n json_data = req.json()\n\n total_pages = int(json_data['pagination']['total']/json_data['pagination']['per_page']) + 1\n page = json_data['pagination']['current_page']\n while page <= total_pages:\n req = requests.get(get_url(page))\n json_data = req.json()\n menus = json_data['menus']\n for menu in menus:\n aggregate[(menu)['id']] = menu['child_ids']\n if not 'parent_id' in menu:\n roots.append((menu)['id'])\n page += 1\n return aggregate","title":""},{"docid":"c0ff301db1885a2ebfb90ad7d1553910","score":"0.5346458","text":"def build_page(self, x_y):\n\n x, y = x_y\n min_x, min_y = x * self.value_delta, y * self.value_delta\n max_x, max_y = min_x + self.value_delta, min_y + self.value_delta\n points = [\n (i, j)\n for i in range(min_x, max_x, self.step_size)\n for j in range(min_y, max_y, self.step_size)\n ]\n altis = RgeAltiClient().fetch_points(points)\n page = np.array(altis).reshape((self.page_size, self.page_size))\n return page","title":""},{"docid":"07f2dbe50cbb6a2325e177e6b9264610","score":"0.53431183","text":"def build_blog_list(query_list, date_range, phantom_path):\n \n driver = webdriver.PhantomJS(executable_path=phantom_path)\n\n buzz_info = []\n list_of_posts = []\n for query in query_list:\n searchword = query[0] \n print (searchword)\n for date in date_range:\n search_url = url_generator(searchword, date[0], date[1], '')\n print(search_url)\n soup = page_source_retriever(driver, search_url) \n num_of_posts, max_num = max_num_finder(soup)\n \n for i in range(max_num):\n current_page = str(10*i+1)\n search_url_by_page = url_generator(searchword, date[0], date[1], current_page)\n soup = page_source_retriever(driver, search_url_by_page)\n \n sections = soup.find_all('li', {'class':'sh_blog_top'})\n for section in sections:\n href = get_hrefs(section)\n date_posted = get_date(section)\n list_of_posts.append([searchword, date_posted,href]) \n buzz_info.append([searchword,date[0],date[1], num_of_posts]) \n\n driver.quit()\n \n return buzz_info, list_of_posts","title":""},{"docid":"6c707e789296591918c1a5bef739489a","score":"0.53393954","text":"def test_scrap_for_pages(self):\n given_url = \"https://danbooru.donmai.us/posts?tags=short_sleeves\"\n \n expected_ouput = [\"https://danbooru.donmai.us/posts?tags=short_sleeves\",\n \"https://danbooru.donmai.us/posts?page=2&tags=short_sleeves\",\n \"https://danbooru.donmai.us/posts?page=3&tags=short_sleeves\",\n \"https://danbooru.donmai.us/posts?page=4&tags=short_sleeves\"]\n \n out = self.dan.scrap_for_pages(given_url, 4)\n for i in range(4):\n self.assertEqual(out[i], expected_ouput[i])","title":""},{"docid":"2da5ba60bab5138e8e5fbc4880e29ccf","score":"0.53393036","text":"def pages(request):\n current_site = get_site()\n\n language = get_language({'country_code': ''})\n page = get_index_page(language)\n\n menuitems = get_main_menuitems('', page)\n\n meta_data = get_metadata(page)\n scripts = get_scripts()\n\n site_content = {\n 'site': current_site,\n 'languages': get_languages(),\n 'current_language': language,\n 'menuitems': menuitems,\n 'page': page,\n 'scripts': scripts,\n 'metadata': meta_data,\n }\n\n if has_other_menu():\n site_content['other_menuitems'] = get_other_menuitems()\n\n return {'site_content': site_content}","title":""},{"docid":"4dcc28780ebec978c5a7f6a484657910","score":"0.53388333","text":"def scrape_pages(self, page):\n\n while True:\n print 'Scraping page ' + str(self.page)\n\n # Format of url is http://www.wyzant.com/New_York_City_tutors.aspx?sl=80075877&sort=27&pagesize=5&pagenum=1 where the 1 at the end is the page number. Increase page number by 1 each iteration of while loop in order to crawl through pages.\n self.current_url = self.region_url + str(self.page)\n ufile = urllib2.urlopen(self.current_url)\n\n if ufile.geturl()[-1:] == self.base_url:\n break # Check for end of list\n\n people = BeautifulSoup(ufile).findAll('div', {'class':'tutorFR hide medium-show'}) # picks out each person on the page\n\n self.scrape_people(people)\n\n self.page += 1 # Next page","title":""},{"docid":"f6c9b705e2eb1fc4f8722c2398d2ab44","score":"0.53386146","text":"def get_all(self, **kwargs):\n\n all_params = ['page_index', 'page_size', 'sort', 'q']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method get_all\" % key\n )\n params[key] = val\n del params['kwargs']\n\n resource_path = '/build-configurations'.replace('{format}', 'json')\n method = 'GET'\n\n path_params = {}\n\n query_params = {}\n if 'page_index' in params:\n query_params['pageIndex'] = params['page_index']\n if 'page_size' in params:\n query_params['pageSize'] = params['page_size']\n if 'sort' in params:\n query_params['sort'] = params['sort']\n if 'q' in params:\n query_params['q'] = params['q']\n\n header_params = {}\n\n form_params = {}\n files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['application/json'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, method,\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=files,\n response_type='BuildConfigurationPage',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","title":""},{"docid":"d3b93b5a9cace236e810063ab2a02783","score":"0.53377736","text":"def pages(per_page: Optional[int] = 1, show_page: Optional[bool] = True) -> Callable:\n def page_source(coro: Callable[[MenuBase, Any], Coroutine[Any, Any, discord.Embed]]) -> Type[menus.ListPageSource]:\n async def create_page_header(self, menu: MenuBase, entry: Any) -> Union[discord.Embed, str]:\n result = await discord.utils.maybe_coroutine(coro, self, menu, entry)\n return menu.generate_page(result, self._max_pages)\n\n def __init__(self, list_pages: Iterable):\n super(self.__class__, self).__init__(list_pages, per_page=per_page)\n kwargs = {\n '__init__': __init__,\n 'format_page': (coro, create_page_header)[show_page]\n }\n return type(coro.__name__, (menus.ListPageSource,), kwargs)\n return page_source","title":""},{"docid":"90c9cf9b266149fcd97052e5a475ca18","score":"0.5332687","text":"def pushFront(self, *pages):\n self.pages = list(pages) + self.pages\n self.evaluatePages()","title":""},{"docid":"4f269c434799334c8e2b01e790d63aa0","score":"0.533135","text":"def loadPage(self):\n\n #depending on currentPage, create the required page \n if self.currentPage == 0:\n self.page = InfoPage(self.screen, data.getChild(self.menuNode, \"info\"), self.poke)\n elif self.currentPage == 1:\n self.page = SkillsPage(self.screen, data.getChild(self.menuNode, \"skills\"), self.poke)\n elif self.currentPage == 2:\n self.page = MovesPage(self.screen, data.getChild(self.menuNode, \"moves\"), self.poke)","title":""}],"string":"[\n {\n \"docid\": \"d4904c1eae48a04ca36df142f4678fa7\",\n \"score\": \"0.71009964\",\n \"text\": \"def generate_all_pages():\\n fetcher = getattr(parent, fetcher_str)\\n\\n first_page = fetcher.get(**kwargs)\\n kwargs['page_size'] = len(first_page)\\n count = fetcher.current_total_count\\n yield first_page\\n\\n if count > kwargs['page_size']:\\n num_pages = int(ceil(float(count) / kwargs['page_size']))\\n for page in range(1, num_pages):\\n kwargs['page'] = page\\n yield fetcher.get(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8eec126418e333d5e2c9d0a81577084\",\n \"score\": \"0.68907714\",\n \"text\": \"def _setup_pages(self):\\n stdkwargs = {\\n 'template': 'nav_playground.html',\\n 'language': 'en',\\n 'published': True,\\n 'in_navigation': True,\\n }\\n page_1 = create_page(\\\"page_1\\\", **stdkwargs) # first page slug is /\\n page_2 = create_page(\\\"page_2\\\", parent=page_1, **stdkwargs)\\n page_3 = create_page(\\\"page_3\\\", parent=page_2, **stdkwargs)\\n page_4 = create_page(\\\"page_4\\\", parent=page_3, **stdkwargs)\\n page_5 = create_page(\\\"page_5\\\", parent=page_1, **stdkwargs)\\n page_6 = create_page(\\\"page_6\\\", parent=page_5, **stdkwargs)\\n return [page_1,\\n page_2,\\n page_3,\\n page_4,\\n page_5,\\n page_6,\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2944cb58de08e6eb89206ca25af8c078\",\n \"score\": \"0.67420703\",\n \"text\": \"def build_pages(n):\\n pages = list()\\n template = jinja_env.get_template('template.html')\\n # i is the current page and we want the current\\n # page to point to the next page and all of the \\n # pages before it if it isn't the last page (page n).\\n # Add 1 to n because we start from 1.\\n for i in range(1, n + 1):\\n links = list(range(1, i))\\n # Add the next page if (i + 1) if it isn't the last page (n + 1)\\n if i + 1 != n + 1:\\n links.append(i + 1)\\n context = dict(title='Page{}'.format(i), links=links)\\n pages.append(template.render(context))\\n\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff579bb2917c912ed7f607b6573dc837\",\n \"score\": \"0.6695581\",\n \"text\": \"def generate(self, **options):\\n from pyClanSphere.application import url_for, get_application, \\\\\\n DEFAULT_THEME_SETTINGS\\n\\n if self._skip_theme_defaults:\\n settings = DEFAULT_THEME_SETTINGS\\n else:\\n settings = get_application().theme.settings\\n\\n def _getopt(name):\\n value = options.pop(name, None)\\n if value is not None:\\n return value\\n return settings['pagination.' + name]\\n normal = _getopt('normal')\\n active = _getopt('active')\\n commata = _getopt('commata')\\n ellipsis = _getopt('ellipsis')\\n threshold = _getopt('threshold')\\n left_threshold = _getopt('left_threshold')\\n right_threshold = _getopt('right_threshold')\\n prev_link = _getopt('prev_link')\\n next_link = _getopt('next_link')\\n gray_prev_link = _getopt('gray_prev_link')\\n gray_next_link = _getopt('gray_next_link')\\n simple = _getopt('simple')\\n if options:\\n raise TypeError('generate() got an unexpected keyword '\\n 'argument %r' % iter(options).next())\\n\\n was_ellipsis = False\\n result = []\\n prev = None\\n next = None\\n get_link = lambda x: url_for(self.endpoint, page=x,\\n per_page=self.per_page,\\n post_id=self.post_id, **self.url_args)\\n\\n if simple:\\n result.append(active % {\\n 'url': get_link(self.page),\\n 'page': self.page\\n })\\n if self.page > 1:\\n prev = self.page - 1\\n if self.page < self.pages:\\n next = self.page + 1\\n else:\\n for num in xrange(1, self.pages + 1):\\n if num == self.page:\\n was_ellipsis = False\\n if num - 1 == self.page:\\n next = num\\n if num + 1 == self.page:\\n prev = num\\n if num <= left_threshold or \\\\\\n num > self.pages - right_threshold or \\\\\\n abs(self.page - num) < threshold:\\n if result and result[-1] != ellipsis:\\n result.append(commata)\\n link = get_link(num)\\n template = num == self.page and active or normal\\n result.append(template % {\\n 'url': link,\\n 'page': num\\n })\\n elif not was_ellipsis:\\n was_ellipsis = True\\n result.append(ellipsis)\\n\\n if next_link:\\n if next is not None:\\n result.append(u' <a href=\\\"%s\\\" class=\\\"next\\\">%s</a>' %\\n (get_link(next), _(u'Next »')))\\n elif gray_next_link:\\n result.append(u' <span class=\\\"disabled next\\\">%s</span>' %\\n _(u'Next »'))\\n if prev_link:\\n if prev is not None:\\n result.insert(0, u'<a href=\\\"%s\\\" class=\\\"prev\\\">%s</a> ' %\\n (get_link(prev), _(u'« Previous')))\\n elif gray_prev_link:\\n result.insert(0, u'<span class=\\\"disabled prev\\\">%s</span> ' %\\n _(u'« Previous'))\\n\\n return Markup(u''.join(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3945a6a1b440b882a1afb72f01db9c9\",\n \"score\": \"0.66792846\",\n \"text\": \"def _create_pages(self):\\n\\n\\t\\tself.sidebar_modules = [] # add extra sidebar links to this\\n\\n\\t\\t# create a page for each class -- recursive for subclasses/subfunctions\\n\\t\\tif len(self.data['classes']) > 0:\\n\\t\\t\\t# start a subelement list\\n\\t\\t\\tself.sidebar_modules.append('<span>Classes:</span>')\\n\\t\\t\\tself.sidebar_modules.append('<ul>')\\n\\n\\t\\t\\tfor class_item in self.data['classes']:\\n\\t\\t\\t\\tself._doc_class(class_item)\\n\\n\\t\\t\\tself.sidebar_modules.append('</ul>')\\n\\n\\n\\t\\t# create a page for each function\\n\\t\\tif len(self.data['functions']) > 0:\\n\\t\\t\\tself.sidebar_modules.append('<span>Functions:</span>')\\n\\t\\t\\tself.sidebar_modules.append('<ul>')\\n\\t\\t\\t\\n\\t\\t\\tfor function_item in self.data['functions']:\\n\\t\\t\\t\\tself._doc_function(function_item)\\n\\n\\t\\t\\tself.sidebar_modules.append('</ul>')\\n\\n\\n\\t\\t# end\\n\\t\\tif len(self.sidebar_modules) > 0:\\n\\t\\t\\tself.sidebar += \\\"<p>Modules:</p>\\\\n\\\"\\n\\t\\t\\tfor item in self.sidebar_modules:\\n\\t\\t\\t\\tself.sidebar += item + \\\"\\\\n\\\"\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba06ff441157039f6355e13e8514b22b\",\n \"score\": \"0.65176576\",\n \"text\": \"def generate_pages(self, node, depth, kwargs={}):\\n for child in node.children.all():\\n \\n \\n page = self.page_class(child, depth, \\n exporter = self,\\n has_children=child.children.exists(),\\n **kwargs)\\n \\n last_page = self.pages[-1] if self.pages else None\\n if last_page:\\n page.prev_page = last_page\\n last_page.next_page = page\\n self.pages.append(page)\\n self.generate_pages(child, depth + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b91fd69ceb1e16b3b51e4070e8c8f63\",\n \"score\": \"0.6274178\",\n \"text\": \"def generate_index_page_list(self, response):\\n total_houses_str = response.css('h2.total.fl span::text').extract_first().strip()\\n page_count = int(int(total_houses_str) / self.default_house_count + 1)\\n for i in range(1, page_count + 1):\\n index_url = self.index_url_template.format(str(i))\\n self.start_urls.append(index_url)\\n return self.initialized()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"171b8daf2ffedafa10102ff9b453c6a8\",\n \"score\": \"0.62389433\",\n \"text\": \"def generate_pages(self, writer):\\n write = partial(writer.write_file,\\n relative_urls=self.settings['RELATIVE_URLS'],\\n override_output=True)\\n\\n # to minimize the number of relative path stuff modification\\n # in writer, articles pass first\\n # self.generate_articles(write)\\n self.generate_period_archives(write)\\n self.generate_direct_templates(write)\\n\\n # and subfolders after that\\n self.generate_categories(write)\\n self.generate_authors(write)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02710a96eb532d702f34996c92665368\",\n \"score\": \"0.62368083\",\n \"text\": \"def page_links(paginator, current, view_name, suffix=\\\"?page=\\\"):\\n num_pages = paginator.num_pages\\n current = int(current)\\n page_numbers = [current]\\n for num in [1, 2, current - 1, current + 1, num_pages, num_pages - 1]:\\n if num not in page_numbers and num > 0 and num <= num_pages:\\n page_numbers.append(num)\\n page_numbers.sort()\\n\\n new_page_numbers = [page_numbers[0]]\\n j = 1\\n for i in range(1, len(page_numbers)):\\n if (page_numbers[i] - page_numbers[i-1] > 1):\\n new_page_numbers.append(0)\\n new_page_numbers.append(page_numbers[i])\\n page_numbers = new_page_numbers\\n\\n return {\\n \\\"page_numbers\\\": page_numbers,\\n \\\"current\\\": current,\\n \\\"view_name\\\": view_name,\\n \\\"suffix\\\": suffix,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f38c0ed0641ed446af60de3e71527e3\",\n \"score\": \"0.621711\",\n \"text\": \"def pagination(context, page):\\n paginator, page_num = page.paginator, page.number\\n \\n pagination_required = paginator.num_pages > 1\\n if not pagination_required:\\n page_range = []\\n else:\\n ON_EACH_SIDE = 3\\n ON_ENDS = 2\\n \\n # If there are 10 or fewer pages, display links to every page.\\n # Otherwise, do some fancy\\n if paginator.num_pages <= 10:\\n page_range = range(paginator.num_pages)\\n else:\\n # Insert \\\"smart\\\" pagination links, so that there are always ON_ENDS\\n # links at either end of the list of pages, and there are always\\n # ON_EACH_SIDE links at either end of the \\\"current page\\\" link.\\n page_range = []\\n if page_num > (ON_EACH_SIDE + ON_ENDS):\\n page_range.extend(range(0, ON_EACH_SIDE - 1))\\n page_range.append(DOT)\\n page_range.extend(range(page_num - ON_EACH_SIDE, page_num + 1))\\n else:\\n page_range.extend(range(0, page_num + 1))\\n if page_num < (paginator.num_pages - ON_EACH_SIDE - ON_ENDS - 1):\\n page_range.extend(range(page_num + 1, page_num + ON_EACH_SIDE + 1))\\n page_range.append(DOT)\\n page_range.extend(range(paginator.num_pages - ON_ENDS, paginator.num_pages))\\n else:\\n page_range.extend(range(page_num + 1, paginator.num_pages))\\n \\n return {\\n 'context': context,\\n 'page': page,\\n 'pagination_required': pagination_required,\\n 'page_range': page_range,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4759640ebab8a5ce27c16d5840907d\",\n \"score\": \"0.61539274\",\n \"text\": \"def test_paginator(self):\\n nine = [1, 2, 3, 4, 5, 6, 7, 8, 9]\\n ten = nine + [10]\\n eleven = ten + [11]\\n tests = (\\n # Each item is two tuples:\\n # First tuple is pagination.Paginator parameters - object_list, per_page,\\n # orphans, and allow_empty_first_page.\\n # Second tuple is resulting pagination.Paginator attributes - count,\\n # num_pages, and page_range.\\n # Ten items, varying orphans, no empty first page.\\n ((ten, 4, 0, False), (10, 3, [1, 2, 3])),\\n ((ten, 4, 1, False), (10, 3, [1, 2, 3])),\\n ((ten, 4, 2, False), (10, 2, [1, 2])),\\n ((ten, 4, 5, False), (10, 2, [1, 2])),\\n ((ten, 4, 6, False), (10, 1, [1])),\\n # Ten items, varying orphans, allow empty first page.\\n ((ten, 4, 0, True), (10, 3, [1, 2, 3])),\\n ((ten, 4, 1, True), (10, 3, [1, 2, 3])),\\n ((ten, 4, 2, True), (10, 2, [1, 2])),\\n ((ten, 4, 5, True), (10, 2, [1, 2])),\\n ((ten, 4, 6, True), (10, 1, [1])),\\n # One item, varying orphans, no empty first page.\\n (([1], 4, 0, False), (1, 1, [1])),\\n (([1], 4, 1, False), (1, 1, [1])),\\n (([1], 4, 2, False), (1, 1, [1])),\\n # One item, varying orphans, allow empty first page.\\n (([1], 4, 0, True), (1, 1, [1])),\\n (([1], 4, 1, True), (1, 1, [1])),\\n (([1], 4, 2, True), (1, 1, [1])),\\n # Zero items, varying orphans, no empty first page.\\n (([], 4, 0, False), (0, 0, [])),\\n (([], 4, 1, False), (0, 0, [])),\\n (([], 4, 2, False), (0, 0, [])),\\n # Zero items, varying orphans, allow empty first page.\\n (([], 4, 0, True), (0, 1, [1])),\\n (([], 4, 1, True), (0, 1, [1])),\\n (([], 4, 2, True), (0, 1, [1])),\\n # Number if items one less than per_page.\\n (([], 1, 0, True), (0, 1, [1])),\\n (([], 1, 0, False), (0, 0, [])),\\n (([1], 2, 0, True), (1, 1, [1])),\\n ((nine, 10, 0, True), (9, 1, [1])),\\n # Number if items equal to per_page.\\n (([1], 1, 0, True), (1, 1, [1])),\\n (([1, 2], 2, 0, True), (2, 1, [1])),\\n ((ten, 10, 0, True), (10, 1, [1])),\\n # Number if items one more than per_page.\\n (([1, 2], 1, 0, True), (2, 2, [1, 2])),\\n (([1, 2, 3], 2, 0, True), (3, 2, [1, 2])),\\n ((eleven, 10, 0, True), (11, 2, [1, 2])),\\n # Number if items one more than per_page with one orphan.\\n (([1, 2], 1, 1, True), (2, 1, [1])),\\n (([1, 2, 3], 2, 1, True), (3, 1, [1])),\\n ((eleven, 10, 1, True), (11, 1, [1])),\\n )\\n for params, output in tests:\\n self.check_paginator(params, output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25bc5a2c73617805c76987b229f332d\",\n \"score\": \"0.6152125\",\n \"text\": \"def get_pages(self):\\n raise NotImplementedError(\\\"get_pages\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a3f64b467a15dde5fbf886ba5c60a2\",\n \"score\": \"0.6131596\",\n \"text\": \"def test_page_indexes(self):\\n ten = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\\n tests = (\\n # Each item is three tuples:\\n # First tuple is pagination.Paginator parameters - object_list, per_page,\\n # orphans, and allow_empty_first_page.\\n # Second tuple is the start and end indexes of the first page.\\n # Third tuple is the start and end indexes of the last page.\\n # Ten items, varying per_page, no orphans.\\n ((ten, 1, 0, True), (1, 1), (10, 10)),\\n ((ten, 2, 0, True), (1, 2), (9, 10)),\\n ((ten, 3, 0, True), (1, 3), (10, 10)),\\n ((ten, 5, 0, True), (1, 5), (6, 10)),\\n # Ten items, varying per_page, with orphans.\\n ((ten, 1, 1, True), (1, 1), (9, 10)),\\n ((ten, 1, 2, True), (1, 1), (8, 10)),\\n ((ten, 3, 1, True), (1, 3), (7, 10)),\\n ((ten, 3, 2, True), (1, 3), (7, 10)),\\n ((ten, 3, 4, True), (1, 3), (4, 10)),\\n ((ten, 5, 1, True), (1, 5), (6, 10)),\\n ((ten, 5, 2, True), (1, 5), (6, 10)),\\n ((ten, 5, 5, True), (1, 10), (1, 10)),\\n # One item, varying orphans, no empty first page.\\n (([1], 4, 0, False), (1, 1), (1, 1)),\\n (([1], 4, 1, False), (1, 1), (1, 1)),\\n (([1], 4, 2, False), (1, 1), (1, 1)),\\n # One item, varying orphans, allow empty first page.\\n (([1], 4, 0, True), (1, 1), (1, 1)),\\n (([1], 4, 1, True), (1, 1), (1, 1)),\\n (([1], 4, 2, True), (1, 1), (1, 1)),\\n # Zero items, varying orphans, allow empty first page.\\n (([], 4, 0, True), (0, 0), (0, 0)),\\n (([], 4, 1, True), (0, 0), (0, 0)),\\n (([], 4, 2, True), (0, 0), (0, 0)),\\n )\\n for params, first, last in tests:\\n self.check_indexes(params, 'first', first)\\n self.check_indexes(params, 'last', last)\\n # When no items and no empty first page, we should get pagination.EmptyPage error.\\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 0, False), 1, None)\\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 1, False), 1, None)\\n assert_raises(pagination.EmptyPage, self.check_indexes, ([], 4, 2, False), 1, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589fcbcf6705d08c3a728772ae0ba330\",\n \"score\": \"0.60818326\",\n \"text\": \"def _get_gen_pages(self, gen, count: int = None, site=None):\\n original_iter = iter(gen)\\n\\n gen = itertools.islice(original_iter, 0, count)\\n\\n gen_pages = list(gen)\\n\\n with suppress(StopIteration):\\n gen_pages.append(next(original_iter))\\n next(original_iter)\\n if not site:\\n site = gen_pages[0].site\\n gen_pages.append(pywikibot.Page(site, '...'))\\n\\n for page in gen_pages:\\n self.assertIsInstance(page, pywikibot.Page)\\n if site:\\n self.assertEqual(page.site, site)\\n\\n return gen_pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10cc4137c45385bea2f3f26c1e656bfd\",\n \"score\": \"0.60435414\",\n \"text\": \"def _get_pages(self, verb, route, params, model):\\n response_list = []\\n\\n if params is None:\\n params = {}\\n params['per_page'] = 100\\n\\n if 'per_page' not in params:\\n params['per_page'] = 100\\n\\n if 'page' in params:\\n response = self._request(verb, route, params)\\n if response.status_code != 200:\\n logging.error(\\\"Error::{}\\\".format(response.text))\\n return None\\n return CBWParser().parse_response(model, response)\\n\\n response = self._request(verb, route, params)\\n\\n if response.status_code != 200:\\n logging.error(\\\"Error::{}\\\".format(response.text))\\n return None\\n\\n response_list.extend(CBWParser().parse_response(model, response))\\n\\n while 'next' in response.links:\\n next_url = urlparse(response.links['next']['url'])\\n params['page'] = parse_qs(next_url .query)['page'][0]\\n response = self._request(verb, route, params)\\n response_list.extend(CBWParser().parse_response(model, response))\\n return response_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447e591b96bdb8e94c521f23429f22b0\",\n \"score\": \"0.59836143\",\n \"text\": \"def make_pages_list(self, pages):\\n pages_list = \\\"const pages = [\\\"\\n for page in pages:\\n pages_list += '\\\\n \\\"pages/{}/index.html,\\\"'.format(page)\\n pages_list += \\\"\\\\n];\\\\n\\\"\\n return pages_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e2b447f17b53e3918c1a541a0be1072\",\n \"score\": \"0.59680253\",\n \"text\": \"def create_pages(self, item, vbox):\\n adapters = self.get_adapters(item)\\n\\n first = True\\n for _, name, adapter in adapters:\\n try:\\n page = adapter.construct()\\n if page is None:\\n continue\\n if first:\\n vbox.pack_start(page, False, True, 0)\\n first = False\\n else:\\n expander = Gtk.Expander()\\n expander.set_use_markup(True)\\n expander.set_label(f\\\"<b>{name}</b>\\\")\\n expander.add(page)\\n expander.show_all()\\n vbox.pack_start(expander, False, True, 0)\\n except Exception:\\n log.error(\\n \\\"Could not construct property page for \\\" + name, exc_info=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f5074c12d95d44c3d668e6e386016e\",\n \"score\": \"0.5956614\",\n \"text\": \"def makePages(self, pageCnt, pn=1, template=None, name=None, w=None, h=None, **kwargs):\\n for n in range(pageCnt): # First page is n + pn\\n # Parent is forced to self.\\n self.newPage(pn=pn+n, template=template, name=name, w=w, h=h, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"909d71dc88d5a2fb030f624f3da1c4a8\",\n \"score\": \"0.5955188\",\n \"text\": \"def _fill_pages(self):\\n\\t\\tif self.pages is None:\\n\\t\\t\\tself.log.info(\\\"Couldn't get any information about the module.\\\")\\n\\t\\t\\treturn\\n\\n\\t\\t# unless this is the index file, it needs to go in the html subdirectory\\n\\t\\tnewpages = {}\\n\\t\\tfor page in self.pages:\\n\\t\\t\\tif page != \\\"/index.html\\\":\\n\\t\\t\\t\\tnewpages[\\\"html/\\\" + page] = self.pages[page]\\n\\t\\tnewpages[\\\"/index.html\\\"] = self.pages[\\\"/index.html\\\"]\\n\\n\\t\\t# grab the updated version of pages\\n\\t\\tself.pages = newpages\\n\\n\\t\\t# iterate through pages and add html frame\\n\\t\\tfor page in self.pages:\\n\\t\\t\\t# dont stick the css into an html file\\n\\t\\t\\tif page == \\\"style.css\\\":\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# see how many subdirectory levels the page is\\n\\t\\t\\tsubdirnum = len(ghtml.get_subdirs(page))\\n\\n\\t\\t\\t# take the page body information and fill a base html page with it\\n\\t\\t\\tself.pages[page] = ghtml.fill_base( title = self.title,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\troot_url = (subdirnum - 1) * '../',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tbody = self.pages[page],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsidebar = self.sidebar,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfooter = self.footer)\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed30a5201ffd260586cf61a83fd26b51\",\n \"score\": \"0.5953235\",\n \"text\": \"def _generatePaginationLinks(\\n self, offsetval, returned_count, number_found, params):\\n\\n doc_limit = self._getDocLimit()\\n pcopy = params.copy()\\n if offsetval - doc_limit >= 0:\\n pcopy['offset'] = offsetval - doc_limit\\n prev_link = '/psearch?' + urllib.urlencode(pcopy)\\n else:\\n prev_link = None\\n if ((offsetval + doc_limit <= self._OFFSET_LIMIT)\\n and (returned_count == doc_limit)\\n and (offsetval + returned_count < number_found)):\\n pcopy['offset'] = offsetval + doc_limit\\n next_link = '/psearch?' + urllib.urlencode(pcopy)\\n else:\\n next_link = None\\n return (prev_link, next_link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a2eccccb520236572d48d29034c924\",\n \"score\": \"0.59350693\",\n \"text\": \"def get_pagination_parameters(request, paginator, paginated):\\n LEADING_PAGE_RANGE_DISPLAYED = TRAILING_PAGE_RANGE_DISPLAYED = 10\\n LEADING_PAGE_RANGE = TRAILING_PAGE_RANGE = 8\\n NUM_PAGES_OUTSIDE_RANGE = 2\\n ADJACENT_PAGES = 4\\n\\n pages = paginator.num_pages\\n page = paginated.number\\n in_leading_range = in_trailing_range = False\\n pages_outside_leading_range = pages_outside_trailing_range = range(0)\\n if pages <= LEADING_PAGE_RANGE_DISPLAYED + NUM_PAGES_OUTSIDE_RANGE + 1:\\n in_leading_range = in_trailing_range = True\\n page_range = [n for n in range(1, pages + 1)]\\n elif page <= LEADING_PAGE_RANGE:\\n in_leading_range = True\\n page_range = [n for n in range(1, LEADING_PAGE_RANGE_DISPLAYED + 1)]\\n pages_outside_leading_range = [\\n n + pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1)]\\n elif page > pages - TRAILING_PAGE_RANGE:\\n in_trailing_range = True\\n page_range = [n for n in range(\\n pages - TRAILING_PAGE_RANGE_DISPLAYED + 1, pages + 1)\\n if n > 0 and n <= pages]\\n pages_outside_trailing_range = [\\n n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE)]\\n else:\\n page_range = [n for n in range(\\n page - ADJACENT_PAGES, page + ADJACENT_PAGES + 1)\\n if n > 0 and n <= pages]\\n pages_outside_leading_range = [\\n n + pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1)]\\n pages_outside_trailing_range = [\\n n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE)]\\n\\n # Now try to retain GET params, except for 'page'\\n params = request.GET.copy()\\n if 'page' in params:\\n del(params['page'])\\n get_params = params.urlencode()\\n prev = paginated.previous_page_number() if paginated.has_previous() else \\\"\\\"\\n\\n return {\\n 'pages': pages,\\n 'page': page,\\n 'previous': prev,\\n 'next': paginated.next_page_number() if paginated.has_next() else \\\"\\\",\\n 'has_previous': paginated.has_previous(),\\n 'has_next': paginated.has_next(),\\n 'page_range': page_range,\\n 'in_leading_range': in_leading_range,\\n 'in_trailing_range': in_trailing_range,\\n 'pages_outside_leading_range': pages_outside_leading_range,\\n 'pages_outside_trailing_range': pages_outside_trailing_range,\\n 'get_params': get_params,\\n 'count': paginator.count,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520186a2ff96f069d212438fd3609c57\",\n \"score\": \"0.5923028\",\n \"text\": \"def loadpages(self):\\n newpages = {}\\n for page in listfiles(PAGES_DIR):\\n (path,ext) = os.path.splitext(page)\\n if ext == '.markdown' or ext == '.html':\\n path = os.path.join(PAGES_DIR,page)\\n (rest,ext) = os.path.splitext(page)\\n # reuse existing pages where we can\\n if rest in self.pages:\\n newpages[rest] = self.pages[rest]\\n else:\\n p = Page(self,path)\\n for decorators in self.page_decorators:\\n decorators.extend_page()\\n newpages[rest] = p\\n self.pages = newpages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e727adfb5953ef2317650250a05855fc\",\n \"score\": \"0.59183145\",\n \"text\": \"def pagination_management(paginator, selected_page):\\n \\n try:\\n objects = paginator.page(selected_page)\\n except PageNotAnInteger:\\n objects = paginator.page(1)\\n except EmptyPage:\\n objects = paginator.page(paginator.num_pages)\\n return objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a1480319a474bc49a85a94c9b8918c\",\n \"score\": \"0.5917556\",\n \"text\": \"def paginator(context, adjacent_pages=3):\\n \\n # Get the base query string to be used in the pagination\\n request = context[\\\"request\\\"]\\n base_query_string_data = {}\\n for (arg_key, arg_value) in request.GET.items():\\n if arg_key != PAGE_NUMBER_KEY:\\n arg_key_encoded = arg_key.encode(\\\"utf8\\\")\\n arg_value_encoded = arg_value.encode(\\\"utf8\\\")\\n base_query_string_data[arg_key_encoded] = arg_value_encoded\\n\\n paginator_object = context[\\\"paginator\\\"]\\n page_count = paginator_object.num_pages\\n current_page = int(request.GET.get(PAGE_NUMBER_KEY, 1))\\n\\n # Get the links to the pages surrounding the current one\\n bottom_page = max(current_page - adjacent_pages, 1)\\n top_page = min(current_page + adjacent_pages, page_count) \\n page_numbers = range(bottom_page, top_page + 1)\\n\\n return {\\n \\\"current_page\\\": current_page,\\n \\\"page_count\\\": page_count,\\n \\\"page_numbers\\\": page_numbers,\\n \\\"show_first\\\": 1 not in page_numbers,\\n \\\"show_last\\\": page_count not in page_numbers,\\n \\\"previous_page_number\\\": current_page - 1,\\n \\\"next_page_number\\\": current_page + 1,\\n \\\"base_query_string_data\\\": base_query_string_data,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac55713cf835e0fc027ac72d982fb9f\",\n \"score\": \"0.5898167\",\n \"text\": \"def __init__(self) -> None:\\n self.pages = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1c702aa080aefce14205cc430d6b4e\",\n \"score\": \"0.5882981\",\n \"text\": \"def pages(self):\\n if self._pages is None:\\n context = layout.LayoutContext(\\n self.enable_hinting, self.style_for, self.get_image_from_uri)\\n self._pages = list(layout.layout_document(\\n context, self.formatting_structure))\\n return self._pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e59978d90f1a146323b0c1397c3242\",\n \"score\": \"0.58063865\",\n \"text\": \"def show_paginator(context, paginator, page, current_page, request, anchor=\\\"\\\", non_grouped_number_of_results = -1 ):\\r\\n \\r\\n adjacent_pages = 3\\r\\n total_wanted = adjacent_pages * 2 + 1\\r\\n min_page_num = max(current_page - adjacent_pages, 1)\\r\\n max_page_num = min(current_page + adjacent_pages + 1, paginator.num_pages + 1)\\r\\n\\r\\n num_items = max_page_num - min_page_num\\r\\n\\r\\n if num_items < total_wanted and num_items < paginator.num_pages:\\r\\n if min_page_num == 1:\\r\\n # we're at the start, increment max_page_num\\r\\n max_page_num += min(total_wanted - num_items, paginator.num_pages - num_items)\\r\\n else:\\r\\n # we're at the end, decrement\\r\\n min_page_num -= min(total_wanted - num_items, paginator.num_pages - num_items)\\r\\n\\r\\n # although paginator objects are 0-based, we use 1-based paging\\r\\n page_numbers = [n for n in range(min_page_num, max_page_num) if n > 0 and n <= paginator.num_pages]\\r\\n \\r\\n params = urllib.urlencode([(key, value.encode('utf-8')) for (key, value) in request.GET.items() if key.lower() != u\\\"page\\\"])\\r\\n \\r\\n if params == \\\"\\\":\\r\\n url = request.path + u\\\"?page=\\\"\\r\\n else:\\r\\n url = request.path + u\\\"?\\\" + params + u\\\"&page=\\\"\\r\\n\\r\\n return {\\r\\n \\\"page\\\": page,\\r\\n \\\"paginator\\\": paginator,\\r\\n \\\"current_page\\\": current_page,\\r\\n \\\"page_numbers\\\": page_numbers,\\r\\n \\\"show_first\\\": 1 not in page_numbers,\\r\\n \\\"show_last\\\": paginator.num_pages not in page_numbers,\\r\\n \\\"url\\\" : url,\\r\\n \\\"media_url\\\": context['media_url'],\\r\\n \\\"anchor\\\": anchor,\\r\\n \\\"non_grouped_number_of_results\\\": non_grouped_number_of_results\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d08fe4ea612250f5ffc97f408d901a\",\n \"score\": \"0.5784741\",\n \"text\": \"def parse(self, response):\\n s = Selector(response)\\n pagination = s.css(\\\".pagination\\\")\\n pagelinks = pagination.xpath('//a[contains(@href, \\\"&page=\\\")]/@href').extract()\\n for pagelink in pagelinks:\\n request = Request(\\n urljoin(self.root, pagelink),\\n callback=self.parse_jobspage,\\n dont_filter=True,\\n )\\n yield request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08998b293ef44108c4d93b015d1e23c7\",\n \"score\": \"0.57800114\",\n \"text\": \"def build_page(self):\\n if self.query:\\n try:\\n page_no = int(self.request.GET.get('page', 1))\\n except (TypeError, ValueError):\\n raise Http404(\\\"Not a valid number for page.\\\")\\n\\n if page_no < 1:\\n raise Http404(\\\"Pages should be 1 or greater.\\\")\\n\\n start_offset = (page_no - 1) * self.results_per_page\\n self.results[start_offset:start_offset + self.results_per_page]\\n\\n paginator = Paginator(self.results, self.results_per_page)\\n\\n try:\\n page = paginator.page(page_no)\\n except InvalidPage:\\n raise Http404(\\\"No such page!\\\")\\n\\n return (paginator, page)\\n else:\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca2361f00c677ef6c375454c72e564a\",\n \"score\": \"0.5776995\",\n \"text\": \"def generator(self):\\n cat = pywikibot.Category(self.site,\\n \\\"%s:%s\\\"\\n % (self.site.namespaces.CATEGORY.custom_name,\\n self.source))\\n gen = pagegenerators.CategorizedPageGenerator(cat)\\n gen = pagegenerators.NamespaceFilterPageGenerator(\\n gen, self.site.namespaces.FILE.custom_name)\\n if not self.filter:\\n gen = pagegenerators.PreloadingGenerator(gen)\\n # gen = pagegenerators.ImageGenerator(gen)\\n for item in gen:\\n page = DUP_Image(item.site, item.title(),\\n not self.filter and item.get() or None,\\n item.editTime())\\n if self.filter and page.hasRefs:\\n continue\\n if not self.filter and not page.validReasons:\\n continue\\n yield page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b332c06add8221f8a4cff58e7ba47f87\",\n \"score\": \"0.57711005\",\n \"text\": \"def gen_pages_for_shop(shop_id):\\n for i in range(1, 2):\\n uri = ''.join((url_shop, shop_id,\\n '/#reviews?ref=pagination&page=', str(i)))\\n print(uri)\\n yield uri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9744d40d4cf44d65760b67dd57b0b76\",\n \"score\": \"0.5763879\",\n \"text\": \"def allpages(self, start=None, prefix=None, namespace='0', filterredir='all',\\n minsize=None, maxsize=None, prtype=None, prlevel=None,\\n limit=None, dir='ascending', filterlanglinks='all', generator=True,\\n end=None):\\n\\n pfx = listing.List.get_prefix('ap', generator)\\n kwargs = dict(listing.List.generate_kwargs(\\n pfx, ('from', start), ('to', end), prefix=prefix,\\n minsize=minsize, maxsize=maxsize, prtype=prtype, prlevel=prlevel,\\n namespace=namespace, filterredir=filterredir, dir=dir,\\n filterlanglinks=filterlanglinks,\\n ))\\n return listing.List.get_list(generator)(self, 'allpages', 'ap',\\n limit=limit, return_values='title',\\n **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a080ddb3221c9160acdd4fc5e48e91d\",\n \"score\": \"0.576003\",\n \"text\": \"def GetPageCount(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e783672e3d5a5d38787eea8e528fafd5\",\n \"score\": \"0.57418764\",\n \"text\": \"def pages(per_page=1, show_page=True):\\n def page_source(coro):\\n async def create_page_header(self, menu, entry):\\n result = await discord.utils.maybe_coroutine(coro, self, menu, entry)\\n return menu.generate_page(result, self._max_pages)\\n\\n def __init__(self, list_pages):\\n super(self.__class__, self).__init__(list_pages, per_page=per_page)\\n kwargs = {\\n '__init__': __init__,\\n 'format_page': (coro, create_page_header)[show_page]\\n }\\n return type(coro.__name__, (menus.ListPageSource,), kwargs)\\n return page_source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119d5b173254b7d2f06149bbaf91528b\",\n \"score\": \"0.57410246\",\n \"text\": \"def paginate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30775c9803d6cbe75b6b3b08ea2e22e7\",\n \"score\": \"0.573554\",\n \"text\": \"def parse_page(self):\\n self._nav_to_header_parent()\\n self._nav_to_posts_parent()\\n self.parse_library()\\n self.parse_last_pg_url()\\n self.compile_records()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b88d6214f0138d7abd58c251665b1f\",\n \"score\": \"0.57333153\",\n \"text\": \"def paginate(self, url):\\n #browser = Browser(\\\"chrome\\\")\\n r = requests.get(\\\"http://localhost:8950/render.html?\\\", params= {\\\"url\\\":url})\\n for i in range(10):\\n try:\\n browser.find_by_css(\\\".PagerLinks > a\\\")[-2].click()\\n pages.append(browser.html)\\n except:\\n break\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86045b49fa09d83bdbcb9154019baf9f\",\n \"score\": \"0.5726248\",\n \"text\": \"def _get_all_pages(cls, start_url, params=None):\\n entries = []\\n # Composing URL in advance since the link to the next page already have the params of the\\n # first request and using `get_soup` with the params argument creates duplicate params\\n next_url = ds_utils.compose_url_get(start_url, params)\\n while next_url:\\n soup = cls._get_soup(next_url)\\n for entry in soup.feed.find_all('entry'):\\n entries.append(cls(cls._parse_entry(entry)))\\n next_link = soup.find('link', rel=\\\"next\\\")\\n next_url = next_link and next_link.attrs.get('href', None)\\n return entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6494149e83f9c5877bfd1cfb278abc61\",\n \"score\": \"0.5702473\",\n \"text\": \"def allpages(self, limit=100, namespace=0,\\n prefix=None, getinfo=None, **evil):\\n params = {\\n 'action': 'query',\\n 'list': 'allpages',\\n 'aplimit': limit,\\n 'apprefix': prefix,\\n 'apnamespace': namespace,\\n }\\n params.update(evil)\\n return self._generate(\\n params,\\n Page,\\n ('query', 'allpages'),\\n getinfo\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f6153aa78f96b69d3086a47417e0c71\",\n \"score\": \"0.5702232\",\n \"text\": \"def querypages(self,tag=None,category=None,maxitems=None,order=None):\\n \\n if self.preview_mode:\\n for p in self.pages.values():\\n p.refresh()\\n \\n pages = []\\n for p in self.pages.values():\\n if tag is not None and tag not in p.tags:\\n continue\\n if category is not None and category != p.category:\\n continue \\n pages.append(p)\\n if order:\\n if order == 'date_created':\\n pages.sort(key=lambda p:p.date_created())\\n pages.reverse()\\n elif order == 'date_modified':\\n pages.sort(key=lambda p:p.date_modified())\\n pages.reverse()\\n elif order == 'title':\\n pages.sort(key=lambda p:p.title)\\n \\n if maxitems is not None:\\n pages = pages[0:maxitems]\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2061573eaf01340af95789722f841b36\",\n \"score\": \"0.5683553\",\n \"text\": \"def _setup_pages(cls, config):\\n from cms.exceptions import NoHomeFound\\n from cms.models import Page\\n from cms.utils import get_language_list\\n from django.conf import settings\\n from django.utils.translation import override\\n\\n app_page = None\\n get_url = False\\n if getattr(settings, \\\"ALDRYN_SEARCH_CMS_PAGE\\\", False):\\n from aldryn_search.search_indexes import TitleIndex\\n\\n def fake_url(self, obj):\\n return \\\"\\\"\\n\\n get_url = TitleIndex.get_url\\n TitleIndex.get_url = fake_url\\n site = Site.objects.get_current()\\n auto_sites = cls.auto_setup.get(\\\"sites\\\", True)\\n if auto_sites is True or site.pk in auto_sites:\\n if getattr(cls, \\\"app_config\\\", False):\\n configs = cls.app_config.objects.all()\\n if not configs.exists():\\n config = cls._create_config()\\n else:\\n config = configs.first()\\n\\n langs = get_language_list(site.pk)\\n if not Page.objects.on_site(site.pk).filter(application_urls=cls.__name__).exists():\\n for lang in langs:\\n with override(lang):\\n if config:\\n if cls.auto_setup[\\\"config_translated_fields\\\"]:\\n cls._create_config_translation(config, lang)\\n namespace = config.namespace\\n elif cls.app_name:\\n namespace = cls.app_name\\n else:\\n namespace = None\\n try:\\n home = Page.objects.get_home(site.pk).get_draft_object()\\n except NoHomeFound:\\n home = None\\n set_home = hasattr(Page, \\\"set_as_homepage\\\")\\n home = cls._create_page(home, lang, cls.auto_setup[\\\"home title\\\"], site=site, set_home=set_home)\\n app_page = cls._create_page(\\n app_page, lang, cls.auto_setup[\\\"page title\\\"], cls.__name__, home, namespace, site=site\\n )\\n if get_url:\\n TitleIndex.get_url = get_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a5511d8c86c396c1982627dcc07741\",\n \"score\": \"0.5668219\",\n \"text\": \"def generatePageImages(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c97dfdf4d85e08e46c436c499df279f\",\n \"score\": \"0.56672287\",\n \"text\": \"def page_range(self):\\n b_size = self._BATCH_SIZE\\n range_start = max(self.page_number() - 5, 0)\\n range_stop = min(max(self.page_number() + 5, 10), self.page_count())\\n\\n pages = []\\n for p in range(range_start, range_stop):\\n b_start = p * b_size\\n pages.append({'number': p + 1,\\n 'url': self._getNavigationURL(b_start)})\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93df7f6c3692b8dcd223550674198c28\",\n \"score\": \"0.56533444\",\n \"text\": \"def get_pages(self):\\n # Get base url\\n base_url = self.chapter_url\\n max_retries = deepcopy(self.max_retries)\\n wait_retry_time = deepcopy(self.wait_time)\\n\\n while True:\\n # Get javascript blocks\\n r = requests.get(base_url, verify=self.verify_https)\\n soup = bsoup.BeautifulSoup(r.text, 'html.parser')\\n scripts = [script for script in soup.find_all(\\n 'script', attrs={'type': 'text/javascript'})]\\n\\n if scripts:\\n # Get total pages\\n for script in scripts:\\n if script.contents:\\n matched_groups = re.search(\\n 'var total_pages\\\\s?=\\\\s?(\\\\d*)\\\\s?;',\\n script.contents[0])\\n if matched_groups:\\n total_pages = int(matched_groups.group(1))\\n break\\n # Get page urls\\n page_urls = [\\\"%s/%d.html\\\" % (self.chapter_url, i + 1)\\n for i in range(total_pages)]\\n page_num = [i + 1 for i in range(total_pages)]\\n pages = list(zip(page_urls, page_num))\\n shuffle(pages)\\n\\n return True, pages\\n\\n elif (max_retries > 0):\\n # Idea from manga_downloader (which in turn was from wget)\\n sleep(uniform(0.5 * wait_retry_time, 1.5 * wait_retry_time))\\n max_retries -= 1\\n else:\\n return False, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ca102245f15657942a1c554de5d3c4\",\n \"score\": \"0.56481713\",\n \"text\": \"def apply_paginators(self, paginators, max_size=None):\\n pagination = {\\n 'limit': self.DEFAULT_LIMIT,\\n 'offset': self.DEFAULT_OFFSET\\n }\\n for paginator in paginators:\\n try:\\n value = int(paginator.value)\\n\\n # Raise if the maximum page size was exceeded.\\n if max_size is not None and \\\\\\n paginator.strategy in ['limit', 'number'] and \\\\\\n value > max_size:\\n raise ValueError('Maximum query size exceeded.')\\n\\n pagination[paginator.strategy] = value\\n except ValueError:\\n raise errors.InvalidPaginationValue(item=paginator)\\n if 'number' in pagination:\\n limit = pagination['limit']\\n pagination['offset'] = pagination['number'] * limit - limit\\n return self.limit(pagination['limit']).offset(pagination['offset'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e3ea1e0415c435376af4176808b171\",\n \"score\": \"0.5643073\",\n \"text\": \"def paginator(parser, token):\\n args = token.split_contents()\\n\\n # Parameters\\n page = args[1]\\n\\n style=None\\n preload=True\\n for a in args[1:]:\\n if a[0:len('style=')] == 'style=':\\n style = a[len('style='):]\\n if a == 'nopreload':\\n preload = False\\n\\n # Read nodelist\\n nodelist = parser.parse(('endpaginate',))\\n parser.delete_first_token()\\n\\n # Return meta node\\n return PaginateNode(page, nodelist, style, preload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a91e8db52121f3910dd0372ba5ef0ba\",\n \"score\": \"0.56149274\",\n \"text\": \"def __build_pages_matrices(self, keypoint_data_pages, label_data_pages):\\n pages = []\\n for keypoints, labels in zip(keypoint_data_pages, label_data_pages):\\n pages.append(self.__build_page_matrix(keypoints, labels))\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892648a228137fec2beaf03eebed7b94\",\n \"score\": \"0.5593152\",\n \"text\": \"def paginate(objects, page_num, items_per_page=20, padding_pages=3):\\n\\n context = {}\\n paginate = Paginator(objects, items_per_page)\\n\\n try:\\n page = paginate.page(page_num)\\n context['current_page'] = int(page_num)\\n except PageNotAnInteger:\\n # If page is not an integer, deliver first page.\\n page = paginate.page(1)\\n context['current_page'] = 1\\n except EmptyPage:\\n # If page is out of range (e.g. 9999), deliver last page of results.\\n page = paginate.page(paginate.num_pages)\\n context['current_page'] = paginate.num_pages\\n\\n context['objects'] = page.object_list\\n context['totalPages'] = paginate.num_pages\\n\\n startPage = max(context['current_page'] - padding_pages - 1, 0)\\n endPage = context['current_page'] + padding_pages\\n context['pages'] = paginate.page_range[startPage:endPage]\\n\\n if page.has_next():\\n context['nextPage'] = page.next_page_number()\\n context['lastPage'] = paginate.num_pages\\n else:\\n context['nextPage'] = False\\n context['lastPage'] = False\\n\\n if page.has_previous():\\n context['prevPage'] = page.previous_page_number()\\n context['firstPage'] = 1\\n else:\\n context['prevPage'] = False\\n context['firstPage'] = False\\n\\n if not 1 in context['pages']:\\n context['pages'].insert(0, '...')\\n if not paginate.num_pages in context['pages']:\\n context['pages'].append('...')\\n\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d667d9743e74f6e1954708ec6ee21f\",\n \"score\": \"0.55680984\",\n \"text\": \"def get_pages(context):\\n # set the current search context\\n if context is None:\\n context = invoke_context()\\n print('invoked context: %s' % context)\\n limit = request.args.get('limit', None)\\n return jsonify([page.to_json() for page in Page.all(index=context, limit=limit)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"162c62851ddce79676488d4c7c8f6bb4\",\n \"score\": \"0.5564884\",\n \"text\": \"def _page0(self, page=None, pageSize=None):\\n yield self.cpage\\n self.npage = page\\n url0 = self.mkUrl(page=self.npage, pageSize=self.pageSize)\\n r = self.fetch(url=url0)\\n self.cpage = self.extract(list=r.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a694f6aebd5ee1b49db857fc03c39ffd\",\n \"score\": \"0.5563814\",\n \"text\": \"def pages(self, pages):\\n\\n self._pages = pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af89ac1720d4323cce7f21ab3477e31\",\n \"score\": \"0.55627286\",\n \"text\": \"def build(self,flowables,onFirstPage=_doNothing, onLaterPages=_doNothing, canvasmaker=canvas.Canvas):\\n self._calc() #in case we changed margins sizes etc\\n frameT = Frame(self.leftMargin, self.bottomMargin, self.width, self.height, id='normal')\\n self.addPageTemplates([PageTemplate(id='First',frames=frameT, onPage=onFirstPage,pagesize=self.pagesize),\\n PageTemplate(id='Later',frames=frameT, onPage=onLaterPages,pagesize=self.pagesize)])\\n if onFirstPage is _doNothing and hasattr(self,'onFirstPage'):\\n self.pageTemplates[0].beforeDrawPage = self.onFirstPage\\n if onLaterPages is _doNothing and hasattr(self,'onLaterPages'):\\n self.pageTemplates[1].beforeDrawPage = self.onLaterPages\\n BaseDocTemplate.build(self,flowables, canvasmaker=canvasmaker)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a72d8ef682f834541ee089f4705d11\",\n \"score\": \"0.5561412\",\n \"text\": \"def __init__(self, prop, **kwargs):\\n QueryGenerator.__init__(self, prop=prop, **kwargs)\\n self.resultkey = \\\"pages\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b628462d31f4bb7c6c233c08d1ef138\",\n \"score\": \"0.55603456\",\n \"text\": \"def main_paginate():\\n import argparse\\n\\n parser = argparse.ArgumentParser(prog='latexpages-paginate',\\n description='Computes and updates start page numbers in compiled parts and contents')\\n\\n parser.add_argument('--version', action='version',\\n version='%%(prog)s %s' % __version__)\\n\\n parser.add_argument('filename',\\n help='INI file configuring the parts and paginate options')\\n\\n args = parser.parse_args()\\n paginate(args.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42815a9482b425f90187ee638272a70c\",\n \"score\": \"0.555997\",\n \"text\": \"def get_main_pages_urls(self) -> Generator[str, None, None]:\\n return (f\\\"{self.main_url}p={page}\\\" for page in range(1, self.pages_number + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c6c1ec39a0fc6ab05220b4de04c594f\",\n \"score\": \"0.55559534\",\n \"text\": \"def _getPages(request, filter_regex=None):\\n filter = None\\n if filter_regex:\\n filter = re.compile(filter_regex).match\\n pages = request.rootpage.getPageList(filter=filter)\\n pages.sort()\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3001056fd0cc3bebf94aa1ef39f1df\",\n \"score\": \"0.55523103\",\n \"text\": \"def pagination(page, objects_caption, query_string=''):\\n\\n if not hasattr(page, 'has_other_pages') or not page.has_other_pages():\\n return {\\n 'page_object': page,\\n }\\n\\n padding = settings.PAGINATION_PADDING\\n tail = settings.PAGINATION_TAIL\\n\\n current_page = page.number\\n pages_count = page.paginator.num_pages + 1\\n\\n # Create a list of pages which are within +/- `padding` items around the current one\\n main_range_from = max(current_page - padding, 1)\\n main_range_to = min(main_range_from + padding * 2 + 1, pages_count)\\n main_range = range(main_range_from, main_range_to)\\n\\n # Create a list of `tail` pages located at the beginning of the navigation block\\n left_range_from = 1\\n left_range_to = min(left_range_from + tail, main_range_from)\\n left_range = range(left_range_from, left_range_to)\\n\\n # If the lists main_range and left_range overlap, join them\\n # to avoid a discontinuity in the navigation block\\n if (main_range[0] - 1) <= tail:\\n main_range = left_range + main_range\\n left_range = []\\n\\n # Same for the right side of the navigation block\\n right_range_to = pages_count\\n right_range_from = max(right_range_to - tail, main_range_to)\\n right_range = range(right_range_from, right_range_to)\\n\\n if (main_range[-1] + 1) >= (pages_count - tail):\\n main_range = main_range + right_range\\n right_range = []\\n\\n if query_string:\\n query_string = '&' + query_string\\n\\n return {\\n 'left_range': left_range,\\n 'main_range': main_range,\\n 'right_range': right_range,\\n 'page': page,\\n 'query_string': query_string,\\n 'objects_caption': objects_caption,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b5eeb014aa59cbc9acd49b4a2c0f902\",\n \"score\": \"0.5549387\",\n \"text\": \"def make_pagination(query, offset, list_name=\\\"objects\\\"):\\n if type(query) == list:\\n objs = len(query)\\n else:\\n objs = query.count()\\n if objs == 0:\\n return dict()\\n if offset >= objs or offset%5 != 0:\\n raise Exception(\\\"Bad request\\\")\\n\\n page = offset/5 + 1\\n prev_pages = list(enumerate(range(0, offset, 5), start=1))\\n next_pages = list(enumerate(range(offset+5, objs, 5), start=page+1))\\n\\n d = {\\n list_name: query[offset:offset+5],\\n \\\"page\\\": page,\\n \\\"next_pages\\\": next_pages,\\n \\\"prev_pages\\\": prev_pages,\\n }\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"686729ab98feb7d50d5d73706ca7b6a3\",\n \"score\": \"0.553319\",\n \"text\": \"def before():\\n g.pages = PagesRepository(pages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48778d836e6ec813e8fcc57ed67db601\",\n \"score\": \"0.5519491\",\n \"text\": \"def process_multi_page_menu(pages): \\n date = get_date(pages[0])\\n lunch = get_meal(multi_page_classify,pages[1])\\n dinner = get_meal(multi_page_classify,pages[2])\\n\\n return {\\\"dinner\\\":dinner,\\\"lunch\\\":lunch,\\\"base_date\\\":date}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb829c1b7d9c5db3149c1c8e838bdf3\",\n \"score\": \"0.55104864\",\n \"text\": \"def add_pagination_context(context): \\n per_page = context[\\\"paginator\\\"].per_page\\n page_obj = context[\\\"page_obj\\\"]\\n context[\\\"showing_first\\\"] = per_page * (page_obj.number - 1) + 1 if page_obj else 0\\n context[\\\"showing_end\\\"] = (\\n context[\\\"showing_first\\\"] + len(page_obj) - 1 if page_obj else 0\\n )\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c7b0d0ae3a88f4475c25398df9344c\",\n \"score\": \"0.55101967\",\n \"text\": \"def __init__(self, generator, g_content=False, **kwargs):\\n # get some basic information about every page generated\\n if 'prop' in kwargs:\\n kwargs['prop'] += \\\"|info|imageinfo|categoryinfo\\\"\\n else:\\n kwargs['prop'] = 'info|imageinfo|categoryinfo'\\n if g_content:\\n # retrieve the current revision\\n kwargs['prop'] += \\\"|revisions\\\"\\n if \\\"rvprop\\\" in kwargs:\\n kwargs[\\\"rvprop\\\"] += \\\"ids|timestamp|flags|comment|user|content\\\"\\n else:\\n kwargs[\\\"rvprop\\\"] = \\\"ids|timestamp|flags|comment|user|content\\\"\\n if \\\"inprop\\\" in kwargs:\\n if \\\"protection\\\" not in kwargs[\\\"inprop\\\"]:\\n kwargs[\\\"inprop\\\"] += \\\"|protection\\\"\\n else:\\n kwargs['inprop'] = 'protection'\\n if \\\"iiprop\\\" in kwargs:\\n kwargs[\\\"iiprop\\\"] += 'timestamp|user|comment|url|size|sha1|metadata'\\n else:\\n kwargs['iiprop'] = 'timestamp|user|comment|url|size|sha1|metadata'\\n QueryGenerator.__init__(self, generator=generator, **kwargs)\\n self.resultkey = \\\"pages\\\" # element to look for in result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2934ef3a85377fbc0a457a8517d0b8\",\n \"score\": \"0.5501297\",\n \"text\": \"def render_pagination(pagination: Optional[Dict[str, Any]]) -> None:\\n if pagination and (int(pagination[\\\"current\\\"]) + 1 <= int(pagination[\\\"total\\\"])):\\n kwargs = {\\\"page\\\": int(pagination[\\\"current\\\"]) + 1}\\n if plugin.settings.exclude_anime == \\\"true\\\" and \\\"start_from\\\" in pagination:\\n kwargs[\\\"start_from\\\"] = pagination[\\\"start_from\\\"]\\n img = plugin.routing.build_icon_path(\\\"next_page\\\")\\n # Next\\n li = plugin.list_item(\\n name=f\\\"[COLOR FFFFF000]{localize(32016)}[/COLOR]\\\", iconImage=img, thumbnailImage=img\\n )\\n url = plugin.routing.add_kwargs_to_url(**kwargs)\\n xbmcplugin.addDirectoryItem(plugin.handle, url, li, True)\\n img = plugin.routing.build_icon_path(\\\"home\\\")\\n home_url = plugin.routing.build_url(\\\"/\\\")\\n # Home\\n li = plugin.list_item(\\n name=f\\\"[COLOR FFFFF000]{localize(32017)}[/COLOR]\\\", iconImage=img, thumbnailImage=img\\n )\\n xbmcplugin.addDirectoryItem(plugin.handle, home_url, li, True)\\n xbmcplugin.endOfDirectory(plugin.handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cdd07cf54df71c53bc87060436c9aac\",\n \"score\": \"0.5495602\",\n \"text\": \"def gen_tasks(self):\\n self.site.scan_posts()\\n yield self.group_task()\\n\\n # Cache classification sets per language for taxonomies where\\n # add_other_languages_variable is True.\\n classification_set_per_lang = {}\\n for taxonomy in self.site.taxonomy_plugins.values():\\n if taxonomy.add_other_languages_variable:\\n lookup = self.site.posts_per_classification[taxonomy.classification_name]\\n cspl = {lang: set(lookup[lang].keys()) for lang in lookup}\\n classification_set_per_lang[taxonomy.classification_name] = cspl\\n\\n # Collect post lists for classification pages and determine whether\\n # they should be generated.\\n post_lists_per_lang = {}\\n for taxonomy in self.site.taxonomy_plugins.values():\\n plpl = {}\\n for lang in self.site.config[\\\"TRANSLATIONS\\\"]:\\n result = {}\\n for classification, posts in self.site.posts_per_classification[taxonomy.classification_name][lang].items():\\n # Filter list\\n filtered_posts = self._filter_list(posts, lang)\\n if len(filtered_posts) == 0 and taxonomy.omit_empty_classifications:\\n generate_list = generate_rss = generate_atom = False\\n else:\\n # Should we create this list?\\n generate_list = taxonomy.should_generate_classification_page(classification, filtered_posts, lang)\\n generate_rss = taxonomy.should_generate_rss_for_classification_page(classification, filtered_posts, lang)\\n generate_atom = taxonomy.should_generate_atom_for_classification_page(classification, filtered_posts, lang)\\n result[classification] = (filtered_posts, generate_list, generate_rss, generate_atom)\\n plpl[lang] = result\\n post_lists_per_lang[taxonomy.classification_name] = plpl\\n\\n # Now generate pages\\n for lang in self.site.config[\\\"TRANSLATIONS\\\"]:\\n # To support that tag and category classifications share the same overview,\\n # we explicitly detect this case:\\n ignore_plugins_for_overview = set()\\n if 'tag' in self.site.taxonomy_plugins and 'category' in self.site.taxonomy_plugins and self.site.link(\\\"tag_index\\\", None, lang) == self.site.link(\\\"category_index\\\", None, lang):\\n # Block both plugins from creating overviews\\n ignore_plugins_for_overview.add(self.site.taxonomy_plugins['tag'])\\n ignore_plugins_for_overview.add(self.site.taxonomy_plugins['category'])\\n for taxonomy in self.site.taxonomy_plugins.values():\\n if not taxonomy.is_enabled(lang):\\n continue\\n # Generate list of classifications (i.e. classification overview)\\n if taxonomy not in ignore_plugins_for_overview:\\n if taxonomy.template_for_classification_overview is not None:\\n for task in self._generate_classification_overview(taxonomy, lang):\\n yield task\\n\\n # Process classifications\\n for classification, (filtered_posts, generate_list, generate_rss, generate_atom) in post_lists_per_lang[taxonomy.classification_name][lang].items():\\n for task in self._generate_classification_page(taxonomy, classification, filtered_posts,\\n generate_list, generate_rss, generate_atom, lang,\\n post_lists_per_lang[taxonomy.classification_name],\\n classification_set_per_lang.get(taxonomy.classification_name)):\\n yield task\\n # In case we are ignoring plugins for overview, we must have a collision for\\n # tags and categories. Handle this special case with extra code.\\n if ignore_plugins_for_overview:\\n for task in self._generate_tag_and_category_overview(self.site.taxonomy_plugins['tag'], self.site.taxonomy_plugins['category'], lang):\\n yield task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd4dd2f9827651f7a5ae593b6b6026e5\",\n \"score\": \"0.54944646\",\n \"text\": \"def scraping_pages_links(self, void: str) -> List[str]:\\r\\n # Create link\\r\\n link = self.page + void\\r\\n\\r\\n try:\\r\\n # Read website, encode and create HTML parser\\r\\n soup_pages = self.enterPage_parser(link)\\r\\n\\r\\n # Extract max page number\\r\\n res = soup_pages.findAll('script')\\r\\n lengths = [len(str(el)) for el in res]\\r\\n json_object = json.loads(res[lengths.index(max(lengths))].contents[0])\\r\\n max_page_num = json_object[\\\"props\\\"][\\\"pageProps\\\"][\\\"tracking\\\"]['listing']['page_count']\\r\\n\\r\\n # Create all pages links\\r\\n all_pages_links = [link + '?page=' + str(page) for page in range(1, max_page_num + 1)]\\r\\n\\r\\n except:\\r\\n all_pages_links = link\\r\\n\\r\\n return all_pages_links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926c4f2c038863745b487488d4021e1f\",\n \"score\": \"0.54927444\",\n \"text\": \"def paginator(context, adjacent_pages=2):\\n if not 'page' in context:\\n # improper use of paginator tag, bail out\\n return {}\\n\\n page = context['page']\\n page_no = int(page.number)\\n\\n s = max(1, page_no - adjacent_pages - max(0, page_no+adjacent_pages-page.paginator.num_pages))\\n page_numbers = range(s, min(page.paginator.num_pages, s+2*adjacent_pages)+1)\\n\\n return {\\n 'page': page,\\n 'results_per_page': context['results_per_page'],\\n 'page_numbers': page_numbers,\\n 'show_first': 1 not in page_numbers,\\n 'show_last': page.paginator.num_pages not in page_numbers,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d0c81e3c4385fe99299f1a54e5735b\",\n \"score\": \"0.54913855\",\n \"text\": \"def load_pages(self):\\n\\t\\t#A dict of dicts to store the contents of _ directories. The first-\\n\\t\\t# level key is the parent directory of the _ dir; the second-level\\n\\t\\t# key is the name of the _ dir without the '_'. The value is a\\n\\t\\t# list of rendered pages.\\n\\t\\textravars = defaultdict(dict)\\n\\n\\t\\t#Walk through the directory bottom-up so that we get any\\n\\t\\t# subdirectories with extra variables first.\\n\\t\\tfor root, dirs, files in os.walk(self.options['content_dir'],\\n\\t\\t\\ttopdown=False, followlinks=True):\\n\\t\\t\\t#Per-directory environment to get templates from current\\n\\t\\t\\t# directory or its parents\\n\\t\\t\\tenvironment = jinja2.Environment(\\n\\t\\t\\t\\tloader=ParentLoader(root, stop=self.root,\\n\\t\\t\\t\\t\\tdefault=self.options['default_template']),\\n\\t\\t\\t\\textensions=self.options.get('jinja2_extensions', []))\\n\\n\\t\\t\\t#Add any filters specified in options\\n\\t\\t\\tenvironment.filters.update(self.options.get('jinja2_filters', {}))\\n\\n\\t\\t\\troot_basename = os.path.basename(root) #The name only of the current directory\\n\\t\\t\\tparent_dir = os.path.split(root)[0] #The full path of the parent directory\\n\\n\\t\\t\\t#If the subdirectory starts with _, read and parse all .md files\\n\\t\\t\\t# within it and add them as variables with the directory name\\n\\t\\t\\t# (without the _).\\n\\t\\t\\tin_subfiles = root_basename.startswith('_')\\n\\t\\t\\tif in_subfiles:\\n\\t\\t\\t\\troot_basename = root_basename[1:]\\n\\t\\t\\t\\textravars[parent_dir][root_basename] = [] #Drop the _\\n\\n\\t\\t\\t#Go through each file in the current directory (root)\\n\\t\\t\\tfor f in files:\\n\\t\\t\\t\\trname, ext = os.path.splitext(f)\\n\\n\\t\\t\\t\\t#Skip hidden and template files\\n\\t\\t\\t\\tif f.startswith('.') or ext == '.jinja':\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t#Figure out where it should go in output for files to be\\n\\t\\t\\t\\t# copied or written\\n\\t\\t\\t\\tdestroot = os.path.relpath(\\n\\t\\t\\t\\t\\tos.path.join(\\n\\t\\t\\t\\t\\t\\tos.path.join(parent_dir, root_basename) if in_subfiles else root,\\n\\t\\t\\t\\t\\t\\trname), #Filename with no extension\\n\\t\\t\\t\\t\\tstart=self.options['content_dir'])\\n\\t\\t\\t\\tif destroot.split(os.path.sep, 1)[0] == self.options['root_subdir']:\\n\\t\\t\\t\\t\\tdestroot = os.path.relpath(destroot, start=self.options['root_subdir'])\\n\\n\\t\\t\\t\\t#If it's a .md, we should render it\\n\\t\\t\\t\\tif ext == '.md':\\n\\t\\t\\t\\t\\there = os.getcwd()\\n\\t\\t\\t\\t\\tos.chdir(root) #Be sure we're in root for relative paths\\n\\t\\t\\t\\t\\tmeta = self.render(f, environment, extravars.get(root, {}))\\n\\t\\t\\t\\t\\tos.chdir(here)\\n\\t\\t\\t\\t\\t#If we're in a _ dir, put the rendered file in extravars,\\n\\t\\t\\t\\t\\t# otherwise write the rendered result to disk\\n\\t\\t\\t\\t\\tif in_subfiles:\\n\\t\\t\\t\\t\\t\\textravars[parent_dir][root_basename].append(meta)\\n\\t\\t\\t\\t\\telif meta['content']:\\n\\t\\t\\t\\t\\t\\t\\tself.write(meta['content'], destroot + '.html')\\n\\n\\t\\t\\t\\t#Otherwise copy it\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsrc = os.path.join(root, f)\\n\\t\\t\\t\\t\\tdst = os.path.join(self.options['output_dir'], destroot + ext)\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tos.makedirs(os.path.dirname(dst))\\n\\t\\t\\t\\t\\texcept OSError as e:\\n\\t\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\t\\tlogging.info(\\\"Copy file {0} to {1}\\\".format(src, dst))\\n\\t\\t\\t\\t\\tshutil.copy(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"209d366f074719763b074282c9c3a85b\",\n \"score\": \"0.54750156\",\n \"text\": \"def populate(self):\\n # type: () -> None\\n self.container.clear_children()\\n self.recent_pages = GUI.PagedContainer((20, 0), width=self.width - 40, height=60, hideControls=True)\\n self.recent_pages.add_page(self.recent_pages.generate_page())\\n self.btn_left = GUI.Button((0, 0), \\\"<\\\", state.color_palette.get_color(GUI.Palette.accent),\\n state.color_palette.get_color(GUI.Palette.item), 20, width=20, height=60,\\n onClick=self.recent_pages.page_left)\\n self.btn_right = GUI.Button((self.width - 20, 0), \\\">\\\", state.color_palette.get_color(GUI.Palette.accent),\\n state.color_palette.get_color(GUI.Palette.item), 20, width=20, height=60,\\n onClick=self.recent_pages.page_right)\\n per_app = (self.width - 40) / 4\\n current = 0\\n for app in state.application_list.active_applications:\\n if app is not state.active_application and app.parameters.get(\\\"persist\\\", True) and app.name != \\\"home\\\":\\n if current >= 4:\\n current = 0\\n self.recent_pages.add_page(self.recent_pages.generate_page())\\n cont = GUI.Container((per_app * current, 0), transparent=True, width=per_app, height=self.height,\\n border=1, borderColor=state.color_palette.get_color(GUI.Palette.item),\\n onClick=self.activate, onClickData=(app,), onLongClick=self.close_ask,\\n onLongClickData=(app,))\\n cont.SKIP_CHILD_CHECK = True\\n icon = app.getIcon()\\n if not icon:\\n icon = state.icons.get_loaded_icon(\\\"unknown\\\")\\n img = GUI.Image((0, 5), surface=icon)\\n img.position[0] = GUI.get_centered_coordinates(img, cont)[0]\\n name = GUI.Text((0, 45), app.title, state.color_palette.get_color(GUI.Palette.item), 10)\\n name.position[0] = GUI.get_centered_coordinates(name, cont)[0]\\n cont.add_child(img)\\n cont.add_child(name)\\n self.recent_pages.add_child(cont)\\n current += 1\\n if len(self.recent_pages.get_page(0).child_components) == 0:\\n notxt = GUI.Text((0, 0), \\\"No Recent Apps\\\", state.color_palette.get_color(GUI.Palette.item), 16)\\n notxt.position = GUI.get_centered_coordinates(notxt, self.recent_pages.get_page(0))\\n self.recent_pages.add_child(notxt)\\n self.recent_pages.go_to_page()\\n self.add_child(self.recent_pages)\\n self.add_child(self.btn_left)\\n self.add_child(self.btn_right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3718ba5b83568394a4038dea4d13f7dc\",\n \"score\": \"0.5471489\",\n \"text\": \"def get_all(parent, fetcher_str, **kwargs):\\n # overwrite parameters that may interfere\\n kwargs.update({'commit': True, 'page': 0})\\n\\n def generate_all_pages():\\n \\\"\\\"\\\"generator for all pages.\\\"\\\"\\\"\\n fetcher = getattr(parent, fetcher_str)\\n\\n first_page = fetcher.get(**kwargs)\\n kwargs['page_size'] = len(first_page)\\n count = fetcher.current_total_count\\n yield first_page\\n\\n if count > kwargs['page_size']:\\n num_pages = int(ceil(float(count) / kwargs['page_size']))\\n for page in range(1, num_pages):\\n kwargs['page'] = page\\n yield fetcher.get(**kwargs)\\n\\n # flatten the pages into one generator for just the objects\\n return chain.from_iterable(generate_all_pages())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26a017ac4f628e4de52230fc64d2684\",\n \"score\": \"0.5469787\",\n \"text\": \"def get_pages(self) -> List[str]:\\r\\n\\r\\n # Scrape all links for voivodeships in self.voivodeship variable\\r\\n results_pages = self.scraping_all_links(self.scraping_pages_links, self.voivodeships)\\r\\n results_pages = self.flatten(results_pages)\\r\\n\\r\\n # Verify weather there are some missing oferts\\r\\n missed_pages = [oferts for oferts in results_pages if \\\"page\\\" not in oferts]\\r\\n\\r\\n if len(missed_pages) != 0:\\r\\n results_pages = self.flatten(\\r\\n [properties for properties in results_pages if (properties != None) & (\\\"page\\\" in properties)])\\r\\n\\r\\n # Try to scrape missing links once again and join them with scraped before\\r\\n missed_pages_list = self.missed_links_all(missed_offers=missed_pages, func=self.missed_offers_pages,\\r\\n details=False, offers=False,\\r\\n func_pages_or_offers=self.scraping_pages_links)\\r\\n results_pages = self.join_missed_with_scraped(missed_pages_list, results_pages)\\r\\n\\r\\n return self.flatten(results_pages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3bbcd891336824ba4d54606f47acf42\",\n \"score\": \"0.54651093\",\n \"text\": \"def page(self, *criterion, limit=None, offset=None):\\n raise NotImplementedError(\\\"Subclasses should overwrite this Method.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3bbcd891336824ba4d54606f47acf42\",\n \"score\": \"0.54651093\",\n \"text\": \"def page(self, *criterion, limit=None, offset=None):\\n raise NotImplementedError(\\\"Subclasses should overwrite this Method.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa7e5b96053b854a6db20fdd919c089\",\n \"score\": \"0.54617846\",\n \"text\": \"def chunked_pages(self, tags_list: list, n: int):\\n pages = []\\n for chunk in self.chunk_list(tags_list, n):\\n content = ''\\n for col in self.chunk_list(chunk, 3):\\n # the columns wont have the perfect number of elements every time, we need to append spaces if\\n # the list entries is less then the number of columns\\n while len(col) < 3:\\n col.append(' ')\\n\\n # Concatenate the formatted column string to the page content string\\n content += \\\"{: <20} {: <20} {: <20}\\\\n\\\".format(*col)\\n\\n # Append the content string to the list of pages to send to the paginator\\n # Marked as a code block to ensure a monospaced font and even columns\\n pages.append(f'```{content}```')\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6176b69bdc6ee17ebf18f4d304fb119d\",\n \"score\": \"0.545429\",\n \"text\": \"def make_poss(current_page):\\n print \\\"fetching possibilities\\\"\\n possibilities = []\\n\\n for title in current_page.links():\\n try:\\n if debug: print \\\"fetching \\\" + str(title) \\n possibilities.append(Page(title))\\n except Exception, msg:\\n if debug: print \\\"caught an error: \\\" + str(msg)\\n return possibilities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec86b4f5ccf3ba9711e952db01bfb86\",\n \"score\": \"0.54528755\",\n \"text\": \"def _fill_pages(self):\\n tif = self._new_tif()\\n draw = ImageDraw.Draw(tif)\\n page_nb = 0\\n x_pos = self.start_x\\n y_pos = self.start_y\\n if self.verbose:\\n print('Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\\n for true_type in self.true_type_list:\\n if x_pos != self.start_x or y_pos != self.start_y:\\n x_pos = self.start_x\\n y_pos = self.start_y\\n self._save_tif(tif, page_nb) # save individual tif\\n page_nb += 1\\n if self.verbose:\\n print('Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\\n tif = self._new_tif() # new page\\n draw = ImageDraw.Draw(tif) # write on this new page\\n for word in self.text:\\n word += ' ' # add a space between each word\\n wordsize_w, wordsize_h = true_type.getsize(word)\\n # Check if word can fit the line, if not, newline\\n # if newline, check if the newline fits the page\\n # if not, save the current page and create a new one\\n if not word_fits_in_line(self.W, x_pos, wordsize_w):\\n if newline_fits_in_page(self.H, y_pos, wordsize_h):\\n # newline\\n x_pos = self.start_x\\n y_pos += wordsize_h\\n else:\\n # newline AND newpage\\n x_pos = self.start_x\\n y_pos = self.start_y\\n self._save_tif(tif, page_nb) # save individual tif\\n page_nb += 1\\n if self.verbose:\\n print(\\n 'Generating individual tif image %s' % (self.indiv_page_prefix + str(page_nb) + '.tif'))\\n tif = self._new_tif() # new page\\n draw = ImageDraw.Draw(tif) # write on this new page\\n # write word\\n for char in word:\\n char_w, char_h = true_type.getsize(char) # get character height / width\\n offset_x, offset_y = true_type.getoffset(char)\\n top_left = (x_pos + offset_x, y_pos + offset_y) # character top-left corner coordinates\\n bottom_right = (x_pos + char_w, y_pos + char_h) # character bottom-roght corner coordinates\\n draw.text((x_pos, y_pos), char, fill=\\\"black\\\", font=true_type) # write character in tif file\\n if char != ' ':\\n # draw.rectangle([(char_x0, char_y0),(char_x1, char_y1)], outline=\\\"red\\\")\\n self._write_boxline(char, top_left, bottom_right, self.H, page_nb) # add coordinates to boxfile\\n x_pos += char_w\\n self._save_tif(tif, page_nb) # save last tif\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386a714133811877b0279e2577fe78e2\",\n \"score\": \"0.54449975\",\n \"text\": \"def generate(root_url):\\n \\n result = []\\n for c in Conference.objects.order_by('-start')[1:9999]:\\n print '*** generating page for:', c\\n year = str(c.start.year)\\n\\n result.append(\\n build_page(parts={'title':year,'meta':'','fragment':''}, data=fields, url=root_url+'/'+year))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13b1f571c9265ef7f19a9102d5e55c2\",\n \"score\": \"0.5439712\",\n \"text\": \"def paginate(self, *args, **kwargs):\\r\\n op_dict = {'ListObjects': self.list_objects}\\r\\n yield op_dict[self.name](kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a63d69402f59db5cf475640a697079e\",\n \"score\": \"0.5438786\",\n \"text\": \"def make_pages(plugins, src, dst, layout, **params):\\n items = []\\n\\n # Compute a \\\"base path\\\" from the first wildcard in src.\\n # This will be used to compose a slug that includes subdirectories.\\n basepath = get_base_path(src)\\n for src_path in glob.glob(src):\\n content = read_content(src_path, basepath)\\n\\n page_params = dict(params, **content)\\n\\n if page_params.get('draft') != True:\\n # Populate placeholders in content if content-rendering is enabled.\\n if page_params.get('render') == True:\\n rendered_content = render(plugins, page_params['content'], **page_params)\\n page_params['content'] = rendered_content\\n content['content'] = rendered_content\\n\\n items.append(content)\\n\\n dst_path = render(plugins, dst, **page_params)\\n # Certain meta params need processing\\n # if page_params.get('guests')\\n output = render(plugins, layout, **page_params)\\n\\n log('Rendering {} => {} ...', src_path, dst_path)\\n fwrite(dst_path, output)\\n\\n comparison_fun = lambda x: datetime.datetime.strptime(x['date'], '%Y-%m-%d')\\n return sorted(items, key=comparison_fun, reverse=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2a7d18560abfacd9b1edcd9b03a63be\",\n \"score\": \"0.54367083\",\n \"text\": \"def build_menus():\\n # call the extract_iterations() method\\n # to identify the number of pages to\\n # be visited\\n iterations = extract_iterations()\\n print(\\\"Building menus...\\\")\\n for page_num in range(1, iterations+1):\\n # append page number to base_url\\n # to get a specific page\\n url = base_url + str(page_num)\\n r = requests.get(url)\\n data = r.json()\\n menus_value = data['menus']\\n for menu_entry in menus_value:\\n # adding another field 'visited'\\n # to each of the nodes, so as to\\n # manage their visit status\\n menu_entry['visited'] = False\\n menus.insert(menu_entry)\\n\\n # the menus table is now complete\\n # (with the additional 'visited' field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ea197640891059792fe168f1d036ac\",\n \"score\": \"0.5434656\",\n \"text\": \"def available_pagination_pages(self):\\n chunked_posts = list(chunk_list(self.get_posts(), PAGINATION_PAGE_MAX))\\n return list(range(1, len(chunked_posts) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1499067b8abdacb86aa2e1e9fc3dab5\",\n \"score\": \"0.54340935\",\n \"text\": \"def render(self, templ_vars=None):\\n if not templ_vars:\\n templ_vars = {}\\n\\n if 'pagination' in self.meta and 'list' in self.meta['pagination']:\\n extra_pages = self.paginate()\\n else:\\n extra_pages = []\\n\\n if 'page' in templ_vars:\\n logging.debug('Found defaulted page data.')\\n templ_vars['page'].update(self.meta)\\n else:\\n templ_vars['page'] = self.meta\\n\\n if 'pagination' in templ_vars:\\n templ_vars['pagination'].update(self.meta['pagination'])\\n else:\\n templ_vars['pagination'] = self.meta['pagination']\\n\\n logging.debug('templ_vars.keys(): ' + repr(templ_vars.keys()))\\n self.rendered = self.template.render(templ_vars)\\n\\n logging.debug('extra pages is: ' + repr(extra_pages))\\n return extra_pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae6a1906ad53393e6eededa46b95073\",\n \"score\": \"0.54241955\",\n \"text\": \"def generate_data(self):\\n page_home_id = Page.objects.create(title='home').id\\n page_section1_id = Page.objects.create(title='section1', parent_id=page_home_id, url='section1').id\\n page_section2_id = Page.objects.create(title='section2', parent_id=page_home_id, url='section2').id\\n page_abc_id = Page.objects.create(title='abc', parent_id=page_section1_id, url='abc').id\\n Page.objects.create(title='xyz', parent_id=page_abc_id, url='xyz')\\n page_def_id = Page.objects.create(title='def', parent_id=page_section2_id, url='/def/').id # absolute url\\n page_ghi_id = Page.objects.create(title='ghi', parent_id=page_section2_id, url='ghi').id\\n Page.objects.create(title='example', url='http://example.com')\\n\\n page_def = Page.objects.get(id=page_def_id)\\n page_ghi = Page.objects.get(id=page_ghi_id)\\n page_ghi.move_to(page_def, 'right')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1bbb24ffd7c59cbc31e634298f757f7\",\n \"score\": \"0.54222685\",\n \"text\": \"def getPages(\\n self,\\n pageNumSpec,\\n refreshConfig=False,\\n doRules=True,\\n doFilter=True,\\n onlyFnRules=False,\\n ):\\n\\n if not self.good:\\n print(\\\"SKIPPING because of config errors\\\")\\n return\\n\\n fnRules = self.fnRules\\n spaces = self.spaces\\n columns = self.columns\\n\\n self.doRules = doRules\\n self.doFilter = doFilter\\n\\n ruleIndex = self.ruleIndex\\n rulesApplied = self.rulesApplied\\n\\n if refreshConfig:\\n self.getCharConfig()\\n\\n for rn in ruleIndex:\\n rulesApplied[rn] = collections.Counter()\\n\\n for (i, pageNum) in enumerate(self.parsePageNums(pageNumSpec)):\\n self.pageNum = pageNum\\n rep = (\\n f\\\"{i + 1:>4} (page {pageNum:>4})\\\"\\n if pageNum != i + 1\\n else (f\\\"{i + 1:>4}\\\" + \\\" \\\" * 12)\\n )\\n sys.stdout.write(f\\\"\\\\r\\\\t{rep}\\\")\\n sys.stdout.flush()\\n doc = self.doc\\n page = doc[pageNum - 1]\\n\\n theseFnRules = set()\\n\\n for fnRule in page.getDrawings():\\n if RECT in fnRule and fnRule.get(COLOR, None):\\n rect = fnRule[RECT]\\n width = rect.x1 - rect.x0\\n if width > FNRULE_WIDTH:\\n theseFnRules.add(int(round(rect.y1)))\\n\\n fnRules[pageNum] = tuple(sorted(theseFnRules))\\n spaces[pageNum] = {}\\n columns[pageNum] = {}\\n\\n if onlyFnRules:\\n continue\\n\\n textPage = page.getTextPage()\\n data = textPage.extractRAWDICT()\\n self.collectPage(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8c1125ccddccd90b01594e1e488a68\",\n \"score\": \"0.54105824\",\n \"text\": \"def get_paginator(self, search, per_page, orphans=0, **kwargs):\\n return self.paginator_class(search, per_page, orphans=orphans, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd4c55d3d8e6a0cbd7ab492c072acbb\",\n \"score\": \"0.5408659\",\n \"text\": \"def _pagination_iter(self, more_func, *args, **kwargs):\\n\\n\\t\\toffset = 0\\n\\t\\tpage = []\\n\\n\\t\\twhile True:\\n\\t\\t\\tif not page:\\n\\t\\t\\t\\tpage = more_func(offset, *args, **kwargs)\\n\\t\\t\\t\\tif not page:\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\toffset += len(page)\\n\\n\\t\\t\\tyield page.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55c94dbac8f075e797497f3c215a458\",\n \"score\": \"0.5406382\",\n \"text\": \"def render_pages(pages, dry=False):\\n filenames = [(page, dry) for page in pages]\\n open_pool(create_page, filenames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261bd1dbe04abcfaf08bec6f45f087c\",\n \"score\": \"0.5386752\",\n \"text\": \"def search_page_wise(self):\\n results = {}\\n for page in range(0,self.pages):\\n args = {'q' : self.query,\\n 'v' : '1.0',\\n 'start' : page,\\n 'rsz': RSZ_LARGE,\\n 'safe' : SAFE_OFF, \\n 'filter' : FILTER_ON, \\n }\\n q = urlencode(args)\\n search_results = self.__urlopen(URL+q)\\n data = json.loads((search_results.read() or '').decode(\\\"utf-8\\\"))\\n urls = []\\n if 'responseData' in data:\\n for result in data['responseData'].get('results', []):\\n if result and 'unescapedUrl' in result:\\n url = urllib2.unquote(result['unescapedUrl'])\\n urls.append(url) \\n else:\\n self.logger.error('no responseData key found in response')\\n results[page] = urls\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b41fbf7098052f1e7bec48f62ff125bb\",\n \"score\": \"0.5370654\",\n \"text\": \"def build_data(api_key, query, begin_date, end_date, page='0'):\\n \\n dat = get_articles(api_key, query, begin_date, end_date, page='0')\\n time.sleep(1)\\n articles_data = dat['response']['docs']\\n pages = ceil(dat['response']['meta']['hits']/10)\\n for page in range(1, int(pages)):\\n print('Gathering article page {} of {}'.format(page, pages))\\n p = get_articles(api_key, query, begin_date, end_date, page=str(page))\\n articles_data.extend(p['response']['docs'])\\n time.sleep(0.75)\\n \\n return(articles_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6973135c5a3bc750032e28ccc4d517\",\n \"score\": \"0.53705144\",\n \"text\": \"def annotation_pages(result: Optional[dict]) -> Optional[str]:\\n if result:\\n if result[\\\"total\\\"] > 0:\\n last = urlparse(result[\\\"last\\\"])\\n last_page = parse_qs(last.query)[\\\"page\\\"][0]\\n for p in range(0, int(last_page) + 1):\\n page = set_query_field(result[\\\"last\\\"], field=\\\"page\\\", value=p, replace=True)\\n yield page\\n else:\\n return\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e850a43224fbc695d52eafc681d3d0a\",\n \"score\": \"0.53601015\",\n \"text\": \"def get_list(self, *args, **kwargs):\\n \\n # Set up the mongo connection.\\n mongo_object = MongoConnection(db=self.db_name, collection=self.collection_name, auth=self.auth_string)\\n connection = mongo_object.connect()\\n \\n # Get the total count and the number of pages.\\n total_count = connection.find(self.query_dict).count()\\n \\n if (total_count % self.pagination_limit) > 0:\\n self.pages = (total_count / self.pagination_limit) + 1\\n else:\\n self.pages = (total_count / self.pagination_limit)\\n \\n if self.pages == 1:\\n self.page_range = [1]\\n else:\\n self.page_range = range(1,self.pages+1)\\n \\n try:\\n query_kwargs = self.query_filter['kwargs']\\n query_filter = { query_kwargs['mongo_field']: self.kwargs[query_kwargs['url_kwarg']] }\\n \\n except Exception, e:\\n print Exception, e\\n query_filter = None\\n \\n try:\\n # Try to get the page from the URL.\\n self.page = int(self.request.GET['page'])\\n \\n # If we can get the page from the URL, calculate the offset.\\n \\n # First, test if the current page is 0.\\n if self.page == 0:\\n \\n # If so, short circuit the process and return a redirect URL while setting the type as a redirect.\\n redirect_url = '%s?page=%s' % (self.request.META['PATH_INFO'], 1)\\n return redirect_url, 'redirect'\\n \\n # Next, check if the current page is less than the total number of pages.\\n elif self.page <= self.pages:\\n \\n # If so, set the offset to the 1-indexed page number * the pagination limit.\\n self.offset = (self.page-1) * self.pagination_limit\\n \\n # Set the previous/next page numbers.\\n if self.page == 1 and self.page == self.pages:\\n pass\\n \\n elif self.page == 1:\\n self.next_page_number = 2\\n \\n elif self.page == self.pages:\\n self.previous_page_number = self.page - 1\\n \\n else:\\n self.next_page_number = self.page + 1\\n self.previous_page_number = self.page - 1\\n \\n # Finally, check if the current page is greater than the total number of pages.\\n elif self.page > self.pages:\\n \\n # If so, short circuit the process and return a redirect URL while setting the type as a redirect.\\n redirect_url = '%s?page=%s' % (self.request.META['PATH_INFO'], self.pages)\\n return redirect_url, 'redirect'\\n \\n # Check if the sort is absent.\\n if self.query_sort == None:\\n \\n # If there's no sort, just send along the connection query.\\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset)\\n \\n # Otherwise, if the sort is present ...\\n else:\\n \\n # ... send along the connection query with the sort attached.\\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset).sort(self.query_sort['field'], direction=self.query_sort['direction'])\\n \\n except Exception, e:\\n \\n # If there's no page in the URL, set the page to 1.\\n self.page = 1\\n self.offset = 0\\n \\n # If so, set the offset to the 1-indexed page number * the pagination limit.\\n self.offset = (self.page-1) * self.pagination_limit\\n \\n # Set the previous/next page numbers.\\n if self.page == 1 and self.page == self.pages:\\n pass\\n \\n elif self.page == 1:\\n self.next_page_number = 2\\n \\n elif self.page == self.pages:\\n self.previous_page_number = self.page - 1\\n \\n else:\\n self.next_page_number = self.page + 1\\n self.previous_page_number = self.page - 1\\n \\n # Check if the sort is absent.\\n if self.query_sort == None:\\n \\n # If there's no sort, just send along the connection query.\\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset)\\n \\n # Otherwise, if the sort is present ...\\n else:\\n \\n # ... send along the connection query with the sort attached.\\n query = connection.find(query_filter, limit=self.pagination_limit, skip=self.offset).sort(self.query_sort['field'], direction=self.query_sort['direction']) \\n \\n # Set up a query list.\\n query_list = []\\n \\n # Append each item to the query_list.\\n for item in query:\\n item_dict = {}\\n item_as_kwargs = dict(item)\\n item_dict.update(**item_as_kwargs)\\n item_dict['id'] = str(item['_id'])\\n item_dict.pop('_id')\\n query_list.append(item_dict)\\n \\n # Return the query list, and set the type of return to query.\\n return query_list, 'query'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756189bdfc65eac7274551cacdedd181\",\n \"score\": \"0.53503144\",\n \"text\": \"def aggregate_pages(roots):\\n aggregate = {}\\n req = requests.get(get_url(0))\\n json_data = req.json()\\n\\n total_pages = int(json_data['pagination']['total']/json_data['pagination']['per_page']) + 1\\n page = json_data['pagination']['current_page']\\n while page <= total_pages:\\n req = requests.get(get_url(page))\\n json_data = req.json()\\n menus = json_data['menus']\\n for menu in menus:\\n aggregate[(menu)['id']] = menu['child_ids']\\n if not 'parent_id' in menu:\\n roots.append((menu)['id'])\\n page += 1\\n return aggregate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ff301db1885a2ebfb90ad7d1553910\",\n \"score\": \"0.5346458\",\n \"text\": \"def build_page(self, x_y):\\n\\n x, y = x_y\\n min_x, min_y = x * self.value_delta, y * self.value_delta\\n max_x, max_y = min_x + self.value_delta, min_y + self.value_delta\\n points = [\\n (i, j)\\n for i in range(min_x, max_x, self.step_size)\\n for j in range(min_y, max_y, self.step_size)\\n ]\\n altis = RgeAltiClient().fetch_points(points)\\n page = np.array(altis).reshape((self.page_size, self.page_size))\\n return page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f2dbe50cbb6a2325e177e6b9264610\",\n \"score\": \"0.53431183\",\n \"text\": \"def build_blog_list(query_list, date_range, phantom_path):\\n \\n driver = webdriver.PhantomJS(executable_path=phantom_path)\\n\\n buzz_info = []\\n list_of_posts = []\\n for query in query_list:\\n searchword = query[0] \\n print (searchword)\\n for date in date_range:\\n search_url = url_generator(searchword, date[0], date[1], '')\\n print(search_url)\\n soup = page_source_retriever(driver, search_url) \\n num_of_posts, max_num = max_num_finder(soup)\\n \\n for i in range(max_num):\\n current_page = str(10*i+1)\\n search_url_by_page = url_generator(searchword, date[0], date[1], current_page)\\n soup = page_source_retriever(driver, search_url_by_page)\\n \\n sections = soup.find_all('li', {'class':'sh_blog_top'})\\n for section in sections:\\n href = get_hrefs(section)\\n date_posted = get_date(section)\\n list_of_posts.append([searchword, date_posted,href]) \\n buzz_info.append([searchword,date[0],date[1], num_of_posts]) \\n\\n driver.quit()\\n \\n return buzz_info, list_of_posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c707e789296591918c1a5bef739489a\",\n \"score\": \"0.53393954\",\n \"text\": \"def test_scrap_for_pages(self):\\n given_url = \\\"https://danbooru.donmai.us/posts?tags=short_sleeves\\\"\\n \\n expected_ouput = [\\\"https://danbooru.donmai.us/posts?tags=short_sleeves\\\",\\n \\\"https://danbooru.donmai.us/posts?page=2&tags=short_sleeves\\\",\\n \\\"https://danbooru.donmai.us/posts?page=3&tags=short_sleeves\\\",\\n \\\"https://danbooru.donmai.us/posts?page=4&tags=short_sleeves\\\"]\\n \\n out = self.dan.scrap_for_pages(given_url, 4)\\n for i in range(4):\\n self.assertEqual(out[i], expected_ouput[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da5ba60bab5138e8e5fbc4880e29ccf\",\n \"score\": \"0.53393036\",\n \"text\": \"def pages(request):\\n current_site = get_site()\\n\\n language = get_language({'country_code': ''})\\n page = get_index_page(language)\\n\\n menuitems = get_main_menuitems('', page)\\n\\n meta_data = get_metadata(page)\\n scripts = get_scripts()\\n\\n site_content = {\\n 'site': current_site,\\n 'languages': get_languages(),\\n 'current_language': language,\\n 'menuitems': menuitems,\\n 'page': page,\\n 'scripts': scripts,\\n 'metadata': meta_data,\\n }\\n\\n if has_other_menu():\\n site_content['other_menuitems'] = get_other_menuitems()\\n\\n return {'site_content': site_content}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dcc28780ebec978c5a7f6a484657910\",\n \"score\": \"0.53388333\",\n \"text\": \"def scrape_pages(self, page):\\n\\n while True:\\n print 'Scraping page ' + str(self.page)\\n\\n # Format of url is http://www.wyzant.com/New_York_City_tutors.aspx?sl=80075877&sort=27&pagesize=5&pagenum=1 where the 1 at the end is the page number. Increase page number by 1 each iteration of while loop in order to crawl through pages.\\n self.current_url = self.region_url + str(self.page)\\n ufile = urllib2.urlopen(self.current_url)\\n\\n if ufile.geturl()[-1:] == self.base_url:\\n break # Check for end of list\\n\\n people = BeautifulSoup(ufile).findAll('div', {'class':'tutorFR hide medium-show'}) # picks out each person on the page\\n\\n self.scrape_people(people)\\n\\n self.page += 1 # Next page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c9b705e2eb1fc4f8722c2398d2ab44\",\n \"score\": \"0.53386146\",\n \"text\": \"def get_all(self, **kwargs):\\n\\n all_params = ['page_index', 'page_size', 'sort', 'q']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method get_all\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n resource_path = '/build-configurations'.replace('{format}', 'json')\\n method = 'GET'\\n\\n path_params = {}\\n\\n query_params = {}\\n if 'page_index' in params:\\n query_params['pageIndex'] = params['page_index']\\n if 'page_size' in params:\\n query_params['pageSize'] = params['page_size']\\n if 'sort' in params:\\n query_params['sort'] = params['sort']\\n if 'q' in params:\\n query_params['q'] = params['q']\\n\\n header_params = {}\\n\\n form_params = {}\\n files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['application/json'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, method,\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=files,\\n response_type='BuildConfigurationPage',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b93b5a9cace236e810063ab2a02783\",\n \"score\": \"0.53377736\",\n \"text\": \"def pages(per_page: Optional[int] = 1, show_page: Optional[bool] = True) -> Callable:\\n def page_source(coro: Callable[[MenuBase, Any], Coroutine[Any, Any, discord.Embed]]) -> Type[menus.ListPageSource]:\\n async def create_page_header(self, menu: MenuBase, entry: Any) -> Union[discord.Embed, str]:\\n result = await discord.utils.maybe_coroutine(coro, self, menu, entry)\\n return menu.generate_page(result, self._max_pages)\\n\\n def __init__(self, list_pages: Iterable):\\n super(self.__class__, self).__init__(list_pages, per_page=per_page)\\n kwargs = {\\n '__init__': __init__,\\n 'format_page': (coro, create_page_header)[show_page]\\n }\\n return type(coro.__name__, (menus.ListPageSource,), kwargs)\\n return page_source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c9cf9b266149fcd97052e5a475ca18\",\n \"score\": \"0.5332687\",\n \"text\": \"def pushFront(self, *pages):\\n self.pages = list(pages) + self.pages\\n self.evaluatePages()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f269c434799334c8e2b01e790d63aa0\",\n \"score\": \"0.533135\",\n \"text\": \"def loadPage(self):\\n\\n #depending on currentPage, create the required page \\n if self.currentPage == 0:\\n self.page = InfoPage(self.screen, data.getChild(self.menuNode, \\\"info\\\"), self.poke)\\n elif self.currentPage == 1:\\n self.page = SkillsPage(self.screen, data.getChild(self.menuNode, \\\"skills\\\"), self.poke)\\n elif self.currentPage == 2:\\n self.page = MovesPage(self.screen, data.getChild(self.menuNode, \\\"moves\\\"), self.poke)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994479,"cells":{"query_id":{"kind":"string","value":"afe6f0ceb315a8b809fa6471a104b182"},"query":{"kind":"string","value":"Returns the number of days between year1/month1/day1 and year2/month2/day2. Assumes inputs are valid dates in Gregorian calendar."},"positive_passages":{"kind":"list like","value":[{"docid":"d8b73162f072e405ab0afb00e8a23a25","score":"0.75588286","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n # program defensively! Add an assertion if the input is not valid!\n assert month1 in range(1, 13) and month2 in range(1, 13), \" the value of month should be between 1 - 12\"\n # assert day1 < days_in_month(month1, year1), \"the value of day in %d should be <= %d\" % month1 % days_in_month(month1, year1) # zb\n # assert day2 < days_in_month(month2, year2), \"the value of day in %d should be <= %d\" % month2 % days_in_month(month2, year2) # zb\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1), \\\n \"year1-month1-day1 should be earlier than year2-mont2-day2!\"\n days = 0\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\n year1, month1, day1 = nextDay(year1, month1, day1)\n days += 1\n return days","title":""}],"string":"[\n {\n \"docid\": \"d8b73162f072e405ab0afb00e8a23a25\",\n \"score\": \"0.75588286\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n # program defensively! Add an assertion if the input is not valid!\\n assert month1 in range(1, 13) and month2 in range(1, 13), \\\" the value of month should be between 1 - 12\\\"\\n # assert day1 < days_in_month(month1, year1), \\\"the value of day in %d should be <= %d\\\" % month1 % days_in_month(month1, year1) # zb\\n # assert day2 < days_in_month(month2, year2), \\\"the value of day in %d should be <= %d\\\" % month2 % days_in_month(month2, year2) # zb\\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1), \\\\\\n \\\"year1-month1-day1 should be earlier than year2-mont2-day2!\\\"\\n days = 0\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\n year1, month1, day1 = nextDay(year1, month1, day1)\\n days += 1\\n return days\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5bb3242f99f100bec5f8ed8c05ac123d","score":"0.84254104","text":"def days_between_dates(year1, month1, day1, year2, month2, day2):\n\n days = date_in_days(year2, month2, day2) - date_in_days(year1, month1, day1)\n return days","title":""},{"docid":"ff3fe8202499476d844f942bfac8ef9a","score":"0.8176493","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n number_of_days = 0\n next_date = nextDay(year1, month1, day1)\n end_date = (year2, month2, day2)\n while next_date <= end_date:\n number_of_days += 1\n next_date = nextDay(next_date[0], next_date[1], next_date[2])\n return number_of_days","title":""},{"docid":"084e553237e76537fb4ef319147a9d3b","score":"0.81429404","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\r\n #Add an assertion if the input is not valid!\r\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\r\n days = 0\r\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\r\n year1, month1, day1 = nextDay(year1, month1, day1)\r\n days += 1\r\n return days","title":""},{"docid":"ced2c7607d9d53e7497ddedec89dac0a","score":"0.8122042","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n # program defensively! Add an assertion if the input is not valid!\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1) \n days = 0\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\n year1, month1, day1 = nextDay(year1, month1, day1)\n days += 1\n return days","title":""},{"docid":"9cd893805164130d32c781f427341b38","score":"0.8031734","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\r\n\r\n assert ((year1, month1, day1) <= (year2, month2, day2)), \"AssertionError\"\r\n days = 0\r\n while nextDay(year1, month1, day1) <= (year2, month2, day2):\r\n (year1, month1, day1) = nextDay(year1, month1, day1)\r\n days += 1\r\n return days","title":""},{"docid":"a9c8f8b31851b669cbf450fd8c513432","score":"0.79735273","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2): \r\n days = 0\r\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\r\n year1, month1, day1 == nextDay(year1, month1, day1)\r\n days += 1\r\n return days","title":""},{"docid":"7ed807619ac4c5a9d27390bc7cdcbd1c","score":"0.7773415","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n # program defensively! Add an assertion if the input is not valid!\n date1 = str(month1) + '/' + str(day1) + '/' + str(year1)\n date2 = str(month2) + '/' + str(day2) + '/'+ str(year2)\n days_per_month = 365.25 / 12\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\n days = 0\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\n year1, month1, day1 = nextDay(year1, month1, day1)\n days += 1\n print (str(days) + ' days between ' + date1 + ' and ' + date2 + '\\n' +\n str(round(days / days_per_month, 1)) + ' months between ' + date1 + ' and ' + date2)","title":""},{"docid":"d23653ab83fd6f6f979ed9c79add64f9","score":"0.7499938","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n # YOUR CODE HERE!\n day=0\n while daysisbefore(year1, month1, day1, year2, month2, day2):\n year1,month1,day1=nextDay(year1,month1,day1)\n day=day+1\n return day","title":""},{"docid":"028444fc684747c403d2b8858ee80769","score":"0.74602246","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n \n def nextDay(year, month, day):\n daysInCurrentMonth = daysInMonth(year, month)\n if day < daysInCurrentMonth:\n day += 1 \n else:\n day = 1\n month += 1\n if month > 12:\n month = 1\n year += 1\n return year, month, day\n\n def dateIsBefore(year1, month1, day1, year2, month2, day2):\n \"\"\"Returns True if year1-month1-day1 is before\n year2-month2-day2. Otherwise, returns False.\"\"\"\n if year1 < year2:\n return True\n if year1 == year2:\n if month1 < month2:\n return True\n if month1 == month2:\n return day1 < day2\n return False \n\n def isLeapYear(year):\n if year % 4 != 0:\n return False\n elif year % 100 != 0:\n return True\n elif year % 400 != 0:\n return False\n else:\n return True\n\n def daysInMonth(year, month):\n #thirty = [4,6,9,11]\n thirtyOne = [1,3,5,7,8,10,12]\n\n if month == 2:\n if isLeapYear(year):\n return 29\n else:\n return 28\n elif month in thirtyOne:\n return 31\n else:\n return 30\n \n\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\n days = 0\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\n year1, month1, day1 = nextDay(year1, month1, day1)\n days += 1\n return days","title":""},{"docid":"76bcbb13f8f89e830f76e3cc6bb2de89","score":"0.7421402","text":"def days_diff(date1, date2):\n year1, month1, day1 = date1\n year2, month2, day2 = date2\n days1, days2 = date(year1, month1, day1), date(year2, month2, day2)\n return abs(days2 - days1).days","title":""},{"docid":"ad8caad5731700b792a8a1b33020a095","score":"0.7386876","text":"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n\n if(year2 > year1):\n\n if (month2 > month1):\n\n if(day2>day1):\n dayOfDay=day2-day1\n elif(day2==day1):\n dayOfDay=0\n else:\n month2=month2-1\n day2=day2+30\n dayOfDay=day2-day1\n minOfMonth = month2 - month1\n dayOfmonth = minOfMonth * 30\n elif(month2== month1):\n if (day2 > day1):\n dayOfDay = day2 - day1\n elif (day2 == day1):\n dayOfDay = 0\n else:\n year2=year2-1\n month2=month2+12\n month2=month2-1\n day2=day2+30\n dayOfDay=day2-day1\n dayOfmonth=0\n\n else:\n year2=year2-1\n month2=month2+12\n minOfMonth=month2-month1\n dayOfmonth=minOfMonth * 30\n\n minOfYear = year2 - year1\n dayofyear = minOfYear * 365\n\n totalofgreater=dayofyear+dayOfmonth+dayOfDay\n return total","title":""},{"docid":"3437354c3d99bc3533e31e152e4c03e4","score":"0.7249976","text":"def leapdays(year1, year2):\r\n year1 -= 1\r\n year2 -= 1\r\n return (year2 // 4 - year1 // 4) - (year2 // 100 - year1 // 100) + (year2 // 400 - year1 // 400)","title":""},{"docid":"5068893c9210be423a1ab035cda064d3","score":"0.7225645","text":"def days_between_dates(y1, m1, d1, y2, m2, d2): \n return total_days(y2,m2,d2) - total_days(y1,m1,d1)","title":""},{"docid":"476cf4ffa03a32c111d97d70609f45f6","score":"0.7024466","text":"def days_between(cls, date1, date2):\n dd1 = date1.day\n dd2 = date2.day\n mm1 = date1.month\n mm2 = date2.month\n yy1 = date1.year\n yy2 = date2.year\n\n if (dd2 == 31) and (dd1 < 30):\n dd2 = 1\n mm2 += 1\n\n return 360*(yy2-yy1) + 30*(mm2-mm1-1) + max(0, 30-dd1) + min(30, dd2)","title":""},{"docid":"a7c9c5865115d554d326d328828bc25b","score":"0.6815656","text":"def GetNumberOfDays(month, year=Inv_Year, cal=Gregorian):","title":""},{"docid":"0a8a3efccc2c988a4a104143e2f3a30e","score":"0.6784326","text":"def count_days(self, date1, date2):\n\t\t\n\t\tif isinstance(date1,dt.datetime) and isinstance(date2,dt.datetime):\n\t\t\tn_days = date1 - date2\n\t\t\t\n\t\telif isinstance(date1,pd.Series) and isinstance(date2,dt.datetime):\n\t\t\tn_days = date1.apply(lambda x: (x-date2).days)\n\t\t\t\n\t\telif isinstance(date1,dt.datetime) and isinstance(date2,pd.Series):\n\t\t\tn_days = date2.apply(lambda x: (date1-x).days)\n\t\t\n\t\telif isinstance(date1,pd.Series) and isinstance(date2,pd.Series):\n\t\t\tn_days = (date1 - date2).apply(lambda x: x.days)\n\t\t\n\t\treturn n_days","title":""},{"docid":"e4bfdbc17cf3ffb88e7fa0059bb2d435","score":"0.6746539","text":"def diff_year(start_date, end_date):\r\n diffyears = end_date.year - start_date.year\r\n difference = end_date - start_date.replace(end_date.year)\r\n days_in_year = isleap(end_date.year) and 366 or 365\r\n difference_in_years = diffyears + (difference.days + difference.seconds / 86400.0) / days_in_year\r\n return difference_in_years","title":""},{"docid":"9176445cec0e47e020abb63711477cce","score":"0.67233205","text":"def count_days(year_input):\n total_days = 0\n for years in range(1900, year_input):\n if find_leap(years):\n total_days += 366\n else:\n total_days += 365\n return total_days","title":""},{"docid":"90952de76697fdc7f0a2d11c390c2be9","score":"0.67219394","text":"def calc_years_diff(start_date, end_date):\n yd= datetime.strptime(end_date, '%Y-%m-%d')\n xd = datetime.strptime(start_date, '%Y-%m-%d')\n diff = yd - xd\n return round(diff.days / 365, 1)","title":""},{"docid":"43972ebb51e4af62427502061e2b75be","score":"0.67066115","text":"def _get_days_diff(day1, day2):\n return (day1 - day2).days","title":""},{"docid":"259d23a30130cd65714db1f42e62449d","score":"0.66719353","text":"def days_between(d1, d2):\n d1 = datetime.strptime(d1, \"%Y%m%d\")\n d2 = datetime.strptime(d2, \"%Y%m%d\")\n return abs((d2 - d1).days)","title":""},{"docid":"ae4b90e1682b40de60a533f9e1cd8c90","score":"0.65848184","text":"def days_between(d1, d2): \r\n d1 = datetime.datetime.strptime(d1, \"%Y-%m-%d\").date()\r\n d2 = datetime.datetime.strptime(d2, \"%Y-%m-%d\").date()\r\n return abs((d2 - d1).days)","title":""},{"docid":"40a96b3e1394e47d9e77b694e12b2a72","score":"0.6583141","text":"def date_diff(d1, d2):\r\n #Put the date into a list of data: ['year', 'month', 'day']\r\n d1_data = d1.split('-')\r\n d2_data = d2.split('-')\r\n\r\n #Create dates with the correct data\r\n date1 = datetime.date(int(d1_data[0]), int(d1_data[1]), int(d1_data[2]))\r\n date2 = datetime.date(int(d2_data[0]), int(d2_data[1]), int(d2_data[2]))\r\n\r\n #Find the difference between the two dates\r\n diff = date1 - date2\r\n\r\n #Return the days between the two dates\r\n return 0 - diff.days","title":""},{"docid":"28126bc5cb21236175c13b88c59d7a76","score":"0.6575045","text":"def days_between(date1, date2, daycount_method):\n date1 = date_to_datetime(date1).date()\n date2 = date_to_datetime(date2).date()\n if daycount_method == DAYCOUNT_ACT_365:\n return (date2 - date1).days\n\n raise ValueError('Unsupported daycount method.')","title":""},{"docid":"21aa446260afbd8dfd910af0dcb0d6e6","score":"0.6555041","text":"def days_between_dates(date1, date2):\n if date1 is not None and date2 is not None:\n date1 = datetime.strptime(date1, \"%Y-%m-%d\")\n date2 = datetime.strptime(date2, \"%Y-%m-%d\")\n return abs((date2 - date1).days)\n else:\n return None","title":""},{"docid":"79397bbc52dee3488819b5e136cbe34e","score":"0.65446794","text":"def days_between(start: Date, end: Date) -> int:\n return Date.days_between(start=start, end=end)","title":""},{"docid":"0f301185c61798f91e5f114f93833115","score":"0.6533187","text":"def calculate_date_diff(date_one: datetime, date_two: datetime) -> int:\n return (date_two - date_one).days","title":""},{"docid":"7ced56a5b103e32e58e76ba9d4ceca7d","score":"0.64924693","text":"def _days_in_year(year, calendar, use_cftime=True):\n date_type = get_date_type(calendar, use_cftime=use_cftime)\n if year == -1 and calendar in _CALENDARS_WITHOUT_YEAR_ZERO:\n difference = date_type(year + 2, 1, 1) - date_type(year, 1, 1)\n else:\n difference = date_type(year + 1, 1, 1) - date_type(year, 1, 1)\n return difference.days","title":""},{"docid":"01b8e86f011df62a5b53c11bdc65e3b2","score":"0.648655","text":"def days_between(first_date: typing.Iterable, second_date: typing.Iterable) -> int:\n try:\n difference = datetime.datetime(*fill_date(first_date)) - datetime.datetime(*fill_date(second_date))\n return abs(difference.days)\n except Exception as e:\n raise e","title":""},{"docid":"6d31b36a164965508adcaa46f7d6a04d","score":"0.63857275","text":"def _get_number_of_days(self, date_from, date_to):\n from_dt = mx.DateTime.Parser.DateTimeFromString(date_from)\n to_dt = mx.DateTime.Parser.DateTimeFromString(date_to)\n timedelta = to_dt - from_dt\n diff_day = timedelta.days\n return diff_day","title":""},{"docid":"412ab69c1a68f6efde6cbae4edf6a70b","score":"0.63731587","text":"def num_leap_years(year_start: int, year_end: int) -> int:\n start = year_start - 1\n end = year_end - 1\n\n return (end // 4 - start // 4) - (end // 100 - start // 100) + (end // 400 - start // 400)","title":""},{"docid":"55fceb6318aa61b51db1bb024a49e0f4","score":"0.63625443","text":"def calc_day(year,month,day):\n\ti=1\n\ttotal_days=0\n\twhile i<month:\n\t\ttotal_days=total_days+months_length[i]\n\t\ti+=1\n\ttotal_days=total_days+day\n\treturn total_days","title":""},{"docid":"266c2253f04f81d71db5fbfc39f94c97","score":"0.6358348","text":"def days_from(input_date, compare_date):\n\n con_comp = datetime.datetime.strptime(compare_date, '%Y-%m-%d')\n\n con_full = input_date\n\n comp_date = datetime.date(con_full.year, con_comp.month, con_comp.day)\n\n end_year = datetime.date(con_full.year, 12, 31)\n\n abs_diff = abs((con_full.timetuple().tm_yday) -\n (comp_date.timetuple().tm_yday))\n\n if (abs_diff >= 183):\n final = end_year.timetuple().tm_yday - abs_diff\n else:\n final = abs_diff\n return final","title":""},{"docid":"8dda1a6261c27e618f683719652aa858","score":"0.6348582","text":"def dayssince(dt1, dt2):\n return (dt1.date() - dt2.date()).days","title":""},{"docid":"b163c6377258ffc02facdb79bd946da8","score":"0.6344517","text":"def date_arithmetic():\n dt02272000 = datetime.strptime(\"Feb 27, 2000\", \"%b %d, %Y\")\n dt02272017 = datetime.strptime(\"Feb 27, 2017\", \"%b %d, %Y\")\n dt01012017 = datetime.strptime(\"Jan 1, 2017\", \"%b %d, %Y\")\n dt10312017 = datetime.strptime(\"Oct 31, 2017\", \"%b %d, %Y\")\n three_days_after_02272000 = (dt02272000 + timedelta(3)).strftime(\"%b %d, %Y\")\n three_days_after_02272017 = (dt02272017 + timedelta(3)).strftime(\"%b %d, %Y\")\n\n days_passed_01012017_10312017 = (dt10312017 - dt01012017).days\n\n return three_days_after_02272000, three_days_after_02272017, days_passed_01012017_10312017","title":""},{"docid":"3e9a582b783091e188ef637ba80e933f","score":"0.63353944","text":"def dates_between(first: str, second: str) -> int:\n return abs((datetime.strptime(first, '%d.%m.%Y') - datetime.strptime(second, '%d.%m.%Y')).days)","title":""},{"docid":"e44d0253b9869fc2974453af5e60d2ed","score":"0.6318613","text":"def dateDiff(date1,date2):\n d1 = date(int(date1[0:4]), int(date1[4:6]), int(date1[6:8]))\n d2 = date(int(date2[0:4]), int(date2[4:6]), int(date2[6:8]))\n delta = d2 - d1\n return delta.days","title":""},{"docid":"547c223e39f9e5bd3fd1349b618767d9","score":"0.63173074","text":"def date_arithmetic():\n date1 = \"27 Feb 2000\"\n date2 = \"27 Feb 2017\"\n num_days = 3\n date3 = \"31 Oct 2017\"\n date4 = \"1 Jan 2017\"\n d1 = datetime.strptime(date1, \"%d %b %Y\")\n d2 = datetime.strptime(date2, \"%d %b %Y\")\n d3 = datetime.strptime(date3, \"%d %b %Y\")\n d4 = datetime.strptime(date4, \"%d %b %Y\")\n three_days_after_02272000 = d1 + timedelta(days=num_days)\n three_days_after_02272017 = d2 + timedelta(days=num_days)\n days_passed = d3 - d4\n\n return three_days_after_02272000, three_days_after_02272017, days_passed","title":""},{"docid":"bd3baa84e6b1b94989adbba2ceee675f","score":"0.63084006","text":"def years_apart(date1, date2):","title":""},{"docid":"f74320551d35ac59aec1821b1ff4d685","score":"0.6305475","text":"def days_in_year(year: int) -> int:\n return sum(DateBS.months_in_year(year))","title":""},{"docid":"69e2ea4118ae6c8f0d6451c782025bbb","score":"0.6305449","text":"def year_as_days(year):\n\n days = 365 * year\n days += num_leap_years(year)\n return days","title":""},{"docid":"c720904bbca941eafe4382e9b06f0590","score":"0.6300241","text":"def count_leap_year(from_year, to_year):\n\n count = 0\n for ele in range(from_year, to_year):\n if is_leap_year(ele):\n count += 1\n return count","title":""},{"docid":"f2ccf1ecea25489a787ba636d7bf5e10","score":"0.62962836","text":"def get_age(d1, d2):\r\n #Check how many days apart the two dates are\r\n days_apart = date_diff(d1,d2)\r\n\r\n #Divide this number by the number of days in a year and round\r\n #down to get the number of complete years\r\n return int(days_apart/365.2425)","title":""},{"docid":"c727c32d1eeefea2181d747d2b67aa5c","score":"0.62929523","text":"def NumDaysInYear(year):\n\n date_lo = datetime.date(year, 1, 1)\n date_hi = datetime.date(year + 1, 1, 1)\n num_days = (date_hi - date_lo).days\n\n # End of NumDaysInYear().\n return num_days","title":""},{"docid":"0970e9088c9b826f7cd7c151bdd779f7","score":"0.6269547","text":"def date_in_days(year, month, day):\n\n # Year and month are subtracted by one because they are in-progress.\n days = year_as_days(year - 1)\n days += month_as_days(month - 1, year)\n days += day\n return days","title":""},{"docid":"bd1067fad0c4588297c25d55caf676fd","score":"0.6239519","text":"def get_no_of_days(self):\r\n \r\n CurrentDate=dt.datetime(self.Current_year,self.Current_month,self.Current_day)\r\n BirthDate=dt.datetime(self.Current_year,self.Birth_month,self.Birth_day)\r\n\r\n #Intializing the difference in days to zero\r\n diffdays=0\r\n\r\n #Difference is to be calculated only if Current date and Birth date are unidentical\r\n \r\n if(CurrentDate!=BirthDate):\r\n difference=str(BirthDate-CurrentDate)\r\n diffdays=int(difference.split(\" \")[0])\r\n \r\n return diffdays","title":""},{"docid":"c69f9517986f48ca7755556bd0cbcf1c","score":"0.6195864","text":"def days_in_year( year ):\n if year % 4 == 0:\n return 366\n else:\n return 365","title":""},{"docid":"c3f8811ce1ffe5f2c5f8286235d69f77","score":"0.6188364","text":"def days_between(self, date1, date2):\n raise NotImplementedError(\"Method days_between needs to be implemented\")","title":""},{"docid":"62a1d64e12b7d549e83f3aa2f848d431","score":"0.61681944","text":"def calc_months_passed(year, month, day):\r\n if not all(isinstance(x, int) for x in [year, month, day]):\r\n raise TypeError\r\n if (\r\n (year < START_DATE.year)\r\n or (month not in range(1, 13))\r\n or (day not in range(1, monthrange(year, month)[1]))\r\n or (date(year, month, day) < START_DATE)\r\n ):\r\n raise ValueError\r\n\r\n date_to_test = date(year, month, day)\r\n relative_delta = relativedelta(date_to_test, START_DATE)\r\n days_to_month = 1 if relative_delta.days >= MIN_DAYS_TO_COUNT_AS_MONTH else 0\r\n return (\r\n (relative_delta.years * MONTHS_PER_YEAR) + relative_delta.months + days_to_month\r\n )","title":""},{"docid":"3cf94ae60d50e8f42ef760cf8064eacc","score":"0.61270416","text":"def _get_number_of_days(self, date_from, date_to):\n from_dt = datetime.strptime(date_from, DATETIME_FORMAT)\n to_dt = datetime.strptime(date_to, DATETIME_FORMAT)\n timedelta = to_dt - from_dt\n diff_day = timedelta.days + 1 + float(timedelta.seconds) / 86400\n return diff_day","title":""},{"docid":"335ca6b7fa36f5c4efaff9cb8c821895","score":"0.612495","text":"def find_nb_days(start, end):\n # convert dates in numbers\n start = date_in_nm(start)\n end = date_in_nm(end)\n \n nb_days = end - start + 1\n return nb_days","title":""},{"docid":"e92fc7ab9006be200a83645a99474f16","score":"0.611288","text":"def get_age(d1, d2=instantiate_date()):\n age_delta = d2.datetime - d1.datetime\n if d2.month < d1.month: #compared month comes before anniversary month\n next_anniversary = custom_date(d2.year, d1.month, d1.day, d1.hour, d1.minute, d1.second)\n elif d2.month > d1.month: #compared month comes after anniversary month\n next_anniversary = custom_date(d2.year + 1, d1.month, d1.day, d1.hour, d1.minute, d1.second)\n elif d2.day < d1.day: #compared month = anniversary month but compared day comes before anniversary day\n next_anniversary = custom_date(d2.year, d1.month, d1.day, d1.hour, d1.minute, d1.second)\n elif d2.day > d1.day: #compared month = anniversary month but compared day comes after anniversary day\n next_anniversary = custom_date(d2.year + 1, d1.month, d1.day, d1.hour, d1.minute, d1.second)\n next_anniversary_delta = next_anniversary.datetime - d2.datetime\n return age_delta, next_anniversary_delta","title":""},{"docid":"67b855c8ea714a7a1dd01c9c0e6cc58f","score":"0.6107837","text":"def days(ctx, end_date, start_date):\n return datedif(ctx, start_date, end_date, 'd')","title":""},{"docid":"1ae62ef213be35af6060e547d1697fab","score":"0.6094515","text":"def __getPeriod(d1,d2):\r\n\td1 = map(int,d1.split('-'))\r\n\td1 = date(d1[0],d1[1],d1[2])\r\n\td2 = map(int,d2.split('-'))\r\n\td2 = date(d2[0],d2[1],d2[2])\r\n\tdelta = d1-d2\r\n\treturn float(delta.days)/365.0","title":""},{"docid":"8f4147cab3b7e292c4cd8d66e553dc1e","score":"0.60763305","text":"def parse_year_days(year_info):\n _, leap_days = _parse_leap(year_info)\n res = leap_days\n for month in range(1, 13):\n res += (year_info >> (16 - month)) % 2 + 29\n return res","title":""},{"docid":"ceaf2396dba8d0b1db344ea4394c5431","score":"0.60627824","text":"def _difference_in_years(start, end):\n diff = end - start\n diff_in_years = (diff.days + diff.seconds/86400)/365.2425\n return diff_in_years","title":""},{"docid":"5a31b6f8677dcadb94fe633043b0ea2b","score":"0.6026908","text":"def numDays(self, dt, mnt, yr):\n d = 0\n for i in range(1, mnt):\n d += self.mnthDay[str(self.mnth[i])]\n d += dt\n if mnt > 2 and self.leap(yr):\n d += 1\n return d","title":""},{"docid":"ccd1b78448c05c7cf6cf8879c3481e04","score":"0.6019438","text":"def get_date_diff_count(from_date, to_date):\n if from_date and to_date:\n return numpy.busday_count(from_date.date(), to_date.date())\n\n return None","title":""},{"docid":"ac2f04eaa0e607fe2946e6b48fc3e1b3","score":"0.60106456","text":"def days(m, y):\r\n d = 0\r\n if m == 2 and isLeap(y):\r\n d = 29\r\n elif m == 2:\r\n d = 28\r\n elif m % 2 == 0:\r\n d = 30\r\n else:\r\n d = 31\r\n\r\n return d","title":""},{"docid":"7cf008f99ce1c9356b47292917f28994","score":"0.6008513","text":"def baselineDays(self):\n year, month, day = self.toTuple()\n days = firstDayOfYear(year)\n for m in range(month - 1):\n days += GenDate.daysInMonth[m]\n if isLeapYear(year) and month > 2:\n days += 1\n if year == 1582 and (month > 10 or (month == 10 and day > 4)):\n days -= 10\n days += day - 1\n return days","title":""},{"docid":"1a0a8354ca95b4a0f866a620ee1f433d","score":"0.59950364","text":"def doubleday(b1, b2):\n birth1 = datetime.date(b1[0], b1[1], b1[2])\n birth2 = datetime.date(b2[0], b2[1], b2[2])\n \n difference = abs(birth1 - birth2)\n \n if birth1 > birth2:\n datediff = (birth1 + difference)\n elif birth2 > birth1:\n datediff = (birth2 + difference)\n \n return datediff","title":""},{"docid":"9b1fea23299b7c9877b63ffa8166ea44","score":"0.59703875","text":"def _lunar_year_days(self, year: int) -> int:\n days = 0\n months_day = self.G_LUNAR_MONTH_DAYS[year - self.START_YEAR]\n for i in range(1, 13 if self._get_leap_month(year) == 0x0F else 14):\n day = 29 + ((months_day >> i) & 0x01)\n days += day\n return days","title":""},{"docid":"9b1fea23299b7c9877b63ffa8166ea44","score":"0.59703875","text":"def _lunar_year_days(self, year: int) -> int:\n days = 0\n months_day = self.G_LUNAR_MONTH_DAYS[year - self.START_YEAR]\n for i in range(1, 13 if self._get_leap_month(year) == 0x0F else 14):\n day = 29 + ((months_day >> i) & 0x01)\n days += day\n return days","title":""},{"docid":"60c5947e4b63c51f42dab9a615b6c8d6","score":"0.5962528","text":"def _count_days(self):\n delta = self.__end_date - self.__start_date\n return delta.days + 1","title":""},{"docid":"4cd0e63e178922fa1db18a599884d727","score":"0.59481615","text":"def year_fraction_from_days(self, days: int) -> float:\n raise NotImplementedError","title":""},{"docid":"5eb0e37015ff0be4531520355716a392","score":"0.5940397","text":"def is_year(day1, day2):\n return day1.year == day2.year and day1.month == 1 and day1.day == 1 and\\\n day2.month == 12 and day2.day == 31","title":""},{"docid":"f7ffb68bb51eb902a04554dbd2cb1b90","score":"0.5938862","text":"def parse_year_days(year_info):\n year_info = int(year_info)\n res = 29 * 12\n\n leap = False\n if year_info % 16 != 0:\n leap = True\n res += 29\n\n year_info //= 16\n\n for i in range(12 + leap):\n if year_info % 2 == 1:\n res += 1\n year_info //= 2\n return res","title":""},{"docid":"12aea3dae6d9d5d406625131c02735ce","score":"0.59175766","text":"def _get_number_of_days(self, date_from, date_to,employee_id):\n\n timedelta = date_to - date_from\n diff_day = timedelta.days + 1.0\n return diff_day","title":""},{"docid":"716d9c82aeb9c9a7aa79fce919d464c5","score":"0.59130925","text":"def days_between(self,other):\n x = self.copy()\n y = other.copy()\n if x == y:\n return 0\n if x.is_before(y) == False:\n count = 0\n while x != y:\n y.advance_one()\n count += 1 \n elif x.is_before(y) == True:\n count = 0\n while x != y:\n x.advance_one()\n count += -1\n return count","title":""},{"docid":"00b4fd3c6f14b2cb3024a9b6a55b6133","score":"0.5881235","text":"def date_diff(self):\n d1 = datetime.strptime(self.start_date, \"%Y-%m-%d\")\n d2 = datetime.strptime(self.end_date, \"%Y-%m-%d\")\n return abs((d2 - d1).days) + 1","title":""},{"docid":"3133edc823d739283fb5a25949266c15","score":"0.5864947","text":"def diff_days(call_record1, call_record2):\n return (call_record2.format_time - call_record1.format_time).days","title":""},{"docid":"f9c83896968537a074f2eb36a108f3d9","score":"0.5840487","text":"def date2consecutive_day(year=2000, month=12, day=31):\n num = (datetime.date(year, month, day)-datetime.date(year, 1, 1)).days\n if num > 364:\n num = num-1\n return num","title":""},{"docid":"2eee886ab16b4695433fafc439899ba6","score":"0.5840359","text":"def _num_days(self, dt):\n\n return (dt - date(2001, 1, 1)).days","title":""},{"docid":"6332f61cd77fd176910ac4af0960a5dc","score":"0.5811715","text":"def ndays(year: int, month: Optional[int] = None, leap: int = 0) -> int:\n _check_year_range(year)\n if month is None:\n return YEAR_DAYS[year - MIN_LUNAR_YEAR]\n leap = int(bool(leap))\n for _month, _days, _leap in LCalendars.iter_year_month(year):\n if (_month, _leap) == (month, leap):\n return _days\n else:\n raise InvalidLunarDateError(f'[year={year},month={month},leap={leap}]: Invalid month.')","title":""},{"docid":"a182ef65286212f6ef2fe1d08740687a","score":"0.5789213","text":"def _span_days(self, year: int) -> int:\n return sum(self._lunar_year_days(y) for y in range(self.START_YEAR, year))","title":""},{"docid":"04373743ebfc83ecc10dae320e249b42","score":"0.5782142","text":"def DayOfYear(year, month, day):\n total_days = 0\n # Validate the date ranges, months (1 to 12), and days (correct number of days per month), including Leap Year.\n if 0 < month <= 12 and (0 < day <= days_in_month[month - 1] or (IsYearLeap(year) and month == 2)):\n # January is easy, it's the number of days as defined by the value in 'day'.\n if month == 1:\n total_days = day\n return total_days\n # February is January (31) + number of days in February as defined by 'day'.\n if month == 2:\n total_days = day + days_in_month[0]\n return total_days\n # Add all days of the month up until current month-1, then add one (1) for February if Leap Year.\n total_days = day # Add number of days in month to 'total_days' before adding number of days per month before current month.\n for m in range(month - 1):\n total_days += days_in_month[m]\n if IsYearLeap(year):\n total_days += 1 # Leap Year Day, February 29\n return total_days\n else:\n return None # Invalid month or day.","title":""},{"docid":"d917a03b99e8c1c5f2b3cd86931f0bae","score":"0.5781079","text":"def _get_month_days(self, start, end):\n today = datetime.date.today()\n days_worked = h.get_working_days(start, h.previous_day(today))\n days_left = h.get_working_days(today, end)\n return days_worked, days_left","title":""},{"docid":"baf09eed69ac4bcd7b6365509c2658bb","score":"0.5775615","text":"def days_before_year(year):\n y = year - 1\n return y*365 + y//4 - y//100 + y//400","title":""},{"docid":"b8db227d1108477f637b92c34be2a846","score":"0.5769517","text":"def get_number_of_days(date1):\n\n d0 = date1\n d1 = date.today()\n delta = d1 - d0\n return str(delta)","title":""},{"docid":"d7d677ef644a1b35c6c8cb5a3f280625","score":"0.5764401","text":"def _calculate_years(self, year):\n diff = self._today.year - int(year)\n # include current year\n return diff","title":""},{"docid":"f2d5a60d8e9e4f18c7bf9c043d2dfa59","score":"0.5748859","text":"def dateConv(x):\n startDate = datetime.date(1900, 1, 1)\n\n try:\n inputDate = datetime.datetime.strptime(x, \"%d/%m/%Y\")\n inputDate = inputDate.date()\n except ValueError:\n #Return the current year \n return float('nan')\n\n if inputDate.year < startDate.year:\n raise ValueError(\"Conversion of year before 1900 Error\")\n\n tDelta = inputDate - startDate\n\n return tDelta.days","title":""},{"docid":"30cd75937122049f95b177cfb74c45c4","score":"0.5746567","text":"def days_number():\r\n # TODO: Assuming COVID-19 start date is considered as Jan 1st 2020. Jan 1st 2020 to current system date will be XYZ number of days.\r\n # The number of days generated by the program cannot exceed this date, if exceeded the days returned should be '9999'\r\n pass","title":""},{"docid":"2280704122f51b9d919925ee4968007e","score":"0.5737672","text":"def number_of_days(df):\n first_day, last_day = first_and_last_days_df(df)\n return (last_day - first_day)/np.timedelta64(1, 'D')","title":""},{"docid":"69a423e2dab7349ad0b0a851f40d8807","score":"0.5727096","text":"def test_calculate_years(self):\n self.assertEqual(calculate_years('09 FEB 1962', '11 SEP 2017'), 55)\n self.assertEqual(calculate_years('10 SEP 1969', '11 SEP 1999'), 30)\n self.assertEqual(calculate_years('23 OCT 1989', '11 SEP 2017'), 27)","title":""},{"docid":"70cf2e9f6eea601a1d932ae39f1596a7","score":"0.57255656","text":"def days_in_month(year, month):\n month_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n num_days=month_days[month-1] \n for days in month_days:\n if is_leap(year) is True:\n month_days=[31,29,31,30,31,30,31,31,30,31,30,31]\n num_days=month_days[month-1]\n return num_days","title":""},{"docid":"5843d5eaf8f40e9fd0cc0bd64f9a97cb","score":"0.5707042","text":"def num_days_from_mmm_yyyy(date_str):\n date = datetime.datetime.strptime(date_str, '%b-%Y').date()\n delta = datetime.datetime.today().date() - date\n return delta.days","title":""},{"docid":"5007ba143346d0a4216383768a34af80","score":"0.5706403","text":"def days_in_year(year: int, calendar: str = \"default\") -> int:\n return (\n (datetime_classes[calendar](year + 1, 1, 1) - pydt.timedelta(days=1))\n .timetuple()\n .tm_yday\n )","title":""},{"docid":"013c088633ff7051dc2720e846f1aa9d","score":"0.5697428","text":"def get_no_of_days_basic(date):\n formatted_date = re.search(r'^(\\d{4})-(\\d{2})$', date)\n \n if not formatted_date:\n raise InvalidDateFormat('Entered invalid date format!')\n\n year = int(formatted_date.group(1))\n month = int(formatted_date.group(2))\n\n # monthrange returns [weekday of first day of the month, number of days in month]\n return monthrange(year, month)[1]","title":""},{"docid":"333ae70a188431cd5b7f02605267d088","score":"0.56913424","text":"def calc_partial_year(firstDate, month_lengths_arr):\n date, month, year = map(int, firstDate.split(\"/\"))\n\n days_into_year = date - 1\n # accounts for the fact that today is not completed\n\n for i in xrange(0, month - 1):\n # excludes the current month; -1 accounts for zero indexing\n days_into_year += month_lengths_arr[i]\n\n if month > 2:\n # we do not need to check for the 29th here, because date takes care of it\n if is_leap(year):\n days_into_year += 1\n\n return days_into_year","title":""},{"docid":"43b6723260508c962275290abfc93e98","score":"0.56837106","text":"def days_diff(day1: SciDate, day2: SciDate, *, include_first: bool = False, include_last: bool = False):\n\n # SciDate.sub includes the first day but not last, add last to the diff\n # to fulfil the specified include_first and include_last value\n diff_seconds = abs(day1.sub(day2)) + SECONDS_PER_DAY\n\n min_diff = 0\n if not include_first:\n min_diff += SECONDS_PER_DAY\n\n if not include_last:\n min_diff += SECONDS_PER_DAY\n\n if diff_seconds < min_diff:\n return 0\n\n return (diff_seconds - min_diff) // SECONDS_PER_DAY","title":""},{"docid":"2e54538a139f590cefad7990e82f3dbd","score":"0.5673075","text":"def yf(self, start_date, end_date):\n return (end_date - start_date).days / 365.0","title":""},{"docid":"3a90a464d5a8e4e9d138062a8e4ecb69","score":"0.56671846","text":"def get_day_num_in_assign_year(year):\n if calendar.isleap(int(year)):\n return 366\n else:\n return 365","title":""},{"docid":"ae81a1607766293e335f70bb57afb79b","score":"0.5635434","text":"def get_count_days(\n self,\n ) -> List[Union[datetime.timedelta, List[datetime.timedelta]]]:\n days: List[Union[datetime.timedelta, List[datetime.timedelta]]] = []\n dates = self.get_working_dates()\n start_date = self._date\n days.append(dates[0] - start_date)\n\n for date, next_date in zip(dates, dates[1:]):\n start_year = datetime.date(next_date.year, 1, 1)\n\n if date.month == 12 and calendar.isleap(next_date.year):\n common_date, leap_date = start_year - date, next_date - start_year\n days.append([common_date, leap_date])\n\n elif date.month == 12 and calendar.isleap(date.year):\n leap_date, common_date = start_year - date, next_date - start_year\n days.append([common_date, leap_date])\n\n else:\n days.append(next_date - date)\n return days","title":""},{"docid":"285588c22ed2bda902ca793535ee1159","score":"0.5616895","text":"def _span_days(self, year: int) -> int:\n span_days = 0\n for y in range(self.START_YEAR, year):\n span_days += self._lunar_year_days(y)\n return span_days","title":""},{"docid":"fd6e0ed6c13d9dfdbdf3f3866035fd69","score":"0.56074077","text":"def _doy_days_since_doys(\n base: xr.DataArray, start: DayOfYearStr | None = None\n) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray]:\n calendar = get_calendar(base)\n\n base_doy = base.dt.dayofyear\n\n doy_max = xr.apply_ufunc(\n days_in_year, base.dt.year, vectorize=True, kwargs={\"calendar\": calendar}\n )\n\n if start is not None:\n mm, dd = map(int, start.split(\"-\"))\n starts = xr.apply_ufunc(\n lambda y: datetime_classes[calendar](y, mm, dd),\n base.dt.year,\n vectorize=True,\n )\n start_doy = starts.dt.dayofyear\n start_doy = start_doy.where(start_doy >= base_doy, start_doy + doy_max)\n else:\n start_doy = base_doy\n\n return base_doy, start_doy, doy_max","title":""},{"docid":"36c25b220cfdd580b25002ae942370a3","score":"0.56073624","text":"def _divide_by_year(self):\r\n \r\n # clean the user input\r\n from_ = datetime.datetime.strptime(self.from_date, format=\"%Y-%m-%D\")\r\n to_ = datetime.datetime.strptime(self.to_date, format=\"%Y-%m-%D\")\r\n\r\n # recursive functions to get the correct froms and tos\r\n def from_rec(start_date: datetime.datetime):\r\n new_date = datetime.datetime(start_date.year + 1, 1, 1)\r\n if new_date > to_:\r\n return []\r\n else:\r\n return [new_date] + from_rec(new_date)\r\n \r\n def to_rec(end_date: datetime.datetime):\r\n new_date = datetime.datetime(end_date.year - 1, 12, 31)\r\n if new_date < from_:\r\n return []\r\n else:\r\n return [new_date] + from_rec(new_date)\r\n\r\n # get the list with the star and end of each process\r\n from_list = [from_] + from_rec(from_)\r\n to_list = [to_] + from_rec(to_)\r\n to_list = to_list[::-1]\r\n\r\n # this attribute is an iterator\r\n self.correct_dates = zip(from_list, to_list)","title":""},{"docid":"82220768fcfef3bc9b9dda9b1ca85f60","score":"0.5606136","text":"def days_months(months):\n\tif months in [1,3,5,7,8,10,12]:\n\t\tdays=np.arange(1,32)\n\telif months in [4,6,9,11]:\n\t\tdays=np.arange(1,31)\n\telse:\n\t\tdays=np.arange(1,30) #as feb 2016 has 29 days\n\treturn days","title":""},{"docid":"82220768fcfef3bc9b9dda9b1ca85f60","score":"0.5606136","text":"def days_months(months):\n\tif months in [1,3,5,7,8,10,12]:\n\t\tdays=np.arange(1,32)\n\telif months in [4,6,9,11]:\n\t\tdays=np.arange(1,31)\n\telse:\n\t\tdays=np.arange(1,30) #as feb 2016 has 29 days\n\treturn days","title":""},{"docid":"13900f89dd5edbcd8f90bdb0e4d36598","score":"0.5593712","text":"def _days_in_month(year: int, month: int) -> int:\n DAYS_IN_MONTH = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n is_leap = (year % 4 == 0) and ((year % 100 != 0) or (year % 400) == 0)\n days = DAYS_IN_MONTH[(month - 1) % 12]\n if month == 2:\n days += is_leap\n return days","title":""},{"docid":"630a7e868a74cd840565f63e9ef7b1e5","score":"0.55800176","text":"def Days(days):","title":""}],"string":"[\n {\n \"docid\": \"5bb3242f99f100bec5f8ed8c05ac123d\",\n \"score\": \"0.84254104\",\n \"text\": \"def days_between_dates(year1, month1, day1, year2, month2, day2):\\n\\n days = date_in_days(year2, month2, day2) - date_in_days(year1, month1, day1)\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3fe8202499476d844f942bfac8ef9a\",\n \"score\": \"0.8176493\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n number_of_days = 0\\n next_date = nextDay(year1, month1, day1)\\n end_date = (year2, month2, day2)\\n while next_date <= end_date:\\n number_of_days += 1\\n next_date = nextDay(next_date[0], next_date[1], next_date[2])\\n return number_of_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"084e553237e76537fb4ef319147a9d3b\",\n \"score\": \"0.81429404\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\r\\n #Add an assertion if the input is not valid!\\r\\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\\r\\n days = 0\\r\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\r\\n year1, month1, day1 = nextDay(year1, month1, day1)\\r\\n days += 1\\r\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced2c7607d9d53e7497ddedec89dac0a\",\n \"score\": \"0.8122042\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n # program defensively! Add an assertion if the input is not valid!\\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1) \\n days = 0\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\n year1, month1, day1 = nextDay(year1, month1, day1)\\n days += 1\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd893805164130d32c781f427341b38\",\n \"score\": \"0.8031734\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\r\\n\\r\\n assert ((year1, month1, day1) <= (year2, month2, day2)), \\\"AssertionError\\\"\\r\\n days = 0\\r\\n while nextDay(year1, month1, day1) <= (year2, month2, day2):\\r\\n (year1, month1, day1) = nextDay(year1, month1, day1)\\r\\n days += 1\\r\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c8f8b31851b669cbf450fd8c513432\",\n \"score\": \"0.79735273\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2): \\r\\n days = 0\\r\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\r\\n year1, month1, day1 == nextDay(year1, month1, day1)\\r\\n days += 1\\r\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed807619ac4c5a9d27390bc7cdcbd1c\",\n \"score\": \"0.7773415\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n # program defensively! Add an assertion if the input is not valid!\\n date1 = str(month1) + '/' + str(day1) + '/' + str(year1)\\n date2 = str(month2) + '/' + str(day2) + '/'+ str(year2)\\n days_per_month = 365.25 / 12\\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\\n days = 0\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\n year1, month1, day1 = nextDay(year1, month1, day1)\\n days += 1\\n print (str(days) + ' days between ' + date1 + ' and ' + date2 + '\\\\n' +\\n str(round(days / days_per_month, 1)) + ' months between ' + date1 + ' and ' + date2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23653ab83fd6f6f979ed9c79add64f9\",\n \"score\": \"0.7499938\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n # YOUR CODE HERE!\\n day=0\\n while daysisbefore(year1, month1, day1, year2, month2, day2):\\n year1,month1,day1=nextDay(year1,month1,day1)\\n day=day+1\\n return day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028444fc684747c403d2b8858ee80769\",\n \"score\": \"0.74602246\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n \\n def nextDay(year, month, day):\\n daysInCurrentMonth = daysInMonth(year, month)\\n if day < daysInCurrentMonth:\\n day += 1 \\n else:\\n day = 1\\n month += 1\\n if month > 12:\\n month = 1\\n year += 1\\n return year, month, day\\n\\n def dateIsBefore(year1, month1, day1, year2, month2, day2):\\n \\\"\\\"\\\"Returns True if year1-month1-day1 is before\\n year2-month2-day2. Otherwise, returns False.\\\"\\\"\\\"\\n if year1 < year2:\\n return True\\n if year1 == year2:\\n if month1 < month2:\\n return True\\n if month1 == month2:\\n return day1 < day2\\n return False \\n\\n def isLeapYear(year):\\n if year % 4 != 0:\\n return False\\n elif year % 100 != 0:\\n return True\\n elif year % 400 != 0:\\n return False\\n else:\\n return True\\n\\n def daysInMonth(year, month):\\n #thirty = [4,6,9,11]\\n thirtyOne = [1,3,5,7,8,10,12]\\n\\n if month == 2:\\n if isLeapYear(year):\\n return 29\\n else:\\n return 28\\n elif month in thirtyOne:\\n return 31\\n else:\\n return 30\\n \\n\\n assert not dateIsBefore(year2, month2, day2, year1, month1, day1)\\n days = 0\\n while dateIsBefore(year1, month1, day1, year2, month2, day2):\\n year1, month1, day1 = nextDay(year1, month1, day1)\\n days += 1\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76bcbb13f8f89e830f76e3cc6bb2de89\",\n \"score\": \"0.7421402\",\n \"text\": \"def days_diff(date1, date2):\\n year1, month1, day1 = date1\\n year2, month2, day2 = date2\\n days1, days2 = date(year1, month1, day1), date(year2, month2, day2)\\n return abs(days2 - days1).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8caad5731700b792a8a1b33020a095\",\n \"score\": \"0.7386876\",\n \"text\": \"def daysBetweenDates(year1, month1, day1, year2, month2, day2):\\n\\n if(year2 > year1):\\n\\n if (month2 > month1):\\n\\n if(day2>day1):\\n dayOfDay=day2-day1\\n elif(day2==day1):\\n dayOfDay=0\\n else:\\n month2=month2-1\\n day2=day2+30\\n dayOfDay=day2-day1\\n minOfMonth = month2 - month1\\n dayOfmonth = minOfMonth * 30\\n elif(month2== month1):\\n if (day2 > day1):\\n dayOfDay = day2 - day1\\n elif (day2 == day1):\\n dayOfDay = 0\\n else:\\n year2=year2-1\\n month2=month2+12\\n month2=month2-1\\n day2=day2+30\\n dayOfDay=day2-day1\\n dayOfmonth=0\\n\\n else:\\n year2=year2-1\\n month2=month2+12\\n minOfMonth=month2-month1\\n dayOfmonth=minOfMonth * 30\\n\\n minOfYear = year2 - year1\\n dayofyear = minOfYear * 365\\n\\n totalofgreater=dayofyear+dayOfmonth+dayOfDay\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3437354c3d99bc3533e31e152e4c03e4\",\n \"score\": \"0.7249976\",\n \"text\": \"def leapdays(year1, year2):\\r\\n year1 -= 1\\r\\n year2 -= 1\\r\\n return (year2 // 4 - year1 // 4) - (year2 // 100 - year1 // 100) + (year2 // 400 - year1 // 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5068893c9210be423a1ab035cda064d3\",\n \"score\": \"0.7225645\",\n \"text\": \"def days_between_dates(y1, m1, d1, y2, m2, d2): \\n return total_days(y2,m2,d2) - total_days(y1,m1,d1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476cf4ffa03a32c111d97d70609f45f6\",\n \"score\": \"0.7024466\",\n \"text\": \"def days_between(cls, date1, date2):\\n dd1 = date1.day\\n dd2 = date2.day\\n mm1 = date1.month\\n mm2 = date2.month\\n yy1 = date1.year\\n yy2 = date2.year\\n\\n if (dd2 == 31) and (dd1 < 30):\\n dd2 = 1\\n mm2 += 1\\n\\n return 360*(yy2-yy1) + 30*(mm2-mm1-1) + max(0, 30-dd1) + min(30, dd2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c9c5865115d554d326d328828bc25b\",\n \"score\": \"0.6815656\",\n \"text\": \"def GetNumberOfDays(month, year=Inv_Year, cal=Gregorian):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a8a3efccc2c988a4a104143e2f3a30e\",\n \"score\": \"0.6784326\",\n \"text\": \"def count_days(self, date1, date2):\\n\\t\\t\\n\\t\\tif isinstance(date1,dt.datetime) and isinstance(date2,dt.datetime):\\n\\t\\t\\tn_days = date1 - date2\\n\\t\\t\\t\\n\\t\\telif isinstance(date1,pd.Series) and isinstance(date2,dt.datetime):\\n\\t\\t\\tn_days = date1.apply(lambda x: (x-date2).days)\\n\\t\\t\\t\\n\\t\\telif isinstance(date1,dt.datetime) and isinstance(date2,pd.Series):\\n\\t\\t\\tn_days = date2.apply(lambda x: (date1-x).days)\\n\\t\\t\\n\\t\\telif isinstance(date1,pd.Series) and isinstance(date2,pd.Series):\\n\\t\\t\\tn_days = (date1 - date2).apply(lambda x: x.days)\\n\\t\\t\\n\\t\\treturn n_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bfdbc17cf3ffb88e7fa0059bb2d435\",\n \"score\": \"0.6746539\",\n \"text\": \"def diff_year(start_date, end_date):\\r\\n diffyears = end_date.year - start_date.year\\r\\n difference = end_date - start_date.replace(end_date.year)\\r\\n days_in_year = isleap(end_date.year) and 366 or 365\\r\\n difference_in_years = diffyears + (difference.days + difference.seconds / 86400.0) / days_in_year\\r\\n return difference_in_years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9176445cec0e47e020abb63711477cce\",\n \"score\": \"0.67233205\",\n \"text\": \"def count_days(year_input):\\n total_days = 0\\n for years in range(1900, year_input):\\n if find_leap(years):\\n total_days += 366\\n else:\\n total_days += 365\\n return total_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90952de76697fdc7f0a2d11c390c2be9\",\n \"score\": \"0.67219394\",\n \"text\": \"def calc_years_diff(start_date, end_date):\\n yd= datetime.strptime(end_date, '%Y-%m-%d')\\n xd = datetime.strptime(start_date, '%Y-%m-%d')\\n diff = yd - xd\\n return round(diff.days / 365, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43972ebb51e4af62427502061e2b75be\",\n \"score\": \"0.67066115\",\n \"text\": \"def _get_days_diff(day1, day2):\\n return (day1 - day2).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"259d23a30130cd65714db1f42e62449d\",\n \"score\": \"0.66719353\",\n \"text\": \"def days_between(d1, d2):\\n d1 = datetime.strptime(d1, \\\"%Y%m%d\\\")\\n d2 = datetime.strptime(d2, \\\"%Y%m%d\\\")\\n return abs((d2 - d1).days)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4b90e1682b40de60a533f9e1cd8c90\",\n \"score\": \"0.65848184\",\n \"text\": \"def days_between(d1, d2): \\r\\n d1 = datetime.datetime.strptime(d1, \\\"%Y-%m-%d\\\").date()\\r\\n d2 = datetime.datetime.strptime(d2, \\\"%Y-%m-%d\\\").date()\\r\\n return abs((d2 - d1).days)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a96b3e1394e47d9e77b694e12b2a72\",\n \"score\": \"0.6583141\",\n \"text\": \"def date_diff(d1, d2):\\r\\n #Put the date into a list of data: ['year', 'month', 'day']\\r\\n d1_data = d1.split('-')\\r\\n d2_data = d2.split('-')\\r\\n\\r\\n #Create dates with the correct data\\r\\n date1 = datetime.date(int(d1_data[0]), int(d1_data[1]), int(d1_data[2]))\\r\\n date2 = datetime.date(int(d2_data[0]), int(d2_data[1]), int(d2_data[2]))\\r\\n\\r\\n #Find the difference between the two dates\\r\\n diff = date1 - date2\\r\\n\\r\\n #Return the days between the two dates\\r\\n return 0 - diff.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28126bc5cb21236175c13b88c59d7a76\",\n \"score\": \"0.6575045\",\n \"text\": \"def days_between(date1, date2, daycount_method):\\n date1 = date_to_datetime(date1).date()\\n date2 = date_to_datetime(date2).date()\\n if daycount_method == DAYCOUNT_ACT_365:\\n return (date2 - date1).days\\n\\n raise ValueError('Unsupported daycount method.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21aa446260afbd8dfd910af0dcb0d6e6\",\n \"score\": \"0.6555041\",\n \"text\": \"def days_between_dates(date1, date2):\\n if date1 is not None and date2 is not None:\\n date1 = datetime.strptime(date1, \\\"%Y-%m-%d\\\")\\n date2 = datetime.strptime(date2, \\\"%Y-%m-%d\\\")\\n return abs((date2 - date1).days)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79397bbc52dee3488819b5e136cbe34e\",\n \"score\": \"0.65446794\",\n \"text\": \"def days_between(start: Date, end: Date) -> int:\\n return Date.days_between(start=start, end=end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f301185c61798f91e5f114f93833115\",\n \"score\": \"0.6533187\",\n \"text\": \"def calculate_date_diff(date_one: datetime, date_two: datetime) -> int:\\n return (date_two - date_one).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ced56a5b103e32e58e76ba9d4ceca7d\",\n \"score\": \"0.64924693\",\n \"text\": \"def _days_in_year(year, calendar, use_cftime=True):\\n date_type = get_date_type(calendar, use_cftime=use_cftime)\\n if year == -1 and calendar in _CALENDARS_WITHOUT_YEAR_ZERO:\\n difference = date_type(year + 2, 1, 1) - date_type(year, 1, 1)\\n else:\\n difference = date_type(year + 1, 1, 1) - date_type(year, 1, 1)\\n return difference.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b8e86f011df62a5b53c11bdc65e3b2\",\n \"score\": \"0.648655\",\n \"text\": \"def days_between(first_date: typing.Iterable, second_date: typing.Iterable) -> int:\\n try:\\n difference = datetime.datetime(*fill_date(first_date)) - datetime.datetime(*fill_date(second_date))\\n return abs(difference.days)\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d31b36a164965508adcaa46f7d6a04d\",\n \"score\": \"0.63857275\",\n \"text\": \"def _get_number_of_days(self, date_from, date_to):\\n from_dt = mx.DateTime.Parser.DateTimeFromString(date_from)\\n to_dt = mx.DateTime.Parser.DateTimeFromString(date_to)\\n timedelta = to_dt - from_dt\\n diff_day = timedelta.days\\n return diff_day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412ab69c1a68f6efde6cbae4edf6a70b\",\n \"score\": \"0.63731587\",\n \"text\": \"def num_leap_years(year_start: int, year_end: int) -> int:\\n start = year_start - 1\\n end = year_end - 1\\n\\n return (end // 4 - start // 4) - (end // 100 - start // 100) + (end // 400 - start // 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55fceb6318aa61b51db1bb024a49e0f4\",\n \"score\": \"0.63625443\",\n \"text\": \"def calc_day(year,month,day):\\n\\ti=1\\n\\ttotal_days=0\\n\\twhile i<month:\\n\\t\\ttotal_days=total_days+months_length[i]\\n\\t\\ti+=1\\n\\ttotal_days=total_days+day\\n\\treturn total_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266c2253f04f81d71db5fbfc39f94c97\",\n \"score\": \"0.6358348\",\n \"text\": \"def days_from(input_date, compare_date):\\n\\n con_comp = datetime.datetime.strptime(compare_date, '%Y-%m-%d')\\n\\n con_full = input_date\\n\\n comp_date = datetime.date(con_full.year, con_comp.month, con_comp.day)\\n\\n end_year = datetime.date(con_full.year, 12, 31)\\n\\n abs_diff = abs((con_full.timetuple().tm_yday) -\\n (comp_date.timetuple().tm_yday))\\n\\n if (abs_diff >= 183):\\n final = end_year.timetuple().tm_yday - abs_diff\\n else:\\n final = abs_diff\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dda1a6261c27e618f683719652aa858\",\n \"score\": \"0.6348582\",\n \"text\": \"def dayssince(dt1, dt2):\\n return (dt1.date() - dt2.date()).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b163c6377258ffc02facdb79bd946da8\",\n \"score\": \"0.6344517\",\n \"text\": \"def date_arithmetic():\\n dt02272000 = datetime.strptime(\\\"Feb 27, 2000\\\", \\\"%b %d, %Y\\\")\\n dt02272017 = datetime.strptime(\\\"Feb 27, 2017\\\", \\\"%b %d, %Y\\\")\\n dt01012017 = datetime.strptime(\\\"Jan 1, 2017\\\", \\\"%b %d, %Y\\\")\\n dt10312017 = datetime.strptime(\\\"Oct 31, 2017\\\", \\\"%b %d, %Y\\\")\\n three_days_after_02272000 = (dt02272000 + timedelta(3)).strftime(\\\"%b %d, %Y\\\")\\n three_days_after_02272017 = (dt02272017 + timedelta(3)).strftime(\\\"%b %d, %Y\\\")\\n\\n days_passed_01012017_10312017 = (dt10312017 - dt01012017).days\\n\\n return three_days_after_02272000, three_days_after_02272017, days_passed_01012017_10312017\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9a582b783091e188ef637ba80e933f\",\n \"score\": \"0.63353944\",\n \"text\": \"def dates_between(first: str, second: str) -> int:\\n return abs((datetime.strptime(first, '%d.%m.%Y') - datetime.strptime(second, '%d.%m.%Y')).days)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44d0253b9869fc2974453af5e60d2ed\",\n \"score\": \"0.6318613\",\n \"text\": \"def dateDiff(date1,date2):\\n d1 = date(int(date1[0:4]), int(date1[4:6]), int(date1[6:8]))\\n d2 = date(int(date2[0:4]), int(date2[4:6]), int(date2[6:8]))\\n delta = d2 - d1\\n return delta.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"547c223e39f9e5bd3fd1349b618767d9\",\n \"score\": \"0.63173074\",\n \"text\": \"def date_arithmetic():\\n date1 = \\\"27 Feb 2000\\\"\\n date2 = \\\"27 Feb 2017\\\"\\n num_days = 3\\n date3 = \\\"31 Oct 2017\\\"\\n date4 = \\\"1 Jan 2017\\\"\\n d1 = datetime.strptime(date1, \\\"%d %b %Y\\\")\\n d2 = datetime.strptime(date2, \\\"%d %b %Y\\\")\\n d3 = datetime.strptime(date3, \\\"%d %b %Y\\\")\\n d4 = datetime.strptime(date4, \\\"%d %b %Y\\\")\\n three_days_after_02272000 = d1 + timedelta(days=num_days)\\n three_days_after_02272017 = d2 + timedelta(days=num_days)\\n days_passed = d3 - d4\\n\\n return three_days_after_02272000, three_days_after_02272017, days_passed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3baa84e6b1b94989adbba2ceee675f\",\n \"score\": \"0.63084006\",\n \"text\": \"def years_apart(date1, date2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74320551d35ac59aec1821b1ff4d685\",\n \"score\": \"0.6305475\",\n \"text\": \"def days_in_year(year: int) -> int:\\n return sum(DateBS.months_in_year(year))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e2ea4118ae6c8f0d6451c782025bbb\",\n \"score\": \"0.6305449\",\n \"text\": \"def year_as_days(year):\\n\\n days = 365 * year\\n days += num_leap_years(year)\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c720904bbca941eafe4382e9b06f0590\",\n \"score\": \"0.6300241\",\n \"text\": \"def count_leap_year(from_year, to_year):\\n\\n count = 0\\n for ele in range(from_year, to_year):\\n if is_leap_year(ele):\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2ccf1ecea25489a787ba636d7bf5e10\",\n \"score\": \"0.62962836\",\n \"text\": \"def get_age(d1, d2):\\r\\n #Check how many days apart the two dates are\\r\\n days_apart = date_diff(d1,d2)\\r\\n\\r\\n #Divide this number by the number of days in a year and round\\r\\n #down to get the number of complete years\\r\\n return int(days_apart/365.2425)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c727c32d1eeefea2181d747d2b67aa5c\",\n \"score\": \"0.62929523\",\n \"text\": \"def NumDaysInYear(year):\\n\\n date_lo = datetime.date(year, 1, 1)\\n date_hi = datetime.date(year + 1, 1, 1)\\n num_days = (date_hi - date_lo).days\\n\\n # End of NumDaysInYear().\\n return num_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0970e9088c9b826f7cd7c151bdd779f7\",\n \"score\": \"0.6269547\",\n \"text\": \"def date_in_days(year, month, day):\\n\\n # Year and month are subtracted by one because they are in-progress.\\n days = year_as_days(year - 1)\\n days += month_as_days(month - 1, year)\\n days += day\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1067fad0c4588297c25d55caf676fd\",\n \"score\": \"0.6239519\",\n \"text\": \"def get_no_of_days(self):\\r\\n \\r\\n CurrentDate=dt.datetime(self.Current_year,self.Current_month,self.Current_day)\\r\\n BirthDate=dt.datetime(self.Current_year,self.Birth_month,self.Birth_day)\\r\\n\\r\\n #Intializing the difference in days to zero\\r\\n diffdays=0\\r\\n\\r\\n #Difference is to be calculated only if Current date and Birth date are unidentical\\r\\n \\r\\n if(CurrentDate!=BirthDate):\\r\\n difference=str(BirthDate-CurrentDate)\\r\\n diffdays=int(difference.split(\\\" \\\")[0])\\r\\n \\r\\n return diffdays\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69f9517986f48ca7755556bd0cbcf1c\",\n \"score\": \"0.6195864\",\n \"text\": \"def days_in_year( year ):\\n if year % 4 == 0:\\n return 366\\n else:\\n return 365\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f8811ce1ffe5f2c5f8286235d69f77\",\n \"score\": \"0.6188364\",\n \"text\": \"def days_between(self, date1, date2):\\n raise NotImplementedError(\\\"Method days_between needs to be implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a1d64e12b7d549e83f3aa2f848d431\",\n \"score\": \"0.61681944\",\n \"text\": \"def calc_months_passed(year, month, day):\\r\\n if not all(isinstance(x, int) for x in [year, month, day]):\\r\\n raise TypeError\\r\\n if (\\r\\n (year < START_DATE.year)\\r\\n or (month not in range(1, 13))\\r\\n or (day not in range(1, monthrange(year, month)[1]))\\r\\n or (date(year, month, day) < START_DATE)\\r\\n ):\\r\\n raise ValueError\\r\\n\\r\\n date_to_test = date(year, month, day)\\r\\n relative_delta = relativedelta(date_to_test, START_DATE)\\r\\n days_to_month = 1 if relative_delta.days >= MIN_DAYS_TO_COUNT_AS_MONTH else 0\\r\\n return (\\r\\n (relative_delta.years * MONTHS_PER_YEAR) + relative_delta.months + days_to_month\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf94ae60d50e8f42ef760cf8064eacc\",\n \"score\": \"0.61270416\",\n \"text\": \"def _get_number_of_days(self, date_from, date_to):\\n from_dt = datetime.strptime(date_from, DATETIME_FORMAT)\\n to_dt = datetime.strptime(date_to, DATETIME_FORMAT)\\n timedelta = to_dt - from_dt\\n diff_day = timedelta.days + 1 + float(timedelta.seconds) / 86400\\n return diff_day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335ca6b7fa36f5c4efaff9cb8c821895\",\n \"score\": \"0.612495\",\n \"text\": \"def find_nb_days(start, end):\\n # convert dates in numbers\\n start = date_in_nm(start)\\n end = date_in_nm(end)\\n \\n nb_days = end - start + 1\\n return nb_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92fc7ab9006be200a83645a99474f16\",\n \"score\": \"0.611288\",\n \"text\": \"def get_age(d1, d2=instantiate_date()):\\n age_delta = d2.datetime - d1.datetime\\n if d2.month < d1.month: #compared month comes before anniversary month\\n next_anniversary = custom_date(d2.year, d1.month, d1.day, d1.hour, d1.minute, d1.second)\\n elif d2.month > d1.month: #compared month comes after anniversary month\\n next_anniversary = custom_date(d2.year + 1, d1.month, d1.day, d1.hour, d1.minute, d1.second)\\n elif d2.day < d1.day: #compared month = anniversary month but compared day comes before anniversary day\\n next_anniversary = custom_date(d2.year, d1.month, d1.day, d1.hour, d1.minute, d1.second)\\n elif d2.day > d1.day: #compared month = anniversary month but compared day comes after anniversary day\\n next_anniversary = custom_date(d2.year + 1, d1.month, d1.day, d1.hour, d1.minute, d1.second)\\n next_anniversary_delta = next_anniversary.datetime - d2.datetime\\n return age_delta, next_anniversary_delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b855c8ea714a7a1dd01c9c0e6cc58f\",\n \"score\": \"0.6107837\",\n \"text\": \"def days(ctx, end_date, start_date):\\n return datedif(ctx, start_date, end_date, 'd')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae62ef213be35af6060e547d1697fab\",\n \"score\": \"0.6094515\",\n \"text\": \"def __getPeriod(d1,d2):\\r\\n\\td1 = map(int,d1.split('-'))\\r\\n\\td1 = date(d1[0],d1[1],d1[2])\\r\\n\\td2 = map(int,d2.split('-'))\\r\\n\\td2 = date(d2[0],d2[1],d2[2])\\r\\n\\tdelta = d1-d2\\r\\n\\treturn float(delta.days)/365.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4147cab3b7e292c4cd8d66e553dc1e\",\n \"score\": \"0.60763305\",\n \"text\": \"def parse_year_days(year_info):\\n _, leap_days = _parse_leap(year_info)\\n res = leap_days\\n for month in range(1, 13):\\n res += (year_info >> (16 - month)) % 2 + 29\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceaf2396dba8d0b1db344ea4394c5431\",\n \"score\": \"0.60627824\",\n \"text\": \"def _difference_in_years(start, end):\\n diff = end - start\\n diff_in_years = (diff.days + diff.seconds/86400)/365.2425\\n return diff_in_years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a31b6f8677dcadb94fe633043b0ea2b\",\n \"score\": \"0.6026908\",\n \"text\": \"def numDays(self, dt, mnt, yr):\\n d = 0\\n for i in range(1, mnt):\\n d += self.mnthDay[str(self.mnth[i])]\\n d += dt\\n if mnt > 2 and self.leap(yr):\\n d += 1\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd1b78448c05c7cf6cf8879c3481e04\",\n \"score\": \"0.6019438\",\n \"text\": \"def get_date_diff_count(from_date, to_date):\\n if from_date and to_date:\\n return numpy.busday_count(from_date.date(), to_date.date())\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac2f04eaa0e607fe2946e6b48fc3e1b3\",\n \"score\": \"0.60106456\",\n \"text\": \"def days(m, y):\\r\\n d = 0\\r\\n if m == 2 and isLeap(y):\\r\\n d = 29\\r\\n elif m == 2:\\r\\n d = 28\\r\\n elif m % 2 == 0:\\r\\n d = 30\\r\\n else:\\r\\n d = 31\\r\\n\\r\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf008f99ce1c9356b47292917f28994\",\n \"score\": \"0.6008513\",\n \"text\": \"def baselineDays(self):\\n year, month, day = self.toTuple()\\n days = firstDayOfYear(year)\\n for m in range(month - 1):\\n days += GenDate.daysInMonth[m]\\n if isLeapYear(year) and month > 2:\\n days += 1\\n if year == 1582 and (month > 10 or (month == 10 and day > 4)):\\n days -= 10\\n days += day - 1\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0a8354ca95b4a0f866a620ee1f433d\",\n \"score\": \"0.59950364\",\n \"text\": \"def doubleday(b1, b2):\\n birth1 = datetime.date(b1[0], b1[1], b1[2])\\n birth2 = datetime.date(b2[0], b2[1], b2[2])\\n \\n difference = abs(birth1 - birth2)\\n \\n if birth1 > birth2:\\n datediff = (birth1 + difference)\\n elif birth2 > birth1:\\n datediff = (birth2 + difference)\\n \\n return datediff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1fea23299b7c9877b63ffa8166ea44\",\n \"score\": \"0.59703875\",\n \"text\": \"def _lunar_year_days(self, year: int) -> int:\\n days = 0\\n months_day = self.G_LUNAR_MONTH_DAYS[year - self.START_YEAR]\\n for i in range(1, 13 if self._get_leap_month(year) == 0x0F else 14):\\n day = 29 + ((months_day >> i) & 0x01)\\n days += day\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1fea23299b7c9877b63ffa8166ea44\",\n \"score\": \"0.59703875\",\n \"text\": \"def _lunar_year_days(self, year: int) -> int:\\n days = 0\\n months_day = self.G_LUNAR_MONTH_DAYS[year - self.START_YEAR]\\n for i in range(1, 13 if self._get_leap_month(year) == 0x0F else 14):\\n day = 29 + ((months_day >> i) & 0x01)\\n days += day\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c5947e4b63c51f42dab9a615b6c8d6\",\n \"score\": \"0.5962528\",\n \"text\": \"def _count_days(self):\\n delta = self.__end_date - self.__start_date\\n return delta.days + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd0e63e178922fa1db18a599884d727\",\n \"score\": \"0.59481615\",\n \"text\": \"def year_fraction_from_days(self, days: int) -> float:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb0e37015ff0be4531520355716a392\",\n \"score\": \"0.5940397\",\n \"text\": \"def is_year(day1, day2):\\n return day1.year == day2.year and day1.month == 1 and day1.day == 1 and\\\\\\n day2.month == 12 and day2.day == 31\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7ffb68bb51eb902a04554dbd2cb1b90\",\n \"score\": \"0.5938862\",\n \"text\": \"def parse_year_days(year_info):\\n year_info = int(year_info)\\n res = 29 * 12\\n\\n leap = False\\n if year_info % 16 != 0:\\n leap = True\\n res += 29\\n\\n year_info //= 16\\n\\n for i in range(12 + leap):\\n if year_info % 2 == 1:\\n res += 1\\n year_info //= 2\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12aea3dae6d9d5d406625131c02735ce\",\n \"score\": \"0.59175766\",\n \"text\": \"def _get_number_of_days(self, date_from, date_to,employee_id):\\n\\n timedelta = date_to - date_from\\n diff_day = timedelta.days + 1.0\\n return diff_day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716d9c82aeb9c9a7aa79fce919d464c5\",\n \"score\": \"0.59130925\",\n \"text\": \"def days_between(self,other):\\n x = self.copy()\\n y = other.copy()\\n if x == y:\\n return 0\\n if x.is_before(y) == False:\\n count = 0\\n while x != y:\\n y.advance_one()\\n count += 1 \\n elif x.is_before(y) == True:\\n count = 0\\n while x != y:\\n x.advance_one()\\n count += -1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b4fd3c6f14b2cb3024a9b6a55b6133\",\n \"score\": \"0.5881235\",\n \"text\": \"def date_diff(self):\\n d1 = datetime.strptime(self.start_date, \\\"%Y-%m-%d\\\")\\n d2 = datetime.strptime(self.end_date, \\\"%Y-%m-%d\\\")\\n return abs((d2 - d1).days) + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3133edc823d739283fb5a25949266c15\",\n \"score\": \"0.5864947\",\n \"text\": \"def diff_days(call_record1, call_record2):\\n return (call_record2.format_time - call_record1.format_time).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c83896968537a074f2eb36a108f3d9\",\n \"score\": \"0.5840487\",\n \"text\": \"def date2consecutive_day(year=2000, month=12, day=31):\\n num = (datetime.date(year, month, day)-datetime.date(year, 1, 1)).days\\n if num > 364:\\n num = num-1\\n return num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eee886ab16b4695433fafc439899ba6\",\n \"score\": \"0.5840359\",\n \"text\": \"def _num_days(self, dt):\\n\\n return (dt - date(2001, 1, 1)).days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6332f61cd77fd176910ac4af0960a5dc\",\n \"score\": \"0.5811715\",\n \"text\": \"def ndays(year: int, month: Optional[int] = None, leap: int = 0) -> int:\\n _check_year_range(year)\\n if month is None:\\n return YEAR_DAYS[year - MIN_LUNAR_YEAR]\\n leap = int(bool(leap))\\n for _month, _days, _leap in LCalendars.iter_year_month(year):\\n if (_month, _leap) == (month, leap):\\n return _days\\n else:\\n raise InvalidLunarDateError(f'[year={year},month={month},leap={leap}]: Invalid month.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a182ef65286212f6ef2fe1d08740687a\",\n \"score\": \"0.5789213\",\n \"text\": \"def _span_days(self, year: int) -> int:\\n return sum(self._lunar_year_days(y) for y in range(self.START_YEAR, year))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04373743ebfc83ecc10dae320e249b42\",\n \"score\": \"0.5782142\",\n \"text\": \"def DayOfYear(year, month, day):\\n total_days = 0\\n # Validate the date ranges, months (1 to 12), and days (correct number of days per month), including Leap Year.\\n if 0 < month <= 12 and (0 < day <= days_in_month[month - 1] or (IsYearLeap(year) and month == 2)):\\n # January is easy, it's the number of days as defined by the value in 'day'.\\n if month == 1:\\n total_days = day\\n return total_days\\n # February is January (31) + number of days in February as defined by 'day'.\\n if month == 2:\\n total_days = day + days_in_month[0]\\n return total_days\\n # Add all days of the month up until current month-1, then add one (1) for February if Leap Year.\\n total_days = day # Add number of days in month to 'total_days' before adding number of days per month before current month.\\n for m in range(month - 1):\\n total_days += days_in_month[m]\\n if IsYearLeap(year):\\n total_days += 1 # Leap Year Day, February 29\\n return total_days\\n else:\\n return None # Invalid month or day.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d917a03b99e8c1c5f2b3cd86931f0bae\",\n \"score\": \"0.5781079\",\n \"text\": \"def _get_month_days(self, start, end):\\n today = datetime.date.today()\\n days_worked = h.get_working_days(start, h.previous_day(today))\\n days_left = h.get_working_days(today, end)\\n return days_worked, days_left\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf09eed69ac4bcd7b6365509c2658bb\",\n \"score\": \"0.5775615\",\n \"text\": \"def days_before_year(year):\\n y = year - 1\\n return y*365 + y//4 - y//100 + y//400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8db227d1108477f637b92c34be2a846\",\n \"score\": \"0.5769517\",\n \"text\": \"def get_number_of_days(date1):\\n\\n d0 = date1\\n d1 = date.today()\\n delta = d1 - d0\\n return str(delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d677ef644a1b35c6c8cb5a3f280625\",\n \"score\": \"0.5764401\",\n \"text\": \"def _calculate_years(self, year):\\n diff = self._today.year - int(year)\\n # include current year\\n return diff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d5a60d8e9e4f18c7bf9c043d2dfa59\",\n \"score\": \"0.5748859\",\n \"text\": \"def dateConv(x):\\n startDate = datetime.date(1900, 1, 1)\\n\\n try:\\n inputDate = datetime.datetime.strptime(x, \\\"%d/%m/%Y\\\")\\n inputDate = inputDate.date()\\n except ValueError:\\n #Return the current year \\n return float('nan')\\n\\n if inputDate.year < startDate.year:\\n raise ValueError(\\\"Conversion of year before 1900 Error\\\")\\n\\n tDelta = inputDate - startDate\\n\\n return tDelta.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30cd75937122049f95b177cfb74c45c4\",\n \"score\": \"0.5746567\",\n \"text\": \"def days_number():\\r\\n # TODO: Assuming COVID-19 start date is considered as Jan 1st 2020. Jan 1st 2020 to current system date will be XYZ number of days.\\r\\n # The number of days generated by the program cannot exceed this date, if exceeded the days returned should be '9999'\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2280704122f51b9d919925ee4968007e\",\n \"score\": \"0.5737672\",\n \"text\": \"def number_of_days(df):\\n first_day, last_day = first_and_last_days_df(df)\\n return (last_day - first_day)/np.timedelta64(1, 'D')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a423e2dab7349ad0b0a851f40d8807\",\n \"score\": \"0.5727096\",\n \"text\": \"def test_calculate_years(self):\\n self.assertEqual(calculate_years('09 FEB 1962', '11 SEP 2017'), 55)\\n self.assertEqual(calculate_years('10 SEP 1969', '11 SEP 1999'), 30)\\n self.assertEqual(calculate_years('23 OCT 1989', '11 SEP 2017'), 27)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70cf2e9f6eea601a1d932ae39f1596a7\",\n \"score\": \"0.57255656\",\n \"text\": \"def days_in_month(year, month):\\n month_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\\n num_days=month_days[month-1] \\n for days in month_days:\\n if is_leap(year) is True:\\n month_days=[31,29,31,30,31,30,31,31,30,31,30,31]\\n num_days=month_days[month-1]\\n return num_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5843d5eaf8f40e9fd0cc0bd64f9a97cb\",\n \"score\": \"0.5707042\",\n \"text\": \"def num_days_from_mmm_yyyy(date_str):\\n date = datetime.datetime.strptime(date_str, '%b-%Y').date()\\n delta = datetime.datetime.today().date() - date\\n return delta.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5007ba143346d0a4216383768a34af80\",\n \"score\": \"0.5706403\",\n \"text\": \"def days_in_year(year: int, calendar: str = \\\"default\\\") -> int:\\n return (\\n (datetime_classes[calendar](year + 1, 1, 1) - pydt.timedelta(days=1))\\n .timetuple()\\n .tm_yday\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013c088633ff7051dc2720e846f1aa9d\",\n \"score\": \"0.5697428\",\n \"text\": \"def get_no_of_days_basic(date):\\n formatted_date = re.search(r'^(\\\\d{4})-(\\\\d{2})$', date)\\n \\n if not formatted_date:\\n raise InvalidDateFormat('Entered invalid date format!')\\n\\n year = int(formatted_date.group(1))\\n month = int(formatted_date.group(2))\\n\\n # monthrange returns [weekday of first day of the month, number of days in month]\\n return monthrange(year, month)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333ae70a188431cd5b7f02605267d088\",\n \"score\": \"0.56913424\",\n \"text\": \"def calc_partial_year(firstDate, month_lengths_arr):\\n date, month, year = map(int, firstDate.split(\\\"/\\\"))\\n\\n days_into_year = date - 1\\n # accounts for the fact that today is not completed\\n\\n for i in xrange(0, month - 1):\\n # excludes the current month; -1 accounts for zero indexing\\n days_into_year += month_lengths_arr[i]\\n\\n if month > 2:\\n # we do not need to check for the 29th here, because date takes care of it\\n if is_leap(year):\\n days_into_year += 1\\n\\n return days_into_year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b6723260508c962275290abfc93e98\",\n \"score\": \"0.56837106\",\n \"text\": \"def days_diff(day1: SciDate, day2: SciDate, *, include_first: bool = False, include_last: bool = False):\\n\\n # SciDate.sub includes the first day but not last, add last to the diff\\n # to fulfil the specified include_first and include_last value\\n diff_seconds = abs(day1.sub(day2)) + SECONDS_PER_DAY\\n\\n min_diff = 0\\n if not include_first:\\n min_diff += SECONDS_PER_DAY\\n\\n if not include_last:\\n min_diff += SECONDS_PER_DAY\\n\\n if diff_seconds < min_diff:\\n return 0\\n\\n return (diff_seconds - min_diff) // SECONDS_PER_DAY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e54538a139f590cefad7990e82f3dbd\",\n \"score\": \"0.5673075\",\n \"text\": \"def yf(self, start_date, end_date):\\n return (end_date - start_date).days / 365.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a90a464d5a8e4e9d138062a8e4ecb69\",\n \"score\": \"0.56671846\",\n \"text\": \"def get_day_num_in_assign_year(year):\\n if calendar.isleap(int(year)):\\n return 366\\n else:\\n return 365\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae81a1607766293e335f70bb57afb79b\",\n \"score\": \"0.5635434\",\n \"text\": \"def get_count_days(\\n self,\\n ) -> List[Union[datetime.timedelta, List[datetime.timedelta]]]:\\n days: List[Union[datetime.timedelta, List[datetime.timedelta]]] = []\\n dates = self.get_working_dates()\\n start_date = self._date\\n days.append(dates[0] - start_date)\\n\\n for date, next_date in zip(dates, dates[1:]):\\n start_year = datetime.date(next_date.year, 1, 1)\\n\\n if date.month == 12 and calendar.isleap(next_date.year):\\n common_date, leap_date = start_year - date, next_date - start_year\\n days.append([common_date, leap_date])\\n\\n elif date.month == 12 and calendar.isleap(date.year):\\n leap_date, common_date = start_year - date, next_date - start_year\\n days.append([common_date, leap_date])\\n\\n else:\\n days.append(next_date - date)\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285588c22ed2bda902ca793535ee1159\",\n \"score\": \"0.5616895\",\n \"text\": \"def _span_days(self, year: int) -> int:\\n span_days = 0\\n for y in range(self.START_YEAR, year):\\n span_days += self._lunar_year_days(y)\\n return span_days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6e0ed6c13d9dfdbdf3f3866035fd69\",\n \"score\": \"0.56074077\",\n \"text\": \"def _doy_days_since_doys(\\n base: xr.DataArray, start: DayOfYearStr | None = None\\n) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray]:\\n calendar = get_calendar(base)\\n\\n base_doy = base.dt.dayofyear\\n\\n doy_max = xr.apply_ufunc(\\n days_in_year, base.dt.year, vectorize=True, kwargs={\\\"calendar\\\": calendar}\\n )\\n\\n if start is not None:\\n mm, dd = map(int, start.split(\\\"-\\\"))\\n starts = xr.apply_ufunc(\\n lambda y: datetime_classes[calendar](y, mm, dd),\\n base.dt.year,\\n vectorize=True,\\n )\\n start_doy = starts.dt.dayofyear\\n start_doy = start_doy.where(start_doy >= base_doy, start_doy + doy_max)\\n else:\\n start_doy = base_doy\\n\\n return base_doy, start_doy, doy_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c25b220cfdd580b25002ae942370a3\",\n \"score\": \"0.56073624\",\n \"text\": \"def _divide_by_year(self):\\r\\n \\r\\n # clean the user input\\r\\n from_ = datetime.datetime.strptime(self.from_date, format=\\\"%Y-%m-%D\\\")\\r\\n to_ = datetime.datetime.strptime(self.to_date, format=\\\"%Y-%m-%D\\\")\\r\\n\\r\\n # recursive functions to get the correct froms and tos\\r\\n def from_rec(start_date: datetime.datetime):\\r\\n new_date = datetime.datetime(start_date.year + 1, 1, 1)\\r\\n if new_date > to_:\\r\\n return []\\r\\n else:\\r\\n return [new_date] + from_rec(new_date)\\r\\n \\r\\n def to_rec(end_date: datetime.datetime):\\r\\n new_date = datetime.datetime(end_date.year - 1, 12, 31)\\r\\n if new_date < from_:\\r\\n return []\\r\\n else:\\r\\n return [new_date] + from_rec(new_date)\\r\\n\\r\\n # get the list with the star and end of each process\\r\\n from_list = [from_] + from_rec(from_)\\r\\n to_list = [to_] + from_rec(to_)\\r\\n to_list = to_list[::-1]\\r\\n\\r\\n # this attribute is an iterator\\r\\n self.correct_dates = zip(from_list, to_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82220768fcfef3bc9b9dda9b1ca85f60\",\n \"score\": \"0.5606136\",\n \"text\": \"def days_months(months):\\n\\tif months in [1,3,5,7,8,10,12]:\\n\\t\\tdays=np.arange(1,32)\\n\\telif months in [4,6,9,11]:\\n\\t\\tdays=np.arange(1,31)\\n\\telse:\\n\\t\\tdays=np.arange(1,30) #as feb 2016 has 29 days\\n\\treturn days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82220768fcfef3bc9b9dda9b1ca85f60\",\n \"score\": \"0.5606136\",\n \"text\": \"def days_months(months):\\n\\tif months in [1,3,5,7,8,10,12]:\\n\\t\\tdays=np.arange(1,32)\\n\\telif months in [4,6,9,11]:\\n\\t\\tdays=np.arange(1,31)\\n\\telse:\\n\\t\\tdays=np.arange(1,30) #as feb 2016 has 29 days\\n\\treturn days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13900f89dd5edbcd8f90bdb0e4d36598\",\n \"score\": \"0.5593712\",\n \"text\": \"def _days_in_month(year: int, month: int) -> int:\\n DAYS_IN_MONTH = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\\n is_leap = (year % 4 == 0) and ((year % 100 != 0) or (year % 400) == 0)\\n days = DAYS_IN_MONTH[(month - 1) % 12]\\n if month == 2:\\n days += is_leap\\n return days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"630a7e868a74cd840565f63e9ef7b1e5\",\n \"score\": \"0.55800176\",\n \"text\": \"def Days(days):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994480,"cells":{"query_id":{"kind":"string","value":"05bbe2e0b7965809ebd65daeb904cc8c"},"query":{"kind":"string","value":"Helper method to locate row in database"},"positive_passages":{"kind":"list like","value":[{"docid":"dbdd4b554715401408c8f7b90e2a0625","score":"0.0","text":"def check_for_app(app):\n if (app == \"all\"):\n return -1\n count = 0\n for row in cur.execute(\"SELECT * FROM passwords WHERE application=?\", (app,)):\n count+=1\n else:\n return count","title":""}],"string":"[\n {\n \"docid\": \"dbdd4b554715401408c8f7b90e2a0625\",\n \"score\": \"0.0\",\n \"text\": \"def check_for_app(app):\\n if (app == \\\"all\\\"):\\n return -1\\n count = 0\\n for row in cur.execute(\\\"SELECT * FROM passwords WHERE application=?\\\", (app,)):\\n count+=1\\n else:\\n return count\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"297fb191bddda71a9dc5e40d64edc8c6","score":"0.667747","text":"async def find(self, pk_value: Any) -> RowProxy:","title":""},{"docid":"6c84afbfd114dc604b825ace3583251d","score":"0.64952505","text":"def find_row(table, row):\n for idx in range(len(table)):\n if table[idx][0] == row:\n return idx\n return -1","title":""},{"docid":"28839e7d2598847d8979c750a714aad8","score":"0.6296006","text":"def get_rows(column_to_search, value_to_match, table, db_file):\n \n try:\n conn, c = connect_to_db(db_file) \n c.execute('SELECT * FROM {t} WHERE {col}=\"{value}\"'.format(t=safe(table), \n col=safe(column_to_search), value=value_to_match))\n row = c.fetchall()\n conn.close()\n return row\n except Exception as e:\n print(\"Error when trying to get row in table\", table, \"in\", db_file)\n print(e)\n return None","title":""},{"docid":"06ab440f6a0a50b09c16c1ed4cfd8241","score":"0.62917984","text":"def find_row(self, row_id):\n #Calculate what line in the file the row_id will be found at\n looking_for_line = self.__row_id_in_file(row_id)\n\n #Initiate line-counter\n current_line = 1\n with open(self.__data_file_for_row_id(row_id), 'r') as f:\n for line in f:\n if current_line == looking_for_line:\n return json.loads(line)\n current_line += 1\n\n raise Exception('Could not find row_id ' + row_id)","title":""},{"docid":"504967e10d6ef22a6779ba7fc9bb6a3d","score":"0.62687343","text":"def get_row_index(self):\n for row in range(self.model.rowCount()):\n name_item = self.model.item(row, self.COL_NAME)\n fullpath = name_item.data(self.ROLE_FULLPATH)\n if fullpath == self.filepath:\n return row","title":""},{"docid":"ca030c7af9b16e3976333b51a983936a","score":"0.62624425","text":"def search_db(self, key, item):\n db = self.check_db()\n data = [record for record in db if record[key] == item]\n if data:\n return data[0]\n else:\n return False","title":""},{"docid":"17dbbe544a08939ae858d44b9d1cb459","score":"0.6181649","text":"def db_row(self):\n return self._model_cls.query.get(self._pk)","title":""},{"docid":"8ef52de486d6046a1be64aaf8c778956","score":"0.61808544","text":"def find_id(self, uid: int) -> Optional[sqlite3.Row]:\n\n sql = \"\"\"SELECT rowid, url as 'url [url]', title,\n added as 'added [local_datetime]',\n updated as 'updated [local_datetime]',\n deleted as 'deleted [local_datetime]',\n tags, comments\n FROM bookmarks\n WHERE rowid=?\"\"\"\n return self._selectOne(sql, (uid,))","title":""},{"docid":"3e94a68f483c751a05c47fa6a37fc0c8","score":"0.60699207","text":"def findAt(self, index):\n saveCursor = self.cursor\n i = 0\n if self.first():\n if index == i:\n return self.cursor\n else:\n while self.next():\n i += 1\n if index == i:\n return self.cursor\n self.cursor = saveCursor\n return None","title":""},{"docid":"9e6ddd0dc35be2252846c08b164288b5","score":"0.60465103","text":"def WhereAmI (row, headers,table):\r\n\r\n return t.WhereAmI (row, headers,table)","title":""},{"docid":"a0281990eb782393505e2e96e107bae8","score":"0.604043","text":"async def find_by_id(self, _id: int) -> Record:\n conn: Connection\n async with self.db_pool.acquire() as conn:\n return await conn.fetchrow(\n f\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\",\n _id,\n )","title":""},{"docid":"cd5e03e17097b8c93b80158b2fba2911","score":"0.6037111","text":"def search(item):\n row = -1\n try:\n row = filter_rows.index(item)\n except Exception:\n logger.warning(\"%s was not found\", str(item))\n return row","title":""},{"docid":"08227e90db2e48ee0cd434462b34c591","score":"0.60301137","text":"def find(self, value):\n for position in range(self.get_size()):\n if self.table[position] == value:\n return position","title":""},{"docid":"4bc6881c01f37ad933d0b4d0792ee8e7","score":"0.5997052","text":"def rpc_database_get_row_by_id(self, row_id):\n\t\ttable_name = self.path.split('/')[-2]\n\t\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\n\t\tassert table\n\t\tcolumns = DATABASE_TABLES[table_name]\n\t\tsession = db_manager.Session()\n\t\trow = db_manager.get_row_by_id(session, table, row_id)\n\t\tif row:\n\t\t\trow = dict(zip(columns, (getattr(row, c) for c in columns)))\n\t\tsession.close()\n\t\treturn row","title":""},{"docid":"8c68ec8f12fdfa6f58d0854723163697","score":"0.5869959","text":"def _get_row_index(self, row: Row) -> int:\n row_index = -1\n for index, table_row in enumerate(self.table_data):\n if table_row.values == row.values:\n row_index = index\n break\n return row_index","title":""},{"docid":"6d11892e3af4bd3bfe30a3c8e8865d3e","score":"0.58069116","text":"def row_by_value(idl_, table, column, match, default=_NO_DEFAULT):\n tab = idl_.tables[table]\n for r in tab.rows.values():\n if getattr(r, column) == match:\n return r\n if default is not _NO_DEFAULT:\n return default\n raise None","title":""},{"docid":"5e84a966cb35da05610cc0e725ccec47","score":"0.58031994","text":"def get_row(self, pk):\n ans = self.execute(self.commands.get_row(\n cols=self._join_cols(self.columns),\n table=self.name,\n pk_col=self.primary_key_column,\n pk=pk\n ))\n if not ans:\n return None\n return self._dictify(self.columns, ans[0])","title":""},{"docid":"4318deb02a39569b51bd6790e5f7d37c","score":"0.57962567","text":"def get_for_id(id,table):\n # Implement this function\n for row in range(1, len(table)):\n for col in range(len(table[0])):\n if id in table[row][col]:\n return table[row]","title":""},{"docid":"c56499c3188e2df25a425606e4bf2aa2","score":"0.5779984","text":"def search_query(self, num, query, table, field):\n try:\n self.db_connection()\n if self.connection is not None: # If the database connection was established successfully\n with self.connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchone()\n print(type(result))\n if result is not None and result[field] == num: # check if query result is None and pk equal to num\n print(table + str(num), \"was found\")\n return True\n else:\n print(table + str(num), \"wasn't found\")\n return False\n except IntegrityError:\n print(\"A new record couldn't be added\")\n exit()\n finally:\n self.close_connection() # Closing the opened connection","title":""},{"docid":"8982c47ca30a1f6d2fee8e79075b10ec","score":"0.57695395","text":"def pylookup(self, col1, col2, matrix, index=3):\n\n for row in matrix:\n if col1 == row[0] and col2 == row[1]:\n return row[index]\n return None","title":""},{"docid":"3105696bb5e55a73dd6059a48aa35653","score":"0.57678396","text":"def locate(self, column, cond_inp):\n\n try:\n return self.df_input.loc[self.df_input[column] == cond_inp]\n except Exception as e:\n print(e)","title":""},{"docid":"ee34301f45dcffed13f7dc7accf30b7a","score":"0.5747678","text":"def select_from_DB (itemToSearch, tableWhereToSearch):\n session = open_session()\n s = select([itemToSearch.tableWhereToSearch])\n result = session.execute(s)\n for row in result:\n print(row)","title":""},{"docid":"6637a320c7cb67a30929380f2f37c8be","score":"0.5729195","text":"def find_result_row(self, search_text, html_column_name):\n headers = []\n results = []\n html_header = self.driver.find_elements_by_css_selector('.header')\n for header in html_header:\n headers.append(header.text)\n\n html_results = self.driver.find_elements_by_css_selector('.dataGridRow')\n for id, row in enumerate(html_results):\n results.append({headers[id]:item.text for id, item in enumerate(row.find_elements_by_css_selector('td'))})\n\n if results:\n # Find result row, 0 = first entry\n for id, person in enumerate(results):\n if search_text.lower() == person[html_column_name].lower():\n # Match found, but check for duplicates\n temp = []\n for people in results:\n if people[html_column_name] in temp:\n return -2 # Duplicate found\n else:\n temp.append(people[html_column_name])\n return id\n return -1 # No result or no match","title":""},{"docid":"2a82c5f0cca6da9c941109922090673f","score":"0.56894594","text":"def get_result_from_db() -> list:\n global choice, confirmation, res, data, column_names, result, choice_row\n column_names = data[0]\n try:\n confirmation.after(1, confirmation.destroy)\n except AttributeError:\n pass\n choice_row = choice.get()\n res = place_for_enter.get()\n if choice_row in column_names:\n result = simple_search_from_db(data_base, table, choice_row, res)\n return result\n else:\n mistake_select_value()","title":""},{"docid":"8ae0f9ef58912819debaf910055a9924","score":"0.5688523","text":"def search_element_of_table(path, table, nameColumn, data):\n conn = sqlite3.connect(path)\n c = conn.cursor()\n to_return = \"<tr>\"\n for row in c.execute('SELECT * FROM '+table +' where '+nameColumn+\" like '%\"+data+\"%'\").fetchall():\n for x in row:\n to_return = to_return + \"<td>\"+ str(x) + \"</td>\"\n to_return = to_return + \"</tr>\"\n\n conn.close()\n return to_return","title":""},{"docid":"82303fc4f119816dddc36c000b2d804f","score":"0.5684421","text":"def dt_find(search = \"\",\n row = None,\n column = None,\n tableID = \"list\",\n first = False,\n ):\n # 'todo need to fix the searching on numbers\n config = current.test_config\n browser = config.browser\n\n # Calculate the rows that need to be navigated along to find the search string\n colList = []\n rowList = []\n if row == None:\n r = 1\n while True:\n tr = \".//*[@id='%s']/tbody/tr[%s]\" % (tableID, r)\n try:\n elem = browser.find_element_by_xpath(tr)\n rowList.append(r)\n r += 1\n except:\n break\n elif isinstance(row, int):\n rowList = [row]\n else:\n rowList = row\n # Calculate the columns that need to be navigated down to find the search string\n if column == None:\n c = 1\n while True:\n td = \".//*[@id='%s']/tbody/tr[1]/td[%s]\" % (tableID, c)\n try:\n elem = browser.find_element_by_xpath(td)\n colList.append(c)\n c += 1\n except:\n break\n elif isinstance(column, int):\n colList = [column]\n else:\n colList = column\n s3_debug(\"rows %s, columns %s\" % (rowList, colList))\n # Now try and find a match\n result = []\n for r in rowList:\n for c in colList:\n td = \".//*[@id='%s']/tbody/tr[%s]/td[%s]\" % (tableID, r, c)\n try:\n elem = browser.find_element_by_xpath(td)\n s3_debug(\"got %s, needle %s\" % (elem.text, search))\n if elem.text == search:\n if first:\n return (r, c)\n else:\n result.append((r, c))\n except:\n pass\n return result","title":""},{"docid":"81ad858b56a2534b08e265d7a2225c2c","score":"0.56746584","text":"def get(self, question, rows):\n con.query(question)\n result = con.use_result()\n return result.fetch_row(rows)","title":""},{"docid":"44bd82cca6db12cd561101fb3c1c3871","score":"0.56663024","text":"def extract_row(self, column, identifier, df_input=None):\n\n try:\n if df_input is None:\n return self.df_input.loc[self.df_input[column] == identifier]\n else:\n return df_input.loc[df_input[column] == identifier] \n except Exception as e:\n print(e)","title":""},{"docid":"545604459049dd550b1ee2aa5db95af1","score":"0.56301135","text":"def find(self, target):\n try:\n if type(target) is int:\n for key, value in self.index.table.items():\n if value == target:\n return(key)\n elif type(target) is str:\n for key, value in self.index.table.items():\n if key == target:\n return(value)\n except Exception as error:\n print(f\"Error: self.find({target}) -> {error}\")","title":""},{"docid":"2229e71295060ce4193b244fa7f52778","score":"0.5620588","text":"def get_column(col_to_search, value_to_match, col_to_get, table, db_file):\n \n try:\n conn, c = connect_to_db(db_file) \n c.execute('SELECT {cg} FROM {t} WHERE {col}=\"{value}\"'.format(t=safe(table), \n cg=safe(col_to_get), col=safe(col_to_search), value=safe(value_to_match)))\n column = c.fetchone()\n conn.close()\n return column\n except Exception as e:\n print(\"Error when trying to fetch row in table\", table, \"in database file\", db_file)\n print(e)\n return None","title":""},{"docid":"4a08d0e09bbaafef00bbac0522edd781","score":"0.56205857","text":"def getRowPosition(sheetData, text, column):\n for index, row in enumerate(sheetData):\n if row[column] == text:\n return index\n raise ValueError('{0} is not in column {1}'.format(text, column))","title":""},{"docid":"6cb7cf555e3d50761a1b8d3cdd862b91","score":"0.55995876","text":"def get_row(self):\n return self._row_number","title":""},{"docid":"f522d2eebf883389c89309ecbcc6e04b","score":"0.55955905","text":"def get_row(self, row):\n \n return self.sudoku_matrix[row]","title":""},{"docid":"38c3db93e876eb6ee1d8016539e54fd4","score":"0.5590182","text":"def getCurrentRow(self):\n item = self.getSelectedItem()\n if item:\n return item.row()\n return None","title":""},{"docid":"15b66f8d2c58613ebb903de672049399","score":"0.55896556","text":"def get_absolute_row(self, index):\n try:\n return self._table.getRow(index)\n except Registry.Table.INVALID_ROW:\n import traceback \n raise IndexError(\n \"Index %s out of bounds. Original exception: %s\" % \\\n (index, traceback.format_exc()))","title":""},{"docid":"f192d09b2efca6af71a25e100b4413bf","score":"0.5565859","text":"def getTableEntryByAddress(self, address):\n for entry in self.table:\n if entry.hasPage():\n if entry.getPage().contains(address):\n return entry\n return None","title":""},{"docid":"fc85145fa65f3739dd418d30afe4c6a8","score":"0.5562986","text":"def findWhere(cls, args):\n return cls.search(args)[0][0]","title":""},{"docid":"c3092d6220f910c11542b7e4c6227c23","score":"0.55624574","text":"def get_query_single_row(query, *args):\n conn = get_connection()\n cursor = conn.cursor()\n\n rows = cursor.execute(query, args).fetchall()\n\n result = map_row(cursor, rows[0])\n\n cursor.close()\n conn.close()\n\n return result","title":""},{"docid":"c6c0bfcd71d9e0d32208cbd00f0129a3","score":"0.55473316","text":"def db_search_by_path(self, path):\n \n try:\n with self.connection:\n c = self.connection.cursor()\n query = \"SELECT * FROM %s WHERE path=?\" % PUBLICATIONS_TABLE\n c.execute(query, [path])\n return c.fetchone() # This path is a unique absolute path.\n except sqlite.Error as e:\n logging.exception(e)\n return None","title":""},{"docid":"8ddad2f320ea6a8bca786356440c70a1","score":"0.55336905","text":"def get_index_in_table_row(self, table_locator, row, expected, loglevel='INFO'):\n row = int(row)\n row_index = row - 1\n table = self._table_element_finder.find(self._current_browser(), table_locator)\n if table is not None:\n rows = table.find_elements_by_xpath(\"./thead/tr\")\n if row_index >= len(rows): rows.extend(table.find_elements_by_xpath(\"./tbody/tr\"))\n if row_index >= len(rows): rows.extend(table.find_elements_by_xpath(\"./tfoot/tr\"))\n if row_index < len(rows):\n columns = rows[row_index].find_elements_by_tag_name('th')\n columns.extend(rows[row_index].find_elements_by_tag_name('td'))\n column_index = 0\n for element in columns:\n column_index = column_index + 1\n element_text = element.text\n if element_text and expected in element_text:\n return column_index\n self.log_source(loglevel)\n raise AssertionError(\"%s could not be found in row #%s of table %s.\"\n % (expected, str(row), table_locator))","title":""},{"docid":"ef9bc570b6e6387b4217ed40b613e96a","score":"0.55283874","text":"def get_row( self, rowIndex ):\n return self.data[rowIndex,:]","title":""},{"docid":"ed462ee4637137dac2b15c49658b7b99","score":"0.55276054","text":"def get_source_row(self, row):\n return self._dataframe.index[row]","title":""},{"docid":"cf55e9df461bdf8d17311a91903c3737","score":"0.55211335","text":"def find(self, id):\n return self._select_one('''\n select\n *\n from\n {table}\n where\n {primary_key} = %s\n '''.format(table=self.__class__._table,\n primary_key=self.__class__._primary_key), [id])","title":""},{"docid":"a18505f4e884ee890561e9ab664322b8","score":"0.5515408","text":"def find_cell_row(cell_roi_df, master_index_df):\n cell_roi_cols = ['cell_index', 'xy', 'date']\n \n if 'sub_coord' in master_index_df.columns:\n master_index_cols = ['sub_coord', 'xy', 'date']\n elif 'cell_index' in master_index_df.columns:\n master_index_cols = ['cell_index', 'xy', 'date']\n else:\n print(f\"No cell_index or sub_coord column in master_index_df\")\n return None\n\n matching_rows = []\n for row in master_index_df.index:\n\n query = [int(cell_roi_df.loc[0, colname]) for colname in cell_roi_cols]\n master = [int(master_index_df.loc[row, colname]) for colname in master_index_cols]\n\n if query == master:\n matching_rows.append(row)\n\n if len(matching_rows) > 1:\n print(f\"Found multiple rows matching cell_roi_df. Check master index for duplicates at:/n{master_index.path.iloc[0]}\")\n return None\n elif len(matching_rows) == 0:\n print(\"Found no rows in master_index_df matching cell_roi_df\")\n return None\n else:\n matching_row = matching_rows[0]\n return matching_row","title":""},{"docid":"53a9353c3c46d97e6ef02217439e1220","score":"0.55056393","text":"def get_or_raise(cls, pk, exc=Exception(\"数据库错误\")):\n row = DBSession().query(cls).get(pk)\n if row is None:\n raise Exception(\"{}记录不存在\".format(cls.__name__))\n return row","title":""},{"docid":"8b223e28473f3fa112f715653822fc3c","score":"0.5503251","text":"def find(self, key):\n if isinstance(key, (int, long)):\n result = self.table.Find(<long> key).decode('utf8')\n if result == u'':\n raise KeyError(key)\n return result\n else:\n key = as_str(key)\n result = self.table.Find(<char*>key)\n if result == -1:\n raise KeyError(key)\n return result","title":""},{"docid":"902fd6f2180439e169585dc803c9335a","score":"0.5490408","text":"def find(self,item):\n sig = str(item)\n try:\n return self.index[sig]\n except:\n return None","title":""},{"docid":"a593527fb945b6805f8ff64b31e3c4b9","score":"0.5488193","text":"def getRowFromPath(self, path):\n query = \"SELECT resource_id, etag, title FROM docs WHERE local_path = ?\"\n res = self.db.execute(query, (path,)).fetchone()\n return res","title":""},{"docid":"8c398b0681ac3a65593a01e1bd5411e4","score":"0.5487673","text":"def test_get_row_index(self):\n self.dboard.set_row_names([\"1\", \"2\"])\n self.assertEqual(0, self.dboard._get_row_index(\"1\"))\n self.assertEqual(1, self.dboard._get_row_index(\"2\"))","title":""},{"docid":"cb09f0be0e172d40ae402c1c2222725c","score":"0.54837364","text":"def find(self, node):\n saveCursor = self.cursor\n if self.first() and self.cursor.nodeId == node.nodeId:\n return self.cursor\n while self.next():\n if self.cursor.nodeId == node.nodeId:\n return self.cursor\n self.cursor = saveCursor\n return None","title":""},{"docid":"fc4c17cf78c7c55c73a01065c4087b42","score":"0.54808366","text":"def _find_by_key(self, key, find):\n index = hashId(key, self.capacity) # Get the index/ bucket based on hash code of the key\n \n hash_table_cell = self._entry[index]\n found_item = None\n for item in hash_table_cell: #Iterrate the entry array and check the key is matching and if key is same than get the value\n if item[0] == key:\n found_item = item\n break\n\n return find(found_item, hash_table_cell)","title":""},{"docid":"260c045dfeca6f307e20a8e9056b6a30","score":"0.5479559","text":"def query_exists(self, q, param=None):\r\n \"\"\" Queries database and return one row \"\"\"\r\n try:\r\n c = self.connection.cursor()\r\n c.execute(q, param)\r\n self.logger.log(logger.LogLevel.DEBUG, 'database.query_exists: %s | %s' % (q, param)) \r\n if c.fetchone() is None:\r\n return False\r\n else:\r\n return True\r\n except Exception as e:\r\n self.logger.log(logger.LogLevel.ERROR, 'database.query_exists: %s. %s | %s' % (e, q, param))\r\n return False","title":""},{"docid":"d5fdd447af75f01bbd50bbfbfcd6a099","score":"0.54712725","text":"def find_index(row):\n value = row[index]\n if value in seen:\n return seen[value]\n for row_ in merged.iter_dicts(True):\n if row_[index] == value:\n seen[value] = row_[\"index\"]\n return row_[\"index\"]\n return None","title":""},{"docid":"6ff31caa5fabc6b7a788bb37a718cec3","score":"0.54655886","text":"def find(self, key):\n index = self.quadratic_probe(key)\n if index != -1:\n if self.table[index] is not None:\n return self.table[index]\n else:\n return False\n else:\n return False","title":""},{"docid":"0bd41cd66077140c9b9e8a6d809f9d3e","score":"0.54550296","text":"def find(self, primary_key):\n sql = '{} WHERE {} = %s'.format(self._select(), self.PRIMARY_KEY)\n cursor = yield self._pool.execute(sql, [primary_key])\n result = cursor.fetchmany(1)\n return self.convert_result_to_object(result)","title":""},{"docid":"f458214eeeeeca3d2f4365b6d1849ab0","score":"0.54480684","text":"def find(self, value):\n for row in range(self.getHeight()):\n for column in range(self.getWidth()):\n if self[row][column] == value:\n return (row, column)\n return None","title":""},{"docid":"f79a69243ac3fc07171e16b942411cfe","score":"0.54478145","text":"def find_element(grid, target):\n \n # First, iterate over the row indices\n for row_number in range(len(grid)):\n \n# print(\"Checking row\", row_number)\n \n for col_number in range(len(grid[row_number])):\n \n# print(\"Checking column\", col_number)\n \n if grid[row_number][col_number] == target:\n return (row_number, col_number)\n \n return None","title":""},{"docid":"b76737edef07065ba71e15286bf9a4b1","score":"0.54471594","text":"def GetTableCurrentRow (latextable):\r\n\r\n return t.GetTableCurrentRow (latextable)","title":""},{"docid":"4feb83dc1859a58f08952fe4d0b89037","score":"0.54371154","text":"def get_entry_from_row(self, row: [str], column_header: str) -> str:\n column_index = self.header.index(column_header)\n return row[column_index]","title":""},{"docid":"aef97bf5e8805263d44c2a3573aaca0c","score":"0.5434794","text":"def parent_row(self):\n dict_cur.execute('SELECT * FROM \"{}\" WHERE {} = {}'.format(\n self.parent_table(), self.id_col_of_parent(), self.parent_id\n ))\n return dict_cur.fetchone()","title":""},{"docid":"6f12769f65651514e00012bb4e7378cd","score":"0.5425477","text":"def get_run(self, run_id: str) -> sqlite3.Row:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from runs\n WHERE run_id = ?;\n \"\"\",\n (run_id,),\n )\n results = c.fetchall()\n return results[0]","title":""},{"docid":"ea8a9aff2330c25ba9d687615f6b24fc","score":"0.5420388","text":"def find_position(self, element):\n for row in range(self.rows):\n for col in range(self.cols):\n if self.data[row][col] == element:\n return row, col\n return None, None","title":""},{"docid":"ea8a9aff2330c25ba9d687615f6b24fc","score":"0.5420388","text":"def find_position(self, element):\n for row in range(self.rows):\n for col in range(self.cols):\n if self.data[row][col] == element:\n return row, col\n return None, None","title":""},{"docid":"682e2f197fe6f72a35f951069fe31a85","score":"0.5411231","text":"def check_if_row_exists(self, session, data):\n\n row_exists = None\n user_id = 0\n\n try:\n\n user_row = self.get_user_by_id(session, data)\n\n if user_row is not None:\n user_id = user_row.user_id\n else:\n user_id = 0\n\n logger.info('User Row object in DB: %s', str(user_row))\n\n row_exists = session.query(UsersAuthModel).filter(UsersAuthModel.user_id == user_id). \\\n filter(UsersAuthModel.is_active == \"true\").scalar()\n\n logger.info('Row to data: {}, Exists: %s'.format(data), str(row_exists))\n\n except SQLAlchemyError as exc:\n row_exists = None\n logger.exception('An exception was occurred while execute transactions: %s', str(str(exc.args) + ':' +\n str(exc.code)))\n raise mvc_exc.IntegrityError(\n 'Row not stored in \"{}\". IntegrityError: {}'.format(data.get('username'),\n str(str(exc.args) + ':' + str(exc.code)))\n )\n finally:\n session.close()\n\n return row_exists","title":""},{"docid":"af5f55c5472a534f7da261b7b64221da","score":"0.54105145","text":"def fetch_fromDB(self, searchPhrase):\n pass","title":""},{"docid":"e01435938315b83878c38f66df2dc21b","score":"0.5407367","text":"def findItem(self, item):\n found_location = self.__find(item)\n\n if found_location is not None and found_location.item == item:\n return found_location\n else:\n raise NotFoundError(\"The item '\" + str(item) + \"' was not found!\")","title":""},{"docid":"ec253585855ac1e34da4648c657662e2","score":"0.53953576","text":"def __getitem__(self, index):\r\n if isinstance(index, int):\r\n try:\r\n return next(islice(self.iterall(), index, index + 1))\r\n except StopIteration:\r\n raise IndexError\r\n elif isinstance(index, six.string_types):\r\n for column in self.iterall():\r\n if column.name == index:\r\n return column\r\n raise KeyError(\"Column with name '%s' does not exist; \"\r\n \"choices are: %s\" % (index, self.names()))\r\n else:\r\n raise TypeError('row indices must be integers or str, not %s'\r\n % type(index).__name__)","title":""},{"docid":"deaca3e8f066f1c213c58f758de8cb0a","score":"0.5385548","text":"def _check_row_exists(self, pk):\n session = self.session_factory()\n exists = session.query(PipelineRun).filter_by(id=pk).first()\n session.close()\n if exists:\n return True\n return False","title":""},{"docid":"911b697a5889508444da2138328039e8","score":"0.53843147","text":"def row(self):\n return self[\"row\"]","title":""},{"docid":"c5788160017bf4e0bdc13d9ff698f5ae","score":"0.5382522","text":"def row(self):\n return self.address.row","title":""},{"docid":"39a151f6c63b68ff1a3348cd536d0bbe","score":"0.53753215","text":"def select_one(self, table, column, value):\n select_row = \"SELECT * FROM {} WHERE {}='{}' AND delete_status = FALSE;\".format(table, column, value)\n self.cursor.execute(select_row)\n row = self.cursor.fetchone()\n return row","title":""},{"docid":"eedf5bd03cdc73b707c4faa585e330a0","score":"0.5369321","text":"def in_database(entry, column, table):\n \n conn = get_database_connection(port = 2001)\n cursor = conn.cursor()\n \n query = 'SELECT * FROM %s WHERE %s = %s' % (table, column, entry) \n cursor.execute(query)\n \n results = cursor.fetchall()\n \n cursor.close()\n conn.close()\n \n if len(results) == 0:\n return False\n else:\n return True","title":""},{"docid":"92fe7c819cf5c9e1aa85f6dabc6cc820","score":"0.5362303","text":"def retrieve_from_db(self):\n pass","title":""},{"docid":"7d3a5e9df103b1d87afb0076d7bf5b73","score":"0.5359579","text":"def row(self, row_number):\n return self[self.row_cache[row_number]]","title":""},{"docid":"a3fa5e89cd379c20c386b865c7022466","score":"0.53594106","text":"def query_one(self, sql: str) -> Any:\n with self.connection.cursor() as cursor:\n self.connection.ping(reconnect=True)\n cursor.execute(sql)\n row = cursor.fetchone()\n self.connection.commit()\n return row","title":""},{"docid":"eaca5db028cf617a9c092eae301b8fc1","score":"0.53538996","text":"def __getitem__(self, key):\n for db in self.db:\n if db.name == key:\n return db\n raise IndexError","title":""},{"docid":"baedc83f21e5ba4d5c3af593fc64dba8","score":"0.53482795","text":"def fetch_row(self, sql):\n curs = self.q(sql, True)\n cols = curs.column_names\n row = curs.fetchone()\n if curs._have_unread_result():\n curs.fetchall()\n curs.close()\n\n if row:\n result = {}\n for field in cols:\n k = cols.index(field)\n result[cols[k]] = row[k]\n return result\n else:\n return None","title":""},{"docid":"1b0b9fb5f094ca2d9b318c690f7c72f0","score":"0.53389204","text":"def _find(self, hashV):\n return self.table.search(hashV)","title":""},{"docid":"30931ce1742213eaae316ceb4c08caeb","score":"0.5336372","text":"def get_row_by_position(self,row_number):\n\n if self.__rows is None:\n self.logger.debug('rbpos looking for rows matching %s' % (self.locators['row']))\n self.__rows = self.find_elements(self.locators['row'])\n self.logger.debug('self.__rows = %s' % (self.__rows))\n\n nrows = len(self.__rows)\n if (row_number+1) > nrows:\n raise IndexError(\"while attempting to retrieve row by position,\"\\\n + \" row_number == %d, max rows is %d\" % (row_number,nrows))\n\n # if row_number is out of range on the negative side, wrap around\n if row_number < 0:\n row_number = row_number % nrows\n\n # css and xpath use indicies starting from 1,\n # not 0 as was requested by this function.\n row_number = row_number + 1\n\n result = self.__row_class(self.owner,*self.__row_class_args,row_number=row_number)\n\n # make the result compatible with the iframewrap design pattern\n if self.iframe_tracker is not None:\n self.iframe_tracker.wrap_new_object(result)\n\n result.detach_from_owner()\n return result","title":""},{"docid":"b555a60e47d7a433957d5a60281058fc","score":"0.5335463","text":"def get_entry_id(self, table, field, value):\n cur = self.con.execute(\"select rowid from %s where %s='%s'\" % (table, field, value))\n res = cur.fetchone()\n # Check if id exists\n if res is None:\n # Add to table if id does not exist\n cur = self.con.execute(\"insert into %s (%s) values ('%s')\" % (table, field, value))\n return cur.lastrowid\n return res[0]","title":""},{"docid":"83af4c70302c53cefd541661d0776792","score":"0.53331697","text":"def find_in_db(self, *args, **kwargs):\n return self.relation.find_in_db(*args, **kwargs)","title":""},{"docid":"64fdfa07b52b34b71bf710cecc6e6a97","score":"0.5325645","text":"def getItem(self,row,column,default=None):\n data = self.data\n if row in data and column in data[row]:\n return data[row][column]\n else:\n return default","title":""},{"docid":"874a27dfe908150a1e04adbbbf46a830","score":"0.5324717","text":"def get_row_index(self, name):\n # type: (str) -> Tuple[str, int]\n if name == 'but':\n name = 'casc'\n if name not in self._row_lookup:\n raise ValueError('row %s not found.' % name)\n return self._row_lookup[name]","title":""},{"docid":"25e73bba3c6ecf5aced000c21e6943fd","score":"0.5324464","text":"def search_product(df, product_id):\n if product_id in df.index:\n\n return df[product_id]\n\n else:\n\n return -1","title":""},{"docid":"dccf16687d391bc5a2f7b0a3e64e5bb6","score":"0.53240776","text":"def find_asset_for_pathrow(sqlite_path, **kwargs):\n while True:\n # Generate query\n query = generate_query(**kwargs)\n\n # Find records for query\n iterator = find_records(sqlite_path, query)\n\n # Return if found\n try:\n return next(iterator)\n except StopIteration:\n if kwargs.get('max_cloud') >= 100 and kwargs.get(\n 'sort_preference') == 'closest-to-date':\n\n pathrow = kwargs.get('pathrow')\n print(\n f'Unable to find assets for pathrow {pathrow}',\n file=sys.stderr)\n return\n\n # Modify parameters\n kwargs = relax_params(**kwargs)","title":""},{"docid":"99d2eeb901f2a2b543ef9356d27ac539","score":"0.53221595","text":"def find_cell(app, cell):\n if VALID_BLOCK(cell.block) and VALID_CELL(cell):\n app_block = app.board.blocks[cell.block.column][cell.block.row]\n app_cell = app_block.cells[cell.column][cell.row]\n return app_cell\n else:\n return None","title":""},{"docid":"dbd8aad6624c354365a6ada50ad03e30","score":"0.53142","text":"def __getitem__(self, item) -> SurveyRow:\n return self.rows[item]","title":""},{"docid":"f25fab614cdcc4a7c1de5f2dbfdb85ed","score":"0.5305486","text":"def find_by_id(self, uid):\n return self.mapper.find_by_id(uid)","title":""},{"docid":"146418afc754703b7522a95208e98eb0","score":"0.53007025","text":"def dbGetFirstRow(con, cmd, args=[], skipTrace=False):\n cursor = dbExecute(con, cmd, args, skipTrace)\n return cursor.fetchone()","title":""},{"docid":"278c2dfdb750956b08500efd8991b484","score":"0.52967894","text":"def _fetch_entry_from_table(conn, table_name, entry_id):\n # Make sure the table exists.\n if not _table_exists(conn, table_name):\n return None\n # Query for the classifier.\n with conn:\n cur = conn.cursor()\n cur.execute(\n 'SELECT * FROM %s WHERE Id=\\\"%s\\\"' % (table_name, entry_id))\n query_result = cur.fetchone()\n # If found, return the classifier.\n return query_result if query_result else None","title":""},{"docid":"87c84fb02c30c08700689816c4461c58","score":"0.52934545","text":"def send_find(self, statement):\n msg_type, msg = self.protocol.build_find(statement)\n self._execute_prepared_pipeline(msg_type, msg, statement)\n return DocResult(self) if statement.is_doc_based() else RowResult(self)","title":""},{"docid":"caee2485489d80b2d4d89bbb3b2691d9","score":"0.52847517","text":"def find_item(name, currentRoom):\n for i in currentRoom.contents:\n if i.name == name:\n return i\n\n return None","title":""},{"docid":"217d45b12cd7db3dd98a8fd9aaf84c41","score":"0.5283954","text":"def test_data_returns_pointer_held_in_column(\n three_sour_model,\n): # pylint: disable=redefined-outer-name\n # Given\n index = three_sour_model.index(0, 0)\n\n # When\n pointer = three_sour_model.data(index)\n\n # Then\n assert pointer == \"I001\"","title":""},{"docid":"0ea3af67d404921582ce3f3dfb02fe60","score":"0.52785057","text":"def __get_one_by_id(\n self, table_name: str, id_name: str, db_id: str\n ) -> Mapping[str, Any]:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n f\"\"\"\n SELECT * FROM {table_name}\n WHERE ({id_name} = ?)\n \"\"\",\n (int(db_id),),\n )\n results = c.fetchall()\n if len(results) != 1:\n raise EntryDoesNotExistException(\n f\"Table {table_name} has no {id_name} {db_id}\"\n )\n return results[0]","title":""},{"docid":"391823feae6952e3d5831d584efe0aaa","score":"0.527754","text":"def fetch_row(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\n with self.cursor() as cursor:\n cursor.execute(sql, values)\n assert cursor.rowcount <= 1, f\"More than one result row for fetch_row({sql!r}, {values!r})\"\n return cursor.fetchone()","title":""},{"docid":"8660e34e4b00e3fdf15bb58900794bc7","score":"0.52706236","text":"def query(self, columns, table, matchColumn=None, matchValue=None):\n if matchValue and matchColumn:\n sql = \"\"\"SELECT %s FROM %s WHERE %s='%s'\"\"\" % (','.join(columns), table, matchColumn, matchValue)\n print \"SQL Statement: \" + sql\n else:\n sql = \"\"\"SELECT %s FROM %s\"\"\" % (','.join(columns))\n\n self.db.query(sql)\n queryResult = self.db.store_result()\n return queryResult.fetch_row(maxrows=0)","title":""},{"docid":"16ab6e4491239e0544f16857dec26ed6","score":"0.5269898","text":"def locate(self, geometry):\n cells = geometry.find(self.r)\n if cells:\n self._cell = cells[-1]\n else:\n self._cell = None","title":""},{"docid":"0ea94bb710d5032d9a6ad9e76d9c2494","score":"0.5251509","text":"def search_by_time(integer, row):\n clear_screen()\n found = False\n for item in row:\n if item[\"Time\"] == str(integer):\n print_entry(item)\n found = True\n if found is False:\n print(\"No Entries Found..\")","title":""},{"docid":"0c3cf3adefe9ad5b078301c54999afb3","score":"0.52495384","text":"def findUser(username):\n connector = appEngine.connect()\n userId = connector.execute(\"SELECT user.userID FROM user WHERE userName=(?)\", username).fetchone()\n #selectInput = select([user]).where(user.column.userName == username)\n #db.execute(selectInput)\n return userId","title":""},{"docid":"697dc39452225e1be2f44c654ec014e5","score":"0.52432054","text":"def exact_search(string, row):\n clear_screen()\n found = False\n for item in row:\n if string.lower() in item[\"Task\"].lower() \\\n or string.lower() in item[\"Notes\"].lower():\n print_entry(item)\n found = True\n if found is False:\n print(\"No Entries Found..\")","title":""},{"docid":"73c5360cddbec1475bcfddaff7b4b37d","score":"0.5240922","text":"def row(self, index):\n return self.data[index]","title":""}],"string":"[\n {\n \"docid\": \"297fb191bddda71a9dc5e40d64edc8c6\",\n \"score\": \"0.667747\",\n \"text\": \"async def find(self, pk_value: Any) -> RowProxy:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c84afbfd114dc604b825ace3583251d\",\n \"score\": \"0.64952505\",\n \"text\": \"def find_row(table, row):\\n for idx in range(len(table)):\\n if table[idx][0] == row:\\n return idx\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28839e7d2598847d8979c750a714aad8\",\n \"score\": \"0.6296006\",\n \"text\": \"def get_rows(column_to_search, value_to_match, table, db_file):\\n \\n try:\\n conn, c = connect_to_db(db_file) \\n c.execute('SELECT * FROM {t} WHERE {col}=\\\"{value}\\\"'.format(t=safe(table), \\n col=safe(column_to_search), value=value_to_match))\\n row = c.fetchall()\\n conn.close()\\n return row\\n except Exception as e:\\n print(\\\"Error when trying to get row in table\\\", table, \\\"in\\\", db_file)\\n print(e)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ab440f6a0a50b09c16c1ed4cfd8241\",\n \"score\": \"0.62917984\",\n \"text\": \"def find_row(self, row_id):\\n #Calculate what line in the file the row_id will be found at\\n looking_for_line = self.__row_id_in_file(row_id)\\n\\n #Initiate line-counter\\n current_line = 1\\n with open(self.__data_file_for_row_id(row_id), 'r') as f:\\n for line in f:\\n if current_line == looking_for_line:\\n return json.loads(line)\\n current_line += 1\\n\\n raise Exception('Could not find row_id ' + row_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"504967e10d6ef22a6779ba7fc9bb6a3d\",\n \"score\": \"0.62687343\",\n \"text\": \"def get_row_index(self):\\n for row in range(self.model.rowCount()):\\n name_item = self.model.item(row, self.COL_NAME)\\n fullpath = name_item.data(self.ROLE_FULLPATH)\\n if fullpath == self.filepath:\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca030c7af9b16e3976333b51a983936a\",\n \"score\": \"0.62624425\",\n \"text\": \"def search_db(self, key, item):\\n db = self.check_db()\\n data = [record for record in db if record[key] == item]\\n if data:\\n return data[0]\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dbbe544a08939ae858d44b9d1cb459\",\n \"score\": \"0.6181649\",\n \"text\": \"def db_row(self):\\n return self._model_cls.query.get(self._pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef52de486d6046a1be64aaf8c778956\",\n \"score\": \"0.61808544\",\n \"text\": \"def find_id(self, uid: int) -> Optional[sqlite3.Row]:\\n\\n sql = \\\"\\\"\\\"SELECT rowid, url as 'url [url]', title,\\n added as 'added [local_datetime]',\\n updated as 'updated [local_datetime]',\\n deleted as 'deleted [local_datetime]',\\n tags, comments\\n FROM bookmarks\\n WHERE rowid=?\\\"\\\"\\\"\\n return self._selectOne(sql, (uid,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e94a68f483c751a05c47fa6a37fc0c8\",\n \"score\": \"0.60699207\",\n \"text\": \"def findAt(self, index):\\n saveCursor = self.cursor\\n i = 0\\n if self.first():\\n if index == i:\\n return self.cursor\\n else:\\n while self.next():\\n i += 1\\n if index == i:\\n return self.cursor\\n self.cursor = saveCursor\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6ddd0dc35be2252846c08b164288b5\",\n \"score\": \"0.60465103\",\n \"text\": \"def WhereAmI (row, headers,table):\\r\\n\\r\\n return t.WhereAmI (row, headers,table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0281990eb782393505e2e96e107bae8\",\n \"score\": \"0.604043\",\n \"text\": \"async def find_by_id(self, _id: int) -> Record:\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n return await conn.fetchrow(\\n f\\\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\\\",\\n _id,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5e03e17097b8c93b80158b2fba2911\",\n \"score\": \"0.6037111\",\n \"text\": \"def search(item):\\n row = -1\\n try:\\n row = filter_rows.index(item)\\n except Exception:\\n logger.warning(\\\"%s was not found\\\", str(item))\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08227e90db2e48ee0cd434462b34c591\",\n \"score\": \"0.60301137\",\n \"text\": \"def find(self, value):\\n for position in range(self.get_size()):\\n if self.table[position] == value:\\n return position\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc6881c01f37ad933d0b4d0792ee8e7\",\n \"score\": \"0.5997052\",\n \"text\": \"def rpc_database_get_row_by_id(self, row_id):\\n\\t\\ttable_name = self.path.split('/')[-2]\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\\n\\t\\tassert table\\n\\t\\tcolumns = DATABASE_TABLES[table_name]\\n\\t\\tsession = db_manager.Session()\\n\\t\\trow = db_manager.get_row_by_id(session, table, row_id)\\n\\t\\tif row:\\n\\t\\t\\trow = dict(zip(columns, (getattr(row, c) for c in columns)))\\n\\t\\tsession.close()\\n\\t\\treturn row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c68ec8f12fdfa6f58d0854723163697\",\n \"score\": \"0.5869959\",\n \"text\": \"def _get_row_index(self, row: Row) -> int:\\n row_index = -1\\n for index, table_row in enumerate(self.table_data):\\n if table_row.values == row.values:\\n row_index = index\\n break\\n return row_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d11892e3af4bd3bfe30a3c8e8865d3e\",\n \"score\": \"0.58069116\",\n \"text\": \"def row_by_value(idl_, table, column, match, default=_NO_DEFAULT):\\n tab = idl_.tables[table]\\n for r in tab.rows.values():\\n if getattr(r, column) == match:\\n return r\\n if default is not _NO_DEFAULT:\\n return default\\n raise None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e84a966cb35da05610cc0e725ccec47\",\n \"score\": \"0.58031994\",\n \"text\": \"def get_row(self, pk):\\n ans = self.execute(self.commands.get_row(\\n cols=self._join_cols(self.columns),\\n table=self.name,\\n pk_col=self.primary_key_column,\\n pk=pk\\n ))\\n if not ans:\\n return None\\n return self._dictify(self.columns, ans[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4318deb02a39569b51bd6790e5f7d37c\",\n \"score\": \"0.57962567\",\n \"text\": \"def get_for_id(id,table):\\n # Implement this function\\n for row in range(1, len(table)):\\n for col in range(len(table[0])):\\n if id in table[row][col]:\\n return table[row]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56499c3188e2df25a425606e4bf2aa2\",\n \"score\": \"0.5779984\",\n \"text\": \"def search_query(self, num, query, table, field):\\n try:\\n self.db_connection()\\n if self.connection is not None: # If the database connection was established successfully\\n with self.connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchone()\\n print(type(result))\\n if result is not None and result[field] == num: # check if query result is None and pk equal to num\\n print(table + str(num), \\\"was found\\\")\\n return True\\n else:\\n print(table + str(num), \\\"wasn't found\\\")\\n return False\\n except IntegrityError:\\n print(\\\"A new record couldn't be added\\\")\\n exit()\\n finally:\\n self.close_connection() # Closing the opened connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8982c47ca30a1f6d2fee8e79075b10ec\",\n \"score\": \"0.57695395\",\n \"text\": \"def pylookup(self, col1, col2, matrix, index=3):\\n\\n for row in matrix:\\n if col1 == row[0] and col2 == row[1]:\\n return row[index]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3105696bb5e55a73dd6059a48aa35653\",\n \"score\": \"0.57678396\",\n \"text\": \"def locate(self, column, cond_inp):\\n\\n try:\\n return self.df_input.loc[self.df_input[column] == cond_inp]\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee34301f45dcffed13f7dc7accf30b7a\",\n \"score\": \"0.5747678\",\n \"text\": \"def select_from_DB (itemToSearch, tableWhereToSearch):\\n session = open_session()\\n s = select([itemToSearch.tableWhereToSearch])\\n result = session.execute(s)\\n for row in result:\\n print(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6637a320c7cb67a30929380f2f37c8be\",\n \"score\": \"0.5729195\",\n \"text\": \"def find_result_row(self, search_text, html_column_name):\\n headers = []\\n results = []\\n html_header = self.driver.find_elements_by_css_selector('.header')\\n for header in html_header:\\n headers.append(header.text)\\n\\n html_results = self.driver.find_elements_by_css_selector('.dataGridRow')\\n for id, row in enumerate(html_results):\\n results.append({headers[id]:item.text for id, item in enumerate(row.find_elements_by_css_selector('td'))})\\n\\n if results:\\n # Find result row, 0 = first entry\\n for id, person in enumerate(results):\\n if search_text.lower() == person[html_column_name].lower():\\n # Match found, but check for duplicates\\n temp = []\\n for people in results:\\n if people[html_column_name] in temp:\\n return -2 # Duplicate found\\n else:\\n temp.append(people[html_column_name])\\n return id\\n return -1 # No result or no match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a82c5f0cca6da9c941109922090673f\",\n \"score\": \"0.56894594\",\n \"text\": \"def get_result_from_db() -> list:\\n global choice, confirmation, res, data, column_names, result, choice_row\\n column_names = data[0]\\n try:\\n confirmation.after(1, confirmation.destroy)\\n except AttributeError:\\n pass\\n choice_row = choice.get()\\n res = place_for_enter.get()\\n if choice_row in column_names:\\n result = simple_search_from_db(data_base, table, choice_row, res)\\n return result\\n else:\\n mistake_select_value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae0f9ef58912819debaf910055a9924\",\n \"score\": \"0.5688523\",\n \"text\": \"def search_element_of_table(path, table, nameColumn, data):\\n conn = sqlite3.connect(path)\\n c = conn.cursor()\\n to_return = \\\"<tr>\\\"\\n for row in c.execute('SELECT * FROM '+table +' where '+nameColumn+\\\" like '%\\\"+data+\\\"%'\\\").fetchall():\\n for x in row:\\n to_return = to_return + \\\"<td>\\\"+ str(x) + \\\"</td>\\\"\\n to_return = to_return + \\\"</tr>\\\"\\n\\n conn.close()\\n return to_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82303fc4f119816dddc36c000b2d804f\",\n \"score\": \"0.5684421\",\n \"text\": \"def dt_find(search = \\\"\\\",\\n row = None,\\n column = None,\\n tableID = \\\"list\\\",\\n first = False,\\n ):\\n # 'todo need to fix the searching on numbers\\n config = current.test_config\\n browser = config.browser\\n\\n # Calculate the rows that need to be navigated along to find the search string\\n colList = []\\n rowList = []\\n if row == None:\\n r = 1\\n while True:\\n tr = \\\".//*[@id='%s']/tbody/tr[%s]\\\" % (tableID, r)\\n try:\\n elem = browser.find_element_by_xpath(tr)\\n rowList.append(r)\\n r += 1\\n except:\\n break\\n elif isinstance(row, int):\\n rowList = [row]\\n else:\\n rowList = row\\n # Calculate the columns that need to be navigated down to find the search string\\n if column == None:\\n c = 1\\n while True:\\n td = \\\".//*[@id='%s']/tbody/tr[1]/td[%s]\\\" % (tableID, c)\\n try:\\n elem = browser.find_element_by_xpath(td)\\n colList.append(c)\\n c += 1\\n except:\\n break\\n elif isinstance(column, int):\\n colList = [column]\\n else:\\n colList = column\\n s3_debug(\\\"rows %s, columns %s\\\" % (rowList, colList))\\n # Now try and find a match\\n result = []\\n for r in rowList:\\n for c in colList:\\n td = \\\".//*[@id='%s']/tbody/tr[%s]/td[%s]\\\" % (tableID, r, c)\\n try:\\n elem = browser.find_element_by_xpath(td)\\n s3_debug(\\\"got %s, needle %s\\\" % (elem.text, search))\\n if elem.text == search:\\n if first:\\n return (r, c)\\n else:\\n result.append((r, c))\\n except:\\n pass\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ad858b56a2534b08e265d7a2225c2c\",\n \"score\": \"0.56746584\",\n \"text\": \"def get(self, question, rows):\\n con.query(question)\\n result = con.use_result()\\n return result.fetch_row(rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bd82cca6db12cd561101fb3c1c3871\",\n \"score\": \"0.56663024\",\n \"text\": \"def extract_row(self, column, identifier, df_input=None):\\n\\n try:\\n if df_input is None:\\n return self.df_input.loc[self.df_input[column] == identifier]\\n else:\\n return df_input.loc[df_input[column] == identifier] \\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545604459049dd550b1ee2aa5db95af1\",\n \"score\": \"0.56301135\",\n \"text\": \"def find(self, target):\\n try:\\n if type(target) is int:\\n for key, value in self.index.table.items():\\n if value == target:\\n return(key)\\n elif type(target) is str:\\n for key, value in self.index.table.items():\\n if key == target:\\n return(value)\\n except Exception as error:\\n print(f\\\"Error: self.find({target}) -> {error}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2229e71295060ce4193b244fa7f52778\",\n \"score\": \"0.5620588\",\n \"text\": \"def get_column(col_to_search, value_to_match, col_to_get, table, db_file):\\n \\n try:\\n conn, c = connect_to_db(db_file) \\n c.execute('SELECT {cg} FROM {t} WHERE {col}=\\\"{value}\\\"'.format(t=safe(table), \\n cg=safe(col_to_get), col=safe(col_to_search), value=safe(value_to_match)))\\n column = c.fetchone()\\n conn.close()\\n return column\\n except Exception as e:\\n print(\\\"Error when trying to fetch row in table\\\", table, \\\"in database file\\\", db_file)\\n print(e)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a08d0e09bbaafef00bbac0522edd781\",\n \"score\": \"0.56205857\",\n \"text\": \"def getRowPosition(sheetData, text, column):\\n for index, row in enumerate(sheetData):\\n if row[column] == text:\\n return index\\n raise ValueError('{0} is not in column {1}'.format(text, column))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb7cf555e3d50761a1b8d3cdd862b91\",\n \"score\": \"0.55995876\",\n \"text\": \"def get_row(self):\\n return self._row_number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f522d2eebf883389c89309ecbcc6e04b\",\n \"score\": \"0.55955905\",\n \"text\": \"def get_row(self, row):\\n \\n return self.sudoku_matrix[row]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c3db93e876eb6ee1d8016539e54fd4\",\n \"score\": \"0.5590182\",\n \"text\": \"def getCurrentRow(self):\\n item = self.getSelectedItem()\\n if item:\\n return item.row()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b66f8d2c58613ebb903de672049399\",\n \"score\": \"0.55896556\",\n \"text\": \"def get_absolute_row(self, index):\\n try:\\n return self._table.getRow(index)\\n except Registry.Table.INVALID_ROW:\\n import traceback \\n raise IndexError(\\n \\\"Index %s out of bounds. Original exception: %s\\\" % \\\\\\n (index, traceback.format_exc()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f192d09b2efca6af71a25e100b4413bf\",\n \"score\": \"0.5565859\",\n \"text\": \"def getTableEntryByAddress(self, address):\\n for entry in self.table:\\n if entry.hasPage():\\n if entry.getPage().contains(address):\\n return entry\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc85145fa65f3739dd418d30afe4c6a8\",\n \"score\": \"0.5562986\",\n \"text\": \"def findWhere(cls, args):\\n return cls.search(args)[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3092d6220f910c11542b7e4c6227c23\",\n \"score\": \"0.55624574\",\n \"text\": \"def get_query_single_row(query, *args):\\n conn = get_connection()\\n cursor = conn.cursor()\\n\\n rows = cursor.execute(query, args).fetchall()\\n\\n result = map_row(cursor, rows[0])\\n\\n cursor.close()\\n conn.close()\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c0bfcd71d9e0d32208cbd00f0129a3\",\n \"score\": \"0.55473316\",\n \"text\": \"def db_search_by_path(self, path):\\n \\n try:\\n with self.connection:\\n c = self.connection.cursor()\\n query = \\\"SELECT * FROM %s WHERE path=?\\\" % PUBLICATIONS_TABLE\\n c.execute(query, [path])\\n return c.fetchone() # This path is a unique absolute path.\\n except sqlite.Error as e:\\n logging.exception(e)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddad2f320ea6a8bca786356440c70a1\",\n \"score\": \"0.55336905\",\n \"text\": \"def get_index_in_table_row(self, table_locator, row, expected, loglevel='INFO'):\\n row = int(row)\\n row_index = row - 1\\n table = self._table_element_finder.find(self._current_browser(), table_locator)\\n if table is not None:\\n rows = table.find_elements_by_xpath(\\\"./thead/tr\\\")\\n if row_index >= len(rows): rows.extend(table.find_elements_by_xpath(\\\"./tbody/tr\\\"))\\n if row_index >= len(rows): rows.extend(table.find_elements_by_xpath(\\\"./tfoot/tr\\\"))\\n if row_index < len(rows):\\n columns = rows[row_index].find_elements_by_tag_name('th')\\n columns.extend(rows[row_index].find_elements_by_tag_name('td'))\\n column_index = 0\\n for element in columns:\\n column_index = column_index + 1\\n element_text = element.text\\n if element_text and expected in element_text:\\n return column_index\\n self.log_source(loglevel)\\n raise AssertionError(\\\"%s could not be found in row #%s of table %s.\\\"\\n % (expected, str(row), table_locator))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9bc570b6e6387b4217ed40b613e96a\",\n \"score\": \"0.55283874\",\n \"text\": \"def get_row( self, rowIndex ):\\n return self.data[rowIndex,:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed462ee4637137dac2b15c49658b7b99\",\n \"score\": \"0.55276054\",\n \"text\": \"def get_source_row(self, row):\\n return self._dataframe.index[row]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf55e9df461bdf8d17311a91903c3737\",\n \"score\": \"0.55211335\",\n \"text\": \"def find(self, id):\\n return self._select_one('''\\n select\\n *\\n from\\n {table}\\n where\\n {primary_key} = %s\\n '''.format(table=self.__class__._table,\\n primary_key=self.__class__._primary_key), [id])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18505f4e884ee890561e9ab664322b8\",\n \"score\": \"0.5515408\",\n \"text\": \"def find_cell_row(cell_roi_df, master_index_df):\\n cell_roi_cols = ['cell_index', 'xy', 'date']\\n \\n if 'sub_coord' in master_index_df.columns:\\n master_index_cols = ['sub_coord', 'xy', 'date']\\n elif 'cell_index' in master_index_df.columns:\\n master_index_cols = ['cell_index', 'xy', 'date']\\n else:\\n print(f\\\"No cell_index or sub_coord column in master_index_df\\\")\\n return None\\n\\n matching_rows = []\\n for row in master_index_df.index:\\n\\n query = [int(cell_roi_df.loc[0, colname]) for colname in cell_roi_cols]\\n master = [int(master_index_df.loc[row, colname]) for colname in master_index_cols]\\n\\n if query == master:\\n matching_rows.append(row)\\n\\n if len(matching_rows) > 1:\\n print(f\\\"Found multiple rows matching cell_roi_df. Check master index for duplicates at:/n{master_index.path.iloc[0]}\\\")\\n return None\\n elif len(matching_rows) == 0:\\n print(\\\"Found no rows in master_index_df matching cell_roi_df\\\")\\n return None\\n else:\\n matching_row = matching_rows[0]\\n return matching_row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a9353c3c46d97e6ef02217439e1220\",\n \"score\": \"0.55056393\",\n \"text\": \"def get_or_raise(cls, pk, exc=Exception(\\\"数据库错误\\\")):\\n row = DBSession().query(cls).get(pk)\\n if row is None:\\n raise Exception(\\\"{}记录不存在\\\".format(cls.__name__))\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b223e28473f3fa112f715653822fc3c\",\n \"score\": \"0.5503251\",\n \"text\": \"def find(self, key):\\n if isinstance(key, (int, long)):\\n result = self.table.Find(<long> key).decode('utf8')\\n if result == u'':\\n raise KeyError(key)\\n return result\\n else:\\n key = as_str(key)\\n result = self.table.Find(<char*>key)\\n if result == -1:\\n raise KeyError(key)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"902fd6f2180439e169585dc803c9335a\",\n \"score\": \"0.5490408\",\n \"text\": \"def find(self,item):\\n sig = str(item)\\n try:\\n return self.index[sig]\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a593527fb945b6805f8ff64b31e3c4b9\",\n \"score\": \"0.5488193\",\n \"text\": \"def getRowFromPath(self, path):\\n query = \\\"SELECT resource_id, etag, title FROM docs WHERE local_path = ?\\\"\\n res = self.db.execute(query, (path,)).fetchone()\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c398b0681ac3a65593a01e1bd5411e4\",\n \"score\": \"0.5487673\",\n \"text\": \"def test_get_row_index(self):\\n self.dboard.set_row_names([\\\"1\\\", \\\"2\\\"])\\n self.assertEqual(0, self.dboard._get_row_index(\\\"1\\\"))\\n self.assertEqual(1, self.dboard._get_row_index(\\\"2\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb09f0be0e172d40ae402c1c2222725c\",\n \"score\": \"0.54837364\",\n \"text\": \"def find(self, node):\\n saveCursor = self.cursor\\n if self.first() and self.cursor.nodeId == node.nodeId:\\n return self.cursor\\n while self.next():\\n if self.cursor.nodeId == node.nodeId:\\n return self.cursor\\n self.cursor = saveCursor\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4c17cf78c7c55c73a01065c4087b42\",\n \"score\": \"0.54808366\",\n \"text\": \"def _find_by_key(self, key, find):\\n index = hashId(key, self.capacity) # Get the index/ bucket based on hash code of the key\\n \\n hash_table_cell = self._entry[index]\\n found_item = None\\n for item in hash_table_cell: #Iterrate the entry array and check the key is matching and if key is same than get the value\\n if item[0] == key:\\n found_item = item\\n break\\n\\n return find(found_item, hash_table_cell)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260c045dfeca6f307e20a8e9056b6a30\",\n \"score\": \"0.5479559\",\n \"text\": \"def query_exists(self, q, param=None):\\r\\n \\\"\\\"\\\" Queries database and return one row \\\"\\\"\\\"\\r\\n try:\\r\\n c = self.connection.cursor()\\r\\n c.execute(q, param)\\r\\n self.logger.log(logger.LogLevel.DEBUG, 'database.query_exists: %s | %s' % (q, param)) \\r\\n if c.fetchone() is None:\\r\\n return False\\r\\n else:\\r\\n return True\\r\\n except Exception as e:\\r\\n self.logger.log(logger.LogLevel.ERROR, 'database.query_exists: %s. %s | %s' % (e, q, param))\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5fdd447af75f01bbd50bbfbfcd6a099\",\n \"score\": \"0.54712725\",\n \"text\": \"def find_index(row):\\n value = row[index]\\n if value in seen:\\n return seen[value]\\n for row_ in merged.iter_dicts(True):\\n if row_[index] == value:\\n seen[value] = row_[\\\"index\\\"]\\n return row_[\\\"index\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff31caa5fabc6b7a788bb37a718cec3\",\n \"score\": \"0.54655886\",\n \"text\": \"def find(self, key):\\n index = self.quadratic_probe(key)\\n if index != -1:\\n if self.table[index] is not None:\\n return self.table[index]\\n else:\\n return False\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd41cd66077140c9b9e8a6d809f9d3e\",\n \"score\": \"0.54550296\",\n \"text\": \"def find(self, primary_key):\\n sql = '{} WHERE {} = %s'.format(self._select(), self.PRIMARY_KEY)\\n cursor = yield self._pool.execute(sql, [primary_key])\\n result = cursor.fetchmany(1)\\n return self.convert_result_to_object(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f458214eeeeeca3d2f4365b6d1849ab0\",\n \"score\": \"0.54480684\",\n \"text\": \"def find(self, value):\\n for row in range(self.getHeight()):\\n for column in range(self.getWidth()):\\n if self[row][column] == value:\\n return (row, column)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f79a69243ac3fc07171e16b942411cfe\",\n \"score\": \"0.54478145\",\n \"text\": \"def find_element(grid, target):\\n \\n # First, iterate over the row indices\\n for row_number in range(len(grid)):\\n \\n# print(\\\"Checking row\\\", row_number)\\n \\n for col_number in range(len(grid[row_number])):\\n \\n# print(\\\"Checking column\\\", col_number)\\n \\n if grid[row_number][col_number] == target:\\n return (row_number, col_number)\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b76737edef07065ba71e15286bf9a4b1\",\n \"score\": \"0.54471594\",\n \"text\": \"def GetTableCurrentRow (latextable):\\r\\n\\r\\n return t.GetTableCurrentRow (latextable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4feb83dc1859a58f08952fe4d0b89037\",\n \"score\": \"0.54371154\",\n \"text\": \"def get_entry_from_row(self, row: [str], column_header: str) -> str:\\n column_index = self.header.index(column_header)\\n return row[column_index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef97bf5e8805263d44c2a3573aaca0c\",\n \"score\": \"0.5434794\",\n \"text\": \"def parent_row(self):\\n dict_cur.execute('SELECT * FROM \\\"{}\\\" WHERE {} = {}'.format(\\n self.parent_table(), self.id_col_of_parent(), self.parent_id\\n ))\\n return dict_cur.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f12769f65651514e00012bb4e7378cd\",\n \"score\": \"0.5425477\",\n \"text\": \"def get_run(self, run_id: str) -> sqlite3.Row:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from runs\\n WHERE run_id = ?;\\n \\\"\\\"\\\",\\n (run_id,),\\n )\\n results = c.fetchall()\\n return results[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea8a9aff2330c25ba9d687615f6b24fc\",\n \"score\": \"0.5420388\",\n \"text\": \"def find_position(self, element):\\n for row in range(self.rows):\\n for col in range(self.cols):\\n if self.data[row][col] == element:\\n return row, col\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea8a9aff2330c25ba9d687615f6b24fc\",\n \"score\": \"0.5420388\",\n \"text\": \"def find_position(self, element):\\n for row in range(self.rows):\\n for col in range(self.cols):\\n if self.data[row][col] == element:\\n return row, col\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682e2f197fe6f72a35f951069fe31a85\",\n \"score\": \"0.5411231\",\n \"text\": \"def check_if_row_exists(self, session, data):\\n\\n row_exists = None\\n user_id = 0\\n\\n try:\\n\\n user_row = self.get_user_by_id(session, data)\\n\\n if user_row is not None:\\n user_id = user_row.user_id\\n else:\\n user_id = 0\\n\\n logger.info('User Row object in DB: %s', str(user_row))\\n\\n row_exists = session.query(UsersAuthModel).filter(UsersAuthModel.user_id == user_id). \\\\\\n filter(UsersAuthModel.is_active == \\\"true\\\").scalar()\\n\\n logger.info('Row to data: {}, Exists: %s'.format(data), str(row_exists))\\n\\n except SQLAlchemyError as exc:\\n row_exists = None\\n logger.exception('An exception was occurred while execute transactions: %s', str(str(exc.args) + ':' +\\n str(exc.code)))\\n raise mvc_exc.IntegrityError(\\n 'Row not stored in \\\"{}\\\". IntegrityError: {}'.format(data.get('username'),\\n str(str(exc.args) + ':' + str(exc.code)))\\n )\\n finally:\\n session.close()\\n\\n return row_exists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5f55c5472a534f7da261b7b64221da\",\n \"score\": \"0.54105145\",\n \"text\": \"def fetch_fromDB(self, searchPhrase):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01435938315b83878c38f66df2dc21b\",\n \"score\": \"0.5407367\",\n \"text\": \"def findItem(self, item):\\n found_location = self.__find(item)\\n\\n if found_location is not None and found_location.item == item:\\n return found_location\\n else:\\n raise NotFoundError(\\\"The item '\\\" + str(item) + \\\"' was not found!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec253585855ac1e34da4648c657662e2\",\n \"score\": \"0.53953576\",\n \"text\": \"def __getitem__(self, index):\\r\\n if isinstance(index, int):\\r\\n try:\\r\\n return next(islice(self.iterall(), index, index + 1))\\r\\n except StopIteration:\\r\\n raise IndexError\\r\\n elif isinstance(index, six.string_types):\\r\\n for column in self.iterall():\\r\\n if column.name == index:\\r\\n return column\\r\\n raise KeyError(\\\"Column with name '%s' does not exist; \\\"\\r\\n \\\"choices are: %s\\\" % (index, self.names()))\\r\\n else:\\r\\n raise TypeError('row indices must be integers or str, not %s'\\r\\n % type(index).__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deaca3e8f066f1c213c58f758de8cb0a\",\n \"score\": \"0.5385548\",\n \"text\": \"def _check_row_exists(self, pk):\\n session = self.session_factory()\\n exists = session.query(PipelineRun).filter_by(id=pk).first()\\n session.close()\\n if exists:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911b697a5889508444da2138328039e8\",\n \"score\": \"0.53843147\",\n \"text\": \"def row(self):\\n return self[\\\"row\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5788160017bf4e0bdc13d9ff698f5ae\",\n \"score\": \"0.5382522\",\n \"text\": \"def row(self):\\n return self.address.row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39a151f6c63b68ff1a3348cd536d0bbe\",\n \"score\": \"0.53753215\",\n \"text\": \"def select_one(self, table, column, value):\\n select_row = \\\"SELECT * FROM {} WHERE {}='{}' AND delete_status = FALSE;\\\".format(table, column, value)\\n self.cursor.execute(select_row)\\n row = self.cursor.fetchone()\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedf5bd03cdc73b707c4faa585e330a0\",\n \"score\": \"0.5369321\",\n \"text\": \"def in_database(entry, column, table):\\n \\n conn = get_database_connection(port = 2001)\\n cursor = conn.cursor()\\n \\n query = 'SELECT * FROM %s WHERE %s = %s' % (table, column, entry) \\n cursor.execute(query)\\n \\n results = cursor.fetchall()\\n \\n cursor.close()\\n conn.close()\\n \\n if len(results) == 0:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92fe7c819cf5c9e1aa85f6dabc6cc820\",\n \"score\": \"0.5362303\",\n \"text\": \"def retrieve_from_db(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d3a5e9df103b1d87afb0076d7bf5b73\",\n \"score\": \"0.5359579\",\n \"text\": \"def row(self, row_number):\\n return self[self.row_cache[row_number]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fa5e89cd379c20c386b865c7022466\",\n \"score\": \"0.53594106\",\n \"text\": \"def query_one(self, sql: str) -> Any:\\n with self.connection.cursor() as cursor:\\n self.connection.ping(reconnect=True)\\n cursor.execute(sql)\\n row = cursor.fetchone()\\n self.connection.commit()\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaca5db028cf617a9c092eae301b8fc1\",\n \"score\": \"0.53538996\",\n \"text\": \"def __getitem__(self, key):\\n for db in self.db:\\n if db.name == key:\\n return db\\n raise IndexError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baedc83f21e5ba4d5c3af593fc64dba8\",\n \"score\": \"0.53482795\",\n \"text\": \"def fetch_row(self, sql):\\n curs = self.q(sql, True)\\n cols = curs.column_names\\n row = curs.fetchone()\\n if curs._have_unread_result():\\n curs.fetchall()\\n curs.close()\\n\\n if row:\\n result = {}\\n for field in cols:\\n k = cols.index(field)\\n result[cols[k]] = row[k]\\n return result\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0b9fb5f094ca2d9b318c690f7c72f0\",\n \"score\": \"0.53389204\",\n \"text\": \"def _find(self, hashV):\\n return self.table.search(hashV)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30931ce1742213eaae316ceb4c08caeb\",\n \"score\": \"0.5336372\",\n \"text\": \"def get_row_by_position(self,row_number):\\n\\n if self.__rows is None:\\n self.logger.debug('rbpos looking for rows matching %s' % (self.locators['row']))\\n self.__rows = self.find_elements(self.locators['row'])\\n self.logger.debug('self.__rows = %s' % (self.__rows))\\n\\n nrows = len(self.__rows)\\n if (row_number+1) > nrows:\\n raise IndexError(\\\"while attempting to retrieve row by position,\\\"\\\\\\n + \\\" row_number == %d, max rows is %d\\\" % (row_number,nrows))\\n\\n # if row_number is out of range on the negative side, wrap around\\n if row_number < 0:\\n row_number = row_number % nrows\\n\\n # css and xpath use indicies starting from 1,\\n # not 0 as was requested by this function.\\n row_number = row_number + 1\\n\\n result = self.__row_class(self.owner,*self.__row_class_args,row_number=row_number)\\n\\n # make the result compatible with the iframewrap design pattern\\n if self.iframe_tracker is not None:\\n self.iframe_tracker.wrap_new_object(result)\\n\\n result.detach_from_owner()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b555a60e47d7a433957d5a60281058fc\",\n \"score\": \"0.5335463\",\n \"text\": \"def get_entry_id(self, table, field, value):\\n cur = self.con.execute(\\\"select rowid from %s where %s='%s'\\\" % (table, field, value))\\n res = cur.fetchone()\\n # Check if id exists\\n if res is None:\\n # Add to table if id does not exist\\n cur = self.con.execute(\\\"insert into %s (%s) values ('%s')\\\" % (table, field, value))\\n return cur.lastrowid\\n return res[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83af4c70302c53cefd541661d0776792\",\n \"score\": \"0.53331697\",\n \"text\": \"def find_in_db(self, *args, **kwargs):\\n return self.relation.find_in_db(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64fdfa07b52b34b71bf710cecc6e6a97\",\n \"score\": \"0.5325645\",\n \"text\": \"def getItem(self,row,column,default=None):\\n data = self.data\\n if row in data and column in data[row]:\\n return data[row][column]\\n else:\\n return default\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874a27dfe908150a1e04adbbbf46a830\",\n \"score\": \"0.5324717\",\n \"text\": \"def get_row_index(self, name):\\n # type: (str) -> Tuple[str, int]\\n if name == 'but':\\n name = 'casc'\\n if name not in self._row_lookup:\\n raise ValueError('row %s not found.' % name)\\n return self._row_lookup[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e73bba3c6ecf5aced000c21e6943fd\",\n \"score\": \"0.5324464\",\n \"text\": \"def search_product(df, product_id):\\n if product_id in df.index:\\n\\n return df[product_id]\\n\\n else:\\n\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccf16687d391bc5a2f7b0a3e64e5bb6\",\n \"score\": \"0.53240776\",\n \"text\": \"def find_asset_for_pathrow(sqlite_path, **kwargs):\\n while True:\\n # Generate query\\n query = generate_query(**kwargs)\\n\\n # Find records for query\\n iterator = find_records(sqlite_path, query)\\n\\n # Return if found\\n try:\\n return next(iterator)\\n except StopIteration:\\n if kwargs.get('max_cloud') >= 100 and kwargs.get(\\n 'sort_preference') == 'closest-to-date':\\n\\n pathrow = kwargs.get('pathrow')\\n print(\\n f'Unable to find assets for pathrow {pathrow}',\\n file=sys.stderr)\\n return\\n\\n # Modify parameters\\n kwargs = relax_params(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d2eeb901f2a2b543ef9356d27ac539\",\n \"score\": \"0.53221595\",\n \"text\": \"def find_cell(app, cell):\\n if VALID_BLOCK(cell.block) and VALID_CELL(cell):\\n app_block = app.board.blocks[cell.block.column][cell.block.row]\\n app_cell = app_block.cells[cell.column][cell.row]\\n return app_cell\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbd8aad6624c354365a6ada50ad03e30\",\n \"score\": \"0.53142\",\n \"text\": \"def __getitem__(self, item) -> SurveyRow:\\n return self.rows[item]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25fab614cdcc4a7c1de5f2dbfdb85ed\",\n \"score\": \"0.5305486\",\n \"text\": \"def find_by_id(self, uid):\\n return self.mapper.find_by_id(uid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146418afc754703b7522a95208e98eb0\",\n \"score\": \"0.53007025\",\n \"text\": \"def dbGetFirstRow(con, cmd, args=[], skipTrace=False):\\n cursor = dbExecute(con, cmd, args, skipTrace)\\n return cursor.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278c2dfdb750956b08500efd8991b484\",\n \"score\": \"0.52967894\",\n \"text\": \"def _fetch_entry_from_table(conn, table_name, entry_id):\\n # Make sure the table exists.\\n if not _table_exists(conn, table_name):\\n return None\\n # Query for the classifier.\\n with conn:\\n cur = conn.cursor()\\n cur.execute(\\n 'SELECT * FROM %s WHERE Id=\\\\\\\"%s\\\\\\\"' % (table_name, entry_id))\\n query_result = cur.fetchone()\\n # If found, return the classifier.\\n return query_result if query_result else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c84fb02c30c08700689816c4461c58\",\n \"score\": \"0.52934545\",\n \"text\": \"def send_find(self, statement):\\n msg_type, msg = self.protocol.build_find(statement)\\n self._execute_prepared_pipeline(msg_type, msg, statement)\\n return DocResult(self) if statement.is_doc_based() else RowResult(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caee2485489d80b2d4d89bbb3b2691d9\",\n \"score\": \"0.52847517\",\n \"text\": \"def find_item(name, currentRoom):\\n for i in currentRoom.contents:\\n if i.name == name:\\n return i\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"217d45b12cd7db3dd98a8fd9aaf84c41\",\n \"score\": \"0.5283954\",\n \"text\": \"def test_data_returns_pointer_held_in_column(\\n three_sour_model,\\n): # pylint: disable=redefined-outer-name\\n # Given\\n index = three_sour_model.index(0, 0)\\n\\n # When\\n pointer = three_sour_model.data(index)\\n\\n # Then\\n assert pointer == \\\"I001\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea3af67d404921582ce3f3dfb02fe60\",\n \"score\": \"0.52785057\",\n \"text\": \"def __get_one_by_id(\\n self, table_name: str, id_name: str, db_id: str\\n ) -> Mapping[str, Any]:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n f\\\"\\\"\\\"\\n SELECT * FROM {table_name}\\n WHERE ({id_name} = ?)\\n \\\"\\\"\\\",\\n (int(db_id),),\\n )\\n results = c.fetchall()\\n if len(results) != 1:\\n raise EntryDoesNotExistException(\\n f\\\"Table {table_name} has no {id_name} {db_id}\\\"\\n )\\n return results[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"391823feae6952e3d5831d584efe0aaa\",\n \"score\": \"0.527754\",\n \"text\": \"def fetch_row(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\\n with self.cursor() as cursor:\\n cursor.execute(sql, values)\\n assert cursor.rowcount <= 1, f\\\"More than one result row for fetch_row({sql!r}, {values!r})\\\"\\n return cursor.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8660e34e4b00e3fdf15bb58900794bc7\",\n \"score\": \"0.52706236\",\n \"text\": \"def query(self, columns, table, matchColumn=None, matchValue=None):\\n if matchValue and matchColumn:\\n sql = \\\"\\\"\\\"SELECT %s FROM %s WHERE %s='%s'\\\"\\\"\\\" % (','.join(columns), table, matchColumn, matchValue)\\n print \\\"SQL Statement: \\\" + sql\\n else:\\n sql = \\\"\\\"\\\"SELECT %s FROM %s\\\"\\\"\\\" % (','.join(columns))\\n\\n self.db.query(sql)\\n queryResult = self.db.store_result()\\n return queryResult.fetch_row(maxrows=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ab6e4491239e0544f16857dec26ed6\",\n \"score\": \"0.5269898\",\n \"text\": \"def locate(self, geometry):\\n cells = geometry.find(self.r)\\n if cells:\\n self._cell = cells[-1]\\n else:\\n self._cell = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea94bb710d5032d9a6ad9e76d9c2494\",\n \"score\": \"0.5251509\",\n \"text\": \"def search_by_time(integer, row):\\n clear_screen()\\n found = False\\n for item in row:\\n if item[\\\"Time\\\"] == str(integer):\\n print_entry(item)\\n found = True\\n if found is False:\\n print(\\\"No Entries Found..\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c3cf3adefe9ad5b078301c54999afb3\",\n \"score\": \"0.52495384\",\n \"text\": \"def findUser(username):\\n connector = appEngine.connect()\\n userId = connector.execute(\\\"SELECT user.userID FROM user WHERE userName=(?)\\\", username).fetchone()\\n #selectInput = select([user]).where(user.column.userName == username)\\n #db.execute(selectInput)\\n return userId\",\n \"title\": \"\"\n },\n {\n \"docid\": \"697dc39452225e1be2f44c654ec014e5\",\n \"score\": \"0.52432054\",\n \"text\": \"def exact_search(string, row):\\n clear_screen()\\n found = False\\n for item in row:\\n if string.lower() in item[\\\"Task\\\"].lower() \\\\\\n or string.lower() in item[\\\"Notes\\\"].lower():\\n print_entry(item)\\n found = True\\n if found is False:\\n print(\\\"No Entries Found..\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c5360cddbec1475bcfddaff7b4b37d\",\n \"score\": \"0.5240922\",\n \"text\": \"def row(self, index):\\n return self.data[index]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994481,"cells":{"query_id":{"kind":"string","value":"61f0f13a2701498ff9538f00e55c8cc6"},"query":{"kind":"string","value":"Returns the log of probability estimates."},"positive_passages":{"kind":"list like","value":[{"docid":"6719902eb571034a7a96440a4eb1d871","score":"0.68925774","text":"def predict_log_proba(self, X):\n return np.log(self.predict_proba(X))","title":""}],"string":"[\n {\n \"docid\": \"6719902eb571034a7a96440a4eb1d871\",\n \"score\": \"0.68925774\",\n \"text\": \"def predict_log_proba(self, X):\\n return np.log(self.predict_proba(X))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e0cf42a38133d6cf396650edba63c72c","score":"0.73786455","text":"def priorlogprob(self):\n return numpy.array([p.logpdf(x) for p, x in zip(self.getpriors(), self.getparams())])","title":""},{"docid":"01f0ec5bea8d6e48c9938f08f2177d9b","score":"0.7042134","text":"def logp(self):\n # logp for all the parameters\n return np.sum(\n [\n param.logp()\n for param in f_unique(flatten(self.data))\n if param.vary\n ]\n )","title":""},{"docid":"167ecd81421b7fa8e53ceb2c10966c34","score":"0.7031832","text":"def log_probability(self, weights):\n strict_log_probability = sum(\n [self.strict_log_probability(preference, weights) for preference in self.strict_preferences])\n indifferent_log_probability = sum(\n [self.indifferent_log_probability(preference, weights) for preference in self.indifferent_preferences])\n log_prior = sum([self.priors[i](weight) for i, weight in enumerate(weights)])\n return strict_log_probability + indifferent_log_probability + log_prior","title":""},{"docid":"266a554f4cb0f1c65daa397d65f3c6ad","score":"0.7021919","text":"def log_prob(self, X):\n p = self.p\n r = self.r\n\n # broadcasting\n if xr != None and isinstance(p, xr.DataArray) and isinstance(r, xr.DataArray) and isinstance(X, xr.DataArray):\n p, r, X = xr.align(p, r, X)\n else:\n p, r, X = np.broadcast_arrays(p, r, X)\n\n # return scipy.stats.nbinom(n=r, p=1 - p).logpmf(X)\n coeff = gammaln(r + X) - gammaln(X + 1) - gammaln(r)\n return coeff + r * np.log(1 - p) + X * np.log(p)","title":""},{"docid":"976925ca9b633b9bfc5b3b2ea67f5ab1","score":"0.699978","text":"def log_prob(self, x):\n function = lambda x: x * np.exp(-x ** 2)\n return torch.sum(np.log(self.pi) + self.gaussian1.log_prob(x)\n + np.log1p(((1 - self.pi) / self.pi) * function(self.sigma1 / self.sigma2)))","title":""},{"docid":"2367ce660f041700be584b7822f80fc0","score":"0.69937867","text":"def lnprob(pars):\n model = residual(pars, logx)\n return -0.5 * np.sum(((model - logy) / logyerr)**2 + np.log(2 * np.pi * logyerr**2))","title":""},{"docid":"f32edd9c8c1f078ed9f7ce9a69ffa15d","score":"0.6952415","text":"def log_probability(theta, V, EW, FeH, e_ew, e_feh, power):\n lp = log_prior(theta)\n if not np.isfinite(lp):\n return -np.inf\n return lp + log_likelihood(theta, V, EW, FeH, e_ew, e_feh, power)","title":""},{"docid":"334169aea74692f33f07de4347728e4e","score":"0.69425666","text":"def _logProbability(self, x, mask, mask_):\n z = self._inference(x, mask, mask_, True)\n return self.prior.logProbability(z) + self._inferenceLogjac","title":""},{"docid":"289574b65fb4ab3786f40f310d3bf4c0","score":"0.6909309","text":"def predict_log_proba(self, X):\n y_prob = self.predict_proba(X)\n return np.log(y_prob, out=y_prob)","title":""},{"docid":"7671c58c44bd79041fdbde5b06d885a1","score":"0.6893093","text":"def log_prob(self, x):\n likelihood = self.likelihood(x)\n return likelihood.view(x.shape[0], -1).sum(1)","title":""},{"docid":"8c6a8762384bd2265a41321a0ebd81e4","score":"0.68431693","text":"def logp(self, val):\n return self.rv.logpdf(val)","title":""},{"docid":"3ad7336d40d74fe657264882fc696016","score":"0.6834927","text":"def log_prob(self, x):\n return torch.sum(-0.5 * torch.log(2 * np.pi * self.sigma ** 2)\n - 0.5 * (x - self.mu) ** 2 / self.sigma ** 2)","title":""},{"docid":"9054bee2923aaf52227b7764b8aca041","score":"0.68092906","text":"def log_prob(self, xs, zs):\n # Note there are no parameters we're being Bayesian about. The\n # parameters are baked into how we specify the neural networks.\n X, y = xs['X'], xs['y']\n self.neural_network(X)\n result = self.pi * norm.prob(y, self.mus, self.sigmas)\n result = tf.log(tf.reduce_sum(result, 1))\n return tf.reduce_sum(result)","title":""},{"docid":"ebcb31722ddf2df794a52aae9723d5cc","score":"0.68025124","text":"def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0] #labels.shape=(640,27)","title":""},{"docid":"7ecc6fccf0462a4e65f1beebc7518ea9","score":"0.6796203","text":"def log_probabilities(stprob):\n sprob, tprob = stprob\n with np.errstate(divide='ignore'): # log(0) = -inf OK\n sprob = np.log(sprob)\n tprob = np.log(tprob)\n return sprob, tprob","title":""},{"docid":"2f961529a03d5d39ef442b82351194a9","score":"0.6753031","text":"def _get_log_probs(self, seq):\n # NB: NgramModel returns negative log probs\n return np.negative(np.asarray([self.m1.prob_seq(seq), self.m2.prob_seq(seq)],\n dtype=np.float128))","title":""},{"docid":"756be7fad3cf08e45df2e33f4c493e23","score":"0.6732442","text":"def log_likelihood(self):\n # loop through each word in vocabulary.\n for word in self.vocabulary:\n self.vocabulary[word][\"probability\"] = {} # initialise 'probability' key for each word.\n\n # retrieve the frequency and class of each word.\n for cls, wfr in self.vocabulary[word][\"frequency\"].items():\n # calculate and add log probability to vocabulary\n cf = self.voc_classes[cls]\n\n p = log10((wfr + 1) / (cf + len(self.vocabulary)))\n\n self.vocabulary[word][\"probability\"][cls] = p\n\n # console readout\n if not self.quiet:\n print(\"Log probabilities calculated\\n\")","title":""},{"docid":"15b99934bd316f0a4bf5cd1c8dbda57f","score":"0.6694303","text":"def _log_prob(self, response, predicted_linear_response):\n likelihood = self.as_distribution(predicted_linear_response)\n return likelihood.log_prob(response)","title":""},{"docid":"59ba654e0b0ef8efb38d61bca95eaa4c","score":"0.6693244","text":"def _outcome_log_probs(self):\n if self._logits is None:\n probs = tf.convert_to_tensor(self._probs)\n logits = tf.math.log(probs) - tf.math.log1p(-probs)\n return tf.math.log1p(-probs), tf.math.log(probs), logits\n s = tf.convert_to_tensor(self._logits)\n # softplus(s) = -Log[1 - p]\n # -softplus(-s) = Log[p]\n # softplus(+inf) = +inf, softplus(-inf) = 0, so...\n # logits = -inf ==> log_probs0 = 0, log_probs1 = -inf (as desired)\n # logits = +inf ==> log_probs0 = -inf, log_probs1 = 0 (as desired)\n return -tf.math.softplus(s), -tf.math.softplus(-s), s","title":""},{"docid":"c29f92648f62a159771d1e927c05f989","score":"0.66870743","text":"def log_pred_prob(self, t, x):\n # Posterior predictive: see eq. 40 in (Murphy 2007).\n post_means = self.mean_params[:t]\n post_stds = np.sqrt(self.var_params[:t])\n return norm(post_means, post_stds).logpdf(x)","title":""},{"docid":"aa3e026240a8f584bab99c0801291cbc","score":"0.66751224","text":"def log_prob_from_logits(x):\n # TF ordering\n axis = len(x.size()) - 1\n m, _ = torch.max(x, dim=axis, keepdim=True)\n return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))","title":""},{"docid":"aa3e026240a8f584bab99c0801291cbc","score":"0.66751224","text":"def log_prob_from_logits(x):\n # TF ordering\n axis = len(x.size()) - 1\n m, _ = torch.max(x, dim=axis, keepdim=True)\n return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))","title":""},{"docid":"b2e3e5eef3e56aaab92cbc89114a0d3b","score":"0.66571367","text":"def log_probability(self, X):\n\n\n\t\tX = _check_parameter(_cast_as_tensor(X), \"X\", ndim=3, \n\t\t\tcheck_parameter=self.check_data)\n\t\tself.d = X.shape[1]\n\n\t\tlogps = self.distributions[0].log_probability(X[:, 0])\n\t\tfor i, distribution in enumerate(self.distributions[1:-1]):\n\t\t\tlogps += distribution.log_probability(X[:, :i+2])\n\n\t\tfor i in range(X.shape[1] - self.k):\n\t\t\tj = i + self.k + 1\n\t\t\tlogps += self.distributions[-1].log_probability(X[:, i:j])\n\n\t\treturn logps","title":""},{"docid":"a8abbe6183aaa8f35a813102098eb85d","score":"0.6651732","text":"def log_probability(self, X):\n\n\t\tX = _check_parameter(_cast_as_tensor(X), \"X\", ndim=2, \n\t\t\tshape=(-1, self.d), check_parameter=self.check_data)\n\n\t\tt = (X - self.means) ** 2 / self.covs\n\t\treturn torch.sum(self._lgamma_dofsp1 - self._lgamma_dofs - \\\n\t\t\tself._log_sqrt_dofs_pi_cov -((self.dofs + 1) / 2.0) * \n\t\t\ttorch.log(1 + t / self.dofs), dim=-1)","title":""},{"docid":"2aedbbbac5efd0f65ee60c6678336829","score":"0.6651381","text":"def log_prob(self, model_trace):\n if not self.has_enumerable_sites:\n return model_trace.log_prob_sum()\n log_probs = self._get_log_factors(model_trace)\n with shared_intermediates() as cache:\n return contract_to_tensor(log_probs, self._enum_dims, cache=cache)","title":""},{"docid":"39f1337c62cba202d4342f8a3b2a67e1","score":"0.6640335","text":"def log_prob(self, value):\n raise NotImplementedError","title":""},{"docid":"5b1051e7e81579b76e39cd8b21a19461","score":"0.6623184","text":"def log_probability(self, X):\n\n\t\tcdef int i, j, n\n\t\tcdef numpy.ndarray X_ndarray\n\t\tcdef double* X_ptr\n\t\tcdef double logp\n\t\tcdef numpy.ndarray logp_array\n\t\tcdef double* logp_ptr\n\n\t\tif self.discrete:\n\t\t\tif not isinstance(X[0], (list, tuple, numpy.ndarray)) or len(X) == 1:\n\t\t\t\tn = 1\n\t\t\telse:\n\t\t\t\tn = len(X)\n\n\t\t\tlogp_array = numpy.zeros(n)\n\t\t\tfor i in range(n):\n\t\t\t\tfor j in range(self.d):\n\t\t\t\t\tlogp_array[i] += self.distributions[j].log_probability(X[i][j]) * self.weights[j]\n\n\t\t\tif n == 1:\n\t\t\t\treturn logp_array[0]\n\t\t\telse:\n\t\t\t\treturn logp_array\n\n\t\telse:\n\t\t\tif isinstance(X[0], (int, float)) or len(X) == 1:\n\t\t\t\tn = 1\n\t\t\telse:\n\t\t\t\tn = len(X)\n\n\t\t\tX_ndarray = numpy.array(X, dtype='float64')\n\t\t\tX_ptr = <double*> X_ndarray.data\n\n\t\t\tlogp_array = numpy.empty(n, dtype='float64')\n\t\t\tlogp_ptr = <double*> logp_array.data\n\n\t\t\twith nogil:\n\t\t\t\tself._log_probability(X_ptr, logp_ptr, n)\n\n\t\t\tif n == 1:\n\t\t\t\treturn logp_array[0]\n\t\t\telse:\n\t\t\t\treturn logp_array","title":""},{"docid":"e25ccf73df8a8ed491cfbba05322e650","score":"0.6618147","text":"def indifferent_log_probability(self, preference, weights):\n delta = self.data.loc[preference[0], :].values - self.data.loc[preference[1], :].values\n variance = delta.dot(self.Sigma).dot(delta)\n sd = np.sqrt(variance)\n mean = weights.dot(delta)\n return np.log(norm.cdf(mean + 0.5, loc=0, scale=sd) - norm.cdf(mean - 0.5, loc=0, scale=sd))","title":""},{"docid":"a463d36e0aca733631b4f84f8d116992","score":"0.66125643","text":"def log_prob(self, sentence_list):\n return list(map(lambda x: self.__log_prob_sentence(x), sentence_list))","title":""},{"docid":"d5f5343d29bf424f723ce707c1a28faf","score":"0.660946","text":"def emission_probabilities(dataitems, log_prob=True):\n eprob = np.array([ i.prediction for i in dataitems ])\n if log_prob:\n with np.errstate(divide='ignore'): # log(0) = -inf OK\n eprob = np.log(eprob)\n return eprob","title":""},{"docid":"805f1291799c832355e04af2926a88c8","score":"0.6609043","text":"def log(self):\n return Discrete(np.log(self.pmf), *self.dim)","title":""},{"docid":"be83456c9840d6b1ff06a0758803e54f","score":"0.65975183","text":"def _calc_log_prob_from_prob(self, value, *args, **kwargs):\n return self.log_base(self._prob(value, *args, **kwargs))","title":""},{"docid":"75567363651e0e14eea796c3649ecde9","score":"0.6595068","text":"def prob_to_log_odds(p):\r\n return np.log(p/(1-p))","title":""},{"docid":"797ce1c2d7fb7c636eb66234233bf907","score":"0.6580121","text":"def log_py_xw(self):\n return self.model.local_log_prob('y')","title":""},{"docid":"252678f1f7948117f9136c24f0b08fed","score":"0.65762323","text":"def log_probability(self, sents, base=2.):\n return sum(self.sent_log_prob(sent, base) for sent in sents)","title":""},{"docid":"b79806deebc3c9a1d6ecc7825f9a07de","score":"0.6569243","text":"def prob_ratio(log_probs): \n return np.exp(log_probs[1,:] - log_probs[0,:])","title":""},{"docid":"5598b1aa60ff379e2362cfbc6e4024ce","score":"0.65688914","text":"def compute_log_likelihood(self, indicators, weights):\n scores, _ = self.predict_probability(self.train_feature_x, weights)\n probs = self.predict_probability(self.train_feature_x, weights)\n lp = np.sum((indicators-1)*scores + np.log(probs))\n return lp","title":""},{"docid":"b50f7dc1923ddfb53b244064a4d2db1a","score":"0.65623105","text":"def logprobdist(self):\n # Have the features already been computed and stored?\n if not hasattr(self, 'F'):\n raise AttributeError(\"first set the feature matrix F\")\n\n # Yes:\n # p(x) = exp(params.f(x)) / sum_y[exp params.f(y)]\n # = exp[log p_dot(x) - logsumexp{log(p_dot(y))}]\n\n #log_p_dot = innerprodtranspose(self.F, self.params)\n # Calculate the dot product of F^T and the parameter vector:\n log_p_dot = self.F.T.dot(self.params)\n\n # Do we have a prior distribution p_0?\n if self.priorlogprobs is not None:\n log_p_dot += self.priorlogprobs\n if not hasattr(self, 'logZ'):\n # Compute the norm constant (quickly!)\n self.logZ = logsumexp(log_p_dot)\n return log_p_dot - self.logZ","title":""},{"docid":"7bbbb3e75aaa7626b731dccd61efdd29","score":"0.65508044","text":"def logprob(self, sample, params, n_iters):\n eps = 1e-20\n params = params.reshape(self.dim, self.dim)\n unnormalized_logprob = np.sum(sample * params)\n\n # Approximate the matrix permanent of exp(params) with Bethe permanent of exp (params)\n # In the indigo code there is an additional division by the temperature in here...\n gamma_matrix = self.bethe_permanent_gamma(params, n_iters, 1e-20)\n term_1 = np.sum(params * gamma_matrix)\n term_2 = -np.sum(gamma_matrix * np.log(gamma_matrix + eps))\n term_3 = np.sum((1 - gamma_matrix) * np.log(1 - gamma_matrix + eps))\n log_approx_perm = term_1 + term_2 + term_3\n return unnormalized_logprob - log_approx_perm","title":""},{"docid":"b23ff0d6135b958dbc373c704b6c416a","score":"0.6548126","text":"def log_probability(value, mean, variance): \n\n if variance == 0:\n return 0\n else:\n const = 1 / (2*kPI*variance)**0.5\n ex = exp(-(value - mean)**2 / (2*variance))\n return log(const * ex)","title":""},{"docid":"50d9fe668f84e62b52c1bde0def6bee5","score":"0.6524495","text":"def get_log_probability(self, input_data):\n _, _, masked_array = self.get_probability(input_data)\n log_prob_array = np.log2(masked_array)\n total_log_prob = np.sum(log_prob_array, axis=1, keepdims=True)\n return total_log_prob, log_prob_array, masked_array","title":""},{"docid":"0410f0a909542c4b9f6b4cc0555eabae","score":"0.6519585","text":"def logP(self):\n raise NotImplementedError","title":""},{"docid":"72c3ea3b6018d9e6715f0e9b40542d6c","score":"0.6517506","text":"def log_p(cls, pi, a):\n return pi.log_prob(a).sum(\n axis=-1) # Sum needed for Torch normal distr.","title":""},{"docid":"6bc5dfc002f47d186bd9fad914b3358d","score":"0.6506105","text":"def loglike(self, params):\n return self.loglikeobs(params).sum(0)","title":""},{"docid":"43d7c5fa613540fb4ec41e80053e778d","score":"0.6494451","text":"def log_p(cls, p, a):\n\n return p.log_prob(a)","title":""},{"docid":"719966915af4a5f034d6f512d0251ff2","score":"0.64931244","text":"def logpdf(self, state):\n \n data, opt_vars = state\n param, subgrad, opt_vec = self.penalty.form_optimization_vector(opt_vars)\n gradient = self.loss.gradient(data, param)\n val = - gradient - opt_vec\n generalized_logpdf = self.randomization.logpdf(val).sum() + np.log(self.abs_det(state))\n \n return generalized_logpdf","title":""},{"docid":"b5b081e1baa37ebd3408774505427460","score":"0.6484868","text":"def marginal_log_likelihood(dataSet):\n\n z_shape = (25, 25) # two dimensional shape for prior\n Z_prior, P0, P1 = get_probabilities()\n\n\n # going to the logarithmic space\n Z_prior = np.log(Z_prior)\n P0 = np.log(P0)\n P1 = np.log(P1)\n\n\n logs = np.zeros(len(dataSet))\n\n #getting the values\n i=0\n for X in dataSet:\n #probabilities \n V = np.where(X == 0, P0, P1)\n\n #adding prior\n V = np.c_[Z_prior, V]\n\n #considering logsumexp\n V = np.sum(V, axis = 1)\n V = logsumexp(V)\n\n logs[i] = V\n i += 1\n return logs","title":""},{"docid":"a7a2ef21c8b8ac8836dd3f1e68b3fc57","score":"0.6479175","text":"def log_prob(self, new_obs: List[Tensor], params: List[TensorDict]) -> List[Tensor]:\n return [m.log_prob(new_obs[i], params[i]) for i, m in enumerate(self)]","title":""},{"docid":"c378ad36bdbbf70141ac8f565baebaee","score":"0.64639133","text":"def log_prob_X_given_z(self):\n return self.components.log_marg()","title":""},{"docid":"5cac6215d82f559968ea915318b70623","score":"0.64637846","text":"def predict_log_proba(self, X):\r\n self._check_is_fitted('predict_log_proba')\r\n return self.best_estimator_.predict_log_proba(X)","title":""},{"docid":"a124729a3c0db043d372f237f367fa73","score":"0.6461569","text":"def log_prior(theta):\n return 0.0","title":""},{"docid":"51a8c4a241ffec2e2c8d8dbfe1d830b0","score":"0.64577913","text":"def logprob(self, s, a):\n m = self.dist(s)\n logprob = m.log_prob(a)\n entropy = m.entropy()\n if self.multivariate:\n logprob = logprob.sum(dim=-1)\n entropy = entropy.sum(dim=-1)\n return logprob, entropy","title":""},{"docid":"83f3a48b7ed18abfe17cb17d329ce80f","score":"0.64519083","text":"def predict_log_proba(self, X):\n # Use log properties to multiply the two probabilities in log-space\n # FIXME: The mathematical formulation calls for weighting by class\n # probabilities, but that doesn't seem to have any meaningful impact on the\n # results.\n return np.asarray([self._get_log_probs(s) + self.y_log_probs for s in X],\n dtype=np.float128)\n # return np.asarray([self._get_log_probs(s) for s in X],\n # dtype=np.float128)","title":""},{"docid":"26acc94b8576f25f42543ec8a57563c5","score":"0.64461917","text":"def log_prob(self, value, *args, **kwargs):\n return self._call_log_prob(value, *args, **kwargs)","title":""},{"docid":"a0d0c3c7ba21a88b4c22a751b3f969e8","score":"0.6432684","text":"def priorloggrad(self):\n return numpy.array([p.logpdfgrad(x) for p, x in zip(self.getpriors(), self.getparams())])","title":""},{"docid":"383f96f2d54b7bdbfbd181355c9ae120","score":"0.6432392","text":"def get_probability(self, actions, states):\n\n #mean of the distribution\n mu = self.actor.model.forward(states)\n multivariate_gaussian_distribution = MultivariateNormal(loc=mu, covariance_matrix=self.actor.covariance_matrix)\n logarithmic_probability = multivariate_gaussian_distribution.log_prob(value=actions)\n return logarithmic_probability","title":""},{"docid":"0e706a79f4e9126e009411ff14f1982b","score":"0.64214075","text":"def log_prob(theta: np.ndarray, r: np.ndarray, y: np.ndarray, y_err: np.ndarray,\n m_func, pr) -> np.ndarray:\n lp = log_uniform_prior(theta, pr)\n if not np.isfinite(lp):\n ret_val = -np.inf\n else:\n ret_val = lp + log_likelihood(theta, r, y, y_err, m_func)\n\n if np.isnan(ret_val):\n ret_val = -np.inf\n\n return ret_val","title":""},{"docid":"1a3f48561b8ea5fdce22895d024b81b9","score":"0.64195216","text":"def logpdf(self, state):\n data, opt_vars = state\n param, subgrad, opt_vec = self.penalty.form_optimization_vector(opt_vars)\n gradient = self.loss.gradient(data, param)\n hessian = self.loss.hessian(data, param)\n log_jacobian = self.penalty.log_jacobian(hessian)\n val = - gradient - opt_vec\n\n return self.randomization.logpdf(val).sum() + log_jacobian","title":""},{"docid":"db3fa272ed9e675a8b3a8c949dab6407","score":"0.64097315","text":"def _logProbabilityWithSlice(self, x, sliceDim):\n z = self._inferenceWithSlice(x, sliceDim, True)\n return self.prior.logProbability(z) + self._inferenceLogjac","title":""},{"docid":"27a5faa93d070e8b114a4486277369de","score":"0.6403142","text":"def _log_pdf(x, mean, log_scale):\n\tz = (x - mean) * torch.exp(-log_scale)\n\tlog_p = z - log_scale - 2 * F.softplus(z)\n\n\treturn log_p","title":""},{"docid":"9eb3b03baaf673a2a8fb74bfb75f1e9e","score":"0.6396589","text":"def log_prob(self, sents):\n prob = 0\n for sent in sents:\n prob += self.sent_log_prob(sent)\n\n return prob","title":""},{"docid":"fd557ea817cb09bbbed86e2335c91c63","score":"0.6376421","text":"def log_likelihood(self):\n if self._log_likelihood is None:\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\n return self._log_likelihood","title":""},{"docid":"fd557ea817cb09bbbed86e2335c91c63","score":"0.6376421","text":"def log_likelihood(self):\n if self._log_likelihood is None:\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\n return self._log_likelihood","title":""},{"docid":"a738f46e81b7ea34e869c9c92619e5da","score":"0.6362299","text":"def log_prob_from_logits(x):\n axis = len(x.get_shape())-1\n m = tf.reduce_max(x, axis, keepdims=True)\n return x - m - tf.log(tf.reduce_sum(tf.exp(x-m), axis, keepdims=True))","title":""},{"docid":"65948047bb55ff31b8e723878d82141c","score":"0.636072","text":"def log_prob_from_logits(x):\n axis = len(x.get_shape()) - 1\n m = tf.reduce_max(x, axis, keepdims=True)\n return x - m - tf.log(tf.reduce_sum(tf.exp(x - m), axis, keepdims=True))","title":""},{"docid":"73f5a8e83545592da0346444a037c27e","score":"0.6359348","text":"def log_odds_to_prob(l):\r\n return 1 - 1 / (1 + np.exp(l))","title":""},{"docid":"7a090ac3b517bd5a1ac23c10fe6fb52a","score":"0.63587546","text":"def log_prob_from_logits(x):\n axis = len(x.get_shape()) - 1\n m = tf.reduce_max(x, axis, keepdims=True)\n return x - m - tf.math.log(tf.reduce_sum(tf.exp(x - m), axis, keepdims=True))","title":""},{"docid":"67c3e30477eeb34695bfac53bb6279ee","score":"0.63526064","text":"def log_prob_from_logits(x):\n axis = len(x.get_shape()) - 1\n m = tf.reduce_max(x, axis, keep_dims=True)\n return x - m - tf.log(tf.reduce_sum(tf.exp(x - m), axis, keep_dims=True))","title":""},{"docid":"677af6c31097f00971c2289c2e526f64","score":"0.63486314","text":"def log_prob(params, data, dropout=None):\n W1, b1, W2, b2, W3, b3 = params\n\n if dropout is None:\n dropout = DEFAULT_DROPOUT\n\n a1 = data.inputs\n z2 = a1.dot(W1.T * (1 - dropout[0])) + b1\n a2 = logistic(z2) \n z3 = a2.dot(W2.T * (1 - dropout[1])) + b2\n a3 = logistic(z3)\n U = a3.dot(W3.T * (1 - dropout[2])) + b3\n log_prob = U - np.log(np.sum(np.exp(U), 1))[:,np.newaxis]\n return log_prob","title":""},{"docid":"2e696bc97db94f33bea2c4402e263b6a","score":"0.63455206","text":"def logp(self, obs, acs):","title":""},{"docid":"6e6159b8ea922746783286a87f9c9e0c","score":"0.6341308","text":"def _calc_prob_from_log_prob(self, value, *args, **kwargs):\n return self.exp_base(self._log_prob(value, *args, **kwargs))","title":""},{"docid":"e58dd95733962690e4ddf0ee9b3b8b0c","score":"0.6340186","text":"def __call__(self, theta):\n logl = log_gaussian_pdf(theta, sigma=self.sigma, mu=0)\n return logl, [0.0] * self.nderived","title":""},{"docid":"950036354dc3bb4dad6428dcf085f240","score":"0.6323601","text":"def mixture_log_pdf(x, prior_logits, means, log_scales):\n\tlog_ps = F.log_softmax(prior_logits, dim=-1) \\\n\t\t+ _log_pdf(x.unsqueeze(dim=-1), means, log_scales)\n\tlog_p = torch.logsumexp(log_ps, dim=-1)\n\n\treturn log_p","title":""},{"docid":"2274e9ddbfa6f190e151e1ff818356e5","score":"0.6323542","text":"def log_prob(self, xs):\n # Loop over each random variable.\n # If distribution is univariate, this is over all indices; if\n # distribution is multivariate, this is over all but the last\n # index.\n n = get_dims(xs)[0]\n log_prob = tf.zeros([n], dtype=tf.float32)\n if len(self.shape) == 1:\n if self.is_multivariate:\n idx = ()\n log_prob += self.log_prob_idx(idx, xs)\n else:\n for idx in product(range(self.shape[0])):\n log_prob += self.log_prob_idx(idx, xs)\n\n elif len(self.shape) == 2:\n if self.is_multivariate:\n for idx in product(range(self.shape[0])):\n log_prob += self.log_prob_idx(idx, xs)\n\n else:\n for idx in product(range(self.shape[0]), range(self.shape[1])):\n log_prob += self.log_prob_idx(idx, xs)\n\n elif len(self.shape) == 3:\n if self.is_multivariate:\n for idx in product(range(self.shape[0]), range(self.shape[1])):\n log_prob += self.log_prob_idx(idx, xs)\n\n else:\n for idx in product(range(self.shape[0]), range(self.shape[1]), range(self.shape[2])):\n log_prob += self.log_prob_idx(idx, xs)\n\n else: # len(self.shape) >= 4\n # There should be a generic solution.\n raise NotImplementedError()\n\n return log_prob","title":""},{"docid":"59430b40b3a41dadd2cacddfdabac0aa","score":"0.6309538","text":"def _logprobs(self, text):\n raise NotImplementedError","title":""},{"docid":"096b546099009566dfd1f82dcff84cd8","score":"0.6286248","text":"def strict_log_probability(self, preference, weights):\n delta = self.data.loc[preference[0], :].values - self.data.loc[preference[1], :].values\n variance = delta.dot(self.Sigma).dot(delta)\n sd = np.sqrt(variance)\n mean = weights.dot(delta)\n return norm.logcdf(mean, loc=0, scale=sd)","title":""},{"docid":"b371ae5eed2268e33594c991a4abfe75","score":"0.62859297","text":"def _glm_logp(self, x_vec, x_all):\n # Extract the glm parameters\n x_imp = unpackdict(x_vec, self.glm_shapes)\n set_vars(self.bkgd_syms, x_all['glm']['bkgd'], x_imp)\n lp = seval(self.glm_logprior,\n self.syms,\n x_all)\n\n # Compute the log likelihood for each data sequence\n for data in self.population.data_sequences:\n self.population.set_data(data)\n lp += seval(self.glm_ll,\n self.syms,\n x_all)\n\n return lp","title":""},{"docid":"e054948b8c3a485575e3a2709f49803a","score":"0.62852496","text":"def prior(params):\n\n p_mean = scipy.stats.norm(1, 0.5).logpdf(params[0])\n p_log_amp = scipy.stats.norm(np.log(0.15), np.log(2)).logpdf(params[1])\n p_log_gamma = scipy.stats.norm(np.log(10), np.log(2)).logpdf(np.log(params[2]))\n #print(params[2])\n #print(\" \" + str(p_log_gamma))\n p_log_period = scipy.stats.norm(np.log(4./24.), (12./24.)).logpdf(params[3])\n # log period (period between 0.5hrs and 36hrs)\n #p_log_period = scipy.stats.uniform(np.log(0.5/24), -(np.log(2/3)+np.log(0.5/24))).logpdf((params[3]))\n\n sum_log_prior = p_mean + p_log_amp + p_log_gamma + p_log_period\n\n if np.isnan(sum_log_prior) == True:\n return -np.inf\n\n return sum_log_prior","title":""},{"docid":"3aea952fed0ea63f54dc4e27942400df","score":"0.62807274","text":"def _get_log_pis(self, agent):\n latent_pi, latent_vf, latent_sde = agent.policy._get_latent(self.observations)\n distribution = agent.policy._get_action_dist_from_latent(latent_pi, latent_sde)\n return distribution.distribution.probs","title":""},{"docid":"53fe15b632189af3b6c14d3d7755fbdf","score":"0.62724346","text":"def logistic_log_pdf(log_odds, x, temp, eps=10e-10):\n return (\n tf.log(temp + eps)\n - x * temp\n + log_odds\n - 2.0 * tf.log(1.0 + tf.exp(-x*temp + log_odds) + eps)\n )","title":""},{"docid":"521149dbc7c057aca05ac1752e6aba96","score":"0.6268935","text":"def predict_log_proba(self, X):\n ...","title":""},{"docid":"521149dbc7c057aca05ac1752e6aba96","score":"0.6268935","text":"def predict_log_proba(self, X):\n ...","title":""},{"docid":"2b7affc49c8ee5ce4b6042460263949e","score":"0.6260619","text":"def _log_likelihood(self, **kwargs):\n \n a,b,c,=kwargs[\"hmm\"].asMatrices()\n print np.array(c).sum(axis=0)\n return kwargs[\"hmm\"].loglikelihood(self._create_sequence_set(\n qsr_seq=kwargs[\"qsr_seq\"],\n symbols=self.generate_alphabet(num_symbols=int(NUM_SYMBOLS))\n ))","title":""},{"docid":"548bd0158fa85ce9c7b2e71868d14f20","score":"0.62585104","text":"def logistic_demo_prob():\n logt = sim_log_data(0.5, 0.5, 50, 1, -0.5, -0.5, 50, 1)\n \n probs = [1, 2, 4]\n for p in probs:\n logMod = logistic_mod(logt, p)\n eval_logistic(logMod)\n return 'Done'","title":""},{"docid":"b975e146b1df40e1771b46c217a7a10a","score":"0.6258035","text":"def logp(self, pars, blob=None):\n\n # Unpack parameters from vector\n MFe, MNi, MSi, MCO, lpc, y, w, Eth51, muoff, texp, aNi = pars\n # Total white dwarf mass\n MWD = MFe + MNi + MSi + MCO\n # Neutronization prior (Krueger+ 2012, Seitenzahl+ 2013)\n eta = MNi/(MFe + MNi)\n self.eta_Pmu, self.eta_Psig = SNPhysics.krueger12_eta(lpc)\n # Other prior terms P(theta) for ancillary parameters\n chpri = ((muoff - self.muoff_Pmu) / self.muoff_Psig)**2\n chpri += ((eta - self.eta_Pmu) / self.eta_Psig)**2\n chpri += ((aNi - self.aNi_Pmu) / self.aNi_Psig)**2\n chpri += ((MCO/MWD - 0.0) / 0.05)**2\n return -0.5*chpri","title":""},{"docid":"be9598f23df72114295fcb2f66bcf5e3","score":"0.6253691","text":"def log_prob(self, x):\n if self._validate_args:\n self._validate_sample(value)\n # compute the variance\n scale = self.scale\n var = scale.pow(2.)\n A = self.expamplitude\n xp = self.loc\n \n # For numerically stable wiki implementation\n sigma = scale\n mu = xp\n tau = A\n \n x_inp_dims = x.size()\n x = torch.tensor(x).to(scale.device).view([-1]+[1]*self.loc.ndimension())\n \n z = ((scale/A)-(x-xp).div(scale)).mul(torch.tensor(1./2., device=x.device).sqrt())\n\n # print(z.max())\n # print(z.min())\n\n\n # Compute all 3 wikipedia implementation, then fill in the result based on value of z\n\n # model 1\n #norm_part = sigma.div(tau).mul(torch.tensor(math.pi/2.).sqrt())\n #norm_part = torch.tensor(1.)\n norm_part = (2.*tau).reciprocal()\n\n exp_part = (var-2.*tau*(x[z<3.]-mu)).div(2.*tau.pow(2))\n erf_part = z[z<3.]\n\n ret1 = norm_part.log() + exp_part + (1.-erf_part.erf()).log()\n\n # model 2\n\n exp_part_approx = (x[(z>=3.)*(z<8191.)]-xp).pow(2).div(-2.*var)\n erfcx_part = z[(z>=3.)*(z<8191.)]\n\n ret2 = norm_part.log() + exp_part_approx + erfcx(erfcx_part).log()\n\n\n # model 3\n exp_part_approx_model3 = (x[z>=8191.]-xp).pow(2).div(-2.*var)\n div_part = 1.-(x[z>=8191.]-xp).mul(A).div(var) \n\n ret3 = exp_part_approx_model3 - div_part.log()\n\n #set_trace()\n\n ret_final = torch.zeros_like(z) \n ret_final[z<3.] += ret1\n ret_final[(z>=3.)*(z<8191.)] += ret2\n ret_final[z>=8191.] += ret3\n \n \n \n #ret_final = ret1*(z<3.).float() + ret2*(z<8191.).float()*(z>=3.).float() + ret3*(z>=8191.).float() \n \n # This does not seem to work for backward pass:\n# ret_final = ret3 # Bad estimate for small-ish z, goes over 1 around 4\n# ret_final[z<8191.] = ret2[z<8191.] # based on single-precision float\n# ret_final[z<3.] = ret1[z<3.] # Experimentally, see below\n \n \n if self.loc.ndimension()>0:\n return ret_final.view(*(x_inp_dims + self.loc.size())).squeeze(-self.loc.ndimension()-1)\n else:\n return ret_final.view(*x_inp_dims)\n \n \n \n \n \"\"\"\n # Check which part works best where - seems like the erfcx implementation is not very good\n return torch.stack([ret1[:,1,1], ret2[:,1,1], ret3[:,1,1]], dim=1), z[:,1,1]\n a = ExponentiallyModifiedGaussian(loc = 0.*torch.ones(3,4), \n scale = 0.6*torch.ones(3,4), \n expamplitude=.1*torch.ones(3,4))\n inp = torch.arange(-20., 30., 0.01)\n retval, z = a.log_prob(inp.view(-1,1,1))#[:,1,1]\n #print(retval)\n plot(retval.view(-1,3).exp().clamp(0.,1.), z.view(-1))\n \"\"\"","title":""},{"docid":"78e55da1ecd487aaff4a9994eec1fbbf","score":"0.62512845","text":"def prob(self, X):\n # p = self.p\n # r = self.r\n # return scipy.stats.nbinom(n=r, p=1 - p).pmf(X)\n # return binom(X + r - 1, X) * np.power(p, X) * np.power(1 - p, r)\n return np.exp(self.log_prob(X))","title":""},{"docid":"66abbfbbb6356d5c84f4f6201a84cf5a","score":"0.62478876","text":"def estimate_log_likelihood(self, X, num_samples=200):\n\n\t\timport estimator\n\t\treturn estimator.Estimator(self).estimate_log_probability(X, num_samples)[0].mean()","title":""},{"docid":"730da6b1362effe9a6862c660f7685f6","score":"0.62462026","text":"def predict_log_proba(self, X):\n if self.intercept_fit:\n X = self._add_constant(X)\n return self.model.predict_log_proba(X)","title":""},{"docid":"814ab7b06de102bce114a688163630ec","score":"0.6244322","text":"def log_prob(normal_distribution, action):\n logp_pi = normal_distribution.log_prob(action).sum(axis=-1)\n val = func.softplus(-2 * action)\n logp_pi -= (2 * (np.log(2) - action - val)).sum(axis=1)\n return logp_pi","title":""},{"docid":"0476e66c0a337841d0718be630610bc9","score":"0.6243777","text":"def expected_log_prob(nonconjugate_params,\n conjugate_params,\n expectations,\n data,\n covariates,\n **kwargs):\n df, = nonconjugate_params\n weights, covariance_matrix = conjugate_params\n scale = np.linalg.cholesky(covariance_matrix)\n scale_diag = np.reshape(scale, scale.shape[:-2] + (-1,))[..., ::dim + 1]\n predictions = covariates @ weights.T\n\n E_tau, E_log_tau = expectations\n hdof = 0.5 * df\n # lp = -np.sum(np.log(np.diag(scale)))\n lp = -np.sum(np.log(scale_diag), axis=-1).reshape(scale.shape[:-2])\n lp += 0.5 * E_log_tau\n lp += hdof * np.log(hdof)\n lp -= spsp.gammaln(hdof)\n lp += (hdof - 1) * E_log_tau\n lp -= hdof * E_tau\n tmp = np.linalg.solve(scale, (data - predictions).T).T\n lp -= 0.5 * E_tau * np.sum(tmp**2, axis=1)\n\n # Optional regularization on the degrees of freedom parameter\n lp -= 1e-8 * hdof # regularization (exponential prior)\n return lp","title":""},{"docid":"0318c6a4a528d4e9c5579ae059822a59","score":"0.624108","text":"def perplexity(probabs):\n probabs = np.asarray(probabs)\n return np.exp(-np.sum(np.log(probabs)) / np.max(probabs.shape))","title":""},{"docid":"9477eae1158728497c6a3e4747e5090f","score":"0.6240986","text":"def log_prob_data(w, b_h, b_v, log_z, data):\n log_likelihood = data.dot(b_v) + np.sum(np.log(1+np.exp(b_h+data.dot(w))), axis = 1)\n log_prob_of_data = np.sum(log_likelihood)/data.shape[0] - log_z\n return log_prob_of_data","title":""},{"docid":"7fab67fd7c47fb7cec10d3787f8ad97b","score":"0.6235636","text":"def log_likelihood_list(self,dataset):\r\n lls = []\r\n for instance,count in dataset:\r\n self._mark_decisions(instance)\r\n pr = self.value_top_down(instance)/self.theta_sum\r\n if pr == 0.0:\r\n print \"error: instance has zero probability\"\r\n raise Exception\r\n lls.append(math.log(pr))\r\n self.clear_bits()\r\n self.clear_data()\r\n return lls","title":""},{"docid":"0f50ff60bf05678b4a97cc880594c468","score":"0.62266606","text":"def _py_log_prob(self, zs):\n lp = np.zeros((zs.shape[0]), dtype=np.float32)\n for b, z in enumerate(zs):\n z_dict = OrderedDict()\n idx = 0\n for dim, par in zip(self.model.par_dims, self.model.model_pars):\n elems = np.sum(dim)\n if elems == 0:\n z_dict[par] = float(z[idx])\n idx += 1\n else:\n z_dict[par] = z[idx:(idx+elems)].reshape(dim)\n idx += elems\n\n z_unconst = self.model.unconstrain_pars(z_dict)\n lp[b] = self.model.log_prob(z_unconst, adjust_transform=False)\n\n return lp","title":""},{"docid":"4ce9b7abec40fe157ac54de14f5fe391","score":"0.62235934","text":"def _glm_logp(self, x_vec, x_all):\n # Extract the glm parameters\n x_imp = unpackdict(x_vec, self.glm_shapes)\n set_vars(self.impulse_syms, x_all['glm']['imp'], x_imp)\n lp = seval(self.glm_logprior,\n self.syms,\n x_all)\n\n # Compute the log likelihood for each data sequence\n for data in self.population.data_sequences:\n self.population.set_data(data)\n lp += seval(self.glm_ll,\n self.syms,\n x_all)\n\n return lp","title":""},{"docid":"2c75db278f557f73b204557c41315946","score":"0.6222881","text":"def get_logproba(self, states, actions):\n action_mean, action_logstd = self._forward_actor(states)\n logproba = self.normal_logproba(actions, action_mean, action_logstd)\n return logproba","title":""},{"docid":"37428e05a805d53a665e60a6fe872c3b","score":"0.62193966","text":"def logLikelihoods(self, sequences: List[List[ExternalSymbol]]) -> List[LogProbability]:\n raise NotImplementedError","title":""},{"docid":"b8c9f8219fe2a3171c1228e65ab4e0b6","score":"0.6216099","text":"def log_prior(self,theta):\n return np.sum((-(theta-self.mu_prior)**2)/(2*self.sigma_prior**2))","title":""},{"docid":"bf8989d222c7efdac0cde262ef27f9dc","score":"0.62121755","text":"def probability(cls, logodds: float) -> float:\n return exp(logodds)/(1 + exp(logodds))","title":""}],"string":"[\n {\n \"docid\": \"e0cf42a38133d6cf396650edba63c72c\",\n \"score\": \"0.73786455\",\n \"text\": \"def priorlogprob(self):\\n return numpy.array([p.logpdf(x) for p, x in zip(self.getpriors(), self.getparams())])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f0ec5bea8d6e48c9938f08f2177d9b\",\n \"score\": \"0.7042134\",\n \"text\": \"def logp(self):\\n # logp for all the parameters\\n return np.sum(\\n [\\n param.logp()\\n for param in f_unique(flatten(self.data))\\n if param.vary\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167ecd81421b7fa8e53ceb2c10966c34\",\n \"score\": \"0.7031832\",\n \"text\": \"def log_probability(self, weights):\\n strict_log_probability = sum(\\n [self.strict_log_probability(preference, weights) for preference in self.strict_preferences])\\n indifferent_log_probability = sum(\\n [self.indifferent_log_probability(preference, weights) for preference in self.indifferent_preferences])\\n log_prior = sum([self.priors[i](weight) for i, weight in enumerate(weights)])\\n return strict_log_probability + indifferent_log_probability + log_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266a554f4cb0f1c65daa397d65f3c6ad\",\n \"score\": \"0.7021919\",\n \"text\": \"def log_prob(self, X):\\n p = self.p\\n r = self.r\\n\\n # broadcasting\\n if xr != None and isinstance(p, xr.DataArray) and isinstance(r, xr.DataArray) and isinstance(X, xr.DataArray):\\n p, r, X = xr.align(p, r, X)\\n else:\\n p, r, X = np.broadcast_arrays(p, r, X)\\n\\n # return scipy.stats.nbinom(n=r, p=1 - p).logpmf(X)\\n coeff = gammaln(r + X) - gammaln(X + 1) - gammaln(r)\\n return coeff + r * np.log(1 - p) + X * np.log(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976925ca9b633b9bfc5b3b2ea67f5ab1\",\n \"score\": \"0.699978\",\n \"text\": \"def log_prob(self, x):\\n function = lambda x: x * np.exp(-x ** 2)\\n return torch.sum(np.log(self.pi) + self.gaussian1.log_prob(x)\\n + np.log1p(((1 - self.pi) / self.pi) * function(self.sigma1 / self.sigma2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2367ce660f041700be584b7822f80fc0\",\n \"score\": \"0.69937867\",\n \"text\": \"def lnprob(pars):\\n model = residual(pars, logx)\\n return -0.5 * np.sum(((model - logy) / logyerr)**2 + np.log(2 * np.pi * logyerr**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32edd9c8c1f078ed9f7ce9a69ffa15d\",\n \"score\": \"0.6952415\",\n \"text\": \"def log_probability(theta, V, EW, FeH, e_ew, e_feh, power):\\n lp = log_prior(theta)\\n if not np.isfinite(lp):\\n return -np.inf\\n return lp + log_likelihood(theta, V, EW, FeH, e_ew, e_feh, power)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334169aea74692f33f07de4347728e4e\",\n \"score\": \"0.69425666\",\n \"text\": \"def _logProbability(self, x, mask, mask_):\\n z = self._inference(x, mask, mask_, True)\\n return self.prior.logProbability(z) + self._inferenceLogjac\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289574b65fb4ab3786f40f310d3bf4c0\",\n \"score\": \"0.6909309\",\n \"text\": \"def predict_log_proba(self, X):\\n y_prob = self.predict_proba(X)\\n return np.log(y_prob, out=y_prob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7671c58c44bd79041fdbde5b06d885a1\",\n \"score\": \"0.6893093\",\n \"text\": \"def log_prob(self, x):\\n likelihood = self.likelihood(x)\\n return likelihood.view(x.shape[0], -1).sum(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6a8762384bd2265a41321a0ebd81e4\",\n \"score\": \"0.68431693\",\n \"text\": \"def logp(self, val):\\n return self.rv.logpdf(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad7336d40d74fe657264882fc696016\",\n \"score\": \"0.6834927\",\n \"text\": \"def log_prob(self, x):\\n return torch.sum(-0.5 * torch.log(2 * np.pi * self.sigma ** 2)\\n - 0.5 * (x - self.mu) ** 2 / self.sigma ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9054bee2923aaf52227b7764b8aca041\",\n \"score\": \"0.68092906\",\n \"text\": \"def log_prob(self, xs, zs):\\n # Note there are no parameters we're being Bayesian about. The\\n # parameters are baked into how we specify the neural networks.\\n X, y = xs['X'], xs['y']\\n self.neural_network(X)\\n result = self.pi * norm.prob(y, self.mus, self.sigmas)\\n result = tf.log(tf.reduce_sum(result, 1))\\n return tf.reduce_sum(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcb31722ddf2df794a52aae9723d5cc\",\n \"score\": \"0.68025124\",\n \"text\": \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0] #labels.shape=(640,27)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ecc6fccf0462a4e65f1beebc7518ea9\",\n \"score\": \"0.6796203\",\n \"text\": \"def log_probabilities(stprob):\\n sprob, tprob = stprob\\n with np.errstate(divide='ignore'): # log(0) = -inf OK\\n sprob = np.log(sprob)\\n tprob = np.log(tprob)\\n return sprob, tprob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f961529a03d5d39ef442b82351194a9\",\n \"score\": \"0.6753031\",\n \"text\": \"def _get_log_probs(self, seq):\\n # NB: NgramModel returns negative log probs\\n return np.negative(np.asarray([self.m1.prob_seq(seq), self.m2.prob_seq(seq)],\\n dtype=np.float128))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756be7fad3cf08e45df2e33f4c493e23\",\n \"score\": \"0.6732442\",\n \"text\": \"def log_likelihood(self):\\n # loop through each word in vocabulary.\\n for word in self.vocabulary:\\n self.vocabulary[word][\\\"probability\\\"] = {} # initialise 'probability' key for each word.\\n\\n # retrieve the frequency and class of each word.\\n for cls, wfr in self.vocabulary[word][\\\"frequency\\\"].items():\\n # calculate and add log probability to vocabulary\\n cf = self.voc_classes[cls]\\n\\n p = log10((wfr + 1) / (cf + len(self.vocabulary)))\\n\\n self.vocabulary[word][\\\"probability\\\"][cls] = p\\n\\n # console readout\\n if not self.quiet:\\n print(\\\"Log probabilities calculated\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b99934bd316f0a4bf5cd1c8dbda57f\",\n \"score\": \"0.6694303\",\n \"text\": \"def _log_prob(self, response, predicted_linear_response):\\n likelihood = self.as_distribution(predicted_linear_response)\\n return likelihood.log_prob(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ba654e0b0ef8efb38d61bca95eaa4c\",\n \"score\": \"0.6693244\",\n \"text\": \"def _outcome_log_probs(self):\\n if self._logits is None:\\n probs = tf.convert_to_tensor(self._probs)\\n logits = tf.math.log(probs) - tf.math.log1p(-probs)\\n return tf.math.log1p(-probs), tf.math.log(probs), logits\\n s = tf.convert_to_tensor(self._logits)\\n # softplus(s) = -Log[1 - p]\\n # -softplus(-s) = Log[p]\\n # softplus(+inf) = +inf, softplus(-inf) = 0, so...\\n # logits = -inf ==> log_probs0 = 0, log_probs1 = -inf (as desired)\\n # logits = +inf ==> log_probs0 = -inf, log_probs1 = 0 (as desired)\\n return -tf.math.softplus(s), -tf.math.softplus(-s), s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29f92648f62a159771d1e927c05f989\",\n \"score\": \"0.66870743\",\n \"text\": \"def log_pred_prob(self, t, x):\\n # Posterior predictive: see eq. 40 in (Murphy 2007).\\n post_means = self.mean_params[:t]\\n post_stds = np.sqrt(self.var_params[:t])\\n return norm(post_means, post_stds).logpdf(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa3e026240a8f584bab99c0801291cbc\",\n \"score\": \"0.66751224\",\n \"text\": \"def log_prob_from_logits(x):\\n # TF ordering\\n axis = len(x.size()) - 1\\n m, _ = torch.max(x, dim=axis, keepdim=True)\\n return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa3e026240a8f584bab99c0801291cbc\",\n \"score\": \"0.66751224\",\n \"text\": \"def log_prob_from_logits(x):\\n # TF ordering\\n axis = len(x.size()) - 1\\n m, _ = torch.max(x, dim=axis, keepdim=True)\\n return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e3e5eef3e56aaab92cbc89114a0d3b\",\n \"score\": \"0.66571367\",\n \"text\": \"def log_probability(self, X):\\n\\n\\n\\t\\tX = _check_parameter(_cast_as_tensor(X), \\\"X\\\", ndim=3, \\n\\t\\t\\tcheck_parameter=self.check_data)\\n\\t\\tself.d = X.shape[1]\\n\\n\\t\\tlogps = self.distributions[0].log_probability(X[:, 0])\\n\\t\\tfor i, distribution in enumerate(self.distributions[1:-1]):\\n\\t\\t\\tlogps += distribution.log_probability(X[:, :i+2])\\n\\n\\t\\tfor i in range(X.shape[1] - self.k):\\n\\t\\t\\tj = i + self.k + 1\\n\\t\\t\\tlogps += self.distributions[-1].log_probability(X[:, i:j])\\n\\n\\t\\treturn logps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8abbe6183aaa8f35a813102098eb85d\",\n \"score\": \"0.6651732\",\n \"text\": \"def log_probability(self, X):\\n\\n\\t\\tX = _check_parameter(_cast_as_tensor(X), \\\"X\\\", ndim=2, \\n\\t\\t\\tshape=(-1, self.d), check_parameter=self.check_data)\\n\\n\\t\\tt = (X - self.means) ** 2 / self.covs\\n\\t\\treturn torch.sum(self._lgamma_dofsp1 - self._lgamma_dofs - \\\\\\n\\t\\t\\tself._log_sqrt_dofs_pi_cov -((self.dofs + 1) / 2.0) * \\n\\t\\t\\ttorch.log(1 + t / self.dofs), dim=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aedbbbac5efd0f65ee60c6678336829\",\n \"score\": \"0.6651381\",\n \"text\": \"def log_prob(self, model_trace):\\n if not self.has_enumerable_sites:\\n return model_trace.log_prob_sum()\\n log_probs = self._get_log_factors(model_trace)\\n with shared_intermediates() as cache:\\n return contract_to_tensor(log_probs, self._enum_dims, cache=cache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f1337c62cba202d4342f8a3b2a67e1\",\n \"score\": \"0.6640335\",\n \"text\": \"def log_prob(self, value):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1051e7e81579b76e39cd8b21a19461\",\n \"score\": \"0.6623184\",\n \"text\": \"def log_probability(self, X):\\n\\n\\t\\tcdef int i, j, n\\n\\t\\tcdef numpy.ndarray X_ndarray\\n\\t\\tcdef double* X_ptr\\n\\t\\tcdef double logp\\n\\t\\tcdef numpy.ndarray logp_array\\n\\t\\tcdef double* logp_ptr\\n\\n\\t\\tif self.discrete:\\n\\t\\t\\tif not isinstance(X[0], (list, tuple, numpy.ndarray)) or len(X) == 1:\\n\\t\\t\\t\\tn = 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tn = len(X)\\n\\n\\t\\t\\tlogp_array = numpy.zeros(n)\\n\\t\\t\\tfor i in range(n):\\n\\t\\t\\t\\tfor j in range(self.d):\\n\\t\\t\\t\\t\\tlogp_array[i] += self.distributions[j].log_probability(X[i][j]) * self.weights[j]\\n\\n\\t\\t\\tif n == 1:\\n\\t\\t\\t\\treturn logp_array[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn logp_array\\n\\n\\t\\telse:\\n\\t\\t\\tif isinstance(X[0], (int, float)) or len(X) == 1:\\n\\t\\t\\t\\tn = 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tn = len(X)\\n\\n\\t\\t\\tX_ndarray = numpy.array(X, dtype='float64')\\n\\t\\t\\tX_ptr = <double*> X_ndarray.data\\n\\n\\t\\t\\tlogp_array = numpy.empty(n, dtype='float64')\\n\\t\\t\\tlogp_ptr = <double*> logp_array.data\\n\\n\\t\\t\\twith nogil:\\n\\t\\t\\t\\tself._log_probability(X_ptr, logp_ptr, n)\\n\\n\\t\\t\\tif n == 1:\\n\\t\\t\\t\\treturn logp_array[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn logp_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e25ccf73df8a8ed491cfbba05322e650\",\n \"score\": \"0.6618147\",\n \"text\": \"def indifferent_log_probability(self, preference, weights):\\n delta = self.data.loc[preference[0], :].values - self.data.loc[preference[1], :].values\\n variance = delta.dot(self.Sigma).dot(delta)\\n sd = np.sqrt(variance)\\n mean = weights.dot(delta)\\n return np.log(norm.cdf(mean + 0.5, loc=0, scale=sd) - norm.cdf(mean - 0.5, loc=0, scale=sd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a463d36e0aca733631b4f84f8d116992\",\n \"score\": \"0.66125643\",\n \"text\": \"def log_prob(self, sentence_list):\\n return list(map(lambda x: self.__log_prob_sentence(x), sentence_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f5343d29bf424f723ce707c1a28faf\",\n \"score\": \"0.660946\",\n \"text\": \"def emission_probabilities(dataitems, log_prob=True):\\n eprob = np.array([ i.prediction for i in dataitems ])\\n if log_prob:\\n with np.errstate(divide='ignore'): # log(0) = -inf OK\\n eprob = np.log(eprob)\\n return eprob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805f1291799c832355e04af2926a88c8\",\n \"score\": \"0.6609043\",\n \"text\": \"def log(self):\\n return Discrete(np.log(self.pmf), *self.dim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be83456c9840d6b1ff06a0758803e54f\",\n \"score\": \"0.65975183\",\n \"text\": \"def _calc_log_prob_from_prob(self, value, *args, **kwargs):\\n return self.log_base(self._prob(value, *args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75567363651e0e14eea796c3649ecde9\",\n \"score\": \"0.6595068\",\n \"text\": \"def prob_to_log_odds(p):\\r\\n return np.log(p/(1-p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797ce1c2d7fb7c636eb66234233bf907\",\n \"score\": \"0.6580121\",\n \"text\": \"def log_py_xw(self):\\n return self.model.local_log_prob('y')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252678f1f7948117f9136c24f0b08fed\",\n \"score\": \"0.65762323\",\n \"text\": \"def log_probability(self, sents, base=2.):\\n return sum(self.sent_log_prob(sent, base) for sent in sents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79806deebc3c9a1d6ecc7825f9a07de\",\n \"score\": \"0.6569243\",\n \"text\": \"def prob_ratio(log_probs): \\n return np.exp(log_probs[1,:] - log_probs[0,:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5598b1aa60ff379e2362cfbc6e4024ce\",\n \"score\": \"0.65688914\",\n \"text\": \"def compute_log_likelihood(self, indicators, weights):\\n scores, _ = self.predict_probability(self.train_feature_x, weights)\\n probs = self.predict_probability(self.train_feature_x, weights)\\n lp = np.sum((indicators-1)*scores + np.log(probs))\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50f7dc1923ddfb53b244064a4d2db1a\",\n \"score\": \"0.65623105\",\n \"text\": \"def logprobdist(self):\\n # Have the features already been computed and stored?\\n if not hasattr(self, 'F'):\\n raise AttributeError(\\\"first set the feature matrix F\\\")\\n\\n # Yes:\\n # p(x) = exp(params.f(x)) / sum_y[exp params.f(y)]\\n # = exp[log p_dot(x) - logsumexp{log(p_dot(y))}]\\n\\n #log_p_dot = innerprodtranspose(self.F, self.params)\\n # Calculate the dot product of F^T and the parameter vector:\\n log_p_dot = self.F.T.dot(self.params)\\n\\n # Do we have a prior distribution p_0?\\n if self.priorlogprobs is not None:\\n log_p_dot += self.priorlogprobs\\n if not hasattr(self, 'logZ'):\\n # Compute the norm constant (quickly!)\\n self.logZ = logsumexp(log_p_dot)\\n return log_p_dot - self.logZ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbbb3e75aaa7626b731dccd61efdd29\",\n \"score\": \"0.65508044\",\n \"text\": \"def logprob(self, sample, params, n_iters):\\n eps = 1e-20\\n params = params.reshape(self.dim, self.dim)\\n unnormalized_logprob = np.sum(sample * params)\\n\\n # Approximate the matrix permanent of exp(params) with Bethe permanent of exp (params)\\n # In the indigo code there is an additional division by the temperature in here...\\n gamma_matrix = self.bethe_permanent_gamma(params, n_iters, 1e-20)\\n term_1 = np.sum(params * gamma_matrix)\\n term_2 = -np.sum(gamma_matrix * np.log(gamma_matrix + eps))\\n term_3 = np.sum((1 - gamma_matrix) * np.log(1 - gamma_matrix + eps))\\n log_approx_perm = term_1 + term_2 + term_3\\n return unnormalized_logprob - log_approx_perm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b23ff0d6135b958dbc373c704b6c416a\",\n \"score\": \"0.6548126\",\n \"text\": \"def log_probability(value, mean, variance): \\n\\n if variance == 0:\\n return 0\\n else:\\n const = 1 / (2*kPI*variance)**0.5\\n ex = exp(-(value - mean)**2 / (2*variance))\\n return log(const * ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d9fe668f84e62b52c1bde0def6bee5\",\n \"score\": \"0.6524495\",\n \"text\": \"def get_log_probability(self, input_data):\\n _, _, masked_array = self.get_probability(input_data)\\n log_prob_array = np.log2(masked_array)\\n total_log_prob = np.sum(log_prob_array, axis=1, keepdims=True)\\n return total_log_prob, log_prob_array, masked_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0410f0a909542c4b9f6b4cc0555eabae\",\n \"score\": \"0.6519585\",\n \"text\": \"def logP(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c3ea3b6018d9e6715f0e9b40542d6c\",\n \"score\": \"0.6517506\",\n \"text\": \"def log_p(cls, pi, a):\\n return pi.log_prob(a).sum(\\n axis=-1) # Sum needed for Torch normal distr.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc5dfc002f47d186bd9fad914b3358d\",\n \"score\": \"0.6506105\",\n \"text\": \"def loglike(self, params):\\n return self.loglikeobs(params).sum(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d7c5fa613540fb4ec41e80053e778d\",\n \"score\": \"0.6494451\",\n \"text\": \"def log_p(cls, p, a):\\n\\n return p.log_prob(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719966915af4a5f034d6f512d0251ff2\",\n \"score\": \"0.64931244\",\n \"text\": \"def logpdf(self, state):\\n \\n data, opt_vars = state\\n param, subgrad, opt_vec = self.penalty.form_optimization_vector(opt_vars)\\n gradient = self.loss.gradient(data, param)\\n val = - gradient - opt_vec\\n generalized_logpdf = self.randomization.logpdf(val).sum() + np.log(self.abs_det(state))\\n \\n return generalized_logpdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b081e1baa37ebd3408774505427460\",\n \"score\": \"0.6484868\",\n \"text\": \"def marginal_log_likelihood(dataSet):\\n\\n z_shape = (25, 25) # two dimensional shape for prior\\n Z_prior, P0, P1 = get_probabilities()\\n\\n\\n # going to the logarithmic space\\n Z_prior = np.log(Z_prior)\\n P0 = np.log(P0)\\n P1 = np.log(P1)\\n\\n\\n logs = np.zeros(len(dataSet))\\n\\n #getting the values\\n i=0\\n for X in dataSet:\\n #probabilities \\n V = np.where(X == 0, P0, P1)\\n\\n #adding prior\\n V = np.c_[Z_prior, V]\\n\\n #considering logsumexp\\n V = np.sum(V, axis = 1)\\n V = logsumexp(V)\\n\\n logs[i] = V\\n i += 1\\n return logs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a2ef21c8b8ac8836dd3f1e68b3fc57\",\n \"score\": \"0.6479175\",\n \"text\": \"def log_prob(self, new_obs: List[Tensor], params: List[TensorDict]) -> List[Tensor]:\\n return [m.log_prob(new_obs[i], params[i]) for i, m in enumerate(self)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c378ad36bdbbf70141ac8f565baebaee\",\n \"score\": \"0.64639133\",\n \"text\": \"def log_prob_X_given_z(self):\\n return self.components.log_marg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cac6215d82f559968ea915318b70623\",\n \"score\": \"0.64637846\",\n \"text\": \"def predict_log_proba(self, X):\\r\\n self._check_is_fitted('predict_log_proba')\\r\\n return self.best_estimator_.predict_log_proba(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a124729a3c0db043d372f237f367fa73\",\n \"score\": \"0.6461569\",\n \"text\": \"def log_prior(theta):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a8c4a241ffec2e2c8d8dbfe1d830b0\",\n \"score\": \"0.64577913\",\n \"text\": \"def logprob(self, s, a):\\n m = self.dist(s)\\n logprob = m.log_prob(a)\\n entropy = m.entropy()\\n if self.multivariate:\\n logprob = logprob.sum(dim=-1)\\n entropy = entropy.sum(dim=-1)\\n return logprob, entropy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f3a48b7ed18abfe17cb17d329ce80f\",\n \"score\": \"0.64519083\",\n \"text\": \"def predict_log_proba(self, X):\\n # Use log properties to multiply the two probabilities in log-space\\n # FIXME: The mathematical formulation calls for weighting by class\\n # probabilities, but that doesn't seem to have any meaningful impact on the\\n # results.\\n return np.asarray([self._get_log_probs(s) + self.y_log_probs for s in X],\\n dtype=np.float128)\\n # return np.asarray([self._get_log_probs(s) for s in X],\\n # dtype=np.float128)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26acc94b8576f25f42543ec8a57563c5\",\n \"score\": \"0.64461917\",\n \"text\": \"def log_prob(self, value, *args, **kwargs):\\n return self._call_log_prob(value, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d0c3c7ba21a88b4c22a751b3f969e8\",\n \"score\": \"0.6432684\",\n \"text\": \"def priorloggrad(self):\\n return numpy.array([p.logpdfgrad(x) for p, x in zip(self.getpriors(), self.getparams())])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383f96f2d54b7bdbfbd181355c9ae120\",\n \"score\": \"0.6432392\",\n \"text\": \"def get_probability(self, actions, states):\\n\\n #mean of the distribution\\n mu = self.actor.model.forward(states)\\n multivariate_gaussian_distribution = MultivariateNormal(loc=mu, covariance_matrix=self.actor.covariance_matrix)\\n logarithmic_probability = multivariate_gaussian_distribution.log_prob(value=actions)\\n return logarithmic_probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e706a79f4e9126e009411ff14f1982b\",\n \"score\": \"0.64214075\",\n \"text\": \"def log_prob(theta: np.ndarray, r: np.ndarray, y: np.ndarray, y_err: np.ndarray,\\n m_func, pr) -> np.ndarray:\\n lp = log_uniform_prior(theta, pr)\\n if not np.isfinite(lp):\\n ret_val = -np.inf\\n else:\\n ret_val = lp + log_likelihood(theta, r, y, y_err, m_func)\\n\\n if np.isnan(ret_val):\\n ret_val = -np.inf\\n\\n return ret_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3f48561b8ea5fdce22895d024b81b9\",\n \"score\": \"0.64195216\",\n \"text\": \"def logpdf(self, state):\\n data, opt_vars = state\\n param, subgrad, opt_vec = self.penalty.form_optimization_vector(opt_vars)\\n gradient = self.loss.gradient(data, param)\\n hessian = self.loss.hessian(data, param)\\n log_jacobian = self.penalty.log_jacobian(hessian)\\n val = - gradient - opt_vec\\n\\n return self.randomization.logpdf(val).sum() + log_jacobian\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3fa272ed9e675a8b3a8c949dab6407\",\n \"score\": \"0.64097315\",\n \"text\": \"def _logProbabilityWithSlice(self, x, sliceDim):\\n z = self._inferenceWithSlice(x, sliceDim, True)\\n return self.prior.logProbability(z) + self._inferenceLogjac\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a5faa93d070e8b114a4486277369de\",\n \"score\": \"0.6403142\",\n \"text\": \"def _log_pdf(x, mean, log_scale):\\n\\tz = (x - mean) * torch.exp(-log_scale)\\n\\tlog_p = z - log_scale - 2 * F.softplus(z)\\n\\n\\treturn log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eb3b03baaf673a2a8fb74bfb75f1e9e\",\n \"score\": \"0.6396589\",\n \"text\": \"def log_prob(self, sents):\\n prob = 0\\n for sent in sents:\\n prob += self.sent_log_prob(sent)\\n\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd557ea817cb09bbbed86e2335c91c63\",\n \"score\": \"0.6376421\",\n \"text\": \"def log_likelihood(self):\\n if self._log_likelihood is None:\\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\\n return self._log_likelihood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd557ea817cb09bbbed86e2335c91c63\",\n \"score\": \"0.6376421\",\n \"text\": \"def log_likelihood(self):\\n if self._log_likelihood is None:\\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\\n return self._log_likelihood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a738f46e81b7ea34e869c9c92619e5da\",\n \"score\": \"0.6362299\",\n \"text\": \"def log_prob_from_logits(x):\\n axis = len(x.get_shape())-1\\n m = tf.reduce_max(x, axis, keepdims=True)\\n return x - m - tf.log(tf.reduce_sum(tf.exp(x-m), axis, keepdims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65948047bb55ff31b8e723878d82141c\",\n \"score\": \"0.636072\",\n \"text\": \"def log_prob_from_logits(x):\\n axis = len(x.get_shape()) - 1\\n m = tf.reduce_max(x, axis, keepdims=True)\\n return x - m - tf.log(tf.reduce_sum(tf.exp(x - m), axis, keepdims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f5a8e83545592da0346444a037c27e\",\n \"score\": \"0.6359348\",\n \"text\": \"def log_odds_to_prob(l):\\r\\n return 1 - 1 / (1 + np.exp(l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a090ac3b517bd5a1ac23c10fe6fb52a\",\n \"score\": \"0.63587546\",\n \"text\": \"def log_prob_from_logits(x):\\n axis = len(x.get_shape()) - 1\\n m = tf.reduce_max(x, axis, keepdims=True)\\n return x - m - tf.math.log(tf.reduce_sum(tf.exp(x - m), axis, keepdims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c3e30477eeb34695bfac53bb6279ee\",\n \"score\": \"0.63526064\",\n \"text\": \"def log_prob_from_logits(x):\\n axis = len(x.get_shape()) - 1\\n m = tf.reduce_max(x, axis, keep_dims=True)\\n return x - m - tf.log(tf.reduce_sum(tf.exp(x - m), axis, keep_dims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"677af6c31097f00971c2289c2e526f64\",\n \"score\": \"0.63486314\",\n \"text\": \"def log_prob(params, data, dropout=None):\\n W1, b1, W2, b2, W3, b3 = params\\n\\n if dropout is None:\\n dropout = DEFAULT_DROPOUT\\n\\n a1 = data.inputs\\n z2 = a1.dot(W1.T * (1 - dropout[0])) + b1\\n a2 = logistic(z2) \\n z3 = a2.dot(W2.T * (1 - dropout[1])) + b2\\n a3 = logistic(z3)\\n U = a3.dot(W3.T * (1 - dropout[2])) + b3\\n log_prob = U - np.log(np.sum(np.exp(U), 1))[:,np.newaxis]\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e696bc97db94f33bea2c4402e263b6a\",\n \"score\": \"0.63455206\",\n \"text\": \"def logp(self, obs, acs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6159b8ea922746783286a87f9c9e0c\",\n \"score\": \"0.6341308\",\n \"text\": \"def _calc_prob_from_log_prob(self, value, *args, **kwargs):\\n return self.exp_base(self._log_prob(value, *args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58dd95733962690e4ddf0ee9b3b8b0c\",\n \"score\": \"0.6340186\",\n \"text\": \"def __call__(self, theta):\\n logl = log_gaussian_pdf(theta, sigma=self.sigma, mu=0)\\n return logl, [0.0] * self.nderived\",\n \"title\": \"\"\n },\n {\n \"docid\": \"950036354dc3bb4dad6428dcf085f240\",\n \"score\": \"0.6323601\",\n \"text\": \"def mixture_log_pdf(x, prior_logits, means, log_scales):\\n\\tlog_ps = F.log_softmax(prior_logits, dim=-1) \\\\\\n\\t\\t+ _log_pdf(x.unsqueeze(dim=-1), means, log_scales)\\n\\tlog_p = torch.logsumexp(log_ps, dim=-1)\\n\\n\\treturn log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2274e9ddbfa6f190e151e1ff818356e5\",\n \"score\": \"0.6323542\",\n \"text\": \"def log_prob(self, xs):\\n # Loop over each random variable.\\n # If distribution is univariate, this is over all indices; if\\n # distribution is multivariate, this is over all but the last\\n # index.\\n n = get_dims(xs)[0]\\n log_prob = tf.zeros([n], dtype=tf.float32)\\n if len(self.shape) == 1:\\n if self.is_multivariate:\\n idx = ()\\n log_prob += self.log_prob_idx(idx, xs)\\n else:\\n for idx in product(range(self.shape[0])):\\n log_prob += self.log_prob_idx(idx, xs)\\n\\n elif len(self.shape) == 2:\\n if self.is_multivariate:\\n for idx in product(range(self.shape[0])):\\n log_prob += self.log_prob_idx(idx, xs)\\n\\n else:\\n for idx in product(range(self.shape[0]), range(self.shape[1])):\\n log_prob += self.log_prob_idx(idx, xs)\\n\\n elif len(self.shape) == 3:\\n if self.is_multivariate:\\n for idx in product(range(self.shape[0]), range(self.shape[1])):\\n log_prob += self.log_prob_idx(idx, xs)\\n\\n else:\\n for idx in product(range(self.shape[0]), range(self.shape[1]), range(self.shape[2])):\\n log_prob += self.log_prob_idx(idx, xs)\\n\\n else: # len(self.shape) >= 4\\n # There should be a generic solution.\\n raise NotImplementedError()\\n\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59430b40b3a41dadd2cacddfdabac0aa\",\n \"score\": \"0.6309538\",\n \"text\": \"def _logprobs(self, text):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"096b546099009566dfd1f82dcff84cd8\",\n \"score\": \"0.6286248\",\n \"text\": \"def strict_log_probability(self, preference, weights):\\n delta = self.data.loc[preference[0], :].values - self.data.loc[preference[1], :].values\\n variance = delta.dot(self.Sigma).dot(delta)\\n sd = np.sqrt(variance)\\n mean = weights.dot(delta)\\n return norm.logcdf(mean, loc=0, scale=sd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b371ae5eed2268e33594c991a4abfe75\",\n \"score\": \"0.62859297\",\n \"text\": \"def _glm_logp(self, x_vec, x_all):\\n # Extract the glm parameters\\n x_imp = unpackdict(x_vec, self.glm_shapes)\\n set_vars(self.bkgd_syms, x_all['glm']['bkgd'], x_imp)\\n lp = seval(self.glm_logprior,\\n self.syms,\\n x_all)\\n\\n # Compute the log likelihood for each data sequence\\n for data in self.population.data_sequences:\\n self.population.set_data(data)\\n lp += seval(self.glm_ll,\\n self.syms,\\n x_all)\\n\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e054948b8c3a485575e3a2709f49803a\",\n \"score\": \"0.62852496\",\n \"text\": \"def prior(params):\\n\\n p_mean = scipy.stats.norm(1, 0.5).logpdf(params[0])\\n p_log_amp = scipy.stats.norm(np.log(0.15), np.log(2)).logpdf(params[1])\\n p_log_gamma = scipy.stats.norm(np.log(10), np.log(2)).logpdf(np.log(params[2]))\\n #print(params[2])\\n #print(\\\" \\\" + str(p_log_gamma))\\n p_log_period = scipy.stats.norm(np.log(4./24.), (12./24.)).logpdf(params[3])\\n # log period (period between 0.5hrs and 36hrs)\\n #p_log_period = scipy.stats.uniform(np.log(0.5/24), -(np.log(2/3)+np.log(0.5/24))).logpdf((params[3]))\\n\\n sum_log_prior = p_mean + p_log_amp + p_log_gamma + p_log_period\\n\\n if np.isnan(sum_log_prior) == True:\\n return -np.inf\\n\\n return sum_log_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aea952fed0ea63f54dc4e27942400df\",\n \"score\": \"0.62807274\",\n \"text\": \"def _get_log_pis(self, agent):\\n latent_pi, latent_vf, latent_sde = agent.policy._get_latent(self.observations)\\n distribution = agent.policy._get_action_dist_from_latent(latent_pi, latent_sde)\\n return distribution.distribution.probs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fe15b632189af3b6c14d3d7755fbdf\",\n \"score\": \"0.62724346\",\n \"text\": \"def logistic_log_pdf(log_odds, x, temp, eps=10e-10):\\n return (\\n tf.log(temp + eps)\\n - x * temp\\n + log_odds\\n - 2.0 * tf.log(1.0 + tf.exp(-x*temp + log_odds) + eps)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521149dbc7c057aca05ac1752e6aba96\",\n \"score\": \"0.6268935\",\n \"text\": \"def predict_log_proba(self, X):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521149dbc7c057aca05ac1752e6aba96\",\n \"score\": \"0.6268935\",\n \"text\": \"def predict_log_proba(self, X):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7affc49c8ee5ce4b6042460263949e\",\n \"score\": \"0.6260619\",\n \"text\": \"def _log_likelihood(self, **kwargs):\\n \\n a,b,c,=kwargs[\\\"hmm\\\"].asMatrices()\\n print np.array(c).sum(axis=0)\\n return kwargs[\\\"hmm\\\"].loglikelihood(self._create_sequence_set(\\n qsr_seq=kwargs[\\\"qsr_seq\\\"],\\n symbols=self.generate_alphabet(num_symbols=int(NUM_SYMBOLS))\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548bd0158fa85ce9c7b2e71868d14f20\",\n \"score\": \"0.62585104\",\n \"text\": \"def logistic_demo_prob():\\n logt = sim_log_data(0.5, 0.5, 50, 1, -0.5, -0.5, 50, 1)\\n \\n probs = [1, 2, 4]\\n for p in probs:\\n logMod = logistic_mod(logt, p)\\n eval_logistic(logMod)\\n return 'Done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b975e146b1df40e1771b46c217a7a10a\",\n \"score\": \"0.6258035\",\n \"text\": \"def logp(self, pars, blob=None):\\n\\n # Unpack parameters from vector\\n MFe, MNi, MSi, MCO, lpc, y, w, Eth51, muoff, texp, aNi = pars\\n # Total white dwarf mass\\n MWD = MFe + MNi + MSi + MCO\\n # Neutronization prior (Krueger+ 2012, Seitenzahl+ 2013)\\n eta = MNi/(MFe + MNi)\\n self.eta_Pmu, self.eta_Psig = SNPhysics.krueger12_eta(lpc)\\n # Other prior terms P(theta) for ancillary parameters\\n chpri = ((muoff - self.muoff_Pmu) / self.muoff_Psig)**2\\n chpri += ((eta - self.eta_Pmu) / self.eta_Psig)**2\\n chpri += ((aNi - self.aNi_Pmu) / self.aNi_Psig)**2\\n chpri += ((MCO/MWD - 0.0) / 0.05)**2\\n return -0.5*chpri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9598f23df72114295fcb2f66bcf5e3\",\n \"score\": \"0.6253691\",\n \"text\": \"def log_prob(self, x):\\n if self._validate_args:\\n self._validate_sample(value)\\n # compute the variance\\n scale = self.scale\\n var = scale.pow(2.)\\n A = self.expamplitude\\n xp = self.loc\\n \\n # For numerically stable wiki implementation\\n sigma = scale\\n mu = xp\\n tau = A\\n \\n x_inp_dims = x.size()\\n x = torch.tensor(x).to(scale.device).view([-1]+[1]*self.loc.ndimension())\\n \\n z = ((scale/A)-(x-xp).div(scale)).mul(torch.tensor(1./2., device=x.device).sqrt())\\n\\n # print(z.max())\\n # print(z.min())\\n\\n\\n # Compute all 3 wikipedia implementation, then fill in the result based on value of z\\n\\n # model 1\\n #norm_part = sigma.div(tau).mul(torch.tensor(math.pi/2.).sqrt())\\n #norm_part = torch.tensor(1.)\\n norm_part = (2.*tau).reciprocal()\\n\\n exp_part = (var-2.*tau*(x[z<3.]-mu)).div(2.*tau.pow(2))\\n erf_part = z[z<3.]\\n\\n ret1 = norm_part.log() + exp_part + (1.-erf_part.erf()).log()\\n\\n # model 2\\n\\n exp_part_approx = (x[(z>=3.)*(z<8191.)]-xp).pow(2).div(-2.*var)\\n erfcx_part = z[(z>=3.)*(z<8191.)]\\n\\n ret2 = norm_part.log() + exp_part_approx + erfcx(erfcx_part).log()\\n\\n\\n # model 3\\n exp_part_approx_model3 = (x[z>=8191.]-xp).pow(2).div(-2.*var)\\n div_part = 1.-(x[z>=8191.]-xp).mul(A).div(var) \\n\\n ret3 = exp_part_approx_model3 - div_part.log()\\n\\n #set_trace()\\n\\n ret_final = torch.zeros_like(z) \\n ret_final[z<3.] += ret1\\n ret_final[(z>=3.)*(z<8191.)] += ret2\\n ret_final[z>=8191.] += ret3\\n \\n \\n \\n #ret_final = ret1*(z<3.).float() + ret2*(z<8191.).float()*(z>=3.).float() + ret3*(z>=8191.).float() \\n \\n # This does not seem to work for backward pass:\\n# ret_final = ret3 # Bad estimate for small-ish z, goes over 1 around 4\\n# ret_final[z<8191.] = ret2[z<8191.] # based on single-precision float\\n# ret_final[z<3.] = ret1[z<3.] # Experimentally, see below\\n \\n \\n if self.loc.ndimension()>0:\\n return ret_final.view(*(x_inp_dims + self.loc.size())).squeeze(-self.loc.ndimension()-1)\\n else:\\n return ret_final.view(*x_inp_dims)\\n \\n \\n \\n \\n \\\"\\\"\\\"\\n # Check which part works best where - seems like the erfcx implementation is not very good\\n return torch.stack([ret1[:,1,1], ret2[:,1,1], ret3[:,1,1]], dim=1), z[:,1,1]\\n a = ExponentiallyModifiedGaussian(loc = 0.*torch.ones(3,4), \\n scale = 0.6*torch.ones(3,4), \\n expamplitude=.1*torch.ones(3,4))\\n inp = torch.arange(-20., 30., 0.01)\\n retval, z = a.log_prob(inp.view(-1,1,1))#[:,1,1]\\n #print(retval)\\n plot(retval.view(-1,3).exp().clamp(0.,1.), z.view(-1))\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e55da1ecd487aaff4a9994eec1fbbf\",\n \"score\": \"0.62512845\",\n \"text\": \"def prob(self, X):\\n # p = self.p\\n # r = self.r\\n # return scipy.stats.nbinom(n=r, p=1 - p).pmf(X)\\n # return binom(X + r - 1, X) * np.power(p, X) * np.power(1 - p, r)\\n return np.exp(self.log_prob(X))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66abbfbbb6356d5c84f4f6201a84cf5a\",\n \"score\": \"0.62478876\",\n \"text\": \"def estimate_log_likelihood(self, X, num_samples=200):\\n\\n\\t\\timport estimator\\n\\t\\treturn estimator.Estimator(self).estimate_log_probability(X, num_samples)[0].mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730da6b1362effe9a6862c660f7685f6\",\n \"score\": \"0.62462026\",\n \"text\": \"def predict_log_proba(self, X):\\n if self.intercept_fit:\\n X = self._add_constant(X)\\n return self.model.predict_log_proba(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814ab7b06de102bce114a688163630ec\",\n \"score\": \"0.6244322\",\n \"text\": \"def log_prob(normal_distribution, action):\\n logp_pi = normal_distribution.log_prob(action).sum(axis=-1)\\n val = func.softplus(-2 * action)\\n logp_pi -= (2 * (np.log(2) - action - val)).sum(axis=1)\\n return logp_pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0476e66c0a337841d0718be630610bc9\",\n \"score\": \"0.6243777\",\n \"text\": \"def expected_log_prob(nonconjugate_params,\\n conjugate_params,\\n expectations,\\n data,\\n covariates,\\n **kwargs):\\n df, = nonconjugate_params\\n weights, covariance_matrix = conjugate_params\\n scale = np.linalg.cholesky(covariance_matrix)\\n scale_diag = np.reshape(scale, scale.shape[:-2] + (-1,))[..., ::dim + 1]\\n predictions = covariates @ weights.T\\n\\n E_tau, E_log_tau = expectations\\n hdof = 0.5 * df\\n # lp = -np.sum(np.log(np.diag(scale)))\\n lp = -np.sum(np.log(scale_diag), axis=-1).reshape(scale.shape[:-2])\\n lp += 0.5 * E_log_tau\\n lp += hdof * np.log(hdof)\\n lp -= spsp.gammaln(hdof)\\n lp += (hdof - 1) * E_log_tau\\n lp -= hdof * E_tau\\n tmp = np.linalg.solve(scale, (data - predictions).T).T\\n lp -= 0.5 * E_tau * np.sum(tmp**2, axis=1)\\n\\n # Optional regularization on the degrees of freedom parameter\\n lp -= 1e-8 * hdof # regularization (exponential prior)\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0318c6a4a528d4e9c5579ae059822a59\",\n \"score\": \"0.624108\",\n \"text\": \"def perplexity(probabs):\\n probabs = np.asarray(probabs)\\n return np.exp(-np.sum(np.log(probabs)) / np.max(probabs.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9477eae1158728497c6a3e4747e5090f\",\n \"score\": \"0.6240986\",\n \"text\": \"def log_prob_data(w, b_h, b_v, log_z, data):\\n log_likelihood = data.dot(b_v) + np.sum(np.log(1+np.exp(b_h+data.dot(w))), axis = 1)\\n log_prob_of_data = np.sum(log_likelihood)/data.shape[0] - log_z\\n return log_prob_of_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fab67fd7c47fb7cec10d3787f8ad97b\",\n \"score\": \"0.6235636\",\n \"text\": \"def log_likelihood_list(self,dataset):\\r\\n lls = []\\r\\n for instance,count in dataset:\\r\\n self._mark_decisions(instance)\\r\\n pr = self.value_top_down(instance)/self.theta_sum\\r\\n if pr == 0.0:\\r\\n print \\\"error: instance has zero probability\\\"\\r\\n raise Exception\\r\\n lls.append(math.log(pr))\\r\\n self.clear_bits()\\r\\n self.clear_data()\\r\\n return lls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f50ff60bf05678b4a97cc880594c468\",\n \"score\": \"0.62266606\",\n \"text\": \"def _py_log_prob(self, zs):\\n lp = np.zeros((zs.shape[0]), dtype=np.float32)\\n for b, z in enumerate(zs):\\n z_dict = OrderedDict()\\n idx = 0\\n for dim, par in zip(self.model.par_dims, self.model.model_pars):\\n elems = np.sum(dim)\\n if elems == 0:\\n z_dict[par] = float(z[idx])\\n idx += 1\\n else:\\n z_dict[par] = z[idx:(idx+elems)].reshape(dim)\\n idx += elems\\n\\n z_unconst = self.model.unconstrain_pars(z_dict)\\n lp[b] = self.model.log_prob(z_unconst, adjust_transform=False)\\n\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce9b7abec40fe157ac54de14f5fe391\",\n \"score\": \"0.62235934\",\n \"text\": \"def _glm_logp(self, x_vec, x_all):\\n # Extract the glm parameters\\n x_imp = unpackdict(x_vec, self.glm_shapes)\\n set_vars(self.impulse_syms, x_all['glm']['imp'], x_imp)\\n lp = seval(self.glm_logprior,\\n self.syms,\\n x_all)\\n\\n # Compute the log likelihood for each data sequence\\n for data in self.population.data_sequences:\\n self.population.set_data(data)\\n lp += seval(self.glm_ll,\\n self.syms,\\n x_all)\\n\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c75db278f557f73b204557c41315946\",\n \"score\": \"0.6222881\",\n \"text\": \"def get_logproba(self, states, actions):\\n action_mean, action_logstd = self._forward_actor(states)\\n logproba = self.normal_logproba(actions, action_mean, action_logstd)\\n return logproba\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37428e05a805d53a665e60a6fe872c3b\",\n \"score\": \"0.62193966\",\n \"text\": \"def logLikelihoods(self, sequences: List[List[ExternalSymbol]]) -> List[LogProbability]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c9f8219fe2a3171c1228e65ab4e0b6\",\n \"score\": \"0.6216099\",\n \"text\": \"def log_prior(self,theta):\\n return np.sum((-(theta-self.mu_prior)**2)/(2*self.sigma_prior**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8989d222c7efdac0cde262ef27f9dc\",\n \"score\": \"0.62121755\",\n \"text\": \"def probability(cls, logodds: float) -> float:\\n return exp(logodds)/(1 + exp(logodds))\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9944,"numItemsPerPage":100,"numTotalItems":994482,"offset":994400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzI3NzQ0Niwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc3MzI4MTA0NiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.Vh6sFWbxSlQ7MCaaFLUexReQO7Gf8QckJ94qmyQm03nIGmRv68ZcpdcvYMUWSve8ngMpYp0vOVkUZT9S2AIRDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
e7a2f3379f168ddfaf0cb2364bead9d1 | Parses fastq file looking for first (identifier) and second (sequence) lines only. Outputs a dictionary in which the key is the identifies and the value is the sequence Useful for downstream processing such as finding all the sequences of a particular length or eliminating sequences with invalid characters | [
{
"docid": "528c9a047aa083bba315d5b80e413a8e",
"score": "0.68678826",
"text": "def parse_fastq(self, file_name):\t\n\t\tfile = open(file_name)\n\t\tfile_content = file.readlines()\n\t\ti = 0\n\t\twhile i < len(file_content):\n\t\t\tif i % 4 == 0:\n\t\t\t\tself.fastq_dict[file_content[i].strip('\\n')] = ... | [
{
"docid": "a6f7439dcec856bf127c0d3dc25181c0",
"score": "0.71526283",
"text": "def parse_fastq(fastq):\n with gzip.open(fastq, 'rb') if fastq.endswith('.gz') else open(fastq, 'r') as f:\n counter = 0\n header = ''\n seq = ''\n qual = ''\n fas... |
6095ae754736c9740a4e3ce047fafdc7 | Displays the server info if any. | [
{
"docid": "f1a1fc5a37110aafc100a88402524f90",
"score": "0.6602481",
"text": "async def info(self, ctx):\n\n # Check if we're suppressing @here and @everyone mentions\n if self.settings.getServerStat(ctx.message.guild, \"SuppressMentions\"):\n suppress = True\n else:\n ... | [
{
"docid": "6e2feebea8ad91bef927d600a232cd0f",
"score": "0.7686046",
"text": "async def server_info(self, ctx):\r\n try:\r\n server = ctx.message.server\r\n description = \"**Server owner:** {}#{}\\n\".format(server.owner.name, server.owner.discriminator)\r\n desc... |
187d2f8ecb05b9da510a34ff96078a2c | Get the poll stats. Fetch all the answers from the JotForm API and convert them to cumilative statistics. | [
{
"docid": "debe01e9e473e79dcf0176c0cffc413d",
"score": "0.7560663",
"text": "async def get_poll_stats(uuid: str):\n credentials = redis.get(uuid)\n if credentials is None:\n raise HTTPError(401, \"Unauthorised request.\")\n app_key, poll_id = credentials.decode(\"utf-8\"... | [
{
"docid": "a3a21061c83e3eccdebbeb5612841a12",
"score": "0.6668814",
"text": "def stats(poll_id):\n \n poll_id = int(poll_id)\n poll = Poll.get_by_id(poll_id)\n return render_template(\"stats.html\", \n choice_a=poll.choice_a, \n choice_b=p... |
2f856edd516ea41b56043c18b779c784 | Method used for merging the transactions together. A single paypal payment is often translated into multiple transactions (e.g. conversion to foreign currency means +2 transactions). As this is a redundant information for us, we'd like to merge this into a single record. | [
{
"docid": "9e04f7b2e1c283576dec06245eac7afb",
"score": "0.5725072",
"text": "def combine_transactions(trx_data):\n result = {}\n bar = ChargingBar('3/3 Merging trx ', max=len(trx_data.keys()), suffix = '%(percent).1f%% - %(eta)ds remaining')\n for i in sorted(trx_data.keys()):\n # Fir... | [
{
"docid": "32b3fdd5a2519df39368ae454f3f83c8",
"score": "0.6524009",
"text": "def merge_transactions(base, new):\n\n result = base\n\n # Check the fields one-by-one\n for i in range(max(len(base), len(new))):\n # Generic element handling\n try:\n if result[i] == \"\":\n... |
e1ab27fa6412f3971e20f851c2ccf3ef | Give the user the option of automatically copying an encrypted/decrypted string to the Windows clipboard if Windows is the OS running on the user's computer. | [
{
"docid": "57b0ac6d36cf54d1601e78d07775b737",
"score": "0.79971385",
"text": "def clipboard(cipher_text):\n\n frame = str(inspect.stack())\n\n if platform.system() == 'Windows':\n if 'multiple_encryption' not in frame:\n import win32clipboard\n print cipher_text\n ... | [
{
"docid": "8a5b87d7917bdaa8c4f4fbab9968cf45",
"score": "0.6691027",
"text": "def copy_to_clipboard(text):\n # try windows first\n try:\n import win32clipboard\n\n win32clipboard.OpenClipboard()\n win32clipboard.EmptyClipboard()\n win32clipboard.SetClipboardText(text)\n... |
30cc9bea2320336e2024d02064a23c56 | Return 0/day average if task(s) does not exists | [
{
"docid": "ac21eeb1e1ca9948918a76a50b87232d",
"score": "0.67772514",
"text": "def test_average_completed_report_with_not_existing_tasks(self):\n self.create_test_user()\n token = self.client.post(self.login_url, self.user_login_data, format=\"json\").data['token']\n\n res = self.cl... | [
{
"docid": "a45ccb2b23a9377058f43a9de041582c",
"score": "0.6399138",
"text": "def get_average(days):\n average = 0\n for i in range(days):\n average = average + workloads[-1-i]\n average = average / days\n return average",
"title": ""
},
{
"docid": "334f04a4879085f0a7589af... |
bda7477a24eb68e08cb740b5646017e8 | Get the value of GPIO_DSn | [
{
"docid": "a54678832faf27520d90b3ba3965f6b4",
"score": "0.762052",
"text": "def DS(self):\n val = self.chip[\"GPIOConfig_DS\"][\"GPIO_DS<8:0>\"]\n if val & (1<<self.n)>0:\n return 1\n return 0",
"title": ""
}
] | [
{
"docid": "8cc13613535efa6b50a0e48ee178d9ca",
"score": "0.71249497",
"text": "def get_digital(self):\n [error,digital_value] = self.controller.GPIODigitalGet(self.input_digital_channel)\n return digital_value",
"title": ""
},
{
"docid": "38ae247f1193754b27cd48cbdb120c5b",
... |
0ed61c7c3a1069b92b788921b076cd49 | Exeute a training step of the network | [
{
"docid": "014643795bd534fce953ef32ea38328a",
"score": "0.0",
"text": "def RunTrainingStep(self, _input_data):\n\n data = []\n label = []\n for batch in _input_data:\n data.append(batch['data'])\n label.append(batch['label'])\n\n pred, loss, _ = (self.t... | [
{
"docid": "45e18b87448924a6c7959ae9e64a3345",
"score": "0.78879696",
"text": "def training_step(self, *args, **kwargs):",
"title": ""
},
{
"docid": "eb0802a9681d93c4fa95c1b9150625c2",
"score": "0.7701176",
"text": "def train(self):\n print(\"Training not yet implemented.\")",... |
d7574399af854a5dcf2ec9f029cf30f0 | Command launched when 'Duplicate' QMenuItem is triggered. Duplicate cache version | [
{
"docid": "769d74ecd271bda126770aa98dee5905",
"score": "0.69807845",
"text": "def on_miDuplicateCache(self):\n #-- Check --#\n if not os.path.exists(os.path.normpath(self.pItem.cacheFullPath)):\n raise IOError, \"!!! Cache file not found: %s !!!\" % self.pItem.cacheFullPath\n ... | [
{
"docid": "41371b400127f7426b313b81795478c3",
"score": "0.6907194",
"text": "def on_duplicateSelected():\n toolBoxCmds.duplicateSelected()",
"title": ""
},
{
"docid": "40f1f0a1823da38422e53dadfa4d6dcc",
"score": "0.6071569",
"text": "def copyCommand(self):\n\n selectio... |
fbb66c295298d642ea346b8998a7d514 | TCPSocket specifies an action involving a TCP port. | [
{
"docid": "ea1836355d8f29e6c48cc2de789a993b",
"score": "0.7387273",
"text": "def tcp_socket(self) -> 'outputs.TCPSocketActionResponse':\n return pulumi.get(self, \"tcp_socket\")",
"title": ""
}
] | [
{
"docid": "fb0171d918a4881bd87aa287bf0ed002",
"score": "0.6811422",
"text": "def open_tcp_port(ec2_resource, port, vpc_id):\n try:\n vpc = ec2_resource.Vpc(id=vpc_id)\n defaultSg = list(vpc.security_groups.all())[0]\n defaultSg.authorize_ingress(\n GroupName=defaultSg... |
1d6071a8d7ae909fcf0d44cb3e19d560 | Creates a tree representation as a set of nodes and a mapping from nodes to children Also creates two vptrees for Euclidean and assisting metrics, used for nearest neighbour queries | [
{
"docid": "24194cd4d239b01000f1816a0e4d2fbd",
"score": "0.6277342",
"text": "def __init__(self, root, space, assisting_dist_fn = lambda a, b: np.linalg.norm(a.pos - b.pos)):\n self.root = root\n self.nodes = set()\n self.edges = {}\n self.back_edges = {}\n self.node_a... | [
{
"docid": "4bc6e510335ff66acf124c19b506fc82",
"score": "0.6659278",
"text": "def generate_tree(self):\n calculate_criterion = None\n if(self.criterion == \"entropy\"):\n calculate_criterion = calculate_entropy\n elif self.criterion == \"accuracy\":\n calculate... |
ff56ebfcdc84a9c0a7c66a26c96c92f3 | Yield items for which the indices match | [
{
"docid": "c86d89c67dc21a52ec5d455e38133ce9",
"score": "0.7073103",
"text": "def _yield_subset(iterable, indices):\n if not indices:\n return\n remaining = sorted(indices, reverse=True)\n cur = remaining.pop()\n for idx, item in tqdm(enumerate(iterable)):\n ... | [
{
"docid": "7ac2b4fa3302390e27de2e0f054f793f",
"score": "0.69100356",
"text": "def select_indices(self, indices):\n indices = copy(indices) # passed indices is a reference, need own copy to modify\n for idx, record in enumerate(self):\n if idx in indices:\n yield... |
9afcd2fe6299150fc06b983f24909787 | Successful job submit with incorrect image link | [
{
"docid": "6e4bfc6a94d06c4c548f190db9772e1e",
"score": "0.61634725",
"text": "def test_submit_successful_bad_link(self):\n payload = json.dumps({\n \"count\": 2,\n \"visits\": [\n {\n \"store_id\": \"S00339218\",\n \"imag... | [
{
"docid": "d7ad10b1cdc5147deb72870a688c0aaf",
"score": "0.66116846",
"text": "def saveimage(self, info=False):\r\n request_image_content = requests.get(self.job_content)\r\n if request_image_content.status_code == 404:\r\n print(\"Нет подключения к https://elksale.xyz/\")\r\n ... |
7341d6aa13b1cc286ce04d5827817308 | Dump the value for database storage. | [
{
"docid": "b577095ab886f50393e79e4e1d752c46",
"score": "0.0",
"text": "def db_value(self, val: 'Optional[Union[str, IPAddress]]') -> 'Optional[int]': # pylint: disable=inconsistent-return-statements\n if val is not None:\n if isinstance(val, str):\n val = ipaddress.ip_... | [
{
"docid": "58aad9c81ff71b941b78cdf189ef4a8e",
"score": "0.8058299",
"text": "def dump(self, value):\n return",
"title": ""
},
{
"docid": "58aad9c81ff71b941b78cdf189ef4a8e",
"score": "0.8058299",
"text": "def dump(self, value):\n return",
"title": ""
},
{
"d... |
4262ba2169fb9af58bdba7293847d736 | Edit an entire list. Danger Will Robinson! Only staff members should be allowed to access this view. | [
{
"docid": "fc5bb10fae3692184a40801cd44250b3",
"score": "0.7460019",
"text": "def edit_list(request, list_id):\t\n\tif request.user.is_staff:\n\t\tcan_edit = 1\n\n # Get this list's object (to derive list.name, list.id, etc.)\n\tlist = get_object_or_404(List, pk=list_id)\n\n\tif list.team.uuid == req... | [
{
"docid": "c70565b6317d6080ce923f2da04457d2",
"score": "0.69857556",
"text": "def edit_list(name):\n user = session['username']\n description = ''\n\n user_lists = list_object.show_lists(user)\n\n for s_list in user_lists:\n if s_list['name'] == name:\n description = s_lis... |
feca5e961fa911131bf43c91b42fcfd9 | Write the nifti file to disk | [
{
"docid": "b06bf425afbdbec5b966634675f994ba",
"score": "0.75929457",
"text": "def write_nifti(self, output_path):\n nib.save(self.niftiImage, output_path)\n print('Image saved at: {}'.format(output_path))",
"title": ""
}
] | [
{
"docid": "d9a1fcb314f9516ffb3c235ebf492d2a",
"score": "0.6979252",
"text": "def save_nifti(self, path):\n meta = {'te': self.te, 'tr': self.tr, 'sw': self.sw}\n if self.sequence_type == 'STEAM':\n meta['tm'] = self.tm\n\n # store real and imaginary components in last 2 ... |
fd18bd86a69e92f4b68845489ca18d22 | asks the player if he wants to hit or stand | [
{
"docid": "1d581f916b0bce561d52d363362b7407",
"score": "0.7071528",
"text": "def hit_or_stand(self, _):\n while True:\n move = input(\"Do you want to hit or stand? (H or S): \")\n if move not in ['H', 'S']:\n print(\"Wrong input. Please try again.\")\n ... | [
{
"docid": "e32daeee8ff2c30e0fee55e15db46ec1",
"score": "0.70723826",
"text": "def get_player_action(self) -> None:\n print(f\"\\nYou have: {self.user.hand.cards} totalling to {self.user.hand.value}\")\n while not self.get_game_ending_hands():\n action = self.validate_input(\"Do... |
05445aab07c768eec636fe901ab66bd9 | Turn on the entity. | [
{
"docid": "0413af31cc498d121ee7207c9afb775a",
"score": "0.0",
"text": "def turn_on(self, speed: str = None, **kwargs) -> None:\n if speed is None:\n speed = SPEED_MEDIUM\n self.set_speed(speed)",
"title": ""
}
] | [
{
"docid": "5636f7ded24b3e11bfd3c30745e713ea",
"score": "0.8618475",
"text": "def turn_on(self, **kwargs):\n setattr(self._device, self.entity_description.key, True)",
"title": ""
},
{
"docid": "0787e3138df52f64d7bccfce51a74eff",
"score": "0.81042206",
"text": "async def async... |
f8c00adb9f1397b1a0557dda391b3234 | Parse arguments to the detect module | [
{
"docid": "f27ae0cbc8200bbb4fec7d1bc9817c08",
"score": "0.5837996",
"text": "def arg_parse():\n\n parser = argparse.ArgumentParser(description='YOLO v3 Video Detection')\n parser.add_argument(\"--dataset\", dest=\"dataset\",\n help=\"Dataset on which the network has been trained\", def... | [
{
"docid": "34fe4fe55514d90b708cb95166c13d37",
"score": "0.7203778",
"text": "def parse_args(self):",
"title": ""
},
{
"docid": "97d2ccf7f996b3021796b0ea822e65de",
"score": "0.7183488",
"text": "def parse(args):",
"title": ""
},
{
"docid": "d2d066d8732dafb3e689637147415fd... |
674908d1db925dbcc1adc35da09ca96b | get target network actions from all the agents in the MADDPG object | [
{
"docid": "28a5892506b36c1f6f35cd172af47eef",
"score": "0.72622454",
"text": "def target_act(self, obs_all_agents, noise=0.0):\n target_actions = [agent.target_act(obs) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return target_actions",
"title": ""
}
] | [
{
"docid": "3a6f213e3e825c2203028c4d9e180f76",
"score": "0.731483",
"text": "def target_act(self, obs_all_agents, noise=0.0):\n target_actions = [ddpg_agent.target_act(obs, noise) for ddpg_agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return target_actions",
"title": ""
},
... |
1f7fdb3fa2fb40d9710441a1a0f17b84 | Gathers recursively all inertial object children from the specified object. The inertia objects need to be in the specified objectlist to be encluded. Also, links which are not in the objectlist, will be considered, too. This will gather inertial objects which are child of a link not in the list. | [
{
"docid": "369087941b79d35fd4c096e7677171b3",
"score": "0.8509039",
"text": "def gatherInertialChilds(obj, objectlist):\n\n # only gather the links that are not in the list\n childlinks = [\n link for link in obj.children if link.phobostype == 'link' and link not in objectlist\n ]\n ... | [
{
"docid": "43c145cbaf3e9fe77a99d33e6097f801",
"score": "0.598192",
"text": "def _object_list_helper(self, **params):\n\n if not self.is_folder:\n raise SolveError(\n \"Only folders contain child objects. This is a {}\"\n .format(self.object_type))\n\n ... |
509a286e60fce150300aabb366f83175 | django.test.client.Client gets confused with templates when using the cache, that's why we need to clear it. | [
{
"docid": "e86263ace9d9f65d37bbbb5eae3575f8",
"score": "0.0",
"text": "def setUp(self):\n cache.clear()\n self.author = User.objects.create_user(username='joe', password='qwerty')\n self.teaser = \"this is a teaser\"\n self.body = \"and this is the body\"",
"title": ""
... | [
{
"docid": "4fbfbe6153af5f24a44f085add23e075",
"score": "0.732025",
"text": "def clear_template_cache(request):\n bottle.TEMPLATES.clear()",
"title": ""
},
{
"docid": "aa32e576cf2aba393dcface5a14020e7",
"score": "0.72820395",
"text": "def test_cached_views(self):\n response... |
0263a2c1fabaf9b3dbe87411b3d2a161 | Checks if the server process is healthy | [
{
"docid": "93ddbcc47b26f4c9bc06d42eda0099da",
"score": "0.77359974",
"text": "def server_is_healthy(self):\r\n\r\n if get_settings(active_view(), 'jsonserver_debug', False) is True:\r\n return True\r\n\r\n if self.process.poll() is None:\r\n try:\r\n s... | [
{
"docid": "c750bab4d329fd8a29dd3926ae56d7e8",
"score": "0.7100369",
"text": "def CheckIfSyncServerRunning(port):\n sync_server_healthz_url = ('%s:%s/healthz' % (HTTP_SERVER_URL, port))\n req = urllib2.Request(sync_server_healthz_url)\n try:\n response = urllib2.urlopen(req)\n except urllib2.HTTP... |
101c5fb6c67e4feb997767ddaf69b7dc | today returns today's date | [
{
"docid": "666a6159a5dc480c0343a4ebadb44753",
"score": "0.8839325",
"text": "def today() -> date:\n return datetime.datetime.now().date()",
"title": ""
}
] | [
{
"docid": "c27bb3e457eaa5764e0d5be961713deb",
"score": "0.9091201",
"text": "def get_today_date():\n return datetime.date.today()",
"title": ""
},
{
"docid": "dfdd837ef405d39242b663e7550ab03e",
"score": "0.87969136",
"text": "def get_today_date():\r\n now = datetime.datetime.n... |
9945b198ee2819f25a1b4b7fc922fb45 | Imports an CSV files. First row must be a header row! | [
{
"docid": "b64b6452c8f571043df0a297554acb4d",
"score": "0.6596792",
"text": "def import_csv(self, filename, delimiter=',', quotechar='\"'):\n with open(filename) as data_file:\n reader = csv.reader(data_file, delimiter=delimiter, quotechar=quotechar)\n header = [h.strip() f... | [
{
"docid": "e455c173c1f80b526afba4f9ca27056f",
"score": "0.7136156",
"text": "def import_csv_command(csvdir):\n import_tables_from_csv_files(csvdir)",
"title": ""
},
{
"docid": "4e9f35441ca60f248a8412d0d1fa470b",
"score": "0.70805323",
"text": "def load_lar_csv():\n cur, pg_con... |
381f74c2b4051b21303d642135e54e72 | Fetch graph from one frame; include streams that start in the frame. | [
{
"docid": "03d08c27c27d534ab48214ee8dfa10d2",
"score": "0.545553",
"text": "def fetch_frame(self, start, minutes=0, seconds=0):\n total_secs = minutes * 60 + seconds\n end = start + total_secs\n with self._database:\n g = networkx.MultiDiGraph()\n with self._d... | [
{
"docid": "ff58a4182114fc95691a872a0ed2b5c9",
"score": "0.52892774",
"text": "def fetch(self):\n if self.queries is None:\n version_identifier = get_pg_version(self.connection)\n self.queries = queries_for_version(QUERIES, version_identifier)\n print(\"multigraph %s\... |
018880b1d5d6c22529a88b74d8db0eb8 | Returns the manifest associated with the given tag. | [
{
"docid": "118dccbdaeffbf0f698c7ad2b47e4a0e",
"score": "0.6995916",
"text": "def get_manifest_for_tag(self, tag, backfill_if_necessary=False, include_legacy_image=False):\n try:\n tag_manifest = database.TagManifest.get(tag_id=tag._db_id)\n except database.TagManifest.DoesNotEx... | [
{
"docid": "a1a8b9bf202f253570d1492e7d682672",
"score": "0.72691697",
"text": "def backfill_manifest_for_tag(self, tag):\n # Ensure that there isn't already a manifest for the tag.\n tag_manifest = model.tag.get_tag_manifest(tag._db_id)\n if tag_manifest is not None:\n re... |
4dba8ed3cddff94b4b7072f76b0d80c4 | Test if ID is unique | [
{
"docid": "05ab68083158ee7bba35180202897740",
"score": "0.0",
"text": "def test_RepetitiveID(self):\r\n self.assertRaises(RepetitiveID, lambda: self.x5.analyze())",
"title": ""
}
] | [
{
"docid": "0ac5efe74d321f18377f5ccb07de5d2c",
"score": "0.7788738",
"text": "def isUnique(self) -> bool:\n ...",
"title": ""
},
{
"docid": "147ce7c02042b875c8daf79145db0348",
"score": "0.7677853",
"text": "def check_id(self, id):\n\n if id in self.unique_ids:\n ... |
d2627244a03bd1c854720b1d51c79609 | kth force dependent contribution to the model covariance function. | [
{
"docid": "5c0866cc3035e94fa5bc483edd5b21b9",
"score": "0.0",
"text": "def Lambdag_k(k, vecg, beta, logphi_k, gamma_k, mlfm,\n eval_gradient=True):\n # structure matrices\n if beta is None:\n A = np.asarray(\n [np.zeros((mlfm.dim.K, mlfm.dim.K)),\n *mlfm.... | [
{
"docid": "c7c5de377203ac744f5111948e9c9ed8",
"score": "0.66864216",
"text": "def autocovariance(H, k):\n return 0.5 * (abs(k - 1) ** (2 * H) - 2 * abs(k) ** (2 * H) +\n abs(k + 1) ** (2 * H))",
"title": ""
},
{
"docid": "88a1ca40a00dd305bc1120fc6500d2fb",
"score": "... |
7f7d4113fc302ccc6ad2684a5d6e1a10 | Nothing should be checked w/no Manifest files. | [
{
"docid": "03e3aaa9c501bc5a9576d6fe39437b18",
"score": "0.679476",
"text": "def testNoManifests(self):\n self.file_mock.return_value = [\n '/foo/bar.txt',\n '/readme.md',\n '/manifest',\n '/Manifest.txt',\n ]\n ret = pre_upload._check_manifests('PROJECT', 'COMMIT')\... | [
{
"docid": "e5c8ce4283e35b3c3f0bc3eb507785a3",
"score": "0.6807853",
"text": "def _can_run(self, manifest):\n pass",
"title": ""
},
{
"docid": "3a57e0dad785654907e4bb733f8d9293",
"score": "0.6682673",
"text": "def manifest(self):",
"title": ""
},
{
"docid": "2a3255... |
d908c79cf431b7186e893f10afbe7144 | Add a new permission on the host to the group in the system. | [
{
"docid": "6b3f4d4434a976a4e1a139caaabe4aa1",
"score": "0.6871351",
"text": "def add_host_permission(\n self,\n permission,\n headers=None,\n query=None,\n wait=True,\n **kwargs\n ):\n # Check the types of the parameters:\n Service._check_types... | [
{
"docid": "91f301f95dfc9b19905b0ce6fd7c4a77",
"score": "0.76909554",
"text": "def add(self, group_name, permission_name):\n\n self.permissions[group_name].append(permission_name)",
"title": ""
},
{
"docid": "17571a8f960466ab9f49a77f66a96c26",
"score": "0.74283016",
"text": "d... |
409a756a82c98341bd6b8d5cb1648861 | Return Position instance for given node (or None if no node). | [
{
"docid": "054171e61f10cfe8177f27ab038fc20a",
"score": "0.8076458",
"text": "def _make_position(self, node):\n return self.Position(self, node) if node is not None else None",
"title": ""
}
] | [
{
"docid": "4a6fb3b81190a656753251f00e63b148",
"score": "0.73565936",
"text": "def newPosition(self, node):\n return self.Position(node, self) if node is not None else None",
"title": ""
},
{
"docid": "7ef673963e5db890c4f42bcebc464d13",
"score": "0.6869997",
"text": "def _make... |
762d615cea8ef6f6790668f7190a5359 | Reprioritise tasks to pri if current date is days before due date. Will not reprioritise tasks if they are already higher priority than pri. | [
{
"docid": "50fea5512faf7e418884eb9e87225891",
"score": "0.56365365",
"text": "def main(todo_file, todo_full_sh, days=1, pri=\"A\", list_tasks=False):\n days = int(days)\n tasks_with_due_date = []\n\n # Open todo.txt file\n with open(todo_file, \"r\") as f:\n content = f.readlines()\n... | [
{
"docid": "f48dcd47e2fc719ffe12251564205dd0",
"score": "0.6258063",
"text": "def change_task_prio():\n\n user = current_user.self\n fields = 'proj_name', 'task_name', 'dir'\n fields = proj_name, task_name, dir_ = [request.args.get(i) for i in fields]\n\n if not all(fields) or not dir_ in ('... |
b088dddebe4dd8c763a816df39d4bd07 | Given a boolean array, we select generators from a barcode basis. Example >>> print(base3) Barcode basis [[0 2] [0 1] [1 3]] >>> base5 = base3.bool_select([True,False,True]) >>> print(base5) Barcode basis [[0 2] [1 3]] | [
{
"docid": "c6cf5734120bfe5000e95948f7e28282",
"score": "0.6890537",
"text": "def bool_select(self, selection):\n if self.prev_basis is None:\n return barcode_basis(self.barcode[selection])\n\n return barcode_basis(self.barcode[selection], self.prev_basis,\n ... | [
{
"docid": "c9c6f4e162dc8602c6845ab763118195",
"score": "0.5711035",
"text": "def seqselect(test, list): \n selected = [ ]\n for item in list: \n if test(item) == True: \n selected.append(item)\n return selected",
"title": ""
},
{
"docid": "9dc0b1d43ce0cbc171247... |
1732c168881102eb89121f0aa6d38507 | Create virtualenv for running unit tests | [
{
"docid": "201d6b11ddbc6da6d2c9fc87dd73c84b",
"score": "0.6648784",
"text": "def create_venv_dir():\n SHELL_EXEC = []\n SHELL_EXEC.append(F'set -e')\n SHELL_EXEC.append(F'virtualenv {VENV_DIR}')\n\n return_code = os.system(';'.join(SHELL_EXEC))\n return return_code",
"title": ""
}
... | [
{
"docid": "189f06ad80b7b2ec683c7774ef1026f2",
"score": "0.77171963",
"text": "def create_virtualenv():\n require('virtualenv_root', provided_by=('staging', 'production'))\n args = '--clear --distribute --no-site-packages'\n try:\n run('rm -rf %s' % env.virtualenv_root)\n except:\n ... |
8f3322cbece87d4cd2d4aed1847f9ac1 | Returns a list of words contained by an identifier. | [
{
"docid": "ebf7095286e0d19491a13934e837cdfb",
"score": "0.80292684",
"text": "def identifier_to_words(identifier):\n identifier = identifier.strip('_')\n\n if not identifier:\n return []\n\n lowered = identifier.lower()\n\n split = lowered.split('_')\n if len(split) > 1:\n ... | [
{
"docid": "a7bcb6fba7f8bad6e6745830aff48630",
"score": "0.6666058",
"text": "def get_hate_word_list():\n return list(i['word'] for i in db.hateword.find())",
"title": ""
},
{
"docid": "6f0239e2d00df96a06ea7ff27bfc076d",
"score": "0.64950097",
"text": "def get_words(data):\n ... |
bd00d22d75d3e8398074e98ff4793c1e | Adds a step to the factory to update the script folder. | [
{
"docid": "74f798e23fbee8505eca1454de0ef392",
"score": "0.56004137",
"text": "def AddUpdateScriptStep(self, gclient_jobs=None):\n # This will be run in the '..' directory to udpate the slave's own script\n # checkout.\n command = [chromium_utils.GetGClientCommand(self._target_platform),\n ... | [
{
"docid": "da6f256ea87182c3396057839a71cae9",
"score": "0.607602",
"text": "def test_steps_add(self):\n support.create_project(self, 'bob')\n project = cauldron.project.get_internal_project()\n\n r = support.run_command('steps add first.py')\n self.assertFalse(r.failed, 'sho... |
e65d8166479645f4b886344fd1e0c04b | Convert a pair of rgb and depth map to colored point cloud | [
{
"docid": "7349737b67d3843bc44a0a1fc241a082",
"score": "0.6349855",
"text": "def rgbd_to_colored_pc(self, depth, rgb, f_x, f_y, c_x, c_y, cap=100):\r\n\r\n rgb_height, rgb_width, _ = rgb.shape\r\n x_map, y_map = np.meshgrid(np.arange(rgb_width), np.arange(rgb_height))\r\n xyz_rgb =... | [
{
"docid": "a5754be415e7011d3d6a8987eeae038a",
"score": "0.7130225",
"text": "def world_to_color(params, pcloud, color):\n x, y, z = pcloud[..., 0], pcloud[..., 1], pcloud[..., 2]\n x = x * params['fx_rgb'] / z + params['cx_rgb']\n y = y * params['fy_rgb'] / z + params['cy_rgb']\n x1, xw1, x2, xw2, ... |
ff1b78c3ab6c17b32de402a44284ff0e | Return the default parameters to use for this instrument. | [
{
"docid": "29ecb96dd0a73340f5b590baf576e00a",
"score": "0.63064265",
"text": "def default_pypeit_par(cls):\n par = super().default_pypeit_par()\n\n # Ignore PCA\n par['calibrations']['slitedges']['sync_predict'] = 'nearest'\n # Bound the detector with slit edges if no edges ... | [
{
"docid": "a700ce8225135b8cb4346a035e703959",
"score": "0.8122184",
"text": "def getDefaultParameters():\n param = {}\n return param",
"title": ""
},
{
"docid": "f076a91fe2bc8e56f0b1a28e10c6e71a",
"score": "0.80809766",
"text": "def _get_default_parameters(self):\n ... |
2d61504f4f2f4eb5f0650c9977e4266b | Send all avaleble pages to frontend | [
{
"docid": "83b799e7681ebd13a03275005bae9630",
"score": "0.5553395",
"text": "def all_reed_pages(id):\n if not g.user:\n flash('You have to Login')\n return redirect('/')\n\n \n book = Book.query.get_or_404(id)\n\n if book.user_id != g.user.id:\n flash(\"This is not your... | [
{
"docid": "391186b4f0f909aa1e6774c9c86f8638",
"score": "0.63025695",
"text": "def send_pages(self, response):\n # The internal 'overview' page\n pages = {\"main\": OVERVIEW_ID,\n \"pages\": [OVERVIEW_ID],\n \"names\": [OVERVIEW_ID.title()]}\n\n # Pre... |
5e1a891d3b7866607a29f1974d1ce28e | DMLablike interface for a Craft environment. Given a `scenario` (basically holding an initial world state), will provide the usual DMLab API for use in RL. | [
{
"docid": "1768a3f30f25b192ca42f33c21871d27",
"score": "0.0",
"text": "def __init__(self,\n scenario,\n task_name,\n task,\n max_steps=100,\n visualise=False,\n render_scale=10,\n extra_pickup_penalty=0.3)... | [
{
"docid": "bed98706b436b68588217f2ebed80aaa",
"score": "0.5636561",
"text": "def func_with_scenario():\n Scenario(test=my_scenario)()",
"title": ""
},
{
"docid": "7cecb62700356ff3bdc1e0f8a36300d8",
"score": "0.5341338",
"text": "def _run_scenario(self, scenario):\r\n \r\n s... |
edb5457970fd4d56ccfdc469aad81c35 | DCN+ Dynamic Decoder. Builds decoder graph that iterates over possible solutions to problem until it returns same answer in two consecutive iterations or reaches `max_iter` iterations. | [
{
"docid": "7c7734fd4e07a8e8edda01b29ca9d7cc",
"score": "0.5828644",
"text": "def dcn_decode(encoding, document_length, state_size=100, pool_size=4, max_iter=4, keep_prob=1.0):\n\n with tf.variable_scope('decoder_loop', reuse=tf.AUTO_REUSE):\n batch_size = tf.shape(encoding)[0]\n lstm_d... | [
{
"docid": "a0cc24fd0397519dd87475cb3d28dda1",
"score": "0.5929235",
"text": "def dynamic_bidecode(fw_decoder, bw_decoder,\n output_time_major=False,\n impute_finished=False,\n maximum_iterations=None,\n parallel_iterations=32,\n ... |
bb93233fea2728dbe7a710491dbd20c3 | Override. Filter the orders | [
{
"docid": "64f1bc9be7d931e4af09562d6f9d9f9e",
"score": "0.5655497",
"text": "def get_queryset(self):\n qs = super(OrderListView, self).get_queryset()\n if self.request.user.profile.is_designer:\n qs = qs.filter(preferred_designer=self.request.user)\n\n params = self.get_... | [
{
"docid": "a4e89f8f2340058f683b47dde9083f5d",
"score": "0.7153317",
"text": "def filter(self):\n\n keys = ('ord-af', 'clt-af', 'qty-af', 'dat-af', 'tes-af', 'sta-af')\n new_dict = {}\n if 'orders' in self.data_dict:\n self.data_dict.pop('orders')\n for key in keys... |
02f315b9135f4b22ee9ffddc20ae764a | TABLE(viterbi_combined_cs self) > pmt_vector_cfloat | [
{
"docid": "8edf48582ac73e74d4876415aca92560",
"score": "0.54545975",
"text": "def TABLE(self):\n return _trellis_swig.viterbi_combined_cs_TABLE(self)",
"title": ""
}
] | [
{
"docid": "9ca7f83d9e04bbe56e55a5794b3300c5",
"score": "0.5984601",
"text": "def get_bvec():\n return np.array([0.0000000, 0.0000000, 0.0000000, 0.0000000,\n 0.0000000, 0.0000000, 0.0000000, 0.0000000,\n 0.0000000, 0.0000000, 0.0000000, 0.0000000,\n ... |
346d3ed1deeac006fe47ca51b0c1dd9d | No hint returned. Readonly routing to the SEGMENTS_EXEC_CONNECTION is handled manually in the Segment model. This is 'less surprising' than routing all Segment reads through the SEGMENTS_EXEC_CONNECTION. | [
{
"docid": "8bd1a3437ac212071979be4f57181f99",
"score": "0.0",
"text": "def db_for_read(self, model, **hints):\n return None",
"title": ""
}
] | [
{
"docid": "c4f28fca8b39034d3677e853378cef94",
"score": "0.5777611",
"text": "def test_get_segment_bind(self):\n pass",
"title": ""
},
{
"docid": "a45cab6f8492d86060ab03fadce251a7",
"score": "0.5474118",
"text": "def _get_segment_routing(self):\n return self.__segment_routi... |
767b313f27de008c06873b5506f74731 | Concatenate several GenomicArrays, keeping this array's metadata. This array's data table is not implicitly included in the result. | [
{
"docid": "fef4b50b2b7e631f7ecc7e10a12b1c97",
"score": "0.6265276",
"text": "def concat(self, others):\n result = self.as_dataframe(pd.concat([otr.data for otr in others]))\n result.sort()\n return result",
"title": ""
}
] | [
{
"docid": "15a61d1834cdf3ed63f0ebfd38933e33",
"score": "0.6837463",
"text": "def concatenate(arrays, axis=0):\n if not isinstance(arrays, tuple):\n raise ValueError(\"data type not understood\")\n arrays = tuple([asarray(a) for a in arrays])\n from numpy import concatena... |
7612886b5c1c75952937468ef4de0f84 | Start a busy loop checking for file changes every interval seconds. If blocking is False make one loop then return. | [
{
"docid": "34b86190c20808af2caaeaef05906339",
"score": "0.7577745",
"text": "def loop(self, interval=0.1, blocking=True):\n # May be overridden in order to use pyinotify lib and block\n # until the directory being watched is updated.\n # Note that directly calling readlines() as we... | [
{
"docid": "6d5e0b4b914132edacb2fa8bbd1018de",
"score": "0.64747286",
"text": "def loop_forever(self):\r\n while self.running:\r\n time.sleep(0.1)",
"title": ""
},
{
"docid": "dc72b9d7b7d88b6fc569acd8b1bd22a0",
"score": "0.625984",
"text": "def _loop(self):\n ... |
20616c335706dead03dbd0b355751bb7 | Renvoie un dictionnaire contenant les noeuds de steiner | [
{
"docid": "2d825c430d9e0d9e277a6470bf367ad8",
"score": "0.0",
"text": "def heuristique_ACPM(self,draw=False):\n\n def getVertricesOfPath(edges):\n set_node = set()\n for e in edges:\n id1,id2 = self.getIdVerticesOfEdge(e)\n set_node.add(id1)\n ... | [
{
"docid": "54c14ee0b7392691fb50c0ffb9f8ea26",
"score": "0.6221054",
"text": "def creer_dictionnaire_vide():\n dico = {}\n return dico",
"title": ""
},
{
"docid": "82e0cf18839180689903b7d5cd2d1912",
"score": "0.59172213",
"text": "def get_dictionary(self):\n dct = super(... |
f93c6b5d2fe60a9cdcdbdfcf373aa071 | Look through a list of buildrequests and return bug number from push comments | [
{
"docid": "374a6dcfd50381706b98c26bda1809c1",
"score": "0.7646013",
"text": "def GetBugNumbers(self, buildrequests):\n bugs = []\n for value in buildrequests.values():\n br = value.to_dict()\n for comment in br['comments']:\n # we only want the bug spe... | [
{
"docid": "900b60e5c518d7f522a1b0d26c2ac663",
"score": "0.6280939",
"text": "def ProcessPushType(self, revision, buildrequests, flag_check=True):\n push_type = None\n for value in buildrequests.values():\n br = value.to_dict()\n for comments in br['comments']:\n ... |
7b7f4594694f8cfb3398fee97cad1b09 | Builds a map of all the available XSD files. | [
{
"docid": "be58a6fc0c24417875c01449a15e6348",
"score": "0.72942144",
"text": "def get_xsd_files():\n all_files = []\n xsd_directory = get_xsd_directory()\n for root, unused_dirs, files in os.walk(xsd_directory):\n all_files.extend(\n os.path.join(root, filename)\n for filename in fi... | [
{
"docid": "a31e20d3a39b037126aee79ba974b98f",
"score": "0.59901017",
"text": "def build_file_map(buildset_files, minimal_files,\n full_files, context_files, docs_files):\n\n sys.stderr.write(\"making file map...\\n\")\n file_map = {}\n for f in buildset_files:\n file_m... |
58b4c21a6b2aa69ec9a7f44cb57cdf77 | Should erase itself from self.surface and return a list of dirty rectangles | [
{
"docid": "0f01a94c876eb3204446440d5d404949",
"score": "0.0",
"text": "def erase(self):\r\n raise Exception(\"erase not implemented\")",
"title": ""
}
] | [
{
"docid": "ba4440c20abcd1b1a8ef3e790b5735c9",
"score": "0.740515",
"text": "def erase(self):\r\n if self.is_focus:\r\n dirty_rect=pygame.draw.circle(self.surface, 0, (int(self.cx),int(self.cy)), self.radius, self.border_width)\r\n else:\r\n dirty_rect=pygame.draw.cir... |
b4ee8b559c2f4b6f1cb5f7335cc4859b | Add placeholders and classes, remove autogenerated labels and set autofocus on first field | [
{
"docid": "ba0e42ff03f5470d6030d92c8536c04d",
"score": "0.5561623",
"text": "def __init__(self, *args, **kwargs):\n\n # We call the default init method to set the form up\n # as it would be by default.\n super().__init__(*args, **kwargs)\n\n # Create dictionary of placeholde... | [
{
"docid": "ee58ce594e73263af8c770f223304e5d",
"score": "0.6516639",
"text": "def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n 'title': 'Title',\n 'body': 'Body',\n 'slug': 'Slug'\n }\n\n self.field... |
d1ab94935e1af99e184fbbc6b888b926 | Encodes obj as bytes. | [
{
"docid": "bbfcc47a776f32e4075a6592a8af4227",
"score": "0.8698663",
"text": "def encode(self, obj: Any) -> bytes:",
"title": ""
}
] | [
{
"docid": "bc24fde68354c537e45fcf8c96b600d1",
"score": "0.84944046",
"text": "def encode(self, obj: Any) -> bytes:\n return self.encoder.encode(obj).encode(\"utf8\")",
"title": ""
},
{
"docid": "9eabecd52ef02bc813bb5e953625cca1",
"score": "0.778802",
"text": "def encode(self,... |
08b62f4fb374af1c8cfcc184505c65f9 | Asserts that the cycle parameters are correctly updated. | [
{
"docid": "ffdf0b4a006e974cc807d2374817e258",
"score": "0.67932165",
"text": "def test_cycle_updater_function_wrong_length(self):\n data = CellParameters(**CELL_DATA)\n new_cycle_values = {\n \"phase_0\": 20.0,\n \"phase_1\": 180.0,\n \"phase_2\": 240.0,\n... | [
{
"docid": "3039942404e954fea5c6d5299e65357b",
"score": "0.7294884",
"text": "def test_cycle_updater_function(self):\n data = CellParameters(**CELL_DATA)\n new_cycle_values = {\n \"phase_0\": 20.0,\n \"phase_1\": 180.0,\n \"phase_2\": 240.0,\n \"... |
ffbcdbe876282ffb2f40846e205be13a | obj.text_payload is a base64encoded payload as text. | [
{
"docid": "65369942118cfb69adb3b58495223283",
"score": "0.6641096",
"text": "def test_text_payload(self):\n payload = pem.parse(KEY_PEM_PKCS5_UNENCRYPTED)[0].text_payload\n\n assert (\n KEY_PEM_PKCS5_UNENCRYPTED_PAYLOAD.decode().replace(\"\\n\", \"\")\n == payload\n ... | [
{
"docid": "3b63a512897ae29d232ed7d2ee66b267",
"score": "0.63219917",
"text": "def _encode_text(text):\n message_bytes = text.encode('ascii')\n base64_bytes = base64.b64encode(message_bytes)\n base64_text = base64_bytes.decode('ascii')\n return base64_text",
"title": ""
... |
1a7f9536a35fd590d40a3a1d37600ab3 | Test we handle unexisting board. | [
{
"docid": "857333b605e27b3d26e62b606955f50b",
"score": "0.0",
"text": "async def test_form_cannot_connect(hass: HomeAssistant) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n\n assert result[\"step_id\"] == ... | [
{
"docid": "ccca7c5d0b148ca39efee43c41233dbe",
"score": "0.7050416",
"text": "def test_empty(self):\n with self.assertRaises(Board.InvalidDimensionsError):\n Board(())",
"title": ""
},
{
"docid": "1f214f4a3140b583029b4e1844f0fb37",
"score": "0.6889755",
"text": "def... |
c7b86c990e593c07e47e1f64422f60c7 | Print HTTP headers to sys.stdout. | [
{
"docid": "f7744652dcc4edfcdef486badb665637",
"score": "0.0",
"text": "def set_debug_http(self, handle):\r\n level = int(bool(handle))\r\n for scheme in \"http\", \"https\":\r\n h = self._ua_handlers.get(scheme)\r\n if h is not None:\r\n h.set_http_deb... | [
{
"docid": "be4423dde5e95970d93f0e179b855163",
"score": "0.7317722",
"text": "def print_headers(self, headers={}, nocache=True):\n if self.sent_headers:\n return\n if not headers.has_key('Content-Type'):\n headers['Content-Type'] = 'text/html'\n if nocache:\n ... |
ea836898f81f53deeddd339eb098744f | Redis instance used to cache value. Replace redis instance if you want. | [
{
"docid": "c7b7573e500a55046ada3753831be40d",
"score": "0.7362848",
"text": "def mc(self):\n ctx = stack.top\n if ctx is not None:\n if not hasattr(ctx, 'cache_redis'):\n ctx.cache_redis = self.init_redis()\n return ctx.cache_redis",
"title": ""
... | [
{
"docid": "6881087f66a27160ef90604730473508",
"score": "0.7461873",
"text": "def redis(self):\n if not self._redis:\n self._redis = Redis(self)\n return self._redis",
"title": ""
},
{
"docid": "3e2ee58f21b6db258445b49769a72274",
"score": "0.7202681",
"text":... |
d90d3b15455d45d6b597a96330b7666c | Finds location of the arm in space relative to the base. | [
{
"docid": "56370a7d6be6343b3cbb6bd1907dcf21",
"score": "0.55456394",
"text": "def armLocation(length, theta, position = [0,0]):\n #print \"Angle:\",theta\n width = 263.5\n dx = 125\n dy = 40\n p1 = (position[0]+dx*cos(theta)+dy*cos(pi/2 - theta),position[1]-dx*sin(theta)+dy*sin(pi/2 - th... | [
{
"docid": "978abb5b8a42371efd3b228c57c9b245",
"score": "0.6127112",
"text": "def GetBasePosition(self):\n ...",
"title": ""
},
{
"docid": "5dfe8dc953ab0ca7337b4fb32f866076",
"score": "0.58375865",
"text": "def find_arm(self, arm):\n for idx in range(self.n_arms):\n ... |
632fd86eb40d2badbab4f3c78dc6354a | Constructs a ResNet50 model. | [
{
"docid": "5cd82c73ff832d739fb6bedd9ee42fa5",
"score": "0.75137323",
"text": "def resnet50(pretrained=False, **kwargs):\n model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)\n # if pretrained:\n # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))\n return model",
"tit... | [
{
"docid": "93476a99b995c376193072cffd72e9f0",
"score": "0.7939585",
"text": "def resnet50():\n model = models.resnet50()\n model.apply(weights_init)\n return model",
"title": ""
},
{
"docid": "7d29192d3203989df7a6d6610b2c6abd",
"score": "0.77827954",
"text": "def build_mode... |
a404e163ba2393e46165fbcedf3f1591 | command to display all the regrade requests | [
{
"docid": "34f89319cd2655565707459797320655",
"score": "0.6930176",
"text": "async def display_requests(ctx):\n for name, questions in db.select_query(\n 'SELECT name,questions FROM regrade'\n ):\n if name:\n await ctx.send(name + \" \" + questions)\n else:\n ... | [
{
"docid": "9e26b7c479dffb6328178f7b140df671",
"score": "0.59886205",
"text": "def show_requested(self):\n results = []\n for name, comp in sorted(self._requested):\n results.append(\n ansiformat(\n self._get_color(comp), \"{} {}\".format(name, ... |
20bf984df908d1fb52677a7b15e96b59 | Return an iterator to the link counts. Returned values have the form (start, end), count | [
{
"docid": "acade2d19f779ef287dd95e2b6e85d02",
"score": "0.64247334",
"text": "def iterCounts(self):\n\n if self._obsCount:\n for period in sorted(self._obsCount.keys()):\n if self._obsCount[period]:\n yield period, self._obsCount[period] \n ... | [
{
"docid": "f741166bf40f0bef0de6e35a4ccb277b",
"score": "0.71143895",
"text": "def count_sources(edge_iter: EdgeIterator) -> Counter:\n return Counter(u for u, _, _ in edge_iter)",
"title": ""
},
{
"docid": "ff3aa2b5ab802e53c14ebe5ed8a281e0",
"score": "0.67742",
"text": "def getNu... |
b96fb0c336541696d73fb3e20a0c16e4 | loop over each image as query and evaluate its performance based on top_r selection | [
{
"docid": "abd2b101df9f7c3f228309044a374c6c",
"score": "0.0",
"text": "def oneAgainstAllFaiss(y_true, hash_binary, cls_num_ac, top_r):\n precisions, recalls, dist = [], [], []\n numSample = hash_binary.shape[0]\n # index = faiss.IndexFlatL2(hash_binary.shape[1])\n # index.add(np.ascontiguou... | [
{
"docid": "c36dbba6bc9e1aeb9366f525170254c5",
"score": "0.58435273",
"text": "def query(self, images):\n if self.pool_size == 0:\n return images\n return_images = []\n for image in images:\n if self.num_imgs < self.pool_size:\n self.num_imgs += ... |
b2e98536efdc0cb2f58835cb49f27af1 | The menu is seslected, create a click control. | [
{
"docid": "8972ba31eb913b59f9149eddadeb28bd",
"score": "0.5932137",
"text": "def OnItem(self, e):\n self.process_control(e, \"click\")",
"title": ""
}
] | [
{
"docid": "091f72e3307c61510e4290029546b2c2",
"score": "0.6858837",
"text": "def sig_activate(self, widget, menu):\n menu.event('clicked')",
"title": ""
},
{
"docid": "8ab69f90be7788607bbdb641781d43ac",
"score": "0.6697031",
"text": "def click():",
"title": ""
},
{
... |
3c22b47464a2ef66e958ecb9f5d8c7f5 | Computes mutual information between two images variate from a joint histogram. | [
{
"docid": "559f2a37553f7fa9c6f0e143c7d09a98",
"score": "0.60261965",
"text": "def similarity_measure(image1,image2,norm=True,bin_rule=None,measure=\"MI\"):\n arr1 = image1.ravel()\n arr2 = image2.ravel()\n if bin_rule == None or bin_rule == \"sturges\":\n dx,Nbins = sturges_bin_width(a... | [
{
"docid": "44c5f9982842ac43a6b6ab9c7cab0b07",
"score": "0.6591423",
"text": "def mutual_information(X, Y):\n\n\n # compute limits\n X = X.compute()\n xmin = da.min(X).compute()\n xmax = da.max(X).compute()\n\n Y = Y.compute()\n ymin = da.min(Y).compute()\n ymax = da.max(Y).compute(... |
1a33aa71cb2299b7dac98ae50b67f1f6 | r"""Runs XFuse on the given data | [
{
"docid": "db94527214781b4480b669c00dd34c4f",
"score": "0.0",
"text": "def model(self, xs):\n\n def _go(experiment, x):\n zs = [\n p.sample(\n f\"z-{experiment.tag}-{i}\",\n (\n # pylint: disable=not-callable\... | [
{
"docid": "1a1581ba904d4e719c5c6aa04a0b3511",
"score": "0.61807793",
"text": "def multiFuse(self):\n ...",
"title": ""
},
{
"docid": "1837128fa80992267a139987a47e61af",
"score": "0.5791556",
"text": "def run(data):",
"title": ""
},
{
"docid": "f9424006c706b7763b8b... |
0f103fc209bd989834da306461e14d99 | Copy weights of Trainers model to this models weights. | [
{
"docid": "91516ef8a36fe1c7703d319f56089274",
"score": "0.6777867",
"text": "def copyModel(self, model):\n self.model.set_weights(model.get_weights())",
"title": ""
}
] | [
{
"docid": "f3a34e4992226f0c7fe056c772df87bc",
"score": "0.72203034",
"text": "def set_weights(self, weights):\n # Legacy support\n if legacy_models.needs_legacy_support(self):\n layers = legacy_models.legacy_sequential_layers(self)\n for layer in layers:\n ... |
0cb0fb633d76572e5b881ae74abbc422 | Update the hash by removing the previous item previtm and adding the next item nextitm. Return the updateed hash value. | [
{
"docid": "213175198ac4dd069bc90b5e87438ad7",
"score": "0.71016806",
"text": "def slide(self, previtm, nextitm):\n self.curhash = (self.curhash * self.HASH_BASE) + ord(nextitm)\n self.curhash -= ord(previtm) * (self.HASH_BASE ** self.seqlen)\n return self.curhash",
"title": ""
... | [
{
"docid": "7bcef7fa2b1c6e9d48abb1a27dfdd9d4",
"score": "0.59157664",
"text": "def __re_cal_rhash(self):\n time_stp = datetime.datetime.now()\n self._prev_hashes.append(Hash(self.root_hash, time_stmp))",
"title": ""
},
{
"docid": "304a18c014e428671494e225b163d128",
"score":... |
c3ebe951af73157175644fe9992ba196 | gives a list of the faces | [
{
"docid": "36e3fe5aabc13febf76929dbb5a3bb45",
"score": "0.8191274",
"text": "def list_faces(self):\n\t\tface = sorted(self.faces.items(), key=itemgetter(1), reverse =True);f =[]\n\t\tfor each in face: f.append(each[0])\n\t\treturn f",
"title": ""
}
] | [
{
"docid": "8b5285d9316ec1c056307da87a87dc2c",
"score": "0.76194733",
"text": "def faces(img):\n return detector(img, 1)",
"title": ""
},
{
"docid": "f65231c2483b76edc38e022adfab5345",
"score": "0.7515052",
"text": "def detect_faces(self):\n try:\n response = sel... |
0c052b6fe1ad9e414a31d295b27d6d14 | Return the postprocessed estimated halfwidth of the confidence interval. | [
{
"docid": "c3ef6a9f70af4c303d73ee4b2f351256",
"score": "0.0",
"text": "def epsilon_estimated_processed(self) -> float:\n return self._epsilon_estimated_processed",
"title": ""
}
] | [
{
"docid": "122d794a68ecb72583b6485cbec6a157",
"score": "0.5895502",
"text": "def band_width(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"band_width\")",
"title": ""
},
{
"docid": "480a4bb0243d51ce844d30275d4c3e80",
"score": "0.58554256",
"text": "def get... |
8c455e029d6f65c2169ccaf702a3d6e0 | Gnerating data from homogenized diffusion problem. | [
{
"docid": "05e6111125de46140914ec76980f55c1",
"score": "0.0",
"text": "def GenerateData(n_traj: int, length_traj: float, filename: str):\n\n U,U_fd,U_ref = [],[],[]\n\n # epss=np.logspace(-6,-3,21)\n eps = 1e-5\n\n for _ in range(n_traj):\n ttin = timeit.default_timer()\n prin... | [
{
"docid": "c5fe77eb85d7111315011d81b34c315c",
"score": "0.6364072",
"text": "def diffusion():",
"title": ""
},
{
"docid": "e78cfd5e581ba3e0c686a8f133343e1c",
"score": "0.60929483",
"text": "def g():\r\n\t# use only genes that are correlated with liver disease\r\n\tgene_cor_sorted_in... |
e00d4854127bcc72d1cd6d58082bb8aa | Reads a YAML file and return a dictionary of its contents. | [
{
"docid": "befca0db1c00c8ff4b3347f5daa50d39",
"score": "0.6859964",
"text": "def read_yaml_config(path):\n with open(path) as input_file:\n data = input_file.read()\n try:\n return yaml.load(data)\n except yaml.YAMLError, e:\n raise ValueError('Error parsing YAML file %s: %s' % (path, e))",... | [
{
"docid": "8790fe5b5c4502b493d535daebb90b16",
"score": "0.82360923",
"text": "def read_yaml() -> dict:\n with open(CONFIG_FILE) as config_file:\n return yaml.load(config_file, Loader=yaml.FullLoader)",
"title": ""
},
{
"docid": "f2b97221e12faf0a13d9506dc59f0d55",
"score": "0.8... |
2c0be3bdb13395e5faec089c68c32cc0 | Loop for receiving messages from the server and calling the given handler. | [
{
"docid": "3457228a51d88c8edcc897239a313816",
"score": "0.64994836",
"text": "async def handle(self):\n\n # wait until the socket object is created\n while self.__socket is None:\n await asyncio.sleep(0)\n\n # wait until the socket is ready\n await self.__socket.e... | [
{
"docid": "1f6eb6286e8f5b57133d53eb6f7141b0",
"score": "0.73473716",
"text": "def handle(self):\n sock = self.request\n response = \"\"\n\n while True:\n try:\n objects = _read_objects(sock)\n response = server._callb... |
36afa34acf326a9c7053b818cecc01ff | Convert list of tiles as returned by SH WFS into list of time ranges whose time deltas are not greater than max_timedelta. | [
{
"docid": "c32b8a492c9b5dc658dfb7c466e6b815",
"score": "0.7689309",
"text": "def tile_features_to_time_ranges(cls, tile_features, max_timedelta: Union[str, pd.Timedelta] = '1H') \\\n -> List[Tuple[pd.Timestamp, pd.Timestamp]]:\n max_timedelta = pd.to_timedelta(max_timedelta) if isinst... | [
{
"docid": "322f66d335834d6e7191aa041611eaff",
"score": "0.6455336",
"text": "def split(self, max_delta):\n assert isinstance(max_delta, timedelta)\n chunks = []\n chunk_since = self.since\n while chunk_since < self.until:\n chunk_delta = min(max_delta, self.until ... |
a7c90f3bc19198d782a98754c8220f05 | calculate a and b of y=a.x+b before including in the point p | [
{
"docid": "d6e7219911aa3e8d7f7c2c10c2fa4687",
"score": "0.6427087",
"text": "def evalPoint(self, p):\n if not self._points:\n raise EvalPointError(\"\"\"Cannot calculate slope\n and intercept with a single point.\n \"\"\")\n x = p.x\n... | [
{
"docid": "3420fc09cc7a6c8ccb3050887f7e8572",
"score": "0.7422088",
"text": "def point_add(a, b, p, x0, y0, x1, y1):\n\n # ADD YOUR CODE BELOW\n assert isinstance(a, Bn)\n assert isinstance(b, Bn)\n assert isinstance(p, Bn) and p > 0\n assert (isinstance(x0, Bn) and isinstance(y0, Bn)) o... |
5c23c20f83b7e5f6c4fde5503aa792ee | Run aflshowmap on the target and get the coverage bitmap for a particular testcase. | [
{
"docid": "7976f8ba9da94bc5b4cef400affa0d06",
"score": "0.7753865",
"text": "def run_afl_showmap(target: str, afl_stats: FuzzerStats, testcase: str) -> bytes:\n # Base afl-showmap options and arguements on afl-fuzz options and arguments\n afl_showmap_opts = ['-b', '-q']\n for opt, arg in afl_s... | [
{
"docid": "40e66fffa9607e4942f5d9355e8da9d7",
"score": "0.56286484",
"text": "def show_coverage(self, viewer_id=None, table_group='main'):\n\n view_type = 'coverImage'\n cid = viewer_id if viewer_id else (\"%s-%s\" % (LO_VIEW_DICT[view_type], table_group))\n payload = {'viewerType'... |
7de2e432a41ce01f92bdcf930bbdbe45 | A rule statement that defines a string match search for AWS WAF to apply to web requests. See Byte Match Statement below for details. | [
{
"docid": "588cd4e70cd75d6bde629d1c0c44578a",
"score": "0.54430264",
"text": "def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementRateBasedStatementScopeDownStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")",
"title": ""
}
] | [
{
"docid": "90ea33f01c95e611308c46f7671fe928",
"score": "0.6055283",
"text": "def byte_match_statement(self) -> Optional[pulumi.Input['WebAclRuleStatementAndStatementStatementAndStatementStatementAndStatementStatementByteMatchStatementArgs']]:\n return pulumi.get(self, \"byte_match_statement\")",... |
bce6738a1a6462f06b945c022ba3e93f | Read solution from a binary file. Please refer to README.md for description of the binary file format used here. | [
{
"docid": "eea1f3696bea425597d502f3e1952a69",
"score": "0.6074145",
"text": "def read_solution_from_file(path_to_data):\n\n data = open(path_to_data, \"rb\").read()\n\n # nx: number of x points\n # ----------\n\n start = 0\n end = 4*3\n (_, nx, _) = struct.unpack(\"@iii\", data[start:... | [
{
"docid": "54d9052ff2375d0421dc2be1610b999e",
"score": "0.65914464",
"text": "def read_binary(filename):\n with FileBinaryReader(filename) as f:\n return f.read()",
"title": ""
},
{
"docid": "83beb9771c450aa37bce0e79e62b5fcc",
"score": "0.6584203",
"text": "def load_soluti... |
16d29fd4457273d8c9392acf2e3ee714 | Builds predefined pages and puts them into the paginator. | [
{
"docid": "13d00e00d4e316949f7f6b0d7981e6b5",
"score": "0.758468",
"text": "async def build_pages(self):\n paginator = LinePaginator(prefix=\"\", suffix=\"\")\n\n # Add general info page\n paginator.add_page(Page(\n strings.Info.bot_welcome,\n \"\",\n ... | [
{
"docid": "d4904c1eae48a04ca36df142f4678fa7",
"score": "0.71009964",
"text": "def generate_all_pages():\n fetcher = getattr(parent, fetcher_str)\n\n first_page = fetcher.get(**kwargs)\n kwargs['page_size'] = len(first_page)\n count = fetcher.current_total_cou... |
afe6f0ceb315a8b809fa6471a104b182 | Returns the number of days between year1/month1/day1 and year2/month2/day2. Assumes inputs are valid dates in Gregorian calendar. | [
{
"docid": "d8b73162f072e405ab0afb00e8a23a25",
"score": "0.75588286",
"text": "def daysBetweenDates(year1, month1, day1, year2, month2, day2):\n # program defensively! Add an assertion if the input is not valid!\n assert month1 in range(1, 13) and month2 in range(1, 13), \" the value of month shou... | [
{
"docid": "5bb3242f99f100bec5f8ed8c05ac123d",
"score": "0.84254104",
"text": "def days_between_dates(year1, month1, day1, year2, month2, day2):\n\n days = date_in_days(year2, month2, day2) - date_in_days(year1, month1, day1)\n return days",
"title": ""
},
{
"docid": "ff3fe8202499476d8... |
05bbe2e0b7965809ebd65daeb904cc8c | Helper method to locate row in database | [
{
"docid": "dbdd4b554715401408c8f7b90e2a0625",
"score": "0.0",
"text": "def check_for_app(app):\n if (app == \"all\"):\n return -1\n count = 0\n for row in cur.execute(\"SELECT * FROM passwords WHERE application=?\", (app,)):\n count+=1\n else:\n return count",
"titl... | [
{
"docid": "297fb191bddda71a9dc5e40d64edc8c6",
"score": "0.667747",
"text": "async def find(self, pk_value: Any) -> RowProxy:",
"title": ""
},
{
"docid": "6c84afbfd114dc604b825ace3583251d",
"score": "0.64952505",
"text": "def find_row(table, row):\n for idx in range(len(table)):\n... |
61f0f13a2701498ff9538f00e55c8cc6 | Returns the log of probability estimates. | [
{
"docid": "6719902eb571034a7a96440a4eb1d871",
"score": "0.68925774",
"text": "def predict_log_proba(self, X):\n return np.log(self.predict_proba(X))",
"title": ""
}
] | [
{
"docid": "e0cf42a38133d6cf396650edba63c72c",
"score": "0.73786455",
"text": "def priorlogprob(self):\n return numpy.array([p.logpdf(x) for p, x in zip(self.getpriors(), self.getparams())])",
"title": ""
},
{
"docid": "01f0ec5bea8d6e48c9938f08f2177d9b",
"score": "0.7042134",
... |